<RECORD 1>

Accession number:20214811226843
Title:Review of recent advances in online yield monitoring for grain combine harvester
Title of translation:&#35895;&#29289;&#32852;&#21512;&#25910;&#33719;&#26426;&#22312;&#32447;&#27979;&#20135;&#25216;&#26415;&#30740;&#31350;&#29616;&#29366;&#19982;&#36827;&#23637;
Authors:Wang, Shuai (1); Yu, Zhihong (1); Zhang, Wenjie (1); Yang, Lifang (1); Zhang, Zexin (1); Aorigele (1)
Author affiliation:(1) College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot; 010018, China
Corresponding author:Yu, Zhihong(yzhyq@imau.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:17
Issue date:September 1, 2021
Publication year:2021
Pages:58-70
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 highlights the recent research progress of an online yield monitoring system for grain combine harvesters in modern agriculture. The current measurements for the online yield monitoring were also summarized into the dynamic weighing, volumetric, impulse, and nuclear radiation measurement. Specifically, the dynamic weighing measurement was used for the weighing sensors to weigh the grain bins, screw conveyors, and elevators. The volumetric measurement mainly used the through-beam photoelectric sensors, diffuse reflection photoelectric sensors, and machine vision to measure the volume of grain on the scraper. More importantly, the volume sensor of the scraper wheel was selected to obtain the flow and volume of grain. The impact-based sensors were also adopted to measure the grain flow at the outlet of the elevator. Two kinds of sensors were included, the piezoelectric and strain impact-based grain flow sensors. Nuclear radiation measurement mostly used gamma and X-ray sensors to measure the grain flow. Other measurements were involved to establish the relationship between machine operating parameters and grain quality, such as the torque, tension, current, and speed sensors. At the same time, the limitations of each measurement were evaluated, in terms of stability and accuracy, feasibility, and versatility during the operation of the grain combine harvester. Furthermore, the grains were also dynamically weighed through the grain tank and the scraper due to inertia under specific conditions, such as the machine vibration, the bumps caused by the uneven ground in the field, the tilting, or the turning of ground, and the emergency brake. Nevertheless, the quality and volume were prone to drastic changes in the grain bins or scrapers using the grain volume measurement. Particularly, the accuracy of yield measurement was relatively low, due mainly to the influence of crop varieties, density, and moisture content. Since a large installation space was required for some yield monitoring, such as the auger weighing of net grain in a screw conveyor, capacitance measurement, and working parameters to determine the yield, the compact design of a combine harvester brought the complicated and labor-intensive modification for the installation of measurement system under the limited room. In addition, the low applicability of nuclear radiation was attributed to the possible radiation hazards for the human body during the production test. At the same time, the mechanical damage to the grain was inevitably caused by the collision of grain and impact plate during the impact measurement. As such, the quality and grade of processed grains were reduced for the germination rate of seed, due to the damaged grain was more prone to mildew during storage. Correspondingly, the yield measurement, sensor installation, and location varied significantly in the different physical and chemical properties of various harvested crops, as well as the different net grain transportation structures. Some suggestions were also proposed for crop yield monitoring during this time. Anyway, the simplest and most convenient installation was preferred to meet the needs of different crop harvesting, such as the integrated, miniaturized, high-precision, and intelligent measurement sensors with the communication interface standard. Consequently, an intelligent system can be expected to develop in the combine harvesters for real-time monitoring, such as crop output, harvester geographic location, and output distribution map.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:99
Main heading:Grain (agricultural product)
Controlled terms:Brass - Capacitance - Capacitive sensors - Crops - Gamma rays - Harvesters - Precision agriculture - Screws - Tensile strength - Tractors (truck)  - Weighing - X ray detectors
Uncontrolled terms:Combine harvesters - Grain combines - Grain flow - Measurement technologies - Nuclear radiations - Online yield measurement technology - Precision Agriculture - Yield - Yield measurement - Yield monitoring
Classification code:544.2  Copper Alloys - 546.3  Zinc and Alloys - 605  Small Tools and Hardware - 663.1  Heavy Duty Motor Vehicles - 701.1  Electricity: Basic Concepts and Phenomena - 732  Control Devices - 821.1  Agricultural Machinery and Equipment - 821.3  Agricultural Methods - 821.4  Agricultural Products - 931.3  Atomic and Molecular Physics - 932.1  High Energy Physics - 943.3  Special Purpose Instruments
DOI:10.11975/j.issn.1002-6819.2021.17.007

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

<RECORD 2>

Accession number:20214811226829
Title:Multi-dimensional comprehensive evaluation of multi-energy complementary energy system
Title of translation:&#22810;&#33021;&#20114;&#34917;&#33021;&#28304;&#31995;&#32479;&#22810;&#32500;&#24230;&#32508;&#21512;&#35780;&#20215;&#26041;&#27861;
Authors:Zhao, Fengzhan (1); Li, Qi (1); Zhang, Qicheng (1); Wu, Ming (2); Qu, Xiaoyun (2); Su, Juan (1)
Author affiliation:(1) College of Information and Electrical Engineering, China Agricultural University, Beijing; 100083, China; (2) State Grid Shanghai Energy Internet Research Institute Co., Ltd., Beijing; 100192, China
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:17
Issue date:September 1, 2021
Publication year:2021
Pages:204-210
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">Low-carbon and sustainable development has drawn much attention in social communities in recent years. The conventional single energy system cannot meet the urgent needs of sustainable energy against the background of energy shortage, due mainly to the low utilization efficiency and many deficiencies in technology. As a result, a multi-energy complementary energy system has emerged at present. It is necessary to fully evaluate the implementation of multi-energy complementary energy system for the economic and social benefits. In this article, a system architecture was first designed, according to the "source-network-load-storage" structure, including energy inputs (such as wind, light, gas, and electricity), load demand for electricity, heat, cold, and gas, as well as energy conversion equipment (such as gas turbines, electric refrigerators, and electric boilers). Then, the demand relationship was established among the three stakeholders: energy suppliers, local governments, and energy users. Five levels were set to be evaluated, including the clean and low-carbon, safe and reliable, energy utilization, high-efficiency economic, and social service level. An evaluation index system was then constructed for the multi-energy complementary energy system, including 5 first-level and 45 second-level indicators. The detailed indicators were defined, such as the proportion of electric heating (gas) replacement time, energy storage equipment (battery, heat storage tank, and gas storage tank) peak shaving capacity, user smart energy participation, and energy business online operation rate. Among them, the G1 was used for the subjective weighting of indicators, while the CV was used for the objective weighting, where the subjective and objective weights were combined, according to the optimal comprehensive weight using the maximizing deviation method. As such, this weighting was adopted the expert opinions, together with the indicator data. The improved multi-level matter-element extension was used to comprehensively evaluate the multi-energy complementary energy system. Finally, the evaluation was implemented using G1-CV maximum deviation comprehensive weighting-multi-level matter-element extension, particularly suitable for the three-level evaluation index system. In addition, an example was selected to verify the evaluation index system. Consequently, the comprehensive evaluation can also be expected to serve the construction and operation for different multi-energy complementary energy systems, further to obtain refined evaluation from various dimensions. This finding can also provide a theoretical guidance for the optimal planning of energy system.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:21
Main heading:Gases
Controlled terms:Carbon - Digital storage - Economic and social effects - Electric energy storage - Energy conversion - Energy efficiency - Energy utilization - Heat storage - Sustainable development - Tanks (containers)
Uncontrolled terms:Analyse - Complementary energy - Comprehensive evaluation - Energy - Energy systems - Evaluation indices system - Extension evaluation method - G1-CV maximum deviation comprehensive weighting - Matter-element - Multi energy  - Multi-energy complementary energy system - Multi-level matter-element extension evaluation method - Multilevels
Classification code:525.2  Energy Conservation - 525.3  Energy Utilization - 525.5  Energy Conversion Issues - 619.2  Tanks - 722.1  Data Storage, Equipment and Techniques - 804  Chemical Products Generally - 971  Social Sciences
Numerical data indexing:Time 4.50E+01s
DOI:10.11975/j.issn.1002-6819.2021.17.023

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

<RECORD 3>

Accession number:20214811226895
Title:Variable rate fertilization control system for liquid fertilizer based on genetic algorithm
Title of translation:&#22522;&#20110;&#36951;&#20256;&#31639;&#27861;&#30340;&#28082;&#32933;&#21464;&#37327;&#26045;&#32933;&#25511;&#21046;&#31995;&#32479;
Authors:Tian, Min (1); Bai, Jinbin (1); Li, Jiangquan (1)
Author affiliation:(1) College of Mechanical and Electrical Engineering, Shihezi University, Shihezi; 832003, China
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:17
Issue date:September 1, 2021
Publication year:2021
Pages:21-30
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 rate fertilization has generally been implemented in the field traction liquid fertilizer applicator under a variety of soil and tillage conditions in recent years. However, it is very necessary to improve the precision, even fertilization, and fertilizer saving during operation in modern mechanized agriculture. In this research, a novel fuzzy PID control was proposed using genetic algorithm (GA) for the variable rate fertilization control system of liquid fertilizer. Firstly, a closed-loop negative feedback model was established for the control system of liquid fertilizer variable rate fertilization, thereby obtaining the transfer function of control process. The control process was also optimized, according to the structure of traction variable liquid fertilizer applicator and the composition of electrical components. Among them, the control object was mainly an electric proportional valve in the control system. The feedback channel was read by the flow meter and then transferred the electric signal to the controller. Specifically, the controller was implemented to compare the flow reading with the vehicle speed and the amount of fertilizer required for the current field. The obtained data was converted into the control signal and then output to the electric proportional valve, so as to realize the negative feedback control of system. Some models were established for the traditional, fuzzy, and GA-based fuzzy PID control, according to the requirements of control system. Particularly, the fuzzy PID control model was first established before the GA-based fuzzy PID control model. The input quantity of fuzzy controller was set as the error and the error rate of change, while the output quantity was set as the compensation value of three parameters in the PID controller, where each input and output quantity was set to 7 fuzzy language values. Therefore, there were 49 fuzzy control rules in total. Subsequently, the fuzzy control rules were chromosome-coded within GA. The chromosomes of fuzzy control rules were then simulated and optimized to obtain the optimal fuzzy control rule table using genetic operators, such as selection, crossover, mutation. Correspondingly, the fuzzy PID controller was further set, according to the optimal fuzzy control rules. Finally, MATLAB software was also selected to simulate the traditional, fuzzy, and GA-based fuzzy PID control. Consequently, the response time of electric proportional valve control was 4.86 s for the variable rate fertilization control system using the GA-based fuzzy PID control, which was significantly shorter than the 8.4 s of the traditional PID control, and the fuzzy PID control of 7.32 s. An experimental platform was constructed to carry out the stability and variable control experiment for the flow control in the control system of liquid fertilizer variable rate fertilization. In addition, the flow error was measured during operation in the fertilization stability experiment. The average relative errors of control system were 5.19%, 3.40%, and 1.14%, respectively, corresponding to traditional PID control, fuzzy and GA-base fuzzy PID control during stable operation. The signal was collected and recorded for the actual vehicle speed change in the variable control experiment. The inputs were the collected vehicle speed signal to the controller through a signal generator, thereby measuring the flow when the vehicle speed changed. Consequently, the actual response times were 5.19, 4.12, and 3.21 s, respectively, corresponding to the three control modes. Additionally, the actual response time of GA-based and fuzzy PID reduced by 1.98 and 0.91s, compared with the traditional PID control. Anyway, the GA-based fuzzy PID control presented better response time to flow control in the variable rate fertilization control system than traditional and fuzzy PID control, indicating better operational stability.<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:Genetic algorithms
Controlled terms:Chromosomes - Controllers - Electric control equipment - Feedback control - Fertilizers - Fuzzy control - Histology - Liquids - MATLAB - Membership functions  - Process control - Proportional control systems - Three term control systems
Uncontrolled terms:Control model - Control process - Fertilisation - Fuzzy control rules - Fuzzy-PID control - Liquid fertilizer applicators - Liquid fertilizers - Proportional valves - Variable rate fertilization - Vehicle speed
Classification code:461.2  Biological Materials and Tissue Engineering - 704.2  Electric Equipment - 723.5  Computer Applications - 731  Automatic Control Principles and Applications - 731.1  Control Systems - 732.1  Control Equipment - 804  Chemical Products Generally - 821.2  Agricultural Chemicals - 921  Mathematics
Numerical data indexing:Percentage 1.14E+00%, Percentage 3.40E+00%, Percentage 5.19E+00%, Time 1.98E+00s, Time 3.21E+00s, Time 4.86E+00s, Time 7.32E+00s, Time 8.40E+00s, Time 9.10E-01s
DOI:10.11975/j.issn.1002-6819.2021.17.003

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

<RECORD 4>

Accession number:20214811226866
Title:Super-resolution reconstruction of soil CT images using sequence information
Title of translation:&#22522;&#20110;&#24207;&#21015;&#20449;&#24687;&#30340;&#22303;&#22756;CT&#22270;&#20687;&#36229;&#20998;&#36776;&#29575;&#37325;&#24314;
Authors:Han, Qiaoling (1, 2, 3); Zhou, Xibo (1, 2, 3); Song, Runze (4); Zhao, Yue (1, 2, 3)
Author affiliation:(1) School of Technology, Beijing Forestry University, Beijing; 100083, China; (2) Key Lab of State Forestry Administration for Forestry Equipment and Automation, Beijing; 100083, China; (3) Beijing Laboratory of Urban and Rural Ecological Environment, Beijing; 100083, China; (4) College of Information and Electrical Engineering, China Agricultural University, Beijing; 100091, China
Corresponding authors:Zhao, Yue(zhaoyue0609@126.com); Zhao, Yue(zhaoyue0609@126.com); Zhao, Yue(zhaoyue0609@126.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:17
Issue date:September 1, 2021
Publication year:2021
Pages:90-96
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">Pore boundary is generally blur resulted from the partial volume in the soil CT image. This phenomenon has inevitably posed a great influence on the accuracy of soil pore topology. This study aims to propose a novel Sequence information Generative Adversarial Network (SeqGAN) to realize the Super-Resolution reconstruction of soil CT images. Therefore, the SeqGAN was selected to improve the clarity and accuracy of soil CT images, particularly for the high resolution and feature boundaries. Two improvements of SeqGAN were utilized, including the Sequential Convolution block (SeqConv) structure, and Beginning-to-End Residuals Connection block (BE-Resblock). SeqConv structure involved two convolution block structures. The first convolution block was used to extract the feature of the target image, while the second was used to extract the sequence information of the next and previous image in the sequence, thereby realizing the extraction of sequence information. In the BE-Resblock, more than 8 residual blocks were connected in series to extract the image information. At the same time, the residual blocks of the beginning and end were also connected, where the input information was introduced to reduce the probability of overfitting. Furthermore, twice up-sampling blocks were used to improve the resolution of images, where the final output was a 4x high-definition Super-Resolution image. The experimental samples soil was taken from Keshan Farm in the northwest of Keshan County, Qiqihar City, Heilongjiang Province (125&deg;23'57&Prime;E, 48&deg;18'37&Prime;N). Soil samples were collected with a cutting ring and stored in a plexiglass tube. A submerging test was also conducted to obtain the soil samples. A spiral CT scanner was then used to capture soil CT images. The test datasets were finally taken as the 440 soil CT sequence images with high sequence Structural Similarity (SSIM). Two datasets were obtained after preprocessed, including the high- and low-resolution images with twice the difference in resolution. Specifically, the low-resolution image dataset contained 220 soil CT images, where each image presented a resolution of 128&times;128 pixels and a size of 62.17 mm&times; 62.17 mm. At the same time, the high-resolution image dataset (original image datasets) contained 220 soil CT images, where each image presented a resolution of 256&times;256 pixels and a size of 62.17 mm&times;62.17 mm. Three common models were selected to compare with the improved model. Qualitative experiments showed that the improved model well performed a higher resolution, and lower gray difference, thereby constructing most soil pores in detail. Quantitative experiments showed that the Mean Square Error (MSE) of the improved model was 25% lower than that of Generative Adversarial Network. In addition, the Peak Signal to Noise Ratio (PSNR) of improved model was 1.4 higher than that of Generative Adversarial Network. SSIM of super- and high-resolution image was 0.2% higher than that of Generative Adversarial Network. Consequently, the SeqGAN can be expected to realize the super-resolution reconstruction of soil CT images with high accuracy and high definition. The finding can also provide potential data reliability and benefit to follow-up research on soil pore segmentation and soil skeletonization.<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:Soils
Controlled terms:Computerized tomography - Convolution - Deep learning - Generative adversarial networks - Image enhancement - Optical resolving power - Soil surveys
Uncontrolled terms:CT Image - Deep learning - High definition - High resolution - Image datasets - Images processing - Sequence informations - Soil pores - Super-resolution reconstruction - Superresolution
Classification code:461.4  Ergonomics and Human Factors Engineering - 483.1  Soils and Soil Mechanics - 716.1  Information Theory and Signal Processing - 723.4  Artificial Intelligence - 723.5  Computer Applications - 741.1  Light/Optics
Numerical data indexing:Percentage 2.00E-01%, Percentage 2.50E+01%, Size 6.217E-02m
DOI:10.11975/j.issn.1002-6819.2021.17.010

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

<RECORD 5>

Accession number:20214811226889
Title:Design of the networked precision irrigation system for paddy field crops in intelligent agriculture
Title of translation:&#26234;&#24935;&#20892;&#19994;&#27700;&#30000;&#20316;&#29289;&#32593;&#32476;&#21270;&#31934;&#20934;&#28748;&#28297;&#31995;&#32479;&#35774;&#35745;
Authors:Lu, Xutao (1); Zhang, Lina (2); Liu, Hao (2); Zhi, Chaoqun (2); Li, Jing (3)
Author affiliation:(1) School of Mechatronics Engineering, North University of China, Taiyuan; 030051, China; (2) School of Information and Communication Engineering, North University of China, Taiyuan; 030051, China; (3) School of Electrical and Control Engineering, North University of China, Taiyuan; 030051, China
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:17
Issue date:September 1, 2021
Publication year:2021
Pages:71-81
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">Conventional extensive irrigation of paddy fields has posed a great challenge on crop growth, natural rainfall, and water resources. It is highly demanding for networked intelligent irrigation systems, with the increase in the area of paddy fields during agricultural modernization. However, the current irrigation system is still lacking in the deployment flexibility of underlying equipment, connectivity, and energy consumption of communication networks, as well as the intelligence decision-making of the whole system. Particularly, the energy consumption of equipment inevitably impacts the serving life of the equipment. In addition, the wired information transmission is used in most irrigation systems, where a large number of cables need to be erected. As such, great difficulties have been brought to the deployment and maintenance of equipment, due mainly to the complex environment in paddy fields. In this study, a precision irrigation strategy was proposed to fully utilize the natural precipitation using the sensor, embedded system, wireless networking, and artificial intelligence. Firstly, a networked precision irrigation system was designed for paddy fields using smart agricultural technology. Specifically, five modules were included: data collection node, irrigation control node, handheld control terminal, PC control terminal, and intelligent communication node. The wireless communication was used to flexibly deploy in the paddy fields with the solar power generation devices. Secondly, the fuzzy control was utilized to establish some of the models, including the most optimal deployment model of communication nodes, the most optimal irrigation decision-making model, a prediction model of crop water consumption and precipitation, as well as a decision-making system of precision irrigation. The design strategy was adopted in the decision-making system using the MATLAB+LABVIEW platform, in order to control the system by mutual cooperation. A new deployment model was proposed to optimize the deployment of communication nodes in the irrigation using the improved moth flame optimization and the Voronoi diagram, thereby improving the communication efficiency of the irrigation network, while reducing the energy consumption of communication. As such, the status and network meteorological parameters in the paddy fields were used as the input of the precision irrigation decision-making system. Finally, the irrigation equipment of the paddy field was adaptively controlled for precision irrigation after the decision-making of the system. Taking the rice fields in Jiangsu, China as an example, a field test was carried out to compare with the simulation. It was found that the intelligent irrigation system reduced the action frequency of irrigation equipment by 26.67%, while the irrigation volume of the system reduced by 40.82%, and the drainage volume reduced by 33.89%, compared with the traditional. Consequently, the operating life of the equipment was improved significantly, while the waste of water resources was reduced under the optimal growth water level of paddy field crops. The hardware and software parts of the system also performed well to meet the requirements of design indicators.<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:Data communication systems - Decision making - Energy efficiency - Energy utilization - Fuzzy control - Irrigation - MATLAB - Photovoltaic cells - Precipitation (chemical) - Roads and streets  - Solar energy - Solar power generation - Wireless sensor networks
Uncontrolled terms:Accuracy - Communication nodes - Communications networks - Energy-consumption - Intelligent irrigation systems - Networking - Paddy fields - Precision irrigation - Smart agricultures
Classification code:406.2  Roads and Streets - 525.2  Energy Conservation - 525.3  Energy Utilization - 615.2  Solar Power - 657.1  Solar Energy and Phenomena - 716.3  Radio Systems and Equipment - 722.3  Data Communication, Equipment and Techniques - 723.5  Computer Applications - 731  Automatic Control Principles and Applications - 802.3  Chemical Operations - 821.3  Agricultural Methods - 821.4  Agricultural Products - 912.2  Management - 921  Mathematics
Numerical data indexing:Percentage 2.667E+01%, Percentage 3.389E+01%, Percentage 4.082E+01%
DOI:10.11975/j.issn.1002-6819.2021.17.008

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

<RECORD 6>

Accession number:20214811226891
Title:Optimization of sequential freeze-infrared drying process of jujube slices
Title of translation:&#32418;&#26531;&#29255;&#20919;&#20923;-&#32418;&#22806;&#20998;&#27573;&#32452;&#21512;&#24178;&#29157;&#24037;&#33402;&#20248;&#21270;
Authors:Liu, Decheng (1, 2); Zheng, Xia (1, 2); Xiao, Hongwei (3); Yao, Xuedong (1, 2); Shan, Chunhui (4); Chang, Antai (1, 2); Li, Yican (1, 2); Li, Xiangyu (1, 2)
Author affiliation:(1) College of Mechanical and Electrical Engineering, Shihezi University, Shihezi; 832003, China; (2) Ministry of Agriculture in Northwest China Key Laboratory of Agricultural Equipment, Shihezi; 832003, China; (3) College of Engineering, China Agricultural University, Beijing; 100083, China; (4) College of Food Sciences, Shihezi University, Shihezi; 832003, China
Corresponding authors:Zheng, Xia(124899256@qq.com); Zheng, Xia(124899256@qq.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:17
Issue date:September 1, 2021
Publication year:2021
Pages:293-302
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">Jujube is a very popular fruit in China. Jujube slices is a kind of dried snack with high vitamin C content. In order to decrease drying time and improve quality, the effects of various drying methods (e.g. freeze drying, infrared drying, hot air drying and microwave vacuum drying) on drying characteristics and quality of jujube slices were explored and compared. However, the above mentioned single drying technologies of jujube have low energy efficiency and drying rates, and also can't guarantee the color and nutrient retention of products. In current work, a sequential combined drying method for jujube slices drying was developed. With a moisture content (MC) of 52.06%&plusmn;0.50% (wet basis) were dried in a sequential freeze-drying and infrared drying combined (FD-IRD) from conversion moisture 40%, 35%, 30%, 25%, 20% separately to 10% MC. The conversion moisture, infrared temperature, and slice thickness were selected as influencing factors, drying time and VC retention rate were used as evaluation indicators, and the response surface was adopted. The experiment optimizes the sequential freeze-infrared combined drying process parameters of jujube slices and compares them with single infrared drying. Results showed that: 1) the drying time of freeze and hot air drying is longer than other two methods, which are 8.5 h and 5.75 h respectively, while the shortest is 0.83 h for microwave vacuum drying, and the second shortest is 2.5 h for infrared drying. 2) Freeze-dried products obtained better quality but with average crispness, while infrared-dried products are better than hot air and microwave vacuum-dried products in terms of color, texture (hardness/crispness) and microstructure, and have the best crispness. 3) Conversion moisture content, infrared temperature and slice thickness have significant effects on the combined freeze-infrared drying process of jujube slices (P&lt;0.05), with the main order of effects on drying time are conversion moisture content, infrared temperature and slice thickness, and the main order of effects on VC retention are infrared temperature, conversion moisture content and slice thickness. 4) The response surface method was used to determine the optimal process parameters: conversion moisture content of 34%, infrared temperature of 64 &#8451;, and slice thickness of 5 mm, at which point the drying time was 3.62 h and the VC retention rate was 68.92%. 5) The quality of freeze-infrared combined drying products was better than that of infrared drying, and the drying time was 57.6% shorter than that of freeze-drying, and the VC retention rate was 34.6% higher than that of infrared drying. This paper shows that the combined freeze-infrared drying shortens the drying time and ensures the drying quality at the same time, which can provide a new combined drying technology and theoretical basis for the drying and processing of jujube slices.<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:Optimization
Controlled terms:Energy efficiency - Freezing - Fruits - Infrared drying - Low temperature drying - Microwave heating - Microwaves - Moisture - Moisture determination - Solar dryers  - Surface properties - Textures
Uncontrolled terms:%moisture - Drying process - Drying time - Jujube slice - Retention rate - Sequential drying - Slice thickness - Vacuum-freeze drying - Vitamin C retention - Vitamin C retention rate
Classification code:525.2  Energy Conservation - 642.1  Process Heating - 657.1  Solar Energy and Phenomena - 711  Electromagnetic Waves - 711.1  Electromagnetic Waves in Different Media - 802.1  Chemical Plants and Equipment - 802.3  Chemical Operations - 821.4  Agricultural Products - 921.5  Optimization Techniques - 931.2  Physical Properties of Gases, Liquids and Solids - 944.2  Moisture Measurements - 951  Materials Science
Numerical data indexing:Percentage 1.00E+01%, Percentage 2.00E+01%, Percentage 2.50E+01%, Percentage 3.00E+01%, Percentage 3.40E+01%, Percentage 3.46E+01%, Percentage 3.50E+01%, Percentage 4.00E+01%, Percentage 5.00E-01%, Percentage 5.206E+01%, Percentage 5.76E+01%, Percentage 6.892E+01%, Size 5.00E-03m, Time 1.3032E+04s, Time 2.07E+04s, Time 2.988E+03s, Time 3.06E+04s, Time 9.00E+03s
DOI:10.11975/j.issn.1002-6819.2021.17.034

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

<RECORD 7>

Accession number:20214811226832
Title:Recognition method for adhesive fish based on depthwise separable convolution network
Title of translation:&#22522;&#20110;&#28145;&#24230;&#21487;&#20998;&#31163;&#21367;&#31215;&#32593;&#32476;&#30340;&#31896;&#36830;&#40060;&#20307;&#35782;&#21035;&#26041;&#27861;
Authors:Zhang, Lu (1, 2, 3); Li, Daoliang (1, 2, 3); Cao, Xinkai (1, 2, 3); Li, Wensheng (4); Tian, Ganglu (1, 2, 3); Duan, Qingling (1, 2, 3)
Author affiliation:(1) National Innovation Center for Digital Fishery, China Agricultural University, Beijing; 100083, China; (2) College of Information and Electrical Engineering, China Agricultural University, Beijing; 100083, China; (3) Beijing Engineering and Technology Research Center for the Internet of Things in Agriculture, China Agricultural University, Beijing; 100083, China; (4) Laizhou Mingbo Aquatic Products Co., Ltd., Laizhou; 261400, China
Corresponding authors:Duan, Qingling(dqling@cau.edu.cn); Duan, Qingling(dqling@cau.edu.cn); Duan, Qingling(dqling@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:17
Issue date:September 1, 2021
Publication year:2021
Pages:160-167
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">Fish living in a three-dimensional water environment exhibits various swimming gestures in aquaculture, thereby resulting in the irregular shapes of fish in the images, such as straight or curved bodies, even curved fishtails and bodies. The adhesion between different fish is also complex and varied significantly. In this study, a new depthwise separable convolution network was proposed to improve the accuracy and universality of recognition for the adhesive fish. Image processing was performed on the collected fish images, and then the images of fish-connected areas were segmented to construct an adhesive fish recognition dataset, with a total of 27 836 images. Firstly, some preprocessing operations were performed on the fish images to enhance image contrast, such as color space conversion, color component extraction, and median filtering. Value (V) component of Hue-Saturation-Value (HSV) color space was extracted to serve as the initial image for subsequent processing. Next, the fish images were segmented using the background subtraction. Finally, the open, close, and small-area noise removal operations were implemented to remove the isolated small dots and burrs. After that, the hole filling operation was then conducted to fill the holes on the surface of fish, further to obtain the fish-connected area image. MobileNet was selected as the classification network to build an adhesive fish recognition model using depthwise separable convolution, while the transfer learning was adopted to train the model. Two training mechanisms of transfer learning were designed and then optimized, where the better one was selected according to the experimental data. Specifically, two training were as follows: 1) To freeze all convolutional layers, and only perform the rough training on the fully connected layer; 2) After the first step, subsequently to unfreeze the convolutional layer, and perform fine-tuning training on all layers. As such, the adhesive fish was recognized using the well-trained model. The collected dataset of adhesive fish recognition was randomly divided into a training set and a test set at a ratio of 9:1. Additionally, both images were contained for the model training and validation at a ratio of 9:1 in the training set. Correspondingly, the proposed model was trained and then verified in two transfer learning at three learning rates. The results showed that the model quickly converged with the best training accuracy of 100%, the best validation accuracy of 99.60%, and the best testing accuracy of 99.32%, when the transfer learning was combined the rough training of the fully connected layer, and fine-tuning training of all layers, where the 0.000 1 learning rate was set. Furthermore, the recognition accuracy was increased by 5.46 percentage points and 32.29 percentage points respectively, compared with the Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN). Consequently, the proposed deep learning can also be expected to better perform for the adhesive fish recognition, while adaptively adjust the features, according to actual data and the objects. The combined depthwise separable convolution network and transfer learning can be used to improve the running speed of model. Therefore, the new model can also be conveniently applied in smart terminals, such as mobile phones, particularly for the recognition of adhesive fish automatically and in real-time.<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:Fish
Controlled terms:Adhesion - Aquaculture - Color - Convolution - Convolutional neural networks - Deep learning - Image enhancement - Image recognition - Median filters
Uncontrolled terms:Adhesive fish - Deep learning - Fine tuning - FISH images - Fish recognition - Learning rates - Percentage points - Recognition methods - Training sets - Transfer learning
Classification code:461.4  Ergonomics and Human Factors Engineering - 703.2  Electric Filters - 716.1  Information Theory and Signal Processing - 741.1  Light/Optics - 821.3  Agricultural Methods - 951  Materials Science
Numerical data indexing:Percentage 1.00E+02%, Percentage 9.932E+01%, Percentage 9.96E+01%, Size 2.54E-02m
DOI:10.11975/j.issn.1002-6819.2021.17.018

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

<RECORD 8>

Accession number:20214811226830
Title:Effects of biochar on the photosynthetic and antioxidant characteristics of ryegrass and alfalfa under saline-alkali stress
Title of translation:&#29983;&#29289;&#28845;&#23545;&#30416;&#30897;&#32961;&#36843;&#19979;&#40657;&#40614;&#33609;&#21644;&#32043;&#33457;&#33500;&#34047;&#20809;&#21512;&#21450;&#25239;&#27687;&#21270;&#29305;&#24449;&#30340;&#24433;&#21709;
Authors:Ren, Huaixin (1); Wang, Dongmei (1); Wang, Hui (1); Zhang, Zezhou (1); Liu, Ruosha (1); Huang, Wei (1); Xie, Zhengfeng (1)
Author affiliation:(1) College of Soil and Water Conservation, Beijing Forestry University, Beijing; 100083, China
Corresponding author:Wang, Dongmei(dmwang_bjfu@126.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:17
Issue date:September 1, 2021
Publication year:2021
Pages:116-123
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">Plants generally behave in different response mechanisms under stress. This study aims to reveal the effects of biochar on photosynthesis and antioxidant system of ryegrass and alfalfa at the early stage of salt-alkali stress. Four treatments were conducted in the pot experiment: saline-alkali stress treatment (C0, 150 mmol/L equal mol NaCl, Na<inf>2</inf>CO<inf>3</inf>, NaHCO<inf>3</inf> mixed saline solution), saline-alkali stress +1% biochar (C1), saline-alkali stress +3% biochar (C2), saline-alkali stress +5% biochar (C3), and set blank control (CK) without additives. Conventional cultivation was also carried out before the experiment, and then a stress test was conducted 40 d later. The stress treatment lasted for a total of 14 d, where 100 mL mixed saline solution was added to C0, C1, C2, and C3 treatments at one time, and 100 mL deionized water was added to CK treatment. Subsequently, the response of ryegrass and alfalfa was determined on the 14th day of the experiment, including growth indices, photosynthetic characteristics, malondialdehyde content, and antioxidant enzyme activities under various levels of biochar addition. The results showed as follows: 1) 14 d salt-alkali treatment dominated the growth of ryegrass and alfalfa. Specifically, there was a significant decrease in biomass accumulation, net photosynthetic rate, stomatal conductance, transpiration rate, and intercellular CO<inf>2</inf> concentration. But there was no significant effect on the chlorophyll content and root length. Furthermore, the content of malondialdehyde increased significantly, indicating the antioxidant system responded positively. Similarly, the activities of superoxide dismutase, peroxidase, and catalase increased to alleviate the saline-alkali stress on plants. 2) The addition of biochar effectively improved the stress resistance of perennial ryegrass and alfalfa, thereby inducing a positive response in the antioxidant system. As such, the salinity-alkalinity stress was effectively relieved, particularly from the osmotic stress. The malondialdehyde content decreased obviously, whereas the alfalfa superoxide dismutase increased significantly, indicating that the perennial ryegrass antioxidant system was given a priority with the decomposition of hydrogen peroxide enzyme. The biomass, plant height, and root length of ryegrass and alfalfa increased by 48.50%-82.34%, 31.19%-44.16%, and 17.15%-48.09%, respectively, under 3% biochar treatment, compared with that under the salt-alkali stress. Additionally, the stomatal conductance and transpiration rate increased by 118.69%-358.99%, and 98.66%-526.53%, and the chlorophyll content and net photosynthetic rate increased by 7.97% and 519.09%, respectively. 3) The antioxidant enzyme activities and malondialdehyde content of ryegrass and alfalfa remained stable or decreased with the increase of biochar amount under the short-term salt-alkali stress. A trend was also found that the promoting at a low supplemental level and inhibiting at a high supplemental level for the growth indexes and photosynthetic characteristics. Consequently, there was a significant effect of salinity-alkalinity stress on the growth of perennial ryegrass and alfalfa, where a positive response was found at the beginning of stress. Correspondingly, an optimal addition of biochar can be expected to improve the stress resistance of perennial ryegrass and alfalfa, while the positive reaction of the oxidation system can effectively relieve the short-term salinity-alkalinity stress. The best effect was also achieved in the 3% biochar application. The findings can provide a sound reference for the scientific application of biochar to promote crop growth and yield.<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:Plants (botany)
Controlled terms:Additives - Aldehydes - Antioxidants - Ascorbic acid - Deionized water - Enzymes - Photosynthesis - Saline water - Sodium bicarbonate - Sodium Carbonate  - Sodium chloride
Uncontrolled terms:Antioxidant systems - Antioxidants enzymes - Biochar - Growth indices - Lolium perenne L - Malondialdehyde - Medicago sativa L - Perennial ryegrass - Saline solutions - Saline-alkali stress
Classification code:444  Water Resources - 445.1  Water Treatment Techniques - 741.1  Light/Optics - 802.2  Chemical Reactions - 803  Chemical Agents and Basic Industrial Chemicals - 804  Chemical Products Generally - 804.1  Organic Compounds - 804.2  Inorganic Compounds
Numerical data indexing:Molar concentration 1.50E+02mol/m3, Percentage 1.00E00%, Percentage 1.1869E+02% to 3.5899E+02%, Percentage 1.715E+01% to 4.809E+01%, Percentage 3.00E+00%, Percentage 3.119E+01% to 4.416E+01%, Percentage 4.85E+01% to 8.234E+01%, Percentage 5.00E+00%, Percentage 5.1909E+02%, Percentage 7.97E+00%, Percentage 9.866E+01% to 5.2653E+02%, Volume 1.00E-04m3
DOI:10.11975/j.issn.1002-6819.2021.17.013

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

<RECORD 9>

Accession number:20214811226886
Title:Construction of remote sensing monitoring model of wheat stripe rust based on fractional-order differential spectral index
Title of translation:&#22522;&#20110;&#20998;&#25968;&#38454;&#24494;&#20998;&#20809;&#35889;&#25351;&#25968;&#30340;&#23567;&#40614;&#26465;&#38152;&#30149;&#36965;&#24863;&#30417;&#27979;&#27169;&#22411;&#26500;&#24314;
Authors:Jing, Xia (1); Zhang, Teng (1); Zou, Qin (1); Yan, Jumei (1); Dong, Yingying (2)
Author affiliation:(1) College of Geomatics, Xi'an University of Science and Technology, Xi'an; 710054, China; (2) Aerospace Information Research Institute, Chinese Academy of Science, Beijing; 100094, China
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:17
Issue date:September 1, 2021
Publication year:2021
Pages:142-151
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">Hyper spectral data is the most vulnerable to environmental noise (such as soil background) when monitoring wheat stripe rust. The first- and second-order differential processing of spectral data can be used to eliminate part of the noise, but it is easy to ignore the detailed information of stripe rust. In this study, a fractional-order differential spectral index was proposed to process the hyperspectral data of wheat canopy under the stress of stripe rust. Three two-band and three three-band fractional-order spectral differential indices were constructed after the band combination optimization, according to the current six types of spectral index. Gaussian regression was also applied to estimate the severity of stripe rust disease, compared with the commonly-used reflectivity spectral index. The results showed that the correlation between the fractional-order differential spectrum and the disease index of stripe rust was more significant than that of the original spectrum, where the most obvious significance was found in the range of 0.3-1.3 order differential spectrum. The correlation coefficient was the largest for the 481 nm band of 1.2 order differential spectrum with the severity of wheat stripe rust, 20.9%, 3.9%, and 20.5% higher than that of the original reflectance spectrum, the first-, and the second-order differential spectrum, respectively. Two-band fractional-order differential spectral indices were determined by the maximum correlation coefficient. Specifically, the values of the best order for the fractional-order differential-difference index, ratio index, and normalized difference index were 0.4, 1.3 and 1.2, respectively, where the band combination was 481 and 475 nm, 478 and 622 nm, as well as 481 nm and 673 nm, respectively. In the three-band fractional-order differential-difference index, the best order of fractional-order differential improved difference index was 1.1, and the band combination was 481, 442, and 454 nm. The best order of fractional-order differential improved ratio index was 1.2, and the band combination was 880, 670, and 481 nm. The best order of fractional-order differential photochemical reflectance index was 0.5, and the band combination is 646, 400, and 955 nm. The correlation between the three-band fractional-order differential spectral index and the severity of wheat stripe rust was better than that of the two-band fractional-order differential spectral index, where the fractional-order differential photochemical reflectance index presented the highest correlation with the severity of wheat stripe rust. Furthermore, the Gaussian regression model using the fractional-order differential spectral index indicated a better prediction accuracy for the stripe rust disease index than that for the reflectance spectral index. The determination coefficient between the predicted and measured values of Disease Index (DI) in the training and validation data set increased by 3.8% and 19.1%, respectively, where the Root Mean Square Error (RMSE) decreased by 13.0% and 33.5%, respectively, compared with the reflectance spectral index. Consequently, the fractional-order differential spectral index can be expected to improve the remote sensing detection accuracy of wheat stripe rust. This finding can provide a promising feasible way for the hyper spectral remote sensing to monitor the wheat stripe rust, thereby realizing the large-scale high-precision remote sensing monitoring of crop health.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:40
Main heading:Reflection
Controlled terms:Data handling - Gaussian distribution - Gaussian noise (electronic) - Regression analysis - Remote sensing
Uncontrolled terms:Band combinations - Differential spectra - Fractional order - Fractional-order differential - Gaussian process regression - Remote-sensing - Spectral indices - Stripe rust - Two bands - Wheat stripe rust
Classification code:723.2  Data Processing and Image Processing - 922.1  Probability Theory - 922.2  Mathematical Statistics
Numerical data indexing:Percentage 1.30E+01%, Percentage 1.91E+01%, Percentage 2.05E+01%, Percentage 2.09E+01%, Percentage 3.35E+01%, Percentage 3.80E+00%, Percentage 3.90E+00%, Size 4.54E-07m, Size 4.75E-07m, Size 4.78E-07m, Size 4.81E-07m, Size 6.22E-07m, Size 6.73E-07m, Size 9.55E-07m
DOI:10.11975/j.issn.1002-6819.2021.17.016

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

<RECORD 10>

Accession number:20214811226959
Title:Effects of ionic strength, pH value and humic acid on the settlement of graphitic carbon nitride
Title of translation:&#27700;&#20307;&#31163;&#23376;&#24378;&#24230;, pH&#20540;&#21644;&#33104;&#27542;&#37240;&#27987;&#24230;&#23545;&#30707;&#22696;&#30456;&#27694;&#21270;&#30899;&#27785;&#38477;&#30340;&#24433;&#21709;
Authors:Dong, Shunan (1); Xia, Jihong (1); Wang, Weimu (1); Liu, Hui (1); Sheng, Liting (1)
Author affiliation:(1) College of Agricultural Science and Engineering, Hohai University, Nanjing; 210098, China
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:17
Issue date:September 1, 2021
Publication year:2021
Pages:218-224
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">Graphitic Carbon Nitride (g-C<inf>3</inf>N<inf>4</inf>) has widely been used on the photocatalytic degradation of agricultural organic pollutants (e.g. pesticides, polycyclic aromatic hydrocarbons, and antibiotics). Therefore, it is inevitably entering into the agricultural environment soil, leading to potential risks. This study aims to better evaluate and then predict the photocatalytic efficiency and environmental risk of g-C<inf>3</inf>N<inf>4</inf> application. Experiment, simulation, and theoretical calculation were conducted to investigate the settlement and dispersion stability of g-C<inf>3</inf>N<inf>4</inf> in the aqueous environment. Three typical water factors were also considered, including ionic strength, pH values, and humic acid concentration. Experimental data showed that the water ionic strength was the most important factor in the settlement and dispersion stability of g-C<inf>3</inf>N<inf>4</inf>. A one-site kinetic settlement model was also well established to fit the settlement rate data of g-C<inf>3</inf>N<inf>4</inf> from the experimental measurement. The extended DLVO theory was selected to explain the energy distribution between g-C<inf>3</inf>N<inf>4</inf> particles in the aqueous environment. The settlement of g-C<inf>3</inf>N<inf>4</inf> was remarkably enhanced with the increasing ionic strength, thereby reducing the dispersion stability. Specifically, the final standardized concentration (after 360 min) of g-C<inf>3</inf>N<inf>4</inf> suspension decreased from 0.86 to 0.58, while the fitted settlement rate increased from 0.014 4 to 0.019 1 cm/min, as well as the Zeta potential of g-C<inf>3</inf>N<inf>4</inf> particles increased from -37.1 to -12.7 mV, with the ionic strength increased from 0 to 50 mmol/L. The variation of Zeta potential indicated that the charge shielding of g-C<inf>3</inf>N<inf>4</inf> particles increased with the increasing ionic strength, leading to the compressed electrical double layer and enhanced aggregation of g-C<inf>3</inf>N<inf>4</inf> for better settlement and reduced dispersion stability. Additionally, the water pH showed a relatively low impact on the settlement and dispersion stability of g-C<inf>3</inf>N<inf>4</inf>. The settlement of g-C<inf>3</inf>N<inf>4</inf> was firstly enhanced and then reduced with the increasing pH values. Specifically, the final standardized concentration (after 360 min) of g-C<inf>3</inf>N<inf>4</inf> suspension firstly decreased from 0.63 to 0.57, as the pH values increased from 2 to 4, and then increased from 0.57 to 0.78 with the pH values further increased from 4 to 10. Particularly, the tendency of fitted settlement rate was well consistent with the experimental measurement. By contrast, the hydrodynamic radius of g-C<inf>3</inf>N<inf>4</inf> also firstly increased from 1 116 to 1 271 nm, as the pH values increased from 2 to 4, whereas, then decreased from 1 271 to 862 nm with the pH values further increased from 4 to 10. The Zeta potential of g-C<inf>3</inf>N<inf>4</inf> particles decreased from 18.6 to -57.0 mV with the increasing pH values. Correspondingly, the highest settlement and lowest dispersion stability of g-C<inf>3</inf>N<inf>4</inf> were achieved in the aqueous environment, when the pH value approached the isoelectric point (pH was 4). Furthermore, the electrostatic repulsion and steric hindrance between g-C<inf>3</inf>N<inf>4</inf> particles increased in the presence of humic acid, leading to the reduced settlement and enhanced dispersion stability of g-C<inf>3</inf>N<inf>4</inf> with the increasing concentrations. However, there was a critical point for the enhanced efficiency. More importantly, the final standardized concentration (after 360 min) of g-C<inf>3</inf>N<inf>4</inf> suspension significantly increased from 0.60 to 0.87, as the humic acid concentrations increased from 0 to 5 mg/L, but remained at 0.91 with the humic acid concentrations further increased to 10 mg/L. Anyway, the finding can be expected to well elucidate the settlement and dispersion stability of g-C<inf>3</inf>N<inf>4</inf> in the aqueous environment under typical conditions, particularly for the better understanding of potential behaviors of graphitic carbon nitride in modern agriculture.<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:Kinetics
Controlled terms:Agriculture - Degradation - Ionic strength - Organic pollutants - pH - Polycyclic aromatic hydrocarbons - Stability - Suspensions (fluids) - Zeta potential
Uncontrolled terms:Acid concentrations - Aqueous environment - Dispersion stability - Effect of ionic strength - Graphitic carbon nitrides - Humic acid - pH value - Photo-catalytic - Settlement kinetic - Settlement rate
Classification code:631.1  Fluid Flow, General - 801.1  Chemistry, General - 801.3  Colloid Chemistry - 801.4  Physical Chemistry - 802.2  Chemical Reactions - 804  Chemical Products Generally - 804.1  Organic Compounds - 821  Agricultural Equipment and Methods; Vegetation and Pest Control - 931  Classical Physics; Quantum Theory; Relativity
Numerical data indexing:Force 3.00E+00N, Mass density 0.00E00kg/m3 to 5.00E-03kg/m3, Mass density 1.00E-02kg/m3, Molar concentration 0.00E00mol/m3 to 5.00E+01mol/m3, Size 1.00E-02m, Size 2.71E-07m, Size 2.71E-07m to 8.62E-07m, Time 2.16E+04s, Voltage -1.27E-02V, Voltage -5.70E-02V
DOI:10.11975/j.issn.1002-6819.2021.17.025

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

<RECORD 11>

Accession number:20214811226927
Title:Method for predicting cotton yield based on CNN-BiLSTM
Title of translation:&#22522;&#20110;CNN-BiLSTM&#30340;&#26825;&#33457;&#20135;&#37327;&#39044;&#27979;&#26041;&#27861;
Authors:Dai, Jianguo (1, 2); Jiang, Nan (1, 2); Xue, Jinli (1, 2); Zhang, Guoshun (1, 2); He, Xiangliang (1, 2)
Author affiliation:(1) School of Information Science and Technology, Shihezi University, Shihezi; 832003, China; (2) Geospatial Information Engineering Research Center, Xinjiang Production and Construction Corps, Shihezi; 832003, China
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:17
Issue date:September 1, 2021
Publication year:2021
Pages:152-159
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">Aiming at the imperfections in traditional crop yield estimation methods of model generalization and the question about lack of temporal and spatial features, this study took machine-harvested cotton as the research object, combined with Unmanned Aerial Vehicle (UAV) remote sensing platform and deep learning technology to carry out multi-period remote sensing observation and yield estimation of cotton. Taking the images of the cotton seedling stage, bud stage, and flowering stage as the time series data set, the Convolutional Neural Network and Bidirectional Long Short-Term Memory (CNN-BiLSTM) model combined with convolution neural network and bidirectional long short term memory network was constructed to predict cotton yield, which improved the feature extraction ability of time dimension and spatial dimension, and verified the performance of convolution neural network and bidirectional long short term memory network respectively, as well as the impact of different depth structures on yield estimation. The results showed that in the verification experiments of Long Short-Term Memory (LSTM) and BiLSTM network structure, the BiLSTM<inf>1</inf> model with network depth of 1 layer had the best effect, with the determination coefficient of 0.851, root mean square error of 161.911 g (the input, output and validation of all models are based on 2.3 m&times;2.3 m quadrat data), and average absolute percentage error of 7.304%. The three evaluation indexes were higher than other comparable models. Besides, the accuracy of the LSTM<inf>2</inf> model with 2 hidden layers was the second, the accuracy of the single-layer LSTM<inf>1</inf> model was the third, and the determination coefficients were 0.844 and 0.834 respectively. And the accuracy of LSTM<inf>4</inf>, BiLSTM<inf>2</inf>, and BiLSTM<inf>4</inf> decreased in turn. The results showed that only increased the depth of the network couldn't improve model prediction accuracy. In the verification experiment of CNN-BiLSTM model based on BiLSTM<inf>1</inf>, the CNN<inf>1</inf>-BiLSTM model with 1 convolution layer had the worst effect, its determination coefficient was 0.885, the root mean square error was 147.167 g and the average absolute percentage error was 6.711%, and the three evaluation indexes were lower than other models. It showed that when the convolution layer of the CNN network was less, the shallow layer features extracted by the CNN network couldn't improve the accuracy of the model, even caused interference. However, with the increase of the convolution layer, the prediction accuracy was gradually improved. When the volume layer increases to 10 layers, the determination coefficient of the CNN<inf>10</inf>-BiLSTM model reached 0.857, and the average absolute percentage error decreased to 7.256%. When the convolution layers were 14, the performance index reached the peak and was obviously better than the precision of the BiLSTM model and LSTM model. The coefficient of determination was 0.885, the root mean square error was 147.167 g, and the average absolute percentage error was 6.711%. However, when the number of convolution layers exceeds 14, the increase of convolution layers of CNN didn't help the performance improvement of the model, and it would decrease. For example, the decision coefficient of the CNN<inf>20</inf>-BiLSTM model was only 0.870. In conclusion, based on the multi-phase cotton images collected by the UAV remote sensing platform, the CNN-BiLSTM model adopted in this study could effectively extract the characteristics of spatial dimension and time dimension, and achieve the accurate prediction of cotton yield at time-series scale and cotton field scale, which could provide a reference for the research on crop yield estimation based on time series data.<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:Long short-term memory
Controlled terms:Antennas - Brain - Convolution - Convolutional neural networks - Crops - Feature extraction - Forecasting - Image enhancement - Mean square error - Multilayer neural networks  - Remote sensing - Unmanned aerial vehicles (UAV)
Uncontrolled terms:Bidirectional long short-term memory - Convolutional neural network - Cotton yield - Determination coefficients - Memory modeling - Percentage error - Root mean square errors - Visible light - Yield - Yield estimation
Classification code:461.1  Biomedical Engineering - 652.1  Aircraft, General - 716.1  Information Theory and Signal Processing - 821.4  Agricultural Products - 922.2  Mathematical Statistics
Numerical data indexing:Mass 1.47167E-01kg, Mass 1.61911E-01kg, Percentage 6.711E+00%, Percentage 7.256E+00%, Percentage 7.304E+00%, Size 2.30E+00m
DOI:10.11975/j.issn.1002-6819.2021.17.017

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

<RECORD 12>

Accession number:20214811226951
Title:Yield estimation of winter wheat in China based on CNN-RNN network
Title of translation:&#22522;&#20110;CNN-RNN&#32593;&#32476;&#30340;&#20013;&#22269;&#20908;&#23567;&#40614;&#20272;&#20135;
Authors:He, Xiaohui (1); Luo, Haotian (1); Qiao, Mengjia (2); Tian, Zhihui (1); Zhou, Guangsheng (3)
Author affiliation:(1) School of Earth Science and Technology, Zhengzhou University, Zhengzhou; 450001, China; (2) School of Information Engineering, Zhengzhou University, Zhengzhou; 450001, China; (3) Ecometeorology Joint Laboratory of Zhengzhou University and Chinese Academy of Meteorological Science, Zhengzhou; 450001, China
Corresponding author:Tian, Zhihui(iezhtian@zzu.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:17
Issue date:September 1, 2021
Publication year:2021
Pages:124-132
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 rapid and accurate evaluation of crop yield at large scale is very critical to the planning and management of crop market. The prediction of crop yield includes the extensive manual sampling or the use of remote sensing images at present. Particularly, the remote sensing is preferred to predict the large-scale crop yield, providing a large number of satellite images with long time and high spatial resolution. Much effort has been made on the crop yield prediction using remote sensing images and deep learning. However, the yield generally presents nonlinear spatio-temporal characteristics, due mainly to the growth status of crops changes with the time. A single deep learning cannot make full use of crop growth characteristics in multi-temporal images. In this study, a Convolutional Neural Networks - Gated Recurrent Unit (CNN-GRU) estimation model was proposed to extract rich spatial-spectral features from multi-spectral remote sensing images. The time dependence in the phase remote sensing image was also adaptively learned among the various stages in the growth period of winter wheat. As such, the growth characteristics of winter wheat were integrated from multiple scales, further to predict the yield. Furthermore, the main production areas of winter wheat in the country were taken as the research area, including 1 713 districts and counties. The forecast dataset of winter wheat production was finally constructed using the statistical output of winter wheat, MODIS surface reflectance, day and night surface temperature images, and land cover data from 2001 to 2018, thereby to verify the performance of CNN-GRU production estimation model. The results showed that: 1) The annual average root mean square error (RMSE) and mean absolute error (MAE) of the CNN-GRU production estimation model from 2016 to 2018 were 818.3 and 560 kg/hm², respectively, where the annual average RMSE was reduced by 20.13%, 18.81%, 29.51%, 34.84%, and 36.57%, respectively, compared with the CNN, GRU, Support Vector Regression (SVR), Random Forest (RF), and Decision Tree (DT). Therefore, the CNN-GRU yield estimation model can be expected to accurately predict the winter wheat yield, further to effectively extract the space-spectrum-time information in the entire growth period of winter wheat from multi-temporal remote sensing images. 2) Six time windows were divided during the whole growth period of winter wheat, including the sowing-emergence, tillering-overwintering, green-rising, jointing-booting, heading-flowering, and filling-maturity period. Among them, the highest accuracy of CNN-GRU estimation model was found in the filling-maturation period. Specifically, the annual averages of RMSE and MAE were 817 and 556 kg/hm², respectively, and R² was greater than 0.7. However, the annual averages of RMSE and MAE during the heading-flowering period were 823 and 560 kg/hm², respectively, where the annual average RMSE was 0.7% lower than the filling-maturation period. Therefore, the proposed CNN-GRU yield estimation model can accurately predict the output of winter wheat in the middle and late stages of growth period, particularly for the national yield of winter wheat two months in advance.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:47
Main heading:Convolutional neural networks
Controlled terms:Convolution - Crops - Decision trees - Forecasting - Mean square error - Recurrent neural networks - Remote sensing - Space optics
Uncontrolled terms:Convolutional neural network - Estimation models - Model convolutional neural network model - Multi temporal remote sensing image - Multi-temporal remote sensing - Neural network model - Recurrent neural network model - Remote sensing images - Winter wheat - Yield
Classification code:656.1  Space Flight - 716.1  Information Theory and Signal Processing - 741.1  Light/Optics - 821.4  Agricultural Products - 921.4  Combinatorial Mathematics, Includes Graph Theory, Set Theory - 922.2  Mathematical Statistics - 961  Systems Science
Numerical data indexing:Mass 5.56E+02kg, Mass 5.60E+02kg, Mass 8.17E+02kg, Mass 8.183E+02kg, Mass 8.23E+02kg, Percentage 1.881E+01%, Percentage 2.013E+01%, Percentage 2.951E+01%, Percentage 3.484E+01%, Percentage 3.657E+01%, Percentage 7.00E-01%
DOI:10.11975/j.issn.1002-6819.2021.17.014

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

<RECORD 13>

Accession number:20214811226970
Title:Effects of different mechanized tillage and sowing modes on the seedling quality and yield of winter wheat
Title of translation:&#19981;&#21516;&#26426;&#26800;&#21270;&#32789;&#25773;&#27169;&#24335;&#23545;&#20908;&#23567;&#40614;&#24188;&#33495;&#36136;&#37327;&#21644;&#20135;&#37327;&#30340;&#24433;&#21709;
Authors:Zhao, Lingtian (1); Xian, Yunyu (1); Liu, Guangming (1); Jiang, Hengxin (1); Liao, Pingqiang (1); Zhao, Can (1); Wang, Weiling (1); Huo, Zhongyang (1)
Author affiliation:(1) Jiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology/Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou; 225009, China
Corresponding author:Huo, Zhongyang(huozy69@163.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:17
Issue date:September 1, 2021
Publication year:2021
Pages:31-38
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 provide theoretical and practical basis for high-yield and high-efficiency mechanized cultivation of wheat, different wheat tillage and sowing compound operation machines were selected, and the effects of different mechanized tillage and sowing modes on wheat seedling growth and yield were studied with traditional tillage and sowing machinery and artificial sowing planting as control in this study. The results showed that the compound operation mode of LCB-10 wheat precision fertilization rotary tillage planter (mode 2) and the compound operation mode of biaxial layered cutting, fertilization and sowing, pressing and trenching (mode 1) had high operation efficiency, vegetation coverage and seedling emergence rate, large seedling leaf area and dry matter accumulation in the early stage of overwintering, high overwintering seedling robustness, large population panicles and total grains, high yield and good economic benefit, it was suitable for large-scale popularization. Among them, the operation efficiency of mode 2 and mode 1 was 0%-260%, 100%-620% higher than that of other modes, the average seedling emergence rate in two years was 6.77%-20.60%, 6.13%-19.87%, the average grain yield in two years was 9 894.38 and 9 689.64 kg/hm² respectively, which was 23.84%-42.90%, 21.28%-39.94% higher than that of other modes, and the average economic benefit in two years was 51.54%-112.68% higher than that of other modes 46.94%-106.23%. The sowing depth of model 2 was deep and the uniformity of seedling emergence was not high, while the seed dew rate of model 1 was relatively high. The vegetation coverage rate of 2BFG-10 (6) 230 rotary tillage intelligent fertilization planter compound operation mode (mode 4) was higher than that of mode 3 (LCB-10 wheat precision fertilization rotary tillage planter less no till drill mode) and mode 6 (traditional shallow rotary tillage and manual sowing mode), and the seedling emergence rate and seedling uniformity were higher than that of mode 3, mode 5 (traditional mechanical fertilization rotary tillage and sowing mode) and mode 6, the exposed seed rate was less than that of model 5 and model 6, the population dry matter accumulation is higher, the population panicle number, total grain number, yield and economic benefit at maturity were also significantly higher than that of model 3, model 5 and model 6, in which the average yield in two years was 7.01%-15.39% higher and the economic benefit was 14.23%-40.35% higher, which was also suitable for large-scale popularization and application. Although mode 6 had low mechanical and total investment cost and suitable sowing depth, it was not suitable for large-scale high-yield and high-efficiency production of wheat because of its high labor cost, low operation efficiency, high seed dew rate, poor seedling uniformity, few tillers per plant, few panicles at maturity, small panicle type and lowest yield and economic benefit.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:25
Main heading:Efficiency
Controlled terms:Compressive strength - Cultivation - Drills - Economic and social effects - Grain (agricultural product) - Seed - Vegetation
Uncontrolled terms:Compound operation - Economic benefits - Higher yield - Mechanisation - Operation efficiencies - Rotary tillages - Seedling emergence - Seedling quality - Tillage and sowing mode - Yield
Classification code:603.2  Machine Tool Accessories - 821.3  Agricultural Methods - 821.4  Agricultural Products - 913.1  Production Engineering - 971  Social Sciences
Numerical data indexing:Mass 6.8964E+02kg, Percentage 0.00E00% to 2.60E+02%, Percentage 1.00E+02% to 6.20E+02%, Percentage 1.423E+01% to 4.035E+01%, Percentage 2.128E+01% to 3.994E+01%, Percentage 2.384E+01% to 4.29E+01%, Percentage 4.694E+01% to 1.0623E+02%, Percentage 5.154E+01% to 1.1268E+02%, Percentage 6.13E+00% to 1.987E+01%, Percentage 6.77E+00% to 2.06E+01%, Percentage 7.01E+00% to 1.539E+01%
DOI:10.11975/j.issn.1002-6819.2021.17.004

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

<RECORD 14>

Accession number:20214811226854
Title:Extraction of paddy rice planting areas based on multi-temporal GF-6 remote sensing images
Title of translation:&#22522;&#20110;&#22810;&#26102;&#30456;GF-6&#36965;&#24863;&#24433;&#20687;&#30340;&#27700;&#31291;&#31181;&#26893;&#38754;&#31215;&#25552;&#21462;
Authors:Zhang, Yueqi (1, 2); Li, Rongping (3); Mu, Xihan (2); Ren, Hongrui (1, 2)
Author affiliation:(1) Department of Geomatics, Taiyuan University of Technology, Taiyuan; 030024, China; (2) State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing; 100875, China; (3) Institute of Atmospheric Environment, China Meteorological Administration, Shenyang; 110166, China
Corresponding author:Li, Rongping(rongpingli@aliyun.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:17
Issue date:September 1, 2021
Publication year:2021
Pages:189-196
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">Efficient extraction from high-precision remote sensing images has widely been one of the most important ways to determine the superiority of red-edge information in crop classification. This study aims to quickly and accurately map the paddy rice planting area using GF-6 WFV time-series images in Panjin City, Liaoning Province of China. Six feature types of paddy rice were divided into the construction land, water body, natural vegetation, natural wetland, and dry land, according to the principle of spectral consistency. The Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Ratio Vegetation Index (RVI), and Normalized Difference Red-Edge 1 Index (NDRE1) were established by the GF-6 WFV images taken in the periods of May 11^th, May 25^th, June 1^st, June 6^th, July 20^th and August 22^nd in 2020. Five stages of images were also divided into the trefoil, transplanting, returning green, booting, and heading stage of paddy rice, according to the phenological rhythm in the study area. Among them, the returning green stage image was covered by June 1^st and June 6^th. As such, a remote sensing extraction of paddy rice was established, according to the dynamic change of NDVI, NDWI, RVI, and NDRE1 of various feature types over time. Firstly, the NDRE1 at the transplanting and heading stages of paddy rice were selected to preliminarily extract paddy rice. Secondly, some masks were established to remove the impacts of other feature types. The water body and construction land were eliminated by NDWI and maximum RVI, respectively, from the trefoil to the heading stage. The natural vegetation was eliminated by NDVI of paddy rice at the trefoil stage. The natural wetland was eliminated by NDVI of paddy rice at the transplanting stage, while, the dry land was eliminated by NDWI in transplanting or returning the green stage of paddy rice. Finally, the remaining pixels were taken as the paddy rice. Results showed that the extraction area of paddy rice was 111 058.71 hm² in the study area in 2020, mainly distributed in Dawa District and Panshan County, accounting for 54.47% and 37.95% of the total extraction area, respectively. The overall accuracy was 94.44% under 36 field verification points. Specifically, the overall accuracy was 95.60% with the Kappa coefficient of 0.91, while the mapping accuracy of paddy rice was 95.33% with the user accuracy of 97.28%, after the accuracy verification by 250 visual interpretation points using Google Earth high-resolution images. As such, the distribution map of paddy rice without red-edge bands was obtained using the same remote sensing images and masks, substituting NDVI for NDRE1 in the preliminary paddy rice extraction. More importantly, the extraction with red-edge bands showed increases of 3.20 percentage points, 6.00 percentage points, and 0.06 in the overall accuracy, mapping accuracy of paddy rice, and Kappa coefficient, respectively. By contrast, the extraction with or without red-edge bands was superimposed on the remote sensing image, indicating that the paddy rice distributions were similar, but the extraction without red-edge bands presented an obvious omission. This finding proved that the red-edge bands effectively reduced the classification error and omission of crops. Consequently, the domestic red-edge satellite data can provide a great application potential to crop classification and area extraction.<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:Classification (of information)
Controlled terms:Crops - Extraction - Feature extraction - Image processing - Remote sensing - Satellites - Vegetation - Wetlands
Uncontrolled terms:Area extraction - Edge bands - GF-6 satellite - Normalized difference vegetation index - Normalized difference water index - Paddy-rice - Red edge - Red-edge band - Remote sensing images - Remote-sensing
Classification code:655.2  Satellites - 716.1  Information Theory and Signal Processing - 723.2  Data Processing and Image Processing - 802.3  Chemical Operations - 821.4  Agricultural Products - 903.1  Information Sources and Analysis
Numerical data indexing:Percentage 3.795E+01%, Percentage 5.447E+01%, Percentage 9.444E+01%, Percentage 9.533E+01%, Percentage 9.56E+01%, Percentage 9.728E+01%, Size 1.524E-03m, Size 5.08E-02m
DOI:10.11975/j.issn.1002-6819.2021.17.021

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

<RECORD 15>

Accession number:20214811226836
Title:Optimization and experimental study of tempering process parameters during hot air drying of paddy rice
Title of translation:&#31291;&#35895;&#28909;&#39118;&#24178;&#29157;&#32531;&#33487;&#24037;&#33402;&#21442;&#25968;&#20248;&#21270;&#19982;&#35797;&#39564;
Authors:Wang, Danyang (1); Wang, Jie (1); Qiu, Shuo (1); Zhan, Tingyao (1); Tao, Dongbing (2); Zhang, Benhua (3)
Author affiliation:(1) College of Engineering, Shenyang Agricultural University, Shenyang; 110866, China; (2) Food Science College, Shenyang Agricultural University, Shenyang; 110866, China; (3) College of Mechanical and Electrical Engineering, Suqian University, Suqian; 223800, China
Corresponding author:Zhang, Benhua(benhuazhang@163.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:17
Issue date:September 1, 2021
Publication year:2021
Pages:285-292
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">Paddy rice is the second largest grain crop in the world, and it needs to be dried to a safe moisture content post-harvest for long-term storage. The traditional continuous hot air drying has many problems such as low drying efficiency, serious additional crack percentage, and poor edible quality and nutritional quality. For this reason, the tempering operation is generally added in the drying process of paddy rice. Tempering drying is a periodicity drying technology, in which the continuous heating for paddy rice is stopped, and the moisture in paddy rice is redistributed under the action of water potential. When the drying heat is obtained again, the moisture diffusion rate and surface evaporation rate increase significantly. The parameters of tempering drying mainly include the drying section and tempering section and their matching parameters. However, most of letters have explored the variety of macro indexes of paddy rice for the parameters of the drying section and some of the tempering section such as tempering time. There is a lack of systematic analysis on the conditions of tempering, such as tempering time, tempering temperature and tempering time. And the influence of different parameters on the nutritional quality of paddy rice also needs to be further studied. Aiming at the problem of insufficient research on the correlation between drying technology and quality indicators, this research explores the influence of tempering temperature, starting time of tempering, duration of tempering, and number of tempering cycles on the additional crack rate, head rice rate, protein content, and fatty acid value of paddy rice based on previous research results. Firstly, the change trend of paddy rice drying quality with tempering process parameters was analyzed by a single-factor test, and the weight of additional crack percentage, head rice rate, protein content and fatty acid value were all greater than 20 %, which were the key indexes of paddy rice tempering drying. Secondly, the main factors affect the drying quality of paddy rice and their corresponding levels were determined by the comprehensive evaluation method of the membership function model, including the moisture content of paddy rice at the beginning of tempering, tempering temperature and tempering time. Eventually, in this study, the Central-Composite experimental optimum design methods were used. The tempering temperature, the moisture content of paddy rice at the beginning of tempering, and tempering time as independent variable, and the additional crack percentage, protein content, fatty acid value as response indexes, using response surface to analyze the relationship between the test factors and quality indexes and explaining the causes of the results. The results showed that, the optimal operating parameters were determined as the moisture content of paddy rice at the tempering temperature of 45&#8451;, the moisture content of paddy rice of 21% at the tempering beginning, the tempering time of 1.61 h. Under these conditions, the predicted value of the additional crack percentage of paddy rice after drying was 6.63%, the protein content was 5.39%, the fatty acid value was 11.68%. The results of verification experiments showed that the additional crack rate was 6.86%, the protein content was 5.35%, and the fatty acid value was 11.14%. The average error between the test value and the software optimization parameter value was 2.97%. The results showed that the optimized tempering drying process significantly improved the drying quality of paddy rice, which would provide the theoretical basis for production practice and in-depth exploration of the mechanism of paddy rice quality change.<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:Optimization
Controlled terms:Cracks - Drying - Fatty acids - Grain (agricultural product) - Moisture - Moisture determination - Proteins - Quality control - Tempering
Uncontrolled terms:%moisture - Acid value - Additional crack rate - Drying quality - Nutritional qualities - Paddy-rice - Process parameters - Protein contents - Tempering process - Tempering temperature
Classification code:537.1  Heat Treatment Processes - 804.1  Organic Compounds - 821.4  Agricultural Products - 913.3  Quality Assurance and Control - 921.5  Optimization Techniques - 944.2  Moisture Measurements
Numerical data indexing:Percentage 1.114E+01%, Percentage 1.168E+01%, Percentage 2.00E+01%, Percentage 2.10E+01%, Percentage 2.97E+00%, Percentage 5.35E+00%, Percentage 5.39E+00%, Percentage 6.63E+00%, Percentage 6.86E+00%, Time 5.796E+03s
DOI:10.11975/j.issn.1002-6819.2021.17.033

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

<RECORD 16>

Accession number:20214811226873
Title:Detection model for wine grapes using MobileNetV2 lightweight network
Title of translation:&#37319;&#29992;&#36731;&#37327;&#32423;&#32593;&#32476;MobileNetV2&#30340;&#37247;&#37202;&#33889;&#33796;&#26816;&#27979;&#27169;&#22411;
Authors:Li, Guojin (1); Huang, Xiaojie (1); Li, Xiuhua (1); Ai, Jiaoyan (1)
Author affiliation:(1) School of Electrical Engineering, Guangxi University, Nanning; 530004, China
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:17
Issue date:September 1, 2021
Publication year:2021
Pages:168-176
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">Efficient detection of grape image has widely been one of the most important technologies in automatic grape harvesting robots. In this study, a wine grape detection model (WGDM) was proposed to improve the speed and accuracy of field grape detection using a lightweight network. Firstly, the MobileNetV2 lightweight network was adopted to significantly increase the detection speed for real-time objects in the WGDM model, due to the smaller size, faster speed, and higher accuracy in the image recognition, compared with DarkNet53 in the original YOLOv3. Secondly, the M-Res2Net module was added to the multi-scale detection of YOLOv3, as some standard convolutional layers with 1&times;1 and 3&times;3 convolution kernels were removed, particularly for the better capability of multi-scale feature extraction and higher accuracy of detection in the improved model. Finally, a new location loss function was established using the balanced loss and the intersection over union loss. The classification and object loss stayed the same as the YOLO. As such, a more balance was achieved in the object, classification and location during the model training, thereby to enlarge the precision of object location. Different detection models were trained, including the proposed WGDM, Single Shot Detector (SSD), the original YOLOv3, YOLOv4, and Faster Regions with Convolutional Neural Network (Faster R-CNN). The available wine grape instance segmentation dataset (WGISD) was also selected, including 300 images of wine grape and 300 annotation files with 4 432 objects under the same experimental conditions. Additionally, the resolution of input image was adjusted from the original resolution of 2 048&times;1 365 pixels or 2 048&times;1 536 pixels to 608&times;608 pixels. The experimental results showed that the proposed WGDM model in the test set of wine grape image dataset achieved an average accuracy of 81.20%. The F1-score (a metric function that balances the precision and recall of the model) of the proposed model reached 0.856 3, which was 0.056 3 higher than that of SSD, 0.005 4 higher than that of the original YOLOv3, 0.041 7 higher than that of YOLOv4, and 0.012 5 higher than that of Faster R-CNN. The network structure size of the proposed model was 44 MB, which was 50 MB smaller than that of SSD, 191 MB smaller than that of the original YOLOv3 or YOLOv4, and 83 MB less than that of Faster R-CNN. The average detection time for each grape image in the proposed model was 6.29 ms, which was 4.91 ms shorter than that of SSD, 7.75 ms shorter than that of the original YOLOv3, 14.84 ms shorter than that of YOLOv4, and 158.2 ms shorter than that of Faster R-CNN. Moreover, the number of floating-point operations (the sum of the number of multiplication operations and the number of addition operations) of the proposed model was only 10.14 &times;10⁹, which was 11.58% of SSD 14.54% of the original YOLOv3, 16.05% of YOLOv4, and 5.48%-15.33% of Faster R-CNN. Therefore, the proposed WGDM model presented the faster and more accurate recognition and location of grape fruits in the field, providing a feasible path for the efficient visual detection of grape picking robots.<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:Computer vision
Controlled terms:Convolution - Convolutional neural networks - Feature extraction - Image recognition - Instance Segmentation - Location - Object detection - Pixels - Statistical tests - Wine
Uncontrolled terms:Convolutional neural network - Detection - Detection models - Grape - Images processing - Machine-vision - Res2net - Single-shot - Wine grapes - YOLO
Classification code:716.1  Information Theory and Signal Processing - 723.2  Data Processing and Image Processing - 723.4  Artificial Intelligence - 723.5  Computer Applications - 741.2  Vision - 822.3  Food Products - 922.2  Mathematical Statistics
Numerical data indexing:Percentage 1.158E+01%, Percentage 1.454E+01%, Percentage 1.605E+01%, Percentage 5.48E+00% to 1.533E+01%, Percentage 8.12E+01%, Time 1.484E-02s, Time 1.582E-01s, Time 4.91E-03s, Time 6.29E-03s, Time 7.75E-03s
DOI:10.11975/j.issn.1002-6819.2021.17.019

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

<RECORD 17>

Accession number:20214811226847
Title:Fault diagnosis of tractor diesel engine based on LWD-QPSO-SOMBP neural network
Title of translation:&#37319;&#29992;LWD-QPSO-SOMBP&#31070;&#32463;&#32593;&#32476;&#30340;&#25302;&#25289;&#26426;&#26612;&#27833;&#26426;&#25925;&#38556;&#35786;&#26029;
Authors:Zhou, Junbo (1); Zhu, Yejun (1); Xiao, Maohua (1); Wu, Jianming (2)
Author affiliation:(1) College of Engineering, Nanjing Agricultural University, Nanjing; 210032, China; (2) Dongtai City Agricultural Mechanization Technology Extension Service Station, Yancheng; 224246, China
Corresponding author:Xiao, Maohua(xiaomaohua@njau.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:17
Issue date:September 1, 2021
Publication year:2021
Pages:39-48
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 diesel engine, the power source of a tractor, most directly determines the performance and safety of the tractor. Many efforts have been made on the faults of diesel engines in the agricultural field, due mainly to the complexity of the mechanism, diversity of faults, and the concurrency of multiple faults. Furthermore, fault diagnosis of diesel engines is developing towards artificial intelligence in recent years. Among them, back propagation (BP) neural network with excellent non-linear mapping has widely been used in fault diagnosis of tractor diesel engines. However, BP neural network tends to fall into local the minimum and slow convergence in engineering practical application. In this study, a modified fault diagnosis model was proposed for the tractor diesel engine using Linear Weight Decrease-Quantum Particle Swarm Optimization-Self Organizing Maps Back Propagation (LWD-QPSO-SOMBP) neural network. Firstly, A Self Organizing Maps (SOM) neural network was used to process the input data of the BP neural network. A composite network model was proposed to combine the SOM and BP neural network, in order to alleviate the training pressure of the BP neural network. Secondly, the network structure was modified to optimize the initial network weights, where the Linear Weight Decrease-Quantum Particle Swarm Optimization (LWD-QPSO) was proposed for the network weights and thresholds. Thirdly, the failure mechanism of the tractor diesel engine was analyzed to determine 8 kinds of data signals for the failure. Finally, the structure parameters were determined for the LWD-QPSO-SOMBP neural network model. A fault diagnosis test was then carried out using Controller Area Network (CAN) bus technology. The CAN bus was used to collect and analyze the sensor signal data of the Weichai WP6 tractor diesel engine, thereby evaluating the performance of the LWD-QPSO-SOMBP neural network. A comparison was also made on several neural networks to verify the accuracy of fault diagnosis and performance of LWD-QPSO-SOMBP neural network, including BP, Self Organizing Maps Back Propagation (SOMBP), Particle Swarm Optimization-Self Organizing Maps Back Propagation (PSO-SOMBP), and Linear Weight Decrease-Particle Swarm Optimization-Self Organizing Maps Back Propagation (LWD-PSO-SOMBP), and SOMBP neural network optimized by Improved Quantum Particle Swarm Optimization (IQPSO). The test results show that the LWD-QPSO-SOMBP neural network effectively integrated the SOM neural network in data preprocessing and the PSO in optimizing the initial weight threshold of the BP neural network, compared with the rest. As such, a high-precision fault diagnosis of tractor diesel engines was thus achieved. The LWD-QPSO-SOMBP neural network greatly improved the convergence rate of the framework using the SOM neural network to pre-process the network input data. The iteration times were reduced 97.40% from 2 431 to 63, compared with single BP neural network. At the same time, LWD-QPSO was adopted in the LWD-QPSO-SOMBP neural network to optimize the initial weight threshold of the network. It was found that the particle fitness of traditional PSO was reduced greatly further to improve the convergence accuracy and speed of the network. The PSO particle fitness decreased by 26.67% from 0.15 to 0.11, while, the convergence error of the network decreased by 85.00% from 0.004 to 0.0006, compared with the traditional. The diagnostic accuracy of the LWD-QPSO-SOMBP neural network model was greatly improved, while, the training accuracy increased from 85.00% to 99.44%, compared with the single BP network. Consequently, the LWD-QPSO-SOMBP neural network model presented an excellent diagnostic performance. This finding can provide a sound reference for high-precision intelligent fault diagnosis of tractor diesel engines.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:40
Main heading:Failure analysis
Controlled terms:Agriculture - Backpropagation - Computer aided diagnosis - Conformal mapping - Diesel engines - Fault detection - Outages - Particle swarm optimization (PSO) - Self organizing maps - Tractors (agricultural)  - Tractors (truck)
Uncontrolled terms:Back Propagation - Back-propagation neural networks - Faults diagnosis - Linear weight decrease-quantum particle swarm optimization algorithm - Particle swarm optimization algorithm - Performance - Quanta particle swarm optimizations - Quantum particle swarm optimization algorithm - Self-organizing map neural network - Self-organizing-maps
Classification code:461.1  Biomedical Engineering - 612.2  Diesel Engines - 663.1  Heavy Duty Motor Vehicles - 706.1  Electric Power Systems - 723  Computer Software, Data Handling and Applications - 723.4  Artificial Intelligence - 723.5  Computer Applications - 821  Agricultural Equipment and Methods; Vegetation and Pest Control - 821.1  Agricultural Machinery and Equipment - 921.5  Optimization Techniques
Numerical data indexing:Percentage 2.667E+01%, Percentage 8.50E+01%, Percentage 8.50E+01% to 9.944E+01%, Percentage 9.74E+01%
DOI:10.11975/j.issn.1002-6819.2021.17.005

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

<RECORD 18>

Accession number:20214811226938
Title:Quantitative detection of fox meat adulteration in mutton by hyper spectral imaging combined with characteristic variables screening
Title of translation:&#39640;&#20809;&#35889;&#22270;&#20687;&#32467;&#21512;&#29305;&#24449;&#21464;&#37327;&#31579;&#36873;&#23450;&#37327;&#26816;&#27979;&#32650;&#32905;&#20013;&#29392;&#29432;&#32905;&#25530;&#20551;
Authors:Bai, Zongxiu (1); Zhu, Rongguang (1); Wang, Shichang (1); Zheng, Minchong (1); Gu, Jianfeng (1); Cui, Xiaomin (1); Zhang, Yaoxin (1)
Author affiliation:(1) College of Mechanical and Electrical Engineering, Shihezi University, Shihezi; 832003, China
Corresponding author: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:17
Issue date:September 1, 2021
Publication year:2021
Pages:276-284
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 explore the rapid nondestructive detection of fox meat adulteration in minced mutton using hyperspectral imaging technology combined with characteristic variables screening. A quantitative detection model was also established. A total of 120 adulterated mutton samples were first prepared by adding fox meat into minced mutton at different levels, including 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, and 50%. Spectral information of samples with different adulterated contents was then obtained using visible near-infrared hyperspectral imaging. Some approaches were selected for spectral pre-processing, including First Derivative (1D), the Mean Center (MC), Multiplicative Scattering Correction (MSC), and Standard Normalized Variate (SNV). A Partial Least Squares Regression (PLSR) model was established to determine the 1D optimal pre-processing using the original and the pre-processed spectra. The prediction performance of model was significantly improved, where the determination coefficient (R²) value of calibration set increased from 0.925 to 0.940, the R² value of cross-validation set increased from 0.894 to 0.911, the R² value of validation set increased from 0.896 to 0.912, and the relative error increased from 2.37 to 2.73, indicating better prediction ability of model, compared with no pre-processed spectra. The pre-processed spectra effectively enhanced the difference of spectral data. There were also obvious absorption and reflection bands at specific wavelengths. Furthermore, Genetic Algorithm (GA), Competitive Adaptive Reweighted Sampling (CARS), and Two-Dimensional Correlation (2D-COS) analysis were used to screen the characteristic wavelengths after 1D pre-processing. The PLSR and Support Vector Regression (SVR) models were then established to compare with the total 846 wavelengths and characteristic ones. It was found that 207, 34, and 14 characteristic wavelengths were obtained by GA, CARS, and 2D-COS. More importantly, the performances of all SVR models using the whole wavelengths and characteristic wavelengths were better than that of PLSR model. Among them, the best performance was achieved in the SVR model with 14 characteristic wavelengths from 2D-COS, where the R² value and root mean square error of cross-validation set were 0.928 and 3.00%, respectively, while the relative error of validation set was 4.85. Consequently, the hyperspectral imaging combined with 2D-COS-SVR model can effectively realize the quantitative detection of the fox meat adulterated in the minced mutton. The findings can also provide a strong technical support for the development of a low-cost meat adulteration detection system.<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:Meats
Controlled terms:Calibration - Coherent scattering - Forecasting - Genetic algorithms - Hyperspectral imaging - Infrared devices - Least squares approximations - Regression analysis - Spectroscopy - Spectrum analysis  - Support vector machines
Uncontrolled terms:Adulterated mutton - Characteristic variable - Fox meat - HyperSpectral - Nondestructive detection - Quantitative detection - Spectra analysis - Support vector regression models - Two-dimensional correlation spectroscopy - Validation sets
Classification code:711  Electromagnetic Waves - 723  Computer Software, Data Handling and Applications - 746  Imaging Techniques - 822.3  Food Products - 921.6  Numerical Methods - 922.2  Mathematical Statistics
Numerical data indexing:Percentage 1.00E+01%, Percentage 1.50E+01%, Percentage 2.00E+01%, Percentage 2.50E+01%, Percentage 3.00E+00%, Percentage 3.00E+01%, Percentage 3.50E+01%, Percentage 4.00E+01%, Percentage 4.50E+01%, Percentage 5.00E+00%, Percentage 5.00E+01%, Percentage 9.28E-01%
DOI:10.11975/j.issn.1002-6819.2021.17.032

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

<RECORD 19>

Accession number:20214811226825
Title:Application of reaction force coefficient in the mechanical model for the elastic foundation beam of canal lining during frost heave
Title of translation:&#20923;&#32960;&#21453;&#21147;&#31995;&#25968;&#22312;&#28192;&#36947;&#34924;&#30732;&#20923;&#32960;&#24377;&#24615;&#22320;&#22522;&#26753;&#27169;&#22411;&#20013;&#30340;&#24212;&#29992;
Authors:Li, Zongli (1, 2); Yao, Xiwang (3); Li, Yunbo (2); Wu, Zhengqiao (3); Xiao, Shuaipeng (2); Liu, Shida (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) China Water Resources Bei Fang Investigation Design & Research Co. Ltd., Tianjin; 300222, China
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:17
Issue date:September 1, 2021
Publication year:2021
Pages:97-106
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">Canal foundation soil is generally characterized by nonlinear deformation during the frost heaving process. In this study, a Duncan-Chang constitutive model was established considering the confining pressure and temperature, according to the frozen soil triaxial test. A numerical simulation was conducted to establish the calculation formula for the frost heave reaction force coefficient with the constrained frost heave change, particularly referring to the bed coefficient in the indoor triaxial test. The balance differential equation of the elastic foundation beam was discretized to adapt to the change of frost heave reaction coefficient using the finite difference method. Since the frost heave reaction coefficient greatly varied at different points of lining, the model increased significantly the much more constants than before. Analytical solutions were also applied to verify the new model. In addition, the difference was also determined, when the frost heave reaction force coefficients were variable and constant, particularly for the frost heave mechanical response of trapezoidal canal lining. The results showed that the frost heave reaction force increased by 0.15 MPa on average, while the frost heave reaction force coefficient decreased by 1.12 MPa/m on average at the same freezing temperature for every 1 cm increase in the constrained frost heave. The frost heave reaction force increased by 0.21 times on average for every 5&deg;C decrease at freezing temperature under the same restricted frost heave amount. As such, the hyperbolic function was used to represent this change suitable for engineering applications. In slope and canal bottom lining slabs, the maximum frost heave reaction force calculated by the constant frost heave reaction force coefficient was 1.43 times the variable, and the maximum bending moment was 1.12 times the variable on average. Therefore, the nonlinear deformation of frozen soil was not considered to avoid a larger value than before, if the frost heave reaction coefficient was constant. Moreover, the significant influence of the frost heave reaction coefficient was closely related to the length of the lining slab. The maximum bending moment gradually increased and then became stable, while the position of the maximum shifted to the tip of the slope, as the length of the slope lining slab increased. By contrast, the position of the maximum bending moment changed from the middle to two sides, and the midpoint bending moment first increased and then decreased, as the length of the lining slab at the bottom of the canal increased. This finding can provide strong technical support to the design of large-scale trapezoidal canal lining. The frost heave reaction force coefficient was relatively smaller than the actual value, due mainly to the model without considering water migration well. At the same time, it is also necessary to consider the effect of the separation of lining board from the foundation, and the freezing shrinkage of lining board, on the bending in the future research.<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:Frozen soils
Controlled terms:Bending moments - Canal linings - Deformation - Differential equations - Finite difference method - Freezing - Hydraulic structures - Hyperbolic functions - Tunnel linings
Uncontrolled terms:Elastic foundation beam - Force coefficients - Freezing temperatures - Frost heave - Frost heave reaction coefficient - Maximum bending moments - Mechanical modeling - Nonlinear deformations - Reaction forces - Trapezoidal canal
Classification code:401.2  Tunnels and Tunneling - 407.2  Waterways - 408.2  Structural Members and Shapes - 483.1  Soils and Soil Mechanics - 921  Mathematics - 921.2  Calculus - 921.6  Numerical Methods
Numerical data indexing:Pressure 1.12E+06Pa, Pressure 1.50E+05Pa, Size 1.00E-02m, Temperature 2.78E+02K
DOI:10.11975/j.issn.1002-6819.2021.17.011

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

<RECORD 20>

Accession number:20214811226872
Title:Assessment of land carrying capacity of animal production in Beijing from a wider perspective of combination of planting and animal breeding
Title of translation:&#27867;&#31181;&#20859;&#32467;&#21512;&#35270;&#35282;&#19979;&#21271;&#20140;&#24066;&#20859;&#27542;&#19994;&#22303;&#22320;&#25215;&#36733;&#21147;&#35780;&#20272;
Authors:Guo, Weiyi (1); Cui, Jianyu (1); Zhang, Wang (3); Wang, Tao (1); Liu, Zhong (2); Li, Jinqiao (1); Xue, Fangxu (1); Mu, Kangguo (1); Hu, Lin (1)
Author affiliation:(1) College of Resources and Environment Science/Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation, China Agricultural University, Beijing; 100193, China; (2) College of Land Science and Technology, China Agricultural University, Beijing; 100193, China; (3) Environmental Monitoring Center of Shijiazhuang High Tech Industrial Development Zone, Shijiazhuang; 050000, China
Corresponding author:Hu, Lin(hulin@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:17
Issue date:September 1, 2021
Publication year:2021
Pages:242-250
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 strong market demand is ever increasing for animal protein in China. Correspondingly, this increase inevitably requires the expansion of animal farming industry. However, a trade-off is also highly necessary between the livestock/poultry manure and environmental pollution. Arable land can serve as a disposal site of animal manure in some regions. Furthermore, an optimal combination of planting and animal breeding is also critical to achieve the win-win goals of environmental protection and aquaculture development. Therefore, it is very necessary to accurately estimate the land carrying capacity for animal manure in modern agriculture. In this study, a systematic evaluation was made on the amount of manure production and nitrogen, phosphorus nutrient resources within the animal wastes in 2018 using the livestock and poultry breeding data in Beijing Statistical Yearbook. Six types of land were taken as the sites for the consumption of livestock and poultry manure, including grain field, vegetable field, orchard, available woodland, available grassland, and unused land, compared with only arable land was considered in the previous studies. Both Statistical Yearbook and ArcGIS were used to estimate the above-mentioned land resources. The nitrogen and phosphorus nutrient demand per unit area in the various land types were analyzed comprehensively, thereby to calculate the land carrying capacity of animal breeding industry in the study area. The results showed that the total amount of livestock and poultry manure was 3.801 million tons in Beijing in 2018, where the nitrogen and phosphorus nutrient resources were 5 289.8 and 26 118.2 t, respectively. In terms of nitrogen and phosphorus, the total amount of animal breeding was equivalent to 4.53 million and 5.13 million pig equivalents, respectively. Nevertheless, nitrogen was the significant limiting factor of pollution from the perspective of environmental protection. The total areas was about 218 408.8 hm² available for the animal manure accommodation land in the six types of land. Among them, a major of arable land, the grain and vegetable field reached 112 000 hm², accounting for 52% of the total area of land. The available area of orchard field, woodland, grassland, and unused land were 59 000, 37 600, 9 171.2, and 79.6 hm², respectively. The nutrient requirement value of nitrogen and phosphorus per unit area were 308.9 and 72.9 kg/hm&sup2; for the grain field; 404.2 and 122.9 kg/hm&sup2; for the vegetable field; and 250 and 55 kg/hm&sup2; for the orchard field. Assuming that the plant nutrients were supplied only by organic fertilizer from animal manure, the maximum land carrying capacity for animal breeding was 6.752 million pigs equivalent, if only arable land (including grain and vegetable field) were used as the accommodation sites for livestock and poultry manure. If the six types of land were all used concurrently for the consumption of animal manure, the maximum land carrying capacity for animal breeding increased to the equivalent of 10.89 million pigs, 1.61 times of that of arable land. Assuming that the plant nutrients were provided 50% by organic and 50% by chemical fertilizers, the maximum land carrying capacity for animal breeding was reduced to the equivalent of 3.376 million pigs equivalent, if only arable land was used as the accommodation field of livestock and poultry manure. In this case, the breeding scale exceeded the carrying capacity of arable land in 2018, where it may cause environmental pollution. Fortunately, the land carrying capacity was 5.45 million pigs equivalent for the six kinds of land concurrently, 20.1% more breeding potential, compared with the actual breeding amount in 2018. Therefore, it is necessary to increase the replacement ratio of organic fertilizer to chemical fertilizer during this time, thereby to fully utilize the multiple types of land to absorb livestock and poultry manure. The finding can provide insightful ideas for the long-term coordinated development of environmental protection and animal breeding industry.<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:Nitrogen
Controlled terms:Economic and social effects - Environmental protection - Fertilizers - Grain (agricultural product) - Land use - Mammals - Manures - Nutrients - Orchards - Phosphorus  - Pollution - Vegetables
Uncontrolled terms:Animal manure - Arable land - Carrying capacity - Combination of planting and animal breeding - Land types - Livestock manure - Nitrogen and phosphorus - Plantings - Poultry manure - Vegetable Field
Classification code:403  Urban and Regional Planning and Development - 454.2  Environmental Impact and Protection - 804  Chemical Products Generally - 821.2  Agricultural Chemicals - 821.3  Agricultural Methods - 821.4  Agricultural Products - 821.5  Agricultural Wastes - 971  Social Sciences
Numerical data indexing:Mass 1.229E+02kg, Mass 2.50E+02kg, Mass 3.089E+02kg, Mass 4.042E+02kg, Mass 5.50E+01kg, Mass 7.29E+01kg, Percentage 2.01E+01%, Percentage 5.00E+01%, Percentage 5.20E+01%
DOI:10.11975/j.issn.1002-6819.2021.17.028

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

<RECORD 21>

Accession number:20214811226817
Title:Inversing soil salinity under vegetation cover using Sentinel-2 multispectral satellite remote sensing
Title of translation:Sentinel-2&#22810;&#20809;&#35889;&#21355;&#26143;&#36965;&#24863;&#21453;&#28436;&#26893;&#34987;&#35206;&#30422;&#19979;&#30340;&#22303;&#22756;&#30416;&#20998;&#21464;&#21270;
Authors:Du, Ruiqi (1, 2); Chen, Junying (1, 2); Zhang, Zhitao (1); Xu, Yangyang (1); Zhang, Xing (1); Yin, Haoyuan (1); Yang, Ning (1)
Author affiliation:(1) College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling; 712100, China; (2) Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Yangling; 712100, China
Corresponding authors:Chen, Junying(cjyrose@126.com); Chen, Junying(cjyrose@126.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:17
Issue date:September 1, 2021
Publication year:2021
Pages:107-115
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">Dynamic change of Soil Salinity Content (SSC) is widely used to control soil salinization for high production efficiency in modern agriculture. This study aimed to enhance the correlation between SSC and spectral reflectance under vegetation coverage using Sentinel-2 multispectral remote sensing. Soil samples of salinity and moisture were collected at 100 sampling sites with different depth ranges, including &lt;20, 20-40, and &gt;40-60 cm, in Shahaoqu Irrigation Area, Inner Mongolia, China, from June to August in 2019. The multispectral data of Sentinel-2 satellite was acquired synchronously according to the sample time and location. The specific procedure was as follows. Firstly, a depth decision tree was constructed with the normalized difference vegetation index as a branching criterion, where the best one was then selected to determine the optimal depth range for the SSC retrieval of soil samples. Secondly, a classification decision tree was used to divide the soil samples into different categories, according to the normalized vegetation index and Soil Moisture Content (SMC). As such, the category of each soil sample was determined using the classification decision tree. Thirdly, the optimal spectral combination for each category was calculated to serve as an input variable into the SSC inversion model. Several machine learning models were adopted for the SSC inversion models to monitor the SSC at the optimal depth range from the salinity depth decision tree, including Adaptive boosting algorithm (Adaboost), Partial Least Squares Regression (PLSR), Support Vector Machines (SVM), Gradient Boosting Decision Tree (GBDT), and Random Forest (RF). The results showed that the correlation coefficient between SSC and spectral reflectance was above 0.66, considering the decision tree. In terms of soil depth range, the optimal inversion depth for the SSC under vegetation cover was &gt;40-60 cm, followed by &lt;20 cm, but the SSC inversion model presented some limitations in the middle layer (20-40 cm). Furthermore, the inversion accuracy was ranked in the descending order of RF, GBDT, Adaboost, SVM, and PLSR, where the RF and Adaboost presented similar inversion. Correspondingly, the SSC inversion model using ensemble learning demonstrated a strong generalization ability to achieve the ideal and stable inversion under different application scenarios, compared with the other machine learning. Specifically, the SSC inversion model performed the best using RF, where the coefficient of determination, the root mean square error, the residual predictive interquartile range, and the residual predictive deviations were 0.70, 0.25%, 0.35, and 1.67, respectively. The correlation between SSC and spectral reflectance was 0.38 without considering the decision tree, indicating there was no significance in the SSC inversion model. Considering the decision tree, the coefficient of determination of the SSC inversion model was 0.70, indicating that the decision tree effectively enhanced the sensitivity of spectral reflectance to SSC for the high accuracy, particularly when the vegetation on the surface of the soil. Consequently, this finding can provide a promising potential way to monitor the soil salinization in the irrigated areas during crop growth using multi-spectral satellites in modern agriculture.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:37
Main heading:Decision trees
Controlled terms:Adaptive boosting - Irrigation - Least squares approximations - Reflection - Remote sensing - Soil moisture - Soil surveys - Support vector machines - Vegetation
Uncontrolled terms:Depth range - Hetao irrigation districts - Inversion - Inversion models - Remote-sensing - Salinity - Sentinel-2 satellite - Soil salinity - Soil sample - Spectral reflectances
Classification code:483.1  Soils and Soil Mechanics - 723  Computer Software, Data Handling and Applications - 821.3  Agricultural Methods - 921.4  Combinatorial Mathematics, Includes Graph Theory, Set Theory - 921.6  Numerical Methods - 961  Systems Science
Numerical data indexing:Percentage 2.50E-01%, Size 2.00E-01m, Size 2.00E-01m to 4.00E-01m, Size 4.00E-01m to 6.00E-01m
DOI:10.11975/j.issn.1002-6819.2021.17.012

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

<RECORD 22>

Accession number:20214811226860
Title:Investigation into the catalytic cracking/reforming of biomass pyrolysis gas by biochar supported Ni-Ca catalyst
Title of translation:&#29983;&#29289;&#36136;&#28845;&#36127;&#36733;&#38221;&#38041;&#20652;&#21270;&#21058;&#20652;&#21270;&#35010;&#35299;/&#37325;&#25972;&#29983;&#29289;&#36136;&#28909;&#35299;&#27668;&#30740;&#31350;
Authors:Sun, Zhenjie (1); Huang, Sisi (1); Shi, Hao (1); Li, Huaju (1); Li, Hongyan (1); Guo, Yifeng (1); Du, Yang (1); Pang, Renze (1); Yi, Weiming (2); Li, Xiangqian (1); Dong, Qing (1)
Author affiliation:(1) School of Life Science and Food Engineering, Huaiyin Institute of Technology, Huai'an; 223003, China; (2) School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo; 255049, China
Corresponding author:Dong, Qing(dongq@hyit.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:17
Issue date:September 1, 2021
Publication year:2021
Pages:211-217
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">Pyrolysis is widely considered as one of the most effective thermal chemical technologies to convert biomass into high value-added products. However, the biomass tar is inevitably formed during pyrolysis gaseous production. The removal of biomass tar has posed a great challenge to the further commercialization of biomass gasification in recent years. In the present work, a systematic investigation was made to realize the efficient conversion into syngas (H<inf>2</inf>+CO) using the biochar (BC) supported nickel-calcium catalyst for the biomass tar cracking/reforming. The catalysts were prepared via the one-step pyrolysis of NiCl<inf>2</inf>, together with CaCl<inf>2</inf> pre-loaded biomass at 800&#8451;. Five types of catalysts were also synthesized, including 2Ni-Ca/BC (0.02 mol Ni and 0.01 mol Ca), 2Ni-2Ca/BC (0.02 mol Ni and 0.02 mol Ca), Ni/BC (0.04 mol Ni), Ca/BC (0.04 mol Ca), and BC (without Ni and Ca). Subsequently, the ASAP 2020 Micromeritics instrument was applied to determine the texture structure of catalysts, including specific surface area, the total pore volume, and mean pore diameter. The phase compositions of catalysts were identified by the X-ray Diffractometer (XRD). The morphology and microstructure of catalysts were also characterized using a Scanning Electron Microscope (SEM) and Transmission Electron Microscopy (TEM). A laser Raman spectrometer was used for the surface structure of catalysts. The coke amount of catalyst was determined via a thermal gravimetric analyzer equipped with Mass Spectrometry (MS). The gas composition was finally analyzed using Gas Chromatography (GC). The results indicated that the addition of calcium decreased the crystallite sizes of Ni for the better catalytic performance of catalysts. The graphitization of surface carbon in the 2Ni-Ca/BC catalyst was higher than that in the Ni/BC and Ca/BC catalysts, particularly in the presence of carbon nanotubes. Furthermore, the reaction temperature for all the catalysts greatly contributed to the tar cracking/reforming and syngas production. Alternatively, the catalyst type was another dominant factor during the processing. The catalytic performance was also ranked in the decreased order of 2Ni-Ca/BC, 2Ni-2Ca/BC, Ni/BC, Ca/BC, and BC, in terms of tar cracking and selectivity to the syngas production. Correspondingly, the tar conversion efficiency and syngas yield obtained from 2Ni-Ca/BC catalyst at 700&#8451; were 91.8% and 607.6 mL/g (H<inf>2</inf>/CO=1.05), respectively. More importantly, the tar conversion efficiency and syngas yield increased by only 0.7% and 7.6%, respectively, when the cracking/reforming temperature increased from 700 to 800&#8451;, indicating that an excellent catalytic performance occurred at a relatively low temperature. The 2Ni-Ca/BC catalyst performed well in the higher stability after 210 min at 700^oC, where the tar conversion efficiency, H<inf>2,</inf> and CO yields were achieved about 83%, 275 mL/g, and 268 mL/g, respectively. There was no obvious sintering of active, where only a handful of coke (3.6 mmol/g) was produced within 480 min at 700&#8451;, indicating that the 2Ni-Ca/BC catalyst presented an excellent resistance to sintering and coke deposition.<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:Biomass
Controlled terms:Calcium - Catalysts - Catalytic cracking - Chlorine compounds - Gas chromatography - High resolution transmission electron microscopy - Mass spectrometry - Morphology - Nickel - Nickel compounds  - Pore structure - Scanning electron microscopy - Spectrometers - Surface structure - Synthesis gas - Tar - Textures
Uncontrolled terms:Biochar - Biomass pyrolysis - Biomass tar - Catalytic cracking/reforming - Catalytic performance - Pyrolysis gas - Syn gas - Syngas production - Tar cracking - ]+ catalyst
Classification code:548.1  Nickel - 549.2  Alkaline Earth Metals - 741.3  Optical Devices and Systems - 801  Chemistry - 802.2  Chemical Reactions - 802.3  Chemical Operations - 803  Chemical Agents and Basic Industrial Chemicals - 804  Chemical Products Generally - 931.2  Physical Properties of Gases, Liquids and Solids - 951  Materials Science
Numerical data indexing:Amount of substance 1.00E-02mol, Amount of substance 2.00E-02mol, Amount of substance 4.00E-02mol, Molar concentration 3.60E+00mol/m3, Percentage 7.00E-01%, Percentage 7.60E+00%, Percentage 8.30E+01%, Percentage 9.18E+01%, Specific volume 2.68E-01m3/kg, Specific volume 2.75E-01m3/kg, Specific volume 6.076E-01m3/kg, Time 1.26E+04s, Time 2.88E+04s
DOI:10.11975/j.issn.1002-6819.2021.17.024

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

<RECORD 23>

Accession number:20214811226888
Title:Suppression method for the long flexible arm vibration of orchard inspection robots
Title of translation:&#26524;&#22253;&#24033;&#26816;&#26426;&#22120;&#20154;&#38271;&#33218;&#25238;&#21160;&#25233;&#21046;&#26041;&#27861;
Authors:Jiang, Haiyong (1, 2, 3); Jiang, Wenguang (1, 2); Xing, Yazhou (3); Li, Na (3); Yang, Xin (3)
Author affiliation:(1) Hebei Innovation Center for Equipment Lightweight Design and Manufacturing, Yanshan University, Qinhuangdao; 066004, China; (2) School of Mechanical Engineering, Yanshan University, Qinhuangdao; 066004, China; (3) College of Electrical and Mechanical, Hebei Agricultural University, Baoding; 071001, China
Corresponding authors:Jiang, Wenguang(wgj@ysu.edu.cn); Jiang, Wenguang(wgj@ysu.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:17
Issue date:September 1, 2021
Publication year:2021
Pages:12-20
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">Orchard inspection robot has widely been one of the most important equipment in modern orchard management using information technology. The robot with walking chassis and long arm is more suitable for this work than UAV, in cases where long-term monitoring is required, especially when it must be used in severe weather conditions. But the arm body is prone to shake, when the robot is walking in wind and rain or on uneven ground. If the long arm structure with strong rigidity is used, it will cause the overall bulkiness and the danger of overturning the whole vehicle easily, because of the high center of gravity. Therefore, the low stiffness arm body is beneficial to the safety of vehicle. Nevertheless, the lightweight long flexible arm tends to low-frequency vibration, due mainly to the low stiffness. Such low-frequency vibration can also cause another trouble, that is, high-quality image information cannot be captured during the vibration, and the process of waiting for vibration to subside is too long, which seriously restricts the acquisition efficiency of image information. If the stability control can be realized, this flexible characteristic will be conducive to the overall stability. Alternatively, Finite Element Method (FEM) has widely been one type of reliable calculation, when the analytical dynamic model cannot be obtained, due to the existence of concentrated mass or non-single section in the long flexible arm. FEM modal analysis can be utilized to greatly simplify the control model, where the cantilever part of long flexible arm was equivalent to a two degree of freedom pseudo rigid body. Particularly, this simplification can be carried out under the condition of low-frequency vibration. Therefore, this study aims to effectively suppress the long-flexible arm shaking of orchard inspection robot, further to improve the efficiency of image acquisition. Correspondingly, a vibration suppression control system was also proposed using the synthesis and feedback of elevation angles from three parts of arm. Firstly, a dynamic model of equivalent three-bar two torsion spring was established using the FEM modal analysis on the external extension of arm, where the equivalence of natural frequency and vibration modes depended mainly on the static equilibrium. The readings of three inclination sensors were then synthesized into a system output, according to the Differential Flatness theory. As such, the significant vibration of long flexible arm was rapidly suppressed within 9 s under the control of differential flat output as feedback. Specifically, the amplitude at the end of arm reached 10&deg; decreasing to less than 2&deg; within three control cycles, but the small amplitude and high frequency vibration were difficult to eliminate in the later stage, particularly when the PID controller was used. Fortunately, the profile of system output was relatively soft, and the curved of torque output was saturated twice, significantly less than that of PID, when ADRC controller was adopted. Although large vibration was effectively suppressed after five cycles, it was not easy to occur high-frequency jitter in the later stage. This control system can be expected to serve the long-flexible arm mechanism with impact disturbance or arm body jitter under moving conditions, particularly where the active vibration suppression is needed.<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:Degrees of freedom (mechanics)
Controlled terms:Agriculture - Automobile bodies - Disturbance rejection - Dynamic models - Efficiency - Finite element method - Flexible manipulators - Image enhancement - Inspection - Natural frequencies  - Robotic arms - Stiffness - Vibration analysis
Uncontrolled terms:Active disturbances rejection controls - Differential flatness - Flexible arm - Image information - Inspection robots - Long flexible arm - Low-frequency vibration - Mode equivalent - Orchard inspection robot - Vibration suppression
Classification code:662.4  Automobile and Smaller Vehicle Components - 731  Automatic Control Principles and Applications - 731.5  Robotics - 821  Agricultural Equipment and Methods; Vegetation and Pest Control - 913.1  Production Engineering - 921  Mathematics - 921.6  Numerical Methods - 931.1  Mechanics - 951  Materials Science
Numerical data indexing:Time 9.00E+00s
DOI:10.11975/j.issn.1002-6819.2021.17.002

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

<RECORD 24>

Accession number:20214811226859
Title:Runaway characteristics of bidirectional horizontal axial flow pump with super low head based on entropy production theory
Title of translation:&#22522;&#20110;&#29109;&#20135;&#29702;&#35770;&#30340;&#36229;&#20302;&#25196;&#31243;&#21452;&#21521;&#21351;&#24335;&#36724;&#27969;&#27893;&#35013;&#32622;&#39134;&#36920;&#29305;&#24615;
Authors:Xu, Zhe (1); Zheng, Yuan (1, 2); Kan, Kan (2); Huang, Jiacheng (2)
Author affiliation:(1) College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing; 210098, China; (2) College of Energy and Electric Engineering, Hohai University, Nanjing; 210098, China
Corresponding author:Kan, Kan(kankan@hhu.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:17
Issue date:September 1, 2021
Publication year:2021
Pages:49-57
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 low-head pump station is often required to deliver water in two directions, resulting in the risk of runaway accidents under the bidirectional operation. This study aims to investigate the runaway transition of the pump under the Forward Runaway Condition (FRC) and Backward Runaway Condition (BRC), thereby establishing the full flow system of the low-head horizontal axial flow pump. The volume of fluid (VOF) was applied to determine the position of the free surface, and then to simulate the volume fraction of water and air in the upstream and downstream domains. The Shear Stress Transport (SST) k-&omega; turbulence model was selected to close the governing equations, where the eddy viscosity was modified to account for the transport of the principal turbulent shear stress. The Entropy Production Rate (EPR) mainly included the Entropy Production By Direct Dissipation (EPDD), Turbulence Dissipation (EPTD), and Wall Shear Stress (EPWS). The User-Defined Function (UDF) in the Fluent software was applied to control the real-time speed of the impeller using the torque balance equation. The Grid Convergence Index (GCI) was also calculated to verify the grid independence. A model test was conducted to verify the accuracy of three-dimensional simulation and entropy generation. The results show that the flow rate and rotation speed of the pump decreased first, and then increased, whereas, the torque generally presented a downward trend, while fluctuated around 0 value under FRC and BRC. Furthermore, the torque fluctuation amplitude under FRC was significantly higher than that under BRC in the runaway state, due to the strong Rotor-Stator Interaction (RSI) under FRC. The EPDD was dominant in the total simulation domain, followed by the EPTD and EPWS. Additionally, the total entropy production in the impeller was the highest in each simulation domain, due mainly to the larger velocity gradient and the stronger rotor-stator interaction. Additionally, the guide vane was located in the inflow direction of the impeller under FRC in the turbine or runaway state, where the smoother flow state and the lower EPDD and EPTD under FRC, compared with the BRC. As for the inlet conduit during the runaway state, the EPDD was slightly higher than the EPTD under FRC and BRC. However, the EPDD in the outlet conduit was much higher than the EPTD. More importantly, the upstream transformed from the outflow to inflow domain, and then the EPDD, EPTD, and EDWS were gradually close to zero, whereas, the downstream transformed from inflow to outflow domain, and then the EPDD, EPTD, and EDWS gradually increased during the runaway. There was a seriously unstable flow pattern in the inlet and outlet channel, leading to the strong vortices and reflux areas, particularly when the flow rate was zero (t<inf>Q=0</inf>). Correspondingly, the velocity gradient and turbulent kinetic energy were small at the low velocity, leading to the smaller total entropy production at the inlet and outlet conduit under FRC and BRC. Additionally, the span of velocity gradient in the downstream was larger under BRC than that under FRC, so did the EPDD in the runaway state (42.5 s). The vortex core gathered at the inlet side of the impeller blade under pump condition at t<inf>Q</inf>=0. Consequently, the distribution of vortex and EPR was similar at different blade-to-blade surfaces. The reason was that there was a large velocity gradient near the vortex core, particularly leading to a larger entropy yield, indicating that the vortex was the cause of energy loss.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:25
Main heading:Entropy
Controlled terms:Air - Axial flow - Impellers - Pumps - Rivers - Shear flow - Shear stress - Stators - Turbulence models
Uncontrolled terms:Axial flow pump - Bidirectional condition - Condition - Down-stream - Entropy production - Free surfaces - Runaway conditions - Runaway process - Turbulence dissipation - Velocity gradients
Classification code:601.2  Machine Components - 618.2  Pumps - 631.1  Fluid Flow, General - 641.1  Thermodynamics - 705.1  Electric Machinery, General - 804  Chemical Products Generally
Numerical data indexing:Time 4.25E+01s
DOI:10.11975/j.issn.1002-6819.2021.17.006

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

<RECORD 25>

Accession number:20214811226912
Title:Land use/cover classification based on combining spectral mixture analysis model and object-oriented method
Title of translation:&#34701;&#21512;&#20809;&#35889;&#28151;&#21512;&#20998;&#35299;&#19982;&#38754;&#21521;&#23545;&#35937;&#30340;&#22303;&#22320;&#21033;&#29992;/&#35206;&#34987;&#20998;&#31867;
Authors:Li, Zhuo (1, 2); Han, Wenchao (1); Hu, Qiyuan (1); Gao, Xiang (1); Wang, Linlin (1); Xiao, Fei (2, 3); Liu, Wenchao (2, 3); Guo, Wenhua (2, 3); Sun, Danfeng (1)
Author affiliation:(1) College of Land Science and Technology, China Agricultural University, Beijing; 100193, China; (2) Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen; 518034, China; (3) Information Center of Ministry of Natural Resources, Beijing; 100036, China
Corresponding author:Sun, Danfeng(sundf@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:17
Issue date:September 1, 2021
Publication year:2021
Pages:225-233
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">Complex land use/cover and fragmented land objects have posed a great restriction on the efficiency and accuracy of classification. In traditional classification, a single pixel was often taken as the basic unit, inevitably leading to the low accuracy of the mixed pixels. Thus, the low classification accuracy of land use/cover can be attributed that the mixed image pixels blur the spectral information of land objects. Meanwhile, it is necessary to efficiently utilize the spectral, shape and texture characteristics of land objects during extraction. In an object-oriented model, the adjacent pixels are taken as the objects considering various attributes, such as spectrum, shape and texture, in order to weaken the interference of mix pixels to land use information extraction. However, a large number of feature parameters in the object information extraction can reduce the computational efficiency and classification accuracy. As a result, it is highly demanding for the combined technology to realize the automatic and high-precision land use/cover classification using remote sensing images. In this study, a land use/cover extraction was carried out to integrate the spectral mixture analysis and object-oriented model using the Sentinel-2A images, in order to improve the accuracy of land use/cover classification. Firstly, the rules for land object extraction were constructed by 8 characteristic parameters, such as Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), and Soil Background Level (SBL) at three optimal segmentations, according to the spectral, shape and texture of land objects. Secondly, the spectral mixing was utilized to extract the three generic endmembers in the study area, including substrate (SU, rock and soil), green vegetation (GV, photosynthetic leaves), and dark material (DA, shadow and water). Finally, an illustration was presented for the effects of spectral features of three endmembers on the optimization of extraction. The results showed that: 1) The overall accuracy of land use/cover classification was 80.83% for five land objects using the fuzzy function and threshold in different hierarchical levels, where the Kappa coefficient was 0.76. 2) The spectral extraction significantly improved the overall accuracy of land use/cover classification up to 90.00% using the fusion of three endmembers derived from spectral mixture, where the Kappa coefficient was up to 0.88. 3) The integration of three endmembers with clear physical meaning enhanced the difference of each component in the pixel, especially in cultivated land and construction land. Correspondingly, the deficiency was reduced for traditional spectral indexes in the resolution between vegetation and soil brightness, due mainly to the explicit physical meaning of three endmembers. Besides, this model was conducted from easy decreasing, thereby to decrease uncertain factors layer by layer. Thus, it is also expected to make full use of spectral features, suitable for the medium and high resolution of remote sensing images with multiple spectral bands. The finding can provide great potential to the fine extraction for land use information.<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:Classification (of information)
Controlled terms:Computational efficiency - Efficiency - Extraction - Image analysis - Image classification - Image enhancement - Image segmentation - Information retrieval - Information use - Land use  - Mixtures - Pixels - Remote sensing - Soils - Textures - Vegetation
Uncontrolled terms:Analysis models - Endmembers - Land use/cover - Multiscale segmentation - Object oriented method - Remote-sensing - Sentinel-2a image - Shape and textures - Spectral mixture analyse model - Spectral mixture analysis
Classification code:403  Urban and Regional Planning and Development - 483.1  Soils and Soil Mechanics - 716.1  Information Theory and Signal Processing - 723.2  Data Processing and Image Processing - 802.3  Chemical Operations - 903.1  Information Sources and Analysis - 903.3  Information Retrieval and Use - 913.1  Production Engineering
Numerical data indexing:Electric current -2.00E+00A, Percentage 8.083E+01%, Percentage 9.00E+01%
DOI:10.11975/j.issn.1002-6819.2021.17.026

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

<RECORD 26>

Accession number:20214811226885
Title:Image classification method for Oudemansiella raphanipes using compound knowledge distillation algorithm
Title of translation:&#37319;&#29992;&#22797;&#21512;&#30693;&#35782;&#33976;&#39311;&#31639;&#27861;&#30340;&#40657;&#30382;&#40481;&#26526;&#33740;&#22270;&#20687;&#20998;&#32423;&#26041;&#27861;
Authors:Zhao, Mingyan (1); Li, Yixin (1); Xu, Peng (2); Song, Tianyue (1); Li, Huanran (1)
Author affiliation:(1) College of Mechanical and Electronical Engineering, China Jiliang University, Hangzhou; 310018, China; (2) College of Science, China Jiliang University, Hangzhou; 310018, China
Corresponding author:Xu, Peng(xupeng@cjlu.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:17
Issue date:September 1, 2021
Publication year:2021
Pages:303-309
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 solve the problems of time consuming and less accuracy of the Oudemansiella raphanipes, this paper proposes an Oudemansiella raphanipes classification detection method based on a compound knowledge distillation algorithm, which improves the recognition accuracy and calculation efficiency. The knowledge distillation method uses the teacher model (large parameter model) to train the student model (small parameter model). Compared with learning a single correct label, student model can learn the category weights of the predicted target by the knowledge distillation. These category weights are extracted from the teacher model which the student model cannot be obtained through training. At the same time, in order to make full use of feature information, this paper proposes a detection method based on compound knowledge distillation which uses knowledge distillation in different positions of the model. This study uses 4 800 Oudemansiella raphanipes test set images to pre-train the teacher model (Resnet50), then intercept the output of the first 25 layers of the teacher model and perform parameter training on the first 9 layers of the convolutional model of the student model (Resnet18). The student model can continuously adjust the weight information by learning the output of the teacher model during the training process to obtain the best results. Finally, the first 9-layer convolution model of the pre-trained student model and the last half are spliced together to perform knowledge distillation of the overall model. The recognition accuracy of Resnet18 after compound knowledge distillation is 96.89%, and the time to recognize a single image is 0.032 s. It is found that compared with Resnet50, the compound knowledge distillation algorithm proposed in this paper takes 68.93% less time to recognize a single image. Compared with the Resnet18 model without knowledge distillation and single knowledge distillation, the accuracy is improved by 0.97 percent and 0.52 percent points, respectively. The reason for the improvement in accuracy is that when traditional neural network backpropagation parameters are updated, there is a certain distortion phenomenon after each layer of convolutional layer. As the network structure deepens, the first few layers of the model often fail to get effective update signals. And the Resnet network in this paper makes the structure more sensitive to change parameters. At the same time, the knowledge distillation technology can provide the student model with soft label information that cannot be learned on the hard label. The compound knowledge distillation technology proposed in this article allows the first half of the student model to learn high-level feature information in advance, and then parameterize the overall model. It can make it more fully absorb the knowledge in the teacher model, and improve the phenomenon of gradient dispersion of feedback parameters in the transmission process. The results show that the compound knowledge distillation algorithm proposed in this paper can make the accuracy of small model approach large-scale network model without increasing the running time. The research results can provide technical support for the quality grading production line of Oudemansiella raphanipes, and improve the speed and sorting accuracy of agricultural products based on neural network.<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:Convolutional neural networks
Controlled terms:Convolution - Deep neural networks - Distillation - Learning algorithms - Mean square error - Multilayer neural networks - Personnel training - Students
Uncontrolled terms:Convolutional neural network - Deep learning - Detection methods - Distillation algorithm - Knowledge distillation algorithm - Oudemansiellum raphanipes - Parameter model - Recognition accuracy - Student Modeling - Teacher models
Classification code:461.4  Ergonomics and Human Factors Engineering - 716.1  Information Theory and Signal Processing - 723.4.2  Machine Learning - 802.3  Chemical Operations - 912.4  Personnel - 922.2  Mathematical Statistics
Numerical data indexing:Percentage 5.20E-01%, Percentage 6.893E+01%, Percentage 9.689E+01%, Percentage 9.70E-01%, Time 3.20E-02s
DOI:10.11975/j.issn.1002-6819.2021.17.035

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

<RECORD 27>

Accession number:20214811226821
Title:Dynamic mechanical properties of Pisha sandstone geopolymer cement composite soil using SHPB
Title of translation:&#22522;&#20110;SHPB&#30340;&#30738;&#30722;&#23721;&#22320;&#32858;&#29289;&#27700;&#27877;&#22797;&#21512;&#22303;&#21160;&#24577;&#21147;&#23398;&#29305;&#24615;
Authors:Zhao, Xiaoze (1); Li, Xiaoli (1); Shen, Xiangdong (1); Yang, Jian (1)
Author affiliation:(1) College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot; 010018, China
Corresponding author:Li, Xiaoli(nd-lxl@163.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:17
Issue date:September 1, 2021
Publication year:2021
Pages:310-316
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">Pisha sandstone is widely distributed over the Ordos Plateau in Inner Mongolia, China. This loose rock layer is prone to soil erosion, due to the small overburden thickness, low pressure, low degree of rock formation, and inter-sand cementation. Fortunately, Pisha sandstone can serve as the main body to prepare the Pisha sandstone geopolymer cement composite soil using alkali excitation. The composite soil can also be applied to the channel lining for environmental protection in the cold areas of northern China, where the impact load usually happens, such as ice cream, and sandy water flow. In this study, the dynamic mechanical properties of Pisha sandstone geopolymer cement composite soil were investigated under various impact loads. A test was also carried out using &Phi;80 mm Split Hopkinson Pressure Bars (SHPB) with the air pressures from 0.04 to 0.3 MPa for various strain rates, thereby exploring the kinetic properties of Pisha sandstone ground aggregate cement composite. The results showed that the strain rate of Pisha sandstone geopolymer cement composite soil increased significantly, with the increase of impact air pressure, whereas, the growth rate decreased after the strain rate exceeding 161.69 s^-1. Specifically, the dynamic modulus of elasticity of composite soil was relatively stable and grew less with the increase of strain rate when the strain rate was less than 64.67 s^-1, whereas, the dynamic modulus of elasticity grew rapidly with the increase of strain rate, when the strain rate was greater than 64.67 s^-1. A significant parameter, the dynamic increase coefficient (the ratio of dynamic strength to static strength) was also selected to evaluate the dynamic performance of cement soil. As such, the dynamic increase coefficient presented a linear relationship with the logarithm of strain rate, when the strain rate was less than 64.67 s^-1, whereas, a nonlinear relationship with the logarithm of strain rate was found, when the strain rate was greater than 64.67 s^-1. Furthermore, the continuous fracture was observed to break into several small pieces in the soil specimens under the impact load, where the degrees of fragmentation varied significantly with the impact load. Subsequently, a standard square-hole sieve of 0.63-26.5 mm was used to screen the particles of fragmented soil samples. Fragmentation characteristics of composite soil were established from the perspectives of mechanics and energy. The correlation was also established between various impact loads and the average block size, as well as the fractal dimension of Pisha sandstone fragments. Specifically, the average block size of fragmented specimens decreased as a power function, whereas, the fractal dimension first decreased and then increased, with the increase of strain rate and energy absorption flux density. A cut-off point appeared when the energy absorption flux density was 29.08 J/(s&bull;m²), where the fractal dimension was the smallest. When the fractal dimension was smaller than the cut-off point, the fractal dimension decreased with the increase of strain rate and energy absorption flux density, whereas, when it was larger than the cut-off point, there was an increasing power function relationship with the fractal dimension. This finding can provide a strong theoretical reference for the application of Pisha sandstone geopolymer cement composite soil in specific engineering.<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:Strain rate
Controlled terms:Aggregates - Composite materials - Compressive strength - Dynamic loads - Elastic moduli - Energy absorption - Flow of water - Sandstone - Soil testing - Soils
Uncontrolled terms:Cement composite - Composite soils - Dynamic mechanical property - Flux densities - Fracture characteristics - Geopolymer cement - Impact loads - Pisha sandstone - Split Hopkinson pressure bars - Strain-rates
Classification code:406  Highway Engineering - 408.1  Structural Design, General - 412.2  Concrete Reinforcements - 482.2  Minerals - 483.1  Soils and Soil Mechanics - 631.1.1  Liquid Dynamics - 951  Materials Science
Numerical data indexing:Energy 2.908E+01J, Pressure 4.00E+04Pa to 3.00E+05Pa, Size 6.30E-04m to 2.65E-02m, Time 1.6169E+02s, Time 6.467E+01s
DOI:10.11975/j.issn.1002-6819.2021.17.036

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

<RECORD 28>

Accession number:20214811226907
Title:Parameter optimization and experiment of the disturbance pneumatic plate hole metering device for rapeseed
Title of translation:&#27833;&#33756;&#25200;&#21160;&#27668;&#21147;&#30424;&#24335;&#31348;&#25773;&#25490;&#31181;&#22120;&#21442;&#25968;&#20248;&#21270;&#19982;&#35797;&#39564;
Authors:Li, Zhaodong (1, 2); He, Shun (1); Zhong, Jiyu (1); Han, Jianfeng (1); Chen, Yongxin (1, 2); Song, Yu (1, 2)
Author affiliation:(1) School of Engineering, Anhui Agricultural University, Hefei; 230036, China; (2) Anhui Province Engineering Laboratory of Intelligent Agricultural Machinery and Equipment, Hefei; 230036, China
Corresponding authors:Song, Yu(songyu@ahau.edu.cn); Song, Yu(songyu@ahau.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:17
Issue date:September 1, 2021
Publication year:2021
Pages:1-11
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">Pneumatic precision metering device has widely been used for the planting of rapeseed in recent years. However, it is very necessary to improve the performance of hole-sowing and metering for a higher operation speed of the device in modern mechanized agriculture. In this study, a dynamic analysis was conducted on the disturbance filling process of rapeseed, then to establish a disturbance filling mechanical model. Particularly, an attempt was made on the parameter optimization and experiment of disturbance air-suction hole metering device for rapeseed. The result showed that the filling performance depended mainly on the rotation speed and structure of the seed plate, as well as the working negative pressure. The disturbance tooth presented a disturbing and pushing effect on the population, leading to the increase initial speed of seed migration. As such, the population close to the surface of seed plate was obtained the same initial speed as the suction hole, thereby increasing the contact time between the seed and suction hole, finally making the seeds close to the seed plate easily captured by the suction holes. A simulation model with the EDEM software was also constructed for the motion contact between the rapeseed and seed plate. The different structures of seed plate were used to determine the influence of structural parameters on the disturbance intensity of population, particularly the number and thickness of groove teeth. More importantly, the population in the filling room was divided into the forced and the friction disturbance zone. Correspondingly, a better parameters combination of grooved teeth was achieved to clarify the influence of various factors on population disturbance. Among them, the number and thickness of groove teeth, as well as the rotation speed of seed plate were selected to be the test factors, while, the average speed of population in the forced disturbance zone was used as the test index during parameter optimization. A performance test of bench filling was conducted to verify the simulation. A four-factor three-level orthogonal test was also carried out with the number and thickness of groove teeth, the rotation speed of seed plate, and the working negative pressure as the test factors, whereas, the missing rate and the filling qualification rate as the evaluation index. Additionally, a range analysis was utilized to determine the optimal parameter combination of groove tooth. Specifically, the optimal parameter combination was achieved, where the number of groove tooth was 18, and the thickness of groove tooth was 1.0 mm, indicating better consistency with the simulated ones. The optimized seed plate was selected for a better filling performance of disturbed and undisturbed seed plates under a low negative pressure via the performance test of bench filling. A bench comparison test also clarified that the seed plate with directional disturbance to the population effectively improved the seed filling performance. A three-level factorial design experiment was carried out with the seed plate rotation speed and working negative pressure as the test factors, and the cavity rate and the qualified rate of the seed in the hill as the test indicators, where the optimized seed plate was installed on the disturbance air-suction hole metering device for rapeseed. The regression analysis showed that the cavity rate was lower than 3%, and the qualified rate of seed in the hill was higher than 96% when the seed plate rotation speed was 40-80 r/min, and the working negative pressure was 2 392-2 500 Pa. The verification test with the same conditions was basically consistent with the predicted one. Field experiments demonstrated that the rapeseed planting density was (70&plusmn;4) plants/m², the average of empty broadcast rate was 4.6%, the average of pass rate was 90.54%, suitable for the agronomic requirements of rapeseed. This finding can provide a sound reference for the design of pneumatic drill hole planting and metering system.<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:Filling
Controlled terms:Agricultural machinery - Computer software - Oilseeds - Optimization - Plates (structural components) - Pneumatic materials handling equipment - Rotation - Speed
Uncontrolled terms:Air suction - Disturbance zone - Hole metering device - Initial speed - Metering devices - Optimal parameter combinations - Parameter optimization - Performance - Performance tests - Rotation speed
Classification code:408.2  Structural Members and Shapes - 632.4  Pneumatic Equipment and Machinery - 691.1  Materials Handling Equipment - 691.2  Materials Handling Methods - 723  Computer Software, Data Handling and Applications - 821.1  Agricultural Machinery and Equipment - 821.4  Agricultural Products - 921.5  Optimization Techniques - 931.1  Mechanics
Numerical data indexing:Angular velocity 6.68E-01rad/s to 1.336E+00rad/s, Percentage 3.00E+00%, Percentage 4.60E+00%, Percentage 9.054E+01%, Percentage 9.60E+01%, Size 1.00E-03m
DOI:10.11975/j.issn.1002-6819.2021.17.001

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

<RECORD 29>

Accession number:20214811226956
Title:Remote sensing extraction and spatial pattern analysis of cropping patterns in black soil region of Northeast China at county level
Title of translation:&#19996;&#21271;&#40657;&#22303;&#21306;&#20856;&#22411;&#21439;&#22495;&#31181;&#26893;&#27169;&#24335;&#36965;&#24863;&#35782;&#21035;&#19982;&#31354;&#38388;&#26684;&#23616;&#20998;&#26512;
Authors:Du, Guoming (1, 2); Zhang, Rui (1); Liang, Chang'an (2); Hu, Mingyu (1)
Author affiliation:(1) School of Pubilc Adminstration and Law, Northeast Agricultural University, Harbin; 150030, China; (2) College of Economics and Management, Northeast Agricultural University, Harbin; 150030, China
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:17
Issue date:September 1, 2021
Publication year:2021
Pages:133-141
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">Cropping patterns play a significant role in soil fertility and crop production in the black soil region of Northeast China. It is highly demanding for reasonable cropping patterns to make full use of black soil sources at present. However, it is still lacking in a systematic analysis related to the types and spatial distribution of cropping patterns in black soil areas. Taking Kedong County of Heilongjiang Province in China as the research area, this study aims to determine the remote sensing extraction and spatial county-level cropping patterns in the black soil region, particularly on combining with Geo-information Tupu. The specific procedure was as follows. Firstly, the extraction of crop distribution over six years was realized in ENVI software using the Landsat 8 OLI remote sensing images of six phases from 2012 to 2017. Then, the information Tupu of crop change was obtained using the space superposition function of GIS, where the crop change was classified to identify the types and areas of cropping patterns. Finally, the kernel density estimation was utilized to determine the spatial agglomeration of cropping patterns, while the spatial structure characteristics were calculated for the proportion of cropping patterns in each administrative village. The results show that: 1) The total planting area of soybean and maize exceeded 94% in Kedong County from 2012 to 2017, indicating the changing trend of "decreasing first before increasing" and "increasing first before decreasing". There were also relatively low and stable acreages and variations of rice and other crops. 2) Five cropping patterns were identified, and then sorted by the area from large to small as follows: disordered, soybean continuous, two-year crop rotation, maize continuous, and three-year crop rotation cropping pattern. Among them, the first three cropping patterns accounted for the largest sum of 83.90%. 3) The soybean continuous cropping pattern presented an obvious trend in the west and north county, while, the disordered cropping pattern was distributed in the central, east, and south county. The maize continuous cropping pattern was distributed in the northeast-southwest belt, while, the three- and two-year crop rotation pattern showed the distribution patterns of "local aggregation and global dispersion". 4) The cropping patterns at the administrative village scale were roughly divided into five types, among which "disordered cropping-soybean continuous cropping-two-year crop rotation" dominated, and widely distributed in the northwest-southeast county. Followed by "soybean continuous cropping-disordered cropping-two-year crop rotation" and "disordered cropping-maize continuous cropping - two-year crop rotation", the former was scattered in the eastern county, and the latter was distributed in the northeast-southwest belt. The administrative villages with the patterns of "disordered cropping-two-year crop rotation - soybean continuous cropping" and "disordered cropping-two-year crop rotation-maize continuous cropping" were scattered in the east and southeast county.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:25
Main heading:Remote sensing
Controlled terms:Crops - Cultivation - Extraction - Rotation - Rural areas - Soils - Space optics - Spatial distribution
Uncontrolled terms:Black soil - Black soil region of northeast chinas - County level - Crop rotation - Cropping patterns - Extraction patterns - Geo-information Tupu - Remote-sensing - Spatial pattern analysis - Spatial patterns
Classification code:405.3  Surveying - 483.1  Soils and Soil Mechanics - 656.1  Space Flight - 741.1  Light/Optics - 802.3  Chemical Operations - 821.3  Agricultural Methods - 821.4  Agricultural Products - 902.1  Engineering Graphics - 921  Mathematics - 931.1  Mechanics
Numerical data indexing:Percentage 8.39E+01%, Percentage 9.40E+01%
DOI:10.11975/j.issn.1002-6819.2021.17.015

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

<RECORD 30>

Accession number:20214811226828
Title:Effects of cryoprotectant on 3D printability of frozen shrimp surimi based on principal component analysis
Title of translation:&#37319;&#29992;&#20027;&#25104;&#20998;&#20998;&#26512;&#25239;&#20923;&#21058;&#23545;&#20919;&#20923;&#34430;&#32905;&#31964;3D&#21487;&#25171;&#21360;&#24615;&#30340;&#24433;&#21709;
Authors:Pan, Yanmo (1); Liu, Yang (1); Sun, Qinxiu (1); Liu, Shucheng (1, 2); Wei, Shuai (1); Xia, Qiuyu (1); Ji, Hongwu (1); Shi, Wenzheng (3)
Author affiliation:(1) College of Food Science and Technology, Guangdong Ocean University/Guangdong Provincial Key Laboratory of Aquatic Products Processing and Safety/Guangdong Provincial Engineering Technology Research Center of Marine Food, Guangdong Province Engineering Laboratory for Marine Biological Products/Key Laboratory of Advanced Processing of Aquatic Product of Guangdong Higher Education Institution, Zhanjiang; 524088, China; (2) Collaborative Innovation Center of Seafood Deep Processing, Dalian Polytechnic University, Dalian; 116034, China; (3) College of Food Science and Technology, Shanghai Ocean University, Shanghai; 201306, China
Corresponding authors:Liu, Shucheng(Lsc771017@163.com); Liu, Shucheng(Lsc771017@163.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:17
Issue date:September 1, 2021
Publication year:2021
Pages:266-275
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">Shrimp surimi is a food material suitable for 3D printing, due mainly to that the sol can form an elastic gel with a certain viscosity and fluidity. Nevertheless, the shrimp surimi is usually frozen and stored before 3D printing. However, freezing and storage can easily denature the protein of shrimp surimi, leading to 3D printability. Fortunately, cryoprotectants can be used to effectively prevent the freezing denaturation of protein. Therefore, this study aims to investigate the effects of cryoprotectants on the rheological and textural properties, as well as 3D printability of shrimp surimi. The relationships between them were also established using principal component analysis (PCA). Five treatments were divided for the shrimp surimi used in the experiment. The first treatment was the control treatment (CK) without cryoprotectant, and the rest four treatments were added with commercial cryoprotectants, including 4% sucrose + 4% sorbitol + 0.3% polyphosphate (SSP), 8% trehalose (TH), 0.3% polyphosphate (PP), and 8% trehalose + 0.3% polyphosphate (TP). Shrimp surimi in five treatments was frozen to -20 &#8451; and then stored at -18 &#8451;. Samples were taken at regular intervals to analyze 3D printing accuracy and stability, rheological and textural properties. The results showed as follows. The 3D printing lines of shrimp surimi were rough after frozen storage in the CT treatment, leading to deposition and collapse in the products concurrently with the worst appearance. However, the appearance of 3D printing products was improved to some extent after frozen storage in the treatments added with cryoprotectants. More importantly, the 3D printing lines after the TP treatment were fine and smooth, while the product presented no obvious deposition and collapse, indicating the best appearance. Since shrimp surimi was a kind of pseudoplastic fluid, there was a shear-thinning phenomenon under high-speed shear stress. Particularly, all treatments reduced the accuracy and stability of 3D printing, viscosity constant, the connection strength among rheological units, hardness, springiness, adhesion, and water holding capacity for the shrimp surimi, as the frozen storage days increased. The accuracy and stability of 3D printing of shrimp surimi significantly increased (P&lt;0.05) in four treatments adding cryoprotectants, compared with the CT treatment. At the same time, the decreasing trend of rheological and textural parameters became slow significantly (P&lt;0.05), as the frozen storage days increased. Correspondingly, the best 3D printing accuracy and stability were achieved in the TP treatment. However, the rheological and textural parameters of TP treated products were not the largest or the smallest, but suitable for 3D printing. Therefore, the food raw materials were necessary to behave suitable rheological and textural parameters for post processing. In addition, PCA showed that the accuracy and stability of 3D printing were positively correlated with the viscosity constant, the connection strength among rheological units, springiness, hardness, adhesion, and water holding capacity of shrimp surimi. The 3D printability of shrimp surimi was also positively correlated with these indicators. Consequently, TP (8% trehalose + 0.3% polyphosphate) treatment performed the best inhibition effect on the frozen denaturation of myofibrillar protein in shrimp surimi. The shrimp surimi still displayed better 3D printability after 30 d of frozen storage, where the 3D printing accuracy and stability were 96.64% and 97.61%, respectively. The finding can provide a strong theoretical reference to ensure the supply of raw materials for the 3D printing of shrimp surimi.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:41
Main heading:Textures
Controlled terms:3D printers - Alcohols - Deposition - Freezing - Hardness - Principal component analysis - Proteins - Shear stress - Shear thinning - Sols  - Stability
Uncontrolled terms:3-D printing - 3D-printing - Cryoprotectants - Frozen storage - Polyphosphates - Principal-component analysis - Rheological parameter - Rheological property - Shrimp surimi - Textural parameters
Classification code:631.1  Fluid Flow, General - 745.1.1  Printing Equipment - 802.3  Chemical Operations - 804  Chemical Products Generally - 804.1  Organic Compounds - 922.2  Mathematical Statistics - 951  Materials Science
Numerical data indexing:Percentage 3.00E-01%, Percentage 4.00E+00%, Percentage 8.00E+00%, Percentage 9.664E+01%, Percentage 9.761E+01%
DOI:10.11975/j.issn.1002-6819.2021.17.031

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

<RECORD 31>

Accession number:20214811226808
Title:Effects of subsoiling tillage on N/P ratios in roots, stems, leaves, and aboveground biomass in maize
Title of translation:&#28145;&#26494;&#32789;&#23545;&#29577;&#31859;&#26681;&#33550;&#21494;&#27694;&#30967;&#27604;&#21450;&#22320;&#19978;&#29983;&#29289;&#37327;&#30340;&#24433;&#21709;
Authors:Qi, Peng (1, 2, 3); Wang, Xiaojiao (4); Guo, Gaowen (1); Cai, Liqun (1, 2, 3); Wu, Jun (1, 2, 3)
Author affiliation:(1) College of Resources and Environmental Sciences, Gansu Agricultural University, Lanzhou; 730070, China; (2) Gansu Provincial Key Laboratory of Arid Land Crop Science, Gansu Agricultural University, Lanzhou; 730070, China; (3) Gansu Engineering Research Center for Agriculture Water-saving, Lanzhou; 730070, China; (4) College of Management, Gansu Agricultural University, Lanzhou; 730070, China
Corresponding author:Wang, Xiaojiao(42321964@qq.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:17
Issue date:September 1, 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">Nitrogen and phosphorus in Plant organs have been two of the most important indicators of crop growth. The N/P stoichiometry in various organs has generally been selected to represent the balance of nutrients during plant growth. Alternatively, subsoiling tillage has widely been used as a better farming in the semi-arid area of Loess Plateau. Although subsoiling tillage can boost the crop yield and aboveground biomass, it remains unclear whether subsoiling tillage impacts crop N/P, or whether crop organ N/P can explain the mechanism of aboveground biomass growth. The goal of this research was to examine the mechanism of aboveground biomass production from the aspect of plant N/P. A field split plot experiment was carried out in the Loess Plateau, China, from 2016 to 2018, in order to investigate the effects of subsoiling tillage and fertilization on aboveground biomass, the N/P of roots, stems, and leaves of maize, particularly on the relationship between N/P stoichiometry and aboveground biomass. Furthermore, eight treatments were set, where tillage practices as main factors included subsoiling tillage (T1), rotary tillage (T2), plow tillage (T3), and no-tillage (T4), whereas, as secondary factors included two measures of fertilization: N0 (basic fertilizer 200 kg N/hm²) and N1 (basic fertilizer 200 kg N/hm² + jointing stage fertilizer 100 kg N/hm²). The specific analysis was also associated with the split-plot variance, structural equation modeling, and a linear mixed-effect model. The results revealed that: 1) T1 significantly enhanced the aboveground biomass (P&lt;0.05), where increased by 9.56 %, and 9.29% in 2016, while 4.67%, and 5.94% in 2018, compared with T3 and T4. 2) Similarly, both T1 and T2 considerably reduced the N/P of roots and leaves (P&lt;0.05). T1 presented the greatest drop, with the N/P of roots and leaves of 19.90 and 17.74, respectively. Nevertheless, there was no significant effect of integrated fertilization and tillage practices on N/P of roots, stems, and leaves. 3) Effect of tillage practices on the root, and leaf N/P indirectly transferred 39% of the variation in aboveground biomass, according to structural equation modeling, with a total effect value of 0.24. Specifically, tillage practices indirectly affected aboveground biomass via the N/P of roots and leaves, with the effect values of 0.10 and 0.14, respectively. But there was no direct effect of tillage on the N/P of roots, stems, and leaves. Correspondingly, the nitrogen and phosphorus content of roots and leaves were two key indirect determinants to regulate the aboveground biomass. In addition, the random slope model better characterized the link between root, leaf N/P, and biomass, where the explanatory degree increased by 425% and 133%, respectively, indicating over fixed effects. More importantly, aboveground biomass was inversely linked with the N/P of roots and leaves, but there was no correlation with the N/P of stems, according to the linear mixed effect model. Consequently, T1 can be expected to boost the aboveground biomass via the lower N/P in maize leaves and roots, while the higher nitrogen and phosphorus nutrition balance. The findings can serve as a sound reference for the fertilization and the promotion of deep tillage, while greatly contribute to understanding the effects of tillage and fertilization on maize production, particularly nitrogen and phosphorus balance in farmland ecosystems.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:39
Main heading:Stoichiometry
Controlled terms:Agricultural machinery - Biogeochemistry - Biomass - Crops - Ecology - Fertilizers - Grain (agricultural product) - Landforms - Phosphorus - Plants (botany)  - Sediments - Soils
Uncontrolled terms:Aboveground biomass - Fertilisation - Linear mixed models - Linear mixed-effects model - Loess Plateau - Maize - N:P ratio - Nitrogen and phosphorus - Structural equation models - Tillage practices
Classification code:454.3  Ecology and Ecosystems - 481.1  Geology - 481.2  Geochemistry - 483  Soil Mechanics and Foundations - 483.1  Soils and Soil Mechanics - 801.2  Biochemistry - 801.4  Physical Chemistry - 804  Chemical Products Generally - 821.1  Agricultural Machinery and Equipment - 821.2  Agricultural Chemicals - 821.4  Agricultural Products
Numerical data indexing:Mass 1.00E+02kg, Mass 2.00E+02kg, Percentage 1.33E+02%, Percentage 3.90E+01%, Percentage 4.25E+02%, Percentage 4.67E+00%, Percentage 5.94E+00%, Percentage 9.29E+00%, Percentage 9.56E+00%
DOI:10.11975/j.issn.1002-6819.2021.17.009

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

<RECORD 32>

Accession number:20214811226880
Title:Image classification method for tomato leaf deficient nutrient elements based on attention mechanism and multi-scale feature fusion
Title of translation:&#22522;&#20110;&#27880;&#24847;&#21147;&#26426;&#21046;&#21450;&#22810;&#23610;&#24230;&#29305;&#24449;&#34701;&#21512;&#30340;&#30058;&#33540;&#21494;&#29255;&#32570;&#32032;&#22270;&#20687;&#20998;&#31867;&#26041;&#27861;
Authors:Han, Xu (1, 2, 3); Zhao, Chunjiang (1, 2, 3); Wu, Huarui (2, 3); Zhu, Huaji (2, 3); Zhang, Yan (2, 3)
Author affiliation:(1) College of Information Engineering, Northwest A&F University, Yangling; 712100, China; (2) National Engineering Research Center for Information Technology In Agriculture, Beijing; 100097, China; (3) Beijing Research Center for Information Technology In Agriculture, Beijing; 100097, China
Corresponding authors:Zhao, Chunjiang(zhaocj@nercita.org.cn); Zhao, Chunjiang(zhaocj@nercita.org.cn); Zhao, Chunjiang(zhaocj@nercita.org.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:17
Issue date:September 1, 2021
Publication year:2021
Pages:177-188
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 realize the accurate identification of deficient nutrients elements in tomato leaves. An experiment was conducted on the lack of nutrients in the climate chamber in the laboratory of Big Data Intelligence Department of the Beijing Academy of Agriculture and Forestry Sciences, China. An artificial climate chamber was also selected to regulate the growth environment factors of tomato plants for the specific lack of nutrients. Three types of nutrient deficiency groups were set, namely nitrogen deficiency, phosphorus deficiency, and potassium deficiency, as well as a normal control group. The experiment was started with the appearance of nutrient-deficient traits in seedlings, and the images of nutrient-deficient leaves were then collected according to the growth stages. The experimental results show that there were diversity and differences in the traits of tomato nutrient deficiency. Specifically, there were relatively small changes of leaves in the early stage of tomato nutrient deficiency. Furthermore, it was difficult to capture the details and textures, due to the smaller area of traits. For example, the manifestation of phosphorus deficiency was that the leaves gradually turn purple along the veins. The trait details were hardly identified in the early stage of phosphorus deficiency, due mainly to the mostly small vein structure. Particularly, tomato leaves under different conditions of nutrient deficiency presented similar color and texture characteristics at a certain stage. For example, the leaves were both slightly yellow in the early stage of nitrogen deficiency and the early stage of potassium deficiency. The only slight difference was the characteristic display of morphology in the size of characteristic areas. There were obvious differences in color and texture at different stages under the same nutrient deficiency. The images were collected from the climate chamber to serve as the experimental data. An attempt was made on the inconsistency of feature area size, and the difficulty of feature extraction, resulting from the different types of nutrient deficiency, the insignificant early traits of nutrient deficiency, and the large differences in the characteristics of each growth period. Therefore, an image classification was proposed for the nutrient deficiency of tomato leaves using an attention mechanism and multi-scale feature fusion convolutional neural network (MSFF & AM-CNNs). First of all, a multi-scale feature fusion (MSFF) module was set for nutrient deficiency traits, due to the low efficiency of a fixed-scale convolution kernel for different sizes. The MSFF input image was carried out with multi-channel feature stitching after the MSFF convolution kernel of multiple scales, where the shallow image was multiplied while expanding the number of channels. As such, the fusion of scale features was adopted in this structure. Secondly, an MSFF&AM module was used to improve the large-scale convolutional layer for the extraction of shallow features using the attention mechanism (CBAM). A multi-scale fusion of Bottleneck was also utilized to improve the Dense Block for the extraction of deep features. Deep-MSFF Block aimed to combine the attention mechanism and the MSFF module, where the multiple feature channels were selectively emphasized the global multi-scale information feature function. The recalibration of features in nitrogen deficiency was improved on the tomato leaves the classification accuracy. Finally, a Focal Loss function was introduced as the loss function to reduce the weight of easy-to-differentiate samples. Correspondingly, the image recognition model of tomato elements lacking was widely expected to focus on difficult-to-classify samples during training, particularly for the overall performance of the model. The experiments demonstrated that the MSFF & AM-CNNs can meet the high-precision classification requirements of nutrient-deficient images in tomato leaves, particularly with high recognition accuracy and wide applicability (an average recognition accuracy rate of 95.92%). The model can also be expected for the identification of plant leaf nutrient deficiency.<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:Image recognition
Controlled terms:Color - Convolution - Convolutional neural networks - Forestry - Fruits - Nitrogen - Nutrients - Phosphorus - Plants (botany) - Potassium  - Textures - Wetlands
Uncontrolled terms:Attention mechanisms - Convolutional neural network - Deficient nutrient element - Features fusions - Multi-scale features - Multiscale feature fusion - Nitrogen deficiency - Nutrient deficiency - Nutrient elements - Tomato leaf
Classification code:549.1  Alkali Metals - 716.1  Information Theory and Signal Processing - 741.1  Light/Optics - 804  Chemical Products Generally - 821.0  Woodlands and Forestry - 821.4  Agricultural Products
Numerical data indexing:Percentage 9.592E+01%
DOI:10.11975/j.issn.1002-6819.2021.17.020

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

<RECORD 33>

Accession number:20214811226826
Title:Effects of vibration stress on the browning and antioxidant capacity of Agaricus bisporus
Title of translation:&#25391;&#21160;&#32961;&#36843;&#23545;&#21452;&#23394;&#33735;&#35088;&#21464;&#19982;&#25239;&#27687;&#21270;&#33021;&#21147;&#30340;&#24433;&#21709;
Authors:Chen, Dailiang (1); Chen, Hangjun (1); Liu, Ruiling (1); Han, Yanchao (1); Wu, Weijie (1); Gao, Haiyan (1)
Author affiliation:(1) Food Science Institute, Zhejiang Academy of Agricultural Sciences, Key Laboratory of Postharvest Handing of Fruits, Ministry of Agriculture and Rural Affairs, Key Laboratory of Fruits and Vegetables Postharvest and Processing Technology Research of Zhejiang Province, Key Laboratory of Postharvest of Fruits and Vegetables, China National Light Industry, Hangzhou; 310021, China
Corresponding author:Gao, Haiyan(spsghy@163.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:17
Issue date:September 1, 2021
Publication year:2021
Pages:258-265
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">Agaricus bisporus is one of the most popular foods rich in nutrition and with unique flavor among the edible fungus. However, the edible part of A. bisporus is prone to water loss and mechanical damage during the process of postharvest transportation, thereby leading to browning and decay, due to its high water content without the protective tissue in the outer skin. This study aims to explore the effect of vibration stress on the storage quality of A. bisporus during logistics and transportation for better cushioning packaging, and then to determine the relationship between browning degree and antioxidant capacity of A. bisporus after vibration stress treatment. W192 strain of A. bisporus was treated in a simulated transportation vibration with different duration times (0, 8, 16, and 24 h) at the frequency of 3.33 Hz, and then stored at 4&#8451; for 15 days. The results showed that the browning degree of A. bisporus cap was aggravated under vibration stress, where the brightness (L*) in the vibration treatment group was generally lower than that in the control group (P &lt; 0.05) during the storage. After 15d of storage, the browning index (BI) and total chromatism (&Delta;E) of samples treated by the vibration for 24 h were 1.53 and 1.57 times higher than that in the control, respectively. Vibration treatment increased the permeability of cell membrane, thereby accelerating the consumption of non-enzymatic antioxidants, such as phenols, vitamin C, and glutathione (GSH), particularly for better activities of ascorbate peroxidase (APX), peroxidase (POD), and superoxide dismutase (SOD). Compared with the vibration treatment, the activities of APX, POD, and SOD changed more slowly in the control group, indicating that the higher activities had remained during the later stage of storage. The activities of catalase (CAT) and glutathione reductase (GR) were usually higher in the control group than those in the vibration treatment (P &lt; 0.05) during storage, indicating that the enzyme activities decreased fast, as the duration of vibration stress increased. After 15d of storage, the 1,1-Diphenyl-2-picrylhydrazyl (DPPH) free radical scavenging rate of samples treated by vibration for 24 h was also significantly lower than that in the control (P &lt; 0.05). Correlation analysis showed that there was a great significance between the antioxidant capacity and browning index of A. bisporus under vibration stress. More importantly, the antioxidant capacity of A. bisporus was proportional to the browning degree after vibration treatment. As such, a relatively lower browning was achieved under the stronger antioxidant capacity of A. bisporus after vibration treatment. Therefore, it can be inferred that the damage of A. bisporus caused by vibration stress was a cumulative process, where the mechanical damage was greater for the longer transportation vibration. Correspondingly, shorter transportation and reasonable preservation were recommended for the higher antioxidant capacity of A. bisporus. Particularly, the reduction of relative friction between mushroom bodies and cushioning packaging can be expected to minimize the vibration damage during transportation. The finding can also provide a sound reference for the development of logistics and transportation packaging in most vegetables.<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:Antioxidants
Controlled terms:Cytology - Enzymes - Food storage - Fungi - Moisture - Peptides - Phenols - Stresses
Uncontrolled terms:Agaricus bisporus - Antioxidant capacity - Bisporus - Browning - Control groups - Effect of vibration - Logistics and transportations - Mechanical damages - Vibration - Vibration stress
Classification code:461.2  Biological Materials and Tissue Engineering - 461.9  Biology - 694.4  Storage - 803  Chemical Agents and Basic Industrial Chemicals - 804  Chemical Products Generally - 804.1  Organic Compounds - 804.2  Inorganic Compounds - 822.1  Food Products Plants and Equipment
Numerical data indexing:Age 4.11E-02yr, Frequency 3.33E+00Hz, Time 8.64E+04s
DOI:10.11975/j.issn.1002-6819.2021.17.030

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

<RECORD 34>

Accession number:20214811226819
Title:Citrus psyllid detection based on improved YOLOv4-Tiny model
Title of translation:&#37319;&#29992;&#25913;&#36827;YOLOv4-Tiny&#27169;&#22411;&#30340;&#26577;&#27224;&#26408;&#34417;&#35782;&#21035;
Authors:Hu, Jiapei (1); Li, Zhen (1, 2); Huang, Heqing (1); Hong, Tiansheng (2, 3); Jiang, Sheng (1); Zeng, Jingyuan (4)
Author affiliation:(1) College of Electronic Engineering, South China Agricultural University, Guangzhou; 510642, China; (2) Division of Citrus Machinery, China Agriculture Research System, Guangzhou; 510642, China; (3) College of Engineering, South China Agricultural University, Guangzhou; 510642, China; (4) Guangdong Provincial Key Laboratory of Conservation and Precision Utilization of Characteristic Agricultural Resources in Mountainous Areas, Meizhou; 514015, China
Corresponding author:Zeng, Jingyuan(15140706@qq.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:17
Issue date:September 1, 2021
Publication year:2021
Pages:197-203
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">Citrus psyllids have widely been served as transmitted vectors for a devastating Huanglong disease. It is highly urgent to rapidly identify and monitor citrus psyllids in orchard sites in real-time. The specific measures can be taken for the early prevention and control of disease. However, the field environments are unsuitable for deploying servers. In this study, a citrus psyllid identification was proposed suitable for embedded systems using YOLOv4-Tiny model. A feature fusion network was developed to improve the accuracy of model in recognizing citrus psyllids. A key path was added to reduce the loss of semantic information in the shallow network layer. At the same time, an output feature map was added, which was down sampled eight times relative to the input image. For an image with a 416 &times; 416 input, the improved feature fusion network outputted feature maps of three scales, with pixels of 13 &times; 13, 26 &times; 26, and 52 &times; 52. Cross mini-batch normalization was used instead of batch normalization, due to that this normalization combined the output information of previous mini-batch to calculate the average and standard deviation of current mini-batch. The outputs of convolutional layers were converted into the normal layers with a mean of 0 and a variance of 1 distribution. Learnable parameters were used in linearly transforming the outputs of standardized convolutional layers. Owing to the accumulation of output features, the accuracy of statistical information was improved, thereby improving the recognition accuracy of the model. The ability of model to recognize occluded targets was also improved using mosaic data augmentation during model training, particularly for the occluded citrus psyllids. More importantly, four images were randomly cropped in the training set and then stitched them into a single image. The intersection-over-union indicator was also used to filter the ambiguous target frame in the image generated by mosaic data augmentation. The improved mosaic data augmentation was used to simulate the occlusion of citrus psyllids, thereby to weaken the dependence of model on the characteristics of targets. A handheld camera was used to capture the images of adult citrus psyllids in field environments. Data augmentation was then used to obtain a dataset containing 21 410 images, which was divided into the training set, validation set, and test set in a ratio of 7:1:2, respectively. Various improvements were introduced further to verify by experiments. Results showed that the improved feature fusion network, the introduction of cross mini-batch normalization, and the improved mosaic data augmentation greatly increased the average precision of model in the test set. The differences between the proposed model and existing networks were analyzed, where the same training set was used to train YOLOv4, YOLOv4-Tiny, Faster R-CNN, and the proposed model. Furthermore, comparative tests were performed in the test set, where the model was evaluated in terms of average precision, inference speed, and model size. Specifically, the average precision of the proposed model was 96.16%, the inference speed on the Graphics Processing Unit (GPU) was 3.63 ms/frame, and the model size was 24.50 MB. Consequently, the new model can be expected to accurately and quickly identify citrus psyllids for early warning suitable for the deployment in embedded devices.<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:Convolution
Controlled terms:Artificial intelligence - Disease control - Embedded systems - Image enhancement - Image fusion - Image recognition - Network layers - Precision agriculture - Semantics
Uncontrolled terms:Citrus psyllid - Data augmentation - Features fusions - Images processing - Mosaic data - Normalisation - Precision Agriculture - Psyllid - Test sets - Training sets
Classification code:716.1  Information Theory and Signal Processing - 723  Computer Software, Data Handling and Applications - 723.2  Data Processing and Image Processing - 723.4  Artificial Intelligence - 821.3  Agricultural Methods
Numerical data indexing:Percentage 9.616E+01%, Time 3.63E-03s
DOI:10.11975/j.issn.1002-6819.2021.17.022

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

<RECORD 35>

Accession number:20214811226961
Title:Comparison and application of spatial pattern measurement methods for water surface of dike-pond
Title of translation:&#22522;&#22616;&#27700;&#38754;&#31354;&#38388;&#24418;&#24577;&#24230;&#37327;&#26041;&#27861;&#30340;&#27604;&#36739;&#19982;&#24212;&#29992;
Authors:Zhou, Jinhao (1, 2, 3); Huang, Xiaojun (1, 4); Xiao, Ningchuan (2); Lin, Zeming (1); Luo, Manqi (1)
Author affiliation:(1) Department of Geoinformatic, South China Agricultural University, Guangzhou; 510642, China; (2) Department of Geography, The Ohio State University, Columbus; OH; 43210, United States; (3) Guangdong Provincial Key Laboratory of Land Use and Consolidation, Guangzhou; 510642, China; (4) School of Geography and Remote Sensing, Guangzhou University, Guangzhou; 510006, China
Corresponding author:Xiao, Ningchuan(xiao.37@osu.edu)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:17
Issue date:September 1, 2021
Publication year:2021
Pages:251-257
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">Dike-pond is a landscape system where water and land areas are integrated through ecological and agricultural processes. This type of system is commonly found in the low-lying river delta of China's eastern coast. The water surface of the dike-pond plays an important role in the local economic development and ecological services. However, the use of existing landscape metrics to depict the spatial pattern of the water surface of the dike-pond has not been fully studied. This research aims to fill the gap by comparing a range of such metrics through a case study based on the Xingtan Township of Foshan City, China. We divide the dike-ponds in the study areas into two types: regular and irregular, which represent after and before dike-pond consolidation project respectively. We then test 16 landscape metrics using six cases of compact or scatter pattern. The metric that can distinguish various cases is applied to measure the water pattern of the dike-pond in the study area. Our comparison results suggest that the metric called Weighted Aggregation and Closeness (WAC) is the only one that can accurately capture all the water patterns exhibited in our study area. Among other 15 metrics, Mean Patch Fractal Dimension (MPFD), Area Weighted Mean Patch Fractal Dimension (AWMPFD), Mean Patch Size (MPS), Number of Patches (NumP), and Total Landscape Area (TLA), poorly perceive the pattern difference of the same pond types. And the metrics including Area-Perimeter ratio (AP), Perimeter-Area ratio (PA), Total Edge (TE), Mean Shape Index (MSI), Area Weighted Mean Shape Index (AWMSI), Mean Patch Edge (MPE), and Median Patch Size (MedPS) give measurement results that are contrary to the water pattern. Our application results show that the average WAC value of the regular pond is 40.18% higher than that of the irregular ponds. All the regular ponds have WAC values above 0.20, of which 66.76% are greater than 0.25. Only 4% of the irregular ponds have WAC values above 0.30, but 77.22% of them have WAC values lower than 0.25. These indicate that the pattern of the regular pond is more compact than that of the irregular pond. This is because the regular ponds are formed by large-scale consolidation projects, so they are gridded and closed to each other; the irregular ponds are randomly formed by the individual farmers and scattered around the edge area of the town and the regular ponds. The compact water pattern is beneficial to the dike-pond economy by providing a large proportion for aquaculture and exhibiting agglomeration effects, while the scatter water pattern is beneficial to the dike-pond ecology by promoting the land-water interaction. We note that the pattern difference of the regular pond is not obvious in the study area since the consolidation projects are standardized and uniform. There are more spatial differences of the water pattern of the irregular pond. The WAC values of such type in the central of the study area is 15% higher than that in the northern and southern, due to the central ponds have not been consolidated. These findings are useful to reveal the relationships of spatial pattern with the economic and ecological benefits of the dike-pond. Furthermore, WAC is not only effective for measuring the water pattern of the dike-pond, but also for other patterns with disconnected features.<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:Lakes
Controlled terms:Agglomeration - Ecology - Fractal dimension - Levees
Uncontrolled terms:Consolidation projects - Dike ponds - Disconnected feature - Landscape metric - Shape indexes - Spatial patterns - Study areas - Water surface - Weighted aggregation and closeness - Weighted mean
Classification code:442.1  Flood Control - 454.3  Ecology and Ecosystems - 802.3  Chemical Operations - 921  Mathematics
Numerical data indexing:Percentage 1.50E+01%, Percentage 4.00E+00%, Percentage 4.018E+01%, Percentage 6.676E+01%, Percentage 7.722E+01%
DOI:10.11975/j.issn.1002-6819.2021.17.029

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

<RECORD 36>

Accession number:20214811226813
Title:Temporal and spatial variation of vegetation and its response to topography, climate and land use in Guangxi during 2000-2018
Title of translation:2000-2018&#24180;&#24191;&#35199;&#26893;&#34987;&#26102;&#31354;&#21464;&#21270;&#21450;&#20854;&#23545;&#22320;&#24418;, &#27668;&#20505;&#21644;&#22303;&#22320;&#21033;&#29992;&#30340;&#21709;&#24212;
Authors:Yang, Yanping (1); Chen, Jianjun (1, 2); Qin, Qiaoting (1); Zhou, Guoqing (1, 2); You, Haotian (1, 2); Han, Xiaowen (1, 2)
Author affiliation:(1) College of Geomatics and Geoinformation, Guilin University of Technology, Guilin; 541004, China; (2) Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, Guilin; 541004, China
Corresponding authors:Chen, Jianjun(chenjj@glut.edu.cn); Chen, Jianjun(chenjj@glut.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:17
Issue date:September 1, 2021
Publication year:2021
Pages:234-241
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 typical karst landform is widely distributed in Guangxi Province of southern China. Therefore, it is very necessary to explore the temporal and spatial variation characteristics of vegetation in this area. Particularly, the influencing factors can greatly contribute to further promote green development and ecological environment protection in recent years. Among them, the normalized difference vegetation index (NDVI) can serve as an important indicator to evaluate crop growth, farmland, land management, and crop production. It is highly urgent to monitor and then predict the changes in vegetation cover in this region. This article aims to investigate the characteristics of temporal and spatial variation in the vegetation NDVI in Guangxi Province of China using the MODIS NDVI time series data from 2000 to 2018. The maximum value synthesis and trend analysis were also utilized to combine DEM data, land use types, temperature, and precipitation data, thereby exploring the NDVI spatiotemporal changes of different vegetation types, as well as the response to climate factors under various terrain conditions and land use. The results showed that: (1) NDVI presented an increasing trend from 2000 to 2018 in the study area, with a linear growth rate of 0.004/a, and an average NDVI value of 0.81. The largest NDVI value was also found in the third quarter. Moreover, the spatial distribution of vegetation NDVI was distributed in the north and edge, but less in the south and middle. The specific areas with high vegetation coverage were mainly concentrated in Hechi, Baise, and Guilin City. There was an overall increasing trend of vegetation NDVI from 2000 to 2018, particularly distributed in southern Qinzhou and Nanning City, accounting for 37.0% of the total study area. (2) The vegetation NDVI first increased and then decreased, and finally stabilized above 0.8, as the elevation increased in the study area. Specifically, the distribution of elevation was higher in the northwest and lower in the southeast, in total between 0-1 000 m. Furthermore, the vegetation NDVI first increased to stable and then decreased, as the slope increased, where the slope was mainly distributed in between 0-34&deg;. Correspondingly, there was little effect of slop difference on the vegetation NDVI, except for the plain. (3) The annual average temperature was between 18 and 21&deg;C in the study area from 2000 to 2018, while the average precipitation was between 1 100 mm and 1 900 mm. Temperature and precipitation posed a positive effect on the vegetation NDVI, where the multiple correlation coefficient reached 0.32. The response of vegetation NDVI to climate varied significantly in the different types of land use. Specifically, the vegetation NDVI greatly responded to air temperature, but less to precipitation in residential and unused land. By contrast, the response of vegetation NDVI to precipitation was greater than that to temperature in cultivated land, woodland, grassland, and water area. Consequently, there was a relatively significant growth trend of vegetation NDVI in Guangxi Province from 2000 to 2018, indicating that excellent progress in the continuous implementation of afforestation, the conversion of farmland to forests, and ecological protection projects to curb rocky desertification.<br/></div> &copy; 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:35
Main heading:Land use
Controlled terms:Crops - Cultivation - Geomorphology - Landforms - Regression analysis - Time series - Vegetation
Uncontrolled terms:Climate factors - Development environment - Guangxi - Normalized difference vegetation index - Southern China - Study areas - Temporal and spatial variation - Topographic factor - Variation characteristics - Vegetation index values
Classification code:403  Urban and Regional Planning and Development - 481.1  Geology - 481.1.1  Geomorphology - 821.3  Agricultural Methods - 821.4  Agricultural Products - 922.2  Mathematical Statistics
Numerical data indexing:Percentage 3.70E+01%, Size 0.00E00m, Size 1.00E-01m, Size 5.08E+01m to 5.12572E+01m, Size 9.00E-01m, Temperature 2.91E+02K to 2.94E+02K
DOI:10.11975/j.issn.1002-6819.2021.17.027

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