<RECORD 1>

Accession number:20214811239952
Title:Potential zoning and development paths of homestead renovation for rural revitalization in southern Hunan Province of China
Title of translation:&#38754;&#21521;&#20065;&#26449;&#25391;&#20852;&#30340;&#28248;&#21335;&#23429;&#22522;&#22320;&#25972;&#27835;&#28508;&#21147;&#20998;&#21306;&#21450;&#21457;&#23637;&#36335;&#24452;
Authors:Yang, Hao (1); Lu, Xinhai (1, 2); Chen, Dongjun (3)
Author affiliation:(1) College of Public Administration, Central China Normal University, Wuhan; 430079, China; (2) College of Public Administration, Huazhong University of Science and Technology, Wuhan; 430074, China; (3) Tourism and Urban Management, Jiangxi University of Finance and Economics, Nanchang; 330013, China
Corresponding authors:Lu, Xinhai(xinhailu@163.com); Lu, Xinhai(xinhailu@163.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:18
Issue date:September 15, 2021
Publication year:2021
Pages:263-272
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">Homestead renovation has been one of the most important parts of comprehensive rural land consolidation. The potential of homestead renovation can greatly contribute to scientifically managing land sources, thereby effectively implementing the related policies in functional areas. The purpose of this study is to build an evaluation system from the four dimensions of spatial distribution, the demand, the external environment, and the renovation cost of the homestead. Taking typical towns in southern Hunan of China as the research areas, a landscape and spatial analysis was made to calculate the relevant indicators. TOPSIS method was then combined to rank the potential value using information entropy. The optimized hot spot analysis was utilized to explore the spatial distribution of homestead renovation potential for each village type in the study areas, as well as the future direction of spatial reconstruction and transformation. The results show that: 1) The main influencing factors were achieved, including terrain conditions, potential tapping coefficient of per capita homestead area, and the average nearest neighbor distance of homestead. Specifically, the landscape fragmentation was much higher, as the patch density of homestead increased. 2) The hot spot areas of renovation potential of the homestead were concentrated in the Northeast hills and Southeast mountains in the study areas, while the cold spot areas were concentrated in the central plain of town in the middle and west. 3) Four gradient levels of homestead renovation were divided, according to the combined evaluation of homestead remediation potential and village classification. Specifically, the potential value of village homestead renovation from large to small was ranked in the following order: the fragile ecological environment, gathering and upgrading, traditional village and balanced development, as well as agricultural production leading type. 4) Seven typical areas of comprehensive land consolidation were divided without considering the administrative division of the village level. A transforming development path of "homestead renovation + N" was also summarized to highlight the natural resources and characteristic elements in each typical area. Anyway, the potential of village homestead renovation was dominated by the own endowment, and geographical location. In addition, a micro-scale assessment of renovation potential can widely be expected to explore the homestead exit mechanism, further to guide the township for the comprehensive land renovation in rural revitalization.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:30
Main heading:Rural areas
Controlled terms:Agriculture - Consolidation - Landforms - Regression analysis - Spatial distribution - Spatial variables measurement
Uncontrolled terms:Development path - Hot spot analyse - Hotspots - Hunan province - Land consolidations - Model area - Potential - Potential values - Rural homestead - Study areas
Classification code:405.3  Surveying - 481.1  Geology - 821  Agricultural Equipment and Methods; Vegetation and Pest Control - 902.1  Engineering Graphics - 921  Mathematics - 922.2  Mathematical Statistics - 943.2  Mechanical Variables Measurements
DOI:10.11975/j.issn.1002-6819.2021.18.030

Database:Compendex
Compilation and indexing terms, Copyright 2022 Elsevier Inc.

<RECORD 2>

Accession number:20214811239977
Title:Estimation of aboveground biomass and leaf area index of summer maize using SE<inf>PLS</inf>_ELM model
Title of translation:&#37319;&#29992;SE<inf>PLS</inf>_ELM&#27169;&#22411;&#20272;&#31639;&#22799;&#29577;&#31859;&#22320;&#19978;&#37096;&#29983;&#29289;&#37327;&#21644;&#21494;&#38754;&#31215;&#25351;&#25968;
Authors:Lu, Junsheng (1, 2); Chen, Shaomin (1, 2); Huang, Wenmin (3); Hu, Tiantian (1, 2)
Author affiliation:(1) Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education, Northwest A&F University, Yangling; 712100, China; (2) College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling; 712100, China; (3) Cultivated Land Quality and Agricultural Environmental Protection Station in Shaanxi Province, Xi'an; 710000, China
Corresponding authors:Hu, Tiantian(hutiant@nwsuaf.edu.cn); Hu, Tiantian(hutiant@nwsuaf.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:18
Issue date:September 15, 2021
Publication year:2021
Pages:128-135
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">Hyperspectral remote sensing has widely been used to estimate crop physiological, ecological, and biochemical parameters in recent years. However, most previous studies focused mainly on the selection of sensitive bands or the construction of vegetation index (the combination of sensitive bands) for crop parameter inversion. Particularly, the spectral information of some bands can be lost, and then to reduce the prediction ability of the estimation model. The purpose of this study is to estimate the aboveground biomass and leaf area index of summer maize using all spectral information (spectral bands). Therefore, a three-year (2018-2020) field experiment was also conducted under different water and nitrogen management in the Guanzhong Plain of China. Accordingly, 212 plant samples (aboveground biomass and leaf area index) were collected during the vegetative growth period of summer maize. Prior to plant sample collection, the hyperspectral reflectance data of the summer maize canopy was measured using an ASD FieldSpec 3 portable spectroradiometer. Correspondingly, the estimation model was constructed using Partial Least Squares Regression (PLS), Extreme Learning Machine (ELM), Random Forest (RF), and Stacked Ensemble Extreme Learning Machine (SE<inf>PLS</inf>_ELM, using the PLS stacked ensemble strategy). The results showed that the estimation accuracy (four estimation models) of the leaf area index of summer maize was higher than that of aboveground biomass. The estimation models of PLS and ELM presented a relatively low accuracy for the aboveground biomass and leaf area index of summer maize, where the determination coefficient (R²) for the validation set of the aboveground biomass estimation model was lower than 0.85, and the Root Mean Square Error (RMSE) was higher than 550 kg/hm^-2, whereas, the R² for the validation set of leaf area index estimation model was lower than 0.90, and the RMSE was higher than 0.40 cm²/cm². The estimation model of aboveground biomass and leaf area index of summer maize using RF and SE<inf>PLS</inf>_ELM presented a higher estimation accuracy, particularly that the performance of the SE<inf>PLS</inf>_ELM model was outstanding. The R² values for the validation set of aboveground biomass and leaf area index estimation model using the SE<inf>PLS</inf>_ELM model were 0.955 and 0.969, while the RMSE were 307.3 kg/hm² and 0.24 cm²/cm², and the Residual Predictive Deviation (RPD) were 4.66 and 5.30, respectively. Compared with PLS and ELM, the estimation accuracy of the SE<inf>PLS</inf>_ELM model was significantly improved (the R² increased by more than 8%, RMSE decreased by more than 40%, and RPD increased by more than 70%, respectively) in aboveground biomass and leaf area index estimation. Compared with the RF, the R² of the SE<inf>PLS</inf>_ELM estimation model increased by more than 7%, RMSE decreased by more than 40%, and RPD increased by more than 66% in the aboveground biomass and leaf area index estimation of summer maize, respectively. Consequently, the present study demonstrated that the SE<inf>PLS</inf>_ELM model was highly reliable to predict the aboveground biomass and leaf area index of summer maize. The findings can provide a strong reference for the estimation of crop aboveground biomass and leaf area index using hyperspectral remote sensing.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:34
Main heading:Biomass
Controlled terms:Crops - Decision trees - Ecology - Knowledge acquisition - Least squares approximations - Machine learning - Mean square error - Physiological models - Regression analysis - Remote sensing  - Vegetation
Uncontrolled terms:Aboveground biomass - Ensemble models - Estimation models - Hyper spectra - Leaf Area Index - Partial least square regression - Random forests - Remote-sensing - Stacked ensemble model - Summer maize
Classification code:454.3  Ecology and Ecosystems - 723.4  Artificial Intelligence - 821.4  Agricultural Products - 921.4  Combinatorial Mathematics, Includes Graph Theory, Set Theory - 921.6  Numerical Methods - 922.2  Mathematical Statistics - 961  Systems Science
Numerical data indexing:Mass 3.073E+02kg, Mass 5.50E+02kg, Percentage 4.00E+01%, Percentage 6.60E+01%, Percentage 7.00E+00%, Percentage 7.00E+01%, Percentage 8.00E+00%, Size 2.00E-02m, Size 2.40E-03m, Size 4.00E-03m
DOI:10.11975/j.issn.1002-6819.2021.18.015

Database:Compendex
Compilation and indexing terms, Copyright 2022 Elsevier Inc.

<RECORD 3>

Accession number:20214811239865
Title:Detecting the pest disease of field crops using deformable VGG-16 model
Title of translation:&#22522;&#20110;&#21487;&#24418;&#21464;VGG-16&#27169;&#22411;&#30340;&#30000;&#38388;&#20316;&#29289;&#23475;&#34411;&#26816;&#27979;&#26041;&#27861;
Authors:Zhang, Shanwen (1); Xu, Xinhua (1); Qi, Guohong (1); Shao, Yu (1)
Author affiliation:(1) School of Electronic Information Engineering, Zhengzhou Sias University, Zhengzhou; 451150, China
Corresponding author:Shao, Yu(zswwyy125@163.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:18
Issue date:September 15, 2021
Publication year:2021
Pages:188-194
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">Detection of crop pest has widely been one of the most challenges in modern agriculture, due to the intra- and inter-class pests in the field with various colors, sizes, shapes, postures, positions, and complex backgrounds. Convolutional Neural Network (CNN) has presented an excellent performance on the detection and recognition of complex images. However, the current CNN models cannot adapt to the geometric deformation of pests. In this study, a deformable VGG-16 (DVGG-16) model was constructed and then applied for the detection of crop pest in the field. The framework consisted of six convolutional layers, four deformable convolutional layers, five pooling layers, and one global average pooling layer. Furthermore, the network training was utilized to speed up the global average pooling operation, instead of three fully connected layers of VGG-16. Four convolutional layers in VGG-16 were replaced by four deformable convolutional layers, in order to improve the characteristic expression ability of network and the practicality of VGG-16 to insect image deformation. Moreover, a global pooling layer was used instead of three fully connected layers of VGG-16, in order to reduce the number of the training parameters, while accelerate the network training speed free of the over-fitting. The offset was added in the deformable convolution unit, thereby to serve one part of DVGG-16 structure. Among them, another parallel standard convolution unit was used to calculate and then learn end-to-end through gradient backpropagation. Subsequently, the size of deformable convolution kernels and position were adjusted, according to the current need to identify the dynamic image content of crop pests, particularly suitable for different shapes, sizes, and other geometric deformation of the object. Moreover, data augmentation was performed on the original dataset to increase the number of training samples. A series operations were also included for the better generalization ability and robustness of model, such as bilinear interpolation, cropping and rotating images, and adding salt-pepper noise to the images. A parallel convolution layer was used in DVGG-16 to learn the offset corresponding to the input feature map. The constraint was easily broken for the regular grid of normal convolution, where an offset was added at the corresponding position of each sampling point, while the arbitrary sampling was performed around the sampling location. More importantly, the deformable convolution was greatly contributed to the DVGG-16 model for better suitable for various insect images with different shapes, states, and sizes. An image database of actual field pest was evaluated to compare with two feature extraction and two deep learning, including image-based Orchard Insect Automated Identification (IIAI), Local Mean Color Feature and Support Vector Machine (LMCFSVM), Improved Convolutional Neural Network (ICNN), and VGG-16. Specifically, the detection accuracy of DVGG-16 was 91.14%, which was 28.60 and 26.97 percentage higher than that of IIAI and LMCFSVM, and 7.72 and 9.01 percentage higher than that of ICNN and VGG-16 based models, respectively. The training time of DVGG-16 was 7.98 h longer than that of the ICNN, because the deformable convolution operation was realized by bilinear interpolation, which resulted in the increase of computational complexity and training time of DVGG-16 compared with ICNN. The test time of DVGG-16 based model was 0.02 and 0.17 s faster than that ICNN and VGG-16 based models, respectively. Consequently, the DVGG-16 was effective and feasible to detect the variable pests in the field. The finding can provide a strong reference for the effective detection of pests in the complex field background, further to realize the feature extraction of irregular field insect images without changing the spatial resolution.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:26
Main heading:Convolution
Controlled terms:Classification (of information) - Complex networks - Convolutional neural networks - Crops - Deformation - Geometry - Image classification - Image enhancement - Image recognition
Uncontrolled terms:'current - Bilinear interpolation - Convolutional neural network - Different shapes - Features extraction - Geometric deformations - Insect images - Learn+ - Network training - Pest
Classification code:716.1  Information Theory and Signal Processing - 722  Computer Systems and Equipment - 723.2  Data Processing and Image Processing - 821.4  Agricultural Products - 903.1  Information Sources and Analysis - 921  Mathematics
Numerical data indexing:Percentage 9.114E+01%, Time 1.70E-01s, Time 2.00E-02s, Time 2.8728E+04s
DOI:10.11975/j.issn.1002-6819.2021.18.022

Database:Compendex
Compilation and indexing terms, Copyright 2022 Elsevier Inc.

<RECORD 4>

Accession number:20214811239972
Title:Research status and development prospect of energy and high value utilization of biomass resources
Title of translation:&#29983;&#29289;&#36136;&#36164;&#28304;&#33021;&#28304;&#21270;&#19982;&#39640;&#20540;&#21033;&#29992;&#30740;&#31350;&#29616;&#29366;&#21450;&#21457;&#23637;&#21069;&#26223;
Authors:Wang, Fang (1); Liu, Xiaofeng (2); Chen, Lungang (3); Lei, Tingzhou (4); Yi, Weiming (1); Li, Zhihe (1)
Author affiliation:(1) School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo; 255000, China; (2) Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu; 610041, China; (3) Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou; 510650, China; (4) Henan Academy of Sciences, Zhengzhou; 450002, China
Corresponding author:Yi, Weiming(yiweiming@sdut.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:18
Issue date:September 15, 2021
Publication year:2021
Pages:219-231
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">Biomass has been the only renewable energy that can be directly converted into fuel. The full utilization of biomass can effectively alleviate energy needs in an eco-friendly way. It is also an important path for China to achieve the goal of "carbon neutrality". Therefore, this study aims to focus mainly on the general goal of clean energy production and high-value utilization for straw-based biomass resources in recent years. A systematic analysis was made to evaluate the comprehensive utilization technology and mode of producing gas, liquid, and solid clean energy in the biological or thermo-chemical way. The development status and research progress were concentrated upon the anaerobic digestion for biogas, hydrothermal catalysis for alcohol and hydrocarbon fuel, pyrolysis liquefaction and bio-oil upgrading, and solid fuel production. Particularly, an attempt was addressed on the prospect of biogas, liquid fuel, and solid fuel. More importantly, no matter what biomass conversion technology was adopted, biomass resources utilization should be comprehensive and of high value. Correspondingly, the large-scale application required at least three basic elements. The first was the scale collection and disposal of biomass raw materials at a low cost. The second was the efficient and stable transformation, as well as quality improvement technology. The last was that the terminal fuel products needed to connect smoothly with the current application. Among them, anaerobic digestion for biogas presented the highest level of industrialization in recent years, due mainly to effectively solving raw materials collection in large breeding farms. It infers that the anaerobic digestion and biogas purification technology were relatively mature during this time. As such, biogas was directly used as a source of fuel, power, and thermal production. By contrast, hydrothermal catalysis for alcohol and hydrocarbon fuel, together with pyrolysis liquefaction for bio-oil was relatively difficult to connect with the current application, due mainly to the high conversion cost, difficult product separation, low-quality improvement efficiency, and unstable products. Therefore, the large-scale development level of the two technologies was relatively low during this time. Nevertheless, the technology of biomass preparation was relatively mature for solid fuels. The research and development of supporting stoves also effectively implemented the application of molding fuel. But the biggest difficulty in the scale application lay in the collection and storage of raw materials. Finally, the development prospects were proposed for the biomass conversion technologies. In terms of biogas, the anaerobic digestion was enhanced by multi-ingredients and bio-strengthen to improve biogas production efficiency. A precise control system should be established for the high concentration anaerobic digestion for better stability. Particularly, the comprehensive utilization of biogas slurry was carried out to realize the nutrient recycling, and biological organic fertilizer, or carbon-based fertilizer for nitrogen and carbon fixation, with emphasis on the efficiency of desulfurization and decarbonization. In terms of liquid fuel, the unpolluted depolymerization can be explored from lignocellulose to platform compounds, especially how to realize the efficient separation and quality improvement of platform compounds. During the quality upgrading process, the multi-step reaction combined catalyzer can be used to shorten the process and improve the reaction efficiency. In addition, the efficient conversion of biomass resources into fuel, chemicals, and materials should be developed synchronously for higher-value products. In terms of solid fuel, the heat transfer and bonding mechanism of torrefaction straw should be studied during the molding process, further to realize the low energy consumption and high quality. New efficient molding and combustion equipment needed to be improved the reliability of solid fuel production, particularly on the standardization, series, and package of torrefaction, molding, and combustion equipment. Consequently, the standard production of biomass collection, storage, and combustion should be improved to form biomass solid fuel industry chain from collection, storage, transportation, molding, and distribution. This research can provide a strong reference for the efficient preparation of clean energy and high-value utilization in rural biomass.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:113
Main heading:Biomass
Controlled terms:Anaerobic digestion - Bioconversion - Biogas - Carbon - Catalysis - Chemical analysis - Costs - Liquefaction - Liquid fuels - Liquids  - Pyrolysis
Uncontrolled terms:Bio-oils - Biomass conversion technologies - Biomass resources - Carbon abatement - Clean energy - Development prospects - Hydrocarbon fuel - Quality improvement - Solid fuel productions - Solid fuels
Classification code:522  Gas Fuels - 523  Liquid Fuels - 801.2  Biochemistry - 802.2  Chemical Reactions - 802.3  Chemical Operations - 804  Chemical Products Generally - 821.5  Agricultural Wastes - 911  Cost and Value Engineering; Industrial Economics
DOI:10.11975/j.issn.1002-6819.2021.18.026

Database:Compendex
Compilation and indexing terms, Copyright 2022 Elsevier Inc.

<RECORD 5>

Accession number:20214811239983
Title:Preparation and application of liquid bioformulation of Metschnikowia citriensis on citrus fruits
Title of translation:&#26708;&#26757;&#22855;&#37237;&#27597;&#28082;&#20307;&#21046;&#21058;&#30340;&#21046;&#22791;&#21450;&#20854;&#22312;&#26577;&#27224;&#26524;&#23454;&#19978;&#30340;&#24212;&#29992;
Authors:Chen, Liwei (1); Zhang, Hongyan (1); Deng, Lili (1, 2); Zeng, Kaifang (1, 3)
Author affiliation:(1) College of Food Science, Southwest University, Chongqing; 400715, China; (2) Food Storage and Logistics Research Center, Southwest University, Chongqing; 400715, China; (3) National Citrus Engineering Research Center, Chongqing; 400712, China
Corresponding authors:Zeng, Kaifang(zengkaifang@163.com); Zeng, Kaifang(zengkaifang@163.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:18
Issue date:September 15, 2021
Publication year:2021
Pages:299-306
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">Antagonistic yeast has widely expected to replace chemical fungicides, serving as an effective biological agent to control postharvest diseases of citrus. Metschnikowia citriensis, a new species of Metschnikowia spp, isolated from citrus leaves and identified laboratory, was efficient to control postharvest green mold and sour rot caused by Penicillium digitatum and Geotrichum citri-aurantii on citrus fruit, indicating great application and development value. However, the unstable effect of biocontrol agents and the unclear mechanism of biocontrol have led to a low success rate of commercial application of antagonistic yeast. Therefore, it is highly urgent to develop the antagonistic yeast biological agents with long shelf life and stable control effect for the commercialization of M. citriensis. In this study, an attempt was made to explore the preparation of liquid bioformulation with M. citriensis as the main active ingredient. Single factor and response surface tests were carried out to optimize the formulation of the protective agent for liquid bioformulation. An in vitro and in vivo test was also conducted to further evaluate the control effect of formulation on the main postharvest diseases of citrus fruits, particularly for the application of liquid preparation. The results showed that four protective agents were screened via a single factor test, including trehalose, sodium glutamate, Tween 80, and proline. The formula of the protective agent was selected: trehalose 19.74%, sodium glutamate 1.05%, Tween 80 4.24%, proline 1.11%, where the best protective effect on M. citriensis, and the survival rate of yeast increased from 5.02% to 54.96%. The storage stability of liquid bioformulation showed that the survival rate of M. citriensis was still more than 60% after 30 days of storage when the temperature was lower than 10 &#8451;. More importantly, the shelf life of preparation reached more than 90 days, when the storage temperature was -20 &#8451;. As such, a relatively low storage temperature was beneficial to the longer shelf life of bioformulation. In vitro and fruit tests were carried out to verify the application effect of preparation. It was found that there was no significant change in the inhibitory effect of liquid bioformulation on postharvest pathogen and pigment production capacity of M. citriensis, compared with fresh yeast, where the inhibition zone reached more than 8 mm. There was also no significant difference in the number of fresh and liquid yeast at the fruit wound, both of which grew well at the wound. Fresh yeast performed remarkable control effects on blue and green mold, sour rot, and anthrax on citrus fruits, while liquid preparation presented no significant difference, compared with fresh yeast. Specifically, the formulation reduced the incidence of four citrus postharvest diseases by 25.00%-48.33%. Consequently, the preparation of M. citriensis can widely be expected to effectively retain cell viability and biocontrol efficacy. The liquid bioformulation with M. citriensis as the main active ingredient demonstrated an excellent control effect on postharvest citrus diseases. The finding can provide strong theoretical and practical support to the application of M. citriensis in the biological control of postharvest citrus diseases.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:34
Main heading:Liquids
Controlled terms:Biocontrol - Citrus fruits - Disease control - Sodium - Yeast
Uncontrolled terms:Antagonistic yeast - Biological agents - Biological controls - Citrus - Green molds - Metschnikowia citriensis - Postharvest - Postharvest disease - Protective agents - Shelf life
Classification code:461.1  Biomedical Engineering - 462.4  Prosthetics - 549.1  Alkali Metals - 731.1  Control Systems - 821.4  Agricultural Products - 822.3  Food Products
Numerical data indexing:Age 2.466E-01yr, Age 8.22E-02yr, Percentage 1.05E+00%, Percentage 1.11E+00%, Percentage 1.974E+01%, Percentage 2.50E+01% to 4.833E+01%, Percentage 4.24E+00%, Percentage 5.02E+00% to 5.496E+01%, Percentage 6.00E+01%, Size 8.00E-03m
DOI:10.11975/j.issn.1002-6819.2021.18.034

Database:Compendex
Compilation and indexing terms, Copyright 2022 Elsevier Inc.

<RECORD 6>

Accession number:20214811239859
Title:Mapping of the winter crop planting areas in Huaihe River Basin based on Google Earth Engine
Title of translation:&#22522;&#20110;Google Earth Engine&#30340;&#28142;&#27827;&#27969;&#22495;&#36234;&#20908;&#20316;&#29289;&#31181;&#26893;&#38754;&#31215;&#21046;&#22270;
Authors:Pan, Li (1); Xia, Haoming (1, 2); Wang, Ruimeng (1); Niu, Wenhui (1); Tian, Haifeng (1); Qin, Yaochen (1, 2)
Author affiliation:(1) College of Geography and Environmental Science, Henan University, Henan Key Laboratory of Earth System Observation and Modeling, Key Research Institute of Yellow River Civilization and Sustainable Development & Collaborative Innovation Center of Yellow River Civilization Provincial Co-construction, Kaifeng; 475001, China; (2) Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng; 475001, China
Corresponding authors:Xia, Haoming(xiahm@vip.henu.edu.cn); Xia, Haoming(xiahm@vip.henu.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:18
Issue date:September 15, 2021
Publication year:2021
Pages:211-218
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">The winter crop has been one of the important crop types in China. Accurate and timely spatio-temporal distribution of planting area directly determines the grain output and economy, as well as the national food security. Taking the Huaihe River Basin as an example, this study aims to extract the planting areas of winter crops according to the phenology period using the Google Earth Engine cloud platform and the fusion of Landsat-7/8 and Sentinel-2A/B images. Firstly, a dataset of time-series images was constructed with a spatial resolution of 30 m. A CFMask algorithm was selected to preprocess the images, thereby calculating the Normalized Difference Vegetation Index (NDVI). More importantly, the maximum NDVI of all high-quality images within 10 days was used to obtain time-series data with equal time intervals. The linear interpolation was utilized to fill the pixels without high-quality images. Savitzky-Golay (S-G) filtering (a second-order filter with a moving window of 9 observations) was adopted to smooth the NDVI time series for the removal of noise. As such, a smoothed NDVI time series was obtained with a 10-day interval. Secondly, the peak growth, sowing, and harvest periods were determined to select sample points of winter crops with different spatial distributions, according to the NDVI time series. Subsequently, the winter crops were sowed in mid-late October, when the NDVI values were the lowest. The NDVI values gradually increased, after the emergence of seedlings in early November. The crops stopped growing in January during the overwintering period, where the NDVI stayed the same over the whole period. Furthermore, the NDVI resumed growing and gradually reached the peak growth period, when the winter crops turned green in February. After that, the NDVI reached the peak at the heading stage, and then gradually decreased. Correspondingly, the NDVI dropped to the bottom, when the harvest was over from the end of May to June. According to these characteristics in the process of winter crops growth, the peak growth period was determined from March 20, 2018, to April 20, 2018, the sowing period was determined from October 11, 2017, to November 10, 2018, and the harvest period was determined from May 20, 2018, to June 30, 2018. Particularly, the maximum NDVI was achieved in the peak growth period and the minimum and median of NDVI in the sowing and harvest period. Finally, the classification model of a decision tree was constructed, according to the NDVI boxplots of winter crops and non-winter crops at different time periods. The planting area map of winter crops was also generated for the Huaihe River Basin. The results showed that the planting area of winter crops was 8.762&times;10⁶ hm² in the Huaihe River Basin in 2018. Specifically, the user accuracy was 0.926, the producer accuracy was 0.970, the total accuracy was 0.958, and the Kappa coefficient was 0.912. Consequently, the large-scale planting area of winter crops was extracted accurately for the decision-making in similar areas.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:31
Main heading:Remote sensing
Controlled terms:Crops - Engines - Food supply - Mapping - Pixels - Rivers - Time series - Time series analysis - Watersheds
Uncontrolled terms:Google earth engine - Google earths - Growth period - High quality images - Huaihe river basins - Normalized difference vegetation index - Normalized difference vegetation index time series - Planting areas - Remote-sensing - Winter crops
Classification code:405.3  Surveying - 444.1  Surface Water - 821.4  Agricultural Products - 822.3  Food Products - 922.2  Mathematical Statistics
Numerical data indexing:Age 2.74E-02yr, Electric current -2.00E+00A, Size 3.00E+01m, Size 5.08E-02m
DOI:10.11975/j.issn.1002-6819.2021.18.025

Database:Compendex
Compilation and indexing terms, Copyright 2022 Elsevier Inc.

<RECORD 7>

Accession number:20214811239915
Title:Improved finite analytic method to simulate soil water movement in vadose zones
Title of translation:&#25913;&#36827;&#22411;&#26377;&#38480;&#20998;&#26512;&#27861;&#27169;&#25311;&#21253;&#27668;&#24102;&#22303;&#22756;&#27700;&#20998;&#36816;&#31227;
Authors:Zhang, Zaiyong (1, 2); Wang, Wenke (1, 2); Lu, Yanwei (1, 2); Gong, Chengcheng (1, 2); Ran, Bin (1, 2); Wu, Zeyu (1, 2)
Author affiliation:(1) Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region, Ministry of Education, Chang'an University, Xi'an; 710054, China; (2) School of Water and Environment, Chang'an University, Xi'an; 710054, China
Corresponding authors:Wang, Wenke(wenkew@chd.edu.cn); Wang, Wenke(wenkew@chd.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:18
Issue date:September 15, 2021
Publication year:2021
Pages:55-61
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">Finite Analytic Method (FAM) has widely been used to solve the Richards equation in recent years. The first-order finite difference approximation can usually be utilized in the hybrid FAM (HFAM) to handle the time derivative term during solution. To some extent, the HFAM can obtain satisfactory results, compared with other numerical methods, such as the modified picard Finite Difference (MPFD) method. However, large errors can also be found using the HFAM to simulate the characteristics of sharp wetting fronts during the infiltration process in the vadose zone. Therefore, a better method is required to handle the time derivative term. In this study, an improved FAM (IFAM) was proposed to accurately simulate the soil water movement in the vadose zone. The IFAM was selected to obtain local analytic solutions from both the time and space domain simultaneously, due mainly to totally different from the HFAM method. Three cases were also considered to systematically evaluate the performance of IFAM. In all cases, the one-dimensional vertical soil columns were set as 100 cm. In the first case, the upper boundary condition was a constant flux boundary, and the lower boundary was a constant pressure head. Three soil columns were discretized into 100, 50, and 10 elements, respectively. The Finite Difference Method (FDM), HFAM, analytic solution, and IFAM were utilized to solve the Richards equation for better comparison. In the second case, both upper and lower boundary conditions were constant pressure heads. The vertical discretization spacing was set as 1 cm. The water movement was then simulated in the vadose zone using IFAM and VSAFT2 (a commonly-used software based on the Finite Element Method (FEM) to solve the Richards equation). In the third case, the upper boundary condition was also assumed to be a flux boundary, and the flux was equal to the amount of evaporation and rainfall. The lower boundary condition was a constant pressure head. The results of the first case showed that the best numerical results was achieved in the IFAM among all numerical methods. Furthermore, the minimum mass balance error was obtained in IFAM even under the condition of larger spatial steps (spatial step equal to 10 cm), compared to the analytical solution. The results from both the second and third cases showed that there was similar solutions to the IFAM in contrast with VSAFT 2. Consequently, the IFAM can widely be expected to significantly improve the numerical accuracy of the solution to the Richards equation under the larger vertical discretization spacing. This finding can also provide a better way for the simulation of water movement in the vadose zone, particularly for sustainable water resources management and ecological protection.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:33
Main heading:Solute transport
Controlled terms:Boundary conditions - Errors - Finite difference method - Finite element method - Groundwater - Infiltration - Numerical methods - Numerical models - Soil moisture - Wetting
Uncontrolled terms:%moisture - Constant pressures - Finite analytic methods - Pressure heads - Richards Equation - Soil water movement - Time-derivative terms - Transport - Upper boundary - Vadose Zone
Classification code:444.2  Groundwater - 483.1  Soils and Soil Mechanics - 921  Mathematics - 921.6  Numerical Methods - 931  Classical Physics; Quantum Theory; Relativity
Numerical data indexing:Size 1.00E-01m, Size 1.00E-02m, Size 1.00E00m
DOI:10.11975/j.issn.1002-6819.2021.18.007

Database:Compendex
Compilation and indexing terms, Copyright 2022 Elsevier Inc.

<RECORD 8>

Accession number:20214811239897
Title:Segmentation model for maize plant images based on depth mask
Title of translation:&#22522;&#20110;&#28145;&#24230;&#25513;&#30721;&#30340;&#29577;&#31859;&#26893;&#26666;&#22270;&#20687;&#20998;&#21106;&#27169;&#22411;
Authors:Deng, Hanbing (1, 2); Xu, Tongyu (1, 2); Zhou, Yuncheng (1, 2); Miao, Teng (1, 2, 3); Li, Na (1, 2); Wu, Qiong (1, 2); Zhu, Chao (1); Shen, Dezheng (1)
Author affiliation:(1) College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang; 110866, China; (2) Liaoning Engineering Research Center for Information Technology in Agricultural, Shenyang; 110866, China; (3) Beijing Research Center for Information Technology in Agricultural, Beijing; 100097, China
Corresponding authors:Xu, Tongyu(xutongyu@syau.edu.cn); Xu, Tongyu(xutongyu@syau.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:18
Issue date:September 15, 2021
Publication year:2021
Pages:109-120
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">Supervised deep learning has gradually been one of the most important ways to extract the features and information of plant phenotype in recent years. However, the cost and quality of manual labeling have become the bottleneck of restricting the development of technology, due mainly to the complexity of plant structure and details. In this study, a Depth Mask Convolutional Neural Network (DM-CNN) was proposed to realize automatic training and segmentation for the maize plant. Firstly, the original depth and color images of maize plants were collected in indoor scene using the sensors of Kinect. The parallax between depth and color camera was also reduced after aligning the display range of depth and color images. Secondly, the depth and color images were cropped into the same size to remain from the consistency of spatial and content. The depth density function and nearest neighbor pixel filling were also utilized to remove the background of depth images, while retaining the maize plant pixels. As such, a binary image of the maize plant was represented, where the depth mask annotations were obtained by the maximum connection area. Finally, the depth mask annotations and color images were packed and then input to train the DM-CNN, where automatic images labeling and segmentation were realized for maize plants indoors. A field experiment was also designed to verify the trained DM-CNN. It was found that the training loss of depth mask annotations converged faster than that of manual annotations. Furthermore, the performance of DM-CNN trained by depth mask annotations was slightly better than that of manual ones. For the former, the mean Intersection over Union (mIoU) was 59.13%, and mean Recall Accuracy (mRA) was 65.78%. For the latter, the mIoU was 58.49% and mRA was 65.78%. In addition, the dataset was replaced 10% depth mask samples with manual annotations taken in outdoor scene, in order to verify the generalization ability of DM-CNN. After fine-tuning, excellent performance was achieved for the segmentation with the top view images of outdoor seedling maize, particularly that the mean pixel accuracy reached 84.54%. Therefore, the DM-CNN can widely be expected to automatically generate the depth mask annotations using depth images in indoor scene, thereby realizing the supervised network training. More importantly, the model trained by depth mask annotations also performed better than that by manual annotations in mean intersection over union and mean recall accuracy. The segmentation was also suitable for the different plant height ranges during the maize seedling stage, indicating an excellent generalization ability of the model. Moreover, the improved model can be transferred and used in the complex outdoor scenes for better segmentation of maize images (top view), when only 10% of samples (depth mask annotations) were replaced during training. Therefore, it is feasible to realize automatic annotation and training of deep learning model using depth mask annotations instead of manual labeling ones. The finding can also provide low-cost solutions and technical support for high-throughput and high-precision acquisition of maize seedling phenotype.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:36
Main heading:Image segmentation
Controlled terms:Binary images - Color - Convolution - Convolutional neural networks - Deep learning - Geometrical optics - Land use - Pixels - Plants (botany)
Uncontrolled terms:Colour image - Convolutional neural network - Depth density function - Depth image - Depth masks - Images processing - Images segmentations - Maize - Maize plants - Plant phenotype
Classification code:403  Urban and Regional Planning and Development - 461.4  Ergonomics and Human Factors Engineering - 716.1  Information Theory and Signal Processing - 723.2  Data Processing and Image Processing - 741.1  Light/Optics
Numerical data indexing:Percentage 1.00E+01%, Percentage 5.849E+01%, Percentage 5.913E+01%, Percentage 6.578E+01%, Percentage 8.454E+01%
DOI:10.11975/j.issn.1002-6819.2021.18.013

Database:Compendex
Compilation and indexing terms, Copyright 2022 Elsevier Inc.

<RECORD 9>

Accession number:20214811239941
Title:Design and experiment of shovel-screen combined green onion digging, shaking, and soil tillage device
Title of translation:&#38130;&#31579;&#32452;&#21512;&#24335;&#22823;&#33905;&#25366;&#25496;&#25238;&#22303;&#30095;&#25972;&#35013;&#32622;&#35774;&#35745;&#19982;&#35797;&#39564;
Authors:Hou, Jialin (1, 2); Chen, Yanyu (2); Li, Yuhua (2, 3); Li, Tianhua (2, 3); Li, Guanghua (4); Guo, Hong'en (1)
Author affiliation:(1) Shandong Academy of Agricultural Machinery Sciences, Jinan; 250100, China; (2) College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai'an; 271018, China; (3) Shandong Provincial Engineering Laboratory of Agricultural Equipment Intelligence, Tai'an; 271018, China; (4) Shandong Hualong Agricultural Equipment Co., Ltd., Qingzhou; 262500, China
Corresponding author:Guo, Hong'en(guohongen163@163.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:18
Issue date:September 15, 2021
Publication year:2021
Pages:29-39
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">Green onion has widely been one of the most important cash crops in recent years. Its planting area is ever-increasing year by year. However, the mechanized harvest level is less than 20% in China, which seriously restricts the green onion industry. In this research, a combined shovel-screen device was designed for green onion digging, shaking, and soil tillage, in order to significantly improve the reliability and working efficiency during harvesting. Three parts were mainly composed of the digging shovel, the shaking screen, and the soil tillage mechanism. A mechanical model was also constructed to theoretically clarify the working principle and design idea. Then, the discrete element simulation was conducted for the green onion ridge in the harvest period. A field test was also carried out to verify the performance of the developed device and the original one. The simulation results showed that the soil disturbance and the flow condition of soil particles were better in the combined shovel-screen device than before. The field test results showed that the average impurity and damage rates of green onion were 2.94%, and 1.66%, respectively, while the soil accumulation was less than 10%. Furthermore, the green onion ridge soil was almost free from accumulation and blocking, when the combined shovel-screen device was digging, shaking, and soil tillage in the test. Correspondingly, there were decreases of 0.51 percentage points, 0.77 percentage points, and 40 percentage points in the average impurity and damage rates of green onion, as well as the soil accumulation in the test, respectively, compared with the original. The finding can also provide a strong reference for the research on the technology and equipment of digging, shaking, and soil tillage during harvesting, further promoting the rapid development of the onion industry.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:36
Main heading:Harvesting
Controlled terms:Agricultural machinery - Crops - Shovels - Soils
Uncontrolled terms:Damage rate - Digging - EDEM - Field experiment - Field test - Green onion - Impurity rates - Percentage points - Shaking - Soil tillage
Classification code:483.1  Soils and Soil Mechanics - 821.1  Agricultural Machinery and Equipment - 821.3  Agricultural Methods - 821.4  Agricultural Products
Numerical data indexing:Percentage 1.00E+01%, Percentage 1.66E+00%, Percentage 2.00E+01%, Percentage 2.94E+00%
DOI:10.11975/j.issn.1002-6819.2021.18.004

Database:Compendex
Compilation and indexing terms, Copyright 2022 Elsevier Inc.

<RECORD 10>

Accession number:20214811239905
Title:Classification of mutton location on the animal using improved ResNet18 network model and mobile application
Title of translation:&#25913;&#36827;ResNet18&#32593;&#32476;&#27169;&#22411;&#30340;&#32650;&#32905;&#37096;&#20301;&#20998;&#31867;&#19982;&#31227;&#21160;&#31471;&#24212;&#29992;
Authors:Zhang, Yaoxin (1, 2); Zhu, Rongguang (1, 2); Meng, Lingfeng (1); Ma, Rong (1); Wang, Shichang (1); Bai, Zongxiu (1); Cui, Xiaomin (1)
Author affiliation:(1) College of Mechanical and Electrical Engineering, Shihezi University, Shihezi; 832003, China; (2) Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi; 832003, China
Corresponding authors:Zhu, Rongguang(rgzh_jd@163.com); Zhu, Rongguang(rgzh_jd@163.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:18
Issue date:September 15, 2021
Publication year:2021
Pages:331-338
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">Accurate and timely detection of meat parts has gradually been highly demanding in meat consumption. However, the traditional image classification cannot clearly distinguish the similar color and texture characteristics for different mutton parts under different storage time, particularly with the low generalization and time-consuming. In this study, an improved ResNet18 network model was proposed to classify the different mutton parts, while, the corresponding mobile application software was developed using the optimal model. Firstly, 1 008 mutton images of loin, hind shank, and fore shank under different storage times (0-12 d) were collected, and then 9 types of data-augmentation were used to expand the dataset. After that, 6 000 images were randomly selected from the augmented dataset for modeling, where 80% of the images were used as the training dataset, and the remainder was used as the test dataset. Secondly, Additive Angular Margin Loss (ArcFace) and the depthwise separable convolution were introduced into the ResNet18 network for the improved one. Thirdly, the improved ResNet18 network was trained with the augmented images of different mutton parts. Meanwhile, an evaluation was made to determine the effect of different parameters on the convergence speed and accuracy of improved ResNet18. Optimizers of stochastic gradient descent (SGD) and adaptive moment estimation (Adam), the learning rate of 0.01 and 0.001, weight decay coefficient of 0 and 0.000 5 were adopted for experimental comparison. The optimal classification model was then determined for different mutton parts. Finally, a mobile application software was developed to transplant the TorchScript model that transformed from the improved ResNet18. The results showed that the ArcFace greatly improved the distinguishability of different mutton parts, while the depthwise separable convolution significantly reduced the parameters of the network. Furthermore, the improved ResNet18 network using SGD optimizer presented a higher accuracy and more stable performance than that using the Adam in the test phase. When the learning rate was set to 0.01, the weight decay coefficient was set to 0.000 5, and the SGD optimizer was used to train the improved ResNet18 network, only 25 images of different parts of lamb were classified incorrectly in the 1 200 test sets, where the classification accuracy of the model was 97.92%, while the average classification accuracies of the loin, hind shank, and fore shank were 97.00%, 98.00%, and 98.75%, respectively. Compared with the original, the classification accuracy of the improved ResNet18 was improved by 5.92 percentage points, while the classification accuracies of loin, hind shank, and fore shank were improved by 5.75, 5.50, and 6.50 percentage points, respectively. Compared with the MobileNet model, the classification accuracy of improved ResNet18 was improved by 13.34 percentage points, while the classification accuracies of loin, hind shank, and fore shank were improved by 13.50, 10.75, and 15.75 percentage points, respectively. Moreover, the software using the improved ResNet18 quickly and accurately classified different mutton parts, where the average detection time of each image was about 0.3 s. The finding can provide the technical and theoretical support to improve the level of intelligent detection of meat products for the fair competition of the meat market.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:30
Main heading:Image recognition
Controlled terms:Application programs - Classification (of information) - Convolution - Digital storage - Gradient methods - Image classification - Image enhancement - Image quality - Learning algorithms - Meats  - Optimization - Statistical tests - Stochastic systems - Textures
Uncontrolled terms:Classification accuracy - Classification of mutton part - Images processing - Mobile terminal - Network models - Optimizers - Percentage points - Resnet18 - Stochastic gradient descent - Storage time
Classification code:716.1  Information Theory and Signal Processing - 722.1  Data Storage, Equipment and Techniques - 723  Computer Software, Data Handling and Applications - 723.2  Data Processing and Image Processing - 723.4.2  Machine Learning - 731.1  Control Systems - 822.3  Food Products - 903.1  Information Sources and Analysis - 921.5  Optimization Techniques - 921.6  Numerical Methods - 922.2  Mathematical Statistics - 961  Systems Science
Numerical data indexing:Percentage 8.00E+01%, Percentage 9.70E+01%, Percentage 9.792E+01%, Percentage 9.80E+01%, Percentage 9.875E+01%, Time 3.00E-01s
DOI:10.11975/j.issn.1002-6819.2021.18.038

Database:Compendex
Compilation and indexing terms, Copyright 2022 Elsevier Inc.

<RECORD 11>

Accession number:20214811239961
Title:Research advances of influencing factors and weakening methods to determine Pb²⁺ and Cd²⁺ in soils by anodic stripping voltammetry
Title of translation:&#22303;&#22756;&#38085;&#21644;&#38217;&#28342;&#20986;&#20239;&#23433;&#27861;&#26816;&#27979;&#20013;&#24433;&#21709;&#22240;&#32032;&#21450;&#20854;&#21066;&#24369;&#26041;&#27861;&#30740;&#31350;&#36827;&#23637;
Authors:Liu, Ning (1, 2); Zhao, Guo (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 and Rural Affairs, China Agricultural University, Beijing; 100083, China; (3) College of Artificial Intelligence, Nanjing Agricultural University, Nanjing; 210031, China
Corresponding authors:Liu, Gang(pac@cau.edu.cn); 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:37
Issue:18
Issue date:September 15, 2021
Publication year:2021
Pages:232-243
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">Pb²⁺ and Cd²⁺ are non-biodegradable heavy metal elements with high toxicity. They, even at trace levels, can cause serious damage to the brain, kidneys, blood, nerves, and other organs. Improper human activities have deposited a large amount of Pb²⁺ and Cd²⁺ into the soil, such as sewage irrigation, the abuse of chemical fertilizers and pesticides, as well as the excessive discharge of industrial wastes. The heavy metal ions have inevitably been absorbed by crops and then accumulated in animals. After that, these heavy metals can be enriched thousands of times into the human body, ultimately endangering human health, particularly under the biomagnification of food chain. Therefore, rapid, accurate, and reliable detection of Pb²⁺ and Cd²⁺ in soil has been highly urgent to control heavy metal pollution for product safety in modern agriculture. One of electrochemical technique, Anodic Stripping Voltammetry (ASV) can be used for on-site and real-time detection of Pb²⁺ and Cd²⁺ in soils, indicating high sensitivity, excellent selectivity, convenient operation, equipment portability, and low cost. However, ASV is susceptible to various influencing factors, leading to the decrease in the accuracy of heavy metals detection. The detection performance also varies in the different ASVs. According to the type of voltammetric signal, ASV can be divided into Square-Wave Anodic Stripping Voltammetry (SWASV), Differential Pulse Anodic Stripping Voltammetry (DPASV), and Linear Anodic Sweep Voltammetry. Among them, SWASV and DPASV are often used to detect Pb²⁺ and Cd²⁺ in soils, due to their higher sensitivity and lower detection limit. Voltammetric parameters negatively influence the stripping peak current of Pb²⁺ and Cd²⁺, including pulse amplitude, pulse frequency, and potential increment. Furthermore, the voltammetric response of target heavy metals depends seriously on experimental conditions, such as supporting electrolyte type and pH value, deposition potential, as well as deposition time. More importantly, there is the complex composition in soils, including a variety of metal cations, anions, and rich organic matter, but the content of heavy metal ions is very low. Therefore, the voltammetric signal of heavy metals is relatively weak, particularly that easily interfered with by the complex components in soils. In addition, Cu²⁺ and organic matter are the most common and serious interference factors in soil. In this review, the interference problem of Cu²⁺ was proposed for an efficient Cu²⁺ interference suppression. Moreover, a specific mechanism was also addressed to explore the interference of soil humus on the practical application of Pb²⁺ and Cd²⁺ detection using ASV. Sensitive material modified-electrodes were selected to obtain high signal-to-noise ratio voltammetric signals in recent years. Although these materials improve the sensitivity, selectivity, and stability of electrodes, the complex composition in soils will interfere with the detection performance of electrodes, and the amount of material modification, where the concentration of sensitive materials will also interfere with the voltammetric signal of target heavy metals. To accurate and reliably detect Pb²⁺ and Cd²⁺ in soils using ASV, the following problems must be solved in future research: 1) To propose an efficient suppression of Cu²⁺ interference. 2) To explore the interference mechanism of soil humus on Pb²⁺ and Cd²⁺ detection, aiming to an efficient suppression of interference, and 3) To develop modified materials with high selectivity and stability, thereby to improve the detection performance of electrodes for Pb²⁺ and Cd²⁺. Summarily, the working principle of various ASVs was firstly introduced to analyze the influencing factors on Pb²⁺ and Cd²⁺ detection using ASV from three aspects of voltammetric parameters, experimental conditions, and soil material composition. Then, the influencing mechanism of each factor was explained to summary the research advances of influence mitigation. Finally, this finding can provide a promising future of interference research during the detection of Pb²⁺ and Cd²⁺ in soils using ASV.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:76
Main heading:Heavy metals
Controlled terms:Deposition - Electrolytes - Fertilizers - Irrigation - Metal ions - Pesticides - Pollution control - Sewage - Soil pollution - Soils  - Stripping voltammetry - Trace elements
Uncontrolled terms:Anodic stripping voltammetry - Detection performance - Environment - Experimental conditions - Heavy metal ion - Interference research - Research advances - Soil constituent - Stripping voltammetry - Voltammetric signals
Classification code:452.1  Sewage - 483.1  Soils and Soil Mechanics - 531  Metallurgy and Metallography - 531.1  Metallurgy - 702  Electric Batteries and Fuel Cells - 801.4.1  Electrochemistry - 802.3  Chemical Operations - 803  Chemical Agents and Basic Industrial Chemicals - 804  Chemical Products Generally - 821.2  Agricultural Chemicals - 821.3  Agricultural Methods
DOI:10.11975/j.issn.1002-6819.2021.18.027

Database:Compendex
Compilation and indexing terms, Copyright 2022 Elsevier Inc.

<RECORD 12>

Accession number:20214811239943
Title:Efficient allocation of multiple water sources in irrigation areas considering water cycle process under uncertainty
Title of translation:&#19981;&#30830;&#23450;&#26465;&#20214;&#19979;&#32771;&#34385;&#27700;&#24490;&#29615;&#36807;&#31243;&#30340;&#28748;&#21306;&#22810;&#27700;&#28304;&#39640;&#25928;&#37197;&#32622;
Authors:Li, Mo (1); Cao, Kaihua (1); Fu, Qiang (1); Liu, Wei (1); Hu, Yan (1); Chang, Yuqing (1)
Author affiliation:(1) College of Water Conservancy and Civil Engineering, Northeast Agricultural University, Harbin; 150030, China
Corresponding author:Fu, Qiang(fuqiang@neau.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:18
Issue date:September 15, 2021
Publication year:2021
Pages:62-73
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">Precise configuration of multiple water sources in irrigation areas is closely involved with the whole water cycle process of "atmospheric water-surface water-soil water-groundwater". Particularly, changes in hydrological elements can pose a great complexity on the multi-source configuration of irrigation areas. It is very necessary to consider the water cycle process under uncertainty, thereby efficiently allocating the limit water availability to different growth stages of crops in precision irrigation. In this study, a multi-objective model was established to optimize the efficient allocation of multiple water sources under the combined uncertainty of runoff and precipitation in an irrigation area using the water cycle process. Jensen and water scarcity footprint models were also coupled to achieve the synchronization of economic benefits and water saving. An attempt was made to obtain the response characteristics of efficient water distribution to the combined uncertainty of runoff and precipitation. The results showed that the comprehensive water allocation during the main growth period in the irrigation area was 22.41 million m³ under different combined scenarios of surface water supply and precipitation using the water allocation plan and occurrence probability of each scenario. Specifically, the proportion of surface water and groundwater was 6.5:1, while the water allocation in the field accounted for 95% of the optimal water availability. Furthermore, the goal of economic benefit presented a positive correlation with the field water allocation amount, while the goal of water scarcity footprint presented a negative correlation. The constructed model was also used to weigh the conflict goals of economic benefit, yield and blue water use. In addition, the water productivity increased by 11% in the irrigation area. Nevertheless, the required irrigation at each growth stage greatly varied in the different scenarios. More importantly, the jointing stage was the largest sensitivity to water shortage and the amplitude of water allocation variation. The main water supply source during tillering, jointing, and milk-ripe stages was surface water, while the main water source during heading was groundwater. Correspondingly, the multi-source configuration presented high effectiveness in the irrigation area, where the irrigation reliability fluctuated within the good and medium conditions. Fortunately, irrigation adequacy can widely be expected to serve great potential for improvement in the future. Consequently, the constructed model can be used to clearly represent the impact of dynamic variations in hydrological elements on the allocation of multiple water sources in the irrigation area. A relationship between canal water availability and field water distribution can also greatly contribute to a multi-water source configuration plan with simultaneous improvement of benefits and water efficiency. Particularly, the finding can provide strong decision-making support to the efficient use of agricultural water resources in the irrigation areas.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:46
Main heading:Irrigation
Controlled terms:Economic and social effects - Economics - Farms - Groundwater - Groundwater resources - Runoff - Sensitivity analysis - Soil moisture - Water conservation - Water management  - Water supply - Water supply systems
Uncontrolled terms:Cycle process - Efficient allocations - Irrigation area - Multiple water source - Random uncertainties - Water allocations - Water availability - Water cycle - Water source - Waters resources
Classification code:442.1  Flood Control - 444  Water Resources - 444.1  Surface Water - 444.2  Groundwater - 446.1  Water Supply Systems - 483.1  Soils and Soil Mechanics - 821  Agricultural Equipment and Methods; Vegetation and Pest Control - 821.3  Agricultural Methods - 921  Mathematics - 971  Social Sciences
Numerical data indexing:Percentage 1.10E+01%, Percentage 9.50E+01%
DOI:10.11975/j.issn.1002-6819.2021.18.008

Database:Compendex
Compilation and indexing terms, Copyright 2022 Elsevier Inc.

<RECORD 13>

Accession number:20214811239937
Title:Detection of underwater treasures using attention mechanism and improved YOLOv5
Title of translation:&#37319;&#29992;&#27880;&#24847;&#21147;&#26426;&#21046;&#19982;&#25913;&#36827;YOLOv5&#30340;&#27700;&#19979;&#29645;&#21697;&#26816;&#27979;
Authors:Lin, Sen (1); Liu, Meiyi (2); Tao, Zhiyong (2)
Author affiliation:(1) School of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang; 110159, China; (2) School of Electronic and Information Engineering, Liaoning Technical University, Huludao; 125105, China
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:18
Issue date:September 15, 2021
Publication year:2021
Pages:307-314
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">Underwater treasures, such as sea urchins, sea cucumbers, and scallops, have always been preferred in fish production, due mainly to the high value-added industry. However, two conventional approaches, including net fishing and manual catching, cannot meet the application requirements of rapid detection in the actual large-scale cultivation in modern agriculture, particularly on time-consuming, labor-intensive, and severe destruction of submarine environments in the early days. Alternatively, deep learning has widely been characterized by high resolution and fast speed in recent years. Therefore, it is a promising application potential to the target detection framework using the convolutional neural network in fishery production. It is also highly necessary to improve the detection performance in complex underwater environments. In this study, a YOLOv5 detection of underwater treasure was proposed using the attention mechanism, referred to as CG-YOLOv5, in order to provide a more accurate dataset for underwater robots. The main advantages were as follows: 1) DarkNet-53 was introduced the CBAM to deepen the network for the better performance of feature extraction, further to suppress the worthless features in the network. Specifically, the CBAM combined the channel and spatial attention to filter and weight the feature vectors. The channel attention focused mainly on what the detection target was, whereas, spatial attention was used to determine where the detection target was. As such, the prominent feature information was represented via two combined mechanisms, while weakening the general features. 2) The lightweight Ghost-Bottleneck module was introduced to replace the Bottleneck in YOLOv5. A simpler linear operation in Ghost-Bottleneck was utilized to maintain a higher accuracy with light weights. 3) New anchor points were obtained by clustering the labels of underwater datasets. A new detection scale was also added to the original three detections for higher detection accuracy. CG-YOLOv5 network mainly included CGDarknet-53 backbone network, Focus structure, Spatial Pyramid Pooling structure (SPP), and Path Aggregation Network (PANet). Focus served as a benchmark network with down sampling to change the input size of 640&times;640&times;3 to 320&times;320&times;32. Only one CSP structure was involved in the CG-YOLOv5 to integrate gradient changes completely into the feature map for feature fusion enhancement. The SPP structure was used to maximize the pooling of the feature layer. Four scales were utilized in the pooling layers with the pooling core sizes of 1&times;1, 5&times;5, 9&times;9, and 13&times;13, respectively. As such, the SPP effectively increased the perception field, while isolating significant contextual features. Furthermore, path aggregation networks were used to fuse different feature layers of an image. A specific dataset was also selected to verify the model using the actual underwater environment. There were 781 underwater images, 90% of which were employed as training datasets, and the rest were for testing. The experimental results demonstrated that the model fully met the requirement of detection and recognition for the treasures in complex underwater environments, compared with the current deep learning. The average accuracy was 95.67%. Compared with YOLOv5, the average precision of sea urchin, scallop and sea cucumber increased by 7.48, 6.90 and 2.09 percentage points, and mAP increased by 5.49 percentage points base point. Compared with other classical algorithms, the method has better accuracy and lower complexity. The finding can provide a more accurate and fast way to detect and capture aquatic products.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:31
Main heading:Image recognition
Controlled terms:Computer vision - Convolutional neural networks - Cultivation - Deep learning - Feature extraction - Fisheries - Inspection - Mammals - Molluscs - Shellfish
Uncontrolled terms:Attention mechanisms - Lightweight - Multi-scales - Sea cucumber - Sea-urchin - Spatial attention - Spatial pyramids - Underwater environments - Underwater treasure - YOLOv5
Classification code:461.4  Ergonomics and Human Factors Engineering - 461.9  Biology - 471  Marine Science and Oceanography - 723.5  Computer Applications - 741.2  Vision - 821.3  Agricultural Methods
Numerical data indexing:Percentage 9.00E+01%, Percentage 9.567E+01%
DOI:10.11975/j.issn.1002-6819.2021.18.035

Database:Compendex
Compilation and indexing terms, Copyright 2022 Elsevier Inc.

<RECORD 14>

Accession number:20214811239916
Title:Design and experiment of banana straw crushing and returning machine with anti-wrapping device supported by flailing blade
Title of translation:&#23450;&#29993;&#20992;&#38450;&#32544;&#24335;&#39321;&#34121;&#31224;&#31174;&#31881;&#30862;&#36824;&#30000;&#26426;&#35774;&#35745;&#19982;&#35797;&#39564;
Authors:Li, Yue (1); Guo, Chaofan (1); Yao, Deyu (1); He, Ningbo (1); Zhang, Xirui (1); Wu, Zihan (1); Li, Yuan (2)
Author affiliation:(1) Mechanical and Electrical Engineering, Hainan University, Haikou; 570228, China; (2) Institute of Scientific and Technical Information, Chinese Academy of Tropical Agricultural Sciences, Haikou; 570228, China
Corresponding author:Zhang, Xirui(zhangxirui_999@sina.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:18
Issue date:September 15, 2021
Publication year:2021
Pages:11-19
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">Banana straw is usually broken into pieces to degrade naturally in the farmland. However, severe entanglement of knife roller easily causes the wear of blades, leading to a short service life and low crushing efficiency in the conventional banana straw-crushing and returning machine. A great challenge has also been posed on the effective coordination of fixed knives during operation, especially in the case of high toughness after the aging of banana straw. Therefore, this study aims to improve the smashing rate of banana straw up to the standard requirement, thereby avoiding the winding of banana straw in pulverizers. An anti-wrapping device with a fixed flailing knife was also designed to reduce the entanglement for the banana straw-crushing and returning machine. Specifically, the movable and fixed knife was effectively coordinated in the machine. Three-point support was also formed using the crushing fixed knife and the Y-shaped flailing knife in high-speed crushing operation for the banana straw. As such, the highly efficient straw-crushing was realized to avoid straw entanglement. Among them, the Y-shaped flailing knife was composed of two L-shaped blades combined with a Y-shaped flailing knife and a flail. A systematic investigation was made on the optimization of structural parameters for the key components of crushing, the arrangement and combination of fixed knives, as well as the force analysis of banana straw during crushing. Correspondingly, the main test factors were determined as the forward speed of the returning machine, the speed of the crushing knife roller, and the bending angle of the Y-shaped flailing knife. A three-level three-factor orthogonal field test was then carried out, where the evaluation indicators were set as the crushing qualification rate of banana straw, and the unevenness of throwing. An optimal parameter combination was achieved, where the forward speed was 1.85 m/s, the knife roller speed was 1 500 r/min, and the bending angle of the Y-shaped flailing knife was 140&deg;. In this case, the crushing qualification rate of banana straw was 95.1%, and the unevenness of throwing was 14.6%, indicating suitable for the actual situation of banana straw crushing. A comparison test was also conducted to verify the performance of the improved pulverizer. It was found that the qualified rate of straw smashing increased by 1.7 percentage points in the fixed-blade anti-wrapping banana straw crushing and returning machine, where the anti-wrapping device performed better. Consequently, the anti-wrapped banana straw crushing and returning machine with a fixed flailing knife can be expected to realize the sliding cooperation of the flailing and fixed knife for a better crushing effect, thereby reducing the entanglement of crushing knife roller. As such, the higher squeezing force of the cutter on the straw greatly contributed to effectively improving the crushing performance under the optimal operation requirements in the southern banana areas. The finding can provide strong technical support to the straw crushing and returning to the field in the banana areas.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:32
Main heading:Crushing
Controlled terms:Fruits - Rollers (machine components) - Speed - Straw - Structural optimization
Uncontrolled terms:Anti-wrapping - Banana stalk - Bending angle - Crushing and returning machine - Fixed blade - Flailing blade - Forward speed - High toughness - Test - Y-shaped
Classification code:601.2  Machine Components - 821.4  Agricultural Products - 821.5  Agricultural Wastes - 921.5  Optimization Techniques
Numerical data indexing:Angular velocity 8.35E+00rad/s, Percentage 1.46E+01%, Percentage 9.51E+01%, Velocity 1.85E+00m/s
DOI:10.11975/j.issn.1002-6819.2021.18.002

Database:Compendex
Compilation and indexing terms, Copyright 2022 Elsevier Inc.

<RECORD 15>

Accession number:20214811239899
Title:Model for popularizing "conversion of biogas to toilet" and its application in Sichuan Province of China
Title of translation:"&#27836;&#25913;&#21397;"&#25512;&#24191;&#27169;&#24335;&#30340;&#23454;&#29616;&#21450;&#20854;&#22312;&#22235;&#24029;&#30465;&#30340;&#24212;&#29992;
Authors:Zhou, Kun (1); Ran, Yi (2); Wu, Jin (2); He, Li (2); Kong, Chuixue (2); Xiang, Zhengyi (3); Gao, Zhifei (3); Mao, Jing (3)
Author affiliation:(1) College of Management, Sichuan Agriculture University, Chengdu; 610030, China; (2) Biogas Institute of Ministry of Agriculture and Rural Affairs, Chengdu; 610041, China; (3) Sichuan Provincial Department of Agriculture and Rural Affairs, Chengdu; 610041, China
Corresponding author:Ran, Yi(ranyi@caas.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:18
Issue date:September 15, 2021
Publication year:2021
Pages:273-280
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">Household biogas has widely been one of the most important strategic energy construction to greatly improve the living environment, resource recycling, and energy efficiency in most rural areas of China. However, a large number of biogas digesters have been idle and abandoned in recent years, due mainly to the high maintenance cost of biogas projects against the adjustment of agricultural industrial structure, particularly with the migration of labor force. It is highly urgent to improve the use efficiency of biogas for clean energy in practical application. Alternatively, the rural toilet revolution is an important link to promote the rural living environment and green development in Rural Revitalization. Toilet as one of the symbols of civilization can be used to improve the health level of rural residents. The technical mode of "Conversion of biogas to toilet" can be selected to connect the biogas digester with household sanitary toilet. In this study, a detailed analysis was made on the realistic background, policy arrangement, and main technical modes of "biogas toilet transformation". The typical areas of "biogas toilet transformation" in Sichuan Province were selected to popularize from the promotion mode, implementation, benefit analysis, as well as management and protection mechanism. Most technical modes of biogas conversion were the promotion modes of biogas digesters and toilets to jointly convert toilets or build additional sewage treatment tanks. These modes were effectively utilized biogas digesters for household life and production wastes, particularly for the active role of energy conservation, emission reduction, and environmental improvement. The cost analysis showed that the construction fund of "Conversion of biogas to toilet" reduced by half than before. "Three-grille mode septic tanks" was utilized to effectively revitalize the stock assets of rural household biogas digesters, while realizing the treatment and resource utilization of toilet feces, providing complementary ways of toilet transformation for the rural toilet revolution. Ecological benefits showed that biogas digesters in Sichuan Province indirectly reduced carbon dioxide emissions by about 1.43-2.0 t CO<inf>2</inf> per household. There was also relatively diverse implementation, management, and protection of "technical mode of changing reform of biogas to toilets". The most commonly-used way was found in most areas: "the government as the main investor and manager, social investment promotion and construction, unified management of village collective economic organizations, and contribution and contribution of farmers". Finally, four suggestions were proposed during this time to establish 1) A diversified investment mechanism for rural "reform of biogas to toilet", 2) An integrated promotion mechanism for technological innovation, 3) A new resource utilization model, and 4) A long-term follow-up management and protection mechanism of rural "reform of biogas to toilet". This finding can provide effective support to promote the "reform of biogas to toilet" mode in most rural areas.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:31
Main heading:Economic analysis
Controlled terms:Biogas - Carbon dioxide - Cost benefit analysis - Costs - Emission control - Energy efficiency - Global warming - Rural areas
Uncontrolled terms:Biogas digesters - Case analysis - Conversion of biogas to toilet - Economics analysis - ITS applications - Living environment - Management mechanisms - Protection mechanisms - Resources utilizations - Sichuan province
Classification code:443.1  Atmospheric Properties - 451.2  Air Pollution Control - 522  Gas Fuels - 525.2  Energy Conservation - 804.2  Inorganic Compounds - 821.5  Agricultural Wastes - 911  Cost and Value Engineering; Industrial Economics - 911.2  Industrial Economics - 912.2  Management
DOI:10.11975/j.issn.1002-6819.2021.18.031

Database:Compendex
Compilation and indexing terms, Copyright 2022 Elsevier Inc.

<RECORD 16>

Accession number:20214811239956
Title:High-precision extraction method for maize planting information based on UAV RGB images
Title of translation:&#22522;&#20110;&#26080;&#20154;&#26426;RGB&#24433;&#20687;&#30340;&#29577;&#31859;&#31181;&#26893;&#20449;&#24687;&#39640;&#31934;&#24230;&#25552;&#21462;&#26041;&#27861;
Authors:Zhi, Junjun (1, 2); Dong, Ya (3); Lu, Lican (4); Shi, Jinhui (1); Luo, Wenhui (1); Zhou, Yue (1); Geng, Tao (1); Xia, Jingxia (1); Jia, Cai (1)
Author affiliation:(1) School of Geography and Tourism, Anhui Normal University, Wuhu; 241002, China; (2) Key Laboratory of Earth Surface Processes and Regional Response in the Yangtze-Huaihe River Basin, Wuhu; 241002, China; (3) College of Environmental and Resources Sciences, Zhejiang Forestry University, Hangzhou; 311300, China; (4) Natural Resources and Planning Bureau of Taihu County, Anqing; 246400, China
Corresponding author:Jia, Cai(jc1986@ahnu.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:18
Issue date:September 15, 2021
Publication year:2021
Pages:48-54
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">Ultra-high-resolution aerial images obtained from Unmanned Aerial Vehicles (UAVs) have widely been used to extract crop planting information in recent years. However, some high-resolution multispectral or hyperspectral images were usually costly and time-consuming for data processing. Therefore, it is very necessary to effectively use easily accessible and low-cost high-resolution RGB images, particularly to eliminate the common noises (e.g., shadows and bare land) for a better extraction accuracy of crop planting. In this study, a high-precision extraction method was proposed to obtain the maize planting information using 1.8 cm resolution UAV aerial orthophotos (i.e., RGB images). The experimental maize farm was located in Southeast Africa, where images were taken at noon during the maize growing season. The classification features were also selected from the aspects of the spectrum, color space, and image texture. Then, five types of classification were selected to extract maize planting information, including Bayes, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) algorithms). Firstly, an object-oriented interpreting platform (eCognition9.0 software) was selected to calculate the space transform of Hue, Saturation, and Intensity (HSI) color, eight types of RGB texture, and five vegetation indices, including the normalized green-red difference index, red-green ratio index, vegetation color verification index, visible-band difference vegetation index, and excess green vegetation index. Then, three types of feature space were constructed: 1) The first feature space was composed of three sub-feature spaces, i.e., vegetation indices, HSI color space features, and RGB image texture features; 2) The second feature space was composed of four sub-feature spaces, where three types of features were combined (i.e., vegetation indices, HSI color space, and RGB image texture) in pairs or total; 3) The third feature space was composed of the most optimal factors, where the dimension reduction was performed on the combination of all three types of features using RF. Subsequently, the RGB images were classified into three land-use types, including maize, bare land, and shadow. Bayes, KNN, SVM, DT, and RFs models were finally selected for the supervised classification with error matrix. The results showed that the optimal classification accuracy was obtained using neither a single feature nor all three types of features in total. More importantly, a combination of features was usually achieved higher accuracy than that of a single feature. Specifically, the best choice was the combination of HSI color and RGB image texture features using the RF, particularly with the total highest accuracy of 86.2% and a Kappa coefficient of 0.793. Additionally, the dimension reduction of features using RF models was neither significantly improved nor reduced classification accuracy (except for the SVM). However, the factors retained from the feature dimension reduction were easily explained suitable for the actual background and meaning. Furthermore, both classification efficiency and stability were improved greatly during this time. The finding can provide a specific solution for the high-precision extraction of crop planting information using UAV RGB images.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:30
Main heading:Remote sensing
Controlled terms:Antennas - Classification (of information) - Color - Crops - Data handling - Data mining - Decision trees - Extraction - Feature extraction - Grain (agricultural product)  - Image classification - Image texture - Land use - Nearest neighbor search - Object oriented programming - Spectroscopy - Support vector machines - Textures - Unmanned aerial vehicles (UAV) - Vectors  - Vegetation
Uncontrolled terms:Colour spaces - Feature space - High-precision - Maize - Plantings - Remote-sensing - RGB images - Support vectors machine - Texture features - Vegetation index
Classification code:403  Urban and Regional Planning and Development - 652.1  Aircraft, General - 716.1  Information Theory and Signal Processing - 723  Computer Software, Data Handling and Applications - 723.1  Computer Programming - 723.2  Data Processing and Image Processing - 741.1  Light/Optics - 802.3  Chemical Operations - 821.4  Agricultural Products - 903.1  Information Sources and Analysis - 921.1  Algebra - 921.4  Combinatorial Mathematics, Includes Graph Theory, Set Theory - 921.5  Optimization Techniques - 961  Systems Science
Numerical data indexing:Percentage 8.62E+01%, Size 1.80E-02m
DOI:10.11975/j.issn.1002-6819.2021.18.006

Database:Compendex
Compilation and indexing terms, Copyright 2022 Elsevier Inc.

<RECORD 17>

Accession number:20214811239975
Title:Effects of waterlogging after anthesis on the grain filling characteristics of winter wheat with different waterlogging tolerances
Title of translation:&#33457;&#21518;&#28173;&#27700;&#23545;&#19981;&#21516;&#32784;&#28173;&#22411;&#20908;&#23567;&#40614;&#31869;&#31890;&#28748;&#27974;&#29305;&#24615;&#30340;&#24433;&#21709;
Authors:Wu, Qixia (1, 2); Tan, Jinghong (1, 2); Zhu, Jianqiang (1, 2); Wang, Wei (1, 2); Han, Rui (1, 2); Zou, Juan (3)
Author affiliation:(1) Engineering Research Center of Ecology and Agricultural Use of Wetland, Ministry of Education, Jingzhou; 434025, China; (2) College of Agriculture, Yangtze University, Jingzhou; 434025, China; (3) Institute of Food Crops, Hubei Academy of Agricultural Sciences, Wuhan; 430064, China
Corresponding author:Zou, Juan(zoujuan1010@163.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:18
Issue date:September 15, 2021
Publication year:2021
Pages:74-81
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">Frequent occurrence of waterlogging has posed a great threat to the production of the crops in the middle and late stages of winter wheat growth in the Jianghan Plain, even the middle and lower reaches of the Yangtze River in China. However, the impact of waterlogging on the grain filling process is still unclear during this time. Taking Zhengmai 9023 (tolerant genotype) and Yangmai 20 (sensitive genotype) as research objects, this study aims to explore the effects of water logging after anthesis on grain filling of winter wheat under different tolerances. A systematic experiment was conducted under 5, 9, 13, and 17 d of waterlogging duration after anthesis of winter wheat in the test-pit with a controllable irrigation and drainage system. The soil moisture was kept at 90% field capacity in the waterlogging treatments. Meanwhile, the treatment with soil moisture at 70%-80% field capacity was used as a control. The grain filling was firstly simulated for two varieties of wheat under waterlogging environment stress using the Richard model. Subsequently, the yield component parameters were quantitatively analyzed the dynamic influence on grain filling, further to explore the influence process after anthesis waterlogging on winter wheat yield. The results showed that the waterlogging for 5, 9, 13, and 17 d after anthesis reduced the yield of Zhengmai 9023 (tolerant genotype) by 10.84%, 19.51%, 25.93%, 36.52% and Yangmai 20 (sensitive genotype) by 14.25%, 25.84%, 37.26%, 47.84%, respectively. The main reason was attributed to the decrease of thousand-grain mass. When waterlogging increased by 1 d after anthesis, the thousand-grain mass of Zhengmai 9023 (tolerance genotype) and Yangmai 20 (sensitive genotype) decreased by 0.961 and 0.996 g, respectively. The Richards equation presented better to simulate the grain filling of waterlogged winter wheat after anthesis. Specifically, the determination coefficients of the fitting equation were all above 0.99. Furthermore, there was a different influence mechanism of waterlogging after anthesis on the grain filling of wheat under different waterlogging tolerance. In waterlogging-tolerant wheat, the waterlogging was greatly contributed to shortening significantly the active days of the grain filling after anthesis, and specially shortened significantly the duration days in grain-filling fast increase period and grain-filling slowly increase period. In the waterlogging-sensitive wheat, the waterlogging after anthesis was mainly contributed to significantly reducing the filling rate in three periods of grain filling. Specifically, the waterlogging increased by 1 d after anthesis, and the grain filling active days of Zhengmai 9023 was shortened by 0.827 d, among which the duration days in grain-filling fast increase period and grain-filling slowly increase period was shortened by 0.492 and 0.963 d, respectively. Correspondingly, waterlogging increased by 1 d after anthesis, the maximum grain-filling rate per kernel of Yangmai 20 decreased by 0.046 mg/d, and the mean grain-filling rate per kernel decreased by 0.032 mg/d, the grain-filling rate per kernel in grain-filling pyramid period, grain-filling fast increase period and grain-filling slowly increase the period of winter wheat decreased by 0.011, 0.040 and 0.010 mg/d, respectively. The finding can provide strong theoretical support for the prevention and control of waterlogging disasters in winter wheat.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:36
Main heading:Crops
Controlled terms:Filling - Fits and tolerances - Grain (agricultural product) - Irrigation - Soil moisture - Solute transport - Yield stress
Uncontrolled terms:Characteristics parameters - Field capacity - Grain filling - Grain mass - Grain-filling characteristic parameter - Grain-filling rate - Richards models - Waterlogging stress - Winter wheat - Yield
Classification code:483.1  Soils and Soil Mechanics - 691.2  Materials Handling Methods - 821.3  Agricultural Methods - 821.4  Agricultural Products - 931  Classical Physics; Quantum Theory; Relativity - 951  Materials Science
Numerical data indexing:Mass 1.00E-08kg, Mass 3.20E-08kg, Mass 4.00E-08kg, Mass 4.60E-08kg, Mass 9.61E-04kg, Mass 9.96E-04kg, Percentage 1.084E+01%, Percentage 1.425E+01%, Percentage 1.951E+01%, Percentage 2.584E+01%, Percentage 2.593E+01%, Percentage 3.652E+01%, Percentage 3.726E+01%, Percentage 4.784E+01%, Percentage 7.00E+01% to 8.00E+01%, Percentage 9.00E+01%
DOI:10.11975/j.issn.1002-6819.2021.18.009

Database:Compendex
Compilation and indexing terms, Copyright 2022 Elsevier Inc.

<RECORD 18>

Accession number:20214811239900
Title:Field weed recognition based on improved DenseNet
Title of translation:&#22522;&#20110;&#25913;&#36827;DenseNet&#30340;&#30000;&#38388;&#26434;&#33609;&#35782;&#21035;
Authors:Zhao, Hui (1); Cao, Yuhang (1); Yue, Youjun (1); Wang, Hongjun (1)
Author affiliation:(1) School of Electrical and Electronic Engineering, Tianjin University of Technology, Tianjin Key Laboratory of Complex System Control Theory and Application, Tianjin; 300384, China
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:18
Issue date:September 15, 2021
Publication year:2021
Pages:136-142
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">Accurate and rapid acquisition of crop and weed category information has been one of the most important steps for automatic weeding operations. In this research, a weed recognition model was proposed using improved DenseNet, particularly for the efficient and accurate identification of weeds in crop fields under complex environments. Firstly, data augmentation was utilized to expand the number of images for the collected crop and weed pictures, thereby increasing the diversity of data, but avoiding network learning irrelevant features, and finally enhancing the recognition ability of the model. Secondly, Efficient Channel Attention (ECA) was introduced into the DenseNet-121 network after each convolutional layer. As such, the accuracy of weed recognition was improved to effectively focus the attention on the weeds in the main part of images, where the weight of important features increased further to strengthen the weed features, but to suppress the extraction of background features. At the same time, DropBlock regularization was also added after each DenseBlock block, further to randomly hide some feature maps and noise. Correspondingly, the generalization, robustness, and adaptability of the model were improved to identify different types of weeds. Finally, taking maize seedlings and six types of associated weeds in natural environments as samples, a comparison test was performed on the test set using VggNet-16, ResNet-50, and the unimproved DenseNet-121 model, where the batch size was 64, and the initial learning rate was 0.01. More importantly, an Stochastic Gradient Descent (SGD) optimizer was used to train the CNN model, and the batch size was set to 64, the initial learning rate was set to 0.01, and the VggNet-16, ResNet-50 and the unimproved DenseNet-121 model was compared and tested on the test set. The results show that the improved DenseNet model presented the best performance, where the model size was 26.55 MB, the single image took 0.23 s, and the average recognition accuracy reached 98.63%, increased by 2.09 percentage points before the improvement. It infers that the overall performance of improved DenseNet-121 was significantly higher than that of VggNet-16 and ResNet-50. Gradient-weighted Class Activation Mapping (Grad-CAM) was also used to visualize the heat map for the subsequent comparison. The improved DenseNet decision was obtained, where the important weight position of classification was more focused on the main part of weeds than before. Specifically, the category judgment probability was 0.99, significantly higher than that of the unimproved model, further verifying the effectiveness of the improved model. Consequently, the DenseNet network with ECA attention and DropBlock regularization can widely be expected to improve the recognition accuracy and the generalization of the model, further to ensure the efficient and accurate recognition of weeds in complex environments. The findings can provide a strong reference for the accurate identification of other crops and associated weeds. The versatility of the model in weed identification can also be improved for the technical development of intelligent weeding robots.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:32
Main heading:Crops
Controlled terms:Convolution - Convolutional neural networks - Gradient methods - Image enhancement - Image recognition - Learning algorithms - Learning systems - Optimization - Stochastic models - Stochastic systems
Uncontrolled terms:Attention mechanisms - Complex environments - Convolutional neural network - Dropblock - Efficient channel attention mechanism - Efficient channels - Field weed - Generalisation - Regularisation - Weed recognition
Classification code:716.1  Information Theory and Signal Processing - 723.4.2  Machine Learning - 731.1  Control Systems - 821.4  Agricultural Products - 921.5  Optimization Techniques - 921.6  Numerical Methods - 922.1  Probability Theory - 961  Systems Science
Numerical data indexing:Percentage 9.863E+01%, Time 2.30E-01s
DOI:10.11975/j.issn.1002-6819.2021.18.016

Database:Compendex
Compilation and indexing terms, Copyright 2022 Elsevier Inc.

<RECORD 19>

Accession number:20214811239913
Title:Blueberry maturity recognition method based on improved YOLOv4-Tiny
Title of translation:&#22522;&#20110;&#25913;&#36827;YOLOv4-Tiny&#30340;&#34013;&#33683;&#25104;&#29087;&#24230;&#35782;&#21035;&#26041;&#27861;
Authors:Wang, Lishu (1); Qin, Mingxia (1); Lei, Jieya (1); Wang, Xiaofei (1); Tan, Kezhu (1)
Author affiliation:(1) Institute of Electrical and Information, Northeast Agricultural University, Harbin; 150030, China
Corresponding author:Tan, Kezhu(kztan@neau.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:18
Issue date:September 15, 2021
Publication year:2021
Pages:170-178
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">The accurate identification of blueberry fruit maturity is very important for modern automatic picking and early yield estimation. To realize the accurate and rapid identification of blueberry fruit in the natural environment, by improving the structure of YOLOv4-Tiny network, a target detection network with attention module (I-YOLOv4-Tiny) was proposed. The detection network used CSPDarknet53-Tiny network model as the backbone network, and the convolution block attention module (CBAM) was added to the feature pyramid network (FPN) model. Feature compression, weight generation and reweighting were carried out on the feature channel dimension and feature space dimension of the target detection network, The two dimensions of channel attention and spatial attention selectively integrated the deep and shallow features. High order features guided low-order features for channel attention acquisition, and low-order features reversed guide high-order features for spatial attention screening, which could improve the feature extraction ability of network structure without significantly increasing the amount of calculation and parameters, and realized the real-time detection performance of network structure, the correlation of features between different channels was learned by weight allocation of features of each channel, and the transmission of deep information of network structure was strengthened, to reduce the interference of complex background on target recognition. Moreover, the detection network has fewer network layers and low memory consumption, to significantly improve the accuracy and speed of blueberry fruit detection. The performance evaluation and comparative test results of the research recognition method showed that the Mean Average Precision (mAP) of the trained I-YOLOv4-Tiny target detection network under the verification set was 97.30%, which could effectively use the color images in the natural environment to identify blueberry fruits and detect fruit maturity. The average accuracy and F1 score of I-YOLOv4-Tiny detection network were 97.30% and 96.79% respectively, which were 2.58 percentage points and 2.13 percentage points higher than that of YOLOv4-Tiny target detection network respectively. In terms of the memory occupied by the network structure, I-YOLOv4-Tiny was 1.05 M larger than that of YOLOv4-Tiny, and the detection time was 5.723 ms, which was only 0.078 ms more than that of YOLOv4-Tiny target detection network, which did not affect the real-time detection, However, many indicators have been improved significantly. Compared with I-YOLOv4-Tiny, YOLOv4-Tiny, YOLOv4, SSD-MobileNet and Faster R-CNN target detection networks in different scenes, the average accuracy of I-YOLOv4-Tiny target detection network was the highest, reaching 96.24%, 1.51 percentage points higher than YOLOv4-Tiny, 4.84 percentage points higher than Faster R-CNN, 1.54 percentage points higher than YOLOv4 and 10.74 percentage points higher than SSD-MobileNet. In terms of network structure size, this study was less than one tenth of the size of YOLOv4 network structure, only 24.20 M. In terms of the detection of three blueberries with different maturity, the I-YOLOv4-Tiny target detection network performed best, which could provide accurate positioning guidance for picking robots and early yield estimation. In this study, the target detection network I-YOLOv4-Tiny suffered more interference in the process of blueberry fruit recognition, but the average accuracy of three types blueberry fruits with different maturity was higher than 95%, of which the average accuracy of mature blueberry fruits was the highest. Due to the similar color of immature fruits and background color, the detection accuracy of immature blueberry fruits was relatively poor. It could be seen that the overall performance of the target detection network in this study was the best, which could meet the needs of recognition accuracy and speed at the same time.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:31
Main heading:Convolution
Controlled terms:Computer vision - Deep learning - Feature extraction - Fruits - Image recognition - Network layers - Object recognition
Uncontrolled terms:Blueberry - Convolutional attention block - Deep learning - Detection networks - Machine-vision - Network structures - Percentage points - Recognition methods - Target detection network - Targets detection
Classification code:461.4  Ergonomics and Human Factors Engineering - 716.1  Information Theory and Signal Processing - 723  Computer Software, Data Handling and Applications - 723.5  Computer Applications - 741.2  Vision - 821.4  Agricultural Products
Numerical data indexing:Percentage 9.50E+01%, Percentage 9.624E+01%, Percentage 9.679E+01%, Percentage 9.73E+01%, Time 5.723E-03s, Time 7.80E-05s
DOI:10.11975/j.issn.1002-6819.2021.18.020

Database:Compendex
Compilation and indexing terms, Copyright 2022 Elsevier Inc.

<RECORD 20>

Accession number:20214811239893
Title:Effects of precise temperature control on the storage quality and aroma components of fresh goji fruit
Title of translation:&#31934;&#20934;&#28201;&#24230;&#25511;&#21046;&#23545;&#26552;&#26462;&#40092;&#26524;&#36142;&#34255;&#21697;&#36136;&#21644;&#39321;&#27668;&#25104;&#20998;&#30340;&#24433;&#21709;
Authors:Zhang, Peng (1, 2); Yuan, Xingling (3); Xue, Youlin (3); Jia, Xiaoyu (1, 2); Li, Jiangkuo (1, 2)
Author affiliation:(1) Institute of Agricultural Products Preservation and Processing Technology, Tianjin Academy of Agricultural Sciences, Tianjin; 300384, China; (2) Tianjin Key Laboratory of Postharvest Physiology and Storage of Agricultural Products, Key Laboratory of Storage of Agricultural Products, inistry of Agriculture and Rural Affairs, National Engineering and Technology Research Center for Preservation of Agricultural Products (Tianjin), Tianjin; 300384, China; (3) College of Light Industry, Liaoning University, Shenyang; 110036, China
Corresponding authors:Li, Jiangkuo(lijkuo@sina.com); Li, Jiangkuo(lijkuo@sina.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:18
Issue date:September 15, 2021
Publication year:2021
Pages:322-330
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">Goji (Lycium barbarum L.) is one of the most popular medicinal plants, particularly as a typical nutritional fruit supplement rich with bioactive compounds. However, fruits browning, taste loss, and storage intolerance often occur during postharvest, because of tender and juicy tissue. Among them, the temperature is one of the most significant factors to maintain the quality and extend the shelf life of fruits. It is necessary to place fruits in a constant temperature environment after harvest since temperature fluctuations and variation tend to induce product quality deterioration. This study aims to investigate the effect of precise temperature control on storage quality and aroma components of fresh goji fruits. Three treatments of storage temperature at 0&#8451; were also set, including foam box (CK), foam box + cool storage agent (ice temperature), as well as the precise temperature control box and cool storage agent (phase temperature). The temperature was real-time recorded in different cabinets to evaluate the storage quality. Principal component analysis (PCA) was used to evaluate the aroma components of goji. Results showed that the foam box containing the self-made refrigerant performed better at ambient temperature, compared with that with the ice. Temperatures in the box of CK, ice, and phase temperature group were (0.12&plusmn;0.17), (-0.04&plusmn;0.07) and (-0.05&plusmn;0.04)&#8451;, respectively, during the whole storage period. A cold accumulator provided a cold source in the box to maintain a low-temperature environment. The temperature distribution was more uniform in the precision temperature control box, while the maintenance time of low temperature was longer than before, due to the excellent performance of heat preservation. The time of mildew and rot in the CK group, ice, and phase temperature group were 10, 20, and 40 d, respectively. At the storage of 40 d, the rot rate in the phase temperature group were 4.11%, much lower than 14.85% in the CK group. Chromatic aberration &Delta;E was lower than 3, while Brightness L value reached 34.12. The content of soluble solid, titratable acid, Vitamin C conten, carotenoid were 0.65 percentage, 0.03 percentage, 4.34 mg/100g, 2.90 mg/g higher than CK group, respectively. Correspondingly, the treatment in the phase temperature group inhibited the increase of decay rate and weight loss rate, indicating the delay of &Delta;E and L, as well as a higher soluble solid, titratable acid, VC, and carotenoid content. The principal component analysis demonstrated that the goji quality in three groups was in the order of phase temperature group, ice temperature group and CK group. Moreover, the contents of favorable aroma components, such as lycium aldehydes and terpenoids, were relatively higher in the storage under precise temperature control. Consequently, the ice and phase temperature storage was the most effective in the long-term preservation of goji, where the color change of goji was delayed to facilitate the release of volatile substances, while effectively prolonging the storage period of goji to 30 days. The finding can be greatly beneficial to guide the preservation of goji fruits.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:36
Main heading:Quality control
Controlled terms:Aberrations - Deterioration - Food storage - Fruits - Ice - Odors - Principal component analysis - Temperature control - Temperature distribution
Uncontrolled terms:Aroma components - Cool storage - Goji - Phase temperature - Precise temperature control - Principal-component analysis - Quality components - Storage periods - Storage quality - Temperature control boxes
Classification code:641.1  Thermodynamics - 694.4  Storage - 731.3  Specific Variables Control - 821.4  Agricultural Products - 822.1  Food Products Plants and Equipment - 913.3  Quality Assurance and Control - 922.2  Mathematical Statistics - 951  Materials Science
Numerical data indexing:Age 8.22E-02yr, Mass 1.00E-01kg, Mass 4.34E-06kg, Percentage 1.485E+01%, Percentage 4.11E+00%, null 2.90E+00null
DOI:10.11975/j.issn.1002-6819.2021.18.037

Database:Compendex
Compilation and indexing terms, Copyright 2022 Elsevier Inc.

<RECORD 21>

Accession number:20214811239901
Title:Radial growth repair algorithm for maize root phenotype detection
Title of translation:&#21033;&#29992;&#24452;&#21521;&#29983;&#38271;&#20462;&#22797;&#31639;&#27861;&#26816;&#27979;&#29577;&#31859;&#26681;&#31995;&#34920;&#22411;
Authors:Lu, Wei (1); Shao, Yuning (1); Wang, Ling (1); Luo, Hui (1); Zhou, Ji (2); Deng, Yiming (3)
Author affiliation:(1) College of Artificial Intelligence, Nanjing Agricultural University, Nanjing; 210031, China; (2) Cambridge Crop Research Institute/National Institute of Agricultural Botany, Cambridge; CB30LE, United Kingdom; (3) College of Engineering, Michigan State University, East Lansing; MI; 48824, United States
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:18
Issue date:September 15, 2021
Publication year:2021
Pages:195-202
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">This study aims to improve the detection precision of plant root phenotyping in the images, particularly with broken roots. An algorithm of radial growing repair was proposed to apply to the evaluation of maize seed's resistance to damage. A soil culture experiment was also conducted on the Pukou campus of Nanjing Agricultural University in China every month. After that, four varieties of corn seeds were selected: Yufeng 303 (ordinary control group), Zhengdan 958 (drought resistance group), Liyu 16 (water resistance group), and Zhengdan 958 (salt resistance group). Four kinds of resistant corn seeds were planted in trough-shaped and flat containers for 14d, including ordinary, drought-resistant, water-resistant, and salt-resistant corn seeds. Subsequently, the root system was taken out to rinse the residual soil with tap water, and then placed on a solid-color background plate to level out. Prior to image acquisition, the root length and diameter were measured by a ruler, and the number of main and lateral roots was counted to record. The specific procedure of image processing was as follows. Firstly, a series of operations was used to preprocess the collected images for the extraction of root systems from complex backgrounds, such as adaptive contrast enhancement, histogram grayscale searching, and pepper-salt denoising. As such, the discrimination of root images was improved to remove the noise during image acquisition, such as reflections and water stains. Secondly, the tips of main roots in maize images were detected by training the YOLO-V3 neural network. Finally, the radial growth repair algorithm was presented, including the direction discrimination of main roots in bifurcation points, and adaptive repair in end points. These strategies greatly contributed to extracting phenotypic parameters from main and lateral roots. Maize root datasets were also selected to evaluate the practicality and accuracy of radial growth repair. The results demonstrated that the phenotypic accuracy of repaired main roots lengths and diameter increased from 83.6% and 84.4% to 97.4% and 94.8%, respectively, compared with that processed by region growth algorithm. The phenotypic parameters extracted by radial growth repair algorithm was more precise than that extracted by region growth algorithm, which indicated that radial growth repair algorithm was suitable for extraction of maize root system parameters in different stress environments. The accuracy of radial growth was improved more obviously in the salty environments and drought environments. The results in this study demonstrated that the proposed radial growth repair algorithm could improve the accuracy of root image phenotype detection and could be efficient for maize resistance evaluation, which provided reference for the rapid extraction of root system phenotype.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:24
Main heading:Extraction
Controlled terms:Drought - Genetic algorithms - Image acquisition - Image enhancement - Neural networks - Plants (botany) - Repair - Search engines - Seed - Soils
Uncontrolled terms:Adaptive repairing - Corn seeds - Environmental stress - Images processing - Maize roots - Radial-growth - Repair algorithms - Root length - Root system - Root system phenotype
Classification code:443.3  Precipitation - 444  Water Resources - 483.1  Soils and Soil Mechanics - 723  Computer Software, Data Handling and Applications - 802.3  Chemical Operations - 821.4  Agricultural Products - 913.5  Maintenance
Numerical data indexing:Percentage 8.36E+01%, Percentage 8.44E+01% to 9.74E+01%, Percentage 9.48E+01%
DOI:10.11975/j.issn.1002-6819.2021.18.023

Database:Compendex
Compilation and indexing terms, Copyright 2022 Elsevier Inc.

<RECORD 22>

Accession number:20214811239981
Title:Feedforward PID control method for the automatic leveling of an orchard high-position operation platform
Title of translation:&#26524;&#22253;&#39640;&#20301;&#20316;&#19994;&#24179;&#21488;&#33258;&#21160;&#35843;&#24179;&#21069;&#39304;PID&#25511;&#21046;&#26041;&#27861;
Authors:Lyu, Haotun (1); Hu, Zhaotian (1); Yu, Yongchao (2); Kang, Feng (2); Zheng, Yongjun (1)
Author affiliation:(1) College of Engineering, China Agricultural University, Beijing; 100083, China; (2) College of Engineering, Beijing Forestry University, Beijing; 100083, China
Corresponding author:Zheng, Yongjun(zyj@cau.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:18
Issue date:September 15, 2021
Publication year:2021
Pages:20-28
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">A high-position platform has gradually been utilized to realize the heavy tasks in an orchard, such as thinning flowers and fruit, bagging, and picking in modern mechanized agriculture. However, traditional high-position platforms cannot adjust adaptively in current orchards that are mostly concentrated in hilly and mountainous areas. Particularly, it is easy to cause stress, even the staff falling down from high place when working. Therefore, it is highly urgent to improve the automatic leveling control performance of high-position platform for higher efficiency and safety in hilly areas. In this study, an automatic control system was proposed for the self-developed leveling mechanism of high-position platform using feedforward PID control. A systematic dynamic analysis was also conducted via the electromagnet, proportional valve-controlled hydraulic cylinder, and leveling mechanism. A mathematical model was then established for the feedforward PID control in the automatic leveling system. Three parts were selected to design the model, including the current PI controller, angle PID controller, and feedforward compensator. Specifically, the current PI controller was used to reduce the internal error of the system, whereas, the feedforward compensator was used to increase the response speed with a low steady-state error. Furthermore, the angle information was first transmitted from the inclination sensor to the controller. After processing the received angle information, the feedforward PID controller output the corresponding current for the proportional valve, further to drive the pitch cylinder for the extension or retraction, and finally to tailor the angle of the platform for the standard movement. As such, the simulation of leveling control system demonstrated that the feedforward PID control presented a better performance than PID control. Firstly, the rise time of feedforward PID control was 1.26 s, while the regulation time was 2.05 s, respectively, compared with PID control. Secondly, the steady-state error was 0.020, which was lower than that of PID control. At the same time, a systematic test was also carried out to verify the high-position platform model. Correspondingly, it was found that the experimental and simulated values of rising time, adjustment time, and steady-state error differed by 0.19 s, 0.37 s, and 0.04&deg;, respectively, whereas, those of waveforms were almost the same. It infers that the mathematical model was feasible for the leveling control system of the platform in an orchard. Finally, an automatic leveling control system was built for the high-position platform to conduct static and dynamic tests. The test results showed that the leveling performance of feedforward PID control was better than that of traditional PID control. In the static leveling, the high-position platform was leveled at an angle of -4.9&deg;, -7.4&deg;, and -9.6&deg; relative to the ground. The rise time of feedforward PID control was 1.57, 1.35, and 1.47 s, while the leveling time was 3.15, 2.35, and 2.62 s, excluding the system response time. More importantly, the rise time, adjustment time, and steady-state error were shortened by 20%, 30%, and 0.6%, compared with the PID control. In the dynamic leveling, the high-position platform traveled on undulating roads at a speed of 2 km/h. The maximum error of pitch angle was -3.0&deg;, the average absolute error was 0.79&deg;, the mean square error was 0.58&deg;, and the inclination angle was stable at &plusmn;3&deg; for the workbench. Consequently, the automatic leveling control system can fully meet the operating requirements of high-position platform in hilly and mountainous areas.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:30
Main heading:Three term control systems
Controlled terms:Automation - Controllers - Cylinders (shapes) - Electric control equipment - Errors - Orchards - Proportional control systems
Uncontrolled terms:'current - Automatic - Automatic leveling controls - Hilly and mountainous areas - Leveling control systems - Leveling mechanism - Levelings - Proportional valves - Risetimes - Steady state errors
Classification code:704.2  Electric Equipment - 731  Automatic Control Principles and Applications - 731.1  Control Systems - 732.1  Control Equipment - 821.3  Agricultural Methods
Numerical data indexing:Percentage 2.00E+01%, Percentage 3.00E+01%, Percentage 6.00E-01%, Size 2.00E+03m, Time 1.26E+00s, Time 1.47E+00s, Time 1.90E-01s, Time 2.05E+00s, Time 2.62E+00s, Time 3.70E-01s
DOI:10.11975/j.issn.1002-6819.2021.18.003

Database:Compendex
Compilation and indexing terms, Copyright 2022 Elsevier Inc.

<RECORD 23>

Accession number:20214811239858
Title:Extraction algorithm of the center line of maize row in case of plants lacking
Title of translation:&#32570;&#26666;&#29577;&#31859;&#34892;&#20013;&#24515;&#32447;&#25552;&#21462;&#31639;&#27861;&#30740;&#31350;
Authors:Li, Xiangguang (1); Zhao, Wei (1); Zhao, Leilei (1)
Author affiliation:(1) Vehicle and Transportation Engineering Institute, Henan University of Science and Technology, Luoyang; 471000, China
Corresponding author:Zhao, Wei(zhaowei@haust.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:18
Issue date:September 15, 2021
Publication year:2021
Pages:203-210
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">Identification of crop centerlines has been one of the most essential links in the environmental perception, particularly for the detection of driving paths during operation for the emerging unmanned agricultural machinery at present. However, the current detection of centerlines presents a low accuracy in the extraction of lacking rows for the maize seedling. In this study, an algorithm was proposed to extract the centerlines of maize rows in the lacking seedlings. The collection date was in July 2020, and the experimental subjects were maize seedlings. The height of the seedling was 0.3-0.4 m and the seedling spacing was 0.2-0.3 m at the time of image collection. The height of the camera was 1.5 m and the pitch angle was about 30&deg;. The images of maize seedling rows were also collected in different plots of the experimental fields to ensure the universality of samples. Firstly, the range of HSV color components was limited to segment the seedlings and the background. The average time of threshold processing per frame of the image was 0.013 s. Morphological processing was utilized to fill the holes in the crop areas of denoised images. Secondly, a strip Region of Interest (ROI) was set in the horizontal position at the bottom and middle of the images. The barycenter was extracted from the seedlings contour located in the ROI as the locating points. Specifically, the upper endpoint was determined by the fixed step size in the pixel point of the first line of the image. The row area of the crop within a limited range was scanned using a straight line through the locating points and upper endpoint, where the line that crossed the most seedlings was the optimal line of target seedlings. As such, the contour feature of the seedling was strengthened, and the lack of seedling in the bottom area was filled, when the optimal line was fused with the seedling area. Because the algorithm was used to extract the crop centerline under different conditions of seedlings lacking, the optimal lines at the bottom and the middle of rows were fused with the region to fill the lacking part of the row. Finally, the dynamic ROI was set, where the fitting profile of the maximum area within the region was the target centerlines of seedling rows. The experimental results showed that the algorithm fully met the extracting requirement for the centerlines of seedlings in the field with seedling deficiency, compared with the traditional. It was also utilized to deal with the low detection rate when there was a seedling deficiency. Experimental verification was also performed on 1 190 frames of maize seedlings images for the reliability of the algorithm in the lack of seedlings. The results showed that this algorithm required a relatively small amount of computation. Specifically, the average accuracy rate was 84.2%, and the average detection time of each frame was 0.092 s, indicating a better filling effect on the maize seedlings row with different crop lacked conditions. Consequently, the improved algorithm presented strong robustness and high accuracy for the recognition rate when seedlings were lacking. The finding can provide sound theoretical support to the operation of unmanned agricultural machinery in the field environment of seedlings lacking.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:32
Main heading:Crops
Controlled terms:Agricultural machinery - Computer vision - Image denoising - Image segmentation - Location
Uncontrolled terms:Centerlines - Dynamic region - Dynamic region of interest - Images processing - Machine-vision - Maize - Maize seedlings - Optimal lines - Region-of-interest - Regions of interest
Classification code:716.1  Information Theory and Signal Processing - 723.2  Data Processing and Image Processing - 723.5  Computer Applications - 741.2  Vision - 821.1  Agricultural Machinery and Equipment - 821.4  Agricultural Products
Numerical data indexing:Percentage 8.42E+01%, Size 1.50E+00m, Size 2.00E-01m to 3.00E-01m, Size 3.00E-01m to 4.00E-01m, Time 1.30E-02s, Time 9.20E-02s
DOI:10.11975/j.issn.1002-6819.2021.18.024

Database:Compendex
Compilation and indexing terms, Copyright 2022 Elsevier Inc.

<RECORD 24>

Accession number:20214811239979
Title:Early detection of moldy apple core using symmetrized dot pattern images of vibro-acoustic signals
Title of translation:&#22522;&#20110;&#22768;&#25391;&#20449;&#21495;&#23545;&#31216;&#26497;&#22352;&#26631;&#22270;&#20687;&#30340;&#33529;&#26524;&#38665;&#24515;&#30149;&#26089;&#26399;&#26816;&#27979;
Authors:Zhao, Kang (1); Zha, Zhihua (1); Li, He (1); Wu, Jie (1, 2, 3)
Author affiliation:(1) College of Mechanical and Electrical Engineering, Shihezi University, Shihezi; 832003, China; (2) Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi; 832003, China; (3) Research Center of Agricultural Mechanization for Economic Crop in Oasis, Ministry of Education, Shihezi; 832003, China
Corresponding authors:Wu, Jie(wjie_mac@shzu.edu.cn); Wu, Jie(wjie_mac@shzu.edu.cn); Wu, Jie(wjie_mac@shzu.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:18
Issue date:September 15, 2021
Publication year:2021
Pages:290-298
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">Moldy core in apple (a common internal disease infected by fungal) has widely resulted in quality loss and food safety for fruits and by-products, such as concentrated juice and cider. However, the slightly infected apples are difficult to be picked out, because there are no visible symptoms in the fruit appearance at present. Traditional manual inspection is highly destructive, subjective, and time-consuming, due mainly to require cutting apples into halves for the visual evaluation in the presence or absence of internal defects. In addition, little research has been focused on the early detection of internal disease in fruits. Consequently, there is an urgent demand to develop the nondestructive detection system for the early detection of moldy apple core. Therefore, it is an urgent demand to develop a nondestructive early detection for the apples with a moldy core. In this study, a nondestructive vibro-acoustic setup was employed to detect the apples with slight moldy-core using two identical piezoelectric transducers. The obtained vibro-acoustic signals were transformed to the images using Symmetrized Dot Pattern (SDP) algorithm. SDP images were then used to extract the depth feature using the transfer learning of three Convolutional Neural Networks (CNNs), including AlexNet, VGG16, and ResNet50. The extracted features were fed to train the Support Vector Machine (SVM) classifier, finally to identify the slightly moldy apple core (moldy-core degree less than 7%). Specifically, the largest difference of shape feature was found among the SDP images of sound and moldy-core apples, when the time lag coefficient l was 25 and the angular gain factor was 50&ordm;. As such, various SVM classification models were constructed in this case using the different CNN structures and kernel functions. Correspondingly, the ResNet50-SVM-gaus model performed the higher classification in the training set with less training time and the number of parameters, compared with the AlexNet-SVM-line model. Subsequently, the super parameters were selected to optimize the network structure in the trained ResNet50-SVM-gaus model, including learning rate and epochs. Specifically, the classification accuracy of model was improved from 91.38% to 99.63%. Furthermore, the total classification accuracy of the model in the test set reached 96.97% using an imbalanced dataset with the sound to diseased apples of 10:1. Meanwhile, the Stable Precision (SP), Stable Recall (SR), Stable F1-score (SF), Kappa Coefficient (KC), and Matthews Correlation Coefficient (MCC) of the ResNet50-SVM-gaus model were 80.19%, 90.36%, 86.21%, 82.54%, and 82.68%, respectively. These indicated that the ResNet50-SVM-gaus model achieved the accurate classification for the early detection of apples with a slight moldy core. Therefore, the ResNet50-SVM-gaus model can be expected to enhance the classification performance for the minority variety of moldy-core apples in the early stage. Consequently, the vibro-acoustic approach combined with SDP demonstrated a promising potential to early detect fungal diseases in moldy apple core. The finding can provide the theoretical reference for the early detection of diseases inside the fruits.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:36
Main heading:Support vector machines
Controlled terms:Acoustic waves - Classification (of information) - Convolution - Fruits - Nondestructive examination - Transducers
Uncontrolled terms:Acoustic signals - Convolution neural network - Early detection - Moldy apple core - Non destructive - Pattern images - Support vectors machine - Symmetrized dot patterns - Vibroacoustics - Vibroacoustics methods
Classification code:716.1  Information Theory and Signal Processing - 723  Computer Software, Data Handling and Applications - 751.1  Acoustic Waves - 821.4  Agricultural Products - 903.1  Information Sources and Analysis
Numerical data indexing:Percentage 7.00E+00%, Percentage 8.019E+01%, Percentage 8.254E+01%, Percentage 8.268E+01%, Percentage 8.621E+01%, Percentage 9.036E+01%, Percentage 9.138E+01% to 9.963E+01%, Percentage 9.697E+01%
DOI:10.11975/j.issn.1002-6819.2021.18.033
Funding details:  Number: 4061,EP/P505119/1, Acronym: EPSRC, Sponsor: Engineering and Physical Sciences Research Council;Number: EP/P505739/1, Acronym: FEDER, Sponsor: European Regional Development Fund;
Funding text:This work was supported by the UK Engineering Physical Sciences Research Council (EPSRC DTG: EP/P505119/1) and the ISCE-Chem project (No. 4061) which was co-financed by the European Regional Development Fund (ERDF) through the INTERREGIV A France (Channel) - England cross-border cooperation Programme. RMP acknowledges the EPSRC for a Ph.D. Plus Fellowship (EPSRC: EP/P505739/1). PQ was an undergraduate Chemistry student during this project. PAC acknowledges the EPSRC for doctoral training funding.

Database:Compendex
Compilation and indexing terms, Copyright 2022 Elsevier Inc.

<RECORD 25>

Accession number:20214811239871
Title:Preliminary study on active metabolites of Lactobacillus plantarum against Aspergillus flavus
Title of translation:&#26893;&#29289;&#20083;&#26438;&#33740;&#25233;&#21046;&#40644;&#26354;&#38665;&#27963;&#24615;&#20195;&#35874;&#29289;&#30340;&#21021;&#27493;&#30740;&#31350;
Authors:Chen, Jiamin (1); Zhang, Wenyi (1); Li, Kangning (1); Menghe, Bilige (1)
Author affiliation:(1) Key Laboratory of Dairy Biotechnology and Engineering, Ministry of Education, Key Laboratory of Dairy Products Processing, Ministry of Agriculture and Rural Affairs, Inner Mongolia Agricultural University, Hohhot; 010018, China
Corresponding author:Menghe, Bilige(mhblg@163.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:18
Issue date:September 15, 2021
Publication year:2021
Pages:315-321
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">Lactic acid bacteria have a long history of safe use in food. Among them, the antifungal properties of Lactobacillus plantarum are particularly interesting. Numerous studies have pointed out that the natural compounds produced by L. plantarum can significantly inhibit the growth of fungi and Aspergillus flavus, spores, thereby degrading aflatoxin, especially for the longer shelf life of a variety of food. Moreover, the acid is attributed to the antifungal activity of L. plantarum, where organic acid is the most important acidic metabolite of L. plantarum. In addition, acetic acid is the most effective antifungal metabolite among the many organic acids produced by L. plantarum. At the same time, there is the highest content of lactic acid, acetic acid, and propionic acid in the L. plantarum fermentation supernatant organic acid. However, the inhibitory effect of L. plantarum on A.flavus is mostly focused on the antifungal substances produced by a single strain in recent years. Only a few studies were on the differential metabolites of multiple strains with different antifungal activities. Moreover, standard products are still lacking to verify the activity of inhibiting A.flavus on the quantitative amount of lactic acid, acetic acid, and propionic acid in the supernatant of L. plantarum. Therefore, the purpose of this research is to find the small molecular metabolites that inhibit A.flavus through the comparison between multiple strains, with emphasis on the practical application of L. plantarum. 16 strains of L. plantarum with different antifungal activities were selected, 8 strains of which presented strong antifungal activity (Strong group) and the rest was weak (Weak group). Ultra-high performance liquid chromatography-quadrupole flight Time mass spectrometry combined with PCA (Principal Components Analysis) and OPLS-DA (Orthogonal Partial Least Square-Discriminate Analysis) was utilized to explore the different metabolites between strains with different antifungal activities, further to determine the substances with main antifungal effects. At the same time, standard products of 1, 2, and 3 times the concentration were used to verify the inhibit activity of A.flavus, according to the quantitative determination for the content of lactic acid, acetic acid, and propionic acid in the fermentation supernatant of L. plantarum in the early stage of the laboratory. The results showed that there were significantly different metabolites in the two groups of L. plantarum fermentation supernatants (P&lt;0.05). Database comparison demonstrated 30 significantly different metabolites, including imidazoleacetic acid, tyrosine were identified (P&lt;0.05). Among them, acidic substances were relatively different, such as organic and fatty acids. Correspondingly, the acidic substance in the supernatant was attributed to the main antifungal effect, whereas the antifungal activity depended on the acidic environment of low pH value. Lactic acid, acetic acid, and propionic acid presented excellent antifungal activities among organic acids produced by L. plantarum. Specifically, the antifungal activity was ranked in order of propionic acid&gt;acetic acid&gt;lactic acid. Comprehensively, organic and fatty acids are widely expected to be the main antifungal substances, where the L. plantarum antifungal activity increased with the concentration of the acidic substances.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:32
Main heading:Metabolites
Controlled terms:Acetic acid - Agriculture - Aspergillus - Bacilli - Biomolecules - Fermentation - High performance liquid chromatography - Lactic acid - Mass spectrometry - pH  - Principal component analysis - Propionic acid - Strain
Uncontrolled terms:Agriculture products - Anti-aspergillus flavu activity - Antifungal activities - Aspergillus flavus - L. plantarum - Lactobacillus plantarum - Metabolomics - Non-targeted - Non-targeted metabolomic - Supernatants
Classification code:461.9  Biology - 801  Chemistry - 801.1  Chemistry, General - 801.2  Biochemistry - 804.1  Organic Compounds - 821  Agricultural Equipment and Methods; Vegetation and Pest Control - 922.2  Mathematical Statistics - 951  Materials Science
DOI:10.11975/j.issn.1002-6819.2021.18.036

Database:Compendex
Compilation and indexing terms, Copyright 2022 Elsevier Inc.

<RECORD 26>

Accession number:20214811239955
Title:Optimization of the process for biosynthesis nano-TiO2 from mango leaf extract and its antimicrobial properties
Title of translation:&#33426;&#26524;&#21494;&#25552;&#21462;&#28082;&#29983;&#29289;&#21512;&#25104;&#32435;&#31859;TiO<inf>2</inf>&#24037;&#33402;&#20248;&#21270;&#21450;&#20854;&#25239;&#33740;&#24615;&#33021;
Authors:Xu, Qinglian (1); Huang, Ruihan (1); Li, Xuanlin (1, 2); Xing, Yage (1); Shui, Yuru (1, 2); Wu, Lin (1); Yu, Jinze (3)
Author affiliation:(1) School of Food and Bioengineering, Xihua University, Chengdu; 610039, China; (2) Yibin Research Institute, Xihua University, Yibin; 644004, China; (3) National Engineering Technology Research Center for Preservation of Agricultural Products, Key Laboratory of Storage of Agricultural Products, Ministry of Agriculture and Rural Affairs, Tianjin Key Laboratory of Postharvest Physiology and Storage of Agricultural Products, Tianjin; 300384, China
Corresponding author:Xing, Yage(xingyege@163.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:18
Issue date:September 15, 2021
Publication year:2021
Pages:281-289
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">Fresh fruits and vegetables with a high content of water are easily lost to spoilage by a variety of microorganisms, resulting in short shelf life. Specifically, penicillium (P. steckii) has been the most harmful and frequent disease in postharvest storage of fruits, such as mango and citrus, which are easily infected by moldy pathogens. Fortunately, the nano-TiO<inf>2</inf> particle has widely been used to preserve fruits and vegetables, due mainly to the high chemical stability and antibacterial properties. Two reasons can be attributed to the preservation mechanism. 1) Ethylene (C<inf>2</inf>H<inf>4</inf>) under ultraviolet (UV) irradiation has normally been decomposed into carbon dioxide (CO<inf>2</inf>) and water (H<inf>2</inf>O) in the fruits and vegetables packaging, where the concentration of CO<inf>2</inf> increases, while that of C<inf>2</inf>H<inf>4</inf> decreases. As such, the respiration and ripening rate of fruits and vegetables can be effectively delayed by the gas change, thereby controlling the water loss. 2) Microorganisms are composed of organic compounds, such as bacteria and fungi. Strong oxidation can denature the protein, thus inhibiting the growth of microorganisms or even killing, where Reactive Oxygen Species (ROS) has been produced by nano-TiO<inf>2</inf> under light conditions. Nevertheless, the biosynthesis of nanomaterials has attracted much more attention, with the highly demand for renewable and non-toxic chemicals in recent years. Correspondingly, the nano- TiO<inf>2</inf> biosynthesis can be assumed as a bottom-up approach, including the main reaction of reduction/oxidation without toxic chemicals involved in the synthesis process, particularly suitable for pharmacy, biomedicine, and food. In this study, nano-sized TiO<inf>2</inf> particles were prepared by biosynthesis, where the mango leaf extract was taken as the reducing agent, while metatitanic acid (TiO(OH)<inf>2</inf>) as titanium source. An investigation was also made to explore the effects of extraction times on the reduction ability of mango leaf extracts. Moreover, the Response Surface Method (RSM) in a single factor experiment was selected to optimize the biosynthesis process of nano-TiO<inf>2</inf>. The nano-TiO<inf>2</inf> particles were characterized by X-Ray Diffraction (XRD) and Scanning Electron Microscopy (SEM), together with antimicrobial properties against P. steckii. The results showed as follows. The yield of nano-TiO<inf>2</inf> increased with the extension of extraction time when extracting mango leaves. Specifically, the yield of nano-TiO<inf>2</inf> was 86.74%, when the extraction time was 30 min, which was not significantly different from 87.62% and 87.93% when the extraction time was 40 and 50 min. An optimal combination of synthesis process was achieved, where TiO(OH)<inf>2</inf> addition 0.65 g, reaction time 10.2 h, calcination time 2 h, and calcination temperature 786 &deg;C. In this case, the photoinduced degradation rate of nano-TiO<inf>2</inf> was 96.24%, and the standard deviation from the theoretical value was 0.6%. In addition, the XRD pattern demonstrated that the biosynthetic nano-TiO<inf>2</inf> was anatase type. SEM images showed that the TiO<inf>2</inf> nanoparticles obtained by biosynthesis were quasi-spherical, with the distribution of particle size in the range of 20-40 nm and fewer aggregates, but the modified nano-TiO<inf>2</inf> presented a smaller particle size and fewer aggregates, indicating the better dispersion. Furthermore, the biosynthesized TiO<inf>2</inf> nanoparticles exhibited a certain inhibitory effect on P. steckii, whereas, the modified nano-TiO<inf>2</inf> performed a better antimicrobial effect under the induction of ultraviolet (UV) light. More importantly, the modified nano-TiO<inf>2</inf> in composite coating behaved an obvious inhibitory effect on P. steckii. Consequently, the biosynthesized nano-TiO<inf>2</inf> can widely be expected to serve as the preservation of fruits and vegetables to maintain the quality and prolong the storage life. This preparation process can provide a strong theoretical reference for the synthesis of nano-TiO2 with better photoinduced antibacterial properties.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:27
Main heading:Titanium dioxide
Controlled terms:Biochemistry - Biosynthesis - Carbon dioxide - Chemical stability - Citrus fruits - Ethylene - Food safety - Food storage - Microorganisms - Quality control  - Scanning electron microscopy - Spoilage - Vegetables
Uncontrolled terms:Anti-microbial activity - Anti-microbial properties - Antibacterial properties - Extraction time - Fruit and vegetables - Leaf extracts - Nano-TiO 2 - Nano-TiO2 - Nano-titanium dioxide - Photo-induced
Classification code:461.6  Medicine and Pharmacology - 461.8  Biotechnology - 461.9  Biology - 694.4  Storage - 801  Chemistry - 801.2  Biochemistry - 802.2  Chemical Reactions - 804.1  Organic Compounds - 804.2  Inorganic Compounds - 821.4  Agricultural Products - 822.1  Food Products Plants and Equipment - 822.3  Food Products - 913.3  Quality Assurance and Control
Numerical data indexing:Inductance 2.00E+00H, Mass 6.50E-04kg, Percentage 6.00E-01%, Percentage 8.674E+01%, Percentage 8.762E+01%, Percentage 8.793E+01%, Percentage 9.624E+01%, Size 2.00E-08m to 4.00E-08m, Size 5.08E-02m, Temperature 1.059E+03K, Time 1.80E+03s, Time 2.40E+03s, Time 3.00E+03s, Time 3.672E+04s, Time 7.20E+03s
DOI:10.11975/j.issn.1002-6819.2021.18.032

Database:Compendex
Compilation and indexing terms, Copyright 2022 Elsevier Inc.

<RECORD 27>

Accession number:20214811239882
Title:Detection method of soybean pod number per plant using improved YOLOv4 algorithm
Title of translation:&#37319;&#29992;&#25913;&#36827;YOLOv4&#31639;&#27861;&#30340;&#22823;&#35910;&#21333;&#26666;&#35910;&#33626;&#25968;&#26816;&#27979;&#26041;&#27861;
Authors:Guo, Rui (1); Yu, Chongyu (1); He, Hong (1); Zhao, Yongjian (1); Yu, Hui (2); Feng, Xianzhong (2)
Author affiliation:(1) School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai; 264209, China; (2) Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun; 130102, China
Corresponding author:He, Hong(hehong@sdu.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:18
Issue date:September 15, 2021
Publication year:2021
Pages:179-187
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">Measuring pod number per plant has been one of the most important parts of the pod selection in the soybean growth period. However, traditional manual measurement is costly, time-consuming, and error-prone. Alternatively, artificial intelligence can ever-increasing be used to determine the type and quantity of each pod, thereby accurately predicting soybean yield in modern agriculture. In this study, a soybean phenotyping instrument was employed to collect the phenotype video of soybean plants, and then to process the phenotype data using the YOLOv4 dynamic object detection. The size of the initial anchor box and the complexity of image objects were also considered during the data set training and testing. Specifically, the COCO data set was selected for the prior box in the YOLOv4 model. The size of the objects varied in each category. K-means clustering was selected to adjust the size of original prior box for a higher accuracy of pod recognition. The size and position of container were calculated to test the original anchor frame suitable for pod detection. Accordingly, a total of 9 initial anchor frames were obtained. The average and maximum pooling operations of the global channel information were adopted to obtain the new weights and re-weighed the new weights to generate the output of module, in order to accurately locate pods for the better characterization ability of detection model. The improved attention mechanism module was integrated into the last layer of the backbone network in the YOLOv4 object detection. Migration learning was also utilized to pre-train the neural network for the optimal detection model in the prediction of test set. The experiment was performed on the Pytorch framework under the GPU (Nvidia GeForce RTX 2080 Ti). The parallel computing framework of CUDA10.1 and CUDNN deep neural network acceleration library were used to train the original and the improved YOLOv4 on Linux operating system. Experiment results showed that the improved model greatly improved the accuracy of pod detection. The mean average precision for all categories was 80.55%, 5.67 percentage points higher than the original. The average accuracy rate reached 84.37% after data expansion, The average prediction of the pod with two beans effectively reached 99.46%, indicating more suitable for pod detection. Consequently, the improved model can more accurately identify the most categories that the original model cannot recognize. Some errors were also corrected in the predictions for a better confidence score. In the recognition of pods on a simple background, the prediction mean average precision of the improved model reached 99.1%, 1.81 percentage points higher than the original. More importantly, the improved model presented strong generalization ability and detection performance. The data acquisition was 30-40 times the speed of traditional ones. Moreover, the soybean phenotype instruments performed better to greatly save the human and material resources using the improved model.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:30
Main heading:Forecasting
Controlled terms:Agriculture - Computer operating systems - Deep neural networks - Errors - Image recognition - K-means clustering - Object detection - Object recognition - Optimal detection - Statistical tests
Uncontrolled terms:Attention mechanisms - Data set - Detection methods - Detection models - K-means++ clustering - Percentage points - Pod detection - Pod number - Soybean - YOLOv4
Classification code:461.4  Ergonomics and Human Factors Engineering - 723.2  Data Processing and Image Processing - 821  Agricultural Equipment and Methods; Vegetation and Pest Control - 903.1  Information Sources and Analysis - 921.5  Optimization Techniques - 922.2  Mathematical Statistics
Numerical data indexing:Percentage 8.055E+01%, Percentage 8.437E+01%, Percentage 9.91E+01%, Percentage 9.946E+01%
DOI:10.11975/j.issn.1002-6819.2021.18.021

Database:Compendex
Compilation and indexing terms, Copyright 2022 Elsevier Inc.

<RECORD 28>

Accession number:20214811239959
Title:Design and experiment of the fruit-beating dropping device for chestnut harvesters
Title of translation:&#26495;&#26647;&#25910;&#33719;&#25293;&#25171;&#24335;&#33853;&#26524;&#35013;&#32622;&#35774;&#35745;&#19982;&#35797;&#39564;
Authors:Zong, Wangyuan (1, 2); Huang, Muchang (1); Xiao, Yangyi (1, 2); Li, Mao (1); Deng, Dinglin (1)
Author affiliation:(1) College of Engineering, Huazhong Agricultural University, Wuhan; 430070, China; (2) Key Laboratory of Agricultural Equipment in Mid-lower Yangtze River, Ministry of Agriculture and Rural Affairs, Wuhan; 430070, China
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:18
Issue date:September 15, 2021
Publication year:2021
Pages:1-10
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">Difficult picking is often found in the harvesting of fresh chestnut, particularly in the low efficiency and high labor cost of manual harvesting, as well as the high risk of high-altitude picking. However, only a few research focused on this field in China. In this study, a beating-type fruit dropping device was proposed for a chestnut harvester, according to the characteristics of chestnut trees and the planting mode of Chinese chestnut. The overall structure and working principle were also introduced into the design of the chestnut harvester. Two situations were included to separate the chestnut fruit from the branch. When the beating bars hit the fruit, the beating force was directly transferred to the fruit. As such, the fruit was separated from the branch, if the beating force was greater than the binding force between the fruit and the branch. When the beating bars hit the branch, an inertia force was transmitted from the branch to the fruit, where the chestnut was separated from the branch to complete the chestnut harvest, if the inertia force was greater than the binding force. A beating mechanism was also designed as a crank-rocker without quick returning, in order to ensure the overall performance of the machine, and the stability of the fruit dropping device in the process of operation. The size of the crank-rocker mechanism was also determined under the optimal conditions. A kinematic model was established for the crank-rocker mechanism, further to obtain the kinematic relationships of angular displacement, angular velocity, and angular acceleration of the rocker. A dynamic simulation was also performed on both sides of the beating device in the crank-rocker mechanism. It was found that the motion of the rocker was symmetrical in terms of the motion curve, where the swing angle of the rocker reached 60&deg; suitable for the stability requirements. Furthermore, the variation of separation force between chestnut fruit and branch was obtained at different tension angles. Specifically, the separation force decreased gradually with the increase of tension angle in the range of 0&deg;-90&deg;, where the maximum separation force was 65.24 N at 0&deg; tension angle. The collision model between the beating bars and the branch was established to determine the main factors affecting the beating force, including the speed of the motor, the length of the beating bars, and the beating angle. A three-factor three-level orthogonal test was conducted, where the materials of beating bars were selected as polyurethane, low-density polyethylene, iron sheet, and glass fiber. The results show that the maximum beating force was only 44.31N for the polyurethane, while the maximum separation force of chestnut fruit and branch was 65.24 N, indicating that the maximum beating force provided by polyurethane was less than that of chestnut fruit and branch, fail to meet the requirements of beating force for fruit picking. The maximum beating forces of iron sheet and glass fiber were 87.46 N and 94.03 N, respectively. Nevertheless, the excessive force was easy to damage chestnut branches. Fortunately, the maximum beating force of low-density polyethylene was 70.71 N, similar to that of chestnut fruit and branch (65.24 N), indicating the best beating material. In this case, the optimal combination was achieved, where the motor speed of 600 r/min, the beating bar length of 350 mm, and the tapping angle of 20&deg;. A field test of the chestnut harvesting machine was carried out to verify each area for harvesting. Several areas were selected on the chestnut tree for the harvest experiment after the positioning structure moved the fruit dropping device. The field test shows that the beating force provided by the fruit dropping device can effectively beat the chestnut fruits within 10s, where the fruit drop rate was 90.1%, while less damage to the chestnut trees. Consequently, the beating-type fruit dropping device can fully meet the harvest requirements of chestnut fruits. The finding can provide a strong reference for further research and development of chestnut harvesting machinery.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:29
Main heading:Fruits
Controlled terms:Efficiency - Harvesters - Harvesting - Kinematics - Rotation - Separation - Wages
Uncontrolled terms:Beat - Binding forces - Crank-rocker mechanism - Field test - Fruit dropping device - Glass-fibers - Harvest - Inertia force - Iron sheets - Separation force
Classification code:802.3  Chemical Operations - 821.1  Agricultural Machinery and Equipment - 821.3  Agricultural Methods - 821.4  Agricultural Products - 912.4  Personnel - 913.1  Production Engineering - 931.1  Mechanics
Numerical data indexing:Angular velocity 1.002E+01rad/s, Force 4.431E+01N, Force 6.524E+01N, Force 7.071E+01N, Force 8.746E+01N, Force 9.403E+01N, Percentage 9.01E+01%, Size 3.50E-01m, Time 1.00E+01s
DOI:10.11975/j.issn.1002-6819.2021.18.001

Database:Compendex
Compilation and indexing terms, Copyright 2022 Elsevier Inc.

<RECORD 29>

Accession number:20214811239932
Title:Effects of CO<inf>2</inf> concentration increase and phosphorus deficiency on the stomatal traits and leaf gas exchange parameters of ryegrass
Title of translation:CO<inf>2</inf>&#27987;&#24230;&#21319;&#39640;&#21644;&#30967;&#32032;&#20111;&#32570;&#23545;&#40657;&#40614;&#33609;&#27668;&#23380;&#29305;&#24449;&#21450;&#27668;&#20307;&#20132;&#25442;&#21442;&#25968;&#30340;&#24433;&#21709;
Authors:Zheng, Yunpu (1); Chang, Zhijie (1); Fan, Xiaodong (2); Zhang, Yunxin (1); Liu, Liang (1); Chen, Wenna (3); Liu, Yuanyuan (1); Hao, Lihua (1)
Author affiliation:(1) School of Water Conservancy and Hydropower, Hebei University of Engineering, Handan; 056038, China; (2) School of Water Resources and Architectural Engineering, Northwest A & F University, Yangling; 712100, China; (3) School of Landscape and Ecological Engineering, Hebei University of Engineering, Handan; 056038, China
Corresponding author:Hao, Lihua(haolihua_000@sina.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:18
Issue date:September 15, 2021
Publication year:2021
Pages:82-89
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">In order to further understand the potential mechanisms of grassland structure and function in response to soil phosphorus deficiency under future CO<inf>2</inf> concentration increase, we examined the effects of CO<inf>2</inf> concentration increase and phosphorus deficiency on the stomatal traits and leaf gas exchange parameters of annual ryegrass with environmental growth chambers, where the CO<inf>2</inf> concentration was accurately controlled at 400 &micro;mol/mol or 800 &micro;mol/mol and meanwhile these grasses were fertilized with six phosphorus levels including 0.004 mmol/L (P<inf>0.004</inf>), 0.012 mmol/L (P<inf>0.012</inf>), 0.02 mmol/L (P<inf>0.02</inf>), 0.06 mmol/L (P<inf>0.06</inf>), 0.1 mmol/L (P<inf>0.1</inf>) and 0.5 mmol/L (P<inf>0.5</inf>). The results showed that: 1) The CO<inf>2</inf> concentration increase significantly decreased the stomatal density of plants under lower phosphorus levels, but increased the stomatal density of annual ryegrass grown at higher phosphorus levels (0.1 mmol/L and 0.5 mmol/L); Meanwhile, CO<inf>2</inf> concentration increase obviously decreased the stomatal openness of annual ryegrass, and made the spatial distribution pattern of stomata more regular when plants were grown at the phosphorus level of 0.06 mmol/L. 2) The CO<inf>2</inf> concentration increase substantially enhanced the net photosynthetic rates (P<inf>n</inf>) of annual ryegrass treated with higher phosphorus levels (0.1 and 0.5 mmol/L), but reduced the P<inf>n</inf> of plants subjected to lower phosphorus levels, and thus increased the water use efficiency (WUE) of annual ryegrass at high phosphorus levels. 3) The responses of chlorophyll contents to CO<inf>2</inf> concentration increase were different among the six phosphorus levels, and CO<inf>2</inf> concentration increase substantially changed the ratio of chlorophyll a/b at higher phosphorus levels. 4) The total plant biomass and allocation between aboveground and belowground were obviously changed by phosphorus deficiency, and CO<inf>2</inf> concentration increase featured CO<inf>2</inf> fertilization effect on the aboveground biomass of annual ryegrass at higher phosphorus levels. These results suggested that the responses of stomatal traits and leaf gas exchange efficiency to CO<inf>2</inf> concentration increase were obviously asymmetry between low and high phosphorus levels. These grasses under higher phosphorus levels optimized the leaf gas exchange efficiency by increasing the stomatal density and stomatal openness as well as regulating the spatial distribution pattern of stomata, and thus plants might benefit from CO<inf>2</inf> fertilization effect under CO<inf>2</inf> concentration increase. By contrast, annual ryegrass plants subjected to low phosphorus levels down-regulated the morphological traits of stoma and the regular pattern of distribution, and decreased leaf gas exchange efficiency of annual ryegrass to adapt the low phosphorus conditions under high CO<inf>2</inf> concentration. Our results may have significant importance on further understanding the potential mechanisms of grassland ecosystem structure and function in response to CO<inf>2</inf> concentration increase and phosphorus deficiency under future climate change.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:32
Main heading:Carbon dioxide
Controlled terms:Chlorophyll - Efficiency - Moisture - Phosphorus - Photosynthesis - Plants (botany)
Uncontrolled terms:%moisture - CO 2 concentration - Doubling CO2 concentration - Doublings - Leaf gas exchange - Phosphorus deficiency - Phosphorus levels - Ryegrass - Stomatal trait - Water use efficiency
Classification code:741.1  Light/Optics - 802.2  Chemical Reactions - 804  Chemical Products Generally - 804.1  Organic Compounds - 804.2  Inorganic Compounds - 913.1  Production Engineering
Numerical data indexing:Molar concentration 1.00E-01mol/m3, Molar concentration 1.20E-02mol/m3, Molar concentration 2.00E-02mol/m3, Molar concentration 4.00E-03mol/m3, Molar concentration 5.00E-01mol/m3, Molar concentration 6.00E-02mol/m3
DOI:10.11975/j.issn.1002-6819.2021.18.010

Database:Compendex
Compilation and indexing terms, Copyright 2022 Elsevier Inc.

<RECORD 30>

Accession number:20214811239994
Title:Optimized SOLO segmentation algorithm for the green fruits of persimmons and apples in complex environments
Title of translation:&#22797;&#26434;&#29615;&#22659;&#19979;&#26623;&#23376;&#21644;&#33529;&#26524;&#32511;&#33394;&#26524;&#23454;&#30340;&#20248;&#21270;SOLO&#20998;&#21106;&#31639;&#27861;
Authors:Jia, Weikuan (1, 2); Li, Qianwen (1); Zhang, Zhonghua (1); Liu, Guoliang (3); Hou, Sujuan (1); Ji, Ze (4); Zheng, Yuanjie (1)
Author affiliation:(1) School of Information Science and Engineering, Shandong Normal University, Jinan; 250358, China; (2) Key Laboratory of Facility Agriculture Measurement and Control Technology and Equipment of Machinery Industry, Zhenjiang; 212013, China; (3) School of Control Science and Engineering, Shandong University, Jinan; 250061, China; (4) School of Engineering, Cardiff University, Cardiff; CF24 3AA, United Kingdom
Corresponding author:Zheng, Yuanjie(yjzheng@sdnu.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:18
Issue date:September 15, 2021
Publication year:2021
Pages:121-127
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">To solve the green fruit recognition problem of persimmons and apples, a green fruit segmentation algorithm based on optimized SOLO (Segmenting Objects by Locations) was proposed in this study to achieve accurate segmentation of green fruits in complex environments. The proposed algorithm was a single-stage instance segmentation method, which avoided the disadvantage that detection before segmentation in two-stage methods relied on detection performance. By introducing the concept of instance category, each pixel in the instance was assigned a category according to the location and size of the instance, therefore, the instance segmentation was transformed into a classification problem. This study takes green persimmons and green apples as the research objects. The image collection locations are Shandong Normal University (Changqing Lake Campus) Houshan and the Longwangshan Apple Production Base in Fushan District, Yantai City, Shandong Province. The acquisition device is a Canon EOS 80D SLR camera with an image resolution of 6 000&times;4 000 pixels. Collect under natural light during the day (7:00-17:00) and under LED light at night (19:00-22:00). A total of 568 images of green persimmons and 498 images of green apples were collected in the experiment, including nighttime, overlap, backlighting, forward light, blocked, and after rain. The collected images were annotated by LabelMe software and then were made into a dataset in COCO format. Specifically, first, split-attention networks (ResNeSt) were used to extract features of the target fruit as the backbone network of optimized SOLO, which enhanced the transfer, reuse, and fusion of features in the front and back layers. Then ResNeSt and Feature Pyramid Network (FPN) were combined to solve the multi-scale problem of green fruits. Because FPN defined allocation strategies for different scale features and assigned them to the pyramid levels optimally. Finally, the image features extracted by the ResNeSt+FPN structure were utilized for the subsequent prediction. The optimized SOLO segmentation algorithm was divided into two branches: category prediction and mask generation. While the semantic category was predicted by the category prediction branch, the object instance was segmented by the mask generation branch, in this way, the target fruit segmentation was completed. The experimental results showed that the average recall and precision of the optimized SOLO segmentation algorithm reached 94.84% and 96.16%, respectively, with an average segmentation time of 0.14 s per green target fruit image on Graphics Processing Unit (GPU). Besides, compared with four algorithms, which were the optimized Mask R-CNN fruit recognition algorithm, SOLO, Mask Region Convolutional Neural Network (Mask R-CNN), and Fully Convolutional Instance-aware Semantic Segmentation (FCIS), the recall of the optimized SOLO segmentation algorithm in this study was improved by 1.63, 1.74, 2.23, and 6.52 percentage points, the precision was improved by 1.10, 1.47, 2.61, and 6.75 percentage points, respectively, and the segmentation times were reduced by 0.06, 0.04, 0.11, and 0.13 s, respectively. The relevant results show that the green fruit optimization SOLO segmentation algorithm proposed by the study can meet the real-time performance of green fruit segmentation and improve the accuracy of green fruit segmentation. This research algorithm can provide theoretical reference for segmentation of other target fruits and vegetables to extend the application of orchard yield measurement and robot harvesting.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:26
Main heading:Location
Controlled terms:Forecasting - Fruits - Image resolution - Instance Segmentation - Object recognition - Optimization - Pixels - Semantic Segmentation - Semantics
Uncontrolled terms:Complex environments - Feature pyramid - Feature pyramid network - Fruit recognition - Green fruit - Images processing - Images segmentations - Percentage points - Pyramid network - Segmentation algorithms
Classification code:723.4  Artificial Intelligence - 821.4  Agricultural Products - 921.5  Optimization Techniques
Numerical data indexing:Percentage 9.484E+01%, Percentage 9.616E+01%, Time 1.30E-01s, Time 1.40E-01s
DOI:10.11975/j.issn.1002-6819.2021.18.014

Database:Compendex
Compilation and indexing terms, Copyright 2022 Elsevier Inc.

<RECORD 31>

Accession number:20214811239918
Title:Detecting and counting of spring-see citrus using YOLOv4 network model and recursive fusion of features
Title of translation:&#22522;&#20110;&#29305;&#24449;&#36882;&#24402;&#34701;&#21512;YOLOv4&#32593;&#32476;&#27169;&#22411;&#30340;&#26149;&#35265;&#26577;&#27224;&#26816;&#27979;&#19982;&#35745;&#25968;
Authors:Yi, Shi (1, 2); Li, Junjie (1); Zhang, Peng (1); Wang, Dandan (1)
Author affiliation:(1) School of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu; 610059, China; (2) Key Laboratory of Industrial Internet of Things & Networked Control, Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing; 400065, China
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:18
Issue date:September 15, 2021
Publication year:2021
Pages:161-169
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">Automatic fruit picking has widely been popular for the intelligent estimation of orchard economic harvest in the field of smart agriculture. Object detection using deep learning has presented broad application prospects for spring-see citrus detection and counting. But, it is still a very challenging task to detect and count spring-see fruits, due mainly to the small size of spring-see citrus, the high density of fruit on a single spring-see tree, the similar shape and color of fruits, and the tendency to be heavily shaded by foliage. In this study, a YOLOv4 network model was proposed using a recursive fusion of features (FR-YOLOv4) for spring-see citrus detection. CSPResNext50 network with a smaller receptive field was also selected to detect small targets with higher accuracy. As such, the feature extraction in the original YOLOv4 object detection network model was replaced with the CSPResNext50 network, particularly for the small size of spring-see citrus. Therefore, the difficulty was reduced to greatly improve the detection accuracy of small-scale spring-see citrus, where the feature map of small-scale objects was easily transmitted to the object detector. In addition, the Recursive Feature Pyramid (RFP) network was used to replace the original YOLOv4 Path Aggregation Network (PANet), because of the fuzzy and dense distribution of spring-seeing citrus. Correspondingly, RFP networks significantly enhanced the feature extraction and characterization capabilities of the entire YOLOv4 network at a small computational cost. More importantly, the detection accuracy of the YOLOv4 network was improved for spring-see citrus in a real orchard environment. Additionally, a dataset was collected using images and videos of spring-see citrus captured in various environments in spring-see orchards. Subsequently, three data augmentation operations were performed using OpenCV tools to add Gaussian noise, luminance variation and rotation. The dataset was obtained with a total of 3 600 images, of which the training dataset consisted of 2 520 images and the test dataset consisted of 1 080 images. The experimental results on this test dataset showed that the average detection accuracy of FR-YOLOv4 was 94.6% for spring-see citrus in complex orchard environments, and the frame rate of video detection was 51 frames/s. Specifically, the average detection accuracy increased by 8.9 percentage points, whereas, the frame rate of video detection was only 6 frames/s lower, compared with YOLOv4 before the improvement. Consequently, the FR-YOLOv4 presented an average detection accuracy of 29.3, 14.1, and 16.2 percentages higher than that of Single Shot Multi-Box Detector (SSD), CenterNet, and Faster Region-Convolutional Neural Networks (Faster-RCNN), respectively. The frame rate of video detection was 17 and 33 frames/s higher than SSD and Faster-RCNN, respectively. Anyway, the YOLOv4 network model using a recursive fusion of features (FR-YOLOv4) can widely be expected to detect and count spring citrus suitable for actual complex environments in orchards, indicating a higher detection accuracy with real-time performance.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:34
Main heading:Computer vision
Controlled terms:Citrus fruits - Deep learning - Extraction - Feature extraction - Gaussian noise (electronic) - Object detection - Object recognition - Orchards - Statistical tests
Uncontrolled terms:Detection - Detection accuracy - Frame-rate - Fusion of features - Images processing - Machine-vision - Network models - Spring-see citrus - Video detection - YOLOv4
Classification code:461.4  Ergonomics and Human Factors Engineering - 723.2  Data Processing and Image Processing - 723.5  Computer Applications - 741.2  Vision - 802.3  Chemical Operations - 821.3  Agricultural Methods - 821.4  Agricultural Products - 922.2  Mathematical Statistics
Numerical data indexing:Percentage 9.46E+01%
DOI:10.11975/j.issn.1002-6819.2021.18.019

Database:Compendex
Compilation and indexing terms, Copyright 2022 Elsevier Inc.

<RECORD 32>

Accession number:20214811239876
Title:Segmentation method of the tomato fruits with different maturities under greenhouse environment based on improved Mask R-CNN
Title of translation:&#25913;&#36827;Mask R-CNN&#30340;&#28201;&#23460;&#29615;&#22659;&#19979;&#19981;&#21516;&#25104;&#29087;&#24230;&#30058;&#33540;&#26524;&#23454;&#20998;&#21106;&#26041;&#27861;
Authors:Long, Jiehua (1, 2); Zhao, Chunjiang (1, 2); Lin, Sen (2); Guo, Wenzhong (2); Wen, Chaowu (1, 2); Zhang, Yu (2)
Author affiliation:(1) College of Information Technology, Shanghai Ocean University, Shanghai; 201306, China; (2) Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing; 100097, China
Corresponding author:Lin, Sen(linseng@nercita.org.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:18
Issue date:September 15, 2021
Publication year:2021
Pages:100-108
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">Fruit recognition and segmentation using deep neural networks have widely been contributed to the operation of picking robots in modern agriculture. However, the most current models present a low accuracy of recognition with a low running speed, due mainly to a large number of network parameters and calculations. In this study, a high-resolution segmentation was proposed for the different ripeness of tomatoes under a greenhouse environment using improved Mask R-CNN. Firstly, a Cross Stage Partial Network (CSPNet) was used to merge with Residual Network (ResNet) in the Mask R-CNN model. Cross-stage splitting and cascading strategies were contributed to reducing the repeated features in the backpropagation process for a higher accuracy rate, while reducing the number of network calculations. Secondly, the cross-entropy loss function with weight factor was utilized to calculate the mask loss for the better segmentation effect of the model, due to the imbalance of the whole sample. An experiment was also performed on the test sets of tomato fruits with three ripeness levels. The results showed that the improved Mask R-CNN model with CSP-ResNet50 as the backbone network presented the mean average precision of 95.45%, the precision of 95.25%, the recall of 87.43%, F1-score of 0.912, and average segmentation time was 0.658 s. Furthermore, the mean average precision increased by 16.44, 14.95, and 2.29 percentage points, respectively, compared with the Pyramid Scene Parsing Network (PSPNet), DeepLab v3+, and Mask R-CNN with ResNet50 as the backbone network. Nevertheless, the average segmentation time increased by 14.83% and 27.52%, respectively, compared with PSPNet and DeepLab v3+. More importantly, the average segmentation time of improved Mask R-CNN with CSP-ResNet50 as the backbone network was reduced by 1.98%, compared with Mask R-CNN with ResNet50 as the backbone network. Additionally, the new model performed well in the segmentation of green and half-ripe tomato fruits under different light intensities, especially under low light, compared with PSPNet and DeepLab v3+. Finally, the improved Mask R-CNN model with CSP-ResNet50 as the backbone network was deployed to the picking robot, in order to verify the recognition and segmentation effect on different ripeness of tomato fruits in large glass greenhouses. In a low overlap rate of tomato fruits, the model identified the number of tomato fruits consistent with manual detection, where the accuracy was more than 90%. When the occlusion or overlap rate of tomato fruits exceeded 70%, particularly when the target was far away, the accuracy of 66.67% was achieved in the improved Mask R-CNN model, indicating a large gap with manual detection. Only a few features with the blur pixels were attributed to the difficulty to extract the shape and color features of tomato fruits. In addition, low light also posed a great challenge on recognition difficulty. Correspondingly, it was more difficult to pick tomatoes for the picking robot, particularly a relatively low success rate of picking, as the overlap was more serious. Fortunately, the picking success rate improved greatly, as the occlusions reduced. Consequently, the integrated multiple technologies (such as image acquisition equipment, the performance of the model, the execution end design of robotic arm, and automatic mechanization) can widely be expected to effectively improve the picking rate of mature tomatoes under the complex environment of a specific greenhouse. The new model also demonstrated strong robustness and applicability for the precise operation of tomato-picking robots in various complex environments.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:27
Main heading:Computer vision
Controlled terms:Backpropagation - Deep neural networks - Fruits - Image segmentation
Uncontrolled terms:Back-bone network - CNN models - Cross stage partial network - Greenhouse environment - Images processing - Machine-vision - Maturity segmentation - Picking robot - Residual network - Tomato fruits
Classification code:461.4  Ergonomics and Human Factors Engineering - 723.4  Artificial Intelligence - 723.5  Computer Applications - 741.2  Vision - 821.4  Agricultural Products
Numerical data indexing:Percentage 1.483E+01%, Percentage 1.98E+00%, Percentage 2.752E+01%, Percentage 6.667E+01%, Percentage 7.00E+01%, Percentage 8.743E+01%, Percentage 9.00E+01%, Percentage 9.525E+01%, Percentage 9.545E+01%, Time 6.58E-01s
DOI:10.11975/j.issn.1002-6819.2021.18.012

Database:Compendex
Compilation and indexing terms, Copyright 2022 Elsevier Inc.

<RECORD 33>

Accession number:20214811239993
Title:Quality evaluation and improvement strategies of rural residential areas utilization from the perspective of human and land coordination
Title of translation:&#20154;&#22320;&#21327;&#35843;&#35270;&#35282;&#19979;&#20892;&#26449;&#23621;&#27665;&#28857;&#21033;&#29992;&#36136;&#37327;&#35780;&#20215;&#19982;&#25552;&#21319;&#31574;&#30053;
Authors:Qu, Yanbo (1); Dong, Xiaozhen (1); Ping, Zongli (2); Guan, Mei (2)
Author affiliation:(1) School of Public Administration and Policy, Shandong University of Finance and Economics, Jinan; 250014, China; (2) Shandong Institute of Territorial and Spatial Planning, Jinan; 250014, China
Corresponding author:Ping, Zongli(241420815@qq.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:18
Issue date:September 15, 2021
Publication year:2021
Pages:252-262
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">High-quality utilization of rural residential areas has been a concrete manifestation of rural revitalization. It is highly necessary to comprehensively evaluate the quality of rural residential areas, thereby optimizing the allocation of rural factors for village planning. Taking the Pinggu District of Beijing as an example, the utilization quality of rural residential areas was evaluated from the perspective of coordinated land intensive use and human settlements for the harmonious development of rural production and living spaces. In this study, entropy right-TOPSIS was also selected to evaluate the utilization quality of rural residential areas. According to the utilization types of rural residential areas recognized by an elastic coefficient method modified by the coefficient of variation, the obstacle factors of each type were determined using the barrier diagnosis model. Finally, a promotion strategy was also proposed during this time. The research showed that: 1) The level of land-intensive use was above the average of regional standard in rural residential areas, whereas, the quality of human settlement environment and the comprehensive quality was moderately low with a varying spatial distribution. 2) The types of rural residential areas were characterized by dominant medium-quality, secondary high-quality, and less low-quality, corresponding to the decrease in the number of villages from the decoupling F weak, decoupling T weak, positive hook F strong, negative hook F weak, positive hook T strong, negative hook T weak, positive hook T-F equally strong, and negative hook T-F equally weak type. As such, the maladjusted development was the main obstacle to the high-quality utilization of rural residential areas. 3) The main obstacles were represented by the scale intensity, spatial layout, life environment, and production environment. Moreover, the number of obstacles gradually increased from high- to low-quality type. Specifically, the traffic, terrain, distance from urban areas, and industrial development were also the important influencing factors on the high-quality utilization of rural residential areas. In addition, the environmental development plan needed to be further improved using infrastructure construction. 4) The "integration, intensification, humanization and good governance" was required to be taken as the strategic direction for the systemic rectification in the whole region. Consequently, different measures should be adopted to promote the utilization quality of rural residential areas in an orderly manner. More importantly, it can also be highly demanding to fully maintain the ecological environment in rural residential areas with different utilization qualities. Among them, the rural residential areas with high-quality utilization should carry out appropriate optimized activities, while the medium-quality for synchronous promotion, and those of low-quality for systematic consolidation.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:31
Main heading:Land use
Controlled terms:Houses - Quality control - Rural areas - Zoning
Uncontrolled terms:Classified governance strategy - Classifieds - High quality - Human settlement environment - Human settlements - Low qualities - Media quality - Pinggu districts - Quality evaluation - Rural residential areas
Classification code:402.3  Residences - 403  Urban and Regional Planning and Development - 913.3  Quality Assurance and Control
DOI:10.11975/j.issn.1002-6819.2021.18.029

Database:Compendex
Compilation and indexing terms, Copyright 2022 Elsevier Inc.

<RECORD 34>

Accession number:20214811239875
Title:Water use analysis of cultivated land with typical sand layers in Hetao Irrigation District of Inner Mongolia using HYDRUS-1D model
Title of translation:&#22522;&#20110;HYDRUS-1D&#27169;&#22411;&#30340;&#27827;&#22871;&#28748;&#21306;&#20856;&#22411;&#22841;&#30722;&#23618;&#32789;&#22320;&#27700;&#20998;&#21033;&#29992;&#20998;&#26512;
Authors:Feng, Zhuangzhuang (1, 2); Shi, Haibin (1, 2); Miao, Qingfeng (1, 2); Sun, Wei (1, 2); Liu, Meihan (1, 2); Dai, Liping (1, 2)
Author affiliation:(1) College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot; 010018, China; (2) High Efficiency Water-saving Technology and Equipment and Soil and Water Environment Effect in Engineering Research Center of Inner Mongolia Autonomous Region, Hohhot; 010018, China
Corresponding authors:Shi, Haibin(shi_haibin@sohu.com); Shi, Haibin(shi_haibin@sohu.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:18
Issue date:September 15, 2021
Publication year:2021
Pages:90-99
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">Extensive sand layers are widely distributed over the impact plain of the Yellow River, particularly for Hetao Irrigation Areas in Inner Mongolia of western China. The migration of water and salt under the soil sand layer has posed a great influence on soil water utilization, soil salinization control, and crop growth. In this study, a numerical model of soil water movement was proposed to investigate the field evapotranspiration, crop water consumption, water supply, and deep soil water leakage in the sand layer using the data of field monitoring and laboratory experiments during the growth period of spring maize. Taking the cultivated land of the typical sand layer in the Hetao Irrigation Area as the research object and planting crop as spring corn, two gradients of buried depth were selected: S1 (40-95 cm) and S2 (60-110 cm) of the sand layer in the soils. Three irrigation levels were also set to carry out the field experiment, and then to compare with BWI without sand layer, including W1 (252.5 mm), W2 (315.85 mm), and W3 (378.75 mm). A HYDRUS-1D model was selected to simulate the field evapotranspiration during the growth period of spring maize, deep seepage of soil water, groundwater recharge, and root water absorption. In addition, the temporary water deficit and water productivity were calculated during the whole growth period. Water use in the cultivated land with sand layer was then compared with that without sand layer. The results showed that the soil evaporation loss between grains decreased, whereas, the leaf transpiration water increased, with the increase of buried depth of the sand layer. Specifically, the soil layer above the sand layer was thicker, the soil water storage was larger, the surface soil moisture content was higher, and the soil negative pressure was lower when the buried depth of the sand layer was larger. As such, the deep soil water was less replenished upward through the capillary action. At the same time, there was less leakage to the deep layer below the sand layer after irrigation. Furthermore, the upstream water supply of maize in the field without sand layer increased by 57.01% and 118.53%, respectively, compared with the treatment of shallow (40-95 cm), and deep sand layer (60-110 cm). More importantly, the deep-water leakage of soil under the treatment of sand layer was the least, when the irrigation amount was 315.85mm. Correspondingly, the water absorption of maize roots decreased with the increase in the buried depth of the sand layer, where the largest was found without sand layer during the growth period. Specifically, the shallow (40-95 cm) and deep sand layer (60-110 cm) treatment were 55.51% and 61.31% of evapotranspiration, respectively, whereas, the treatment without sand layer was 66.69%. The irrigation system can be determined for spring maize in the sand layer, according to the sand layer distribution and local conditions. Particularly, the recommended irrigation quota of spring corn can be 315.85 mm during the growth period, when the sand layer was similar to the S1WI and S2WI treatment. The recommendation can be attributed to avoiding the leakage loss of irrigation water in the deep soil water of the farmland with the sand layer. The findings can also provide important theoretical guidance for the formulation of a farmland irrigation system with the sand layer in the Hetao Irrigation District.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:34
Main heading:Soil moisture
Controlled terms:Crops - Digital storage - Evapotranspiration - Grain (agricultural product) - Irrigation - Land use - Plants (botany) - Recharging (underground waters) - Sand - Seepage  - Transpiration - Water absorption - Water supply
Uncontrolled terms:1-D models - Deep seepage - Ground water recharge - HYDRUS-1D - HYDRUS-1d model - Layered soils - Sand layer - Sand-layered soil - Soil transpiration - Soil water content
Classification code:403  Urban and Regional Planning and Development - 444.2  Groundwater - 446.1  Water Supply Systems - 461.9  Biology - 483.1  Soils and Soil Mechanics - 722.1  Data Storage, Equipment and Techniques - 802.3  Chemical Operations - 821.3  Agricultural Methods - 821.4  Agricultural Products
Numerical data indexing:Percentage 1.1853E+02%, Percentage 5.551E+01%, Percentage 5.701E+01%, Percentage 6.131E+01%, Percentage 6.669E+01%, Size 2.525E-01m, Size 3.1585E-01m, Size 3.7875E-01m, Size 4.00E-01m to 9.50E-01m, Size 6.00E-01m to 1.10E+00m
DOI:10.11975/j.issn.1002-6819.2021.18.011

Database:Compendex
Compilation and indexing terms, Copyright 2022 Elsevier Inc.

<RECORD 35>

Accession number:20214811239957
Title:Recognizing wheat seed varieties using hyperspectral imaging technology combined with multi-scale 3D convolution neural network
Title of translation:&#34701;&#21512;&#39640;&#20809;&#35889;&#22270;&#20687;&#25216;&#26415;&#19982;MS-3DCNN&#30340;&#23567;&#40614;&#31181;&#23376;&#21697;&#31181;&#35782;&#21035;&#27169;&#22411;
Authors:Huang, Min (1); Xia, Chao (1); Zhu, Qibing (1); Ma, Hongjuan (2)
Author affiliation:(1) Key Laboratory of Advanced Process Control for Light Industry, Ministry of Education, Jiangnan University, Wuxi; 214122, China; (2) Sinochem Agriculture Holdings, Beijing; 100045, China
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:18
Issue date:September 15, 2021
Publication year:2021
Pages:153-160
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">A hyperspectral image classification model was proposed to detect wheat seeds using a Multi-Scale 3D Convolution Neural Network (MS-3DCNN) in this study, in order to identify wheat seed varieties quickly and accurately. A multi-scale 3D convolution module was used to learn the characteristics of wheat seed from hyperspectral images. A deep learning model was then established to predict wheat varieties. 3D Convolutional Neural Network (3DCNN) was utilized to simultaneously extract the spatial and spectral dimension features of hyperspectral images, compared with the traditional 2D Convolutional Neural Network (2DCNN). The kernel sizes of convolution were set as 5&times;5&times;5, 3&times;3&times;3, 5&times;5&times;3, and 3&times;3&times;5, respectively, considering that the characteristics of spectral dimension occupied a higher position in the application of hyperspectral image data. A Batch Normalization (BN) layer was added after each convolution layer to reduce the over-fitting of the model. The LeaKy_ReLU was adopted in the activation function to prevent neurons from being ineffective when the input was negative. A pooling layer and a fully connected layer were stacked on the last multi-scale convolution module. Finally, the Softmax activation function was used to predict the wheat varieties in the output layer. Dropout was introduced into the fully connected layer to reduce the risk of model overfitting. As such, a total of 6 000 samples were collected for 6 varieties of seeds (1 000 seeds per variety). Specifically, 700 seeds of each variety (4 200 seeds of the 6 varieties) were randomly selected as the training set, and the remaining 1 800 seeds were used as the test set during the specific training. 6 wheat varieties were also selected with certain connections in origin and genetic relationship to evaluate the influence of these factors on the classification model. Nevertheless, there was a relatively large amount of original hyperspectral image data, and a high data redundancy between adjacent hyperspectral bands. Successive Projections (SPA) were selected to combine with the average spectral characteristics of wheat seeds for the less data dimension. Subsequently, 22 optimal bands were selected from 300 bands, where the hyperspectral image data corresponding to the optimal bands was extracted to form a new hyperspectral image space. The reduced dimension data was input into the classification model of MS-3DCNN. The traditional hyperspectral classification model using Support Vector Machine (SVM), 2DCNN, 3DCNN, and Multi-Scale 2D Convolutional Neural Network (MS-2DCNN) were selected to compare the influence of 3D convolution and multi-scale convolution on model. The experimental results showed that the classification performed a higher classification accuracy using the MS-3DCNN model. SVM model using feature fusion, 2DCNN, 3DCNN, and MS-2DCNN models for the test sets achieved the accuracies of 88.33%, 94.17%, 95.17%, and 95.44%, respectively. Particularly, the MS-3DCNN model presented a relatively higher accuracy of 96.72%. Consequently, the improved model can be applied to identify and classify wheat seeds in modern intelligent agriculture.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:32
Main heading:Spectroscopy
Controlled terms:Chemical activation - Classification (of information) - Convolution - Convolutional neural networks - Deep learning - Forecasting - Hyperspectral imaging - Image classification
Uncontrolled terms:Classification models - Convolution neural network - Convolutional neural network - Hyperspectral image datas - Multi-scale 3d convolutional neural network - Multi-scales - Seed recognition - Wheat - Wheat seeds - Wheat varieties
Classification code:461.4  Ergonomics and Human Factors Engineering - 716.1  Information Theory and Signal Processing - 723.2  Data Processing and Image Processing - 746  Imaging Techniques - 802.2  Chemical Reactions - 804  Chemical Products Generally - 903.1  Information Sources and Analysis
Numerical data indexing:Percentage 8.833E+01%, Percentage 9.417E+01%, Percentage 9.517E+01%, Percentage 9.544E+01%, Percentage 9.672E+01%
DOI:10.11975/j.issn.1002-6819.2021.18.018

Database:Compendex
Compilation and indexing terms, Copyright 2022 Elsevier Inc.

<RECORD 36>

Accession number:20214811239969
Title:Heat recovery ventilation system combined with boiler room preheating to improve the indoor environment of rabbit house in severe cold areas
Title of translation:&#28909;&#22238;&#25910;&#36890;&#39118;&#31995;&#32479;&#32467;&#21512;&#38149;&#28809;&#25151;&#39044;&#28909;&#25913;&#21892;&#20005;&#23506;&#22320;&#21306;&#20820;&#33293;&#23460;&#20869;&#29615;&#22659;
Authors:Liu, Peng (1); Guo, Yao (1); Ni, Jiqin (2); Wang, Meizhi (1); An, Lei (1); Tian, Jianhui (1); Wu, Zhonghong (1)
Author affiliation:(1) State Key Laboratory of Animal Nutrition, College of Animal Science and Technology, China Agricultural University, Beijing; 100193, China; (2) Agricultural and Biological Engineering Department, Purdue University, West Lafayette; 47907, United States
Corresponding author:Wu, Zhonghong(wuzhh@cau.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:18
Issue date:September 15, 2021
Publication year:2021
Pages:244-251
Language:English
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">Heat recovery ventilation (HRV) is type of energy saving ventilation. It has been widely used in animal house to alleviate contradiction between thermal comfort and ventilation in recent years. However, air heat exchanger freeze of HRV caused by severe cold weather limited HRV application in northeast China. It is necessary to find an economic way to ensure HRV being operated without freezing. Establish a more reasonable integration of heating and ventilation system in severely cold area. An experiment was conducted in rabbit house during winter in Arun Banner, northeast China. The rabbit house was heated by a coal boiler in the next room. Hot water produced by boiler was supplied to heating radiator inside the rabbit house. The boiler room was warm because of some extra heat release by boiler. To prevent freezing, the boiler room was used to preheat cold fresh air before it entered the heat exchanger. The HRV was installed on the ground of the rabbit house. It was an integration of HRV and boiler heating system. Temperature, relative humidity and concentration of noxious was measured with automatic recording sensor instrument. Data was collected to evaluate the decrease in air pollutants and air temperature of the rabbit house. Preheating effect of the boiler room and the HRV were tested when the system was in operation. The performance of the HRV and prevention of freezing in heat exchanger was evaluated in typical cold weather in northeast China. The economic benefit of the system was analyzed. The results showed that when the HRV and boiler system began to operate, concentrations of ammonia (NH<inf>3</inf>) and carbon dioxide (CO<inf>2</inf>) in rabbit house dropped by 46% and 64%, respectively. Moreover, indoor air temperature only dropped by an average of 1.8 &#8451; when the outdoor air temperature varied from -15.8 to -11.8 &#8451;. It means that the system can significantly improve indoor air quality with barely a slight decline of indoor air temperature. Respiratory and skin disease decreased by 16% and 4%, respectively. It is great potential for better health and production of rabbits. The boiler room heated fresh air by 8.1 &#8451;, and the HRV heated fresh air by 6.4 &#8451; on average. No frozen of the heat exchanger was observed. Heated load of the boiler room and the heat recovery load of the HRV were 6.8 and 5.6 kW, respectively. Sensible heat recovery efficiency of the HRV was 75.6%. Coefficient of performance of the HRV was 6.2. The system was operated efficiently. The method of freezing prevention ensure HRV in good condition among the whole cold season in northeast China. It is an economic option because the system can operated without extra energy consumption. The return of investment is 3 years in this work condition. The integration of HRV and boiler allowed HRV and boiler heating to operate with high efficiency and solve the contradiction between heating and ventilation in severely cold weather in the rabbit house. This finding can provide a sound reference for the design of animal house heating and ventilation system in severely cold climate northeast China.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:22
Main heading:Energy conservation
Controlled terms:Air conditioning - Air quality - Ammonia - Atmospheric temperature - Boilers - Carbon dioxide - Coal - Heat exchangers - Houses - Indoor air pollution  - Preheating - Ventilation - Waste heat - Waste heat utilization
Uncontrolled terms:Animal house - Boiler preheating - Boiler room - Cold weather - Colder climate - Fresh air - Heating system - Northeast China - Rabbit house - Ventilation systems
Classification code:402.3  Residences - 443.1  Atmospheric Properties - 451  Air Pollution - 451.2  Air Pollution Control - 524  Solid Fuels - 525.2  Energy Conservation - 525.3  Energy Utilization - 525.4  Energy Losses (industrial and residential) - 614  Steam Power Plants - 616.1  Heat Exchange Equipment and Components - 642.1  Process Heating - 643.3  Air Conditioning - 643.5  Ventilation - 804.2  Inorganic Compounds
Numerical data indexing:Age 3.00E+00yr, Percentage 1.60E+01%, Percentage 4.00E+00%, Percentage 4.60E+01%, Percentage 6.40E+01%, Percentage 7.56E+01%, Power 5.60E+03W, Power 6.80E+03W
DOI:10.11975/j.issn.1002-6819.2021.18.028
Funding details:  Number: CARS-43-D-2, Acronym: -, Sponsor: -;Number: -, Acronym: -, Sponsor: Agriculture Research System of China;
Funding text:Received date: 2020-11-01 Revised date: 2021-09-01 Foundation items: This study was supported by China Agriculture Research System of MOF and MARA (CARS-43-D-2) Biography: Liu Peng, Ph.D, Engineer, Research interest: Animal husbandry engineering and environment. Email: liupeng010125@163.com &#8251;Corresponding author: Wu Zhonghong, Ph.D, Professor, Research interest: Livestock envronmental engineering, animal environmental physiology. Email: wuzhh@cau.edu.cn

Database:Compendex
Compilation and indexing terms, Copyright 2022 Elsevier Inc.

<RECORD 37>

Accession number:20214811239908
Title:Method for recognizing and locating tomato cluster picking points based on RGB-D information fusion and target detection
Title of translation:&#22522;&#20110;RGB-D&#20449;&#24687;&#34701;&#21512;&#21644;&#30446;&#26631;&#26816;&#27979;&#30340;&#30058;&#33540;&#20018;&#37319;&#25688;&#28857;&#35782;&#21035;&#23450;&#20301;&#26041;&#27861;
Authors:Zhang, Qin (1); Chen, Jianmin (1); Li, Bin (2); Xu, Can (3)
Author affiliation:(1) School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou; 510641, China; (2) School of Automation Science and Engineering, South China University of Technology, Guangzhou; 510641, China; (3) Guangdong Institute of Modern Agricultural Equipment, Guangzhou; 510630, China
Corresponding author:Li, Bin(binlee@scut.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:18
Issue date:September 15, 2021
Publication year:2021
Pages:143-152
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">Spatial position and coordinate points (called picking points) can widely be visualized in intelligent robots for fruit picking in mechanized modern agriculture. Recognition and location of picking points have also been the key technologies to guarantee the efficient, timely, and lossless picking during fruit harvesting. A tomato cluster can be both mature and immature tomato fruits, particularly in various shapes. Meanwhile, the color of fruit stem is similar to that of branches and leaves, while, the shape of fruit stems and petioles are similar. As such, there are large depth value errors or even a lack of depth values captured by the economical RGB-D depth camera using active stereo technology. Therefore, it is very difficult for picking robots to identify the picking points of tomato clusters in a complex planting environment. In this study, a recognition and location algorithm was proposed for the picking points of tomato clusters using RGB-D information fusion and target detection. Firstly, the Region of Interest (ROIs) of tomato clusters and stems were collected via the YOLOv4 target detection, in order to efficiently locate picking targets. Then, the ROIs of pickable stems that connected to the ripe tomato cluster were determined by screening, according to the neighbor relationship between the tomato clusters and stems. Secondly, the comprehensive segmentation was selected using RGB-D information fusion, thereby to accurately recognize the picking points of stems against the ROI color background. Specifically, the tomato clusters from the nearest row were regarded as the foreground in the RGB-D image, while the rest were assumed as the background (i.e., noise), due mainly to only that the nearest row for picking in robots. After that, the depth information segmentation and morphological operations were combined to remove the noise in the pickable stem ROI of RGB images. Subsequently, the pickable stem edges were extracted from the stem ROI using K-means clustering, together with morphological operation and RGB color features. The center point of skeleton along the X axis was set as the picking point (x, y) in image coordinate system, especially after extracting the skeleton of stem via the thinning operation. Thirdly, the RGB image and depth map of pickable stem ROI were fused to locate the picking point. Specifically, the average depth of pickable stem was calculated using the depth information of the whole pickable stem without the noise under the mean filter. Correspondingly, an accurate depth value of picking point was obtained to compare the average with the original. Finally, the picking point was converted to the robot coordinate system from image one. Eventually, the harvesting robot implemented the picking action, according to the coordinates of picking point. A field test was also conducted to verify, where the average runtime of one image was 54 ms, while the picture resolution was 1 280&times;720, the recognition rate of picking points was 93.83%, and the depth value error of picking point was &plusmn;3 mm. Thus, the proposed algorithm can fully meet the practical requirements during field operation in harvesting robots.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:34
Main heading:Fruits
Controlled terms:Color - Extraction - Image recognition - Image segmentation - Information fusion - Information use - Intelligent robots - K-means clustering - Location - Mathematical morphology  - Musculoskeletal system - Object recognition - Stereo image processing
Uncontrolled terms:Depth information - Depth value - Objects recognition - Picking point - Recognition and locations - Region-of-interest - Regions of interest - RGB-D image - Targets detection - Tomato cluster
Classification code:461.3  Biomechanics, Bionics and Biomimetics - 723.2  Data Processing and Image Processing - 731.6  Robot Applications - 741.1  Light/Optics - 802.3  Chemical Operations - 821.4  Agricultural Products - 903.1  Information Sources and Analysis - 903.3  Information Retrieval and Use
Numerical data indexing:Percentage 9.383E+01%, Size 3.00E-03m, Time 5.40E-02s
DOI:10.11975/j.issn.1002-6819.2021.18.017

Database:Compendex
Compilation and indexing terms, Copyright 2022 Elsevier Inc.

<RECORD 38>

Accession number:20214811239962
Title:Analysis and structure optimization of the temperature and flow fields of the belt dryer with multi-temperature zones
Title of translation:&#22810;&#28201;&#21306;&#32593;&#24102;&#24335;&#24178;&#29157;&#26426;&#28909;&#27969;&#22330;&#20998;&#26512;&#19982;&#32467;&#26500;&#20248;&#21270;
Authors:Gong, Zhongliang (1); Wang, Pengkai (1); Li, Dapeng (1); Yi, Zongpei (1); Liu, Hao (1); Wen, Tao (1); Zhang, Zhen (1)
Author affiliation:(1) School of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha; 410004, China
Corresponding author:Li, Dapeng(dapengli@csuft.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:18
Issue date:September 15, 2021
Publication year:2021
Pages:40-47
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">Variable temperature during drying can usually be utilized to improve agricultural product quality in recent years. However, it is difficult to control intentionally the temperature distribution inside traditional multi-layer belt dryers. In this study, a novel type of belt dryer was proposed to select proper structural parameters of the air distribution chamber, further evaluating the performance of the new dryer. Four layers of conveyor belts were also added into the air distribution chamber, under which to supply directly hot air for drying products. Firstly, computational fluid dynamics (CFD) was used to optimize the structural parameters of air outlets in a single air distribution chamber, thereby reducing the velocity non-uniformity coefficient (VNUC). The specific structural parameters included the diameter of the outlet orifice, the distance between orifices, and the arrangement of orifices. Secondly, a CFD model was developed for the whole belt dryer with optimized structure parameters of air distribution chambers. Specifically, the drying material was selected as Camellia oleifera seeds, which were granular suitable for belt dryers. A porous media model was also adopted to evaluate the drying effect of seeds on airflow distribution, considering that the material absorbed the heat from surrounding air, according to the drying kinetics of the material. Thirdly, two typical profiles of air temperature were determined for the inlet of each air distribution chamber, including the inlet temperature increasing layer by layer from top to bottom, while the decreasing counterpart. Finally, two evaluation indicators were defined, including the temperature non-uniformity coefficient (TNUC) and temperature stratification deviation (TSD). The TNUC was used to evaluate temperature uniformity adjacent to each conveyor belt, while the TSD indicated to what extent the actual temperature deviated from the set one. The simulation results show that the distance between orifices presented the most significant effect on air distribution. There was no marked effect on the diameter of the outlet orifice, and the arrangement of orifices. Subsequently, the optimal combination of structural parameters was obtained, where the minimum VNUC was achieved concurrently. Additionally, the independent temperature control can be expected to be effectively realized for each layer inside the dryer during the simulation. In particular, the TNUC of each layer from top to bottom were 3.08%, 2.00%, 1.89%, and 1.60% under the decreasing inlet temperature profile, whereas, 2.37%, 2.04%, 2.42%, and 3.31% under the increasing one. Specifically, the TSDs were 4.94% and 3.57% under the above two profiles, respectively. Furthermore, it was also found that the uniformity and deviation of temperature can be further improved by installing the deflector horizontally in the middle of each layer. Consequently, the deflector decreased the TNUC of each layer by 21.1%, 13.0%, 31.2%, 66.3% for the decreasing profiles, and 34.2%, 24.0%, 29.3%, 51.7% for the increasing one, respectively, whereas, the TSDs were reduced by 10.9% and 10.1%, respectively, compared with the original structure. This finding can provide a valuable reference for the multi-layer belt dryers to perform variable temperature drying with independent temperature control for different zones.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:32
Main heading:Drying
Controlled terms:Agricultural products - Air - Atmospheric temperature - Belt conveyors - Computational fluid dynamics - Dryers (equipment) - Numerical models - Orifices - Porous materials
Uncontrolled terms:Air distribution - Belt dryer - Camellia oleifera seeds - Conveyor belts - Multi-layers - Nonuniformity - Optimisations - Structural parameter - Temperature division - Variable temperature
Classification code:443.1  Atmospheric Properties - 692.1  Conveyors - 723.5  Computer Applications - 804  Chemical Products Generally - 821.4  Agricultural Products - 921  Mathematics - 931.1  Mechanics - 951  Materials Science
Numerical data indexing:Percentage 1.01E+01%, Percentage 1.09E+01%, Percentage 1.30E+01%, Percentage 1.60E+00%, Percentage 1.89E+00%, Percentage 2.00E+00%, Percentage 2.04E+00%, Percentage 2.11E+01%, Percentage 2.37E+00%, Percentage 2.40E+01%, Percentage 2.42E+00%, Percentage 2.93E+01%, Percentage 3.08E+00%, Percentage 3.12E+01%, Percentage 3.31E+00%, Percentage 3.42E+01%, Percentage 3.57E+00%, Percentage 4.94E+00%, Percentage 5.17E+01%, Percentage 6.63E+01%
DOI:10.11975/j.issn.1002-6819.2021.18.005

Database:Compendex
Compilation and indexing terms, Copyright 2022 Elsevier Inc.