<RECORD 1>
Accession number:20171703590863
Title:Citation analysis on articles of Transactions of the CSAE cited by SCI journals
Authors:Zhang, Kunzhu (1); Wang, Liu (2)
Author affiliation:(1) Tsinghua University Library, Beijing; 100084, China; (2) Editorial Department of Transactions of the Chinese Society of Agricultural Engineering, Chinese Society of Agricultural Engineering, Chinese Academy of Agricultural Engineering, Beijng; 100125, China
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:381-387
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:The cited situation of articles in a journal by the journals covered in SCI (Science Citation Index) database is an important symbol of the influence and internationalized level. Based on the index and statistics of SCI database, Author analyzed the data of all the articles cited by the journals covered in SCI, which were from Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE) since founded. The results indicate that from 1985 to 2015, a total number of 3 150 articles are cited and the accumulative citation frequency is 6 871; There are 27 articles of Transactions of the CSAE with top 5 accumulative citation frequency by SCI database in recent 5 years, and the categories of these articles are agricultural information, soil and water engineering, agricultural bioenvironmental and energy engineering, land consolidation and rehabilitation engineering, and agricultural aviation ; There are 10 articles of Transactions of the CSAE with top 5 accumulative citation frequency by SCI database in 30 years, and 5 of them are related to straw resources, the top one citation frequency is 52. 6 045 authors from 86 countries have cited the articles of Transactions of the CSAE and the Chinese Academy of Sciences ranks the first for producing the SCI-cited articles; agriculture and environmental sciences ecology are leading subjects of the articles; and these SCI-cited articles are mainly funded by the National Nature Science Foundation of China. The analysis suggests that the number of SCI-cited articles of Transactions of the CSAE has significantly increased and achieved a high scale. The journals which cite articles of Transactions of the CSAE rank the middle level in the world. In future, Transactions of the CSCE should accept more basic research articles related to energy, environment and ecology, and more articles related to emerging and hot research areas such as agricultural information, land consolidation and rehabilitation engineering and agricultural aviation, and improve the internationalized level to attract more oversea researchers attention. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:8
Main heading:Agriculture
Controlled terms:Agricultural engineering - Database systems - Ecology - Human rehabilitation engineering - Information analysis
Uncontrolled terms:Agricultural informations - Chinese Academy of Sciences - Citation analysis - Impact - Journal - Rehabilitation engineering - Science citation index - Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE)
Classification code:454.3 Ecology and Ecosystems - 461.5 Rehabilitation Engineering and Assistive Technology - 723.3 Database Systems - 821 Agricultural Equipment and Methods; Vegetation and Pest Control - 903.1 Information Sources and Analysis
Numerical data indexing:Age 3.00e+01yr, Age 5.00e+00yr
DOI:10.11975/j.issn.1002-6819.2017.z1.057
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 2>
Accession number:20171703590904
Title:Method of denoising and removing artifacts for farm remote sensing image based on shearlet and total variation
Authors:Mei, Shuli (1); Li, Xiaofei (1); Zhao, Haiying (2); Li, Li (1); Guo, Shujun (1)
Author affiliation:(1) Colleague of Information and Electrical Engineering, China Agricultural University, Beijing; 100083, China; (2) Mobile Media and Cultural Computing Key Laboratory of Beijing, Century College, Beijing University of Post&Telecommunication, Beijing; 102613, China
Corresponding author:Li, Li(lili.li@cau.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:274-280
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:The research on crop phenotype is one of the important measures in crop breeding. Crop breeding is a technology on selecting the good seeds by the crop phenotype. The farm remote sensing image analysis is a simple and effective method for rapid analysis of crop phenotype. However, the farm remote sensing image acquired by UAV (unmanned aerial vehicle) will be affected by the noise. In order to process and analyze the remote sensing image accurately, the remote sensing image should be first denoised. Typical image denoising algorithm of frequency domain is wavelet threshold algorithm. Wavelet transform can identify the singular signal accurately; however, the traditional wavelet can't effectively deal with multidimensional signal. For example, two-dimensional wavelet obtained from one-dimensional wavelet tensor only has horizontal and vertical directions, and the wavelet filter is isotropic, which is not sensitive to the image with more directions of the texture; while using the traditional wavelet threshold denoising algorithm to handle noise image, it is easy to make texture region blurry compared with the common wavelets. It is well known that the shear transformation has been introduced into the definition of the shearlet, which makes shearlet can represent more directions. In order to capture the directions of local geometric features of image, shearlet must be compactly suppressed. Anisotropic expansion scale transform can be applied in multi-resolution analysis. From coarse scale to fine scale approximation signal, namely in the approximation image in smooth regions using coarse scale, and in the approximation image with directions of texture using fine scale, the optimal approximation of singular curves can be achieved through the application of multi-resolution analysis method. Shearlet filter is anisotropic, and it is very sensitive to the direction of the texture; however, with the increase of noise standard deviation, after denoising, using shearlet algorithm generates artifacts easily. In brief, the denoising algorithm based on the total variation model has the advantage of edge preserving and noise reduction. In the application of shearlet for image denoising, we find that the shearlet with noise in the texture region can detect the texture direction, and can effectively remove the noise; but when the noise standard deviation is large, the application of shearlet algorithm in the image can cause the denoised image to tend to produce some artifacts, which will be identified as noise texture perhaps due to the shear wave in the smooth region. In order to solve the problem of discrete shearlet algorithm, this paper proposes a method based on the combination of shearlet and total variation model to eliminate the artifacts. First, select the generated Symmlet quadrature mirror filter, and ascertain anisotropic scale parameters and shear parameters. Perform discrete shearlet transformation to farmland remote sensing image with multi-resolution analysis. Wavelet coefficients can be obtained using the conventional method by anisotropic wavelet transformation. Wavelet coefficients are projected into wavelet of image. Hard threshold algorithm is used to handle noise coefficients. Wavelet coefficients are adopted to reconstruct farm remote sensing image. After using total variation model to smooth artifacts, the mirror extension is used to preprocess the image. In part of the experiment, the algorithm proposed in this paper is compared with the shearlet algorithm and total variation algorithm, and this algorithm gets a PSNR (peak signal to noise ratio) 1dB more than that from total variation model, and an iteration number of total variation less than that from direct total variation. In addition, not only can this algorithm effectively denoise farm remote sensing image noise and visual effects, but also can remove artifact better than the discrete shearlet transform algorithm. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:30
Main heading:Image denoising
Controlled terms:Algorithms - Anisotropy - Crops - Frequency domain analysis - Image analysis - Image processing - Image reconstruction - Image texture - Iterative methods - Mirrors - Multiresolution analysis - Remote sensing - Shear flow - Shear waves - Signal to noise ratio - Statistics - Unmanned aerial vehicles (UAV) - Wavelet transforms
Uncontrolled terms:Image denoising algorithm - Multidimensional signals - PSNR (peak signal to noise ratio) - Removing artifact - Shearlet transforms - Total variation - UAV (unmanned aerial vehicle) - Wavelet threshold de-noising
Classification code:631.1 Fluid Flow, General - 652.1 Aircraft, General - 716.1 Information Theory and Signal Processing - 723.2 Data Processing and Image Processing - 741.3 Optical Devices and Systems - 821.4 Agricultural Products - 921.3 Mathematical Transformations - 921.6 Numerical Methods - 922.2 Mathematical Statistics - 931.1 Mechanics - 931.2 Physical Properties of Gases, Liquids and Solids
Numerical data indexing:Decibel 1.00e+00dB
DOI:10.11975/j.issn.1002-6819.2017.z1.041
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 3>
Accession number:20171703590905
Title:Image dehazing method based on dark channel prior and interval interpolation wavelet transform
Authors:Wei, Yinghui (1); Zhang, Yan'e (1); Mei, Shuli (1); Wei, Shuaijun (1)
Author affiliation:(1) College of Information and Electrical Engineering, China Agricultural University, Beijing; 100083, China
Corresponding author:Zhang, Yan'e(zhang_yane@163.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:281-287
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:Nowadays, smart agriculture has become a research hotspot in the field of agriculture technology. Meanwhile, the image is one of the important data sources for smart agriculture and related technology. Image processing technology has been widely used in modern agricultural research. In the application of outdoor agriculture, environmental conditions are important factors that degrade the quality of the obtained image. In particular, the haze is a very common factor that decreases image quality seriously. Images acquired in bad weather, such as haze, are seriously degraded by the scatting of the atmosphere particles, which reduces the contrast, color saturation and hue shift and makes the object features difficult to identify. In order to remove the negative effect of the haze in degrading image quality, this study proposes a new image dehazing algorithm that combines the dark channel prior model with the interval interpolating wavelet transform. The dark channel prior is based on the statistics of the haze-free outdoor images. Specifically, it is based on a key observation, i.e. most local patches in haze-free outdoor images contain some pixels which have very low intensities in at least one color channel. Using this prior with the haze imaging model, we can directly estimate the thickness of the haze and recover a high quality haze-free image. The wavelet transform is used to carry out multi-scale refinement through the operation of telescopic translation, which can highlight the characteristics of the details of the image. Interval interpolation wavelet may reduce the error caused by the approximation of the wavelet. Firstly, we estimate the transmission and the atmospheric light value by using the dark channel prior theory, and restore the image. Secondly, the obtained image is decomposed by interval interpolation wavelet transform, and then reconstructed by processing high frequency sub-band wavelet coefficients. An experiment is carried out using this method. The results show that after the image processing by using this method, the whole image looks like comparatively bright, and the image contrast and clarity are improved. Finally, it works to filter out the negative effect caused by the haze. The processed image fits the human observation feeling well. It has good visual effect, obvious layering and rich texture detail. For color images, color saturation can be well kept, and the distortion is correspondingly low. Hence, the processed color images are close to the real objects with true color. Moreover, after the image processing, the contour contrast of the scene is obvious and is not blurred. It also makes distant scenery in the image very clear. We compare our haze removal results with that by the dark channel prior algorithm. On average, the standard deviation values of our algorithm in the R, G, and B channels are respectively improved by 25.44%, 27.90% and 26.24%. In sum, this study presents a new method that combines the dark channel prior model with the interval interpolating wavelet transform, and the image can be well dehazed and achieve good restoration in image visibility using this method, and thereby lays the foundation for acquiring accurate image information. Moreover, it is also useful for the application in modern precision agriculture. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:31
Main heading:Image processing
Controlled terms:Agriculture - Algorithms - Color - Demulsification - Image acquisition - Image compression - Image quality - Interpolation - Light transmission - Restoration - Wavelet transforms
Uncontrolled terms:Agricultural research - Agriculture technology - Dark channel priors - Dehazing - Environmental conditions - Image processing technology - Interval interpolation wavelet - Precision Agriculture
Classification code:723 Computer Software, Data Handling and Applications - 741.1 Light/Optics - 802.3 Chemical Operations - 821 Agricultural Equipment and Methods; Vegetation and Pest Control - 921.3 Mathematical Transformations - 921.6 Numerical Methods
Numerical data indexing:Percentage 2.54e+01%, Percentage 2.62e+01%, Percentage 2.79e+01%
DOI:10.11975/j.issn.1002-6819.2017.z1.042
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 4>
Accession number:20171703590898
Title:Analysis of reason for recent slowing maize yield increase under climate change in China
Authors:Yang, Di (1); Xiong, Wei (1); Xu, Yinlong (1); Feng, Lingzhi (2); Zhang, Mengting (1); Liu, Huan (1)
Author affiliation:(1) Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing; 100081, China; (2) Yulin Meteorological Bureau of Shaanxi Province, Yulin; 719099, China
Corresponding author:Xiong, Wei(xiongwei@caas.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:231-238
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:China's maize yield has demonstrated a slowing growth since the end of last century, which has received great concerns for policy makers and agricultural scientists. Reasons for such phenomena are usually ascribed to decreasing fertilizer efficiency, land degradation, decreasing technology inputs such as new crop varieties, and climate change. However, none of the reasons has been clearly investigated and quantified to date, particularly at a very regional scale. Here a gridded and time series database of maize yield, areas, irrigation, fertilizer application, we apply an ensemble empirical mode decomposition (EEMD) analysis and examine the contribution of each factors on past yield growth and their changes along time. We discover that the slowing yield growth has been experienced at the whole China and 15 provinces (autonomous regions, municipalities) in 1981-2008. Based on Cobb-Douglas production function, we create multiple linear regression models for the whole country and the 10 provinces that exhibits slowing yield increase, to isolate the contributions of fertilizer, irrigation, other physical inputs and climate on past maize yield increase. Results showed, at national scale, 1) maize yield was significantly correlated with fertilizer, irrigation, other physical inputs and climate factors during 1981-2008. Maize yield was significantly promoted by inputs of fertilizer, irrigation and others, with a 1% increase of these investments increasing maize yield by 0.39%, 0.06% and 0.04%. Among climate factors, change of precipitation increased maize yield, with a 1% increase in precipitation promoting maize yield by 0.21%. Whereas temperature and cloud cover had negative effects on maize yield change, a 1% increase in temperature and a 1% decrease in solar radiation would decrease maize yield by 0.99% and 1.04% respectively. 2) Past increase of fertilizer application amount contributed most to past yield increase of maize (70.24%), followed by irrigation (9.44%), and other physical inputs (5.43%). Within all climate drivers, increase of temperature reduced maize yield by 1.98%, while decrease in precipitation and solar radiation increased maize yield by 1.08% and 4.72%. 3) Increased fertilizer application significantly increased the production in Northern spring maize region and Huang-Huai-hai summer maize region. Irrigation had positive effects in Northern spring maize region. The other physical inputs had significantly positive effects in Huang-Huai-hai summer maize region. For climate drivers, increase of temperature could promote maize yield significantly in Northern spring maize region and Huang-Huai-hai summer maize region. The reducing solar radiation had significantly negative effects on maize yield in two maize producing regions. Although statistic model is able to isolate the contribution of various factors, it's accuracy depends on the training date and the models that have been selected. Our results only focus on the major factors that affecting China's maize production, which to some extent limits its explanation ability as many other factors such as environmental degradation, pest and diseases, labor loss have started to affect the national crop production. Nevertheless, our results are consistent with previous studies showing that fertilizer is the major player for past maize yield growth, while its decreasing contribution has caused the recent slowing the maize yield increase. Climate change is becoming an important factor in fluctuating the production and affecting the changing trends. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:34
Main heading:Climate change
Controlled terms:Climate models - Crops - Cultivation - Economics - Fertilizers - Irrigation - Linear regression - Radiation effects - Regression analysis - Solar radiation - Time series analysis
Uncontrolled terms:Cobb-Douglas production function - Driving factors - Ensemble empirical mode decompositions (EEMD) - Fertilizer applications - Fertilizer efficiency - Maize yield - Multiple linear regression models - Time Series Database
Classification code:443 Meteorology - 443.1 Atmospheric Properties - 657.1 Solar Energy and Phenomena - 804 Chemical Products Generally - 821.3 Agricultural Methods - 821.4 Agricultural Products - 921 Mathematics - 922.2 Mathematical Statistics - 971 Social Sciences
Numerical data indexing:Percentage 1.00e+00%, Percentage 1.04e+00%, Percentage 1.08e+00%, Percentage 1.98e+00%, Percentage 2.10e-01%, Percentage 3.90e-01%, Percentage 4.00e-02%, Percentage 4.72e+00%, Percentage 5.43e+00%, Percentage 6.00e-02%, Percentage 7.02e+01%, Percentage 9.44e+00%, Percentage 9.90e-01%
DOI:10.11975/j.issn.1002-6819.2017.z1.035
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 5>
Accession number:20171703590907
Title:Early-warning threshold extraction method of agricultural SCADA server based on clustering analysis
Authors:Yang, Lili (1); Wu, Chunhui (1); Zhang, Dawei (1); Su, Juan (1)
Author affiliation:(1) College of Information and Electrical Engineering, China Agricultural University, Beijing; 100083, China
Corresponding author:Su, Juan(sujuan@cau.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:293-299
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:In the agricultural SCADA (supervisory control and data acquisition) system, the early-warning threshold of server is usually set in a manual way. This way is neither precise nor punctual. To solve this problem, our paper proposes a method based on data-mining which takes Apache server in agricultural SCADA system as research object. In the agricultural SCADA system, front end user access and state real-time display of the devices monitoring indicators, such as CPU (Central Processing Unit) temperature and CPU fan speed, are based on the Apache server. Apache server handles a large number of concurrent access requests from front end users. When the number of devices and front end users is huge, this may cause a server access pressure. What's worse, the server will crash. A way to solve this matter is to give out a warning signal before the situation goes worse. When and how to give a signal need to be solved, so our paper proposes a method. First step, Apache operating data and the early-warning message of some exceptions for a period of time are collected. When the data are enough to be analyzed, we select feature from the data collected. This step is mainly to eliminate interference factors. And the feature subset obtained by feature selection means that the dimensions in the feature subset have more influence on the Apache running performance. To verify the influence of different feature subset on the clustering analysis, we set 2 different feature weight thresholds to gain different feature subset in this paper, and the feature weight threshold will filter the features whose weight is less than the threshold. Due to that the distribution shape of the data is unknown and the clustering algorithm has requirements in data distribution shape, in the next step, we cluster the result of feature selection with K-means algorithm and CURE (Clustering Using Representatives) algorithm respectively, which can gain the common characteristic of the server when the exception is about to happen. At last, we extract the early-warning threshold with the better result of clustering. In our method, the clustering algorithm with better result means it is more suitable to deal with the data, but when the data have another distribution shape, it may works badly. And this is why we choose 2 algorithms to do the clustering analysis with avoiding data shape preferences. We apply our method to experiment, and the result shows that feature selection can find out the performance bottleneck of the Apache server in SCADA system, and CURE algorithm gets a better clustering result. The verification test with the operating data of the server proves that our method can find out the potential risk of the system more early than the manual way. The research of this paper provides a new thinking in the extraction of early-warning threshold in computer monitoring for agricultural SCADA system. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:30
Main heading:Clustering algorithms
Controlled terms:Agriculture - Alarm systems - Cluster analysis - Curing - Data acquisition - Data mining - Display devices - Extraction - Feature extraction - Filtration - Program processors - SCADA systems - Servers - Set theory
Uncontrolled terms:Clustering using representatives - Distribution shapes - Dynamic extraction - Interference factor - Monitoring indicators - Performance bottlenecks - Supervisory control and data acquisition - Threshold
Classification code:722.2 Computer Peripheral Equipment - 723 Computer Software, Data Handling and Applications - 723.2 Data Processing and Image Processing - 731.1 Control Systems - 802.2 Chemical Reactions - 802.3 Chemical Operations - 821 Agricultural Equipment and Methods; Vegetation and Pest Control - 903.1 Information Sources and Analysis - 921.4 Combinatorial Mathematics, Includes Graph Theory, Set Theory
DOI:10.11975/j.issn.1002-6819.2017.z1.044
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 6>
Accession number:20171703590873
Title:Design and experiment of rapid detection system of cow subclinical mastitis based on portable computer vision technology
Authors:Cai, Yixin (1, 2); Ma, Li (1, 3); Liu, Gang (1, 2)
Author affiliation:(1) Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing; 100083, China; (2) Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing; 100083, China; (3) College of Information Science and Technology, Agricultural University of Hebei, Baoding; 071001, China
Corresponding author:Liu, Gang(pac@cau.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:63-69
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:With the enhancement of living conditions, the demand for milk is increasing rapidly, the quality of milk is paid more and more attention, and the improvement of the quality of milk has already become an important issue. However, subclinical mastitis in dairy cows is the most dangerous and costly disease which is difficult to control in dairy farm. In recent years, about 1/3 cows of the world are suffering from mastitis, especially subclinical mastitis in dairy cattle. Among them, the incidence of subclinical mastitis is 40%-80% in China, which is seriously harmful to the healthy development of dairy industry. In order to solve the problem of rapid detection of subclinical mastitis in dairy cows, a fast test system based on the computer vision technology of subclinical mastitis was proposed in this paper. Firstly, 25 dairy cows were selected randomly in the experiment, including 5 dairy cows with recessive mastitis, 5 dairy cows with severe mastitis and other 15 healthy dairy cows. Each cow has 4 breasts, so there were 100 sets of data in total. The Foss 5 000 milk somatic cell counts detector was used to obtain the number of somatic cells per sample. At the same time, the samples were dropped on the pH test paper, whose images were collected by USB (Universal Serial Bus) camera connected with the computer. The collected milk pH test paper images were changed into 500 × 500 pixels, and transformed from RGB (red, green, blue) color space to HSV (hue, saturation, value) color space. According to the color characteristics of the pH test paper, the threshold value was selected and the collected images were binarized. On the other hand, the segmented image was processed by morphological processing to remove the segmentation error and edge burr. Finally, the segmentation results were achieved by fusing the 2 results. Linear regression, power regression, quadratic regression, and principal component regression were used to establish estimation models using 75 sets of data. Those models were compared using the remaining 25 sets of data. The power regression of the principal component had a higher correlation coefficient, a lower standard error, and the highest determination coefficient (R<sup>2</sup>) of 0.970. System function and user interface were designed based on Android programming technology. The second experiment was carried out in the cattle farm to validate the favorable model by using the designed mobile terminal equipment which was connected with the USB camera. Using the 20 sets of data to validate the model, the correlation coefficient of the estimated milk somatic cell counts and the measurement value was 0.970, the estimated average relative error was 3.67%, and the standard deviation was 1.88%. The established estimation model of milk somatic cell counts using R and G indices estimated the milk somatic cell counts better than the model using only one index and the model combining 3 indices. Through the model comparison using the 100 sets of data and the validation in the real farm, the detection system of milk somatic cell count is more accurate, and can be used for the rapid detection of subclinical mastitis in dairy cows. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:28
Main heading:Dairies
Controlled terms:Cameras - Cells - Color - Computer terminals - Computer vision - Cytology - Disease control - Diseases - Errors - Image processing - Image segmentation - Microcomputers - Models - Principal component analysis - Regression analysis - Standards - System buses - User interfaces
Uncontrolled terms:Average relative error - Computer vision technology - Correlation coefficient - Cow mastitis - Determination coefficients - Morphological processing - Principal component regression - Rapid detection systems
Classification code:461.2 Biological Materials and Tissue Engineering - 722.2 Computer Peripheral Equipment - 722.4 Digital Computers and Systems - 741.1 Light/Optics - 741.2 Vision - 742.2 Photographic Equipment - 822.1 Food Products Plants and Equipment - 902.2 Codes and Standards - 922.2 Mathematical Statistics
Numerical data indexing:Percentage 1.88e+00%, Percentage 3.67e+00%, Percentage 4.00e+01% to 8.00e+01%
DOI:10.11975/j.issn.1002-6819.2017.z1.010
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 7>
Accession number:20171703590880
Title:Design and experiment of portable wireless soil moisture measuring device based on frequency-domain method
Authors:Meng, Delun (1); Meng, Fanjia (1); Duan, Xiaofei (1); Wang, Yiwen (1)
Author affiliation:(1) Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing; 100083, China
Corresponding author:Meng, Fanjia(mengfanjia@126.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:114-119
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:At present, soil moisture content is mainly monitored by wireless sensor networks and determined by portable soil moisture sensors. Generally, the sensor needs to be embedded into the specified depth of the soil, which obviously disturbs soil. The construction is difficult, and the cost of the equipment input and maintenance is high. When using the soil moisture measuring device which adopts split design, the instrument and sensor probe need to be connected by wire. For a large number of field measurements, it is inconvenient. According to the needs of soil moisture measurement, a portable wireless soil moisture measurement device was developed. The portable wireless soil moisture measuring device mainly consists of 3 parts: PVC (polyvinyl chloride) handle, support rod and soil moisture sensor. The integrated design of device structure adopts "T" structure. The soil moisture sensing unit of the measuring device, the information collecting and receiving unit and the battery unit are integrated into the handle. The power switch and the power charging interfaces are respectively arranged at both ends of the handle. Soil moisture can be measured at different depths of 0-300 mm. The soil moisture sensor and the information collection and transmission unit are combined to reduce the volume and weight of the device, which makes it easier to operate in the field. The handle adopts a closed structure to realize water-proof and dust-proof performance. Acquisition and wireless transmission system consists of soil moisture sensor, information collection and transmission unit and mobile phone with Android system. Soil moisture sensor uses frequency domain method to measure soil water content, and the soil moisture content information is sent to the collection and transmission unit in the form of analog voltage signal. The information collection and transmission unit is composed of MSP430F149 microcontroller, GPS (global positioning system) module, FLASH memory and Bluetooth module. This unit transmits the soil moisture content of soil moisture sensor to the mobile phone with Android system through the Bluetooth transmission mode and stores the measurement information by App (application). By the App, mobile phone analyzes the data processing, controls the test device and stores data, realizing the large capacity storage and intelligent processing of farmland data. In the laboratory environment, in order to obtain the function relationship between the output voltage of the sensor and the soil volumetric water content, the sand and loam were used to calibrate the measuring device. The relationship between soil moisture content and sensor output voltage obeyed the quadratic curve relationship, and the correlation coefficients were above 0.99. However, because of the different texture of soil samples in the 2 kinds of soil, the correlation coefficients were different. Therefore, in order to improve the measurement accuracy of soil moisture sensor, the soil texture should be calibrated before used. The measurement device was compared with the Poland Easy Test TDR (time-domain reflectometry) soil tester. Fifteen groups of soil samples with different moisture content were randomly taken and measured by the Poland Easy Test TDR soil tester and the portable wireless soil moisture measuring device respectively. The measurement results showed a linear correlation, and the correlation coefficient was 0.987, which indicated that the measuring device can accurately measure the soil moisture content. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:30
Main heading:Soil moisture
Controlled terms:Android (operating system) - Bluetooth - Cellular telephone systems - Cellular telephones - Construction equipment - Data handling - Design - Digital storage - Electric power transmission - Flash memory - Frequency domain analysis - Global positioning system - Mobile phones - Moisture - Moisture control - Moisture determination - Moisture meters - Mooring - Polyvinyl chlorides - Sensors - Soil surveys - Soils - Telephone sets - Time domain analysis - Water content - Wireless sensor networks
Uncontrolled terms:Different moisture contents - Frequency domains - Gps (global positioning system) - Measuring device - Soil volumetric water contents - Tdr (time domain reflectometry) - Wireless transmission systems - Wireless transmissions
Classification code:405.1 Construction Equipment - 483.1 Soils and Soil Mechanics - 706.1.1 Electric Power Transmission - 716.3 Radio Systems and Equipment - 718.1 Telephone Systems and Equipment - 722.1 Data Storage, Equipment and Techniques - 722.3 Data Communication, Equipment and Techniques - 723 Computer Software, Data Handling and Applications - 723.2 Data Processing and Image Processing - 815.1.1 Organic Polymers - 921 Mathematics - 921.3 Mathematical Transformations - 944.1 Moisture Measuring Instruments - 944.2 Moisture Measurements
Numerical data indexing:Size 0.00e+00m to 3.00e-01m
DOI:10.11975/j.issn.1002-6819.2017.z1.017
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 8>
Accession number:20171703590882
Title:Prediction of soil moisture based on multilayer neural network with multi-valued neurons
Authors:Ji, Ronghua (1); Zhang, Shulei (1); Zheng, Lihua (1); Liu, Qiuxia (1)
Author affiliation:(1) College of Information and Electrical Engineering, China Agricultural University, Beijing; 100083, China
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:126-131
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:In order to improve the multi-step ahead predication accuracy of soil moisture, we established a multi-step soil moisture prediction model using multilayer neural network with multi-valued neurons (MLMVN). It is a complex-valued neural network with derivative-free back-propagation learning algorithm and error-correction learning rule. Different to the traditional network, the inputs/outputs and weights of MLMVN are complex numbers located on the unit circle, and its learning does not require a derivative of the activation function; all these make it possible to increase the functionality of the network. In order to learn the characteristics of soil moisture better, we employed continuous multi-valued neurons (MVN) as the basic neurons of MLMVN. MVN is based on the principle of multiple-valued threshold function, and the function maps the complex plane into a whole unit circle. The experiment was carried out in an experimental filed of China Agricultural University located in Zuozhou City, Hebei Province during the spring growing season of maize (from March 15 to September 30 in 2015) to measure soil moisture hourly, and the performance of the MLMVN network was tested. At first, in the pre-process, the outlier values and missing values in the sample were replaced by the average values of the data points in the interval of 100 preceding and 100 succeeding data points to smooth the data. Moreover, timing analysis and autocorrelation analysis of preprocessed soil moisture showed that soil moisture was nonlinear non-stationary time series. Secondly, taking rainfall as the key environmental factor according to correlation analysis between soil moisture and atmospheric environmental factors (rainfall, temperature and wind speed), the correlation value of the rainfall was 0.875, which was the maximum among the 3 correlation values. Finally, real-valued soil moisture, rainfall and target values were transformed to complex numbers by a linear transformation, which could be used as MLMVN inputs and outputs. It is important to specify the value ranges below the maximum value and above the minimum value to avoid closeness between the maximal value and the minimal values. On the basis of the experiments, taking into account the network stability and prediction accuracy, two-hidden-layer MLMVN 240- 15-1200-1 was set up as the predictive neural network structure (240 neurons in the input layer, 15 neurons in the 1st hidden layer, 1 200 neurons in the 2nd layer and 1 neuron in the output layer). In addition, two-hidden-layer MLMVN with large number in the 2nd hidden layer worked closely to a high-pass filter. In detail, training dataset contained 3 312 samples, testing dataset contained 1 200 samples and input length was set as 240 steps (which corresponded to soil moisture and rainfall of 5 days). In order to study soil moisture sequence characteristics comprehensively, the training dataset was distributed throughout the maize growing period evenly. Experimental results showed that, when specifying the tolerance threshold for RMSE (root mean square error) was 0.1 radian, and one step ahead prediction accuracy of MLMVN neural network was 0.883, and using iterative method to make 72 steps ahead prediction, the prediction accuracy reached 0.853, showing small accumulating errors. The results also showed that MLMVN outperformed the real-valued BP (back propagation) neural network, which enhanced prediction precision by 9.1%. The study has validated that the soil moisture prediction model based on MLMVN neural network can predict the soil moisture precisely and is significant for the management of water-saving irrigation. Additionally, MLMVN is able to generalize the development of the soil moisture and show a good generalization ability. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:29
Main heading:Soil moisture
Controlled terms:Autocorrelation - Backpropagation - Backpropagation algorithms - Complex networks - Error correction - Errors - Forecasting - High pass filters - Iterative methods - Learning algorithms - Linear transformations - Mathematical transformations - Mean square error - Models - Moisture - Multilayer neural networks - Multilayers - Neural networks - Neurons - Rain - Soil testing - Soils - Statistical tests - Time series analysis - Water conservation - Water management - Wind
Uncontrolled terms:Backpropagation learning algorithm - BP (back propagation) neural network - Complex-valued neural networks - Multi-valued - Non-stationary time series - Predictive neural network - RMSE (root mean square error) - Spring maize
Classification code:443.1 Atmospheric Properties - 443.3 Precipitation - 444 Water Resources - 461.9 Biology - 483.1 Soils and Soil Mechanics - 703.2 Electric Filters - 722 Computer Systems and Equipment - 723.4 Artificial Intelligence - 921 Mathematics - 922.2 Mathematical Statistics
Numerical data indexing:Age 1.37e-02yr, Percentage 9.10e+00%
DOI:10.11975/j.issn.1002-6819.2017.z1.019
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 9>
Accession number:20171703590903
Title:Design and implementation of automatic orthorectification system based on GF-1 big data
Authors:Ye, Sijing (1, 3); Zhang, Chao (2); Wang, Yuan (1); Liu, Diyou (2); Du, Zhenbo (2); Zhu, Dehai (1)
Author affiliation:(1) Key Laboratory of Agricultural Information Acquisition Technology (Beijing), Ministry of Agriculture, China Agricultural University, Beijing; 100083, China; (2) Key Laboratory of Agricultural Land Quality, Ministry of Land and Resources, China Agricultural University, Beijing; 100083, China; (3) Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences, National Engineering Research Center for Remote Sensing Applications, Beijing; 100101, China
Corresponding author:Zhu, Dehai(dehaizhu@qq.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:266-273
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:Remote-sensing imaging is a complicated process. It is influenced by many factors like optical distortion, sensor attitude change, satellite platform movement, curvature change of the earth, terrain fluctuation, and so on, which cause geometric distortion such as excursion, extension, and shrink compared with location of real ground objects, and the extent of distortion turns severe with the increase of distance between pixel and sub-satellite point. Therefore in practical application, correcting geometric distortion caused by terrain fluctuation or satellite platform, becomes one of the basic works. We analyzed data organizing mode and metadata structure of GF-1 satellite image data, and on that basis RPC (rational polynomial coefficient) model-based forward and inverse transformation was combined with the DEM (digital elevation model) data extraction; the process of RPC model-based images orthorectification was elaborately calculated; automatic orthorectification system (GF1AMORS) were designed and implemented, which could fit 2, 8 and 16 m resolution images. There are the critical questions: 1) Method for DEM data rapid extraction; 2) Strategy for image blocking. Firstly, DEM data were reorganized and coded based on 0.5°×0.5° geographic grid system in order that DEM data could be read to system memory rapidly according to coordinate range of image being rectified, and relevant test showed that our grid-based DEM data dynamical extraction method could achieve good efficiency with different image range, while the system memory might overflow when the image range turned larger than 3.5°×3.5°. Secondly, comparative experiments were done to study the relation between image block size and orthographic correction efficiency of GF-1 WFV (wide field of view, 16 m resolution) multi-spectral images. Experiments showed that the computational efficiency of single image converged to 98 s when the block size was set as from 384×384 to 480×480. To test the conversion accuracy of our automatic orthorectification process, 20 GF-1 PMS (Pansharpen/Multispectral Sensor, 8 m resolution) multi-spectral images that covered area with different terrain features (mountainous and plain terrain) in Heilongjiang Province with less cloud were extracted, and on that basis 400 control points were selected and compared to their homonymy points selected in Google Earth (by ENVI "SPEAR Google Earth Bridge") to analyze error and convergence. The experiment showed that our automatic orthorectification process exhibited a nice accuracy and stability in both mountainous terrain and plain terrain: For mountainous terrain, the maximum error in X orientation was less than 16.863 m and in Y orientation was less than 16.811 m, and the standard deviation in X orientation was less than 5.514 m while that in Y orientation was between 2.872 and 4.336 m; for plain terrain, the maximum error in X orientation was less than 10.959 m and in Y orientation was less than 13.546 m, and the standard deviation in X orientation was less than 3.051 m while that in Y orientation was less than 3.761 m. The maximum distance error was 23 m, and the distance error of 92.25% control points was less than 16 m (namely 2 pixels), and that of 38.75% control points was less than 8 m (namely 1 pixel). At last, we presented the limitation and our future work about our automatic orthorectification method. Considering that there is no physical significance for each parameter of RPC model, the calibration precision of our system needs to be improved (e.g. by integrating control points to weaken the system error) before it is used in some applications with higher accuracy requirement; furthermore, there is still a large optimization space of computational efficiency for our system, and high performance computing method (e.g. graphics processing unit) will be integrated based on data block feature to improve the calculating speed. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:29
Main heading:Image processing
Controlled terms:Big data - Calibration - Computational efficiency - Computer graphics - Data handling - Data processing - Digital instruments - Efficiency - Errors - Extraction - Geographic information systems - Geomorphology - Graphics processing unit - Image analysis - Image coding - Inverse problems - Landforms - Metadata - Pixels - Program processors - Remote sensing - Satellites - Space optics - Spectroscopy - Statistics - Surveying
Uncontrolled terms:Comparative experiments - Design and implementations - Digital elevation model - GF-1 - High performance computing - Ortho-rectification - Ortho-rectification process - Rational polynomial coefficients
Classification code:405.3 Surveying - 481.1 Geology - 481.1.1 Geomorphology - 655.2 Satellites - 656.1 Space Flight - 723.2 Data Processing and Image Processing - 723.5 Computer Applications - 802.3 Chemical Operations - 903.3 Information Retrieval and Use - 913.1 Production Engineering - 922.2 Mathematical Statistics
Numerical data indexing:Percentage 3.88e+01%, Percentage 9.22e+01%, Size 1.10e+01m, Size 1.35e+01m, Size 1.60e+01m, Size 1.68e+01m, Size 1.69e+01m, Size 2.30e+01m, Size 3.05e+00m, Size 3.76e+00m, Size 5.51e+00m, Size 8.00e+00m, Time 9.80e+01s
DOI:10.11975/j.issn.1002-6819.2017.z1.040
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 10>
Accession number:20171703590864
Title:Development status and prospect of mechanical rolling shutter technology in solar greenhouse in China
Authors:Zhang, Guoxiang (1, 3); Fu, Zetian (1, 2, 3); Zhang, Lingxian (2, 4); Yan, Jin (2); Zhang, Biao (1, 3); Li, Xinxing (2, 3, 4)
Author affiliation:(1) College of Engineering, China Agricultural University, Beijing; 100083, China; (2) College of Information and Electrical Engineering, China Agricultural University, Beijing; 100083, China; (3) Beijing Laboratory of Food Quality and Safety, Beijing; 100083, China; (4) Key Laboratory of Agricultural Informationization Standardization (Beijing), Ministry of Agriculture, Beijing; 100083, China
Corresponding author:Li, Xinxing(lxxcau@cau.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:1-10
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:This paper describes the development of facility agriculture, which is represented by the facility vegetable. The facility vegetable has promoted the production of mechanical rolling shutter technology in solar greenhouse in the north of China since the 2000s. Solar greenhouse is a typical greenhouse type in the northern China, which can reduce greenhouse heat loss in the cold night by covering roller shutter and maintain the temperature indoor at a relatively steady state to meet the needs of crops growth inside solar greenhouse. The roller shutter can prevent crops from being irradiated by strong sunshine in the daytime and provide the necessary protection for the crops in the rain and snow weather. The length of a solar greenhouse is longer, if covering roller shutter completely relies on manpower. It is time-consuming and difficult to meet the light demands of crops growth in the daytime, which dominates crop production. In order to address this problem and meet the market demand, solar greenhouse rolling machine begins to appear on the market, which can replace manpower to achieve the rolling works and reduce the intensity of labor. And work time for rolling can be reduced to less than 20 min from 2 h in traditional manual work. Besides, solar greenhouse rolling machine can increase the illumination time of greenhouse crops, and promote crop growth and develop by its fast shutter speed. The paper points out that mechanical rolling shutter technology of solar greenhouse provides people a lot of convenience. Especially the rolling shade machine plays a positive role in planting crops in greenhouses in cold area of North China and promotes farmers to become rich. Rolling shade machine in China has the full intellectual property rights in technology. There are many types of rolling shade machines on the market. According to the different forms of the rolling shutter, the rolling shade machine can be divided into 2 types, the rear upper pulling type and the front upper pushing type. Besides, according to the difference of the support bar position, the front upper pushing type rolling shade machine can be divided side reel push type and push on the front reel. This paper introduces the development history of 2 kinds of rolling shade machines, and makes a comparison and detailed analysis of them, including the mechanical structure, working principle, and so on. In addition, in the development of mechanical rolling shutter technology, there are 3 key technologies: the speed redactor for mechanical rolling shutter, position measuring and controlling of rolling shutter. The speed redactor of rolling shade machine mainly includes spur geared wheel, worm and wormwheel, spur geared worm and worm-wheel, planetary gear with small teeth difference and external cylinder bevel gear. The position measuring and controlling part and technology of rolling shade machine mainly include mechanical limit switch, angle sensor and non-contact type position, sensor time-delay relay and indoor environmental sensor. In order to ensure the safety of rolling shade machine, the paper points out the present problems which occur frequently in solar greenhouse. The paper also puts forward a series of solutions which can be divided into 2 parts i.e. execution security and control security. At last, the paper points out the development trend of mechanical rolling shutter technology in solar greenhouse: The mechanical rolling shutter device will have higher efficiency and applicability, and mechanical rolling shutter security technology will get more attention. With the application of environmental sensor technology, and non contact measurement and control technology, the environment prediction model and control system for mechanical rolling shutter in solar greenhouse will get quick development and application. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:47
Main heading:Greenhouses
Controlled terms:Agriculture - Cold rolling - Commerce - Crops - Cultivation - Delay control systems - Environmental technology - Gear teeth - Intellectual property - Laws and legislation - Machinery - Position control - Rollers (machine components) - Solar heating - Time delay - Vegetables - Wheels
Uncontrolled terms:Development - Information - Intellectualization - Position measuring - Shutter - Shutter security
Classification code:454 Environmental Engineering - 535.1.2 Rolling Mill Practice - 601.2 Machine Components - 657.1 Solar Energy and Phenomena - 713 Electronic Circuits - 731.1 Control Systems - 731.3 Specific Variables Control - 821 Agricultural Equipment and Methods; Vegetation and Pest Control - 902.3 Legal Aspects - 971 Social Sciences
Numerical data indexing:Time 1.20e+03s, Time 7.20e+03s
DOI:10.11975/j.issn.1002-6819.2017.z1.001
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 11>
Accession number:20171703590891
Title:Design and experiment of agricultural diseases and pest image collection and diagnosis system with distributed and mobile device
Authors:Yao, Qing (1); Zhang, Chao (1); Wang, Zheng (1); Yang, Baojun (2); Tang, Jian (2)
Author affiliation:(1) Department of Information, Zhejiang Sci-Tech University, Hangzhou; 310016, China; (2) China National Rice Research Institute, Hangzhou; 310006, China
Corresponding author:Tang, Jian(tangjian@caas.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:184-191
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:In order to easily collect images of agricultural diseases and pests and make real-time diagnose, a distributed mobile system was designed with a number of portable image collection devices and one image processing server. Each image collection device consisted of an embedded camera, a stretchable handheld pole and an Android phone equipped with an APP of control capability. The embedded camera was fixed on the end of the handheld pole via universal joints. The handheld pole could extend to about 2 m in length. The embedded camera was built upon a development board with iTOP 4412 and a set of modules, including WIFI control, camera control, image collection, H.264/JPEG coding, RTSP/RTP video transmission, GPS information collection and writing, file transfer, and image preprocessing, which were developed in Linux platform. The mobile application was developed in Android platform with a set of modules, including video streaming preview, network, image browsing and camera control. The image processing sever could receive the images from the image collection devices, record GPS information, diagnose agricultural diseases and pests, and return the diagnosis and control information of agricultural diseases and pests to the mobile phone. Among the components of this system, the handheld pole was used to deliver the embedded camera to some unreachable agricultural disease and pest area, and the mobile phone was used for browsing images and controlling camera to collect the disease and pest images. TCP/UDP protocols and SoftAp technique were used for data exchange among the embedded camera and the mobile phone, which could be independent from cable networks and wireless local area networks. HTTP protocols were used for data exchange and distributed computing among the image collection devices and the image processing server, which can reduce the mobile phone charges and the server overhead. To test the distributed mobile agricultural system, a diagnosis algorithm of damage levels of rice sheath blight was deployed to the image processing server. This algorithm mainly included image feature extraction, disease identification, disease area computation and damage level judgment. The images of rice sheath blight were collected using the image collection device in paddy fields located in China National Rice Research Institute in 2016. After the segmentation of disease area was finished in the embedded camera, the segmented images were uploaded to the image processing server. The diagnosis algorithm in the server was implemented to process these images and the diagnosis results and control information were returned to the mobile phone. The technicians or farmers could control the rice sheath blight based on the diagnosis suggestions. Our experiment indicated that the image collection device could easily collect the images of agricultural diseases and pests, especially on some places where hands and sight were hard to reach. The system could work effectively with low image browse latency, accurate camera control, reliable device-to-server communication and real-time image processing and diagnosis. The accurate rate of 83.5% was achieved to diagnose the damage levels of rice sheath blight based on our algorithm. Therefore, the system is expected to be widely applicable to agricultural disease and pest image collection and diagnosis. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:30
Main heading:Image processing
Controlled terms:Agriculture - Android (operating system) - Cameras - Cellular telephone systems - Cellular telephones - Computer operating systems - Damage detection - Design - Diagnosis - Disease control - Diseases - Distributed computer systems - Electronic data interchange - Feature extraction - Global positioning system - Image coding - Image communication systems - Image segmentation - Learning algorithms - Mobile devices - Mobile phones - Poles - Telephone sets - Video streaming - Wireless local area networks (WLAN)
Uncontrolled terms:Diagnosis algorithms - Distributed computations - Distributed mobile systems - Image collections - Image feature extractions - Information collections - Real-time image processing - Server communications
Classification code:408.2 Structural Members and Shapes - 461.6 Medicine and Pharmacology - 716 Telecommunication; Radar, Radio and Television - 718.1 Telephone Systems and Equipment - 722.4 Digital Computers and Systems - 723 Computer Software, Data Handling and Applications - 723.2 Data Processing and Image Processing - 742.2 Photographic Equipment - 821 Agricultural Equipment and Methods; Vegetation and Pest Control
Numerical data indexing:Percentage 8.35e+01%, Size 2.00e+00m
DOI:10.11975/j.issn.1002-6819.2017.z1.028
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 12>
Accession number:20171703590876
Title:Performance prediction of single-channel centrifugal pump with steady and unsteady calculation and working condition adaptability for turbulence model
Authors:Wu, Xianfang (1); Feng, Jinsheng (2); Liu, Houlin (2); Ding, Jian (2); Chen, Huilong (1)
Author affiliation:(1) School of Energy and Power Engineering, Jiangsu University, Zhenjiang; 212013, China; (2) Research Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang; 212013, China
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:85-91
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:In order to evaluate the performance prediction accuracy of single-channel pump under computational fluid dynamics, a single-channel pump was taken as the study object. The standard k-Ε, RNG k-Ε (renormalization group k-Ε), standard k-ω and SST k-ω (shear stress transport k-ω) were used to predict the performance of single-channel pump numerically. Meanwhile the grid dependence was checked by employing 5 sets of meshes to improve the computational accuracy. The results of steady and unsteady numerical simulation were compared with that of the experiment on the head and efficiency. The unsteady results were closer to the experimental value. Therefore, the unsteady simulation method should be applied to simulate the internal flow of the single-channel pump. The head, efficiency and power were predicted under 3 different discharges (0.6 Q<inf>d</inf>, 1.0 Q<inf>d</inf>, 1.4 Q<inf>d</inf>, Q<inf>d</inf>is flow rate on design operating conditions, 220 m<sup>3</sup>/h) by using CFX (computational fluid dynamics X) 14.0. Energy performance prediction error was analyzed by compared with experimental results. The results showed that there was different degree error between the performance prediction values of the different turbulent models and experimental values. For head prediction of the discharge of 0.6Q<inf>d</inf>, the standard k-ω model, compared with the other 3 models, had higher prediction accuracy and the head error was 0.008%, followed by the SST k-ω model. However, for efficiency prediction of the discharge of 0.6 Q<inf>d</inf>, the SST k-ω model had the minimum error value. Therefore, for the simulations at low flow rates, head, efficiency and power errors were 0.38%, 3.12 percentage points and 5.59% respectively with the SST k-ω model. At the design condition, while the head calculation results of the RNG k-Ε model were closer to the experimental results than other turbulent models, the standard k-Ε model got the best results for the efficiency calculation. The efficiency error of the RNG k-Ε model was about 0.1 percentage points lower than the standard k-Ε model, so the RNG k-Ε model was applied to performance prediction under the design operating condition. When the single-channel pump was operating at the large flow condition (1.4Q<inf>d</inf>), the RNG k-Ε model possessed the highest precision of the head prediction and the standard k-Ε model was the best in the efficiency prediction. For comprehensive evaluation of the data, among all 4 turbulent models the RNG k-Ε model was the best one to predict the performance of single-channel pumps at the large flow rate. For the internal flow simulation of single-channel pump, the flow separation existed on the blade pressure surface under various operating conditions. As the decrease of the flow rate, the flow near the inlet edge tended to be disordered, especially with a wide range of backflow at the low flow rate. At the same time, there was large backflow on interior areas of the blade pressure surface. There were stagnation points near the inlet edge of blade suction surface and on the backboard. However, serious separation and recirculation flow would occur within the flow passage of the blade pressure side under the low flow conditions. Low pressure on the ring surface of impeller outlet was upstream to the blade outlet and near the outlet. The conclusions in this paper will provide a reliable performance prediction data and practice basis for the single-channel pump, also point the way for developing turbulent models for further study of single-channel pumps. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:22
Main heading:Forecasting
Controlled terms:Computational fluid dynamics - Computer simulation - Errors - Flow rate - Flow separation - Fluid dynamics - Numerical models - Pumps - Shear stress - Statistical mechanics - Turbulence models
Uncontrolled terms:Adaptability - Comprehensive evaluation - Computational accuracy - Efficiency calculations - Efficiency predictions - Performance - Performance prediction - Unsteady numerical simulations
Classification code:618.2 Pumps - 631 Fluid Flow - 723.5 Computer Applications - 921 Mathematics - 931.1 Mechanics
Numerical data indexing:Percentage 3.80e-01%, Percentage 5.59e+00%, Percentage 8.00e-03%
DOI:10.11975/j.issn.1002-6819.2017.z1.013
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 13>
Accession number:20171703590881
Title:Design and experiment of prototype soil pretreatment device for ISE-based soil nitrate-nitrogen detection
Authors:Li, Yanhua (1); Zhang, Miao (1, 2); Zheng, Jie (1); Pan, Linpei (1); Kong, Pan (1); Lei, Zongmu (1)
Author affiliation:(1) Key Laboratory on Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing; 100083, China; (2) Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing; 100083, China
Corresponding author:Zhang, Miao(zhangmiao@cau.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:120-125
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:Rapid lower-cost detection methods for soil macronutrient, including soil samping, sample pretreatment and nutrient determination, are urgently needed. In this paper, a prototype soil rapid pretreatment device was introduced for the ion-selective electrode (ISE) based in-situ soil nitrate-nitrogen detection. Necessary pretreatment steps of soil weighting, extracting and filtration were achieved in the integrated device. The mechanical structure of the device was composed of 4 major components, including a two-dimensional work bench, a high-speed centrifuge, a liquid pump and a self-designed electric control unit. The two-dimensional work bench was built with a vertical sliding table and a rotary table. On the purpose of eliminating the "suspension disturbance" of ISE, the centrifuge was used to obtain the clear soil extract solution. The peristaltic liquid pump, which conducted the quantitative injection of the soil extractant solution into centrifugal tube, was involved for the soil extractant injection and stirring. The electric control unit consisted of a microprocessor of STM32F103 and its interface circuit, which was used for the precise control of the two-dimensional work bench. The soil moisture sensor was applied to reduce the interference of soil water content. Net weight of the testing sample could be calculated. According to the testing results, the displacement accuracy of the vertical sliding table was less than 0.1 cm, while the root mean square error (RMSE) and mean relative error (MRE) were less than 0.04 cm and 0.4% respectively. The one-way displacement of 10 cm consumed less than 5 s. The accuracy of the angular movement for the rotary table was 1°. The rotating speed was calculated through the ratio between rotation angle and time, which could reach 10°/s. The two-dimensional work bench possessed good repeatability with the coefficient of variation (CV) of less than 0.40%. The accuracy of the liquid pump was also evaluated. Two injection volumes of 12.5 and 25 mL were selected. A volume accuracy of 0.2 mL was obtained. The RMSE, the MRE and the injection time were examined to be 0.07 mL, 0.24% and 13 s, respectively. The continuous injection operation possessed a high repeatability with the CV of less than 0.1%. Therefore, the extractant injection performance was verified to be reliable and accurate. The performance of the self-designed device was estimated in the nitrate-nitrogen detection of wet soil. Comparisons were conducted among 3 groups of ISE-based detections and the standard spectrometric detection. The 3 groups of ISE-based detections included the fast pretreatment group, the soil suspension group and the conventional pretreatment group. The fast pretreatment group was processed by the self-developed pretreatment device discussed above. Samples of the soil suspension group were prepared without filtration operation. The conventional pretreatment group was conducted under the national site-specific soil testing recommendation. Compared with the standard spectrometric detection, the poor detection accuracy with the biggest absolute error (AE) of 76.86 mg/kg was determined in the soil suspension group. The slope of regression equation was 2.27, which indicated a non-negligible deviation from the real condition. As expected, the ISE detection, conducted by the self-designed device, was much closer to the standard spectrometric. The adjusted determination coefficient (R<sup>2</sup>) of the regression equation was 0.87 and the equation slope was 1.31, which was less than 2.27 obviously. The average AE was decreased to be 20.97 mg/kg. The RMSE of 22.34 mg/kg was found. Meanwhile, the labor intensity was greatly reduced. However, the fast pretreatment group still exhibited the obvious difference with the conventional pretreatment group. In all, the device discussed in this study demonstrates a promising result, which might provide references for the ISE applications of in-situ soil macronutrient rapid detections. Further modification on the mechanical structure for extracting is required to enhance its performance. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:26
Main heading:Soil testing
Controlled terms:Centrifuges - Electrodes - Errors - Filtration - Ion selective electrodes - Liquids - Mean square error - Moisture control - Nitrates - Nitrogen - Nutrients - Pumps - Soil moisture - Soils - Spectrometry - Water content
Uncontrolled terms:Adjusted determination coefficient - Coefficient of variation - Electric control units - In-situ detections - Injection performance - Root mean square errors - Soil moisture sensors - Spectrometric detection
Classification code:483.1 Soils and Soil Mechanics - 618.2 Pumps - 731.3 Specific Variables Control - 802.1 Chemical Plants and Equipment - 802.3 Chemical Operations - 804 Chemical Products Generally - 804.2 Inorganic Compounds - 922.2 Mathematical Statistics - 941.4 Optical Variables Measurements
Numerical data indexing:Angular_Velocity 1.75e-01rad/s, Percentage 1.00e-01%, Percentage 2.40e-01%, Percentage 4.00e-01%, Size 1.00e-01m, Size 1.00e-03m, Size 4.00e-04m, Time 1.30e+01s, Time 5.00e+00s, Volume 1.25e-05m3, Volume 2.00e-07m3, Volume 2.50e-05m3, Volume 7.00e-08m3
DOI:10.11975/j.issn.1002-6819.2017.z1.018
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 14>
Accession number:20171703590901
Title:Extraction method of pig body size measurement points based on rotation normalization of point cloud
Authors:Wang, Ke (1, 2); Guo, Hao (1, 2); Liu, Weilin (1); Ma, Qin (1, 2); Su, Wei (1); Zhu, Dehai (1, 2)
Author affiliation:(1) College of Information and Electrical Engineering, China Agricultural University, Beijing; 100083, China; (2) Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing; 100083, China
Corresponding author:Zhu, Dehai(zhudehai@263.net)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:253-259
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:The body size of the livestock is an important indicator for breeding and animal production, however, the traditional measurement of body size of the livestock is a hard manual operation and it is inaccurate, expensive and time-consuming. In the field of computer vision measurement, the location of the pig body size measurement points is an essential work. To solve the automatic extraction of pig body size measurement points based on point clouds, a method of automatic extraction of pig body size measurement points was developed based on rotation normalization approach. In order to acquire the measurement points of the livestock, several processing steps were applied, and the steps were as follows: (1) Random sample consensus algorithm was used to acquire the plane parameter of the ground and the ground calculated by the parameter was then removed. The target pig and the ground normal vector were acquired. (2) Since the livestock acquired was from different coordinate systems, we proposed a rotation normalization method. First, the method of principal component analysis was used to get the coordinate axis of data cloud of the pig body and the initial measuring coordinate system was obtained. Secondly, the direction of the y axis was replaced by the direction of the ground normal vector acquired. The normal direction had 2 possibilities, that was, the normal vector was either pointing straight to the pig or going back away from the pig. The direction of the normal vector was determined by whether there was intersection between the constructed cuboid region and the pig. The cuboid region was constructed from the point on the ground along the normal vector. Finally, by using the geometrical relationship among the coordinate axis, standard measuring coordinate system defined in this paper was acquired. (3) Some of the measurement points of the livestock were usually the extreme points on the livestock body contour. By cutting the cloud points of the pig along the x axis direction with a step length, the slice data of the pig were acquired and the point cloud of contour line was obtained. By using the structural relation between the measurements points and the geometric feature of the measurement points either at the contour points or on the whole pig, the measurements points were calculated. Landrace sow specimens with 100 days old were selected as the experimental samples. To validate the rotation normalization performance of the proposed method, the scene point cloud including the specimen was used to evaluate the effects objectively. The point cloud was acquired by the 3D (three-dimensional) camera Xtion sensor using KinectFusion technology. Xtion is a sensor based on technology of structured light and consists of an infrared emitter, an infrared camera and an RGB (red, green, blue) camera. KinectFusion provides a function of 3D object scanning using a sensor. The results indicated that our approach could produce a reasonable rotation normalization result, and the directions of the length, height and width of the pig were basically consistent to the directions of the x, y and z axis. In order to verify the precision of measurement point's position, the proposed method was applied to locate the neck midpoint, body length measuring point, body width measuring point, body height measuring point and hip height measuring point. Average error of the results between the automatic extraction and artificial measurement of the selected points was less than 16 mm. The method in this paper can provide a reference for the automatic body measurement of the point cloud of the pig. This work is expected to be a useful contribution for animal production and breeding genetics. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:30
Main heading:Coordinate measuring machines
Controlled terms:Agriculture - Animals - Anthropometry - Automation - Cameras - Extraction - Independent component analysis - Infrared devices - Mammals - Measurements - Principal component analysis - Rotation - Vectors
Uncontrolled terms:3-D (three-dimensional) - Automatic extraction - Body sizes - Co-ordinate system - Geometrical relationship - Point cloud - Random sample consensus - Rotation normalization
Classification code:461.3 Biomechanics, Bionics and Biomimetics - 731 Automatic Control Principles and Applications - 742.2 Photographic Equipment - 802.3 Chemical Operations - 821 Agricultural Equipment and Methods; Vegetation and Pest Control - 921.1 Algebra - 922.2 Mathematical Statistics - 931.1 Mechanics - 943.3 Special Purpose Instruments
Numerical data indexing:Age 2.74e-01yr, Size 1.60e-02m
DOI:10.11975/j.issn.1002-6819.2017.z1.038
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 15>
Accession number:20171703590872
Title:Analysis on vibratory harvesting mechanism for trained fruit tree based on finite element method
Authors:Wang, Dong (1); Chen, Du (1, 2); Wang, Shumao (1, 2); Chen, Zhi (3); Zhang, Feng (1)
Author affiliation:(1) College of Engineering, China Agricultural University, Beijing; 100083, China; (2) Beijing Key Laboratory of Optimized Design for Modern Agricultural Equipment, Beijing; 100083, China; (3) China National Machinery Industry Corporation, Beijing; 100080, China
Corresponding author:Chen, Du(tchendu@cau.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:56-62
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:Currently, mass shake-and-catch tree fruit harvesting approach could cause fruit detachment efficiency in low level. Tree structure plays an important role in both harvest efficiency and machine performance for tree fruit. To obtain better harvest efficiency, it is important to optimize the machine parameters and provide proper excitation to the trees according to their growth morphology. This paper presented a finite element modeling method for the trained tree dynamic response study in relation to shaker type. Firstly, mathematic models of 3 types of shakers, including reciprocal shaker, orbital shaker and multidirectional shaker, were built, which were further used to analyze the basic working principles of these shakers. The basic principle of developed shaking models was analyzed according to their characteristics. In order to study the influence of different shakers on the dynamic response of trained tree, 3 training structures i.e. spindle, open center and vertical plane were selected. Their 3D (three-dimensional) physical models including trunk and secondary limbs were constructed in Pro/Engineer. Other elements, such as leaves and twigs, were removed from the model. Then, these models were imported into ANSYS software to analyze their modal shapes. The modal shape and resonance frequency were obtained in a frequency range of 1-50 Hz. Results showed that the resonance frequency and modal shape were influenced by the growth morphology of trees. Three types of trained tree model (spindle, open center and vertical plane) could obtain ideal response at the 10th, 14th, and 19th phase respectively. For the developed trained tree model, the natural frequency range was mainly contained from 7.0 to 20.0 Hz, corresponding to 420-1200 r/min of mechanical shaker. As having thick tree truck, the initial mode frequency of spindle-shape tree was greater than the other 2 types. To further study the dynamic response of the developed tree models, harmonic response simulation was conducted with 3 types of excitation patterns. For the spindle-shape tree, 3 excitations could all induce violent vibration at far-end of the limb (i.e. NODE4 and NODE5). Simulation results showed that multidirectional excitation could cause a relatively ideal response at 13.5 Hz (10th phase) for spindle-shape tree model. Multidirectional excitation could also induce the most violent response among the 3 methods and obtain the maximum displacement at featured location up to 1.844 m. Orbital excitation with 12.0 Hz (11st phase) could cause evident response with the maximum displacement of 2.485 m, and also provide relatively uniform response for open center tree model. However, a small part of area could only achieve a relatively low vibration response, as a portion of vibration energy was absorbed by the tree crotch. To obtain higher detachment efficiency, shaking from different directions would increase the machine performance for this type of trained tree. For vertical plane tree model, both reciprocal and orbital shaker could cause superior response considering mechanical harvest. Results obtained from field experiment validated the correctness of simulation results. The illustration method including modal analysis and harmonic response simulation, could be useful for new mechanical shaker design for trained fruit tree harvest. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:28
Main heading:Finite element method
Controlled terms:Dynamic response - Dynamics - Fruits - Harvesting - Modal analysis - Natural frequencies - Orchards - Resonance - Trees (mathematics) - Vibration analysis
Uncontrolled terms:3-D (three-dimensional) - Fruit trees - Maximum displacement - Orbital excitations - Resonance frequencies - Tree fruit harvesting - Vibrations - Vibratory response
Classification code:821.3 Agricultural Methods - 821.4 Agricultural Products - 921 Mathematics - 931.1 Mechanics
Numerical data indexing:Frequency 1.00e+00Hz to 5.00e+01Hz, Frequency 7.00e+00Hz to 2.00e+01Hz, Rotational_Speed 4.20e+02RPM to 1.20e+03RPM, Size 1.84e+00m, Size 2.48e+00m
DOI:10.11975/j.issn.1002-6819.2017.z1.009
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 16>
Accession number:20171703590871
Title:Design and simulation of artificial limb picking robot based on somatosensory interaction
Authors:Xu, Changlei (1); Wang, Qing (1); Chen, Hong (1); Mei, Shuli (1); Du, Liqiang (1)
Author affiliation:(1) College of Information and Electrical Engineering, China Agricultural University, Beijing; 100083, China
Corresponding author:Wang, Qing(wangqingait@sina.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:49-55
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:In order to improve the automation degree in agriculture, reduce the workload of farmers, enhance labor efficiency and product quality and ensure that the fruit can be harvested in real time, we designed a robot system which can imitate human picking behavior and pick fruits in real time. In this paper, we first analyzed the physiological characteristics of human upper limb and built a motion model according to human upper limb movement. In the model we selected the key joints and bones as the acquisition object, while the acquisition information includes its location, angle, speed and some other information. In addition, based on the bionics characteristics, we built a motion model of the robot manipulator. The system can gather the skeleton information of the human body using the depth camera Kinect device when the human picking movement occurs. After that the system calculates the position deviation of the joint and the rotation angle of the skeleton by calculating the space vector. At the same time, the system calculates the robot arm for each joint position and rotation angle information. According to the information of instructions, the system can translate the mathematical information into many instructions and send them to the robot manipulator. The robot manipulator will follow the instruction and fulfill the task of picking in real time when instructions have been received correctly. The action system includes 3 main sub-modules: information acquisition module, instruction transition module and instruction execution module. The information acquisition module uses a depth camera Kinect device and acquires the desired three-dimensional (3D) physiological data from the human behavior. The degree of mechanical freedom of human arm is 7 while five-axis robot limb is enough to arrive anywhere human limb can arrive. So the paper built a model with 5 degrees of mechanical freedom to imitate the human picking behavior. The 3D information includes 3 kinds of physiological parameters: position, velocity and angle information. The position information can be obtained through Kinect SDK (software development kit). Based on the position data the module can calculate the vector data automatically. Additionally, through Kinect SDK the module can get 30 frames of position data per second which can support velocity calculation when the data are denoised and smoothed by a filter. The instruction transition module will compare the motion data with the data already saved in the database to find out the most similar movement instruction. The instruction transition module consists of a reverse calculation sub-module which can calculate all the location, mechanical rotating angle and speed information of the robot arm joint according to the destination position and motion model data. The mechanical data in addition to instruction will be sent to the instruction execution module. The instruction execution module in fact is a robot arm. Its mechanical arm shape is very similar to that of human arm. There are mechanical joints corresponding to human upper limb joints on the mechanical limb. The 3 linkage mechanisms can simulate the movement of human upper limbs, which can help the robot finish the picking task. In this paper, we analyzed the mechanism of human upper limb, built a mathematical model based on the bionics principle of robot, built a model of manipulator with 5 degrees of freedom, and studied the actual coordinate movement of the mathematical model of the mechanical arm in the process of fruit picking. And on the basis, we put forward a mathematical method, which is relatively simple and effective. In order to verify the validity of the model, we performed a simulation test, and the results of the test were in the acceptable range, which verified the validity of the model. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:27
Main heading:Manipulators
Controlled terms:Agriculture - Beam plasma interactions - Behavioral research - Biological organs - Biomimetics - Cameras - Computer aided software engineering - Degrees of freedom (mechanics) - Flexible manipulators - Fruits - Industrial robots - Joints (anatomy) - Machine design - Mechanisms - Models - Modular robots - Musculoskeletal system - Physiological models - Physiology - Robot applications - Robotic arms - Robots - Software design - Vector spaces
Uncontrolled terms:Information acquisitions - Manipulator arms - Mathematical information - Motion capture - Physiological characteristics - Physiological parameters - Skeleton models - Somatosensory
Classification code:461 Bioengineering and Biology - 601 Mechanical Design - 601.3 Mechanisms - 723.1 Computer Programming - 731.5 Robotics - 731.6 Robot Applications - 742.2 Photographic Equipment - 821 Agricultural Equipment and Methods; Vegetation and Pest Control - 821.4 Agricultural Products - 921 Mathematics - 931.1 Mechanics - 932.3 Plasma Physics - 971 Social Sciences
DOI:10.11975/j.issn.1002-6819.2017.z1.008
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 17>
Accession number:20171703590884
Title:Rapid determination of moisture content in maize leaf based on transmission spectrum
Authors:Chen, Xiang (1); Li, Minzan (1, 2); Sun, Hong (1); Yang, Wei (1); Zhang, Junyi (1); Mao, Bohui (2)
Author affiliation:(1) Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing; 100083, China; (2) Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing; 100083, China
Corresponding author:Sun, Hong(sunhong@cau.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:137-142
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:Water is essential for plant growth, and water shortage will have an impact on plant yield, growth and quality, therefore, rapid and nondestructive detection of water content in maize leaves is of great significance for scientific guidance of irrigation, and it is also important for improving crop yield. In order to rapidly detect the moisture content of maize leaves, transmission spectrum on the band of 300-1 700 nm was selected to predict moisture content of maize leaves. The testing system included the light source and sensors. The light source part was provided by Wuling Optical Instrument Company. The transmission spectral range of 350-820 nm was obtained with ocean STS-VIS spectrometer, and the spectrum of 900-1 700 nm was measured by Wuling NIRez near-infrared spectrometer. Although the 820-900 nm transmission spectrum was absent, the transmittance curves obtained from different water gradients showed the same trend. Ten maize plants at stage V9 were detected. In order to eliminate the interference of different blade thickness on the experimental results, the first piece of fully expanded leaves from the top of the plant was selected as the experimental sample. The samples were taken back to the laboratory, and 10 cm long strips were cut along the veins from the middle of the leaves. The experiment measured and recorded transmission spectra curve and the moisture content of maize leaves under different water gradient. The moisture content measured by fresh weight moisture content formula, the transmission rate calculated according to Lambert-Beer law. The total number of experimental samples was 98, validation set was 33, and modeling set was 65. The total moisture content of maize leaves was 10%-85% and the average moisture content was 57.9%. In order to eliminate the influence of the noise caused by the spectrometer itself, the Savitzky-Golay method was used for pretreatment. The data was processed using correlation analysis between the transmission spectra and the moisture content, and principal co. As a result, the sensitive wavelengths at 800, 932 and 1 423 nm were selected by the correlation analysis, and the sensitive wavelengths at 478, 748, 1058 and 1323 nm were extracted by principal component analysis. To further improve the determination ability of the model, four sensitive wavelengths were selected, which were 800, 1 323, 1 058 and 1 423 nm. Using the combination of these four wavelengths, 12 vegetation index such as ratio vegetation index, difference vegetation index and normalized difference vegetation index were obtained. In the 12 vegetation indices, the modeling accuracy and prediction accuracy were significantly higher than other vegetation indices, so the DVI (1 423, 800) and DVI (1 423, 1 503) were respectively used to predict the moisture content. The results showed that DVI, which was derived from the combination of strong water absorption band (1 423 nm) and weak water absorption wavelength (800 and 1503 nm), could effectively restrain the disturbance caused by structural changes, and it could sensitively reflect the moisture content of maize leaves. Multivariate linear regression model was established based on DVI (1 423, 800), T<inf>1323</inf>and T<inf>1058</inf>, the calibration R<sup>2</sup>reached to 0.968 8, the validation R<sup>2</sup>reached to 0.951 9, and the root mean square error of prediction was 0.061. The results showed that water prediction model established by transmission spectroscopy could provide efficient guidance for plant leaf water rapid detecting instrument development. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:20
Main heading:Moisture determination
Controlled terms:Correlation methods - Crops - Data handling - Data processing - Forecasting - Infrared devices - Infrared spectrometers - Light sources - Linear regression - Mean square error - Moisture - Plants (botany) - Principal component analysis - Regression analysis - Spectrometers - Spectrum analysis - Vegetation - Water absorption - Water content
Uncontrolled terms:Leaves - Multivariate linear regression model - Near infrared spectrometer - Normalized difference vegetation index - Ratio vegetation indices - Root-mean-square error of predictions - Transmission spectroscopy - Transmitted spectra
Classification code:723.2 Data Processing and Image Processing - 741.3 Optical Devices and Systems - 802.3 Chemical Operations - 821.4 Agricultural Products - 922.2 Mathematical Statistics - 944.2 Moisture Measurements
Numerical data indexing:Percentage 1.00e+01% to 8.50e+01%, Percentage 5.79e+01%, Size 1.00e-01m, Size 1.06e-06m, Size 1.32e-06m, Size 1.42e-06m, Size 1.50e-06m, Size 3.00e-07m to 1.70e-06m, Size 3.50e-07m to 8.20e-07m, Size 8.00e-07m, Size 8.20e-07m to 9.00e-07m, Size 9.00e-07m to 1.70e-06m, Size 9.32e-07m
DOI:10.11975/j.issn.1002-6819.2017.z1.021
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 18>
Accession number:20171703590908
Title:Development of distributed data sharing platform for multi-source IOT sensor data of agriculture and forestry
Authors:Chen, Dong (1); Wu, Baoguo (1); Chen, Tian'en (2); Dong, Jing (2)
Author affiliation:(1) School of Information Science and Technology, Beijing Forestry University, Beijing; 100083, China; (2) National Engineering Research Center for Information Technology in Agriculture(NERCITA), Beijing; 100097, China
Corresponding author:Wu, Baoguo(wubg@bjfu.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:300-307
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:Agriculture and forestry IOT sensor data are the basis of agricultural informatization and forest ecological monitoring. At present, the phenomenon of information alone between various IOT data application systems makes it difficult to realize IOT sensor data sharing. However, with the developing of agricultural whole industry chain information management and forest ecological monitoring domain, the demand for each link of IOT sensor data of agriculture and forestry are getting stronger. In order to overcome the problem, a sharing platform for distributed multi-source IOT sensor data of agriculture and forestry was designed. The platform was divided into 5 parts, including data center subsystem, data adapter subsystem, data storage subsystem, data publishing subsystem and data transmission bus. The data center subsystem was responsible for managing the basic information of the sensor, and realized basic information storage function and registration function of the sensor nodes. This subsystem was the core of the platform for data management, which provided basic information of the sensor nodes for other subsystems. The data adapter subsystem was responsible for receiving and analyzing data from different sensor nodes, and it used a non-blocking Socket interface and a data acquisition interface based on Http and WebService protocol to receive data from different data sources. In this subsystem, the adapter was designed according to the difference format of the received data, and the final data to be analyzed were sent to the data storage subsystem. The data storage subsystem was responsible for storing sensor nodes data using MySQL database. The data publishing subsystem provided standardized data query interfaces of sensor nodes, which included a single sensor node real-time data and historical data query interface, multi sensor nodes real-time data and historical data query interface. The data transmission bus was realized by Active MQ server software, and concurrent capability and response speed of the platform were tested, Concurrent capability test results showed that, error rate was 0 and data throughput was 2 724 KB when the data concurrent access link number was below level 10<sup>2</sup>, which could meet the requirements of concurrent. Response speed test results of the platform showed that, response time of single sensor real-time data query was 63 ms, response time of less than 100 sensors real-time data query was 90.40 ms, average response time of historical data query was 1 846.72 ms when data were less than 1 000, and average response time of historical data query was 3 353.86 ms when data were more than 10 000, which met design requirements. The sharing platform has been deployed in laboratory of National Engineering Research Center for Information Technology in Agriculture, and the performance has been well since began to run. Currently, 550 sensor nodes' data of Shunyi, Xinjiang, Yangling, Tongzhou and other 17 units have been accessed, with the amount of accessing data more than 10 000 every day. The platform sets up a data bridge between different IOT sensor devices and the data application system in agriculture and forestry field, and realizes the unification management of the agricultural and forestry IOT sensor data. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:28
Main heading:Information management
Controlled terms:Agriculture - Data acquisition - Data communication systems - Data storage equipment - Data transfer - Digital storage - Ecology - Forestry - HTTP - Hypertext systems - Information technology - Internet - Query processing - Response time (computer systems) - Sensor nodes - Software testing - Timber - Wireless sensor networks
Uncontrolled terms:Concurrent access - Data application - Distributed architecture - Ecological monitoring - Sensor data - Server softwares - Sharing of data - Sharing platforms
Classification code:454.3 Ecology and Ecosystems - 722 Computer Systems and Equipment - 723 Computer Software, Data Handling and Applications - 821 Agricultural Equipment and Methods; Vegetation and Pest Control
Numerical data indexing:Time 3.54e-01s, Time 6.30e-02s, Time 8.47e-01s, Time 9.04e-02s
DOI:10.11975/j.issn.1002-6819.2017.z1.045
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 19>
Accession number:20171703590869
Title:Grain yield data transformation model based on photoelectric principle and its validation
Authors:An, Xiaofei (1, 2); Fu, Xinglan (1, 2, 3); Meng, Zhijun (1, 2); Yin, Yanxin (1, 2); Li, Liwei (1, 2)
Author affiliation:(1) Beijing Research Center for Intelligent Agricultural Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing; 100097, China; (2) National Research Center of Intelligent Agricultural Equipment for Agriculture, Beijing; 100097, China; (3) Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming; 650000, China
Corresponding author:Meng, Zhijun(mengzj@nercita.org.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:36-41
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:In order to obtain real time grain yield data information, a kind of grain yield monitor system based on photoelectric principle was developed. It was consisted of sensor module, data collection module, GPS module and grain yield calculation terminal. The optical reflectance type grain volume sensor was installed on one side of the combine elevator. When the grain was conveyed through the grain flow sensor, the scraper and grain would be block the light path, intermittently. As a result, a pulse width signal would be generated. And the pulse width signal was proportional to the thickness of scraper and grain volume. Other sensors signals such as elevator speed sensor signal and GPS signal would also be generated at the same time. After the data collection module, all the signals would be transmitted to the liquid crystal display (LCD) terminal by RS485 bus. The grain volume monitor software could display grain instantaneous output volume, grain production information, harvest area and other information. After analyzing the working status of combine harvester and the simulation of scraper heap shape, a subsection type grain yield transformation model was proposed. As the accuracy of grain yield monitor system was affected by the elevator speed seriously, the model had also considered the elevator speed as an input parameter. When the combine harvester worked at the normal status, grain volume had linear relationship with scraper grain thickness. In order to further optimize the quality of yield data, a new preprocessing method was also proposed based on elevator speed dynamic threshold value filter. The experiment selected a field of 2.67 hm<sup>2</sup>Xiaotangshan National Demonstration Station in Beijing, located in E116°26'54.66″-116°27'02.41″, N40°11'15.50″-40°11'11.61″. The grain yield monitor system was installed TB60 (4LZ-6 B) type self-propelled combine harvester produced by Zoomlion Corporation. According to the combine elevator in different speed conditions of no-load scraper thickness, the upper and lower bounds of normal production were determined as the original data of singular value standard. Once the data was below 10% of the real time calculated scraper thickness, it was removed. Once it was above 5 times of the real time calculated scraper thickness, it was replaced by the normal value one second before. In order to evaluate this new preprocessing method, original data, average filter data and dual threshold filter data were used to validate the model. The test results showed that the proposed data preprocessing method could eliminate the singularity and improve the smooth of yield data, obviously. The coefficient of variation (CV) also decreased to 0.33 from 0.53. The field experiment showed that validation error of the grain yield monitor model was less than 3.50%, which could satisfy the practical need. Compared with foreign similar type grain yield monitor system, such as SMARTYIELD Pro system produced by Raven Corporation and the Yield Monitor System produced by Trimble Corporation, the developed system had the advantage of simple calibration step and convenient installation method. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:31
Main heading:Monitoring
Controlled terms:Data acquisition - Display devices - Elevators - Grain (agricultural product) - Harvesters - Liquid crystal displays - Metadata - Photoelectric devices - Photoelectricity - Sensors - Speed - Tools
Uncontrolled terms:Coefficient of variation - Combine harvesters - Data collection modules - Liquid crystal display(LCD) - Photoelectric principle - Threshold filter - Transformation model - Upper and lower bounds
Classification code:692.2 Elevators - 701.1 Electricity: Basic Concepts and Phenomena - 722.2 Computer Peripheral Equipment - 723.2 Data Processing and Image Processing - 821.1 Agricultural Machinery and Equipment - 821.4 Agricultural Products
Numerical data indexing:Percentage 1.00e+01%, Percentage 3.50e+00%
DOI:10.11975/j.issn.1002-6819.2017.z1.006
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 20>
Accession number:20171703590909
Title:Intelligent exhibition platform of Chinese ancient farming virtual scene based on Unity3D
Authors:Li, Chunxiao (1); Sun, Ruizhi (1, 2); Dai, Yizhou (1); Cai, Saihua (1); Li, Qian (1); Li, Jiayao (1)
Author affiliation:(1) College of Information and Electrical Engineering, China Agricultural University, Beijing; 100083, China; (2) Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing; 100083, China
Corresponding author:Sun, Ruizhi(sunrz_cn@sina.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:308-314
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:The limitation of the current mode of transmission has hindered the spread and development of farming culture and spirit. In view of the mentioned facts, a method of developing an intelligent exhibition platform based on virtual reality was proposed. In order to avoid the error and delay caused by interaction, we proposed an algorithm of model interactive control that could realize observing the 3D (three-dimensional) model by rotating and zooming it accurately. Meanwhile, we put forward a virtual scene roaming algorithm for orientation changing aiming at the problem that the present virtual scene path roaming algorithm cannot change the object's orientation, and as a result, the effect achieved by this algorithm was more similar to behavior of human. The intelligent exhibition platform of virtual farming scene was designed and developed based on the Unity3D platform, with 3D Max as the 3D modeling tool and C# as the scripting language. Virtual farming scene should be built on the basis of the objective facts, so that it can be in line with the historical background and reality. But one of the difficulties of this platform was that we had no real scene for reference, so we proposed a method of observation and measurement to solve the problem. According to the analysis and design of the data, the display platform was divided into 2 parts, the north ancient China with dry land and the south ancient China with paddy fields. Each part would be shown from 3 aspects, i.e. scene, farming tools and typical production skills. By observing the appearance, recording the structure, size and angle measurement of real farming tools and its components, we obtained the basic data to complete 3D modeling. There were 2 frequently used methods for 3D modeling: entity modeling and fractal modeling. Through the analysis of physical characteristics of objects, different modeling methods were adopted to complete modeling. With its convenient and intelligent features, key frame animation has become the most basic computer animation technology. In order to achieve the final animation effect, combined with the characteristics of the 3D models of this platform, we adopted key frame animation technology to complete the animation. In order to achieve the intelligent display of scenes, we used 3D virtual roaming technology, and the scene roaming was divided into 2 modes, automatic mode and manual mode. The first way could browse the virtual scene automatically by using the virtual scene roaming algorithm for orientation changing that we have proposed, while the second provided user an opportunity to view the scenes manually through a virtual character. We adopted human-computer interaction technology by using the algorithm of model interactive control that we have proposed. And the interaction accuracy was favorable. Unity3D was utilized as the development software for the platform. The completed 3D models were imported into Unity3D project file with a file format of.FBX. Both the scene roaming and interactive function of the platform were achieved by coding C# scripts. The virtual scene that could show experiment results of the platform rendered a good effect. The research result showed that blended with roaming and controlling, the intelligent platform restored an ancient farming scene, which publicizes the typical production skills, greatly improves the practicability of the platform, and offers a kind of new method for culture exhibition. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:27
Main heading:Three dimensional computer graphics
Controlled terms:Animation - Beam plasma interactions - Behavioral research - Control - Exhibitions - Farms - Human computer interaction - Modeling languages - Virtual reality
Uncontrolled terms:3-D (three-dimensional) - 3-d modeling - Historical background - Intelligent displays - Interactive functions - Observation and measurement - Physical characteristics - Virtual scenes
Classification code:723 Computer Software, Data Handling and Applications - 821 Agricultural Equipment and Methods; Vegetation and Pest Control - 932.3 Plasma Physics - 971 Social Sciences
DOI:10.11975/j.issn.1002-6819.2017.z1.046
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 21>
Accession number:20171703590889
Title:Method for measurement of maize stem diameters based on RGB-D camera
Authors:Qiu, Ruicheng (1); Zhang, Man (1); Wei, Shuang (2); Li, Shichao (2); Li, Minzan (1); Liu, Gang (1)
Author affiliation:(1) Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing; 100083, China; (2) Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing; 100083, China
Corresponding author:Zhang, Man(cauzm@cau.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:170-176
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:Stem diameters of maize are important phenotype parameters and can characterize the crop growth and lodging resistance, drawing more attentions from breeders. Traditional measurement about stem diameters is usually manual measurement, which is timeconsuming, laborious, and subject to human error. In order to rapidly measure stem diameters of maize in field, a method based on RGB-D (red, green, blue - depth) camera was proposed in this paper to extract stem diameters of maize. The color images and depth images of the maize plants at the small bell stage were captured by a RGB-D camera in field. First, maize stem was extracted by processing the color image. It was hard to recognize maize just according to the color differences in red, green and blue component between maize and background due to the illumination variations. To solve the problem, the component that represented the difference between green signals and illumination brightness was calculated and applied to segment maize with Otsu algorithm, and the binary image of maize was generated. And then erosion operation was conducted within region of interest to cut off the connection between little leaves and maize stem, and small regions were eliminated to remove weed and little leaves. The largest region of maize was saved after dilation operation. After that, skeletonization was conducted for main stem. There were crossing points at the points of contact between leaves and stem, and ending points at the points of contact between ground and stem, and the potential measurement region of stem could be identified by searching crossing points and ending points. The color coordinates of the potential measurement region were saved and corresponding point cloud data were generated based on the mapping relationship between color coordinate, depth coordinate and camera coordinate. Second, stem diameters were calculated by processing point cloud data. Noise points affected measurement accuracy of stem diameters, and K-nearest method was applied to remove scattered points from point cloud data. Then the filtered point cloud data of potential measurement region were clustered. There were some point cloud data on the edge of stem due to the measurement of time of flight (ToF), which were background noises. K-means method was used to divide the filtered point cloud data into 2 groups, and only the group whose central point was nearer to the camera was saved to represent maize stem. The saved point cloud data were one side of stem, and ellipse fitting based on least square method was carried out for the point cloud data. Long axis parameter and short axis parameter of ellipse were calculated respectively to indicate the stem diameters of maize. 20 samples were tested to verify aforementioned method, and the experimental results showed that the method proposed in this paper had a good performance in segmenting and identifying maize stem, though ellipse fitting method needed to be improved. The mean errors, standard deviation and mean relative errors of measuring stem diameters were 3.31 mm, 3.01 mm, 10.27% for long axis and 3.33 mm, 2.39 mm, 12.71% for short axis, respectively, indicating that the proposed method could be applicable for plant phenotyping. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:31
Main heading:Least squares approximations
Controlled terms:Binary images - Cameras - Color - Crops - Data handling - Errors - Geometry - Image recognition - Image segmentation - Measurements - Plants (botany)
Uncontrolled terms:Ellipse fitting method - Illumination variation - Plant phenotyping - Point cloud - Potential measurements - Processing point clouds - Rgb-d cameras - Stem diameter
Classification code:723.2 Data Processing and Image Processing - 741.1 Light/Optics - 742.2 Photographic Equipment - 821.4 Agricultural Products - 921 Mathematics - 921.6 Numerical Methods
Numerical data indexing:Percentage 1.03e+01%, Percentage 1.27e+01%, Size 2.39e-03m, Size 3.01e-03m, Size 3.31e-03m, Size 3.33e-03m
DOI:10.11975/j.issn.1002-6819.2017.z1.026
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 22>
Accession number:20171703590868
Title:Design and experiment of harvest boundary online recognition system for rice and wheat combine harvester based on laser detection
Authors:Wei, Liguo (1, 2); Zhang, Xiaochao (1); Wang, Fengzhu (1); Che, Yu (1); Sun, Xiaowen (1); Wang, Ziwei (3)
Author affiliation:(1) Chinese Academy of Agricultural Mechanization Sciences, Beijing; 100083, China; (2) State Key Laboratory in Areas of Soil-Plant-Machine System Technology, Beijing; 100083, China; (3) School of Automation, Beijing Institute of Technology, Beijing; 100081, China
Corresponding author:Zhang, Xiaochao(zxchao2584@163.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:30-35
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:At present, the combine harvester is developing towards the direction of large scale and high speed. It is more and more difficult to recognize the harvest boundary only by people's eyesight, which is to ensure the consistency of cutting when combine harvester operates. When the harvester works in the field, it usually works in full cutting conditions, which requires the driver has high driving skills, and the whole tracking operations keep for a long time; the labour intensity of the driver and the dust of field work make it difficult to rely on the naked eye to obtain accurate boundary. Combine harvester yield monitoring system is according to the need of harvest cutting and actual speed for real-time calculation of harvest area at home and abroad, and combine harvester yield measuring system mainly relies on the operator's input of harvesting information manually, but the actual harvest is difficult to ensure the full harvest. In the detection of combine harvester's feeding quantity, cut width, density, and speed of operation are needed to measure for the straw obtaining. The automatic driving system of combine harvester can automatically track driving according to the harvest boundary. Therefore, the on-line detection of harvest boundary is important for intelligent monitoring system of combine harvester. Aiming at the problem of on-line recognition of harvesting boundary of combine harvester, an on-line recognition system for harvesting boundary of combine harvester was developed by laser non-destructive detection technology. Firstly, the composition of the system, and the selection and working principle of the laser sensor were introduced, and the polar coordinate of the sensor output data is converted to the right angle coordinate. The laser sensor is a 1D (one-dimensional) scanning laser rangefinder supplied by the SICK company. It uses an infra-red laser beam and it works on the principle of light propagation time measurement. A short luminous impulse is emitted. The luminous ray is then deviated by a revolving mirror and thus covers a half-plane. When the ray meets an obstacle, retro diffused light is collected by the detector. The distance from the sensor to the object is then calculated from the time interval between the emission and the reception of the impulse. As the harvest process will produce a lot of dust, it will have a laser detection distance and signal reflection. The laser ray from the sensor is retro diffused when it meets suspended particles of dust. In consequence, the measured distance is shorter than that separating the top of the vegetation from the rangefinder. Through the comparison with crop characteristic threshold, the error data affected by dust are effectively identified and eliminated. Using moving average digital filtering algorithm, system measurement noise is eliminated. Through the signal step change pattern recognition algorithm, the on-line detection of the harvest boundary is realized, and the cutting amplitude of the combine harvester is calculated accurately. The test results show that the system can realize on-line monitoring, and the measurement error is not more than 12 cm, which can provide reference for the practical application of intelligent monitoring system of combine harvester. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:26
Main heading:Harvesters
Controlled terms:Automobile drivers - Dust - Error detection - Grain boundaries - Harvesting - Laser beams - Lasers - Monitoring - Pattern recognition - Range finders
Uncontrolled terms:Automatic driving system - Intelligent monitoring systems - Nondestructive detection - Real-time calculations - Scanning laser rangefinders - Swath - Tracking operations - Wheat combine harvesters
Classification code:432 Highway Transportation - 451.1 Air Pollution Sources - 744.1 Lasers, General - 744.8 Laser Beam Interactions - 821.1 Agricultural Machinery and Equipment - 821.3 Agricultural Methods - 943.1 Mechanical Instruments
Numerical data indexing:Size 1.20e-01m
DOI:10.11975/j.issn.1002-6819.2017.z1.005
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 23>
Accession number:20171703590857
Title:Development of portable determinator for fast detection of lead ions
Authors:Wu, Zihan (1, 2, 3); Sun, Ming (1, 2, 3); Zou, Ling (1, 2, 3)
Author affiliation:(1) College of Information and Electrical Engineering, China Agricultural University, Beijing; 100083, China; (2) Key Laboratory of Agricultural Information Acquisition Technology, Ministy of Agriculture, Beijing; 100083, China; (3) Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing; 100083, China
Corresponding author:Sun, Ming(sunming@cau.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:343-347
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:With the growing deterioration of water environment, heavy metal pollution has become increasingly prominent, and causes a matter of concern. Heavy metals have a strong interaction with a variety of enzymes and proteins in the human body, where the protein and enzymes lose activity. Heavy metals maybe enrich in certain organs of the body, and if its content exceeds the limits of the content that human body can tolerate, it will cause human acute poisoning, subacute poisoning, and chronic poisoning. Development of portable rapid detection equipment of heavy metal becomes necessary. In this study, heavy metal lead ions (Pb<sup>2+</sup>) are the research object, and the research is based on colorimetry theory, Lambert-Beer's law and spectrometry to develop a portable detector for Pb<sup>2+</sup>. According to the analysis of the physical and chemical properties of Pb<sup>2+</sup>and the research of colorimetric reaction between Pb<sup>2+</sup>and dithizone, existing colorimetric detection method of Pb<sup>2+</sup>is improved, and complex and cumbersome pre-process of detector is simplified. Under suitable conditions of room temperature, pH value of 9.0 and certain volume, Pb<sup>2+</sup>reacts with dithizone solution whose color is blue-green, which can generate orange complex, so that the measured value of Pb<sup>2+</sup>can be converted to easily measured mathematic data to build detection model for the determinator, and other metal ions do not interfere in the determination by adding the masking agents. The determinator designed includes the optical circuit part and electric circuit part. The optical circuit part consists of light source, optical fiber, and silicon detector, which is used to collect the optical signal. Optical module uses the 510 nm wavelength LED (light- emitting diode) with narrow band filter as the active light source, and optical fiber is the transmission channel to make sure the monochromatic light source gives the parallel and vertical light striking the detector. Additionally, a silicon photodetector is the optical detector. The optical part is aimed to detect the band transmittance. Electric circuit is designed to convert the light signal to electrical digital signal, amplify the signal, decrease the noises, data process and store, and real-time display and communication. The electric circuit includes microcontroller, LED drive circuit, detection circuit, communication circuit, keyboard circuit, and liquid crystal display circuit, and lithium-ion battery is used as power supply. PS0308 type photodiode with spectral response range 300-1 100 nm converts the optical signal to electric signal and also effectively guarantees the linearity of the instrument. The system software is used to detect electric quantity, measure and manage data, and so on. All the connection parts are fixed together through the metal pieces to prevent deviation by the movement of light path. When the device has been installed, system performance is analyzed to assure the accuracy. Power consumption and anti-interference of the software and hardware are tested, also repetitive testing is done to verify the accuracy of the measurement. Experimental results show that decision coefficient of the predicted and the real concentration values in training set is 0.934, and the value is 0.822 2 in prediction set, and the detection range of determinator is 0.01-0.2 mg/L, indicating it can detect the Pb<sup>2+</sup>with lower concentration. Relative standard deviation of Pb<sup>2+</sup>concentration is less than 1.0%. The test results indicate that this determinator is simple to operate with satisfactory precision, accuracy, and repeatability, realizing the miniaturization of instrument and on-site rapid detection. At the same time, the determinator has simple and stable structure with low consumption. Based on the instrument, in the future work, the portable rapid detection of Pb<sup>2+</sup>can be adapted to a variety of heavy metals in aqueous media with a low cost, low detection limits, and simple pre-treatment. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:28
Main heading:Light emitting diodes
Controlled terms:Chemical analysis - Chemical detection - Chemicals removal (water treatment) - Color - Colorimeters - Colorimetric analysis - Colorimetry - Digital storage - Electric network analysis - Electric network parameters - Electric power systems - Enzymes - Heavy metals - Information management - Light - Light sources - Light transmission - Liquid crystal displays - Lithium alloys - Lithium-ion batteries - Metal implants - Metal ions - Metals - Monochromators - Optical fibers - Optical signal processing - Photodetectors - Pollution - Proteins - Silicon detectors - Software testing - Spectrometry - Water pollution
Uncontrolled terms:Colorimetric detection - Communication circuits - Determinator - Fast detections - Lead ions - Physical and chemical properties - Relative standard deviations - Software and hardwares
Classification code:453 Water Pollution - 531 Metallurgy and Metallography - 531.1 Metallurgy - 542.4 Lithium and Alloys - 703.1 Electric Networks - 703.1.1 Electric Network Analysis - 706.1 Electric Power Systems - 714.2 Semiconductor Devices and Integrated Circuits - 722.1 Data Storage, Equipment and Techniques - 723.5 Computer Applications - 741 Light, Optics and Optical Devices - 801 Chemistry - 803 Chemical Agents and Basic Industrial Chemicals - 804 Chemical Products Generally - 804.1 Organic Compounds - 941.3 Optical Instruments - 941.4 Optical Variables Measurements - 944.7 Radiation Measuring Instruments
Numerical data indexing:Mass_Density 1.00e-05kg/m3 to 2.00e-04kg/m3, Percentage 1.00e+00%, Size 3.00e-07m to 1.10e-06m, Size 5.10e-07m
DOI:10.11975/j.issn.1002-6819.2017.z1.051
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 24>
Accession number:20171703590910
Title:Online identification method of parameters for greenhouse temperature prediction self-adapting mechanism model
Authors:Chen, Lijun (1); Du, Shangfeng (1); Li, Jiapeng (1); He, Yaofeng (1); Liang, Meihui (1)
Author affiliation:(1) College of Information and Electrical Engineering, China Agricultural University, Beijing; 100083, China
Corresponding author:Du, Shangfeng(13520760485@126.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:315-321
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:The greenhouse climate model has the characteristics of nonlinearity, strong disturbance and time variance. The commonly used methods to deal with the complex model are to linearize the original nonlinear models or to do some offline identification research. Without considering the complex characteristics completely, the usual modeling methods cannot predict the greenhouse climate dynamic behaviour effectively. In this paper, on account of the complex characteristics of greenhouse climate system, taking the temperature model as the example, the continuous-discrete recursive error algorithm was used to identify combined parameter and state online. Firstly, describe the greenhouse temperature model. The greenhouse temperature is affected by the heat load imposed on the greenhouse by the sun, the energy lost to the external air because of transmission through the greenhouse cover, the heat transfer between the internal air and soil, the heat loss by crop transpiration, the heat lost through natural ventilation of the roof windows and the energy supply from the heating system. In all of the model parameters, due to the fact that external solar radiation has a great effect on greenhouse temperature, radiation conversion factor changes over time and the parameters related to heating and ventilation are fundamental but difficult to obtain, this paper attempted to estimate and update 5 key parameters online. Secondly, continuous-discrete recursive prediction error algorithm to estimate combined parameters and states online was developed. This algorithm is appropriate for a continuous-discrete system, which is defined as a dynamic system with a continuous state function, and the observation function is discrete. The algorithm estimates the parameters and states by minimizing the error sum of squares between predicted values and measured values, which is usual in this technology. Compared with other traditional estimation algorithm such as the extended Kalman filter, the big difference of the algorithm is that there is no need to set the initial value of system noise precisely. It defines the system noise in real time by introducing an extra parameter as gain matrix and estimating it online. The algorithm adjusts the system noise to match the model predicted values and actual values by regulating the gain matrix. As estimating the system noise for the greenhouse temperature system beforehand is extremely difficult, the advantage of the proposed algorithm enhances the feasibility of its application in practice. At last, in order to test the developed algorithm, the model identification results were compared between the continuous-discrete recursive prediction error algorithm and the extended Kalman filter in MATLAB. The simulation was based on the measured data containing outside temperature, outside solar radiation, control inputs and temperature of an experiment greenhouse. The results showed that the proposed algorithm could lead to a higher model fit value of 93.7% compared with 89.5% of the extended Kalman filter. The gain matrix varied from zero to non-zero, and it nearly maintained stable at a non-zero constant in the end of each test day. The changing process indicated that there were errors between the model predicted value and the measured value in the initial, and the errors could be compensated by regulating the gain matrix. The different values of gain matrix in 2 days showed that system noise may vary largely in different condition and it should not be set as a constant. From the simulation results and the data analysis, it can be known that the proposed continuous-discrete recursive prediction error algorithm can estimate the temperature well and improve the model accuracy and validity. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:35
Main heading:Parameter estimation
Controlled terms:Climate models - Errors - Extended Kalman filters - Forecasting - Greenhouse effect - Greenhouses - Heat transfer - Kalman filters - MATLAB - Matrix algebra - Models - Solar radiation - Temperature - Ventilation
Uncontrolled terms:Complex characteristics - Greenhouse temperature - Model identification - On-line identification methods - Real-time identification - Recursive algorithms - Recursive prediction error algorithm - Temperature modeling
Classification code:443 Meteorology - 451 Air Pollution - 641.1 Thermodynamics - 641.2 Heat Transfer - 643.5 Ventilation - 657.1 Solar Energy and Phenomena - 821.6 Farm Buildings and Other Structures - 921 Mathematics - 921.1 Algebra
Numerical data indexing:Age 5.48e-03yr, Percentage 8.95e+01%, Percentage 9.37e+01%
DOI:10.11975/j.issn.1002-6819.2017.z1.047
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 25>
Accession number:20171703590890
Title:Internet eggplant image retrieval method and system based on mixed features
Authors:Zhu, Ling (1); Li, Zhenbo (1, 3, 4); Yang, Zhaolu (2); Li, Chen (1); Wu, Jing (1); Yue, Jun (2)
Author affiliation:(1) College of Information and Electrical Engineering, China Agricultural University, Beijing; 100083, China; (2) College of Information and Electrical Engineering, LuDong University, Yantai; 264000, China; (3) Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing; 100083, China; (4) Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing; 100083, China
Corresponding author:Li, Zhenbo(zhenboli@126.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:177-183
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:With the explosive growth of image data on the Internet, effective image retrieval becomes more and more important. Different from text retrieval, effective image retrieval is still an open problem. Specific crop images correspond to specific agricultural knowledge. In this paper, in order to search eggplant knowledge more effectively, a system for eggplant images retrieval based on hybrid features is proposed. The hybrid features include Hu invariant moments, color moment vector and contour. Hu invariant moments are used as geometric invariant features, and 7 Hu invariant moments are constructed by using the 2 and 3 order normalized central moments. The main idea of invariant moment is to use the few moments based on region as the shape feature, which are invariant to rotation, translation and scale. For color features, we use the color moment method. The color space is changed from RGB (red, green, blue) color space to HSV (hue, saturation, value) color space firstly. Then the H, S and V channels of HSV color space are used to construct the 9-dimensional color moment vector, which is used as the descriptor of color features. The similarity vector of color features can be calculated using the Manhattan distance. For contour features, sobel operator is used for edge detection firstly, and then the watershed algorithm is used to segment the image and extract the contour feature from the image. The watershed algorithm is divided into 2 steps, one is the sorting process, and the other is the submerging process. Firstly, the images are converted from color images to gray images. The gray levels of all the pixels in the images are ordered from low to high. Then the submerging process is executed from low to high orderly. For each local minimum value in the h-order height of the domain using FIFO (first in first out) data structure to determine and label. The main purpose of watershed algorithm is to find the connected region of image. Finally, the descriptor of eggplant object in images is made by combing the geometric invariant features, color and contour features, which are assigned with 3 different weights respectively. The eggplant images retrieval system is developed based on the combined features descriptor. In order to distinguish long eggplant and round eggplant, we observe that the ratio of length to width of contour is an ideal distinguishing feature. The minimum bounding rectangle of each contour is calculated after image segmentation. The ratio of the length to width of each minimum bounding rectangle is used to distinguish long eggplant and round eggplant in the image. Experiments verify that the system achieves 87.6% in recall and precision ratio in our test data sets, and we list all the images according to the computed similarity values. Compared to the results only using Hu invariant moment (the recall and precision ratio is 31.75%) and that using color features combined with Hu (the recall and precision ratio is 52.8%), our hybrid features are more robust and precise. The effectiveness of the proposed method is verified. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:27
Main heading:Search engines
Controlled terms:Aspect ratio - Color - Contour measurement - Edge detection - Feature extraction - Geometry - Image processing - Image retrieval - Image segmentation - Information retrieval - Method of moments - Vector spaces - Watersheds
Uncontrolled terms:Colour moments - HSV color spaces - Hu invariant moments - Minimum bounding rectangle - Normalized central moment - Ratio of length to widths - Target object - Water-shed algorithm
Classification code:444.1 Surface Water - 723 Computer Software, Data Handling and Applications - 741.1 Light/Optics - 903.3 Information Retrieval and Use - 921 Mathematics
Numerical data indexing:Percentage 3.18e+01%, Percentage 5.28e+01%, Percentage 8.76e+01%
DOI:10.11975/j.issn.1002-6819.2017.zl.027
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 26>
Accession number:20171703590888
Title:Optimization of spectroscopy parameters and prediction of chlorophyll content at seeding stage of winter wheat
Authors:Mao, Bohui (1); Li, Minzan (1); Sun, Hong (1); Liu, Haojie (1); Zhang, Junyi (1); Zhang, Qin (2)
Author affiliation:(1) Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing; 100083, China; (2) Center for Precision & Automated Agricultural System, Washington State University, Prosser; WA; 99350, United States
Corresponding author:Li, Minzan(limz@cau.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:164-169
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:Accurate prediction of winter wheat chlorophyll content at seedling stage is important for guiding precision management in the field. In order to acquire chlorophyll content of winter wheat leaves, traditional detection methods require to squash winter wheat leaves and applying chemical methods, which would have bad influence on crops growth and cause unnecessary waste of time on some level. It is proved that the spectroscopy analysis is an effective method to predict chlorophyll content of winter wheat. However, the drift and offset of spectral baseline has a great influence on the predicting accuracy. So, this study was carried out to eliminate the influence of the drift and offset. The experimental farm was randomly divided into 70 different sampling areas in Xiaotangshan, Beijing, and the winter wheat leaves were collected on April 20th in the period of seedling stage. The visible and near infrared canopy spectral reflectance of winter wheat was measured by an ASD FieldSpec handheld spectroradiometer at seedling stage. The chlorophyll contents of sampling leaves were detected by the spectrophotometer in the laboratory on the same day. The obtained data of the canopy spectral reflectance and chlorophyll content were assembled for each region individually. The multiple scattering correction (MSC) was used on the bands of 325-1025 nm wavelength, because many scattering errors were introduced into the measured spectral data due to the physical factors. The MSC method first requires establishing an ideal spectrum of all samples, and modifying all the other samples of near infrared spectra on the basis of ideal spectrum to, and spectral reflectance changes with the content of chlorophyll components in the sample meet the direct linear relationship. The absolute intensity difference of spectral reflectance of winter wheat canopy was weakened after the MSC pretreatment, and then scattering effect was effectively reduced. Baseline shift and offset problems were resolved, and the correlation coefficients of spectral reflectance and chlorophyll content were increasing through the MSC pretreatment. Furthermore, genetic algorithm (GA) was proposed for sensitive band selection. GA is a high efficient and globally random search optimization method which simulates Darwin's evolution by natural selection and genetic mechanism of biological evolution. According to the principles of choosing high frequency bands as characteristic wavelength, 486, 599, 699 and 762 nm crop canopy reflectances were selected to calculate vegetation indices, including ratio vegetation index (RVI), difference vegetation index (DVI), normalized difference vegetation index (NDVI) and soil-adjusted vegetation index (SAVI). The correlation between each vegetation index and chlorophyll content of winter wheat was analyzed. It was found that the correlation between each vegetation index and chlorophyll content of winter wheat significantly increased after the MSC. The results showed that DVI and SAVI could refrain interference of soil background during seedling period, and the optimal parameters were DVI(762, 599), SAVI(762, 599), DVI(762, 699) and SAVI(762, 699), and the correlation coefficients were all above 0.6. The DVI(762, 699) and SAVI(762, 599) were selected to establish the multiple linear regression (MLR) prediction model and the least squares-support vector regression (LS-SVR) prediction model. The 70 winter wheat samples were divided into 2 groups, 50 samples for model calibration and the other 20 samples for model verification. The results of MLR showed the determination coefficient of the calibration model was 0.528 and that of the validation model was 0.487. In order to improve the precision of the forecast model, the LS-SVR prediction model was applied, and the determination coefficient of the calibration model was 0.681 and that of the validation model was 0.611. It showed that the fitting result was ideal. With the application of spectral technology, it provides a feasible method to detect the winter wheat growth status at seedling stage. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:28
Main heading:Vegetation
Controlled terms:Biology - Chemical detection - Chlorophyll - Crops - Forecasting - Frequency bands - Genetic algorithms - Infrared devices - Linear regression - Multiple scattering - Near infrared spectroscopy - Optimization - Plants (botany) - Reflection - Spectroscopy
Uncontrolled terms:Canopy reflectance - Canopy spectral reflectance - Determination coefficients - Least squares support vector regression - Multiple linear regressions - Normalized difference vegetation index - Vegetation index - Winter wheat
Classification code:461.9 Biology - 801 Chemistry - 804.1 Organic Compounds - 821.4 Agricultural Products - 921.5 Optimization Techniques - 922.2 Mathematical Statistics
Numerical data indexing:Size 3.25e-07m to 1.03e-06m, Size 6.99e-07m, Size 7.62e-07m
DOI:10.11975/j.issn.1002-6819.2017.z1.025
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 27>
Accession number:20171703590893
Title:Monitoring system for brine well in production of potash fertilizer based on wireless sensor network
Authors:Zhang, Xiaoshuan (1); Liu, He (2); Cui, Yan (2); Zhu, Tianyu (2); Fu, Zetian (1)
Author affiliation:(1) College of Engineering, China Agricultural University, Beijing; 100083, China; (2) College of Information and Electrical Engineering, China Agricultural University, Beijing; 100083, China
Corresponding author:Fu, Zetian(fzt@cau.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:199-205
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:Underground brine of Qinghai Saline Lake, which contains many kinds of mineral deposits such as potash, magnesium, sodium, and lithium, is a kind of important raw material for chemical products such as salt, potash fertilizer, and lithium carbonate. The pumps are easily prone to be out of order due to the fact they must keep running around the clock in a complicated climate environment around the saline lake mining sites. However, traditional monitoring for the chemical brine pump in Qinghai Saline Lake is a manual monitoring technology with high cost, non-real time, inflexibility and high energy consumption. This paper proposed a remote monitoring system based on the wireless sensor network (WSN) for the brine well in the production of potash. It consists of 2 units: one is real-time monitoring unit based on WSN with ZigBee protocol and CC2530 wireless sensor SoC, and the other unit is remote management information system (RMIS) of brine well based on a PHP (Hypertext Preprocessor) software platform. The real-time monitoring unit based on WSN is responsible for acquiring and transmitting the data, which consists of a number of sensor or router nodes and a network coordinator, and is deployed at the site of the brine well mining. Meanwhile, the RMIS serves as the management system for end-users, which has 4 functions: 1) Managing static information of the brine well; 2) Maintaining the database for the data acquired by the WSN; 3) Providing functions to automatically control the speed of brine pump according to real-time operating state data of the brine pump or shut down the brine pump when the monitoring indicators exceed the threshold; 4) And generating a list of mining brine failure reports of mining equipment. The system is evaluated by testing the power consumption of sensor nodes first. The experimental results showed that the package loss ratio (PLR) of the nodes gradually decreased with the increase of the transmission power. However, the battery consumption increased with the increase of the transmission power. The transmission power was configured as 1 dBm which could prolong the lifetime of sensor nodes to 13.5 months. And then the PLR and RSSI (received signal strength indicator) of sensor nodes were tested in different distance and transmitting power, respectively. In the case of the same transmission power, the PLR and RSSI had the opposite change trend with the change of distance: The PLR increased and the RSSI decreased with the increase of the distance. The effective transmission distance of the nodes was 30 m when they were placed on the ground and their PLR was less than 3.6%. Last, the RSSI was also tested within 24 h, to analyze the influence of the field deployment environment on the link quality. The experimental results showed the RSSI was relatively better during the daytime (5:40-19:20) which ranged from -80 to -56 dBm. And during the night (00:00-5:40 and 21:30-24:00), the RSSI had a significant drop (around -85 and -95 dBm, respectively). And the system test under the potash production environment proves that monitoring data can accurately reflect the operating status of the pump and the brine level. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:36
Main heading:Monitoring
Controlled terms:Energy utilization - Fertilizers - Hypertext systems - Information management - Lakes - Lithium deposits - Magnesium deposits - Mineral resources - Mining equipment - Potash - Potassium fertilizers - Pumps - Reliability - Remote control - Routers - Sensor nodes - Sodium deposits - System-on-chip - Wireless sensor networks - Zigbee
Uncontrolled terms:Effective transmission - High energy consumption - Hypertext preprocessor - Monitoring indicators - Real time monitoring - Real-time operating state - Received signal strength indicators - Remote monitoring system
Classification code:502.2 Mine and Quarry Equipment - 504.1 Light Metal Mines - 525.3 Energy Utilization - 618.2 Pumps - 714.2 Semiconductor Devices and Integrated Circuits - 722 Computer Systems and Equipment - 722.3 Data Communication, Equipment and Techniques - 731.1 Control Systems - 804 Chemical Products Generally - 804.2 Inorganic Compounds
Numerical data indexing:Age 1.13e+00yr, Decibel_milliwatts -8.00e+01dBm to -5.60e+01dBm, Decibel_milliwatts -9.50e+01dBm, Decibel_milliwatts 1.00e+00dBm, Percentage 3.60e+00%, Size 3.00e+01m, Time 8.64e+04s
DOI:10.11975/j.issn.1002-6819.2017.z1.030
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 28>
Accession number:20171703590913
Title:Design and simulation of automatic fertilizing machine for greenhouse
Authors:Fu, Zetian (1, 2, 3); Dong, Yuhong (1, 3); Zhang, Lingxian (2, 4); Yang, Han (2); Wang, Jieqiong (1, 3); Li, Xinxing (2, 3, 4)
Author affiliation:(1) College of Engineering, China Agricultural University, Beijing; 100083, China; (2) College of Information and Electrical Engineering, China Agricultural University, Beijing; 100083, China; (3) Beijing Laboratory of Food Quality and Safety, Beijing; 100083, China; (4) Key Laboratory of Agricultural Informationization Standardization (Beijing), Ministry of Agriculture, Beijing; 100083, China
Corresponding author:Li, Xinxing(lxxcau@cau.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:335-342
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:As a large agricultural country, there are still more than 40% of people in China engaging in agricultural production activities. In recent years, the soil erosion and desertification have been in serious state, and the arable land has decreased year by year, which means that it is very necessary to develop agricultural technology to improve crop yield per unit area. Therefore, in recent years, China has vigorously developed agricultural greenhouse cultivation which has characteristics of high input, high technology and high yield. Agricultural greenhouse cultivation is an important part of China's agricultural production. In 2014, the area of greenhouse in China was up to 4.109 million hm<sup>2</sup>. With the promotion of precision agriculture, the technology of modern agricultural greenhouse cultivation is developing towards precision. Precision fertilization is one of the important parts of precision agriculture operation system. Due to that the structure of modern agricultural greenhouse is closed, and the planting area is limited in China, the large agricultural machinery cannot be applied to greenhouse operations, leading to that the mechanization level in greenhouse production is lower than other developed countries in the world. At present, the fertilization in greenhouse in China is still mainly relying on manpower. Because of the increasing labor cost and decreasing yield in greenhouse production, the profits of greenhouse is reduced. In the pursuit of more profit, people's requirements of precision fertilization in greenhouse are increasingly urgent. This paper mainly introduced a small, precise and practical fertilizing machine. This machine was mainly used in greenhouse and it was intelligent and automatic. The fertilization machine used ARM9 S3C2440 microprocessor as the core of circuit control module. It was equipped with a fertilizer level monitoring alarm device, and would alarm automatically when the fertilizer level was less than 15 cm, so that the staff could add fertilizer in time. The machine used the Geneva mechanism as fertilizer metering mechanism. By controlling the pulse frequency of the stepping motor, the amount of fertilization could be controlled so as to achieve quantitative precision fertilization. The three-dimensional model of the fertilization machine was built by the SolidWorks. The intensity check was performed for its main parts of the force with simulation. All those verified that the structure could meet the requirements of hardness and intensity. The fertilization effect was simulated by the EDEM (enhanced discrete element method), a general CAE (computer-aided engineering) software applied to the discrete element method. In the simulation, the speed of the sheave was set to 10 r/min, the output time step was 0.05 s, and the total simulation time was 5 s. It was found that there weren't flying particles during the process of simulation, which showed that the selected rotational speed of the sheave and the parameter of the simulation time step were reasonable. The sheave grooves in the process of simulation were almost filled with the fertilizer particles, which proved that the device could get a good effect of fertilization and the parameter of the sheave was reasonable. The brush structure in the fertilization device could effectively get rid of the extra fertilizer particles and supplement fertilizer particles for other incompletely filled groove in order to make sure every groove could discharge a certain amount of fertilizer. The simulation results showed that the fertilization device could discharge the fertilizer particles evenly, stably and continuously. By properly controlling the rotation speed of the drive shaft, the fertilization machine could achieve the goal of precise fertilization. The results show that maximum error of fertilizer test actual value and the theoretical value is 2.42%, less than 3% under four kinds rotational speed (5, 10, 15, 20 r/min). Therefore, the machine designed in this paper could greatly improve the automation level for greenhouse production and reduce the labor intensity of fertilization in greenhouse. It has broad applicability and popularity in our facility greenhouse production. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:21
Main heading:Agricultural machinery
Controlled terms:Agriculture - ARM processors - Compensation (personnel) - Computer aided engineering - Computer aided software engineering - Control equipment - Cultivation - Design - Fertilizers - Greenhouses - Machine design - Profitability - Stepping motors - Wages
Uncontrolled terms:Agricultural greenhouse - Agricultural productions - Agricultural technologies - Design and simulation - Greenhouse production - Precision Agriculture - Precision fertilizations - Three-dimensional model
Classification code:601 Mechanical Design - 705.3 Electric Motors - 721 Computer Circuits and Logic Elements - 723.5 Computer Applications - 732.1 Control Equipment - 804 Chemical Products Generally - 821 Agricultural Equipment and Methods; Vegetation and Pest Control - 911.2 Industrial Economics - 912.4 Personnel
Numerical data indexing:Percentage 2.42e+00%, Percentage 3.00e+00%, Percentage 4.00e+01%, Rotational_Speed 1.00e+01RPM, Rotational_Speed 2.00e+01RPM, Size 1.50e-01m, Time 5.00e+00s, Time 5.00e-02s
DOI:10.11975/j.issn.1002-6819.2017.z1.050
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 29>
Accession number:20171703590862
Title:Isolation, identification and biological characteristics of pathogenic fungus from Chinese wolfberry fruit
Authors:Liu, Yu (1, 2); Wang, Hai (1, 2); Wang, Yandan (3); Sun, Wenyi (4); Guo, Xuexia (1, 2); Ran, Guowei (1, 2); Zhang, Huiyuan (1, 2)
Author affiliation:(1) Institute of Agro-Products Processing, Chinese Academy of Agricultural Engineering, Beijing; 100125, China; (2) Key Laboratory of Agro-Products Postharvest Handling, Ministry of Agriculture, Beijing; 101309, China; (3) Boda College, Jilin Normal University, Siping; 136000, China; (4) School of Life Sciences, Jilin Normal University, Siping; 136000, China
Corresponding author:Wang, Hai(wanghai948@126.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:374-380
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:Chinese wolfberry fruit is the mature fruit of Ningxiawolfberry. It contains rich bioactive substances and nutrients, such as wolfberry polysaccharide, total sugar, amino acids, betaine, carotene, niacin and trace elements etc. It has the function of immune regulation, anti-tumor, antioxidant, anti-fatigue, lowering blood lipid and blood sugar, etc. The mature period of wolfberry fruit is from June to September, and flowering, fruitage and maturation at the same time. The fresh Chinese wolfberry fruit is easy to get mildew due to its thin peel, high moisture and sugar content. Under normal environment and without any treatment, mildew will occur within 1 day after wolfberry fruit being picked. The mildew rate will reach 30%-40% 2 days later, and as high as 50%-80% after 3 days. Commodity value of the mildewed wolfberry is seriously lost. The reproduction of microorganisms is the cause of mildew. Drying process is commonly used to prolong its shelf life. At present, the drying methods mainly include natural drying and hot air drying. However, mildew is still serious during the early stage of drying process due to the varieties of pathogenetic mould. In order to study the mildew problem of Chinese wolfberry, the pathogenetic mould were isolated from the fresh Chinese wolfberry fruit and identified by morphological characters and identification. The biological characteristics related to mold rate were determined. Six strains were isolated from the moldy Chinese wolfberry fruit, which are 1 Fusarium species, 2 Alternaria sp., 6 Fusarium oxysporum, 7 Penicillium sp., 11 Gibberella fujikuroi and 21 Penicillium oxalicum. When the mould was separated, the single colony on the culture medium was counted. The strains were identified by optical microscopy. Fungal separation test isolated 58 fungal single colonies. The detection rate of Fusarium and green fungus was the highest. Artificial infection test showed that all of these six molds were able to cause health Chinese wolfberry fruit mildew, and the same moulds were isolated again from the mould spots. The lethal temperatures on these six strains were difference. The highest was 54℃. Six strains stopped growing at this temperature. The optimal growth pH value of six strains ranged from 4 to 8, with the highest mould spore germination rate at pH value 6. Six strains could grow when the optimal growth relative humidity was from 30% to 90% with the most optimal range between 75% and 85%. The pathogenic fungus which caused the rot of fresh wolfberry were isolated and identified, the effects of temperature, humidity and pH value on the growth of mold were studied. Together, these results provide the theoretical basis for the control of microorganisms growth, and also provide technical support for Chinese wolfberry storage and drying process in the future. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:28
Main heading:Fungi
Controlled terms:Agricultural products - Biotechnology - Blood - Drying - Fruits - Microorganisms - Molds - pH - Temperature - Trace elements
Uncontrolled terms:Bioactive substances - Biological characteristic - Chinese wolfberry - Effects of temperature - Gibberella fujikuroi - Isolation and identification - Morphological characters - pH value
Classification code:461 Bioengineering and Biology - 641.1 Thermodynamics - 801.1 Chemistry, General - 821.4 Agricultural Products
Numerical data indexing:Age 2.74e-03yr, Age 5.48e-03yr, Age 8.22e-03yr, Percentage 3.00e+01% to 9.00e+01%, Percentage 5.00e+01% to 8.00e+01%, Percentage 7.50e+01% to 8.50e+01%
DOI:10.11975/j.issn.1002-6819.2017.z1.056
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 30>
Accession number:20171703590875
Title:Path tracking algorithm of vehicles based on fuzzy hyperbolic tangent model
Authors:Yang, Jue (1); Shi, Guangsi (1); Zhang, Wenming (1); Zhao, Xuan (1); Dun, Haiyang (1); Si, Jixiang (1)
Author affiliation:(1) School of Mechanical Engineering, University of Science & Technology Beijing, Beijing; 100083, China
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:78-84
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:To solve the autonomous driving problems of underground mining truck, especially the path tracking problem, predecessors have researched articulated vehicle kinematic and control through the analysis of the kinematics modeling. Although articulated vehicle dynamic was taken into consideration in path tracking control fields, there would be also lack of accurate system modeling. So considering this multi variable, strong coupling, highly complex nonlinear dynamic system of articulated vehicles, it's difficult not only for establishment of an accurate model, but also for design of a precise control algorithm. Fuzzy hyperbolic model and the method of pole placement controller design in this article could provide a better way to solve these problems above. In this method, information with driver driving the truck included the vehicle kinematics relations, and by repeat of the same process, the vehicle dynamics relationship would be clearer. This method reduced the complexity in process modeling. The fuzzy hyperbolic model could cleverly convert the coefficient matrix of the nonlinear system to a constant matrix, then neural networks as a supervised learning method could be used to identify the parameters, which made the model more closed to the true model and it would be convenient to design a control algorithm. Pole assignment as a classical control algorithm was simple and effective and it could be appropriately applied into this kind of model. Following steps were needed to establish the fuzzy tangent model and the pole assignment method. Initially, the sample data, including the lateral displacement error and orientation error, were collected through the driver controlling articulated vehicle at the speed of 3 m/s. During the multiple times of driving in the same roadway, little change could be achieved for the lateral displacement error and the orientation error. After that, by using the improved adaptive BP neural network model and the mediation rate of error estimator based on the method of Cauchy robust, the system error of neural network learning was effectively reduced, and the fitting error and relative error of data were decreased. So the system was better matched, and the weights were well identified. Finally, the pole assignment method with choosing the appropriate poles was designed to control articulate angle. The Hardware-In-the-Loop (HIL) simulation platform was set up on the basis of PXI and C-RIO as a host computer. The kinematic model established in the Adams platform was downloaded into the PXI as the simulation plant, and the path tracking algorithm compiled by Simulink was embedded to the C-RIO as the real electronic control unit. The host computer coupled the vehicle model and the path tracking algorithm via the Labview platform and displayed the simulation status in the upper monitor. The results suggested in the process of driving this method could describe both the quantitative relation of the horizontal position deviation and course angle deviation with their rates of change, and could provide an accurate control. HIL results showed that the lateral displacement error, orientation error and the articulate angle of the vehicle were respectively controlled at 0.008 m, 0.07 rad (0.5°), 0.21 rad (12°). All the deviations were asymptotically stable. Overshoot and the response time were less than expectations and eventually stabilize. Articulated vehicles were maintained to the low level deviation and reference trajectory coincides with the track and also the simulation time and calculating time were all 80 s in the hardware in the loop simulation process, so the controller met the requirement of the real-time control performance. The method is demonstrated to be effective and reliable in path-tracking for the underground vehicles. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:30
Main heading:Process control
Controlled terms:Algorithms - C (programming language) - Complex networks - Computer hardware - Control theory - Controllers - Dynamical systems - Errors - Fuzzy neural networks - Hardware - Kinematics - Matrix algebra - Mine trucks - Models - Navigation - Neural networks - Nonlinear dynamical systems - Poles - Poles and zeros - Problem solving - Real time control - Synthetic apertures - Tracking (position) - Traction (friction) - Trucks - Underground mine transportation - Vehicles
Uncontrolled terms:Complex nonlinear dynamics - Hard-ware-in-the-loop - Hardware in-the-loop simulation - Hyperbolic tangent models - Path tracking - Pole placement controller - Pole placement methods - Supervised learning methods
Classification code:408.2 Structural Members and Shapes - 605 Small Tools and Hardware - 663.1 Heavy Duty Motor Vehicles - 716.2 Radar Systems and Equipment - 722 Computer Systems and Equipment - 723.1.1 Computer Programming Languages - 723.4 Artificial Intelligence - 731 Automatic Control Principles and Applications - 731.1 Control Systems - 732.1 Control Equipment - 921 Mathematics - 921.1 Algebra - 931.1 Mechanics
Numerical data indexing:Size 8.00e-03m, Velocity 3.00e+00m/s
DOI:10.11975/j.issn.1002-6819.2017.z1.012
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 31>
Accession number:20171703590883
Title:Prediction of soil moisture in multiple depth based on time delay neural network
Authors:Ji, Ronghua (1); Li, Xin (1); Zhang, Shulei (1); Zheng, Lihua (1)
Author affiliation:(1) College of Information and Electrical Engineering, China Agricultural University, Beijing; 100083, China
Corresponding author:Zheng, Lihua(zhenglh@cau.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:132-136
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:It is very important to improve water use efficiency and achieve precision irrigation that soil moisture content is predicted accurately. The spatial and temporal variability of soil moisture content is complex because soil moisture content can be affected by various factors, such as soil properties, plant, and environment. The time-series field soil moisture data were nonlinear. There was strong relationship of soil moisture content between the adjacent depths. The work presented in this paper aimed to contribute to predicting soil moisture content in different depths by proposing a time-delay neural network (TDNN). TDNN is an artificial neural network model in which all the neuron-like units (nodes) are fully connected by directed connections. Each unit has a time-varying real-valued activation and each connection has a modifiable real-valued weight. It has 3 layers: input layer, hidden layer and output layer. In this paper, a prediction model based on the TDNN was presented to predict soil moisture content in 6 field depths (10, 20, 30, 40, 50 and 70 cm). The framework of the prediction model based on the TDNN included input layer with 6 units, hidden layer with 10 units and output layer with 6 units. Three training algorithms, which were Levenberg-Marquardt (L-M) method, conjugate gradient method and momentum increase method, were tested. The simulation results show that the L-M method was the best, followed by the conjugate gradient method, and the momentum increase method was the worst. The original experimental data were acquired from maize field in Shangzhuang experiment station, China Agricultural University in Beijing. The sample data set of soil moisture content prediction model based on the TDNN was generated from the original data set, which was calibrated by drying method and preprocessing algorithm. The prediction accuracy of the soil moisture content prediction model was influenced greatly by the training sample's size. The experiment results showed that the best training accuracy could be gotten when the number of training samples was more than 40% of that of all samples. The prediction model would over fit when the number of training samples was more than 50%. In this paper, every 20 sample data were divided into a set and 9 data were set as training data and other 11 data as test data. The sample data set of soil moisture content prediction model was divided into training set and test set. The results showed that the relative error of soil moisture content prediction for 10 and 20 cm was less than 7%. As for 30, 40, 50 and 70 cm, the relative error was less than 4.5%. The prediction model based on the TDNN presented in this paper can be used to predict the soil moisture content in different depth and can be treated as a solution to grasp the distribution of soil moisture content. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:30
Main heading:Soil surveys
Controlled terms:Conjugate gradient method - Deep neural networks - Forecasting - Measurements - Moisture - Moisture determination - Neural networks - Personnel training - Sampling - Soil moisture - Soil testing - Soils - Statistical tests - Time delay - Timing circuits - Verification
Uncontrolled terms:Artificial neural network modeling - Multiple depth - Pre-processing algorithms - Precision irrigation - Real-valued activation - Spatial and temporal variability - Time delay neural networks - Water use efficiency
Classification code:483.1 Soils and Soil Mechanics - 713 Electronic Circuits - 713.4 Pulse Circuits - 721.1 Computer Theory, Includes Formal Logic, Automata Theory, Switching Theory, Programming Theory - 912.4 Personnel - 921 Mathematics - 922.2 Mathematical Statistics - 944.2 Moisture Measurements
Numerical data indexing:Percentage 4.00e+01%, Percentage 4.50e+00%, Percentage 5.00e+01%, Percentage 7.00e+00%, Size 1.00e-01m, Size 2.00e-01m, Size 5.00e-01m, Size 7.00e-01m
DOI:10.11975/j.issn.1002-6819.2017.z1.020
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 32>
Accession number:20171703590866
Title:Design and test of online flatness measuring system for large-scale chassis of combine harvester
Authors:Wang, Dong (1); Zhang, Yawei (1); Wang, Shumao (1, 2); Chen, Zhi (3); Chen, Du (1, 2)
Author affiliation:(1) College of Engineering, China Agricultural University, Beijing; 100083, China; (2) Beijing Key Laboratory of Optimized Design for Modern Agricultural Equipment, Beijing; 100083, China; (3) China National Machinery Industry Corporation, Beijing; 100080, China
Corresponding author:Chen, Du(tchendu@cau.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:17-22
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:The large-scale harvester chassis is usually made up of 2 long stringers and several crossbeams by welding, and most important parts of harvester are mounted on its upper surface, such as engine, cab and gearbox. Because of the welding thermal stress, the upper surface of chassis cannot be a perfect plane, and the important parts would not be in the pre-set position. If the error is too big, the harvester would not run smoothly as hoped. So it is important to get the flatness error before chassis goes toward the production line. Three-coordinate measuring machine, laser measuring machine and 3D (three-dimensional) camera are traditional flatness error measuring devices, and their precision is high, but they cannot measure such a big chassis with fast enough speed; in addition, their price is very high. In this paper, a test system equipped with laser ranging finders is proposed to measure the flatness error of chassis. The system includes a hydraulic lifting platform and a reference platform. Hydraulic lifting platform is made up of positioning devices, clamping devices and shearing mechanism, and it can lift chassis to a certain height and keep still. The reference platform is a 0 stage precision granite platform, 2 parallel linear guide rails are mounted on its surface, and 2 groups of laser ranging finders (3 finders for each group) will move along the guide rails to get the distance between reference platform and measuring point on chassis. In this system, hydraulic lifting platform is just used to fix chassis, and the position error between hydraulic lifting platform and reference platform can only change the coordinate value of measuring point, and has nothing to do with the flatness error result. The measurement process is carried out in 3 steps. First, hydraulic lifting platform lifts the chassis to the position where stringers are under the laser ranging finders. Second, laser ranging sensors scan the chassis to get the coordinate values of measured points on chassis. At last, computer calculates the flatness error result, and the chassis is put down and took away at the same time. After measuring, the chassis plane is transformed into a great number of spatial coordinates with the same rectangle distribution, and then software will call the MATLAB to calculate the error result. The measurement and control software above-mentioned based on LabWindows/CVI (C for Virtual Instrumentation) is developed to control the system, which can make the test system autonomous or semi-autonomous working, and all the data are stored in an excel file. The system is tested by 9 times on one chassis of a certain type corn combine harvester. The length is 4 000 mm, the width is 1 110 mm, and 66 measuring points are measured along the 2 stringers. The flatness error of 10.27 mm is obtained with the least square method, which takes all the measuring points into account, and the uncertainty degree of this result is just ±0.05 mm. All these show that the result is authentic and credible, and the system and error evaluation method can meet the accuracy and speed requirements of the test, so there is a great significance to ensure the assembly quality of large-scale harvesters and improve the competition of the products. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:28
Main heading:Chassis
Controlled terms:Agricultural machinery - C (programming language) - Design - Errors - Harvesters - Hydraulic machinery - Least squares approximations - Machine design - MATLAB - Measurements - Quality control - Software testing - Stringers - Uncertainty analysis - Welding
Uncontrolled terms:3-D (three-dimensional) - Flatness - Laser measuring machines - Least square methods - Measurement and control - Three-coordinate measuring machines - Uncertainty - Virtual Instrumentation
Classification code:408.2 Structural Members and Shapes - 538.2 Welding - 601 Mechanical Design - 632.2 Hydraulic Equipment and Machinery - 662.4 Automobile and Smaller Vehicle Components - 723.1.1 Computer Programming Languages - 723.5 Computer Applications - 821.1 Agricultural Machinery and Equipment - 913.3 Quality Assurance and Control - 921.6 Numerical Methods - 922.1 Probability Theory
Numerical data indexing:Size 1.03e-02m, Size 1.11e+00m, Size 4.00e+00m
DOI:10.11975/j.issn.1002-6819.2017.z1.003
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 33>
Accession number:20171703590885
Title:Remote intelligent management system for soil sampling based on 3S, ZigBee and radio frequency identification
Authors:Wang, Xingming (1); Yang, Wei (1); Li, Minzan (1); Zheng, Lihua (1); Chen, Yuqing (1)
Author affiliation:(1) Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing; 100083, China
Corresponding author:Yang, Wei(cauyw@cau.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:143-149
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:In order to realize intelligent management of soil sampling in farmland, remote intelligent management system of soil sampling was designed based on the technologies including 3S (GIS, geographic information system; GPS, global positioning system; RS, remote sensing), ZigBee wireless communication, RFID (Radio frequency identification), the 4G (4th generation) communication system, and so on. The system consists of acquisition node, gateway of the coordinator, mobile terminal and remote management software. The acquisition node can obtain the geographic location information of soil samples, the RFID electronic tag and the temperature and humidity of the soil environment; key technologies used in acquisition node are ZigBee wireless communication protocol, serial port communication protocol, and modules of data parsing, packaging, sending, and so on. The gateway of the coordinator is constituted by ZigBee coordinator and 4G module, realizing data transformation from the ZigBee wireless network to 4G network. And the data of acquisition node would be transmitted to management software of remote server when the 4G DTU (data transfer unit) was configured with server IP (Internet Protocol), port number and other configuration information. PDA (personal digital assistant) is used as mobile terminal, which has been remodeled by embedding a micro ZigBee coordinator and a management software in the system. So it is convenient and portable to achieve monitoring and controlling of soil samples collection in time for user, and another important function of PDA is it can realize the transformation of the data from soil samples acquisition node to remote server management software through GSM/GPRS (Global System for Mobile communication / General Packet Radio Service) network if there is no 4G network in remote farmland area. The 4G network is primary communication means under normal circumstances because of its higher efficiency and reliability. Therefore, this method can enhance reliability of the whole system with the double communication links. Management software of remote server develops the data reception, Baidu map, automatic data mapping (two-dimensional, three-dimensional) and other function modules based on Web, SQL (structured query language) Server, Socket and other technologies. Data will be saved to different groups for each test in data reception module, so it is convenient to process data for user. The real-time tracking of soil sampling can be realized through Baidu map with latitude and longitude information from GPS module, and the spatial distribution figure of soil sampling can be automatically generated by calling data collected in database or uploading the local experiment data. We can use system to collect soil sample information and obtain according total nitrogen content of soil sample in laboratory at the same time. In this way, total nitrogen content data of soil sample were imported into information bar by corresponding RFID label in soil management software, generating spatial distribution figure of soil nitrogen, which will provide the decision support for subsequent variable fertilization. The stability test of the system shows it has a high reliability, the packet loss rate of which is 0.2%. So the remote intelligent management system for soil sampling can work stably and accurately, and can be used in production practice. With the further study of the system, it will continue to be improved. The next step will be to increase the number of ZigBee acquisition nodes, and sensor is integrated with different functions of field information detection. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:30
Main heading:Information management
Controlled terms:4G mobile communication systems - Agronomy - Computer terminals - Data transfer - Decision support systems - Farms - Gateways (computer networks) - Geographic information systems - Global positioning system - Global system for mobile communications - Internet protocols - Management - Metadata - Mobile phones - Mobile telecommunication systems - Network protocols - Nitrogen - Packet networks - Personal digital assistants - Query languages - Query processing - Queueing networks - Radio communication - Radio frequency identification (RFID) - Radio waves - Reliability - Remote control - Remote sensing - Sampling - Soil surveys - Soils - Spatial distribution - Wireless telecommunication systems - Zigbee
Uncontrolled terms:Monitoring and controlling - Remote intelligent management systems - Serial port communication - Structured Query Language - Temperature and humidities - Variable fertilizations - ZigBee wireless communication - ZigBee wireless networks
Classification code:483.1 Soils and Soil Mechanics - 711 Electromagnetic Waves - 716 Telecommunication; Radar, Radio and Television - 716.3 Radio Systems and Equipment - 722 Computer Systems and Equipment - 731.1 Control Systems - 804 Chemical Products Generally - 821 Agricultural Equipment and Methods; Vegetation and Pest Control - 821.3 Agricultural Methods - 903.3 Information Retrieval and Use - 912.2 Management - 921 Mathematics
Numerical data indexing:Percentage 2.00e-01%
DOI:10.11975/j.issn.1002-6819.2017.z1.022
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 34>
Accession number:20171703590878
Title:Regional weed identification method from wheat field based on unmanned aerial vehicle image and shearlets
Authors:Wang, Haihua (1, 2); Zhu, Mengting (1); Li, Li (1); Wang, Liyan (1); Zhao, Haiying (3); Mei, Shuli (2)
Author affiliation:(1) Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing; 100083, China; (2) College of Information and Electrical Engineering, China Agricultural University, Beijing; 100083, China; (3) Mobile Media and Cultural Computing Key Laboratory of Beijing, Century College, Beijing University of Post&Telecommunication, Beijing; 102613, China
Corresponding author:Mei, Shuli(meishuli@163.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:99-106
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:Weeds is one of the main harmful factors to the yield and quality of wheat and other main crops during seedling stage. Image processing technology is often used in weed recognition, but the method mainly cares about the weeds between different rows, which is always inefficient and wasteful for unmanned aerial vehicle (UAV) and machine spraying ways. In order to overcome the limitations above, this paper proposes a regional weed identification method, which takes advantage of properties of shearlets. Shearlets have attracted much attention in the field of image recognition because of its good sensitivity and fast computation in texture recognition. Meanwhile, it is a multi-scale analysis method with the characteristic of direction independence. Through the comparison of the regional images of the wheat and weed, it shows that the texture of the weed leaves is more complex while the wheat leaves are relatively regular. So we first choose 8 images including 4 wheat images and 4 weed images. Then we obtain shearlet transform coefficient (STC) at diverse scales and directions according to the different texture characteristics of wheat and weeds. In the STC images of different scales, the brightness from black to white represents different coefficient value. Moreover, the complexity of bright regional distribution represents the textural complexity, which can be used to distinguish wheat and weeds. Shearlets have self-adaptability because of different directions on these scales, so that obvious textural features in images taken from different angles can be detected. In our research, we take the self-adaptability of shearlets and the differences of STC images into account, and we choose the STC in the second scale of vertical cone to distinguish wheat weeds as experimental object. The result shows that the STC mean of wheat in the second scale is lower than that of weeds. Additionally, the fluctuation of STC mean of wheat is smaller than that of weeds. This study chooses 16 wheat images and 16 weeds images, aiming to distinguish weed and wheat more intuitively; we take a further statistical analysis on the mean and variance of coefficient matrixes of shearlets in the second scale of vertical cone. After normalization treatment, the distinction mean values and mean square error between wheat seedling and weeds are about 0.07 and 0.08 respectively. We randomly select 13 pictures of weeds and wheat seedling, and the recognition accuracy is 69.2%. The experimental results of contrast experiment show that the shearlet-transform method performs better than gray level co-occurrence matrix (GLCM) method to distinguish wheat seedling and weeds. We can get an explanation for the experimental results from the different theory of the shearlet-transform and GLCM. The theory of shearlet-transform shows that it can get different directions information adaptively. On the contrary, GLCM can only get the directions assigned, so the number of directions for image processing can't be changed. In addition, the method of splitting blocks of larger image gathered by UAV is used to realize the effective identification of non-wheat region. From the experimental results, we can see that the difference between wheat and weeds is based on effective shearlet-transform, and we can generalize our method to other image classification based on textural features. Furthermore, this method performs with high flexibility and stability and it has the potential for herbicide spraying in the field. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:28
Main heading:Image texture
Controlled terms:Cones - Crops - Image processing - Image recognition - Matrix algebra - Mean square error - Unmanned aerial vehicles (UAV) - Vehicles
Uncontrolled terms:Gray co-occurrence matrix - Regional image - Shearlets - Weed identification - Wheat seedlings
Classification code:652.1 Aircraft, General - 723.2 Data Processing and Image Processing - 821.4 Agricultural Products - 921.1 Algebra - 922.2 Mathematical Statistics
Numerical data indexing:Percentage 6.92e+01%
DOI:10.11975/j.issn.1002-6819.2017.z1.015
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 35>
Accession number:20171703590896
Title:High-throughput maize grain type identification system based on sparse representation algorithm
Authors:Ma, Qin (1, 2); Wang, Yue (1); Guo, Hao (1, 2); Zhu, Dehai (1, 2); Liu, Zhe (1); Zhang, Xiaodong (1); Li, Shaoming (1)
Author affiliation:(1) College of Information and Electrical Engineering, China Agricultural University, Beijing; 100083, China; (2) Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing; 100083, China
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:219-224
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:Maize grain type is one of important phenotype parameters to evaluate maize yield and quality. In order to improve the recognition rate of the maize grain type and achieve high throughput and nondestructive measurement, the maize grain type identification system based on sparse representation is established. For the maize ear's irregular shape and uneven growth, the phenotypic trait acquisition of ear needs to meet the all-dimensional requirements. The hardware acquisition equipment is designed to capture vertically dropping maize ear by 3 high-speed cameras crossed with an angle of 120° mutually in black box. The ear falls down very fast during the process, and thus the high speed of camera's shutter is needed. In addition, enough supplemental lighting is essential because of the high speed of camera. The size of black box is 800 mm × 800 mm × 350 mm. The high-speed CCD (charge coupled device) camera model is DH-SV2001GC, and the image resolution is 1 628×1 236 pixels. The grain images of 3 varieties i.e. flint grains, dent grains and half-dent grains are taken as the research objects. Firstly frame difference method is used to acquire maize contour, and then G channel separation, median filter and Otsu algorithm are used to segment grain contour. Use concave points matching algorithm to solve grain adhesion problem. Then the color feature parameters (average of L-channel, average of a-channel, average of b-channel), the shape feature parameters (cross sectional area, round degree, elongation, rectangular degree) and the texture feature parameters (angular second moment, contrast, inverse difference moment, entropy, correlation) are extrated, which can distinguish different types of grain as the typical characteristics. A total of 200 grains for each grain type is randomly selected to form the dictionary of the sparse representation method. After that, normalize the over-complete dictionary and every test sample. For each test sample, calculate the sparse representation coefficient, and then determine grain type according to the minimum reconstruction error. The classification algorithm is tested by computer which is configured as Intel(R) Core(TM) i7-4710MQ CPU @2.50GHz and the RAM (random access memory) is 8 GB. The test code is written by C++ and the IDE (integrated development environment) is Visual Studio 2013. The image processing library is OpenCV 2.4.9 and the compressed sensing library is KL1p. Experimental results show that the identification accuracy of that algorithm for the maize grains is 94.8%, and the Kappa coefficient of confusion matrix is 0.923, obtaining a high-level discriminant consistency. The recognition accuracy of half-dent grain is not as high as flint grain and dent grain, and because half-dent grain is in the intermediate state between flint grain and dent grain, the difference between it and the other 2 types is not obvious and false recognition maybe occurs. Experiment shows the measurement speed is up to 28 spikes per minute, which meets the demand of high throughput variety test. So the maize grain type identification system proposed in this paper provides an important technique and method for maize variety test and automatic breeding. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:31
Main heading:Image processing
Controlled terms:C++ (programming language) - Cameras - Charge coupled devices - Computer vision - High speed cameras - Identification (control systems) - Image resolution - Inverse problems - Median filters - Nondestructive examination - Random access storage - Speed - Throughput
Uncontrolled terms:Frame difference methods - Grain types - High throughput - Image processing libraries - Integrated development environment - Non-destructive measurement - Over-complete dictionaries - Sparse representation
Classification code:703.2 Electric Filters - 714.2 Semiconductor Devices and Integrated Circuits - 722.1 Data Storage, Equipment and Techniques - 723.1.1 Computer Programming Languages - 723.5 Computer Applications - 731.1 Control Systems - 742.2 Photographic Equipment
Numerical data indexing:Percentage 9.48e+01%, Size 3.50e-01m
DOI:10.11975/j.issn.1002-6819.2017.z1.033
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 36>
Accession number:20171703590892
Title:Design and application of grape production information collecting system based on data quality controlling
Authors:Feng, Jianying (1); Wei, Xuejian (1); Xiao, Guangting (1); Tian, Dong (1)
Author affiliation:(1) College of Information and Electrical Engineering, China Agricultural University, Beijing; 100083, China
Corresponding author:Tian, Dong(td_tiandong@cau.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:192-198
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:The survey of viticulture through paper-and-pencil questionnaires and field interviews has some drawbacks: long period, high cost and low data quality. Establishing a standard set of specifications for data collection process in vineyard management and analysis helps to achieve online research of the grape industry and provide decision support in time. Aiming at the information intelligent acquisition for grape production, MVC (model, view, and controller) framework, PHP (Hypertext Preprocessor) language and B/S (browser/server) structure are adopted to development the production information collecting system. During the process of system designing, according to the characteristics of grape production business and production input and output data, the vineyard information is classified into by basic information and production information and managed separately, which can ensure the integrity and accurateness of data in every process such as acquisition, transformation, storage and computation. The production information collecting system, which includes 3 functional modules: online data collection, data quality control and system management for grape production, is designed and developed. The main function of online survey module is to implement the input and reporting of vineyard basic information and production information. In order to ensure the quality of data, each item of data will be determined by the system with constrain rules after user inputting the forms. The development of user input constraint rules aims to reduce errors in input; outlier detection based on robust regression estimation method is designed to reduce the adverse effects of abnormal data on analysis results; different strategies are made to fill the missed data, so that more complete data can be set. The above method constitute a set of data pre-processing and empirical calculations prove that the pre-treatment process can ensure the accuracy of the system analysis results in a greater degree. Then a set of data quality control method is put forward based on constraint rules management, MM (multiple maximum likelihood type estimates) robust estimation and EM (expectation maximization) algorithm. Meanwhile, this study establishes a pretreatment process of costs and benefits of grape production statistically. In order to test the performance of the system, data of 779 questionnaires from 22 provinces, municipalities and autonomous districts in 2012 were chosen to detect outlier by MM robust estimation, and the result showed the model had good fitness. In terms of data quality control, the accuracy and the normality of processed data have a great improvement, which makes the cycle of data processing reduce a lot. By using the proposed method, questionnaires with outliers can be found. Taking Shanghai as an example, the vacancy value, i.e. labor cost, can be filled. The filled data fit well to the model. Application results show that the system can meet the needs of different users, and it provides a high efficient data collection tool and reliable data quality control technology for the grape industry information analysis, so it can improve the efficiency and effectiveness for survey. In order to facilitate the farmers to upload and manage data anytime and anywhere, optimizing data quality control algorithms and mobile phone applications are an important directions for future research. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:28
Main heading:Data reduction
Controlled terms:Compensation (personnel) - Context free grammars - Control system applications - Costs - Data acquisition - Data handling - Decision support systems - Design - Digital storage - Hypertext systems - Information management - Maximum likelihood - Maximum likelihood estimation - Maximum principle - Metadata - Online systems - Quality assurance - Quality control - Statistics - Surveys - Systems analysis - Wages
Uncontrolled terms:Abnormal data - Data collection process - Data quality - Empirical calculations - Expectation - maximizations - Information acquisitions - Mobile phone applications - Survering
Classification code:721.1 Computer Theory, Includes Formal Logic, Automata Theory, Switching Theory, Programming Theory - 722.1 Data Storage, Equipment and Techniques - 722.4 Digital Computers and Systems - 723 Computer Software, Data Handling and Applications - 723.2 Data Processing and Image Processing - 731.2 Control System Applications - 911 Cost and Value Engineering; Industrial Economics - 912.4 Personnel - 913.3 Quality Assurance and Control - 922 Statistical Methods - 961 Systems Science
DOI:10.11975/j.issn.1002-6819.2017.z1.029
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 37>
Accession number:20171703590870
Title:Photoelectric automatic rotation direction-finding detection method and mechanism design of cherry tomato petiole
Authors:Wang, Meng (1, 2); Li, Jianping (1, 3); Zhu, Pan'an (4); Yu, Qingcang (5); Xu, Zhihao (6); Ji, Mingdong (1)
Author affiliation:(1) College of Bio-systems Engineering and Food Science, Zhejiang University, Hangzhou; 310058, China; (2) College of Machinery and Electronics, Ningbo Polytechnic, Ningbo; 315800, China; (3) Key Laboratory of Equipment and Informationization in Environment Controlled Agriculture, Ministry of Agriculture, Hangzhou; 310058, China; (4) Department of Agriculture and Bio-technology, Wenzhou Vocational College of Science and Technology, Wenzhou; 325006, China; (5) School of Informatics and Electronics, Zhejiang Sci-Tech University, Hangzhou; 310018, China; (6) Horticulture Institute, Zhejiang Academy of Agricultural Sciences, Hangzhou; 310021, China
Corresponding author:Li, Jianping(jpli@zju.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:42-48
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:In order to remove the axillary bud of cherry tomato automatically by a robot, the camera should be in specific locations to capture images of axillary bud and petiole. Camera optical axis perpendicular to the main plane coexisted with main stem, petiole and axillary bud. The horizontal projection direction angle of petiole in horizontal plane should be measured. In this paper, a photoelectric automatic rotating direction-finding mechanism was designed to get the value of the angle. The end effector of the robot was composed of a direction-finding mechanism, a camera and a pneumatic shear. Telescoping devices were used to control the extension and contraction of direction-finding mechanism. The two half-circle of active circle was closed by mechanical claw, and combined into a complete circle around the main stem. Then active circle moved upward under manipulator control. Meanwhile, the active circle rotated repeatedly at 90 degree angle. The inside diameter of active circle was 60 mm. There were 8 photoelectric sensors uniformly distributed on the active circle, and the included angle between every two sensors was 45°. The photoelectric sensors would be triggered when the petiole appeared 10 mm above the active circle. If the inside diameter of active circle was too big, sensors might well be misguided by leaves on which branches closest to the main stem, and produced great error. On the contrary, if the inside diameter of active circle was too small, active circle might be stuck at the parts of the main stem which deviated from the hang-off line. 8 photoelectric sensors fixed on the active circle were used to detect the existence of petiole, and the angle of petiole in horizontal plane was got through the angle of sensors and the rotation angle of active circle. The values of angles were sent to control module, which moved the end effector to the normal direction of the main plane. The end effector should always be kept in front of the tomato plant. The relations among angular velocity, linear velocity and coefficient were obtained by analysis of active circle movement trajectory. Angular velocity was determined by the linear velocity and the scanning coefficient, which should be set previously, and scanning coefficient must be greater than 0.5. If the value of coefficient was smaller than 0.5, direction-finding mechanism would miss the petiole, and damage the cherry tomato plant. Angular velocity was directly proportional to linear velocity. The greater the linear velocity was, the greater the angular velocity would be, and the greater the errors of horizontal projection direction angle would be. The camera was installed under the direction-finding mechanism. Robot arm drove the end effector up to make the main stem, petiole and axillary bud displayed completely in viewfinder display of camera. This process of height compensation was determined by the height difference between the optical axis of the camera and the active circle. Height compensation made the complete axillary bud displayed on camera lens, so that the robot could locate the growth point of the petiole and then remove the axillary bud. Experimental results showed that detection successful rate was 95% with scanning coefficient as 1.5, angular velocity as 1.5π rad/s, and linear velocity as 20 mm/s. The successful rate was 93% if the height compensation was set as 28 mm. 153 petiole samples had been tested, and the successful rate was 88.2% for the height of cherry tomato plant less than 1.8 m. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:29
Main heading:End effectors
Controlled terms:Angular velocity - Cameras - Design - Fruits - Machine design - Photoelectricity - Plants (botany) - Robots - Rotation - Scanning - Sensors - Velocity
Uncontrolled terms:Cherry tomatoes - Direction finding - Horizontal angles - Horizontal projections - Manipulator control - Movement trajectories - Petiole - Photoelectric sensors
Classification code:601 Mechanical Design - 701.1 Electricity: Basic Concepts and Phenomena - 731.5 Robotics - 742.2 Photographic Equipment - 821.4 Agricultural Products - 931.1 Mechanics
Numerical data indexing:Percentage 8.82e+01%, Percentage 9.30e+01%, Percentage 9.50e+01%, Size 1.00e-02m, Size 1.80e+00m, Size 2.80e-02m, Size 6.00e-02m, Velocity 2.00e-02m/s
DOI:10.11975/j.issn.1002-6819.2017.z1.007
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 38>
Accession number:20171703590886
Title:Evaluation of groundwater quality in Changping piedmont plain of Beijing based on BP neural network
Authors:Kong, Gang (1, 2); Wang, Quanjiu (1); Huang, Qiang (1)
Author affiliation:(1) College of Water Resources and Hydropower, Xi'an University of Technology, Xi'an; 710048, China; (2) Center of Water Assessment of Beijing, Beijing; 100161, China
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:150-156
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:Groundwater quality is closely related with human health and environmental safety. In the suburb of Beijing, the groundwater quality is heavily concerned. In this study, the groundwater quality in Changping piedmont plain was evaluated based on the single factor evaluation method and comprehensively evaluated based on BP neural network. The Changping district is located was in the northwestern area of Beijing. Considering that the main area affected by human activity was the shallow groundwater, we arranged a total of 12 monitoring wells around the plain area. The depths of wells 1#, 2# and 11# were 130 m, the depth of wells 3#, 4# and 7# were about 125 m, and the depths of wells 5#, 6#, 8#, 9#, 10#, 12# were about 120 m. The groundwater samples were collected on April 16, 2015. A total of 27 indexes were determined including pH value, chloride, sulfide, nitrate nitrogen, ammonia nitrogen, heavy metals, fluoride, and so on. In the single factor evaluation, the groundwater quality was evaluated according to the National Groundwater Quality Standards (GB/T14848-93). Based on the single factor evaluation method, water quality in 5 wells of 1#, 4#, 7#, 9# and 11# exceeded the standards. In the 1# well, the turbidity, total hardness, total dissolved solids and nitrate nitrogen exceeded the standards by 1.67, 1.81, 1.48, and 3.08 times, respectively. In the 4# well, the turbidity was 1.33 times exceeding the standard. In the 7# well, the total hardness and total dissolved solids exceeded the standards by 1.2 and 1.15 times, respectively. In the 9# well, the fluoride exceeded the standard by 1.58 times. The ammonia nitrogen in the 11# well exceeded the standard by 3.25 times. Compared the values in 1999, the area with total hardness exceeding the standard was expanded largely. Among the 27 indexes, we chose 17 indexes for the comprehensive evaluation based on BP neural network, including the turbidity, iron, chloride, sulfate, TDS, total hardness, manganese, zinc, potassium permanganate index, nitrate nitrogen, nitrite nitrogen, ammonia nitrogen, fluoride, and so on. The BP evaluation showed that all the 12 wells had the water quality above III. Among the wells, 1 was V grade, 5 were IV grade and 6 were III grade. Compared with the single evaluation results, the comprehensive evaluation based on BP neural network was reasonable. The 1# well had 4 indexes exceeding the standards and thus the water quality was V grade. Compared with previous study, the pollution of nitrate and ammonia nitrogen might be due to surface pollutants infiltration. In the future, the continuous monitoring of shallow groundwater should be conducted, and the surface pollutants infiltration prevention control should be strengthened. The study may provide valuable information for the management of the groundwater in Beijing. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:32
Main heading:Quality control
Controlled terms:Ammonia - Chlorine compounds - Fluorine compounds - Groundwater - Groundwater pollution - Hardness - Heavy metals - Infiltration - Neural networks - Nitrates - Nitrogen - Pollution - Standards - Turbidity - Water hardness - Water quality - Zinc chloride
Uncontrolled terms:Beijing - Comprehensive evaluation - Continuous monitoring - Evaluation - Potassium permanganate - Single factor evaluation - Total dissolved solids - Total hardness
Classification code:444.2 Groundwater - 445.2 Water Analysis - 531 Metallurgy and Metallography - 741.1 Light/Optics - 804 Chemical Products Generally - 804.2 Inorganic Compounds - 902.2 Codes and Standards - 913.3 Quality Assurance and Control - 951 Materials Science
Numerical data indexing:Size 1.20e+02m, Size 1.25e+02m, Size 1.30e+02m
DOI:10.11975/j.issn.1002-6819.2017.z1.023
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 39>
Accession number:20171703590860
Title:Nondestructive measurement of firmness and sugar content of blueberries based on hyperspectral imaging
Authors:Li, Rui (1); Fu, Longsheng (1)
Author affiliation:(1) College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling; 712100, China
Corresponding author:Fu, Longsheng(fulsh@nwafu.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:362-366
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:Blueberry is an important fruit in the world, and its consumption has increased significantly in recent years because of its flavor and antioxidant capacity for anti-aging. In China, blueberry has increased greatly with rapid development of internet shopping and express. However, fruits with low firmness are easily to be damaged with juice leaking out, which is and may unacceptable by the consumer. Sugar content is also important to consumer. Accurate determination of blueberry quality is challenging since individual fruit are small and dark in color, and vary greatly in external and internal quality characteristics. Traditionally, blueberry quality was inspected by human in situ at the sorting line, which was inefficient and unreliable. Moreover, it's difficult to sort fruit by human based on sugar content and firmness, two quality attributes that are not only important to the consumer, but also directly impact the shelf life of blueberries. Therefore, hyperspectral imaging technique for predicting the firmness and sugar content of blueberries was researched. A pushbroom hyperspectral imaging system was used to acquire hyperspectral reflectance images from 490 `bluecrop' blueberries in two fruit orientations (i.e., stem and calyx ends) for the spectral region of 900-1 700 nm. Each fruit was then segregated by building a binary mask to recognize the fruit from the background using threshold segmentation in the hypercube. This was accomplished on the spectral image at 1 542 nm, which gave the maximum contrast between the fruit and the background. After that, each berry was identified by combining tilting and labeling operations on the masked image. From the regions of interest of each segmented blueberry image, mean reflectance was computed by averaging over all pixels. Finally, prediction models were developed based on partial least squares method using cross validation and were externally tested with 25% of the samples. Effect of fruit orientations on hyperspectral imaging prediction was evaluated by designated the spectral data in three treatment (stem end, calyx end, and whole fruit which averaged two mean spectra for the stem and calyx end regions to obtain on spectrum). Results showed that better firmness predictions (R<inf>C</inf>=0.911, R<inf>V</inf>=0.871) were obtained, compared to sugar content predictions (R<inf>C</inf>=0.891, R<inf>V</inf>=0.774). Fruit orientation had no or insignificant effect on the firmness and sugar content predictions. Further analysis showed that blueberries could be sorted into two classes of firmness. The result showed that hyperspectral imaging is promising for online sorting and grading of blueberries for firmness and perhaps sugar content as well. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:26
Main heading:Hyperspectral imaging
Controlled terms:Forecasting - Fruits - Grading - Image processing - Imaging techniques - Least squares approximations - Nondestructive examination - Reflection - Sorting - Spectroscopy - Spectrum analysis
Uncontrolled terms:Blueberries - Firmness - Nondestructive detection - Partial least-squares method - Sugar content
Classification code:746 Imaging Techniques - 821.4 Agricultural Products - 921.6 Numerical Methods
Numerical data indexing:Percentage 2.50e+01%, Size 1.54e-06m, Size 9.00e-07m to 1.70e-06m
DOI:10.11975/j.issn.1002-6819.2017.z1.054
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 40>
Accession number:20171703590874
Title:Automatic navigation path search and turning control of agricultural machinery based on GNSS
Authors:Wei, Shuang (1); Li, Shichao (2); Zhang, Man (1); Ji, Yuhan (1); Xiang, Ming (1); Li, Minzan (1)
Author affiliation:(1) Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing; 100083, China; (2) Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing; 100083, China
Corresponding author:Zhang, Man(cauzm@cau.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:70-77
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:In order to improve the performance of the agricultural machinery automatic navigation system, an automatic navigation path searching method of agricultural machinery based on GNSS (global navigation satellite system) was proposed. According to different demands of farm working, the system could generate the straight line or curve path for agricultural machinery automatic navigation according to users setting. In order to get the straight navigation path, the user should drive the tractor and record the current position marked as point A, and then choose the position marked as Point B at least 10 m far away from Point A. The straight line presupposed navigation path could be obtained by connecting Point A and B and extending the segment AB. The way of obtaining the curve presupposed navigation path is similar to the straight path searching method; the curve fits with several segments, and every segment is analyzed with the straight path searching method. When the navigation task began, the system would compare the current position and heading information of the tractor with the presupposed path to get the lateral deviation and heading deviation. In addition, a pure pursuit mode based on preview points research was proposed for steering control. The method didn't involve the complicated control theory, so that it could adapt the navigation system better. In the aspect of turning control, arcuate turning and pyriform turning patterns were selected as the major research objects. The turning path could be generated by the navigation system according to the tractor working width and the minimum turning radius after the users chose the kind of turning pattern, and a series of points could be chosen according to the tractor speed and each point was evenly spaced. When the navigation task began, the searching radius and preview point should be set according to the speed of the tractor. There were several points on the default navigation path falling in the searching circle; the point with the largest ID (identification) number would be selected as the preview point, and then the path of the tractor arriving to the preview point and the control turning angle would be obtained. To verify the path search method and the model of pure tracking performance, a tractor automatic navigation software was designed and implemented. The industrial computer as the carrier of navigation software, processed the GNSS data, IMU (inertial measurement unit) data and PLC (programmable logic controller) data, and then generated the corresponding decisions. A John Deere tractor was used as the platform for experiments, and the straight line / curve navigation experiments based on GNSS positioning technology were designed. The results of experiments were as follows: In the straight line navigation experiments, when the speed of the tractor was 0.8, 1.0 and 1.2 m/s, the root-mean-square error was 3.79, 4.28 and 5.39 cm respectively; in the turning navigation experiments, when the speed of the tractor was 0.6 m/s, the root-mean-square error of the arcuate turning navigation was 25.23 cm and the root-mean-square error of the pyriform turning navigation was 14.42 cm; for the comparison experiment, the root-mean-square error using the proposed method and fuzzy control method was 4.30 and 5.95 cm respectively in straight line navigation module, and 13.73 and 21.40 cm respectively in curve navigation module. The path searching method and the pure pursuit mode based on the researching of preview points can satisfy the requirement of the farmland works effectively. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:34
Main heading:Global positioning system
Controlled terms:Agricultural machinery - Agriculture - Communication satellites - Curve fitting - Digital storage - Errors - Experiments - Fuzzy control - Mean square error - Motion planning - Navigation - Navigation systems - Online searching - Tractors (agricultural) - Tractors (truck) - Units of measurement
Uncontrolled terms:Automatic navigation systems - Fuzzy control methods - Global Navigation Satellite Systems - Inertial measurement unit - PLC (programmable logic controller) - Pure pursuits - Root mean square errors - Tracking performance
Classification code:655.2.1 Communication Satellites - 663.1 Heavy Duty Motor Vehicles - 722.1 Data Storage, Equipment and Techniques - 731 Automatic Control Principles and Applications - 821 Agricultural Equipment and Methods; Vegetation and Pest Control - 821.1 Agricultural Machinery and Equipment - 901.3 Engineering Research - 902.2 Codes and Standards - 903.3 Information Retrieval and Use - 921.6 Numerical Methods - 922.2 Mathematical Statistics
Numerical data indexing:Size 1.00e+01m, Size 1.37e-01m, Size 2.14e-01m, Size 2.52e-01m, Size 4.28e-02m, Size 4.30e-02m, Size 5.39e-02m, Size 5.95e-02m, Velocity 1.00e+00m/s, Velocity 1.20e+00m/s, Velocity 6.00e-01m/s
DOI:10.11975/j.issn.1002-6819.2017.z1.011
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 41>
Accession number:20171703590867
Title:Design, experiment and error analysis of impulse radio-ultra wide band indoor positioning system used in agricultural warehousing
Authors:Sun, Xiaowen (1); Zhang, Xiaochao (1); Zhao, Bo (1); Wang, Lili (1); Wei, Liguo (1); Jia, Quan (1)
Author affiliation:(1) National Key Laboratory of Soil-Plant-Machine System, Chinese Academy of Agricultural Mechanization Science, Beijing; 100083, China
Corresponding author:Zhang, Xiaochao(zxchao2584@163.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:23-29
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:For the problem of low positioning accuracy about agricultural goods and AGV (automated guided vehicle) in the warehousing environment, a system model applicable to agricultural warehouse is developed, which uses IR-UWB (impulse radio - ultra wide band) technology. This system adopts SDS-TWR (symmetric double sided - two-way ranging) scheme to set up positioning system model, and uses TOA (time of arrival) location algorithm to locate the mobile station. First of all, this system measures the distance between base station and mobile station, and then gets the optimal solution of error function using nonlinear least squares method. The optimal solution is the coordinate of the mobile station. This system also takes into account the factors of carrier frequency deviation, researches the source of error and tries to find out the method to reduce it. The analysis shows the main source of positioning error in 2 aspects, namely the method of distance measurement, and the method of calculating the position of the target nodes by the distance value. And we put forward the corresponding countermeasures for these 2 aspects, such as the selection of ranging algorithm, and the arrangement of the base stations. IR-UWB has many advantages such as strong anti-interference ability, high range accuracy, low power consumption, fast transmission speed and good security. Because of the convenience to layout network nodes and no strict requirements in the field environment, it is suitable for the field of agricultural warehousing. Finally, we design the positioning system of mobile and base station nodes based on DW1000 RF (radio frequency) chip, and respectively carry out static ranging experiments, static positioning experiments and dynamic positioning experiments. To increase contrast, this article also adds comparative trial, which uses TWR ranging algorithm. The static ranging experiments of this system adopt 6 distances of 10, 15, 20, 30, 40 and 50 m, and collects around 3 000 sets of data respectively. The experiments show that the error of the mean value between the actual distance and the ranging distance is less than 50 mm, and the root mean-square error is less than 41 mm using SDS-TWR ranging algorithm. However, the former is more than 110 mm and the latter is more than 60 mm using TWR ranging algorithm in the same experiment condition. In the static positioning experiments we conduct some experiments to measure coordinates of some spots under the fixed coordinate system. The result shows the positioning error is less than 50 mm and the root mean-square error is less than 69 mm with SDS-TWR ranging algorithm. And the positioning error is more than 90 mm and the root mean-square error is more than 115 mm with TWR ranging algorithm in the same experiment condition. In the dynamic positioning experiments, according to the actual situation of agricultural material warehouse, we move the mobile station along 5 produce aisles and 9 routes, and obtain the distance between the gathered data and the actual path. The experiments show that the positioning accuracy is 85 mm, which can meet requirement of 150 mm positioning accuracy. Comprehensive experiments show the system set up by this paper can satisfy the requirements of practical application in indoor agricultural material storage. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:29
Main heading:Time of arrival
Controlled terms:Agriculture - Automatic guided vehicles - Base stations - Broadband networks - Digital storage - Dynamic positioning - Error analysis - Errors - Impulse noise - Indoor positioning systems - Least squares approximations - Mean square error - Optimal systems - Position control - Radio - Signal receivers - Transportation - Ultra-wideband (UWB) - Warehouses
Uncontrolled terms:Automated guided vehicles - Carrier frequency deviation - Fixed coordinate systems - Impulse Radio - Nonlinear least squares methods - Root mean square errors - Time of arrival (TOA) - Wireless positioning
Classification code:694.4 Storage - 716.1 Information Theory and Signal Processing - 716.3 Radio Systems and Equipment - 722.1 Data Storage, Equipment and Techniques - 731.3 Specific Variables Control - 731.6 Robot Applications - 821 Agricultural Equipment and Methods; Vegetation and Pest Control - 921.6 Numerical Methods - 922.2 Mathematical Statistics - 961 Systems Science
Numerical data indexing:Size 1.10e-01m, Size 1.15e-01m, Size 1.50e-01m, Size 4.00e+01m, Size 4.10e-02m, Size 5.00e+01m, Size 5.00e-02m, Size 6.00e-02m, Size 6.90e-02m, Size 8.50e-02m, Size 9.00e-02m
DOI:10.11975/j.issn.1002-6819.2017.z1.004
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 42>
Accession number:20171703590902
Title:Aquaculture information recommendation based on collaborative filtering algorithm and web logs
Authors:Zhen, Zhumi (1); Wang, Lianzhi (1); Zhang, Yan'e (1)
Author affiliation:(1) College of Information and Electrical Engineering, China Agricultural University, Beijing; 100083, China
Corresponding author:Wang, Lianzhi(ndjsj862@cau.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:260-265
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:With the development of Internet, aquaculture information is increasing rapidly in decade years in both internet and the Internet of Things (IoT). Now users are confronted with the information overload, and most of the information has not been effectively utilized. In order to select requested resource for specific users from ubiquitous resource, an aquaculture recommender system was built in this paper. Several IoTs had been deployed in many provinces of China, like Beijing, Jiangsu and Shandong. What's more, an IoT platform was established to collect IoT environment information in real time and crawl aquaculture information from Internet. In this research, item based collaborative filtering algorithm was combined with Web usage mining to generate recommendation. Web usage mining technology processed Web logs in host server and analyzed browsing behavior of users to establish user preference model. Furthermore, item based collaborative filtering used collective intelligence to recommend items that was similar with the items user prefers. The producers of this research were as follows. First, an aquaculture household interest questionnaire was designed after field research with aquaculture households. The questionnaire included user basic investigation, pond basic investigation and user interest investigation. Second, users rated the items in the questionnaire according to their interests. These items included aquaculture technology, fish disease prevention knowledge, government policies, pond production information, disease warning information, weather information, information output prediction and fault information etc. So that user tendency was initialized through the questionnaire and system registration. Third, further user interest could be collected in the form of Web logs. Web logs recorded user browsing behavior, such as user name, IP, access date and time and visited pages. User browsing behavior indicated user interests. To a certain extent, the longer time user browsers a page, the more interest user have. This technology was called Web usage mining. After user initial model and Web usage mining, the user-item rating matrix was calculated. Fourth, item-based collaborative filtering calculated the similarity between two items. The methods of similarity calculation mainly included Pearson similarity, cosine similarity, general modified cosine similarity and improved modified cosine similarity. Considering different user rating behaviors, some users would rate much higher or lower score than the average. So improved modified cosine similarity method was used to reduce the impact of user rating behavior. Using this method, all ratings of one user would be divided by highest rating, and the new user-item rating matrix was obtained. Fifth, the prediction rating from users to items was produced based on item similarity. The recommendation system pushed the items with highest prediction rating to the corresponding users using the top neighborhood method. Finally, this research was evaluated by mean absolute error. Results showed that when recommend items were more than 4, MAE of improved modified cosine method were the least in all methods. So that, the improved modified cosine method (IMCM) was chosen to calculate item similarity. In conclusion, the cold start problem of recommender system could be solved by initial user interest like user registration and questionnaire investigation. And the IMCM method improves the accuracy by reducing impact of user behavior. The aquaculture system recommends precisely according to user interest from both Internet and Internet of Things (IoT), such as trading information, farming technology, government policies and Internet of Things data. There are further works to do in the future. Context aware can be taken into consideration to improve recommendation precision, such as time, location and fault information in pond. Time presents the different production season of aquatic products; location presents where the pond is; and the fault information presents the abnormal status of IoT in pond. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:23
Main heading:Information filtering
Controlled terms:Algorithms - Aquaculture - Behavioral research - Blogs - Collaborative filtering - Data mining - Electronic document exchange - Forecasting - Internet of things - Lakes - Public policy - Rating - Recommender systems - Signal filtering and prediction - Surveys - Web browsers - Web crawler
Uncontrolled terms:Collaborative filtering algorithms - Collective intelligences - Information recommendation - Item-based collaborative filtering - Recommendation precision - User interests - User preference modeling - Web log mining
Classification code:716.1 Information Theory and Signal Processing - 723 Computer Software, Data Handling and Applications - 821.3 Agricultural Methods - 903.1 Information Sources and Analysis - 971 Social Sciences
DOI:10.11975/j.issn.1002-6819.2017.z1.039
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 43>
Accession number:20171703590861
Title:Lipid oxidation and color degradation kinetics under different storage conditions of pollen
Authors:Wang, Jun (1); Wang, Dong (2); Luo, Qingsong (1); Xiao, Hongwei (1); Zhang, Xiaolin (1); Fang, Xiaoming (3); Gao, Zhenjiang (1); Han, Shengming (3)
Author affiliation:(1) College of Engineering, China Agricultural University, Beijing; 100083, China; (2) College of Mechanical and Electrical Engineering, Shaanxi University of Science and Technology, Xi'an; 710021, China; (3) Institute of Apicultural Research, Chinese Academy of Agricultural Sciences, Beijing; 100094, China
Corresponding author:Fang, Xiaoming(153886891@qq.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:367-373
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:Pollen is the male reproductive material of plants and rich in various nutrient and functional substances, including protein, lipid of pollen, polysaccharide, vitamins and mineral, which are benefit for human health. Because of its relatively high moisture content, and its sensitive to microbial spoilage and fermentation, fresh pollen has very short shelf-life. Drying is the most frequently used method for pollen preservation as it can prevent the growth and reproduction of microorganisms and minimize many of the moisture-mediated degradation reactions to enhance shelf-life. However, due to high lipid content of pollen, their oxidation and rancidity arise serious quality problems during unsuitable storage, such as undesirable color, odour, flavor, etc. Therefore, a quantitative investigation of the change kinetics of pollen quality during storage is critical for selecting proper package method and storage conditions. As one of the most important organoleptic evaluation indicators of most foods and agricultural products, unsuitable color changes affect the market value and sale quantity significantly. Yellowness value, as the trait color of pollen, was selected as one of the quality attributes in current research. Lipid oxidation is closely related to quality degradation and flavor of food. The peroxide value (POV) and thiobarbituric acid reactive substances value (TBArs) were used to evaluate the first-order and second-order lipid oxidation of pollen during storage, respectively. The higher the POV and TBArs values are, the more severe the oxidation of lipid is. In current work, effects of different package factors (illumination and oxygen) and storage temperatures (4, 20, and 30℃) on the changes of yellowness value (b* value), peroxide (peroxide value, POV) and thiobarbituric acid reactive substances (TBArs) were investigated during pollen storage. The aims of the study were: 1) to explore the impact of temperatures on color and lipid oxidation at vacuum and without illumination package; 2) to explore the impact of illumination on color and lipid oxidation at 4℃ with vacuum package; 3) to explore the impact of oxygen on color and lipid oxidation at 4℃ without illumination package; 4) to establish the kinetics model of b* value, POV and TBArs based on zero-order and first-order models during 30 d storage period. Results showed that, a higher b* value of pollen was obtained under storage temperature of 20 and 30℃ compared with 4℃ (P<0.05); no significant change(P>0.05) was found for the POV value of pollen during storage at 4℃ for 30 days, while significant increasing (P<0.05) were observed for storage at temperature of 20 and 30℃, respectively. In addition, the effect of light on POV of pollen during storage was not significant. But, the illumination could enhance the acceleration of TBArs value, which increased 7.42 mg/kg after 30 days storage. Zero-order model could well predict the TBArs changes kinetics of pollen during storage, while the first-order model could well describe b* value and POV value variation. The results could provide a theoretical support for selecting of storage strategies and conditions, which are benefits for reducing the quality degradation and prolong the shelf-life of pollen. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:35
Main heading:Food storage
Controlled terms:Agricultural products - Biodegradation - Color - Degradation - Energy storage - Enzyme kinetics - Kinetics - Moisture - Oxidation - Peroxides - Plants (botany) - Spoilage - Storage (materials)
Uncontrolled terms:Color parameter - High moisture contents - Organoleptic evaluation - Peroxide value - Pollen - Quantitative investigation - Thiobarbituric acid - Thiobarbituric acid reactive substances
Classification code:461.8 Biotechnology - 461.9 Biology - 525.7 Energy Storage - 694.4 Storage - 741.1 Light/Optics - 802.2 Chemical Reactions - 821.4 Agricultural Products - 931 Classical Physics; Quantum Theory; Relativity
Numerical data indexing:Age 8.22e-02yr
DOI:10.11975/j.issn.1002-6819.2017.z1.055
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 44>
Accession number:20171703590894
Title:3D reconstruction of strawberry leaves based on contour segmentation
Authors:Zhang, Xue (1); Guo, Cailing (1); Zong, Ze (1); Zhang, Weijie (1); Liu, Gang (1)
Author affiliation:(1) Key Laboratory of Modern Precision Agriculture System Integration Research, China Agricultural University, Beijing; 100083, China
Corresponding author:Liu, Gang(pac@cau.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:206-211
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:Based on the data measured by instruments, the plant three-dimensional reconstruction is an important part of the plant digital research. Through the establishment of the plant three-dimensional model, not only can the growth rule of the plants in real environment be researched by measuring the crop parameters quickly, but also the spatial structure of local visual scene of plant can be explored by analyzing plant leaf distribution feature. In order to construct three-dimensional structure of situ strawberry plants precisely for further study in plant spatial structure, the paper took strawberry plants of elevated cultivation environment as the research object and proposed a three-dimensional strawberry canopy morphology reconstruction algorithm based on multi-source image contour segmentation. We divided the main algorithm into 3 parts: multi-source image pre-processing, coarse segmentation of intensity image and model fitting. To make full use of the advantages of color images in color segmentation, color image and intensity image in different resolution are registered and merged. The paper used the improved multi-source information fusion algorithm of strawberry plants based on feature. By using feature-based multi-source information fusion algorithm, feature information of each source image is extracted and analyzed. Then the invariable feature points are selected. Later by checking the similarity of the feature points and adding appropriate parameter constraints, the registration information is obtained. Multi-source image mapping relationship is established by applying registration information. Then by fusing the preprocessed images, the information complement of color image and intensity map is realized and finally the intensity image to be split is obtained by the image preprocessing. Calculating local center of the vector field of intensity image to be segmented means calculating vector field for each pixel. Then the largest local pixels are picked out. Later the vector direction of each pixel is divided into 3 categories by a symbolic function. Clustering potential scattering point set in an array and the local control point are determined by applying a given threshold. Finally by applying the active contour model of the parameters and the central control point to the segmented intensity image we get a coarse segmentation image of the blade. A method of model reconstruction based on surface fitting was proposed for further processing. Intensity image's segmentation contour is regard as the edge contour, and the contour interior point cloud is extracted. The method of region marking is used to mark the used point cloud which belongs to original depth point cloud data, and by checking the number of unmarked point clouds we can know whether the extraction is completed. The plane fitting selection mechanism based on point cloud is designed to compare the minimum mean square deviation of the surface and the plane after fitting, and the optimal fitting model is selected. All the optimal models are displayed in a coordinate system, and the points are colored one by one. Finally, the reconstruction and display of the three-dimensional model of strawberry are finished. To verify the effectiveness of the algorithm, the paper took the distance difference between average single-leaf length and leaf distance as an evaluation index. Experimental results showed that the correction rate of number of blades was 85.6%, that of single-leaf model was 88.4% and that of distance difference was 82.4%. The results can be applied to the spatial position measurement of in situ strawberry plants. The research provides a new method for the construction of plant spatial structure in local vision scenes of agricultural robots. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:31
Main heading:Three dimensional computer graphics
Controlled terms:Color - Crops - Cultivation - Fruits - Image acquisition - Image processing - Image reconstruction - Image segmentation - Information fusion - Pixels - Plants (botany) - Surface reconstruction
Uncontrolled terms:3D reconstruction - Morphology reconstruction - Multi-source information fusion - Multi-source informations - Point cloud - Three-dimensional model - Three-dimensional reconstruction - Three-dimensional structure
Classification code:723 Computer Software, Data Handling and Applications - 741.1 Light/Optics - 821.3 Agricultural Methods - 821.4 Agricultural Products - 903.1 Information Sources and Analysis
Numerical data indexing:Percentage 8.24e+01%, Percentage 8.56e+01%, Percentage 8.84e+01%
DOI:10.11975/j.issn.1002-6819.2017.z1.031
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 45>
Accession number:20171703590911
Title:Target extraction method of ripe tomato in greenhouse based on Niblack self-adaptive adjustment parameter
Authors:Wang, Lili (1, 2); Wei, Shu (2); Zhao, Bo (2); Mao, Wenhua (2); Hu, Xiaoan (2); Fan, Jinwei (1)
Author affiliation:(1) College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing; 100124, China; (2) State Key Laboratory of Soil-Plant-Machine System Technology, Chinese Academy of Agricultural Mechanization Sciences, Beijing; 100083, China
Corresponding author:Fan, Jinwei(jwfan@bjut.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:322-327
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:Tomato is one of the most popular and widely grown vegetables in the world. Manual harvesting of tomatoes is laborious, time-consuming and inefficient, thus making it somewhat impractical for large-scale plantations. Intelligent robots have been developed for harvesting tomato. However, as the tomato is very soft and thus especially prone to bruising, many significant technical challenges remain to be solved. In China, the research on the harvesting robot is still in its infancy, but considerable progress has been made in many aspects, such as the manipulator, image recognition, and motion control. Tomato targets extraction is the basis for location and picking of tomato. Early extraction methods have certain limitations, which are difficult to meet the demand of harvest. In this study, Niblack self-adaptive adjustment parameter selection method was put forward and successfully applied in extracting ripe tomato in greenhouse. This segmentation algorithm was based on traditional Niblack algorithm using the correlation between global and local grayscale change information of tomato image. The original tomato image was firstly transformed to gray space, and the gray-level image was obtained using the normalized color difference method, and segmented into the foreground and the background. The normalized color difference method could eliminate the light intensity information in the red and green components. Then a new Niblack threshold segmentation algorithm was used to segment the gray image. The adjustment parameter was calculated through the expected value of each window and normalized standard deviation. After denoising, the ripe tomato object could be easily extracted from segmented image by using the minimum critical rectangle method. In order to compare different segmentation algorithms, traditional Niblack algorithm, Otsu algorithm and Niblack self-adaptive adjustment parameter selection algorithm had been selected to perform the comparative analysis. Experiments showed that the Otsu algorithm could extract the target of interest in the image, which contributed significantly to the subsequent target recognition and the reduction in computation time. However, this method may fail to segment overlapping tomatoes into individual ones. For Otsu algorithm, the threshold selection in each region lacked the image characteristics, which caused the binary result to contain a lot of background noise. Traditional Niblack algorithm exaggerated image details and got a lot of unnecessary edge information, which made it difficult to separate the target from background. Niblack self-adaptive adjustment parameter selection algorithm could effectively overcome the problem of pseudo noise. This approach has gotten a good applying result in the extraction of ripe tomato object from original images in greenhouse environment. The accuracy rate of ripe tomato recognition could reach 98.3%. Compared with Otsu algorithm based on normalized difference of red and green, and traditional Niblack segmentation algorithm, segmentation algorithm based on Niblack self-adaptive adjustment parameter selection is more efficient, and its noise is smaller and the process is faster. It can meet the need of the subsequent identification of tomato image and solve the problems of low adaptation and pseudo noise block with traditional methods. But because of the complexity of the object-picking environment, the new algorithm remains to be further improved in the practical application. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:30
Main heading:Image segmentation
Controlled terms:Algorithms - Color - Colorimetry - Edge detection - Extraction - Fruits - Greenhouses - Harvesting - Image processing - Image recognition - Intelligent robots - Manipulators - Parameter estimation
Uncontrolled terms:Adaptive adjustment - Greenhouse environment - Large-scale plantations - Normalized color differences - Normalized differences - Normalized standard deviations - Segmentation algorithms - Threshold segmentation
Classification code:731.6 Robot Applications - 741.1 Light/Optics - 802.3 Chemical Operations - 821 Agricultural Equipment and Methods; Vegetation and Pest Control - 941.4 Optical Variables Measurements
Numerical data indexing:Percentage 9.83e+01%
DOI:10.11975/j.issn.1002-6819.2017.z1.048
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 46>
Accession number:20171703590895
Title:Method of fruit tree canopy leaf reconstruction based on point cloud
Authors:Wu, Sheng (1); Zhao, Chunjiang (1, 2, 3, 4); Guo, Xinyu (2, 3, 4); Wen, Weiliang (2, 3, 4); Xiao, Boxiang (2, 3, 4); Wang, Chuanyu (2, 3, 4)
Author affiliation:(1) School of Information Science & Technology, Beijing Forestry University, Beijing; 100083, China; (2) National Engineering Research Center for Information Technology in Agriculture, Beijing; 100097, China; (3) Beijing Research Center for Information Technology in Agriculture, Beijing; 100097, China; (4) Beijing Key Lab of Digital Plant, Beijing; 100097, China
Corresponding author:Zhao, Chunjiang(zhaocj@nercita.org.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:212-218
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:Accurate three-dimensional canopy structure of fruit trees is an important carrier for the study of functional structure model, and the canopy leaf is an important part of the fruit tree canopy structure. The spatial distribution and morphological structure of leaf canopy play an important role in fruit yield and quality. Compared with field crops, fruit trees have high branches, complex branches and luxuriant branches, so the rapid and accurate reconstruction of tree canopy is the focus of current research. With the development of laser measurement technology, 3D (three-dimensional) laser scanner is widely used in 3D reconstruction of plants because of its high precision, wide collection range, high efficiency and non contact. An accurate and automatic canopy leaf reconstruction method of fruit tree is presented based on point cloud. Firstly, using FARO Laser Scanner Focus3D to obtain dense point cloud of fruit tree canopy leaf, and according to global characteristics (the point cloud density distribution meets the ellipsoid layered characteristics) and local characteristics (different organs and organs of different parts have different point cloud density) of the leafy trees point cloud, this paper proposes a point cloud density shrinkage method with ellipsoidal layer to realize the separation of leaf point cloud. The model's parameters (the leaf width and the radius of main branches) are obtained by manual measurement. And experiment shows that point cloud segmentation effect is the best when the relation coefficient is 0.75 between the density threshold and the average density of the point cloud. After the point cloud density calculation, high-density point cloud on each leaf is gathered in the veins, and high-density point cloud of branches is gathered in the intersection of branch and branch bulge so as to realize the segmentation between organs. Then we calculate leaf characteristic parameter by the principal component analysis algorithm of the neighboring point cloud, in which the K mean algorithm is used to simplify the leaf point cloud, and the principal component analysis is used to calculate the normal vector of the leaf point cloud. Then, after removing the leaf point cloud, the branches point cloud is contracted into a connected skeleton by the Laplacian shrinkage algorithm which is a classical point cloud shrinkage algorithm, so as to realize the automatic reconstruction of canopy leaf combined leaf template. Finally, with C++ and Point Cloud Library (PCL), the automatic reconstruction system of point cloud of canopy leaf is developed on the PlantCAD development platform, and the method is validated by different types of fruit trees (Fuji apple tree, Wanglin apple tree, Maogu citrus tree). The results show that the recognition accuracy of leaf is higher than 90%, the correct rate of leaf area index is more than 95%, the number of leaf inclination deviation within 5 degrees is more than 90% of the total leaf, the efficiency is more than 7 times that of the artificial, and there is less artificial participation in the whole process. It gets better effect of visualization, and strong sense of reality and canopy leaf reconstruction precision, which provides effective technical support for the research on the photosynthesis, pruning and training of the tree as well as experimental simulations in the later period. Nevertheless, this method depends on the density of point cloud data, and has poor robustness to non ellipsoidal tree and severe noise point cloud (acquiring under windless weather). In the future, we will study the adjacent relation of the leaf point cloud, and make the algorithm adaptive to different tree structure and sparse canopy leaf point cloud. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:32
Main heading:Trees (mathematics)
Controlled terms:Citrus fruits - Clustering algorithms - Crops - Efficiency - Fruits - Image reconstruction - Laser applications - Lasers - Orchards - Parameter estimation - Principal component analysis - Scanning - Shrinkage
Uncontrolled terms:3-D (three-dimensional) - Automatic reconstruction - Experimental simulations - Fruit trees - Leaf - Morphological structures - Point cloud - Point cloud segmentation
Classification code:744.1 Lasers, General - 744.9 Laser Applications - 821.3 Agricultural Methods - 821.4 Agricultural Products - 903.1 Information Sources and Analysis - 913.1 Production Engineering - 921.4 Combinatorial Mathematics, Includes Graph Theory, Set Theory - 922.2 Mathematical Statistics - 951 Materials Science
Numerical data indexing:Percentage 9.00e+01%, Percentage 9.50e+01%
DOI:10.11975/j.issn.1002-6819.2017.z1.032
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 47>
Accession number:20171703590858
Title:Influence of urban rainwater of city on yield, quality and heavy metal content of hydroponic lettuce
Authors:Mu, Dawei (1, 2); Zhang, Rui (1); Li, Changwei (3); Li, Yanrong (2); Tian, Libo (4); Wang, Lanying (5)
Author affiliation:(1) School of Architecture, Tianjin University, Tianjin; 300072, China; (2) College of Civil Engineering and Architecture, Hainan University, Haikou; 570228, China; (3) School of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou; 310058, China; (4) Horticulture and Landscape College, Hainan University, Haikou; 570228, China; (5) College of Environment and Plant Protection, Hainan University, Haikou; 570228, China
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:348-354
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:Rainwater has become a big problem of the city, because rainwater led to urban waterlogging, affect urban transport. Urban waterlogging is also a kind of hidden danger for urban residents. At the other side, government spends a lot of money to meet municipal water demand, for example the South-to-North Water Transfer Project. Many cities land subsidence caused by groundwater exploitation. Urban agriculture which used rainwater as irrigation water is an effective way for urban rainwater as resources. The safety of urban agricultural products irrigated by rainwater is attention focus, because urban rainwater is polluted by automobile exhaust, asphalt, cement and so on. This paper study the food safety and nutrient quality of lettuce irrigated by urban rainwater aims to explore the feasibility of urban rainwater as urban agricultural irrigation water. A hydroponics experiment was carried out from October 2014 to January 2015 in Hainan University. Four kinds of treatments were designed using hydroponic lettuce design with four replicates, respectively: hydroponic lettuce using tap water (CK), hydroponic lettuce using rainwater (T<inf>1</inf>), hydroponic lettuce using rooftop rainwater (T<inf>2</inf>), and hydroponic lettuce using road rainwater (T<inf>3</inf>). 3 times rainwater from rainwater, rooftop rainwater of office and road rainwater were collected from October in 2014 to January in 2015. The rainwater was mixed respectively, allowed to stand for precipitating impurities, was used as nutrient solution water. Lettuce was cultured in tap plastic pipe. Plant height, leaf number, physiological indexes (chlorophyll content, net photosynthetic rate, root activity), nutritional indexes (soluble protein content, soluble sugar content, vitamin C content, nitrate content), and element (Ca, Fe, Mg, Zn, As, Cd, Pb) content in the lettuce mature stage under different rainwater nutrient solution and tap water nutrient solution were measured. The results show that heavy metal content of rainwater is lower than national agricultural irrigation water standards, urban rainwater is safety to be used as urban agriculture irrigation water. As content of lettuce was respectively 5.83 (CK), 4.10 (T<inf>1</inf>), 4.53 (T<inf>2</inf>), 4.60 μg/kg (T<inf>3</inf>), Cd content of lettuce was respectively 0.76 (CK), 0.78 (T<inf>1</inf>), 2.59(T<inf>2</inf>), 1.37 μg/kg (T<inf>3</inf>), Pb content of lettuce was respectively 102.37 (CK), 118.63 (T<inf>1</inf>), 151.53 (T<inf>2</inf>), 123.37 μg/kg (T<inf>3</inf>). They were less than the national limit values (As content 500 μg/kg, Cd content 200 μg/kg, Pb content 300 μg/kg), the lettuce meet the food safety requirements. Proteins content, sugars content and vitamin C content of lettuce cultivated in rainwater was not lower than that in rainwater; Ca, Mg content of lettuce cultivated in rainwater is higher than that in tap water, Fe content of lettuce cultivated in rainwater is less than that in tap water. The quantity of lettuce cultivated in rainwater is better than that in tap water. Chlorophyll content and net photosynthetic rate of lettuce cultivated in rainwater is lower than that cultivated in tap water. Root activity of lettuce cultivated in rainwater was increased. Root-shoot ratio of lettuce cultivated in rainwater was respectively 0.19(T<inf>1</inf>), 0.11(T<inf>2</inf>), 0.12 (T<inf>3</inf>), they were bigger than that cultivated in tap water(0.08). Lettuce cultivated in rainwater has worse growth power and lower biomass yield than lettuce cultivated in tap water. Lettuce biomass yield cultivated in road rainwater is lowest in three kind of lettuce cultivated in rainwater. In short, the urban rainwater and the agricultural produces cultivated by urban rainwater is safety, and the quality of agricultural produce cultivated in urban rainwater is better. At the same time, crop irrigated by urban rainwater grows weak and has lower yield. Experiments on lettuce demonstrate that urban rainwater is used as urban agriculture irrigation water is feasible. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:26
Main heading:Urban transportation
Controlled terms:Agricultural products - Agriculture - Biomass - Calcium - Chemical contamination - Chlorophyll - Crops - Food safety - Groundwater - Heavy metals - Irrigation - Lead - Magnesium - Nutrients - Physiology - Plastic pipe - Proteins - Quality control - Roads and streets - Transportation - Water
Uncontrolled terms:Agricultural irrigation water - Groundwater exploitation - Lettuce - Net photosynthetic rate - Physiological indices - Soluble sugar contents - South to North Water Transfer Project - Urban rainwater
Classification code:406.2 Roads and Streets - 432 Highway Transportation - 433 Railroad Transportation - 444.2 Groundwater - 461.9 Biology - 531 Metallurgy and Metallography - 546.1 Lead and Alloys - 549.2 Alkaline Earth Metals - 619.1 Pipe, Piping and Pipelines - 804.1 Organic Compounds - 821 Agricultural Equipment and Methods; Vegetation and Pest Control - 822.3 Food Products - 913.3 Quality Assurance and Control
DOI:10.11975/j.issn.1002-6819.2017.z1.052
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 48>
Accession number:20171703590859
Title:Spatial optimization simulation of land use pattern in Yellow River Delta Nature Reserve
Authors:Gong, Xi (1, 2); Cao, Mingchang (1); Wang, Dongsheng (3, 4); Le, Zhifang (1); Sun, Xiaoping (5); Xu, Haigen (1)
Author affiliation:(1) Nanjing Institute of Environmental Sciences, Ministry of Environmental Protection, Nanjing; 210042, China; (2) School of Geography and Remote Sensing, Nanjing University of Information Science and Technology, Nanjing; 210044, China; (3) School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang; 212001, China; (4) International WIC Institute, Beijing University of Technology, Beijing; 100022, China; (5) Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing; 210037, China
Corresponding author:Cao, Mingchang(caomingc@163.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:355-361
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:The Yellow River Delta Nature Reserve (YRDNR) is a national nature reserve designed to protect the newly built wetland ecosystem and the rare and endangered birds. It is one of the fastest growing coastal wetland ecosystems around the world and an important site for red-crowned cranes during migration and wintering periods. It is an urgent task to effectively manage and control resource utilization activities and protect the wetland habitat of red-crowned cranes in the YRDNR. Spatial optimization model can be applied to a variety of spatial planning problems to identify trade-offs between conflicting objectives and solve the optimum allocation problem (such as allocation of reserve sites or management actions). In this paper, we firstly had a secondary development with C++ language to construct a land use pattern spatial optimization model based on LUPO (land use pattern optimization) model developed by Annelie Holzkamper et al. The LUPO model can be applied to identify trade-offs between habitat conservation of red-crowned cranes and sustainable development of community economy in the nature reserve, and to optimize allocation of land use in YRDNR. Secondly, we designed 3 target-driven scenarios according to management requirement in YRDNR to balance ecological protection and economic development. Finally, we simulated the spatial optimization of land use pattern in YNRDR under the 3 scenarios by integrating the scenarios into the LUPO model. The results showed as follows: 1) Under Scenario A, it obviously achieved the ecological conservation target with a 30% increase of suitable habitat area of red-crowned crane by converting the areas of reed ponds and mudflats to seepweed tidal flats and Chinese tamarisk-seepweed tidal flats, which are the birds preferred habitats. However, the regional economic benefit had a small increase of only 11%; 2) Under Scenario B, it achieved 54% regional economic growth by converting the area of farmlands, reed ponds and mudflats to shrimp ponds, which owned a highly economic value. In the meanwhile, the habitat area change of red-crowned crane was not obvious, and became more fragmented in Scenario B; 3) Under Scenario C, it achieved the target of 24% suitable habitat area expansion of red-crowned crane along with 41.3% regional economic growth. It may mainly respectively result from significant expansion of seepweed tidal flats and shrimp ponds converted from mudflats and farmlands. In conclusion, it is more reasonable and practical under Scenario C than Scenarios A and B in YNRDR due to the fact the trade-off between wetland habitat conservation and sustainable development is comprehensively considered. We consider that our LUPO model can offer a good technique support for habitat protection of red-crowned crane and wetland landscape management in YRDNR. However, we also recommend a further secondary development of LUPO model to improve the accuracy, feasibility and operability of the model in the future. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:34
Main heading:Economic and social effects
Controlled terms:Birds - C++ (programming language) - Cranes - Ecology - Economic analysis - Economics - Ecosystems - Environmental protection - Farms - Lakes - Land use - Models - Optimization - Planning - Ponds - Regional planning - Rivers - Shellfish - Site selection - Sustainable development - Tides - Wetlands
Uncontrolled terms:Conflicting objectives - Ecological conservation - Land use pattern optimizations - LUPO - Regional economic growths - Spatial optimization - Spatial optimization model - Yellow river delta nature reserves
Classification code:403 Urban and Regional Planning and Development - 403.2 Regional Planning and Development - 454.2 Environmental Impact and Protection - 454.3 Ecology and Ecosystems - 461.9 Biology - 471.4 Seawater, Tides and Waves - 693.1 Cranes - 723.1.1 Computer Programming Languages - 821 Agricultural Equipment and Methods; Vegetation and Pest Control - 911.2 Industrial Economics - 912.2 Management - 921.5 Optimization Techniques - 971 Social Sciences
Numerical data indexing:Percentage 2.40e+01%, Percentage 3.00e+01%, Percentage 4.13e+01%, Percentage 5.40e+01%
DOI:10.11975/j.issn.1002-6819.2017.z1.053
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 49>
Accession number:20171703590897
Title:Virtual machine load prediction model for agricultural cloud video platform based on semi-supervised partial least squares
Authors:Gao, Wanlin (1); Hu, Hui (1); Xu, Dongbo (1); Zhang, Ganghong (1)
Author affiliation:(1) College of Information and Electrical Engineering, China Agricultural University, Key Laboratory of Agricultural Information Acquisition Technology, Beijing; 100083, China
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:225-230
Language:English
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:In order to optimize the infrastructure resource of agricultural cloud video platform efficiently, the virtual machine (VM) placement algorithms need to know the current and future efficiency of VM resource as accurately as possible for potential actions, such as service deployment, VM deployment, migration or cancellation. However, the data available for analysis are limited, as the samples used for prediction are usually very small. In the study, a sliding window model that considering time factor was designed to learn from small set of data. More importantly, the existing prediction algorithms still have much room to reduce the error. So a sliding window based mathematical method was provided to calculate the aforementioned forecasts, which was combined with PLS and semi-supervised learning (semi-supervised partial least squares, SS-PLS). The feasibility and advantages were analyzed in VM load forecasting based on SS-PLS method. Compared with auto regression moving average (ARMA), experimental results showed that the sliding window based model combined with SS-PLS made noticeable improvements to the forecasting accuracies, with root mean square error (RMSE) improved 5.47% to 1.777 86, mean absolute error (MAE) improved 6.37% to 1.331 2, and mean absolute percentage error (MAPE) improved 6.12% to 0.238 36, respectively. The results demonstrated that the proposed algorithm based on semi-supervised partial least squares is accurate in forecasting virtual machine load is effective in terms of the forecast accuracy. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:30
Main heading:Least squares approximations
Controlled terms:Agricultural machinery - Agriculture - Algorithms - Communication channels (information theory) - Decoding - Errors - Forecasting - Loading - Mean square error - Network security - Supervised learning - Virtual machine
Uncontrolled terms:Auto regression moving averages - Infrastructure resources - Mean absolute percentage error - Partial least square (PLS) - Prediction algorithms - Root mean square errors - Semi- supervised learning - Sliding Window
Classification code:691.2 Materials Handling Methods - 716.1 Information Theory and Signal Processing - 723 Computer Software, Data Handling and Applications - 723.2 Data Processing and Image Processing - 821 Agricultural Equipment and Methods; Vegetation and Pest Control - 821.1 Agricultural Machinery and Equipment - 921.6 Numerical Methods - 922.2 Mathematical Statistics
DOI:10.11975/j.issn.1002-6819.2017.z1.034
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 50>
Accession number:20171703590900
Title:Automatic tracking of pig feeding behavior based on particle filter with multi-feature fusion
Authors:Li, Yiyang (1); Sun, Longqing (1); Sun, Xinxin (1, 2)
Author affiliation:(1) College of Information and Electrical Engineering, China Agricultural University, Beijing; 100083, China; (2) Tongfang Co., Ltd., Beijing; 100083, China
Corresponding author:Sun, Longqing(sunlq@cau.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:246-252
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:The basic behavioral characteristics of live pigs are mainly shown through daily food intake frequency, water intake frequency, and excretion frequency. These factors indicate the health states of pig growth. Monitoring and analyzing the behavioral characteristics of pigs are important basis to understand their health situations. Currently, we mainly use artificial way to monitor livestock behavior in China. This method consumes large amounts of human labor and energy, and the observed data obtained in this way is subjective. It is difficult to ensure the accuracy and the continuity of the records. We take good advantage of pig detection and tracking technology based on machine vision to monitor the behavior of pigs to evaluate the health status of pigs in time, and to reduce the morbidity and mortality of pigs and increase the slaughtering rate of pigs. It has important practical significance and application value in improving people's confidence in pork quality and increasing the income of farmers. Target tracking technology is the basis of the moving target identification and abnormal behavior tracking, recording and analysis. We research the real-time monitoring of the target pigs foraging based on the particle filter target tracking technology. Particle filter algorithm closely approximates Bayesian filtering algorithm based on Monte Carlo simulation, and it is used in target tracking widely. Conceptually, a particle filter tracker maintains a probability distribution over the state (location, scale, and so on) of the object being tracked. Particle filters represent this distribution as a set of weighted samples, or particles. Each particle represents a possible instantiation of the state of the object. In other words, each particle is a guess representing one possible location of the object being tracked. The set of particles contain more weight at locations where the object being tracked is more likely to be. This weighted distribution is propagated through time using a set of equations known as the Bayesian filtering equations, and we can determine the trajectory of the tracked object by the particle with the highest weight or the weighted mean of the particle set at each time step. In view of the pig behavior characteristics and the actual situation of the farms' video image acquisition, this paper takes a group of pigs raised as detection tracking target. On the basis of analyzing and summarizing in particle filter tracking algorithm, we carried out particle filter target tracking technology for pigs which is based on the color characteristics to achieve the goal of tracking pigs. In order to solve the problems in the color characteristics of particle filter target tracking for pigs, we fused the color characteristics and the target contour centroid feature. The specific methods were as follows: First of all, according to the particle filter tracking algorithm based on single color feature of target tracking on the position of the rectangle coordinates, and the height and width of the target tracking rectangular box, we calculated the center of the target tracking rectangle coordinates. Secondly, we determined the centroid position of moving pigs on the basis of the comparison and analysis of moving target centroid position and the minimum circumscribed rectangle length-width ratio. Finally, according to the target contour centroid location and the center of the tracking target rectangle coordinates, we calculated the amount of deviation between them. When the deviation of target contour centroid and tracking rectangular box was too large, we took a second correction for tracking the target coordinates based on the particle filter algorithm with multi-feature fusion. The improved algorithm presented in this paper updated the tracking rectangular coordinates through the target contour centroid coordinates, and gave the new tracking rectangular box. This paper constructs the target pig tracking system based on particle filter algorithm, achieves a multi-feature fusion particle filter tracking algorithm through area real-time monitoring, and completes the statistics of the target pig's feeding time and food intake frequency. Experiment results prove that this algorithm can automatically accurately track, record and analyze the feeding behaviour of the target pigs, and effectively deal with the problems such as target short-time missing. The feeding frequency and time of the target pigs are almost the same as the manual statistics. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:29
Main heading:Target tracking
Controlled terms:Agriculture - Algorithms - Bandpass filters - Clutter (information theory) - Color - Distributed computer systems - Feeding - Geometry - Health - Image processing - Intelligent systems - Location - Mammals - Monte Carlo methods - Probability distributions - Signal filtering and prediction - Surface discharges - Tracking (position) - Verification
Uncontrolled terms:Behavioral characteristics - Color features - Comparison and analysis - Contour centroids - Moving target identification - Particle filter - Particle filter algorithms - Rectangular coordinates
Classification code:461.6 Medicine and Pharmacology - 691.2 Materials Handling Methods - 701.1 Electricity: Basic Concepts and Phenomena - 703.2 Electric Filters - 716.1 Information Theory and Signal Processing - 721.1 Computer Theory, Includes Formal Logic, Automata Theory, Switching Theory, Programming Theory - 722.4 Digital Computers and Systems - 723.4 Artificial Intelligence - 741.1 Light/Optics - 821 Agricultural Equipment and Methods; Vegetation and Pest Control - 921 Mathematics - 922.1 Probability Theory - 922.2 Mathematical Statistics
DOI:10.11975/j.issn.1002-6819.2017.z1.037
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 51>
Accession number:20171703590887
Title:Chlorophyll content diagnosis model of winter wheat at heading stage applied in miniature spectrometer
Authors:Cheng, Meng (1); Zhang, Junyi (1, 2); Li, Minzan (1); Liu, Haojie (1); Sun, Hong (1); Zheng, Tao (1)
Author affiliation:(1) Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing; 100083, China; (2) Henan University of Animal Husbandry and Economy, Zhengzhou; 450000, China
Corresponding author:Sun, Hong(sunhong@cau.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:157-163
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:Crop spectrum characteristic analysis plays an important role on identification of crops, production estimation, condition monitoring, and nutrition diagnosis crop management. In order to predict the nutrient content of crop non-destructively and quickly, a reflectance spectrum detection system for winter wheat was developed to measure the reflectance of 350-820 nm. The system has three parts: optical sensor, data storage and transmission module and the controller. The optical system was developed based on the Ocean Optics STS-VIS sensor. The sensor was controlled by an enhanced electronic device to obtain reflected light with a high-sensitivity 1 024 pixel linear CCD array detector. The spectral information collection and analysis software was developed on Windows 7 platform, using PHP. Software included three modules: acquisition parameters, acquisition control and data management. After the controller and sensor connected successfully, users should optimize the system parameters, such as the integral time, the scan average times and boxcar width. Then it needed to choose the collection function, DN value. Finally, the reflectance data could be analyzed and stored. In order to test the performance of the spectrum analyzer, calibration experiment was carried out. A gray calibration board with four different gray gradations was involved. The correlation was analyzed between the spectral reflectance measured by spectrum analyzer and ASD Field Spec Hand Held 2. The result showed that the average correlation coefficient value was 0.94. it also showed that the developed spectrum analyzer was worked with good stability under different light conditions. The system was used to collect 350-820 nm (visible-near infrared) reflectance of winter wheat at heading stage in the field to detect the chlorophyll of winter wheat. In order to decrease the noise influence, canopy reflectance spectra curves were pre-processed by 1-order differential method. And the data smoothing was conducted by Bior Nr.Nd biorthogonal wavelet packet analysis method. After that, Monte Carlo sampling method was used to remove 5 outliers. And 8 sensitive wavelengths (719, 572, 562, 605, 795, 527, 705, 514 nm) were chosen using Random frog algorithm. In order to indicate the effective of preprocessing method, the chlorophyll content detecting PLSR (partial least squares regression) model was established based on original reflectance spectra. The modeling determination coefficient was 0.70 and predictive determination coefficient was 0.10. Meanwhile, the revised chlorophyll content detecting model was established. The involved data was pre-processed after 1-order differential and wavelet packet decomposition. The modeling determination coefficient was 0.69. The root mean square error of model was 1.364 8. And predictive determination coefficient was 0.52. The root mean square error of prediction was 1.839 7. The modling results showed the background interference and noise of wheat canopy reflectance were removed effectively. The system based on miniature spectrometer could estimate the chlorophyll of wheat leaves and help to diagnose the crop nutrition of winter wheat at heading stage. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:25
Main heading:Crops
Controlled terms:Calibration - Charge coupled devices - Chlorophyll - Condition monitoring - Data handling - Data processing - Digital storage - Information management - Infrared devices - Least squares approximations - Light - Mean square error - Monte Carlo methods - Nutrition - Optical data storage - Optical systems - Plasma diagnostics - Reflection - Spectrometers - Spectrum analysis - Spectrum analyzers - Wavelet analysis - Wavelet decomposition
Uncontrolled terms:Average correlation coefficients - Biorthogonal wavelet packets - Chlorophy II - Partial least squares regression - Root-mean-square error of predictions - Wavelength selection - Wavelet Packet Decomposition - Wheat canopy
Classification code:461.7 Health Care - 714.2 Semiconductor Devices and Integrated Circuits - 722.1 Data Storage, Equipment and Techniques - 723.2 Data Processing and Image Processing - 741.1 Light/Optics - 741.3 Optical Devices and Systems - 804.1 Organic Compounds - 821.4 Agricultural Products - 921 Mathematics - 922.2 Mathematical Statistics - 932.3 Plasma Physics
Numerical data indexing:Size 3.50e-07m to 8.20e-07m, Size 5.14e-07m
DOI:10.11975/j.issn.1002-6819.2017.z1.024
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 52>
Accession number:20171703590899
Title:Data fusion method of livestock and poultry breeding internet of things based on improved support function
Authors:Duan, Qingling (1, 2); Xiao, Xiaoyan (1); Liu, Yiran (1); Zhang, Lu (1); Wang, Kang (1)
Author affiliation:(1) College of Information and Electrical Engineering, China Agricultural University, Beijing; 100083, China; (2) Beijing Engineering Research Center of Agricultural Internet of Things, Beijing; 100097, China
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:239-245
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:Currently, IoT (Internet of Things) technology has been widely applied in livestock breeding. For the characteristics of perception data collected from livestock breeding IoT (such as multi-source, heterogeneous, real-time, and so on), we propose a livestock breeding IoT data fusion model. Firstly, it's easy to produce abnormal data in livestock breeding IoT due to the harsh work environment, transmission network noise and other factors. In order to guarantee the accuracy of livestock breeding IoT data, we check the consistency of original data collected by livestock breeding IoT sensors through the regression prediction method based on sliding window. We determine the sliding window size, then estimate the sensor measurement value at a certain moment through the regression forecast method and calculate the prediction interval, next determine whether the actual measurement value is abnormal compared with prediction interval, and finally replace abnormal data with predictive value. Secondly, there is the problem of uneven distribution of environmental monitoring values in breeding room. In order to comprehensively analyze and evaluate the breeding environment and provide accurate basis for automatic control equipment, we propose a homogeneous data weighted fusion algorithm which fuses homogeneous data that are collected from the same type of sensors based on support function. The support function has been proposed to describe a certain support degree or proximity between 2 numbers, and used to calculate the weights of a set of data. The weighted algorithm in this paper improves support functions so as to improve the accuracy of data fusion. We calculate the support degree of a set of pig breeding environment perception data to get corresponding optimal weights through the improved support function and then the weighted fusion for the same type of livestock breeding IoT sensor set data. Finally, in view of the characteristics different with livestock breeding multi-source perception data storage format and sensor devices encoding, the encoding rules format and data organization for livestock breeding IoT are formulated, which can uniformly describe heterogeneous data in the process of livestock and poultry breeding. Such a standard can be converted into a standard data format, which can work as the basis of data analysis and data applications. Areal production data from pig breeding IoT in Tianjin Huikang breeding pig farm show that in the data consistency test phase, the abnormal sensory data detection rate is 96.67%, which ensures the accuracy of the data, and in homogeneous data fusion phase, the improved support function in this paper has the minimum fusion variance of 0.192 5 compared with other 2 kinds of support functions in the multi-source data fusion calculation, which improves the accuracy of homogeneous data fusion and provides the accurate and reasonable basis for the automatic control of equipment. And coding standard of livestock IoT environment factors and sensor equipment makes unified expression to the sensors, which are from different manufacturers and environmental data with varied representations. Livestock breeding IoT data fusion model proposed in this paper can effectively improve the accuracy of data fusion, and meet the requirements of livestock breeding IoT data analysis. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:30
Main heading:Sensor data fusion
Controlled terms:Agriculture - Automation - Control equipment - Data fusion - Data handling - Digital storage - Encoding (symbols) - Equipment - Forecasting - Information analysis - Internet of things - Mammals - Measurements - Process control - Sensors
Uncontrolled terms:Breeding environments - Data format - Environmental Monitoring - Livestock and poultry breeding - Regression predictions - Sensor measurements - Standard data format - Support functions
Classification code:722.1 Data Storage, Equipment and Techniques - 723 Computer Software, Data Handling and Applications - 723.2 Data Processing and Image Processing - 731 Automatic Control Principles and Applications - 732.1 Control Equipment - 821 Agricultural Equipment and Methods; Vegetation and Pest Control - 901 Engineering Profession - 903.1 Information Sources and Analysis
Numerical data indexing:Percentage 9.67e+01%
DOI:10.11975/j.issn.1002-6819.2017.z1.036
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 53>
Accession number:20171703590906
Title:Identification of transgenic and non-transgenic cotton seed based on terahertz range spectroscopy
Authors:Shen, Xiaochen (1, 2); Li, Bin (1, 3); Li, Xia (2); Long, Yuan (1)
Author affiliation:(1) National Engineering Research Center for Information Technology in Agriculture, Beijing; 100097, China; (2) Mechanical and Electrical Engineering College of Shihezi University, Shihezi; 832000, China; (3) Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing; 100097, China
Corresponding author:Li, Bin(lib@nercita.org.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:288-292
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:Terahertz radiation is the electromagnetic wave, which occupies a large portion between the infrared and microwave bands. Most biological macromolecules have fingerprint characteristics in this band. Firstly, the device for experiment i.e. the terahertz time-domain spectroscopy system (THz-TDS) was introduced. Then, in order to seek the terahertz spectrum characteristics of transgenic mechanism and the trait expression, 3 kinds of transgenic cotton seeds (Zhongmian No.838, Lumianyan No.28, and Guoshen No.7886) and a non-transgenic cotton seed (Xinluzao No.51) were studied in identifying experiments by terahertz time-domain spectroscopy. Because of the grease-like substance in the cotton seed, tablet mold was occlusive when compressing cotton seed. It made the tablet shaping difficult and the surface of the tablets rough and resulted in unperfect data. To solve the problem, when compressing tablet, a small amount of polyethylene powder was taken to spread in the bottom of the compression mould, then samples powder was spread on polyethylene powder, and then on the surface of samples powder a small amount of the powdered polyethylene was put again, and at last the tablet was compressed. During the experiment, the samples were placed in the THz-TDS to scan and reduce the accidental error. Each tablet was scanned in triplicate for different parts and the obtained spectral information is averaged. To avoid the interference such as the strong absorption of the water vapor and other polar molecules in the air, terahertz wave generation and detection device were sealed and filled with dry nitrogen. And the relative humidity of testing environment was less than 4%. The experimental temperature was kept at 298 K. By using terahertz time-domain spectroscopy equipment, we collected the terahertz spectral information of 4 species of cotton seeds in the effective time-domain range. After processed with the fast Fourier transform algorithm, THz spectroscopy time-domain information was transformed into the frequency-domain spectral information. Then a series of transformations were used to get their refractive index and absorption spectra. Qualitative analysis of the terahertz spectrum feature information was made. Terahertz absorption characteristics of 4 kinds of samples especially in 0.9-1.5 THz band and the reasons for this result were discussed in detail. The results showed that 4 kinds of cotton seeds presented different spectral response characteristics in 0.9-1.5 THz band. The 3 transgenic cotton varieties showed obvious absorption at 1.21, 1.23 and 1.41 THz, while non-transgenic cotton variety showed weak absorption or no absorption. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:29
Main heading:Terahertz spectroscopy
Controlled terms:Cotton - Electromagnetic waves - Fast Fourier transforms - Frequency domain analysis - Identification (control systems) - Laser pulses - Molds - Plasmons - Polyethylenes - Reflectometers - Refractive index - Seed - Signal receivers - Spectrophotometers - Spectroscopy - Spectrum analysis - Terahertz wave detectors - Terahertz waves - Time domain analysis - Water absorption
Uncontrolled terms:Absorption co-efficient - Biological macromolecule - Fast Fourier transform algorithm - Spectral response characteristics - Tera Hertz - Terahertz time domain spectroscopy - Terahertz wave generation - Transgenics
Classification code:711 Electromagnetic Waves - 731.1 Control Systems - 732.2 Control Instrumentation - 741.1 Light/Optics - 741.3 Optical Devices and Systems - 744.1 Lasers, General - 802.3 Chemical Operations - 815.1.1 Organic Polymers - 821.4 Agricultural Products - 921 Mathematics - 921.3 Mathematical Transformations - 931.1 Mechanics - 931.3 Atomic and Molecular Physics - 941.3 Optical Instruments
Numerical data indexing:Frequency 1.23e+12Hz, Frequency 1.41e+12Hz, Frequency 9.00e+11Hz to 1.50e+12Hz, Percentage 4.00e+00%, Temperature 2.98e+02K
DOI:10.11975/j.issn.1002-6819.2017.z1.043
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 54>
Accession number:20171703590912
Title:Green ripe tomato detection method based on machine vision in greenhouse
Authors:Li, Han (1); Zhang, Man (1); Gao, Yu (2); Li, Minzan (1); Ji, Yuhan (1)
Author affiliation:(1) Key Laboratory for Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing; 100083, China; (2) School of Electronic and Information Engineering, Hebei University of Technology, Tianjin; 300401, China
Corresponding author:Zhang, Man(cauzm@cau.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:328-334
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:During the detecting and locating process of green tomatoes based on obtained visible images, problems such as shadow caused by uneven illumination source, occlusion of stems and leaves, and occlusion between fruits, need to be solved. In this study, a machine vision algorithm was put forward, which aimed to determine fruit location and size of green tomatoes. Normalized cross-correlation function (NCC) is a feature detection method using template matching. The algorithm firstly detected the potential location of green tomatoes through fast normalized cross-correlation function (FNCC). Then a gray histogram based classifier was used to classify if the location corresponded to green fruit. The histogram based classifier was built based on fruit areas and non-fruit areas of a certain size (30×30 pixels) extracted from the obtained image. Seven texture features, including mean, standard deviation, smoothness, third moment, uniformity, entropy, and gray level range were calculated for those fruit areas and non-fruit areas. Three classifiers including k-nearest neighbor (KNN), SVM (support vector machine), and Naive Bayes, were used to classify fruit and non-fruit areas using those 7 texture features as input vectors. SVM was chosen based on its performance. The non-fruit location was filtered out, and the number of fruit locations was estimated. Meanwhile, the image was segmented based on color analysis. Red and Blue component from RGB (red, green, blue) image, and Hue component from HSV (hue, saturation, value) image transformed from RGB images, were used as the basis for color analysis. Using the fruit potential location number estimated using FNCC as an input parameter of circular Hough transform (CHT), CHT was then applied to the edge image of the segmented result. The center coordinates and radius value of each circle were calculated. Finally, the detection results were merged based on the analysis of the distances of 2 centers of fruit circles detected using CHT. If the distance between 2 circles is smaller than the minimum fruit radius, the circle with a larger radius will be kept, while the other circle will be flagged as repeatedly detected one. Thus, the recognition and positioning of the green tomato were realized. When green fruits and red fruits appear on the same image, a red fruit detection algorithm based on the local maximum value method and random circle round transform detection, which was proposed by the author in another paper, would be carried out on the obtained image. Then the red fruit detection result was combined with the green fruit detection result. The proposed method combined the texture, color, and shape information of the image, and eliminated the disturbance of the color similarity between the green tomatoes and green leaves and stems. A total of 70 images were used in this study, in which 35 images were used as training images, and the other 35 images were used as validation images. The correct detection ratio for 72 fruits in the training dataset was 89.2%, and the correct detection ratio for 105 fruits in the validation dataset was 86.7%. The proposed method has provided a reference for the development of tomato harvesting robots for both red and green mature tomato fruits. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:33
Main heading:Color image processing
Controlled terms:Algorithms - Color - Computer vision - Correlation detectors - Feature extraction - Fruits - Graphic methods - Hough transforms - Image analysis - Image processing - Location - Nearest neighbor search - Support vector machines - Template matching
Uncontrolled terms:Circular Hough transforms - Fast normalized cross correlations - Harvesting robot - K nearest neighbor (KNN) - Machine vision algorithm - Normalized cross-correlation functions - SVM(support vector machine) - Uneven illuminations
Classification code:723 Computer Software, Data Handling and Applications - 723.5 Computer Applications - 741.1 Light/Optics - 741.2 Vision - 821.4 Agricultural Products - 921.3 Mathematical Transformations - 921.5 Optimization Techniques
Numerical data indexing:Percentage 8.67e+01%, Percentage 8.92e+01%
DOI:10.11975/j.issn.1002-6819.2017.z1.049
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 55>
Accession number:20171703590879
Title:Effects of chemical fertilizer and slow-release compound fertilizer on surface electrochemical properties in neutral and slight alkaline soils
Authors:Wang, Fei (1, 2); Gu, Shoukuan (1); Yuan, Ting (1); Wang, Zhengyin (1)
Author affiliation:(1) College of Resources and Environment, Southwest University, Chongqing; 400716, China; (2) Chongqing Industry Polytechnic College, Chongqing; 401120, China
Corresponding author:Wang, Zhengyin(wang_zhengyin@163.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:107-113
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:Soil surface electrochemical properties can reflect the characterization of fertilizer preserving and supplying, and further reveal soil fertility. Researching on the effects of slow/controlled release fertilizers on the soil surface electrochemical properties can uncover the essences of improving soil fertility and promoting the crop growth. In this research, two experiments were carried out: In the first experiment, neutral and slight alkaline soil were cultured 90 days at 25℃ indoor, with 6 treatments designed as no fertilization (CK), chemical fertilizer (CF), common compound fertilizer (CCF), slow-release compound fertilizer with different levels (SRF1, SRF2 and SRF3). The application rate of nitrogen, phosphorus, potassium in SRF1, SRF2 and SRF3 treatments were respectively 200, 120, 120 mg/kg, 400, 240, 240 mg/kg and 800, 480, 480 mg/kg, and the fertilizer application rate of CF and CCF was same as SRF1. Soil surface electrochemistry properties, such as surface potential, surface charge density, specific surface area and surface charge number, were investigated, and the correlations between physical-chemical properties and surface electrochemical properties of soil were also studied. To ensure the accuracy of the investigation, field experiment was conducted to study the effects of chemical fertilizer (CF), common compound fertilizer (CCF) and slow-release compound fertilizer (SRF) on soil surface electrochemical properties and the yield of eggplants in neutral and slight alkaline soils. The application rate of nitrogen, phosphorus, potassium of different fertilizer treatments were 350, 200 and 200 kg/hm<sup>2</sup>in field experiment. The results showed that SRF1 treatment increased the neutral soil surface potential, surface charge density, surface charge number compared to CF in the first experiment under the constant temperature incubation condition. Comparing with CK, soil specific surface area of all fertilizer treatments were significantly (P<0.05) increased by 34.2%-81.2%. Moreover, the surface charge density and specific surface area of neutral soil had an increasing trend with increasing of the application rate of slow-release compound fertilizer. In addition, the correlation analysis showed that soil surface charge density and specific surface area were positively correlated with soil pH value and organic matter. In the field experiment, the effect of slow-release compound fertilizer on neutral soil surface potential, surface charge density and surface charge number was greater than that of chemical fertilizer. The slight alkaline soil surface charge number in SRF treatment was significantly higher than that of chemical fertilizer and common compound fertilizer. Compared to CF and CCF, the slight alkaline soil surface charge density, the SRF treatment was significantly increased by 7.9% and 6.5% in soil planted Heisheng eggplant and 12.9% and 5.3% in soil planted Zhuqie. The correlation analysis showed that there was a significant (P<0.05) or extremely significant (P<0.01) correlations between soil surface electrochemical properties and the yield of eggplants. In conclusion, slow-release compound fertilizer with appropriate content and proportion, which constantly released a variety of forms of nitrogen and organic matter, was the material basis to stabilize soil surface electrochemical properties, improve the characteristics of keeping soil fertility, so nutrient substances could be continuously supplied in a long period for the growth of crop. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:25
Main heading:Soil surveys
Controlled terms:Alkalinity - Biogeochemistry - Biological materials - Charge density - Correlation methods - Crops - Electrochemical properties - Electrochemistry - Fertilizers - Nitrogen - Nitrogen fertilizers - Organic compounds - Phosphorus - Potassium - Soils - Specific surface area - Surface charge - Surface potential - Surface properties
Uncontrolled terms:Alkaline soils - Chemical fertilizers - Compound fertilizer - Fertilizer applications - Neutral soils - Physical chemical property - Slow/controlled release - Surface electrochemical properties
Classification code:461.2 Biological Materials and Tissue Engineering - 483.1 Soils and Soil Mechanics - 549.1 Alkali Metals - 701.1 Electricity: Basic Concepts and Phenomena - 801 Chemistry - 804 Chemical Products Generally - 804.1 Organic Compounds - 821.4 Agricultural Products - 922.2 Mathematical Statistics - 951 Materials Science
Numerical data indexing:Age 2.47e-01yr, Percentage 1.29e+01%, Percentage 3.42e+01% to 8.12e+01%, Percentage 5.30e+00%, Percentage 6.50e+00%, Percentage 7.90e+00%
DOI:10.11975/j.issn.1002-6819.2017.z1.016
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 56>
Accession number:20171703590877
Title:Matching algorithm for plant protecting unmanned aerial vehicles and plant protecting jobs based on R-tree spatial index
Authors:Yang, Ze (1); Zheng, Lihua (1); Li, Minzan (1); Yang, Wei (1); Sun, Hong (1)
Author affiliation:(1) Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing; 100083, China
Corresponding author:Zheng, Lihua(zhenglh@cau.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:92-98
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:Compared with the traditional plant-protecting machine, plant protection unmanned aerial vehicle (UAV) has advantages of high efficiency, high performance, good precision and good spraying effect. Most importantly, the promotion of physical protection is very significant. In order to fully guarantee that the plant protecting assignments can be allocated scientifically and the plant protecting UAV resources can be deployed efficiently, as well as for meeting the needs of plant protecting spray jobs, a high efficient algorithm for matching UAVs with plant protecting jobs was designed, and a high effective plant protecting assignments scheduling system was developed. The system can not only provide the UAV users with appropriate matching plant protecting assignments, but also help to find the appropriate matching UAVs for the users who need to rent the specific UAVs to carry out plant protection spray. In this paper, the existing technologies were analyzed and compared to find out which one could be used to fulfill matching algorithm for UAVs and assignments, the features of plant protection assignment for UAV were clarified, and a matching algorithm for UAV and its plant protection assignment based on the R-tree spatial indexing was designed. The R-tree is a completely dynamic spatial index of data structure, and sub-algorithms such as node inserting, deleting and querying operations are mutually independent. The matching algorithm for plant protection assignment includes the algorithms of inserting plant protecting assignment into the R-tree, querying plant protection assignment from the R-tree, and deleting some assignment when it is finished or canceled. By using the matching algorithm, the plant protection assignment intelligent recommendation system was developed, and it mainly included the region querying function and intelligent recommendation function. The region search function allows UAV users to search all the plant protection assignments within the scope of any rectangle dragged on the map. Meanwhile the intelligent recommendation function based on the user's current location can recommend the UAV users with the plant protection assignments nearby which meet the UAV's spray features, and also can help the users who have the specific plant protecting assignment to find the right UAVs to rent. We built the entire system by using Django web framework and programmed with Python language and JavaScript language. The results of system test showed that the matching algorithm for UAVs and their plant protection assignments based on the R-tree could handle more than 2 000 concurrent requests at the same time even in a lower configuration server. The querying algorithm's response time was less than 1 ms when processing a single request. The test of plant protection job inserted into the R-tree in batches showed that inserting 1 000 jobs took less than 1 s, and thanking to the dynamic nature of the R-tree, we could read and write the R-tree at the same time, so the insertion of the R-tree did not affect the query operation. It illustrates that the algorithm is flexible, accurate, dynamic and highly efficient, and it can be used to match UAVs with the corresponding appropriate plant protecting assignments reasonably and intelligently. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:30
Main heading:Trees (mathematics)
Controlled terms:Algorithms - Decision trees - Forestry - High level languages - Indexing (of information) - Scheduling - Unmanned aerial vehicles (UAV) - Vehicles
Uncontrolled terms:Concurrent requests - Intelligent scheduling - JavaScript language - Matching - Mutually independents - Plant protection - Recommendation functions - Spatial indexes
Classification code:652.1 Aircraft, General - 723.1.1 Computer Programming Languages - 903.1 Information Sources and Analysis - 912.2 Management - 921.4 Combinatorial Mathematics, Includes Graph Theory, Set Theory
Numerical data indexing:Time 1.00e+00s, Time 1.00e-03s
DOI:10.11975/j.issn.1002-6819.2017.z1.014
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
<RECORD 57>
Accession number:20171703590865
Title:Design and experiment of seeding quality infrared monitoring system for no-tillage seeder
Authors:Che, Yu (1); Wei, Liguo (1, 2); Liu, Xingtao (1, 3); Li, Zhuoli (1); Wang, Fengzhu (1)
Author affiliation:(1) Chinese Academy of Agricultural Mechanization Sciences, Beijing; 100083, China; (2) State Key Laboratories in Areas of Soil-Plant-Machine System Technology, Beijing; 100083, China; (3) Beijing Agricultural Machinery Experiment Appraisal Popularize Station, Beijing; 100079, China
Corresponding author:Wei, Liguo(weilg78@126.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:33
Issue date:February 1, 2017
Publication year:2017
Pages:11-16
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:Precision seeding machine has been widely used in recent years. There are still some mechanical failures with the machine. And seeding stoppage caused by the field environment will appear during seeding with machine. So, it is essential to monitor the seeding quality. With the research of monitoring technology, visual instrument detection system for precision seeding-metering device is used in laboratory but not suitable in the field. Image processing technology is efficient and accurate but complex and high-cost. The photoelectric inspection technology is low-cost, reliable and quite accurate. First of all, we introduced the theory of the monitoring technology with the infrared detection, which is a common photoelectric inspection technology. We designed a kind of opposite-type photoelectric sensor. Transmitter using the infrared LED (light emitting diode) gave the infrared signal to receiver. The signal had narrow band and directivity when the receiver LED was sensitive to prevent the interference of the visible light and the dirt. The photo diode of receiver was matched with the transmitter, and the power of the signal was measured to detect the passing seeds. When there was a seed between the transmitter and receiver, the infrared signal may be kept out by the seed. Therefor, the power of the signal was getting weak, and the seed was detected. There was a differential circuit after the receiver processed the signal received. The circuit formed a pulse signal which was sent to the micro processer to count the passing seeds. The time of pulse was 0.4 to 4 ms. It had adequate resolution to distinguish adjacent seeds. Then with the theory of the monitoring technology, we developed the seeding quality monitoring system, including the multiplex sensors, the driver gear sensor, and the terminal device with LCD (liquid crystal display) and alarm. We designed a kind of structure to fix the sensor with the tube, and aligned the transmitter with the receiver. The driver gear sensor was used to detect the drive shaft of the seeding apparatus. When it detected the seeder is seeding, the alarm would be available, and it would be triggered if there was no pulse signal of seeding to the processer. It could help to avoid false alarm when the seeder is not seeding. The processer could count and make statistic of the seeding quality, and display the result on the LCD. The monitoring system is reasonable in structure, convenient to install, and will give an alarm signal when seeding is blocked, leaked, lacking seeds or in other stoppage. At the meantime, the system counts and displays the number of seeding and the stoppage caused by different reasons. At last the paper introduced the field test results. Field experiments were carried out to measure the count of seeding, the leakage seeding rate, the lack of seed and the blockage of the seeding tube. It showed that the monitoring system could adapt to the working environment. The field test presented a monitoring success rate of more than 95%. Shooting the trouble of on-line monitoring of seeding quality, the system avoids miss-seeding, performs the efficient seeding and improves the seeding quality. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:26
Main heading:Monitoring
Controlled terms:Alarm systems - Cloud seeding - Costs - Display devices - Failure (mechanical) - Image processing - Image quality - Infrared detectors - Inspection equipment - Light - Light emitting diodes - Liquid crystal displays - Photoelectricity - Processing - Sensors - Signal receivers - Transmitters
Uncontrolled terms:Differential circuits - Field experiment - Image processing technology - Infrared detection - Inspection technology - Monitoring technologies - Precision seeding - Transmitter and receiver
Classification code:443.3 Precipitation - 714.2 Semiconductor Devices and Integrated Circuits - 722.2 Computer Peripheral Equipment - 741.1 Light/Optics - 911 Cost and Value Engineering; Industrial Economics - 913.3.1 Inspection - 913.4 Manufacturing - 944.7 Radiation Measuring Instruments
Numerical data indexing:Percentage 9.50e+01%, Time 4.00e-04s to 4.00e-03s
DOI:10.11975/j.issn.1002-6819.2017.z1.002
Database:Compendex
Compilation and indexing terms, Copyright 2017 Elsevier Inc.
期刊信息
- 主管单位 中国科学技术协会
- 主办单位 中国农业工程学会
- 主编 朱明
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