2021年10月第37卷第19期

2021年10月第37卷第19期

Accession number:20215211376510
Title:Effects of Vetiveria zizanioides hedgerow on the erosion of purple soil of slope land in the Three Gorges Reservoir Area of China
Title of translation:香根草植物篱对三峡库区坡地紫色土侵蚀的影响
Authors: (1); (1, 2); (1); (1); (1); (1, 2); (1, 2)
Author affiliation:(1) Key Laboratory of Geological Hazards on Three Gorges Reservoir Area (China Three Gorges University), Ministry of Education, Yichang; 443002, China; (2) Engineering Research Center of Eco-environment in Three Gorges Reservoir Region, Ministry of Education, China Three Gorges University, Yichang; 443002, China
Corresponding authors:;
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:105-112
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"></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:39
Main heading:Soils
Controlled terms:Efficiency - Erosion - Rain - Reservoirs (water) - Runoff - Sediments - Soil conservation - Water conservation - Wooden fences
Uncontrolled terms:Aboveground part - Contribution rate - Purple soils - Reservoir area - Runoff and sediment reduction benefit - Runoff and sediments - Runoff reduction - Sediment reduction - Three Gorge reservoir - Underground part
Classification code:415.3 Wood Structural Materials - 441.2 Reservoirs - 442.1 Flood Control - 443.3 Precipitation - 444 Water Resources - 444.1 Surface Water - 483 Soil Mechanics and Foundations - 483.1 Soils and Soil Mechanics - 913.1 Production Engineering
Numerical data indexing:Size 1.20E-01m, Size 6.00E-02m, Percentage 1.12E+01% to 2.619E+01%, Percentage 2.945E+01%, Percentage 3.775E+01%, Percentage 3.956E+01%, Percentage 4.613E+01%, Percentage 4.828E+01%, Percentage 5.172E+01%, Percentage 6.044E+01%, Percentage 6.225E+01%, Percentage 7.154E+01% to 8.363E+01%, Percentage 7.559E+01%

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

Accession number:20215211376471
Title:Thermal performance analysis and parameter optimization of a tractor exhaust waste heat plate-fin evaporator
Title of translation:拖拉机排气余热板翅式蒸发器热力性能分析与参数优化
Authors: (1, 2); (1, 3); (1); (4); (1, 2); (1, 2)
Author affiliation:(1) College of Engineering, Huazhong Agricultural University, Wuhan; 430070, China; (2) Key Laboratory of Agricultural Equipment in Mid-lower Reaches of the Yangtze River, Ministry of Agriculture and Rural Affairs, Wuhan; 430070, China; (3) School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan; 430074, China; (4) Department of Mechanical Engineering, Michigan State University, East Lansing; 48824, United States
Corresponding authors:;
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:7-17
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"></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:29
Main heading:Waste heat
Controlled terms:Channel flow - Computational fluid dynamics - Diesel engines - Efficiency - Evaporation - Evaporators - Fins (heat exchange) - Fuels - Heat convection - Heat transfer coefficients - Neural networks - Nozzles - Numerical methods - Optimization - Rankine cycle - Specific heat - Tractors (truck) - Turbulence models - Waste heat utilization
Uncontrolled terms:Exhaust heat - Exhaust waste heat - Flow channels - Fuel efficiency - Load condition - Optimisations - Plate fins - Thermal Performance - Tractor - Working fluid
Classification code:525.3 Energy Utilization - 525.4 Energy Losses (industrial and residential) - 612.2 Diesel Engines - 616.1 Heat Exchange Equipment and Components - 631.1 Fluid Flow, General - 641.1 Thermodynamics - 641.2 Heat Transfer - 663.1 Heavy Duty Motor Vehicles - 723.5 Computer Applications - 802.1 Chemical Plants and Equipment - 802.3 Chemical Operations - 913.1 Production Engineering - 921.5 Optimization Techniques - 921.6 Numerical Methods - 931.1 Mechanics
Numerical data indexing:Angular velocity 0.00E00rad/s, Angular velocity 8.35E+00rad/s, Mass flow rate 3.00E-02kg/s to 8.00E-02kg/s, Percentage 1.50E+01% to 3.50E+01%, Percentage 3.80E+01% to 4.50E+01%, Percentage 5.20E+00%, Power 1.946E+04W, Power 6.989E+04W, Size 1.90E-01m, Size 2.00E-03m

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

Accession number:20215211376554
Title:Coupling scheme optimization of Panax notoginseng considering yield, quality and water-fertilizer use efficiency
Title of translation:基于产量品质及水肥利用效率的三七水肥耦合方案优选
Authors: (1); (1); (1); (1); (1)
Author affiliation:(1) Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming; 650500, China
Corresponding author:
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:139-146
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"></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:34
Main heading:Irrigation
Controlled terms:Efficiency - Fertilizers - Fruits - Productivity - Quality control - Sustainable development - Water pollution - Water supply
Uncontrolled terms:Fertilisation - Field capacity - Panax notoginseng - Partial factor productivity - Partial factor productivity of fertilizer - Plantings - Water and fertilizer coupling - Water use efficiency - Yield - Yield quality
Classification code:446.1 Water Supply Systems - 453 Water Pollution - 804 Chemical Products Generally - 821.2 Agricultural Chemicals - 821.3 Agricultural Methods - 821.4 Agricultural Products - 913.1 Production Engineering - 913.3 Quality Assurance and Control
Numerical data indexing:Linear density 1.65E+00kg/m, Mass 1.09E+00kg, Mass 4.40E+02kg, Mass 9.7642E+02kg, Percentage 1.00E+01%, Percentage 1.297E+01%, Percentage 1.50E+01%, Percentage 2.00E+01%, Percentage 2.50E+01%, Percentage 3.00E+01%, Percentage 4.00E+01%, Size 1.50E+01m, Size 2.00E+00m, Size 2.0701E-02m, Size 5.00E-03m

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

Accession number:20215211376252
Title:Monitoring and influencing factors of dune movement speed along the Yellow River using UAV technology
Title of translation:基于无人机技术黄河沿岸沙丘移动速度监测及影响因素分析
Authors: (1); (1); (2); (1); (2); (2); (2)
Author affiliation:(1) Yinshanbeilu Grassland Eco-hydrological National Observation and Research Station, China Institute of Water Resources and Hydropower Research, Beijing; 100038, China; (2) Institute of Desert Control Science and Engineering, Inner Mongolia Agricultural University, Hohhot; 010018, China
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:57-64
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"></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:41
Main heading:Landforms
Controlled terms:Antennas - Data acquisition - Sand - Speed - Unmanned aerial vehicles (UAV) - Wind - Wind power
Uncontrolled terms:Drift direction - Drift potential - Dune movement - Movement speed - Sand drift - Sand-driving wind - Study areas - Ulan buh desert - Wind variabilities - Yellow river
Classification code:443.1 Atmospheric Properties - 481.1 Geology - 483.1 Soils and Soil Mechanics - 615.8 Wind Power (Before 1993, use code 611 ) - 652.1 Aircraft, General - 723.2 Data Processing and Image Processing
Numerical data indexing:Percentage 4.076E+01% to 5.693E+01%, Percentage 5.209E+01%, Percentage 7.324E+01%, Size 1.08E+00m to 2.27E+00m, Velocity 8.00E+00m/s to 1.00E+01m/s, Velocity 8.00E+00m/s to 1.20E+01m/s, Velocity 8.00E+00m/s

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

Accession number:20215211376341
Title:Algorithm for the uniform petal carving of Hami melon based on three-dimensional reconstruction
Title of translation:基于三维重构的哈蜜瓜均瓣雕花算法
Authors: (1); (1); (2); (1); (1); (1); (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:
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:276-283
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"></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:30
Main heading:Particle swarm optimization (PSO)
Controlled terms:Cutting - Image reconstruction - Iterative methods - Surface reconstruction - Three dimensional computer graphics
Uncontrolled terms:3D reconstruction - Cutting depth - Cutting paths - Images processing - Point cloud splicing - Point-clouds - Real- time - Three-dimensional reconstruction - Triangular meshing - Uniform petal carving
Classification code:723 Computer Software, Data Handling and Applications - 723.2 Data Processing and Image Processing - 723.5 Computer Applications - 921.5 Optimization Techniques - 921.6 Numerical Methods
Numerical data indexing:Percentage 5.00E+00%, Size 1.50E-03m, Size 2.50E-02m, Size 3.25E-02m, Size 3.40E-02m, Size 7.62E-02m

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

Accession number:20215211376393
Title:Prediction of tea drying moisture content based on PSO Elman algorithm
Title of translation:基于PSO-Elman算法的茶叶烘干含水率预测
Authors: (1); (1); (1); (1); (1); (1); (1)
Author affiliation:(1) College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao; 266109, China
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:284-292
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"></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:31
Main heading:Neural networks
Controlled terms:Drying - Feeding - Forecasting - Linear regression - Moisture - Moisture determination - Particle swarm optimization (PSO) - Temperature
Uncontrolled terms:%moisture - Drum speed - Drying temperature - Dynamic changes - Feeding amount - Hot air drying - Neural-networks - Prediction modelling - Swarm optimization - Tea-leaves
Classification code:641.1 Thermodynamics - 691.2 Materials Handling Methods - 723 Computer Software, Data Handling and Applications - 921.5 Optimization Techniques - 922.2 Mathematical Statistics - 944.2 Moisture Measurements
Numerical data indexing:Angular velocity 3.34E-01rad/s to 5.01E-01rad/s, Mass 2.00E-01kg, Percentage 4.00E+00% to 5.00E+00%

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

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

Accession number:20215211376526
Title:Influence of guide vane length on the velocity characteristics of spiral flow in cross-sections between capsules
Title of translation:导叶长度对囊体间断面螺旋流流速特性的影响
Authors: (1); (1); (1); (1); (1); (1); (1); (1)
Author affiliation:(1) College of Water Resource Science and Engineering, Taiyuan University of Technology, Taiyuan; 030024, China
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:48-56
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"></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:25
Main heading:Flow velocity
Controlled terms:Agricultural products - Containers - Flow fields - Flow of water - Hydraulics - Pipelines - Pneumatic tubes - Velocity - Velocity distribution
Uncontrolled terms:Axial velocity - Capsule - Circumferential velocity - Flow velocity characteristics - Guide-vane - Hydraulic transportation - Length of guide vane - Radial velocity - Spiral flow - Velocity gradients
Classification code:619.1 Pipe, Piping and Pipelines - 631 Fluid Flow - 631.1 Fluid Flow, General - 631.1.1 Liquid Dynamics - 632.1 Hydraulics - 632.4 Pneumatic Equipment and Machinery - 821.4 Agricultural Products - 922.2 Mathematical Statistics - 943.2 Mechanical Variables Measurements
Numerical data indexing:Velocity 1.20E+00m/s, Velocity 1.20E+00m/s to 3.50E+00m/s, Velocity 1.60E+00m/s to 1.20E+00m/s, Velocity 6.00E-01m/s to 1.20E+00m/s

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

Accession number:20215211376617
Title:Agricultural drought evolution characteristics and driving mechanisms in the Yellow River Basin under climate and land use changes
Title of translation:气候和土地利用变化下黄河流域农业干旱时空演变及驱动机制
Authors: (1, 2); (2); (3); (1)
Author affiliation:(1) Ecological Security and Protection Key Laboratory of Sichuan Province, Mianyang Normal University, Mianyang; 621000, China; (2) State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an; 710048, China; (3) School of Water Conservancy and Hydroelectric Power, Hebei University of Engineering, Handan; 056038, China
Corresponding author:
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:84-93
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"></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:29
Main heading:Land use
Controlled terms:Agriculture - Climate change - Climate models - Disasters - Drought - Economics - Food supply - Rivers - Soil moisture - Sustainable development - Water supply - Watersheds
Uncontrolled terms:Agricultural drought - Climate - Disaster areas - Driving mechanism - Evolution characteristics - Land-use land-cover changes - Soil moisture index - Study areas - The yellow river basin
Classification code:403 Urban and Regional Planning and Development - 443 Meteorology - 443.1 Atmospheric Properties - 443.3 Precipitation - 444 Water Resources - 444.1 Surface Water - 446.1 Water Supply Systems - 483.1 Soils and Soil Mechanics - 821 Agricultural Equipment and Methods; Vegetation and Pest Control - 822.3 Food Products - 921 Mathematics - 971 Social Sciences
Numerical data indexing:Age 2.00E+01yr, Age 8.33E-02yr to 1.8326E+00yr, Percentage 1.00E+01% to 5.00E+01%, Percentage 1.50E+01%, Percentage 5.00E+01%, Percentage 5.00E+01% to 9.00E+01%, Percentage 6.00E+01% to 9.00E+01%

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

Accession number:20215211376654
Title:Effects of soil bulk density and water content on the mechanical stability of soil structure using rheological method
Title of translation:基于流变学法研究容重和含水率对土壤结构力学稳定性的影响
Authors: (1, 2); (1, 2, 3); (1); (2, 3); (1, 2); (1, 2); (3)
Author affiliation:(1) College of Natural Resources and Environment, Northwest A&F University, Yangling; 712100, China; (2) State Key Laboratory of Soil Erosion and Dryland Farming on Loess Plateau, Northwest A&F University, Yangling; 712100, China; (3) Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling; 712100, China
Corresponding authors:; ;
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:147-155
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"></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:40
Main heading:Aggregates
Controlled terms:Deformation - Deterioration - Elasticity - Friction - Irrigation - Mechanical stability - Shear strength - Shear stress - Slope protection - Slope stability - Soil moisture - Tribology - Viscoelasticity - Water management - Yield stress
Uncontrolled terms:Bulk water - Lou soils - Shear strength parameters - Shears strength - Soil bulk density - Soil particles - Soil structure - Soil-structure - Visco-elastic parameters - Yield points
Classification code:406 Highway Engineering - 406.2 Roads and Streets - 412.2 Concrete Reinforcements - 483.1 Soils and Soil Mechanics - 821.3 Agricultural Methods - 931 Classical Physics; Quantum Theory; Relativity - 931.2 Physical Properties of Gases, Liquids and Solids - 951 Materials Science
Numerical data indexing:Linear density 1.30E-01kg/m

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

Accession number:20215211376652
Title:Monocular distance measurement algorithm for pomelo fruit based on target pixels change
Title of translation:基于目标像素变化的柚果单目测距算法
Authors: (1, 2, 3); (1); (1); (1); (1); (1)
Author affiliation:(1) College of Engineering, Huazhong Agricultural University, Wuhan; 430070, China; (2) Key Laboratory of Agricultural Equipment in Mid-lower Yangtze River, Ministry of Agriculture and Rural Affairs, Wuhan; 430070, China; (3) Citrus Mechanization Research Base, Ministry of Agriculture and Rural Affairs, Wuhan; 430070, China
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:183-191
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"></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:25
Main heading:Fruits
Controlled terms:Cameras - Curve fitting - Data acquisition - Forestry - MATLAB - Orchards - Pixels - Regression analysis - Trees (mathematics)
Uncontrolled terms:Data collection - Data groups - Fruit samples - Identification - Imaging data - Imaging distances - Monocular distance measurement - Multiple regressions - Number of pixel - Orchard
Classification code:723.2 Data Processing and Image Processing - 723.5 Computer Applications - 742.2 Photographic Equipment - 821.0 Woodlands and Forestry - 821.3 Agricultural Methods - 821.4 Agricultural Products - 921 Mathematics - 921.4 Combinatorial Mathematics, Includes Graph Theory, Set Theory - 921.6 Numerical Methods - 922.2 Mathematical Statistics
Numerical data indexing:Percentage 5.00E+00%, Size 1.20E+00m, Size 1.25E+00m, Size 1.25E+00m to 1.375E+00m, Size 1.30E+00m, Size 1.50E+00m to 1.20E+00m, Size 1.50E+00m, Size 2.50E-01m to 1.25E+00m, Size 2.50E-01m to 1.50E+00m, Size 2.50E-02m

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

Accession number:20215211376251
Title:Characteristics of water and nitrogen loss under subsurface pipe-open ditch controlled drainage in paddy fields
Title of translation:明沟-暗管组合控排下稻田水氮流失特征
Authors: (1); (1, 2); (1); (1, 2); (3); (3)
Author affiliation:(1) College of Agricultural Science and Engineering, Hohai University, Nanjing; 211100, China; (2) State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing; 210098, China; (3) Urban Water Scheduling and Information Management Department of Kunshan, Suzhou; 215300, China
Corresponding authors:;
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:113-121
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"></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:31
Main heading:Drainage
Controlled terms:Efficiency - Nitrogen fertilizers - Subirrigation - Water supply
Uncontrolled terms:Controlled-drainage - Drainage ditches - Drainage systems - Fertilisation - Nitrogen loss - Nonpoint-source pollution (NPS) - Open-ditch - Paddy fields - Subsurface pipe - Total nitrogen
Classification code:446.1 Water Supply Systems - 804 Chemical Products Generally - 821.2 Agricultural Chemicals - 821.3 Agricultural Methods - 913.1 Production Engineering
Numerical data indexing:Percentage 2.44E+01%, Percentage 3.93E+01%, Percentage 4.26E+01%, Percentage 4.40E+01%, Percentage 7.07E+01%

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

Accession number:20215211376523
Title:Design and experiment of double-storage turntable cotton vertical disc hole seeding and metering device
Title of translation:双仓转盘式棉花竖直圆盘穴播排种器设计与试验
Authors: (1, 2); (1); (1, 2); (1); (1); (1); (3)
Author affiliation:(1) College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi; 830052, China; (2) Xinjiang Key Laboratory of Intelligent Agricultural Equipment, Urumqi; 830052, China; (3) Xinjiang Tiancheng Agricultural Machinery Manufacturing Limited Company, Tiemenguan; 841007, China
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:27-36
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"></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:33
Main heading:Cotton
Controlled terms:Agricultural machinery - Optimization - Seed - Warehouses
Uncontrolled terms:Cotton seeds - Damage rate - Hole seeding device - Metering devices - Offset angle - Optimisations - Precision hole seeding - Precision holes - Seed-metering device - Single-grains
Classification code:694.4 Storage - 821.1 Agricultural Machinery and Equipment - 821.4 Agricultural Products - 921.5 Optimization Techniques
Numerical data indexing:Angular velocity 3.9913E-01rad/s, Percentage 1.30E-01%, Percentage 9.00E-02%, Percentage 9.43E+01%, Percentage 9.60E+01%, Size 1.47E-03m, Size 2.08E-03m, Size 2.20E-01m, Size 4.70E-03m, Size 5.20E-03m, Size 9.20E-03m

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

Accession number:20215211376663
Title:Seedling crop row extraction method based on regional growth and mean shift clustering
Title of translation:基于区域生长均值漂移聚类的苗期作物行提取方法
Authors: (1, 2); (1, 2); (1, 2); (3); (1, 2)
Author affiliation:(1) School of Agricultural Engineering, Jiangsu University, Zhenjiang; 212013, China; (2) Jiangsu Provincial Key Laboratory of Agricultural Equipment and Intelligent High Technology Research, Zhenjiang; 212013, China; (3) State Key Laboratory of Soil Plant Machine System Technology, Chinese Academy of Agricultural Mechanization Sciences, Beijing; 100083, China
Corresponding authors:;
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:202-210
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"></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:24
Main heading:Least squares approximations
Controlled terms:Agricultural machinery - Binary images - Color - Color image processing - Colorimetry - Computer vision - Crops - Efficiency - Entropy - Extraction - Feature extraction - Hough transforms - Image segmentation - Iterative methods
Uncontrolled terms:Center points - Clustering centers - Clusterings - Crop rows - Images processing - Least-squares- methods - Machine-vision - Mean shift - Mean-Shift Clustering - Regional growth
Classification code:641.1 Thermodynamics - 723.2 Data Processing and Image Processing - 723.5 Computer Applications - 741.1 Light/Optics - 741.2 Vision - 802.3 Chemical Operations - 821.1 Agricultural Machinery and Equipment - 821.4 Agricultural Products - 913.1 Production Engineering - 921.3 Mathematical Transformations - 921.6 Numerical Methods - 941.4 Optical Variables Measurements
Numerical data indexing:Percentage 9.818E+01%, Time 4.80E-01s

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

Accession number:20215211376522
Title:Drag reduction mechanism of the 3D geometry of foreleg's claw toe of the mole cricket (Gryllotalpa orientalis)
Title of translation:蝼蛄前足爪趾三维几何构形的减阻机理
Authors: (1, 2); (1, 2); (1, 2); (1, 2); (1, 2); (3)
Author affiliation:(1) Tianjin Key Laboratory of Integrated Design and On-line Monitoring for Light Industry & Food Machinery and Equipment, Tianjin; 300222, China; (2) College of Mechanical Engineering, Tianjin University of Science & Technology, Tianjin; 300222, China; (3) Tianjin Limin Condiment Co., Ltd., Tianjin; 300308, China
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:309-315
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"></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:26
Main heading:Bionics
Controlled terms:Agricultural machinery - Agriculture - Biomimetics - Curve fitting - Cutting - Cutting tools - Drag reduction - Energy conservation - Genetic algorithms - Geometry - Machine design - MATLAB - Software testing - Soils
Uncontrolled terms:Bionic design - Characteristic curve - Cutting process - Cutting resistance - Cutting resistance test - Mole cricket - Optimisations - Performance - Resistance tests - Two-dimensions
Classification code:461.1 Biomedical Engineering - 461.8 Biotechnology - 461.9 Biology - 483.1 Soils and Soil Mechanics - 525.2 Energy Conservation - 601 Mechanical Design - 603.2 Machine Tool Accessories - 723.5 Computer Applications - 821 Agricultural Equipment and Methods; Vegetation and Pest Control - 821.1 Agricultural Machinery and Equipment - 921 Mathematics - 921.6 Numerical Methods
Numerical data indexing:Percentage 5.696E+01%, Size 1.50E-02m, Time 2.00E+01s, Velocity 1.00E-02m/s

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

Accession number:20215211376388
Title:Improvement and experiment of the seedling clamping device of apple tree planting machines
Title of translation:苹果树栽植机幼苗夹持装置改进与试验
Authors: (1); (1); (1); (2); (3)
Author affiliation:(1) Mechanical and Electrical Engineering Institute, Qingdao Agricultural University, Qingdao; 266109, China; (2) College of Engineering, Northeast Agricultural University, Harbin; 150030, China; (3) Weifang Gaomi Secondary Specialized School, Weifang; 261501, China
Corresponding author:
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:1-6
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"></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:32
Main heading:Fruits
Controlled terms:Agricultural machinery - Conceptual design - Conveying - Cultivation - Efficiency - Industrial research - Machine design - Orchards - Reforestation - Seed - Structural optimization
Uncontrolled terms:Apple seedling - Apple trees - Coefficients of variations - Perpendicularity - Plant spacing - Plantings - Slip rates - Tree seedlings - Two-point - Two-point clamping
Classification code:601 Mechanical Design - 692.1 Conveyors - 821.0 Woodlands and Forestry - 821.1 Agricultural Machinery and Equipment - 821.3 Agricultural Methods - 821.4 Agricultural Products - 901.3 Engineering Research - 912.1 Industrial Engineering - 913.1 Production Engineering - 921.5 Optimization Techniques
Numerical data indexing:Percentage 5.03E+00% to 3.74E+00%, Percentage 9.063E+01% to 9.714E+01%, Percentage 9.143E+01% to 9.333E+01%, Size 5.00E-01m

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

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

Accession number:20215211376420
Title:Development and performance analysis of an automatic weighing rain gauge
Title of translation:全自动称重式雨量计的研制及性能分析
Authors: (1, 2); (1, 2); (2); (3); (1, 2)
Author affiliation:(1) State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling; 712100, China; (2) Institute of Soil and Water Conservation, Chinese Academy of Sciences & Ministry of Water Resources, Yangling; 712100, China; (3) Xi'an San Intelligent Technology Co., Ltd., Xi'an; 710075, China
Corresponding author:
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:122-128
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"></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:34
Main heading:Rain
Controlled terms:Analog to digital conversion - Errors - Graphic methods - Irrigation - Monitoring - Normal distribution - Rain gages - Remote control - Water management - Weighing
Uncontrolled terms:Automatic monitoring - Complex fields - Fundamental component - Global water cycle - High-precision - Performances analysis - Rain gauges - Rainfall intensity - Tipping bucket rain gauge - Total rainfall
Classification code:443.2 Meteorological Instrumentation - 443.3 Precipitation - 731.1 Control Systems - 821.3 Agricultural Methods - 922.1 Probability Theory - 943.3 Special Purpose Instruments
Numerical data indexing:Percentage -1.32E+00%, Percentage 5.00E+00%, Percentage 7.411E+01%, Percentage 8.50E+01%, Percentage 9.867E+01%, Size 1.00E-02m to 2.50E-02m, Size 1.00E-05m, Size 3.33E-03m, Size 5.00E-03m, Size 5.228E-01m, Size 8.30E-04m, Size 8.50E-04m

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

Accession number:20215211376392
Title:Determination of seed germination index and selection of sensitive seeds for phytotoxicity evaluation of composting
Title of translation:堆肥种子发芽指数测定方法与敏感性种子筛选
Authors: (1); (1); (1); (2); (1); (1)
Author affiliation:(1) Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation, College of Resource and Environmental Science, China Agricultural University, Beijing; 100193, China; (2) Academy of Agricultural Planning and Engineering, Ministry of Agriculture and Rural Affairs, Beijing; 100125, China
Corresponding author:
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:220-227
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"></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:50
Main heading:Composting
Controlled terms:Additives - Biogeochemistry - Carbon - Cultivation - Fertilizers - Mammals - Manures - Nitrogen - Seed
Uncontrolled terms:Carbon additives - Chinese cabbage - Composting process - Determination - Germination index - Index calculation - Organic fertilizers - Phytotoxicity - Pig manures - Seed germination
Classification code:481.2 Geochemistry - 801.2 Biochemistry - 803 Chemical Agents and Basic Industrial Chemicals - 804 Chemical Products Generally - 821.2 Agricultural Chemicals - 821.3 Agricultural Methods - 821.4 Agricultural Products - 821.5 Agricultural Wastes
Numerical data indexing:Percentage 1.00E+02%, Percentage 1.30E+02%, Percentage 1.671E+01% to 9.298E+01%, Percentage 2.00E+01% to 2.17E+02%, Percentage 2.654E+01% to 8.049E+01%, Percentage 2.828E+01% to 1.1074E+02%, Percentage 4.338E+01% to 1.1969E+02%, Percentage 4.50E+01%, Percentage 7.00E+01%, Size 1.00E-03m

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

Accession number:20215211376470
Title:Estimation of the leaf area index and chlorophyll content of wheat using UAV multi-spectrum images
Title of translation:利用无人机多光谱估算小麦叶面积指数和叶绿素含量
Authors: (1, 2); (1); (2); (1); (1); (1)
Author affiliation:(1) College of Mechanical & Electrical Engineering, Henan Agricultural University, Zhengzhou; 450002, China; (2) School of Resources and Environment, Henan University of Economics and Law, Zhengzhou; 450002, China
Corresponding author:
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:65-72
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"></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:26
Main heading:Unmanned aerial vehicles (UAV)
Controlled terms:Antennas - Backpropagation - Chlorophyll - Crops - Data acquisition - Forestry - Image resolution - Infrared devices - Least squares approximations - Maximum likelihood - Remote sensing - Soils - Spectroscopy - Timber - Vegetation
Uncontrolled terms:Chlorophyll contents - Crop growth - Different heights - Leaf Area Index - Multi-spectral - Multispectral images - Partial least square regression - Spectral indices - Vehicle platforms - Wheat
Classification code:483.1 Soils and Soil Mechanics - 652.1 Aircraft, General - 723.2 Data Processing and Image Processing - 723.4 Artificial Intelligence - 804.1 Organic Compounds - 821.0 Woodlands and Forestry - 821.4 Agricultural Products - 921.6 Numerical Methods - 922.1 Probability Theory
Numerical data indexing:Size 1.20E+02m, Size 3.00E+01m, Size 6.00E+01m

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

Accession number:20215211376661
Title:Inversion of the thermal property parameters of fish feed based on adjoint equation method
Title of translation:基于伴随方程法的鱼饲料热特性参数反演
Authors: (1); (1); (1); (2); (1)
Author affiliation:(1) College of Engineering, China Agricultural University, Beijing; 100083, China; (2) COFCO Grains Holdings Limited, Beijing; 100020, China
Corresponding author:
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:316-322
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"></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:26
Main heading:Specific heat
Controlled terms:Aluminum - C (programming language) - Diffusion - Fish - Heat conduction - MATLAB - Moisture - Moisture determination - Software testing - Temperature distribution - Thermal conductivity - Thermal diffusivity
Uncontrolled terms:%moisture - Adjoint equations - Cast aluminium - Cast aluminum - Feed samples - Fish feed - Grass carp - Infrared thermal image - Infrared thermal imager - Inversion
Classification code:541.1 Aluminum - 641 Heat and Mass Transfer; Thermodynamics - 641.1 Thermodynamics - 641.2 Heat Transfer - 723.1.1 Computer Programming Languages - 723.5 Computer Applications - 921 Mathematics - 944.2 Moisture Measurements
Numerical data indexing:Energy 1.71E+03J to 1.84E+03J, Mass 1.00E-02kg, Percentage 1.10E+01% to 1.70E+01%, Size 4.00E-03m, Size 5.701E+00m to 1.0003E+01m

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

Accession number:20215211376601
Title:Design and experiments of the centrifugal side throwing fertilizer spreader for lotus root fields
Title of translation:离心侧抛式藕田撒肥器设计与试验
Authors: (1, 2); (1, 2); (1, 2); (1, 2); (1, 2); (1, 2)
Author affiliation:(1) College of Engineering, Huazhong Agricultural University, Wuhan; 430070, China; (2) Key Laboratory of Agricultural Equipment in Mid-lower Yangtze River, Ministry of Agriculture and Rural Affairs, Wuhan; 430070, China
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:37-47
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract"></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:31
Main heading:Computer software
Controlled terms:Agricultural machinery - Deflection (structures) - Fertilizers - Finite difference method - Rotating machinery - Speed - Spreaders
Uncontrolled terms:Deflection angles - Discrete elements - Feeding rate - Fertilizer spreaders - Inclination angles - Lotus root field - Peak distance - Rotating speed - Side throwing fertilizer spreader - Variation coefficient
Classification code:408.2 Structural Members and Shapes - 601.1 Mechanical Devices - 723 Computer Software, Data Handling and Applications - 804 Chemical Products Generally - 821.1 Agricultural Machinery and Equipment - 821.2 Agricultural Chemicals - 921.6 Numerical Methods
Numerical data indexing:Angular velocity 4.175E+00rad/s, Angular velocity 5.01E+00rad/s, Mass flow rate 1.50E-01kg/s, Mass flow rate 3.16E-01kg/s, Percentage 1.142E+01%, Percentage 1.295E+01%, Percentage 1.551E+01%, Percentage 1.943E+01%, Percentage 2.195E+01%, Percentage 2.456E+01%, Size 1.00E+01m, Size 1.86E+01m, Size 2.10E+01m, Size 2.40E+01m, Size 2.45E+01m, Size 2.90E+01m

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

Accession number:20215211376593
Title:Yield effects of irrigated acreage change under climate change in China
Title of translation:气候变化条件下中国灌溉面积变化的产量效应
Authors: (1, 2); (1, 4); (3); (1, 4); (1, 4); (2, 3)
Author affiliation:(1) Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing; 100101, China; (2) University of Chinese Academy of Sciences, Beijing; 100049, China; (3) Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing; 100101, China; (4) Yucheng Comprehensive Experiment Station, Chinese Academy of Science, Beijing; 100101, China
Corresponding authors:;
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:94-104
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"></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:37
Main heading:Irrigation
Controlled terms:Climate change - Climate models - Crops - Efficiency - Expansion - Precipitation (chemical) - Rain - Water supply
Uncontrolled terms:Adverse effect - Climate - Irrigation waters - Northeast China - Water consumption - Yield increase - Yield-increasing - Yield-increasing efficiency - Yield-increasing potential
Classification code:443 Meteorology - 443.1 Atmospheric Properties - 443.3 Precipitation - 446.1 Water Supply Systems - 802.3 Chemical Operations - 821.3 Agricultural Methods - 821.4 Agricultural Products - 913.1 Production Engineering - 921 Mathematics - 951 Materials Science
Numerical data indexing:Linear density 1.00E-01kg/m, Linear density 1.20E-01kg/m, Linear density 2.10E-01kg/m, Linear density 7.00E-02kg/m, Linear density 8.00E-02kg/m, Linear density 9.00E-02kg/m, Percentage 1.00E+01% to 1.80E+01%, Percentage 2.00E+00% to 1.70E+01%, Percentage 2.20E+01% to 3.30E+01%, Percentage 3.00E+00% to 1.30E+01%, Percentage 4.00E+00% to 4.00E+01%, Percentage 4.00E+01% to 6.20E+01%, Percentage 4.30E+01% to 6.00E+01%, Percentage 4.50E+01%, Percentage 7.00E+00% to 3.30E+01%, Percentage 7.00E+01%, Percentage 7.50E+01%, Percentage 8.00E+01%, Percentage 8.50E+01%, Percentage 9.00E+01%, Size 5.08E-02m

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

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

Accession number:20215211376677
Title:Combining lightweight wheat spikes detecting model and offline Android software development for in-field wheat yield prediction
Title of translation:结合轻量级麦穗检测模型和离线Android软件开发的田间小麦测产
Authors: (1, 2); (3); (1); (1); (3); (4); (3); (1, 5)
Author affiliation:(1) Academy for Advanced Interdisciplinary Studies/Plant Phenomics Research Center/Jiangsu Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing; 210095, China; (2) College of Engineering, Nanjing Agricultural University, Nanjing; 210095, China; (3) College of Agriculture, Nanjing Agricultural University, Nanjing; 210095, China; (4) College of Economics and Management, Nanjing Agricultural University, Nanjing; 210095, China; (5) National Institute of Agricultural Botany/Cambridge Crop Research, Cambridge; CB3 0LE, United Kingdom
Corresponding authors:;
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:156-164
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"></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:35
Main heading:Smartphones
Controlled terms:Android (operating system) - Application programs - Crops - Data acquisition - Data handling - Deep learning - Image acquisition - Image analysis - Learning algorithms - Open source software - Open systems
Uncontrolled terms:Android system development - Android systems - Lightweight deep learning - Per unit - Spike detection - System development - Wheat - Wheat spike detection - Yield - Yield prediction
Classification code:461.4 Ergonomics and Human Factors Engineering - 718.1 Telephone Systems and Equipment - 723 Computer Software, Data Handling and Applications - 723.2 Data Processing and Image Processing - 723.4.2 Machine Learning - 821.4 Agricultural Products
Numerical data indexing:Linear density 1.7641E-02kg/m, Percentage 8.443E+01%, Percentage 9.105E+01%, Percentage 9.196E+01%

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

Accession number:20215211376658
Title:Optimization of the drying process parameters for lentinus edodes in segment variable temperature infrared assisted spouted bed
Title of translation:香菇分段变温红外喷动床干燥工艺参数优化
Authors: (1, 2); (1); (1, 2); (1); (1); (1)
Author affiliation:(1) College of Food and Biological Engineering, Henan University of Science and Technology, Luoyang; 471023, China; (2) Collaorative Innovation Center of Grain Storage Security, Henan Province, Zhengzhou; 450001, China
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 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"></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:39
Main heading:Optimization
Controlled terms:Antioxidants - Drying - Electric power utilization - Nutrients - Shrinkage
Uncontrolled terms:Brightness values - Drying process - Infrared assisted spouted bed drying - Lentinus edodes - Polysaccharide contents - Shrinkage ratio - Specific power consumption - Spouted bed - Spouted bed drying - Wind temperature
Classification code:706.1 Electric Power Systems - 803 Chemical Agents and Basic Industrial Chemicals - 804 Chemical Products Generally - 804.1 Organic Compounds - 804.2 Inorganic Compounds - 921.5 Optimization Techniques - 951 Materials Science
Numerical data indexing:Percentage 5.30E+01%, Percentage 8.315E+01%, Percentage 9.927E+01%, Specific energy 1.4352E+08J/kg, Specific energy 1.6095E+08J/kg, Temperature 3.28E+02K, Temperature 3.29E+02K, Temperature 3.33E+02K, Temperature 3.33E+02K to 3.43E+02K, Temperature 3.43E+02K, Temperature 3.45E+02K, Temperature 3.48E+02K, null 1.044E+01null, null 9.33E+00null, null 9.98E+00null

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

Accession number:20215211376648
Title:Feasibility of the air source heat pump system for heating swine houses in Beijing
Title of translation:北京猪舍空气源热泵供暖的可行性
Authors: (1); (1, 2); (1); (1); (1)
Author affiliation:(1) College of Animal Science and Technology, China Agriculture University, Beijing; 100193, China; (2) People's Government of Miaoyu Town, Wushan County, Chongqing; 404100, China
Corresponding author:
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:236-242
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"></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:30
Main heading:Space heating
Controlled terms:Air source heat pumps - Ammonia - Carbon dioxide - Coal - Coal combustion - Costs - Emission control - Energy conservation - Energy utilization - Floors - Gas emissions - Geothermal heat pumps - Greenhouse gases - Heating equipment - Houses - Mammals - Natural gas - Ventilation
Uncontrolled terms:Air-source heat pump systems - Air-source heat pumps - Clean energy - Coefficient of Performance - Energy-saving and emission reductions - Heating system - Indoor temperature - Pig house - Return water - Supply waters
Classification code:402 Buildings and Towers - 402.3 Residences - 451.1 Air Pollution Sources - 451.2 Air Pollution Control - 521 Fuel Combustion and Flame Research - 522 Gas Fuels - 524 Solid Fuels - 525.2 Energy Conservation - 525.3 Energy Utilization - 616.1 Heat Exchange Equipment and Components - 641.2 Heat Transfer - 643.1 Space Heating - 643.5 Ventilation - 804.2 Inorganic Compounds - 911 Cost and Value Engineering; Industrial Economics
Numerical data indexing:Mass 6.36E+02kg, Percentage 6.60E+01%, Size 4.20E+01m, Size 9.30E+00m

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

Accession number:20215211376647
Title:Establishment of the mining model for sweet molecules in food
Title of translation:食品中甜味分子发掘模型构建
Authors: (1); (2); (3); (1); (1); (1); (2); (1, 3)
Author affiliation:(1) Key Laboratory of Dairy Science, Ministry of Education, Northeast Agricultural University, Harbin; 150030, China; (2) Center for Education Technology, Northeast Agricultural University, Harbin; 150030, China; (3) Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing; 100193, China
Corresponding authors:;
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:303-308
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"></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:32
Main heading:Forecasting
Controlled terms:Decision trees - Food additives - Food processing - Least squares approximations - Mean square error - Molecules - Nearest neighbor search - Principal component analysis - Regression analysis - Statistical tests - Support vector machines
Uncontrolled terms:Accuracy rate - Descriptors - Evaluation index - Principal Components - Qualitative structure-activity relationship - Quantitative structure activity relationship - Random forests - Root mean square errors - Structure-activity relationships - Test sets
Classification code:723 Computer Software, Data Handling and Applications - 803 Chemical Agents and Basic Industrial Chemicals - 822.2 Food Processing Operations - 822.3 Food Products - 921.4 Combinatorial Mathematics, Includes Graph Theory, Set Theory - 921.5 Optimization Techniques - 921.6 Numerical Methods - 922.2 Mathematical Statistics - 931.3 Atomic and Molecular Physics - 961 Systems Science
Numerical data indexing:Percentage 2.00E+01%, Percentage 8.00E+01%

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

Accession number:20215211376536
Title:Seismic performance analysis of Dutch Venlo greenhouse structure
Title of translation:荷兰Venlo型温室结构抗震性能分析
Authors: (1); (1)
Author affiliation:(1) China Triumph International Engineering Co., Ltd., Shanghai; 200063, China
Corresponding author:
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:243-249
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"></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:27
Main heading:Finite element method
Controlled terms:Aerodynamic loads - Agriculture - Earthquakes - Greenhouses - Vibration analysis - Wind stress - Yield stress
Uncontrolled terms:Covering material - Earthquake - Greenhouse structure - Netherlands - Seismic action - Seismic Performance - Seismic precautionary intensity - Structure design - Venlo - Wind load
Classification code:443.1 Atmospheric Properties - 484 Seismology - 651.1 Aerodynamics, General - 821 Agricultural Equipment and Methods; Vegetation and Pest Control - 821.6 Farm Buildings and Other Structures - 921.6 Numerical Methods - 951 Materials Science
Numerical data indexing:Mass 1.00E-04kg, Mass 1.50E-04kg, Mass 2.00E-04kg, Mass 3.00E-04kg, Percentage 1.16E+01%, Pressure 1.9602E+08Pa, Pressure 2.1195E+08Pa, Pressure 2.1696E+08Pa, Size 3.18E-02m, Time 1.75E+00s

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

Accession number:20215211376394
Title:Classification of perch ingesting condition using lightweight neural network MobileNetV3-Small
Title of translation:基于轻量级神经网络MobileNetV3-Small的鲈鱼摄食状态分类
Authors: (1, 3); (1, 2); (1, 2); (1); (1); (1)
Author affiliation:(1) College of Engineering, Huazhong Agricultural University, Wuhan; 430070, China; (2) Key Laboratory of Agricultural Equipment in Mid-lower Yangtze River, Ministry of Agriculture and Rural Affairs, Wuhan; 430070, China; (3) Engineering Research Center of Green Development for Conventional Aquatic Biological Industry in the Yangtze River Economic Belt, Ministry of Education, Wuhan; 430070, China
Corresponding authors:;
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:165-172
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"></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:30
Main heading:Image recognition
Controlled terms:Aquaculture - Cameras - Computer vision - Deep learning - Feeding - Fish - Forecasting - Gaussian noise (electronic) - Image enhancement - Neural networks - Testing - Video recording
Uncontrolled terms:Aquaculture industry - Condition - Control groups - Culture environments - Deep learning - Machine-vision - Neural-networks - Outdoor cultures - Perch - Training sets
Classification code:461.4 Ergonomics and Human Factors Engineering - 691.2 Materials Handling Methods - 716.4 Television Systems and Equipment - 723.5 Computer Applications - 741.2 Vision - 742.2 Photographic Equipment - 821.3 Agricultural Methods
Numerical data indexing:Percentage 5.00E+01%, Percentage 5.56E+00%, Percentage 9.96E+01%, Time 8.00E+01s to 1.10E+02s

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

Accession number:20215211376649
Title:Multi-target cow mouth tracking and rumination monitoring using Kalman filter and Hungarian algorithm
Title of translation:利用Kalman滤波和Hungarian算法的多目标奶牛嘴部跟踪及反刍监测
Authors: (1, 2, 3); (1, 2); (1, 3); (1, 2, 3); (1, 2, 3)
Author affiliation:(1) College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling; 712100, China; (2) Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling; 712100, China; (3) Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling; 712100, China
Corresponding authors:; ;
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:192-201
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"></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:31
Main heading:Computer vision
Controlled terms:Cell proliferation - Clutter (information theory) - Consumer behavior - Farms - Image recognition - Image segmentation - Kalman filters - Mammals - Surface discharges - Target tracking
Uncontrolled terms:Cow - Hungarian algorithm - Kalman filter algorithms - Machine-vision - Mouth region - Multi-target-tracking - Multi-targets - Rapid head - Ruminant behavior - YOLOv4
Classification code:461.9 Biology - 701.1 Electricity: Basic Concepts and Phenomena - 716.1 Information Theory and Signal Processing - 723.5 Computer Applications - 741.2 Vision - 821 Agricultural Equipment and Methods; Vegetation and Pest Control - 931.3 Atomic and Molecular Physics
Numerical data indexing:Percentage 9.246E+01%, Percentage 9.319E+01%, Percentage 9.392E+01%, Percentage 9.693E+01%, Percentage 9.989E+01%, Time 1.48E+00s

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

Accession number:20215211376463
Title:Building saliency-map-based attention-driven deep network to detect vegetable pests of sticky trap images
Title of translation:利用显著图构建注意力深度网络检测诱虫板蔬菜害虫
Authors: (1); (1, 2); (1); (1, 2)
Author affiliation:(1) College of Mathematics and Informatics, South China Agricultural University, Guangzhou; 510642, China; (2) Guangzhou Key Laboratory of Intelligent Agriculture, Guangzhou; 510642, China
Corresponding authors:;
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:211-219
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"></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:28
Main heading:Image recognition
Controlled terms:Computer vision - Convolution - Convolutional neural networks - Crops - Deep learning - Image segmentation - Vegetables
Uncontrolled terms:Bounding-box - Convolutional neural network - Deep learning - Insects pests - Intelligent computer-vision-based detection - Machine-vision - Non-maximum suppression - Pest detection - Saliency map - Vision-based detection
Classification code:461.4 Ergonomics and Human Factors Engineering - 716.1 Information Theory and Signal Processing - 723.5 Computer Applications - 741.2 Vision - 821.4 Agricultural Products
Numerical data indexing:Percentage 8.64E+01%

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

Accession number:20215211376505
Title:Soil salinity accumulation and model simulation of cotton under mulch drip irrigation with different salinity level water
Title of translation:不同矿化度咸水膜下滴灌棉花土壤盐分累积规律及其数值模拟
Authors: (1); (1); (1); (1); (1); (1); (1); (1); (2, 3)
Author affiliation:(1) Key Laboratory of Xinjiang Production and Construction Corps for Ecological and Water Conservancy Engineering in the Cold and Drought Area, School of Water Conservancy and Construction Engineering, Shihezi University, Shihezi; 832000, China; (2) University of Chinese Academy of Sciences, Beijing; 100049, China; (3) Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing; 100101, China
Corresponding author:
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:73-83
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"></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:43
Main heading:Cotton
Controlled terms:Cultivation - Electric conductivity - Groundwater - Irrigation - Magnesium compounds - Numerical models - Sodium bicarbonate - Sodium chloride - Sodium sulfate - Soil moisture - Sulfur compounds - Water treatment
Uncontrolled terms:Different soils - Drip irrigation - Growth period - Irrigation waters - Mulch drip irrigation - Salinity - Salinity levels - Salinity treatment - Soil layer - Soil salts
Classification code:444.2 Groundwater - 445.1 Water Treatment Techniques - 483.1 Soils and Soil Mechanics - 701.1 Electricity: Basic Concepts and Phenomena - 804 Chemical Products Generally - 804.2 Inorganic Compounds - 821.3 Agricultural Methods - 821.4 Agricultural Products - 921 Mathematics
Numerical data indexing:Mass density 4.00E+00kg/m3, Mass density 5.00E+00kg/m3, Mass density 6.00E+00kg/m3, Size 1.00E-01m, Size 2.00E+00m, Size 3.00E-01m, Size 4.508E+02m, Size 5.00E-01m, Size 6.00E-01m to 7.00E-01m

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

Accession number:20215211376679
Title:Long-term comprehensive effects of recommended fertilization based on nutrient expert system of maize in Northeast China
Title of translation:基于养分专家系统推荐施肥在东北玉米上的长期综合效应
Authors: (1); (1); (2); (1); (1); (1); (1); (1)
Author affiliation:(1) Key Laboratory of Plant Nutrition and Agro-Environment in Northeast Region, Ministry of Agriculture and Rural Affairs, P.R. China/Agricultural Resources and Environment Research Institute, Jilin Academy of Agricultural Sciences, Changchun; 130033, China; (2) Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing; 100081, China
Corresponding author:
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:129-138
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"></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:39
Main heading:Soils
Controlled terms:Crops - Decision support systems - Efficiency - Expert systems - Nitrogen fertilizers - Nutrients - Phosphorus - Soil testing
Uncontrolled terms:Fertilisation - Fertilizer use - Fertilizer use efficiency - Inorganics - Maize yield - Net incomes - Northeast China - Nutrient balance - Nutrient expert system - Use efficiency
Classification code:483.1 Soils and Soil Mechanics - 723 Computer Software, Data Handling and Applications - 723.4.1 Expert Systems - 804 Chemical Products Generally - 821.2 Agricultural Chemicals - 821.4 Agricultural Products - 912.2 Management - 913.1 Production Engineering
Numerical data indexing:Percentage 2.20E+01% to 3.17E+01%, Percentage 2.90E+01% to 4.01E+01%, Percentage 3.13E+01% to 4.43E+01%, Size 0.00E00m to 3.00E-01m, Size 3.00E-01m to 9.00E-01m

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

Accession number:20215211376665
Title:Accurate extraction method for cropland in mountainous areas based on field parcel
Title of translation:地块尺度的山区耕地精准提取方法
Authors: (1, 2); (1); (1); (1, 2); (3); (4); (1, 2)
Author affiliation:(1) Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing; 100101, China; (2) University of Chinese Academy of Sciences, Beijing; 100049, China; (3) School of Earth Sciences and Engineering, Hohai University, Nanjing; 211100, China; (4) School of Earth Sciences and Engineering, Hebei University of Engineering, Handan; 056000, China
Corresponding authors:;
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:260-266
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"></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:31
Main heading:Extraction
Controlled terms:Edge detection - Farms - Feature extraction - Image enhancement - Information management - Land use - Pixels - Remote sensing - Semantic Segmentation - Semantics - Spatial distribution - Surveys - Textures
Uncontrolled terms:Cropland-parcel extraction - Cultivated lands - Extraction method - High-resolution images - High-resolution remote sensing images - Hilly and mountainous areas - Land parcels - Mountainous area - Remote-sensing - Semantic segmentation
Classification code:403 Urban and Regional Planning and Development - 405.3 Surveying - 723.4 Artificial Intelligence - 802.3 Chemical Operations - 821 Agricultural Equipment and Methods; Vegetation and Pest Control - 902.1 Engineering Graphics - 921 Mathematics
Numerical data indexing:Percentage 8.284E+01%, Percentage 9.291E+01%, Size 5.30E-01m

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

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

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

Accession number:20215211376665
Title:Accurate extraction method for cropland in mountainous areas based on field parcel
Title of translation:地块尺度的山区耕地精准提取方法
Authors:Zhou, Nan (1, 2); Yang, Peng (1); Wei, Chunshan (1); Shen, Zhanfeng (1, 2); Yu, Juanjuan (3); Ma, Xiaoyu (4); Luo, Jiancheng (1, 2)
Author affiliation:(1) Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing; 100101, China; (2) University of Chinese Academy of Sciences, Beijing; 100049, China; (3) School of Earth Sciences and Engineering, Hohai University, Nanjing; 211100, China; (4) School of Earth Sciences and Engineering, Hebei University of Engineering, Handan; 056000, China
Corresponding authors:Shen, Zhanfeng(shenzf@aircas.ac.cn); Shen, Zhanfeng(shenzf@aircas.ac.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:260-266
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 mountainous and hilly areas, cultivated land resources are scarce, and the phenomenon of cultivated land fragmentation is serious. The long and narrow arable land plots are complex in structure, which makes it difficult to obtain the information of cultivated land at the land level quickly and accurately, and hinders the application of precise digital agricultural services based on high resolution remote sensing images in mountainous and hilly areas. Parcle-level cultivated land information can intuitively show the spatial distribution, boundary details of farmland, and is of great significance for precision agriculture management, distribution of planting subsidies, and agricultural resource survey. Existing edge detection/semantic segmentation networks-based farmland extraction methods ignore the structural features of the parcel, thus have limit performance for handling narrow and small plots, and there is also a blurring boundary problem. To address these issues, we proposed an accurate extraction method of cropland in mountainous area based on geographic parcels. This method combines the advantages of semantic segmentation and edge detection, and effectively extracts and integrates the linear features of the boundary and the internal texture features of the parcel, so as to improve the recognition accuracy of the cultivated land. The main features of the model in this paper are as follows: 1) The edge of cultivated land is regarded as a new class independent of cultivated land parcels, so that the semantic segmentation network can better distinguish the edge and internal area of cultivated land parcels; 2) A cascaded semantic segmentation and edge detection network is introduced to correlate the prediction of cultivated land surface and line, realize the fusion of boundary and texture features of cultivated land parcels and strengthen the edge features of cultivated land, so as to improving the accuracy of cultivated land block edge detection; 3) A focus training technique is proposed to address the problem that the edge pixels of cultivated land are far fewer than non-edge pixels, by enforcing the model pay more attention to the important but underrepresented edge pixels in high resolution remote sensing images in the training process, so as to improve the edge detection accuracy. We conduct experiments in Shaodong County, Hunan Province in the southwest mountainous area, using the Google Earth high-resolution remote sensing images as the data source, with a spatial resolution of 0.53 m. After manual selection, a total of 1000 512×512 image patches are obtained. Among them, 600 pieces are used as the training set, 200 pieces are used as the verification set, and 200 pieces are used as the test set. Experimental results show that the presented model achieves satisfying results with an overall accuracy of 92.91% and IoU (Intersection-Over-Union) of 82.84% on the test set, which was 4.28 percentage points and 8.01 percentage points higher than the baseline method respectively. Compared with the existing methods, the cultivated land extracted in this study is more consistent with the actual distribution pattern of cultivated land, which provides a practical method for accurate extraction of cultivated land information at the plot scale in mountainous and hilly regions.<br/></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:31
Main heading:Extraction
Controlled terms:Edge detection - Farms - Feature extraction - Image enhancement - Information management - Land use - Pixels - Remote sensing - Semantic Segmentation - Semantics - Spatial distribution - Surveys - Textures
Uncontrolled terms:Cropland-parcel extraction - Cultivated lands - Extraction method - High-resolution images - High-resolution remote sensing images - Hilly and mountainous areas - Land parcels - Mountainous area - Remote-sensing - Semantic segmentation
Classification code:403 Urban and Regional Planning and Development - 405.3 Surveying - 723.4 Artificial Intelligence - 802.3 Chemical Operations - 821 Agricultural Equipment and Methods; Vegetation and Pest Control - 902.1 Engineering Graphics - 921 Mathematics
Numerical data indexing:Percentage 8.284E+01%, Percentage 9.291E+01%, Size 5.30E-01m
DOI:10.11975/j.issn.1002-6819.2021.19.030

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

Accession number:20215211376651
Title:Parameter optimization on convective heat transfer of cold plate for cold storage temperature control box based on multi-parameter coupling
Title of translation:基于多参数耦合的蓄冷温控箱冷板对流换热参数优化
Authors:Guo, Jiaming (1, 2); Wu, Xudong (1, 2); Lin, Shitao (1, 2); Zeng, Zhixiong (1, 2); Shen, Hao (1, 2); Wei, Xinyu (1, 2); Lyu, Enli (1, 2)
Author affiliation:(1) Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, South China Agricultural University, Guangzhou; 510642, China; (2) College of Engineering, South China Agricultural University, Guangzhou; 510642, China
Corresponding authors:Lyu, Enli(enlilv@scau.edu.cn); Lyu, Enli(enlilv@scau.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:228-235
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 transport container with a controlled temperature was developed, where the temperature was regulated using low-temperature phase-change materials. The cold energy was first stored in low-temperature phase-change materials and then released when the temperature in the container was out of target range under an intelligent control system. However, there were still some issues that need to be solved, such as the difficulties in controlling the release rate of cold energy, the prediction of remaining cold energy during the transportation work. The release rate of cold energy depended directly on the convective heat transfer coefficient between the surface of the cold storage plate and the ambient air. In this study, an experimental platform was developed to investigate the influence of different environments and parameters of cold storage plates on the convective heat transfer coefficient between the cold storage plate surface and the ambient air. A quadratic regression orthogonal experiment was adopted to clarify the coupling effects among the factors, including the air velocity and temperature at the entrance of the cool storage area, heat transfer area of the cold storage plate, and the space between them on the surface convective heat transfer coefficient. After that, the experimental data were analyzed. A second-order prediction model of surface convective heat transfer coefficient was built that the relationships between the influence factors and the surface convective heat transfer coefficient and the factors with significant effects were obtained, as well as the optimal values of such factors. Consequently, there was the most significant interaction between the entrance air temperature and the heat transfer area of the cold storage plate. The prediction model of surface convective heat transfer coefficient built by response surface method presented a higher accuracy, where the best combination of parameters was velocity=4 m/s, temperature=25 ℃, area=0.455 m<sup>2</sup>, spacing=0.04 m, and the determination coefficient value was 0.927 4 and the coefficient of variation was 5.78%. The calculated results of such regression model were in good agreement with the experimental, with the maximum error of 3.58% and an average relative error of 2.69%, indicating that such model can be used to quickly and accurately predict the convective heat transfer coefficient between the surface of cold storage plate and the ambient air under different conditions. The finding can provide accurate control on the release rate of cold energy in the temperature phase-change materials, and the prediction of remaining cooling energy for transport containers with controlled temperature.<br/></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:34
Main heading:Temperature
Controlled terms:Air - Cold storage - Containers - Digital storage - Forecasting - Heat convection - Heat transfer coefficients - Regression analysis - Surface properties
Uncontrolled terms:Ambient air - Cold energy - Cold storage plate - Cold-storage container - Convection transfer rate - Orthogonal test - Release rate - Response surfaces methods - Storage container - Transfer rates
Classification code:641.1 Thermodynamics - 641.2 Heat Transfer - 644.3 Refrigeration Equipment and Components - 694.4 Storage - 722.1 Data Storage, Equipment and Techniques - 804 Chemical Products Generally - 922.2 Mathematical Statistics - 931.2 Physical Properties of Gases, Liquids and Solids - 951 Materials Science
Numerical data indexing:Percentage 2.69E+00%, Percentage 3.58E+00%, Percentage 5.78E+00%, Size 4.00E-02m, Size 4.55E-01m, Velocity 4.00E+00m/s
DOI:10.11975/j.issn.1002-6819.2021.19.026

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

Accession number:20215211376505
Title:Soil salinity accumulation and model simulation of cotton under mulch drip irrigation with different salinity level water
Title of translation:不同矿化度咸水膜下滴灌棉花土壤盐分累积规律及其数值模拟
Authors:Yang, Guang (1); Li, Wanjing (1); Ren, Futian (1); He, Xinlin (1); Wang, Chunxia (1); Qiao, Changlu (1); Li, Xiaolong (1); Lei, Jie (1); Li, Fadong (2, 3)
Author affiliation:(1) Key Laboratory of Xinjiang Production and Construction Corps for Ecological and Water Conservancy Engineering in the Cold and Drought Area, School of Water Conservancy and Construction Engineering, Shihezi University, Shihezi; 832000, China; (2) University of Chinese Academy of Sciences, Beijing; 100049, China; (3) Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing; 100101, China
Corresponding author:He, Xinlin(hexinlin2002@163.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:73-83
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">Mulch drip irrigation has great benefits to save water for high cotton production in Xinjiang, China. Among them, freshwater has widely been used in agricultural irrigation. Highly efficient exploitation and utilization of saltwater resources have been the potential urgent to alleviate the ongoing freshwater shortages. In this study, a three-year growing season field experiment was conducted with different salinity level water irrigation, thereby analyzing the soil salt content within the different soil layers during the whole growth period of cotton. A salinity threshold of cotton was determined under saltwater mulch drip irrigation. An HYDRUS-2D model of soil salt was also built in cotton field under saltwater drip irrigation using the theory of soil water and solute movement. A numerical simulation was conducted for the distribution and accumulation characteristics of soil salt. The experiment was carried out in Shihezi University, China. The base presented an average altitude of 450.8 m and a geographical location of 85°59'47″ E, 44°19'26″ N. Cotton was planted in each plot with the size of 2 m×2 m×2 m. The irrigation water was treated with five salinity levels: 2, 3, 4, 5, and 6 g/L. The ratio of chemicals NaHCO<inf>3</inf>, Na<inf>2</inf>SO<inf>4</inf>, NaCl, CaCl<inf>2</inf>, and MgCl<inf>2</inf> were 1:7:8:1:1, according to the composition of local groundwater. The results showed that: 1) The salt accumulated less under 2, 3, and 4 g/L salinity treatments, where the plant height, chlorophyll, and yield of cotton were higher than those under 5 and 6 g/L salinity treatments. Therefore, 4 g/L salinity level was the threshold of irrigation water. 2) The soil salt gradually accumulated with the growth period of irrigationand reached the peak at the opening period. The soil electrical conductivity at the emitter increased at first and then decreased with the peak value in 60-70 cm soil layer, as the soil depth increased. The soil electrical conductivity of different salinity treatments were 3.04, 3.18, 3.15, 3.00, and 3.12 dS/m, respectively. 3) There was a more obvious accumulation trend with the increase in the salinity of irrigation water sources. The simulated salt accumulation was ranked in the order of 30 cm > 50 cm > 10 cm soil layers. There was in good agreement, where the Mean Absolute Error (MAE)<0.168, Mean Relative Error (MRE)< 15.321, Root Mean Square Error (RMSE)<0.2, Coefficient of determination R<sup>2</sup>>0.79 between the measured and simulated values of different soil layers. Therefore, the 4 g/L salinity level was suitable for the mulch drip irrigation of cotton using saline water. The finding can provide promising guidance for further exploitation and utilization of saltwater resources, particularly for the sustainable development of irrigated agriculture in semi-arid and arid areas.<br/></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:43
Main heading:Cotton
Controlled terms:Cultivation - Electric conductivity - Groundwater - Irrigation - Magnesium compounds - Numerical models - Sodium bicarbonate - Sodium chloride - Sodium sulfate - Soil moisture - Sulfur compounds - Water treatment
Uncontrolled terms:Different soils - Drip irrigation - Growth period - Irrigation waters - Mulch drip irrigation - Salinity - Salinity levels - Salinity treatment - Soil layer - Soil salts
Classification code:444.2 Groundwater - 445.1 Water Treatment Techniques - 483.1 Soils and Soil Mechanics - 701.1 Electricity: Basic Concepts and Phenomena - 804 Chemical Products Generally - 804.2 Inorganic Compounds - 821.3 Agricultural Methods - 821.4 Agricultural Products - 921 Mathematics
Numerical data indexing:Mass density 4.00E+00kg/m3, Mass density 5.00E+00kg/m3, Mass density 6.00E+00kg/m3, Size 1.00E-01m, Size 2.00E+00m, Size 3.00E-01m, Size 4.508E+02m, Size 5.00E-01m, Size 6.00E-01m to 7.00E-01m
DOI:10.11975/j.issn.1002-6819.2021.19.009

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

Accession number:20215211376679
Title:Long-term comprehensive effects of recommended fertilization based on nutrient expert system of maize in Northeast China
Title of translation:基于养分专家系统推荐施肥在东北玉米上的长期综合效应
Authors:Hou, Yunpeng (1); Kong, Lili (1); Xu, Xinpeng (2); Yin, Caixia (1); Zhang, Lei (1); Zhao, Yinkai (1); Liu, Zhiquan (1); Wang, Lichun (1)
Author affiliation:(1) Key Laboratory of Plant Nutrition and Agro-Environment in Northeast Region, Ministry of Agriculture and Rural Affairs, P.R. China/Agricultural Resources and Environment Research Institute, Jilin Academy of Agricultural Sciences, Changchun; 130033, China; (2) Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing; 100081, China
Corresponding author:Wang, Lichun(wlc1960@163.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:129-138
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 long-term site-specific experiment of fertilizer recommendation from 2012 to 2020 was conducted to evaluate the comprehensive effects of nutrient expert decision support system on yield, benefit, fertilizer use efficiency, and soil nutrient of spring maize in northeast China. Five treatments included no-fertilizer (CK) as the control, currently traditional farmers' practices (FP), fertilizer recommended using a nutrient expert system (NE), slow/controlled-release nitrogen fertilizer using the same rate as NE treatment (NE<inf>R</inf>), and conventional fertilizer recommendation using soil testing (ST). The current study also investigated the long-term changes of maize yield, benefit, fertilizer use efficiency, the contents of inorganic N, available P, and available K in the soil, as well as the balance of nutrient input/output during the nine-year period. The results showed that the maximum, minimum, or mean values of fertilizer inputs in NE, NE<inf>R,</inf> and ST treatments were all significantly lower than that of FP treatment (P<0.05), respectively. In different nutrients, NE, NE<inf>R</inf> and ST treatments significantly reduced the amounts of nitrogen (N) and phosphorus (P) fertilizers application (P<0.05), but significantly increased the amount of potassium (K) fertilizer application (P<0.05), compared with that of FP treatment, respectively. As compared to FP treatment, NE, NE<inf>R</inf> and ST treatments significantly improved maize yield and net income. In which, NE treatment achieved the highest value, with the average increment by11.0% and14.3%, respectively. In all the treatments, NE treatment achieved the highest stability, and followed by ST and NE<inf>R</inf> treatment.The recovery efficiency, agronomic efficiency, and partial factor productivity in the NE, NE<inf>R,</inf> and ST treatments were significantly higher than those in FP treatment, where increased by 29.0%-40.1%, 31.3%-44.3%, and 22.0%-31.7%, respectively. Specifically, the highest value was observed in NE treatment, followed by NE<inf>R</inf> and ST treatments. Compared with FP treatment, NE, NE<inf>R</inf> and ST treatments significantly improved the inorganic N content in 0-30 cm soil layer (P<0.05), but significantly reduced inorganic N content in >30-90 cm soil layer (P<0.05) and available P content in 0-30 cm soil layer (P<0.05). But the soil available K content wasn't significantly different (P>0.05) among different fertilization treatments. The contents of soil inorganic N, available P, and available K in NE treatment were close to the initial testing values before planting. The nutrient input/output balance was obtained during the nine-year period, where the N and P balances were surplus under all fertilization treatments, whereas, the K balance was deficient. But the surplus of N, P, and the deficiency of K in NE treatment attained the lowest values, followed by NE<inf>R</inf> and ST treatments. In conclusion, compared with farmers' practices and the conventional fertilizer recommendation system, the NE fertilization recommendation system fully met the nutrient requirements of maize, with optimal fertilization rate, time, and ratio. Consequently, the NE fertilization recommendation system has the potential to improve maize yield, benefits, and fertilizer use efficiency, thereby maintaining the soil nutrient stability under the reduction of fertilizer application amount, compared with farmers' practices. However, the simplified fertilization of slow/controlled-release nitrogen fertilizer in the NE fertilization recommendation system also obtained higher benefits, but reduced labor cost. Therefore, the NE system is an appropriate fertilizer recommendation for maize in northeast China.<br/></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:39
Main heading:Soils
Controlled terms:Crops - Decision support systems - Efficiency - Expert systems - Nitrogen fertilizers - Nutrients - Phosphorus - Soil testing
Uncontrolled terms:Fertilisation - Fertilizer use - Fertilizer use efficiency - Inorganics - Maize yield - Net incomes - Northeast China - Nutrient balance - Nutrient expert system - Use efficiency
Classification code:483.1 Soils and Soil Mechanics - 723 Computer Software, Data Handling and Applications - 723.4.1 Expert Systems - 804 Chemical Products Generally - 821.2 Agricultural Chemicals - 821.4 Agricultural Products - 912.2 Management - 913.1 Production Engineering
Numerical data indexing:Percentage 2.20E+01% to 3.17E+01%, Percentage 2.90E+01% to 4.01E+01%, Percentage 3.13E+01% to 4.43E+01%, Size 0.00E00m to 3.00E-01m, Size 3.00E-01m to 9.00E-01m
DOI:10.11975/j.issn.1002-6819.2021.19.015

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

Accession number:20215211376620
Title:Temporal-spatial evolution and influencing factors of land green use efficiency of resource-based cities in the Yellow River Basin, China
Title of translation:黄河流域资源型城市土地绿色利用效率时空演变及影响因素
Authors:Ding, Yi (1); Guo, Qingxia (1); Qin, Mingxing (1)
Author affiliation:(1) College of Resources and Environment, Shanxi Agricultural University, Taigu; 030801, China
Corresponding author:Guo, Qingxia(gqx696@163.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:250-259
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">The Yellow River Basin has been one of the most important energy bases in China. The land green use efficiency of resource-based cities directly dominates the sustainable and high-quality development in this region. In this study, taking the resource-based cities in the Yellow River Basin as the case study area, a novel index system of land green use efficiency was developed, where the resource and environmental consumption were as input indicators, while the economic, social, and environmental benefits as output indicators. SSBM (Super Slack Based Measure) model was also used to measure the land green use efficiency of resource-based cities in the study area from 2009 to 2018. A spatial autocorrelation and GTWR (Geographically and Temporally Weighted Regression) model were selected to analyze the spatial evolution characteristics and driving factors of land green use efficiency, respectively. The results showed that: 1) There was a trend of fluctuation on the land green use efficiency of resource-based cities in the study areas from 2009 to 2018. There was also a relatively low proportion of high-value cities in the land green use efficiency, where there was not a significant change over the past 10 years. Furthermore, there was a low growth rate of land green use efficiency with the insufficient rising power in the cities, due mainly to the regional restrictions and economic development. Specifically, the land green use efficiency increased the fastest in Henan and Shaanxi Provinces from 2009 to 2018. Ningxia, Shandong, and Inner Mongolia were in the slow growth stage, while Shanxi and Gansu showed a downward trend. 2) The Global Moran's I index of land green use efficiency was between -0.179 and 0.192 for the resource-based cities in the study area, indicating that the global spatial evolution was ranging from the relatively strong to weak correlation from 2009 to 2018. There were also the small-scale agglomeration and large-scale dispersion in the spatial distributions of land green use efficiency for the resource-based cities in the study area, according to the LISA index. As such, it is necessary to further strengthen the coordinated development among upper, middle, and lower reaches in the study areas. 3) There was spatial heterogeneity in the driving factors of land green use efficiency. The economic and industrial structure factors were consistently dominated the land green use efficiency in the study area. The most critical factor in the main driving factors was gradually shifted to the level of science and technology for different types of resource-based cities in 2018. Furthermore, the growing or declining resource-based cities depended strongly on the economy, industrial structure, and policy. The mature resource-based cities were mainly influenced by technology and urbanization. More importantly, technology and industrial structure posed a strong impact on renewable resource-based cities. Consequently, the finding can widely be used to guide the decision-making for better efficiency of land green use in various resource-based cities.<br/></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:29
Main heading:Land use
Controlled terms:Economic analysis - Economic and social effects - Efficiency - Regression analysis - Rivers - Spatial variables measurement - Watersheds
Uncontrolled terms:Geographically and temporally weighted regression model - Influencing factor - Measure model - Regression modelling - Resource-based city - Slack-based measures - Spatial temporals - Spatial-temporal differentiation - Super slack based measure model - Weighted regression - Yellow River basin
Classification code:403 Urban and Regional Planning and Development - 444.1 Surface Water - 911.2 Industrial Economics - 913.1 Production Engineering - 922.2 Mathematical Statistics - 943.2 Mechanical Variables Measurements - 971 Social Sciences
Numerical data indexing:Age 1.00E+01yr
DOI:10.11975/j.issn.1002-6819.2021.19.029

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

Accession number:20215211376392
Title:Determination of seed germination index and selection of sensitive seeds for phytotoxicity evaluation of composting
Title of translation:堆肥种子发芽指数测定方法与敏感性种子筛选
Authors:Wang, Guoying (1); Yuan, Jing (1); Kong, Yilin (1); Shen, Yujun (2); Yang, Yan (1); Li, Guoxue (1)
Author affiliation:(1) Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation, College of Resource and Environmental Science, China Agricultural University, Beijing; 100193, China; (2) Academy of Agricultural Planning and Engineering, Ministry of Agriculture and Rural Affairs, Beijing; 100125, China
Corresponding author:Li, Guoxue(ligx@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:19
Issue date:October 1, 2021
Publication year:2021
Pages:220-227
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">Composting has historically been carried out by farmers for manure management, which is a reliable way to recycle manure for use on croplands in sustainable agriculture. However, poor management of the composting process can result in un-mature compost product, which inhibits plant growth and deteriorates soil condition. The seed Germination Index (GI), an authoritative parameter to evaluate the maturity of compost, has been widely utilized in evaluating the phytotoxicity and other aspects of composting process. However, there are still lack of unified standards for the measurement method of GI and the representative type of experimental seed until now, which leads to the diverse results of GI (fluctuated from 20%-217%), and the newest standard of organic fertilizer (NY525-2021) requirements GI≥70% of organic fertilizer. Thus, it is meaningless to compare these values between different studies in related fields and urgently for public to provide a reference measurement process for GI during composting. Therefore, this study protocoled four treatments, composting chicken manure with or without auxiliary material (carbon additive) for different phytotoxicity compost, and selected four types of seeds for sensitive assays according to previous studies, mainly included cucumber, radish, Chinese cabbage and oil rape. Besides, in the process of germination experiments and indexes calculation, detailly instruction for the whole process of GI determination and calculation was documented, which stipulated the definition of radical length for 0, 1 mm and the start position for measure (with diagram). Furthermore, systematically detecting the response relationships between influenced parameters and GI of different composting treatments. The results showed that, according to the GI of final compost, the maturity increased in the order of pure pig manure treatment (26.54%-80.49%), pure sheep manure treatment (16.71%-92.98%), pig manure and straw treatment (28.28%-110.74%), sheep manure and straw treatment (43.38%-119.69%). Carbon additive of straw could significantly improve temperature of composting systems and further maturity of the final products. Increasing pH and decreasing phytotoxicity of electronical conductivity, low molecular organic acid and organic matter contents at the same time. Seeds' GI decreased with the order of radish (≤130%), cucumber (≤100%), oil rape (≤70%), Chinese cabbage (≤45%). The sensitives were reflected with correlation analysis, which between physical and chemical indicators (temperature, oxygen, pH, carbon and nitrogen ratio) and biological toxicity factors (electronical conductivity, ammonium nitrogen, organic acid and organic matter) and GI with different seeds (cucumber, radish, cabbage, rape) of four composting processes, and fond the factors influenced GI with the sequence of NH<inf>4</inf><sup>+</sup>, pH value, O<inf>2</inf>, VS, VFAs, EC, C/N, temperature, which was in accordance with the affected sequence of radish seed. Besides, radish seed showed the highest responsivity (3.69, r=0.96) of GI for influenced factors, which was the most scientific and representative type of seeds for comprehensively reflecting maturity and phytotoxicity of compost of discrepancy qualities compost. Furthermore, radish has been widely studied for the reasons of fasting germination speed, moderating molecular size, obtaining easily and lowering price. These results can provide scientific references for the determination and comparation of GI, the establishment of organic fertilizer standards and the safe application of compost in farmlands.<br/></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:50
Main heading:Composting
Controlled terms:Additives - Biogeochemistry - Carbon - Cultivation - Fertilizers - Mammals - Manures - Nitrogen - Seed
Uncontrolled terms:Carbon additives - Chinese cabbage - Composting process - Determination - Germination index - Index calculation - Organic fertilizers - Phytotoxicity - Pig manures - Seed germination
Classification code:481.2 Geochemistry - 801.2 Biochemistry - 803 Chemical Agents and Basic Industrial Chemicals - 804 Chemical Products Generally - 821.2 Agricultural Chemicals - 821.3 Agricultural Methods - 821.4 Agricultural Products - 821.5 Agricultural Wastes
Numerical data indexing:Percentage 1.00E+02%, Percentage 1.30E+02%, Percentage 1.671E+01% to 9.298E+01%, Percentage 2.00E+01% to 2.17E+02%, Percentage 2.654E+01% to 8.049E+01%, Percentage 2.828E+01% to 1.1074E+02%, Percentage 4.338E+01% to 1.1969E+02%, Percentage 4.50E+01%, Percentage 7.00E+01%, Size 1.00E-03m
DOI:10.11975/j.issn.1002-6819.2021.19.025

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

Accession number:20215211376470
Title:Estimation of the leaf area index and chlorophyll content of wheat using UAV multi-spectrum images
Title of translation:利用无人机多光谱估算小麦叶面积指数和叶绿素含量
Authors:Liu, Tao (1, 2); Zhang, Huan (1); Wang, Zhiye (2); He, Chao (1); Zhang, Quanguo (1); Jiao, Youzhou (1)
Author affiliation:(1) College of Mechanical & Electrical Engineering, Henan Agricultural University, Zhengzhou; 450002, China; (2) School of Resources and Environment, Henan University of Economics and Law, Zhengzhou; 450002, China
Corresponding author:Jiao, Youzhou(jiaoyouzhou@126.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:65-72
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">Monitoring the crop growth by using Unmanned Aerial Vehicle (UAV) based remote sensing technique is one of important directions for the development of precision and smart agriculture in China. In recent years, the development of UAV technology has greatly promoted the timely and rapid acquisition and long-term dynamic monitoring of agricultural and forestry ecological environment elements such as crop vegetation, water and soil. Compared with the data acquisition methods of satellite remote sensing and aerial remote sensing, UAV has the advantages of flexibility, convenience, low data acquisition cost and high image resolution. UAV remote sensing image is gradually becoming the main data source for the development of intelligent agriculture and forestry. In order to explore the inversion potential of Leaf Area Index (LAI) and chlorophyll content (SPAD) of wheat from UAV multi-spectral images, the multispectral images at three levels of flight altitudes (30, 60 and 120 m) by using the DJI Phantom4-M UAV platform which integrated five multispectral sensors (blue, green, red, red edge and near infrared) and TimeSync time synchronization system were collected to achieve centimeter-level positioning accuracy with more than 2 million pixel resolution, in Yuanyang wheat breading based, Xinxiang City, Henan Province. Based on the collected multispectral images, four different kinds of spectral indexes including: DSI (Difference Spectral Index), Ratio Spectral Index (RSI), Normalized Spectral Index (NDSI) and Empirical Vegetation Index (EDVI) were used to compute the wheat canopy LAI and chlorophyll content (SPAD). The correlation analysis between different spectral index from different height UAV images and in-situ measured LAI and SPAD data were applied to select the optimal spectral index at different height. The Multiple Linear Stepwise Regression (MLSR), Partial Least Squares Regression (PLSR) and Back Propagation (BP) neural network model were constructed respectively for estimation of LAI and SPAD values. The experimental result showed that: 1) At 30 m height, the correlation coefficient between the green-red ratio spectral index and wheat LAI was the highest, with the value of 0.84. At the height of 60 m, the correlation coefficient between red-blue ratio spectral index and wheat chlorophyll content was the highest, with the value of 0.68. 2) At the height of 60 m, the correlation between EDVI and LAI and chlorophyll content of wheat were both good, and the maximum correlation coefficients were 0.77 and 0.50, respectively. 3) The accuracy of wheat LAI inversion using partial least squares regression was the highest, with a determination coefficient of 0.732 and a root mean square error of 0.055. The accuracy of chlorophyll content inversion using artificial neural network model is the highest, the determination coefficient is 0.804, and the root mean square error is 0.135. This study provides a theoretical basis for high-throughput crop monitoring based on UAV platform, and provides an application reference for selecting UAV multi-spectral bands to achieve rapid estimation of crop growth parameters.<br/></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:26
Main heading:Unmanned aerial vehicles (UAV)
Controlled terms:Antennas - Backpropagation - Chlorophyll - Crops - Data acquisition - Forestry - Image resolution - Infrared devices - Least squares approximations - Maximum likelihood - Remote sensing - Soils - Spectroscopy - Timber - Vegetation
Uncontrolled terms:Chlorophyll contents - Crop growth - Different heights - Leaf Area Index - Multi-spectral - Multispectral images - Partial least square regression - Spectral indices - Vehicle platforms - Wheat
Classification code:483.1 Soils and Soil Mechanics - 652.1 Aircraft, General - 723.2 Data Processing and Image Processing - 723.4 Artificial Intelligence - 804.1 Organic Compounds - 821.0 Woodlands and Forestry - 821.4 Agricultural Products - 921.6 Numerical Methods - 922.1 Probability Theory
Numerical data indexing:Size 1.20E+02m, Size 3.00E+01m, Size 6.00E+01m
DOI:10.11975/j.issn.1002-6819.2021.19.008

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

Accession number:20215211376402
Title:Cultivated land protection in the periphery of the main urban areas based on potential land use conflict identification
Title of translation:基于潜在土地利用冲突识别的主城区周边耕地保护
Authors:Qian, Fengkui (1, 2, 3); Wang, Hexing (1, 2, 3); Xiang, Zixuan (1, 2, 3)
Author affiliation:(1) College of Land and Environment, Shenyang Agriculture University, Shenyang; 110161, China; (2) Key Laboratory of Trinity Protection and Monitoring of Cultivated Land, Shenyang; 110161, China; (3) National Engineering Research Center for Efficient Utilization of Soil and Fertilizer Resources, Shenyang; 110161, China
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:267-275
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">Accurate and rapid conflict identification of potential land use can effectively coordinate the conflicting land types to deal with the ever-increasingly severe land use in recent years. Taking the main urban area of Anshan as the research object, a multi-objective suitability evaluation was carried out to protect the cultivated land near the main urban areas using ArcGIS software. A mapping function and coupling coordination degree model were also selected to identify the conflict zones. Auxiliary decision-making support was provided to integrate with the third line in the land and space planning, particularly to implement differentiated use and protection of surrounding cultivated land. The research results show that the height suitable for cultivated land was 2 459.00 hm<sup>2</sup>; accounting for 24.72%, and the moderate cultivated land was 7 423.05 hm<sup>2</sup>, accounting for 74.64%, indicating more than 90% of the surrounding areas of the main urban area. The cultivated land presented strong farming suitability. The areas of highly suitable construction and moderately suitable construction were 1 736.63 hm<sup>2</sup> and 7 666.78 hm<sup>2</sup> respectively, accounting for 5.43% and 77.09%, respectively. Most cultivated land around the city presented strong construction suitability, indicating a greater risk of potential cultivated land-use conflicts. Cultivated land around the main urban area was divided into 9 types of areas, including 3 types of potential land use conflict zones, accounting for 22.20% of the total area. These lands were mainly distributed around the main urban area and along the main roads, indicating superior natural resource conditions and significant geographical advantages. Geographical location factors, 3 types of farming advantages were non-conflict areas, accounting for 67.33% of the total area. The area presented complete irrigation and drainage facilities, as well as high organic matter content, but it was far away from the main road with low construction suitability, particularly suitable for agricultural purposes. 3 types of construction advantages were conflict-free areas, accounting for 10.47% of the total area. The effective soil thickness of cultivated land in this area was thin, mainly clay soil and lack of irrigation and drainage facilities, but the distance from the central city and roads was relatively short. Recently, good location and high construction suitability were a candidate area for newly-added construction land. Taking into account the natural, geographical and socio-economic conditions of cultivated land around the city, the high-quality contiguous cultivated land in the high and medium potential conflict areas and the farming advantaged areas were designated as permanent basic farmland, particularly benefits to the higher level of mechanization and large-scale agricultural production. There was construct scattered cultivated land in advantageous areas as land for flexible development of urban development boundaries. The land greatly contributed to the vitality of inventory, the degree of land saving, and intensive use in the main urban area, but to reduce the occupation of cultivated land around the city. The cultivated land in the low potential conflict area within the ecological red line was designated as Ecological land to ensure the stability of the ecological security pattern around the city. The finding can provide strong references for the conflict discrimination of suitable types of cultivated land use, while the protection of cultivated land around cities.<br/></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:33
Main heading:Land use
Controlled terms:Clay - Decision making - Drainage - Irrigation - Roads and streets
Uncontrolled terms:Conflict - Conflict areas - Conflict zones - Cultivated land protection - Cultivated lands - Identification - Land use conflicts - Main roads - Suitability evaluation - Urban areas
Classification code:403 Urban and Regional Planning and Development - 406.2 Roads and Streets - 483.1 Soils and Soil Mechanics - 821.3 Agricultural Methods - 912.2 Management
Numerical data indexing:Percentage 1.047E+01%, Percentage 2.22E+01%, Percentage 2.472E+01%, Percentage 5.43E+00%, Percentage 6.733E+01%, Percentage 7.464E+01%, Percentage 7.709E+01%, Percentage 9.00E+01%
DOI:10.11975/j.issn.1002-6819.2021.19.031

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

Accession number:20215211376420
Title:Development and performance analysis of an automatic weighing rain gauge
Title of translation:全自动称重式雨量计的研制及性能分析
Authors:Zhan, Xiaoyun (1, 2); Zhao, Jun (1, 2); Shui, Junfeng (2); Zhao, Xianghui (3); Guo, Minghang (1, 2)
Author affiliation:(1) State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling; 712100, China; (2) Institute of Soil and Water Conservation, Chinese Academy of Sciences & Ministry of Water Resources, Yangling; 712100, China; (3) Xi'an San Intelligent Technology Co., Ltd., Xi'an; 710075, China
Corresponding author:Shui, Junfeng(jfshui@ms.iswc.ac.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:122-128
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">Precipitation has widely been recognized as a fundamental component of the global water cycle. Accurate measurement of precipitation is very necessary for the main input into hydrological models. Hydraulic structures are then required to adequately design for efficient management of water resources. Several types of automatic rain gauges have been used in recent years, such as ultrasonic and laser rain gauges, but tipping-bucket rain gauges are still the common choice. Particularly, the tipping-bucket rain gauge can provide a better temporal resolution for the rainfall intensity. However, questions still remain on the accuracy of graphical representation for the actual rainfall. In this study, a real-time and automatic monitoring instrument was developed for the weighing rain gauge with high precision for precipitation. Three parts were composed of collector, weighing, measurement, and control subsystem. These subsystems were applied to multi-scenario conditions and performed well under the complex field. As such, the instrument was able to realize sample collection and measurement, data transmission and calculation, remote control, and diagnosis synchronously, compared with the traditional. The A/D conversion chip was utilized in the STM32 single-chip microcomputer to amplify the voltage signal of the weighing sensor. Subsequently, two important parameters of rainfall and rainfall intensity were achieved at a minute level with a resolution of 0.01 mm. Finally, a peristaltic pump was selected to verify the calibration of the developed instrument. The target intensities of rainfall were set as 0.02, 0.08, 0.17, 0.25, 0.50, 0.67, 0.83, 1.67, and 3.33 mm/min. The samples with the rainfall intensity of 0.83 mm/min were measured 30 times, and the rest were run five times. The results showed that the average rainfall intensity was 0.85 mm/min, where the histograms of target rainfall intensity presented a normal distribution, indicating higher precision of developed instrument than before. The best fitting linear regression was also represented by a slope with the R<sup>2</sup> value close to 1. Additionally, the average error of the designed instrument was -1.32%, while the highest accuracy was 98.67%, and the relative error of less than 5% accounted for more than 85% of the total samples. The measured data of the developed instrument was also much larger than that of the tipping-bucket rain gauge under simulated rainfall conditions. The high resolution and sensitivity to light rain were contributed to the increase of effective rainfall rate and total rainfall. Finally, the performance of the developed instrument was verified under field conditions in the Wangdonggou watershed for one consecutive year. It was found that the annual rainfall was 522.80 mm, particularly concentrated from May to September. Correspondingly, the single rainfall ≤5 mm was the predominant contributor in natural precipitation, accounting for 74.11% of the total number of rainfall events, whereas, the single rainfall of >10-25 mm was the most important to total rainfall. Consequently, the self-designed instrument can widely be expected to automatically monitor the large variation of rainfall in most complex fields.<br/></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:34
Main heading:Rain
Controlled terms:Analog to digital conversion - Errors - Graphic methods - Irrigation - Monitoring - Normal distribution - Rain gages - Remote control - Water management - Weighing
Uncontrolled terms:Automatic monitoring - Complex fields - Fundamental component - Global water cycle - High-precision - Performances analysis - Rain gauges - Rainfall intensity - Tipping bucket rain gauge - Total rainfall
Classification code:443.2 Meteorological Instrumentation - 443.3 Precipitation - 731.1 Control Systems - 821.3 Agricultural Methods - 922.1 Probability Theory - 943.3 Special Purpose Instruments
Numerical data indexing:Percentage -1.32E+00%, Percentage 5.00E+00%, Percentage 7.411E+01%, Percentage 8.50E+01%, Percentage 9.867E+01%, Size 1.00E-02m to 2.50E-02m, Size 1.00E-05m, Size 3.33E-03m, Size 5.00E-03m, Size 5.228E-01m, Size 8.30E-04m, Size 8.50E-04m
DOI:10.11975/j.issn.1002-6819.2021.19.014

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

Accession number:20215211376388
Title:Improvement and experiment of the seedling clamping device of apple tree planting machines
Title of translation:苹果树栽植机幼苗夹持装置改进与试验
Authors:Lin, Yuexiang (1); Shang, Shuqi (1); Lian, Zhengguo (1); Wang, Mingcheng (2); Zhang, Jingguo (3)
Author affiliation:(1) Mechanical and Electrical Engineering Institute, Qingdao Agricultural University, Qingdao; 266109, China; (2) College of Engineering, Northeast Agricultural University, Harbin; 150030, China; (3) Weifang Gaomi Secondary Specialized School, Weifang; 261501, China
Corresponding author:Shang, Shuqi(sqingnong@126.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:1-6
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">At this stage, the domestic development of apple tree seedling planting machines is still in its infancy, and few planting machines had been developed for apple trees. Planting apple trees requires a large amount of labor, but the current shortage of labor is a prominent contradiction. The problem of non-mechanized planting of apple tree seedlings urgently needs to be solved. In order to solve the problems in the apple planting process under the current high density dwarfing orchard cultivation mode: low manual planting efficiency, unsatisfactory erection of fruit seedlings with the developed machines, and slightly higher coefficient of variation of plant spacing caused by the slippage of the clamping belt. Under the guidance of agricultural machinery and agronomic integration technology, a two-point clamping apple seedling planting machine was developed by a combination of extensive research, virtual design, prototype manufacturing, and field trials. This machine is based on the existing continuous ditching fixed-distance planting machine previously developed by the same team, and implements structural improvements and performance optimization. Firstly, the fruit seedling clamping device is improved, and the fruit seedlings are clamped at the upper and lower points. On top of the previous generation planting machine, a layer of clamping and conveying device is installed above the lower clamping and conveying device, and the upper and lower layers are perpendicular to each other. The distance is set to be 50 cm, which is based on the comprehensive consideration of the height and status of the apple tree seedlings and the comparison of the planting effectiveness at different distances. The upper and lower clamping points work with each other during the transportation of apple tree seedlings to clamp the apple tree seedlings together. The apple tree seedlings will not rotate during operation and maintain a good initial status. It overcomes the problem that it is difficult to guarantee the initial angle of the tall seedlings relative to the ground because of the original one-point clamping, and significantly improves the perpendicularity after planting; Secondly, the transportation method is improved and the coefficient of variation of plant spacing is reduced. Considering that the trunk and bark of apple tree seedlings should not be damaged during the clamping and conveying process of the transplanter, the solution was still sought in the belt category. The initial idea was to add more tension wheels to change the belt slippage. However, the experimental results observed showed that this method makes almost no effect on reducing the belt slip rate. The timing belt can ensure that the apple tree seedlings are protected from damage during the clamping and conveying process, and it has a good transmission effect with little sliding. In order to improve clamping, the conveying mode adopts two synchronous belts to clamp and convey apple seedlings, which has a lower slip rate than the original V-belt clamping and conveying, and significantly reduces the coefficient of variation of the plant spacing; in addition, the power matching is optimized by the corresponding calculation formula. The depth-limiting wheel is installed to improve the stability of the planting depth, and the ditching machine, plant spacing control and other parts follow the first-generation machine plan. According to the planting requirements of different varieties of seedlings, the depth and width of the ditch can be adjusted, and the planting distance can also be adjusted as needed, and the adjustment is simple and convenient. Field tests showed that the qualified rate of apple seedlings planted by the machine is increased from 90.63% to 97.14%, the average planting depth qualified rate is increased from 91.43% to 93.33%, and the average plant spacing coefficient of variation is reduced from 5.03% to 3.74%, and the planting efficiency is increased from 11.89 plants/min to 12.26 plants/min, which was 37 times faster than that of manual planting. Compared with the existing machines, all performances have been improved, laying a solid foundation for the subsequent mechanization of apple's production.<br/></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:32
Main heading:Fruits
Controlled terms:Agricultural machinery - Conceptual design - Conveying - Cultivation - Efficiency - Industrial research - Machine design - Orchards - Reforestation - Seed - Structural optimization
Uncontrolled terms:Apple seedling - Apple trees - Coefficients of variations - Perpendicularity - Plant spacing - Plantings - Slip rates - Tree seedlings - Two-point - Two-point clamping
Classification code:601 Mechanical Design - 692.1 Conveyors - 821.0 Woodlands and Forestry - 821.1 Agricultural Machinery and Equipment - 821.3 Agricultural Methods - 821.4 Agricultural Products - 901.3 Engineering Research - 912.1 Industrial Engineering - 913.1 Production Engineering - 921.5 Optimization Techniques
Numerical data indexing:Percentage 5.03E+00% to 3.74E+00%, Percentage 9.063E+01% to 9.714E+01%, Percentage 9.143E+01% to 9.333E+01%, Size 5.00E-01m
DOI:10.11975/j.issn.1002-6819.2021.19.001

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

Accession number:20215211376601
Title:Design and experiments of the centrifugal side throwing fertilizer spreader for lotus root fields
Title of translation:离心侧抛式藕田撒肥器设计与试验
Authors:Zhang, Guozhong (1, 2); Wang, Yang (1, 2); Liu, Haopeng (1, 2); Ji, Chao (1, 2); Hou, Qunxi (1, 2); Zhou, Yong (1, 2)
Author affiliation:(1) College of Engineering, Huazhong Agricultural University, Wuhan; 430070, China; (2) Key Laboratory of Agricultural Equipment in Mid-lower Yangtze River, Ministry of Agriculture and Rural Affairs, Wuhan; 430070, China
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:37-47
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">Mechanized fertilization has widely been one of the most important steps for the high yield of crops. In this study, a centrifugal side throwing fertilizer spreader was designed for the lotus root field. Firstly, atheoretical model was established for the force of a single fertilizer particle on the blade. The main parameters were then determined for the particle motion, such as the rotating speed of the fertilizer tray, the blade inclination angle, and the blade deflection angle. Secondly, EDEM discrete element simulation software was utilized to optimize the performance of the spreader. A single factor test was carried out, where the fertilizer feed rate and the above factors were taken as the influencing factors. The results showed that the fertilizer increased first and then decreased in a single statistical area when taking the center of the spreader as the origin along the throwing direction. Specifically, the quality of fertilizer with the most distribution in a single statistical region was called the peak value, and the distance between this region and the fertilizer spreader was called the peak distance, to accurately quantify the distribution index of fertilizer. The rotating speed of the fertilizer tray and the blade inclination angle posed a significant impact on the peak value and peak distance of fertilizer distribution, whereas, the blade deflection angle and feed rate on the peak value. An orthogonal rotation simulation test was carried out with the uniformity variation coefficient and peak distance as the evaluation indexes. It was found that there was an extremely significant influence on the uniformity variation coefficient (P<0.01), including the rotating speed of fertilizer tray, the blade inclination angle, the blade deflection angle, the interaction between them, the interaction term between the inclination angle of blade and feeding rate, as well as the quadratic term of blade inclination angle. There was an extremely significant effect on the peak distance (P<0.01), including the rotating speed of fertilizer tray, the blade inclination angle, and the quadratic term, the quadratic term of feeding rate. Design-Expert software was utilized to optimize the structure of the spreader. The blade structure parameters were then calculated to minimize the uniformity variation coefficient, when the peak distance was 10 and 21 m, the blade inclination, and deflection angle were 8.5° and 17.5°, 11.5° and -1.9°, respectively. Subsequently, the simulation and actual fertilizer application were carried out to evaluate the performance of the fertilizer spreader with the optimal structural parameters. The simulation results show that the uniformity variation coefficient, peak distance, and working width were 19.43%, 21 m, and 29 m, respectively, when the rotating speed of the fertilizer tray was 1 250 r/min, and the feeding rate was 0.316 kg/s. By contrast, the specific parameters in an actual contrast test were 21.95%, 18.6 m, and 24.5 m, respectively, where the errors with the simulation were 12.95%, 11.42%, and 15.51%, respectively. A performance test of fertilizer spreader was carried out with large particle urea, compound fertilizer, and phosphorus fertilizer commonly used in lotus root field topdressing. The results showed that the operation effect of large particle urea was better at 1 300 r/min, feeding rate of 0.15 kg/s, and operation width of 24 m. At this time, the uniformity variation coefficient was 24.56%. The analysis of variance showed that the rotating speed of fertilizer tray, feeding rate, fertilizer type, and the interaction term between fertilizer type and feeding rate presented an extremely significant impact on the coefficient of variation (P<0.01). The rotating speed of fertilizer tray, feeding rate, and type presented an extremely significant impact on the operation width (P<0.01). The finding can provide an important reference for the design of fertilizer spreading machinery in the lotus root field.<br/></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:31
Main heading:Computer software
Controlled terms:Agricultural machinery - Deflection (structures) - Fertilizers - Finite difference method - Rotating machinery - Speed - Spreaders
Uncontrolled terms:Deflection angles - Discrete elements - Feeding rate - Fertilizer spreaders - Inclination angles - Lotus root field - Peak distance - Rotating speed - Side throwing fertilizer spreader - Variation coefficient
Classification code:408.2 Structural Members and Shapes - 601.1 Mechanical Devices - 723 Computer Software, Data Handling and Applications - 804 Chemical Products Generally - 821.1 Agricultural Machinery and Equipment - 821.2 Agricultural Chemicals - 921.6 Numerical Methods
Numerical data indexing:Angular velocity 4.175E+00rad/s, Angular velocity 5.01E+00rad/s, Mass flow rate 1.50E-01kg/s, Mass flow rate 3.16E-01kg/s, Percentage 1.142E+01%, Percentage 1.295E+01%, Percentage 1.551E+01%, Percentage 1.943E+01%, Percentage 2.195E+01%, Percentage 2.456E+01%, Size 1.00E+01m, Size 1.86E+01m, Size 2.10E+01m, Size 2.40E+01m, Size 2.45E+01m, Size 2.90E+01m
DOI:10.11975/j.issn.1002-6819.2021.19.005

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

Accession number:20215211376661
Title:Inversion of the thermal property parameters of fish feed based on adjoint equation method
Title of translation:基于伴随方程法的鱼饲料热特性参数反演
Authors:Chen, Jiyuan (1); Wang, Liangju (1); Wang, Hongying (1); Zhang, Guodong (2); Wang, Wei (1)
Author affiliation:(1) College of Engineering, China Agricultural University, Beijing; 100083, China; (2) COFCO Grains Holdings Limited, Beijing; 100020, China
Corresponding author:Wang, Hongying(hongyingw@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:19
Issue date:October 1, 2021
Publication year:2021
Pages:316-322
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">Thermal properties of feed and artificial experience have been generally considered, when adjusting the process parameters of hygrothermal treatment in the production of fish feed, including cooling and drying. Specific heat, thermal conductivity, and thermal diffusivity are the important thermal properties of fish feed. In this study, grass carp (adult fish) extruded feed was taken as the research object. The inversion algorithm was established to obtain the temperature distribution of feed using the adjoint equation. A test was also carried out using the self-developed heat conduction device and infrared thermal imager. When testing, the feed was quickly spread on the cast aluminum soaking plate, where the feed was closely arranged in a single layer, with a thickness of about 4 mm and a total of about 10 g. The infrared thermal images were captured for the feed layer surface and the upper surface on the cast aluminum soaking plate using the infrared thermal imager (accuracy±0.1℃). SmartView software was selected to process the infrared thermal images for the temperature-time data of the feed sample test surface (x=h) and heating surface (x=0), from the T-t data of heating surface x=0 and the feed sample test surface x=h. MATLAB software was selected first to solve the adjoint equation for the adjoint variables, then obtain the gradient value, and finally obtain specific heat c and thermal conductivity k. As such, the specific heat, thermal conductivity, and thermal diffusivity of feed were obtained, according to temperature distribution with the moisture content of 11%-17% and the temperature range of 20-80℃. The results show that the specific heat of grass carp extruded feed was 1.710-1.840 kJ/(kg•℃). Specific heat of feed increased significantly with the increase of temperature (P<0.05). When the moisture content increased from 11% to 17%, the specific heat of feed increased significantly (P<0.05), indicating a linear law. The thermal conductivity of grass carp extruded feed was 0.086-0.148 w/(m•K). When the temperature increased from 20℃ to 80℃, the thermal conductivity of grass carp extruded feed increased significantly (P<0.05). The effect of water content was also significant (P<0.05). The thermal diffusivity of feed ranged from 5.701 to 10.003 m<sup>2</sup>/s, depending significantly on temperature and moisture content (P<0.05). At the same time, the specific heat and thermal conductivity of feed were measured by Differential Scanning Calorimetry (DSC) and thermal characteristic analyzer, respectively, where the thermal diffusivity was calculated as the measured value. Before the test, the feed particles needed to be crushed. The inversion datum was taken as the calculated values. The linear fitting showed that R<sup>2</sup> was equal or greater than 0.980, indicating the feasible determination of thermal characteristic parameters of fish extruded feed using the inversion. The finding can provide a new idea for the determination of the thermal properties of fish feed.<br/></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:26
Main heading:Specific heat
Controlled terms:Aluminum - C (programming language) - Diffusion - Fish - Heat conduction - MATLAB - Moisture - Moisture determination - Software testing - Temperature distribution - Thermal conductivity - Thermal diffusivity
Uncontrolled terms:%moisture - Adjoint equations - Cast aluminium - Cast aluminum - Feed samples - Fish feed - Grass carp - Infrared thermal image - Infrared thermal imager - Inversion
Classification code:541.1 Aluminum - 641 Heat and Mass Transfer; Thermodynamics - 641.1 Thermodynamics - 641.2 Heat Transfer - 723.1.1 Computer Programming Languages - 723.5 Computer Applications - 921 Mathematics - 944.2 Moisture Measurements
Numerical data indexing:Energy 1.71E+03J to 1.84E+03J, Mass 1.00E-02kg, Percentage 1.10E+01% to 1.70E+01%, Size 4.00E-03m, Size 5.701E+00m to 1.0003E+01m
DOI:10.11975/j.issn.1002-6819.2021.19.037

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

Accession number:20215211376677
Title:Combining lightweight wheat spikes detecting model and offline Android software development for in-field wheat yield prediction
Title of translation:结合轻量级麦穗检测模型和离线Android软件开发的田间小麦测产
Authors:Chen, Jiawei (1, 2); Li, Qing (3); Tan, Qiaoxing (1); Gui, Shiquan (1); Wang, Xiao (3); Yi, Fujin (4); Jiang, Dong (3); Zhou, Ji (1, 5)
Author affiliation:(1) Academy for Advanced Interdisciplinary Studies/Plant Phenomics Research Center/Jiangsu Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing; 210095, China; (2) College of Engineering, Nanjing Agricultural University, Nanjing; 210095, China; (3) College of Agriculture, Nanjing Agricultural University, Nanjing; 210095, China; (4) College of Economics and Management, Nanjing Agricultural University, Nanjing; 210095, China; (5) National Institute of Agricultural Botany/Cambridge Crop Research, Cambridge; CB3 0LE, United Kingdom
Corresponding authors:Zhou, Ji(Ji.zhou@njau.edu.cn); Zhou, Ji(Ji.zhou@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:19
Issue date:October 1, 2021
Publication year:2021
Pages:156-164
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">The number of spikes per unit area is a key yield component for cereal crops such as wheat, which is popularly used in wheat research for crop improvement. With the fast maturity of smartphone imaging hardware and recent advances in image processing and lightweight deep learning techniques, it is possible to acquire high-resolution images using a smartphone camera, followed by the analysis of wheat spikes per unit area through pre-trained artificial intelligence algorithms. Then, by combining detected spike number with variety-based spikelet number and grain weight, it is feasible to carry out a near real-time estimation of yield potential for a given wheat variety in the field. This AI-driven approach becomes more powerful when a range of varieties are included in the training datasets, enabling an effective and valuable approach for yield-related studies in breeding, cultivation, and agricultural production. In this study, we present a novel smartphone-based software application that combines smartphone imaging, lightweight and embedded deep learning, with yield prediction algorithms and applied the software to wheat cultivation experiments. This open-source Android application is called YieldQuant-Mobile (YQ-M), which was developed to measure a key yield trait (i.e. spikes per unit area) and then estimate yield based on the trait. Through YQ-M and smartphones, we standardized the in-field imaging of wheat plots, streamlined the detection of spikes per unit area and the prediction of yield, without a prerequisite of in-field WiFi or mobile network. In this article, we introduce the YQ-M in detail, including: 1) the data acquisition designed to standardize the collection of wheat images from an overhead perspective using Android smartphones; 2) the data pre-processing of the acquired image to reduce the computational time for image analysis; 3) the extraction of wheat spike features through deep learning (i.e. YOLOV4) and transfer learning; 4) the application of TensorFlow.lite to transform the trained model into a lightweight MobileNetV2-YOLOV4 model, so that wheat spike detection can be operated on an Android smartphone; 5) finally, the establishment of a mobile phone database to incorporate historic datasets of key yield components collected from different wheat varieties into YQ-M using Android SDK and SQLite. Additionally, to ensure that our work could reach the broader research community, we developed a Graphical User Interface (GUI) for YQ-M, which contains: 1) the spike detection module that identifies the number of wheat spikes from a smartphone image; 2) the yield prediction module that invokes near real-time yield prediction using detected spike numbers and related parameters such as wheat varieties, place of production, accumulated temperature, and unit area. During our research, we have tested YQ-M with 80 representative varieties (240 one-square-meter plots, three replicates) selected from the main wheat producing areas in China. The computed accuracy, recall, average accuracy, and F1-score for the learning model are 84.43%, 91.05%, 91.96%, and 0.88, respectively. The coefficient of determination between YQ-M predicted yield values and post-harvest manual yield measurement is 0.839 (n=80 varieties, P<0.05; Root Mean Square Error=17.641 g/m<sup>2</sup>). The results suggest that YQ-M presented here has a high accuracy in the detection of wheat spikes per unit area and can produce a consistent yield prediction for the selected wheat varieties under complex field conditions. Furthermore, YQ-M can be easily accessed and expanded to incorporate new varieties and crop species, indicating the usability and extendibility of the software application. Hence, we believe that YQ-M is likely to provide a step change in our abilities to analyze yield-related components for different wheat varieties, a low-cost, accessible, and reliable approach that can contribute to smart breeding, cultivation and, potentially, agricultural production.<br/></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:35
Main heading:Smartphones
Controlled terms:Android (operating system) - Application programs - Crops - Data acquisition - Data handling - Deep learning - Image acquisition - Image analysis - Learning algorithms - Open source software - Open systems
Uncontrolled terms:Android system development - Android systems - Lightweight deep learning - Per unit - Spike detection - System development - Wheat - Wheat spike detection - Yield - Yield prediction
Classification code:461.4 Ergonomics and Human Factors Engineering - 718.1 Telephone Systems and Equipment - 723 Computer Software, Data Handling and Applications - 723.2 Data Processing and Image Processing - 723.4.2 Machine Learning - 821.4 Agricultural Products
Numerical data indexing:Linear density 1.7641E-02kg/m, Percentage 8.443E+01%, Percentage 9.105E+01%, Percentage 9.196E+01%
DOI:10.11975/j.issn.1002-6819.2021.19.018

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

Accession number:20215211376593
Title:Yield effects of irrigated acreage change under climate change in China
Title of translation:气候变化条件下中国灌溉面积变化的产量效应
Authors:Li, Zhonghe (1, 2); Zhan, Chesheng (1, 4); Hu, Shi (3); Ning, Like (1, 4); Wu, Lanfang (1, 4); Guo, Hai (2, 3)
Author affiliation:(1) Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing; 100101, China; (2) University of Chinese Academy of Sciences, Beijing; 100049, China; (3) Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing; 100101, China; (4) Yucheng Comprehensive Experiment Station, Chinese Academy of Science, Beijing; 100101, China
Corresponding authors:Zhan, Chesheng(zhancs@igsnrr.ac.cn); Zhan, Chesheng(zhancs@igsnrr.ac.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:94-104
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">Irrigation of crops can effectively increase yields and reduce inter-annual fluctuations in yields, thus mitigating the adverse effects of climate change on food production. However, there are significant differences in climate among different regions of China, and water and soil resources are not balanced among regions of China. The degree to which increasing irrigation in different regions can alleviate the impact of climate change is unknown. Therefore, in this study, the yield datasets under different climate scenarios published by ISIMIP were used to study the yield increase effect of irrigation expansion in different regions of China. Firstly, the irrigation water consumption of three crop models (GEPIC, PEFIC and LPJml) driven by four climate models (GFDL-ESM2M, HadGEM2-ES, IPSL-CAM5-LR, MIROC5) was evaluated using the actual irrigation water consumption in different regions of China from 2006 to 2019. Secondly, the first five model combinations with better simulation results were selected according to the skill scores S1 and S2. Then, the ensemble mean of the first five combinations with better performance was carried out to analyze the yield changes of maize, rice, soybean and wheat in China from 2021 to 2050 under RCP2.6 and RCP6.0 scenarios. Finally, the yield increase effect of irrigated area expansion under the assumption on the irrigation of rain-fed land in different areas was evaluated. The results showed that the increase of precipitation from 2021 to 2050 would increase the yield of rice and soybean, corn and wheat in northern China, among which about 80% of maize area in Northeast China and 70% of maize area in Northwest China would have the increasing of the maize yield by 0.2 - 0.8 t/hm<sup>2</sup>. About 85% of rice area and soybean area in Northeast and Northwest regions would have the increasing of the yield by 1.0 t/hm<sup>2</sup> and 0.5 t/hm<sup>2</sup>, respectively. About 90% of wheat area in Northeast and 75% of wheat area in Northwest regions had the increasing of the by 1.0-2.0 t/hm<sup>2</sup> and 0.5-1.0 t/hm<sup>2</sup>, respectively. The decrease of precipitation resulted in the decrease of maize and wheat yields by 0.2 t/hm<sup>2</sup> in the 45% area of south southwest of China. Under the condition of expanding the irrigated area, the crop yield in Northwest and Southwest China would increase greatly during 2021-2050. Under RCP2.6 and RCP6.0 scenarios, maize (northwest: 43%-60%; southwest: 4%-40%) was the highest, followed by soybean (northwest: 40%-62%; southwest: 2%-17%) and wheat (northwest: 10%-18%; southwest: 22%-33%). Under RCP2.6 and RCP6.0 scenarios, the total yield of rice (3%-13%) and soybean (7%-33%) in Northeast China increased significantly from 2021 to 2050. In terms of yield increase potential per unit irrigation amount, the expansion of irrigated area of wheat in Northeast China and North China during 2021 and 2050 had obvious yield increase benefit. Under RCP2.6 and RCP6.0 scenarios, the wheat yield increase efficiency of irrigation was 0.21 kg/m<sup>3</sup> and 0.12 kg/m<sup>3</sup>, respectively. The irrigation area expansion of maize (rice) in central region (east) had obvious yield increase benefit. The maize (rice) yield increase efficiency under RCP2.6 and RCP6.0 scenarios was 0.1 and 0.09 kg/m<sup>3</sup> (0.08 and 0.07 kg/m<sup>3</sup>), respectively. Therefore, the expansion of irrigated area for wheat in northern China can effectively cope with the adverse effects of climate change.<br/></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:37
Main heading:Irrigation
Controlled terms:Climate change - Climate models - Crops - Efficiency - Expansion - Precipitation (chemical) - Rain - Water supply
Uncontrolled terms:Adverse effect - Climate - Irrigation waters - Northeast China - Water consumption - Yield increase - Yield-increasing - Yield-increasing efficiency - Yield-increasing potential
Classification code:443 Meteorology - 443.1 Atmospheric Properties - 443.3 Precipitation - 446.1 Water Supply Systems - 802.3 Chemical Operations - 821.3 Agricultural Methods - 821.4 Agricultural Products - 913.1 Production Engineering - 921 Mathematics - 951 Materials Science
Numerical data indexing:Linear density 1.00E-01kg/m, Linear density 1.20E-01kg/m, Linear density 2.10E-01kg/m, Linear density 7.00E-02kg/m, Linear density 8.00E-02kg/m, Linear density 9.00E-02kg/m, Percentage 1.00E+01% to 1.80E+01%, Percentage 2.00E+00% to 1.70E+01%, Percentage 2.20E+01% to 3.30E+01%, Percentage 3.00E+00% to 1.30E+01%, Percentage 4.00E+00% to 4.00E+01%, Percentage 4.00E+01% to 6.20E+01%, Percentage 4.30E+01% to 6.00E+01%, Percentage 4.50E+01%, Percentage 7.00E+00% to 3.30E+01%, Percentage 7.00E+01%, Percentage 7.50E+01%, Percentage 8.00E+01%, Percentage 8.50E+01%, Percentage 9.00E+01%, Size 5.08E-02m
DOI:10.11975/j.issn.1002-6819.2021.19.011

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

Accession number:20215211376561
Title:Design and simulation of side air supply drying room based on temperature and velocity homogeneity
Title of translation:基于温度和速度均匀性的侧送风烘房设计及仿真
Authors:Chen, Zhongjia (1); Lei, Wenwen (1); Wang, Qingchun (1)
Author affiliation:(1) School of Engineering, Beijing Forestry University, Beijing; 100083, China
Corresponding author:Wang, Qingchun(wangqingchun@bjfu.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:18-26
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">Coating has widely been a commonly-used way to prevent corrosion failure of machinery parts. Coating of agricultural machinery parts is also conducive to improving the working reliability and service life of machinery. Drying is thus the key link of parts coating. However, improper drying can cause the reduction of film stress, film cracks, shrinkage, and pinholes. In this study, a side air supply drying room was designed suitable for the drying of agricultural machinery parts. The circulation of hot air was used to exchange the heat with the workpiece, further to reduce the moisture content of the coating, and finally to realize the curing of the coating. Computational fluid dynamics software was selected to numerically simulate the velocity and temperature field of the drying room for agricultural machinery parts. The working parameters of the drying room were also optimized to clarify the influence on the velocity and temperature field. As such, the working performance of the drying room was improved to clarify the influence of air supply mode, the number of air supply outlets, and air supply angle on velocity and temperature distribution. The results show that the unilateral air supply performed better than that of the double, indicating better gas circulation in the drying room. The uniformity of speed and temperature was better, particularly with the higher speed of return air and excellent gas circulation, when the number of air inlets was 8 rather than 6 and 10. Furthermore, the uniformity of speed and temperature was better, when the air inlet angle was 15° rather than 0° and 30°, where the return airspeed and temperature were higher. Therefore, an optimal parameter combination of drying room was achieved, where the unilateral side air supply, 8 air supply outlets, and air supply angle of 15°, indicating excellent gas circulation, as well as the better uniformity of speed and temperature. The measured values of each index were in good agreement with the theoretical optimization, where the temperature error was less than 1.68%, and the wind speed error was less than 33%, indicating reliable and effective simulation. At the same time, the overall coefficient of temperature inhomogeneity was less than 6%, where the designed drying room worked stably and reliably for agricultural machinery parts, fully meeting the design requirements. This finding can provide a strong reference for the design of the subsequent drying rooms.<br/></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:31
Main heading:Drying
Controlled terms:Agriculture - Air - Air intakes - Computational fluid dynamics - Corrosion prevention - Machine design - Temperature - Velocity - Wind
Uncontrolled terms:Air supply - Coefficient of temperature inhomogeneity - Coefficient of velocity - Coefficient of velocity inhomogeneity - Drying room design - Flow-field design - Gas circulation - Inhomogeneities - Temperature inhomogeneity - Velocity and temperature fields
Classification code:443.1 Atmospheric Properties - 539.2 Corrosion Protection - 601 Mechanical Design - 631.1 Fluid Flow, General - 641.1 Thermodynamics - 723.5 Computer Applications - 804 Chemical Products Generally - 821 Agricultural Equipment and Methods; Vegetation and Pest Control - 931.1 Mechanics
Numerical data indexing:Percentage 1.68E+00%, Percentage 3.30E+01%, Percentage 6.00E+00%
DOI:10.11975/j.issn.1002-6819.2021.19.003

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

Accession number:20215211376463
Title:Building saliency-map-based attention-driven deep network to detect vegetable pests of sticky trap images
Title of translation:利用显著图构建注意力深度网络检测诱虫板蔬菜害虫
Authors:Guo, Qingwen (1); Wang, Chuntao (1, 2); Xiao, Deqin (1); Huang, Qiong (1, 2)
Author affiliation:(1) College of Mathematics and Informatics, South China Agricultural University, Guangzhou; 510642, China; (2) Guangzhou Key Laboratory of Intelligent Agriculture, Guangzhou; 510642, China
Corresponding authors:Wang, Chuntao(wangct@scau.edu.cn); Wang, Chuntao(wangct@scau.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:211-219
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">Digital imaging has widely been used to detect pest diseases for crops in modern agriculture, particularly on deep learning and intelligent computer vision. However, accurate and rapid detection of insect pests in images still remains a great challenge in the crop field. In this study, a task-specified detector was developed to accurately detect vegetable pests of sticky trap images using an attention-driven deep network from saliency maps. Prevailing pest detectors were mainly adopted anchors to detect pests in sticky trap images. Nevertheless, the anchor-based detection accuracy depended mainly on the balance between positives and negatives, as well as the model training, due mainly to the relatively small sizes and distribution of crop insect pests in the sticky trap images. Therefore, a saliency map was established to filter simple background regions. An attention-driven neural network was also selected to better focus on key regions and then accurately detect crop insect pests of sticky trap images. Firstly, saliency maps and threshold-based techniques were employed to construct masks for rough region proposals, according to connected graphs of acquired masks. Secondly, two fully convolutional neural networks were used in a sliding window fashion to produce refined region proposals from rough region proposals, in order to deal with occlusion issues. Thirdly, each refined region proposal was then classified as one target pest category with a convolutional neural network classifier, thereby detecting the bounding boxes of target vegetable pests. Finally, an enhanced non-maximum suppression was utilized to eliminate the bounding boxes of redundant detection, where a target pest was captured by only one detection bounding box. As such, the target pest number was easily obtained to count the bounding boxes of rest detection during automatic management of vegetable insect pests. Furthermore, a piece of specific monitoring equipment was designed to evaluate the vegetable pest detector, where sticky trap images of two vegetable pests were collected, including Plutellaxylostella (Linnaeus) and Bactroceracucuribitae (Coquillett). Several experiments were also conducted on the labeled data set of collected images. The results demonstrate that the vegetable pest detector achieved a mean average precision of 86.40% and an average mean absolute error of 0.111, indicating better performance than the commonly-used pest detectors, such as SSD, R-FCN, CenterNet, Faster R-CNN, and YOLOv4. In addition, two ablation experiments were carried out to verify the attention mechanism of saliency maps and the enhanced non-maximum suppression. It was found that the attention mechanism remarkably contributed to the detection accuracy and the performance of enhanced non-maximum suppression. In the future, both top- and low-level feature maps were required in a convolutional neural network, further enchancing the robustness of the attention mechanism in the vegetable pest detector.<br/></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:28
Main heading:Image recognition
Controlled terms:Computer vision - Convolution - Convolutional neural networks - Crops - Deep learning - Image segmentation - Vegetables
Uncontrolled terms:Bounding-box - Convolutional neural network - Deep learning - Insects pests - Intelligent computer-vision-based detection - Machine-vision - Non-maximum suppression - Pest detection - Saliency map - Vision-based detection
Classification code:461.4 Ergonomics and Human Factors Engineering - 716.1 Information Theory and Signal Processing - 723.5 Computer Applications - 741.2 Vision - 821.4 Agricultural Products
Numerical data indexing:Percentage 8.64E+01%
DOI:10.11975/j.issn.1002-6819.2021.19.024

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

Accession number:20215211376394
Title:Classification of perch ingesting condition using lightweight neural network MobileNetV3-Small
Title of translation:基于轻量级神经网络MobileNetV3-Small的鲈鱼摄食状态分类
Authors:Zhu, Ming (1, 3); Zhang, Zhenfu (1, 2); Huang, Huang (1, 2); Chen, Yanyan (1); Liu, Yadong (1); Dong, Tao (1)
Author affiliation:(1) College of Engineering, Huazhong Agricultural University, Wuhan; 430070, China; (2) Key Laboratory of Agricultural Equipment in Mid-lower Yangtze River, Ministry of Agriculture and Rural Affairs, Wuhan; 430070, China; (3) Engineering Research Center of Green Development for Conventional Aquatic Biological Industry in the Yangtze River Economic Belt, Ministry of Education, Wuhan; 430070, China
Corresponding authors:Huang, Huang(wmyhuang@qq.com); Huang, Huang(wmyhuang@qq.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:165-172
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">Intelligent feeding has widely been used to determine the amount of feed from a smart prediction about the hunger degree of fish, thereby effectively reducing the waste of feed in the modern aquaculture industry, especially for outdoor intensive fish breeding environments. However, redundant data collected by mobile monitoring devices has caused a huge calculation load for most control systems. An accurate classification of the hunger degree of fish still remains an unsolved problem. Taking the captive perch as the tested object, this work aims to design an image capture system for the perch feeding using MobileNetV3-Small of lightweight neural network. The system also consisted of 2 captive fonds, a camera, and a video recorder. In the test, 4202 perches were randomly fed with adequate or inadequate feed, where a camera was selected to record the water surface every day. 10 000 images were collected after 2-week monitoring to record the perch ingesting condition in the period of 80~110 seconds after per round feeding condition, where 50% belonged to "hungry" condition, and the rest was "non-hungry" condition. These initial images were then divided as training, validation, and testing set, according to a rate of 6:2:2. Four image processing operations were applied on the training set, containing random flipping, random cropping, adding Gaussian noise, and color dithering, thereby expanding the training set from 6 000 to 12 000 images. As such, the more generalized model greatly enhanced the image features and training samples. Next, a MobileNetV3-Small of lightweight Neural Network was selected to classify the ingesting condition of perches. The model was trained, tested, and established on the Tensorflow2 platform, where the images of the training set were selected as the input, whereas, the ingesting condition as the output. Finally, a 2-week feeding contrast test was carried out in the outdoor culture environment to verify the accuracy of the model. Two groups were set for 4202 perches in this test, 2096 of the test group and 2106 of the control group, where the amount of feed was determined according to the classification of model and conventional experience. Meanwhile, the total mass and quantity of the two groups were recorded at the beginning and end of the test, as well as the total amount of consumed feed. Correspondingly, it was found that the MobileNetV3-Small network model achieved a combined accuracy of 99.60% in the test set with an F1 score of 99.60%. The MobileNetV3-Small model presented the smallest Floating Point Operations of 582 M and the largest average classification rate of 39.21 frames/s, compared with ResNet-18, ShuffleNetV2, and MobileNetV3-Large deep learning models. Specifically, the combined accuracies of the MobileNetV3-Small model were 12.74, 23.85, 3.6, and 2.78 percentage points higher than that of the traditional machine learning models KNN, SVM, GBDT, and Stacking. Furthermore, the test group of perch was achieved a lower Feed Conversion Ratio of 1.42, and a higher Weight Gain Ratio of 5.56%, compared with the control group, indicating that the MobileNetV3-Small model performed a better classification on the ingesting condition in a real outdoor culture environment. Consequently, the classification of the ingesting condition can widely be expected for the efficient decision-making for the amount of fish feed, particularly suitable for the growth of fish. The finding can provide a further reference for efficient and intelligent feeding in an intensive cultural environment.<br/></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:30
Main heading:Image recognition
Controlled terms:Aquaculture - Cameras - Computer vision - Deep learning - Feeding - Fish - Forecasting - Gaussian noise (electronic) - Image enhancement - Neural networks - Testing - Video recording
Uncontrolled terms:Aquaculture industry - Condition - Control groups - Culture environments - Deep learning - Machine-vision - Neural-networks - Outdoor cultures - Perch - Training sets
Classification code:461.4 Ergonomics and Human Factors Engineering - 691.2 Materials Handling Methods - 716.4 Television Systems and Equipment - 723.5 Computer Applications - 741.2 Vision - 742.2 Photographic Equipment - 821.3 Agricultural Methods
Numerical data indexing:Percentage 5.00E+01%, Percentage 5.56E+00%, Percentage 9.96E+01%, Time 8.00E+01s to 1.10E+02s
DOI:10.11975/j.issn.1002-6819.2021.19.019

Accession number:20215211376536
Title:Seismic performance analysis of Dutch Venlo greenhouse structure
Title of translation:荷兰Venlo型温室结构抗震性能分析
Authors:Li, Yizhe (1); Pian, Chao (1)
Author affiliation:(1) China Triumph International Engineering Co., Ltd., Shanghai; 200063, China
Corresponding author:Pian, Chao(pianchao@ctiec.net)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:243-249
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 Venlo-type greenhouse has widely been introduced from Netherland for modern agriculture in China at present. This type of greenhouse is also the largest, most commonly-used, and state-of-the-art glass structure with advanced equipment and control systems for super greenhouses in the world. However, few national standards are released for the structure design of modern agricultural greenhouse, although most industry or group standards in China. Furthermore, the combination of earthquake load other than wind load cannot be usually considered for the structure design in most studies, due to the light-weight components and covering materials in the greenhouse structure. If the greenhouse that designed by the Netherlands was directly introduced to adopt the Dutch structural standards in China, there is a safety hazard of seismic action for the damage of main structure, particularly for nearly half of land areas in high-level zones of seismic intensity in China, even above 7 degrees. Taking a Venlo-type greenhouse designed by a Dutch company in Shandong province as an example, this study aims to explore the impact of seismic action on structural safety according to the Dutch code, considering the stress diaphragm of covering material on the structural stiffness. A finite element software MIDAS Gen with the response spectrum modal was also selected to simulate the whole structure of the greenhouse with the seismic precautionary intensity of 7 (0.10 g), 7 (0.15 g), 8 (0.20 g), and 8 degrees (0.30 g). A systematic analysis was made on the mode periods, vibration patterns, the maximum stresses, and displacements for the structure. The results showed that the longest period of natural vibration was 1.75 s in the greenhouse structure, indicating a relatively flexible performance of the structural system. The first and second vibration patterns were the flat motion in Y and X -direction, especially a similar seismic performance in the 2 principal axes. Additionally, the maximum bearing capacity of the structure was 216.96 MPa for different seismic precautionary intensities under the wind load. The maximum stress was still less than the yield strength of components. When the seismic precautionary intensity exceeded 8 degrees (0.30 g), the maximum tensile and compressive stresses of components were 211.95 and 196.02 MPa for the X-directional seismic action, respectively. In addition, the maximum displacement was 31.80 mm under the X-directional wind load without considering seismic action. The structural deformation was also mainly influenced by the seismic load. Specifically, the Y-directional seismic action was about 11.6% than that in the X-direction under the same load combination. Consequently, the greenhouse structure introduced from Netherland can always be expected to fully meet the code requirements within the elastic range of components, when the seismic intensity was lower than 8 degrees (0.30 g). Nevertheless, the maximum deformation exceeded the standard requirement of 1/250 of the elastic inter-story displacement angle, according to the code for seismic design of buildings in China.<br/></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:27
Main heading:Finite element method
Controlled terms:Aerodynamic loads - Agriculture - Earthquakes - Greenhouses - Vibration analysis - Wind stress - Yield stress
Uncontrolled terms:Covering material - Earthquake - Greenhouse structure - Netherlands - Seismic action - Seismic Performance - Seismic precautionary intensity - Structure design - Venlo - Wind load
Classification code:443.1 Atmospheric Properties - 484 Seismology - 651.1 Aerodynamics, General - 821 Agricultural Equipment and Methods; Vegetation and Pest Control - 821.6 Farm Buildings and Other Structures - 921.6 Numerical Methods - 951 Materials Science
Numerical data indexing:Mass 1.00E-04kg, Mass 1.50E-04kg, Mass 2.00E-04kg, Mass 3.00E-04kg, Percentage 1.16E+01%, Pressure 1.9602E+08Pa, Pressure 2.1195E+08Pa, Pressure 2.1696E+08Pa, Size 3.18E-02m, Time 1.75E+00s
DOI:10.11975/j.issn.1002-6819.2021.19.028

Accession number:20215211376647
Title:Establishment of the mining model for sweet molecules in food
Title of translation:食品中甜味分子发掘模型构建
Authors:Ren, Haibin (1); Feng, Baolong (2); Fan, Bei (3); He, Binbin (1); Li, Zhilu (1); Wang, Qinghua (1); Gao, Fei (2); Wang, Yutang (1, 3)
Author affiliation:(1) Key Laboratory of Dairy Science, Ministry of Education, Northeast Agricultural University, Harbin; 150030, China; (2) Center for Education Technology, Northeast Agricultural University, Harbin; 150030, China; (3) Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing; 100193, China
Corresponding authors:Wang, Yutang(wangyt@neau.edu.cn); Wang, Yutang(wangyt@neau.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:303-308
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">Sweet taste is one of the most important tastes in food flavor and quality. Sweet molecules that can be used to produce new sweeteners have also been actively explored in food processing. However, the traditional methods cannot meet the rapid development of the economy and market demand, due mainly to time-consuming, laborious, and inefficient methods. Therefore, an effective and reliable strategy is essential to produce the sweet stuff. Currently, machine learning and structure-activity relationship can be utilized to realize accurate predictions of sweet molecules in the food industry. In this study, a new database of sweeteners and non-sweeteners together with the scores of sweetness was established using molecular sweetness and structure-activity correlation between molecular structures. MOE software was selected to compute molecular descriptors, to fully characterize the properties of molecules. These descriptors were then filtered through neighborhood variance screening, collinearity removal, and principal component contribution rate screening. Specifically, the feature descriptors were screened by removing the descriptors with high correlation. 80% of the dataset was then divided into training sets for model construction, and 20% were divided into test sets for model validation. Random forest and support vector machines were utilized to establish a qualitative structure-activity relationship for the prediction and identification of potential sweet molecules. Evaluation indexes were taken as the area under the receiver characteristic curve (AUC) and accuracy rate. The higher the AUC and accuracy rate represented the better classification. As such, the optimal model was obtained. Subsequently, the principal component, K-nearest neighbor, random forest, and partial least squares regression were used to establish the quantitative structure-activity relationship for better prediction of sweet molecules. The determination coefficient R<sup>2</sup> and Root Mean Square Error (RMSE) were used as evaluation indexes of the quantitative structure-activity model. The higher R<sup>2</sup> and lower RMSE showed the better model. The optimal model was obtained to compare the performance. The food composition database (FooDB) was applied to predict the possible sweet food ingredients and the sweetness. Correspondingly, the publicly accessible dataset was established ranging from artificially revised and continuously updated on sweetener, non-sweetener substances, and sweetness values. A new model was established to identify sweet molecules using the random forest. The accuracy of the model was 0.966 on the test set, and the area under the ROC curve was 0.987, indicating excellent predictive ability. The prediction model of sweetness was also established using the random forest. Specifically, the R<sup>2</sup> was 0.82 and RMSE was 0.60. A manually modified data set was established to combine qualitative and quantitative sweetener prediction. 542 potential sweetener molecules, including lycopene were discovered in the food composition database. All data and code were then stored at the website of https://gitee.com/wang_lab/EMMSM for a better extension. Consequently, the new model indicated universal applicability and high practical application in searching for new sweet molecules.<br/></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:32
Main heading:Forecasting
Controlled terms:Decision trees - Food additives - Food processing - Least squares approximations - Mean square error - Molecules - Nearest neighbor search - Principal component analysis - Regression analysis - Statistical tests - Support vector machines
Uncontrolled terms:Accuracy rate - Descriptors - Evaluation index - Principal Components - Qualitative structure-activity relationship - Quantitative structure activity relationship - Random forests - Root mean square errors - Structure-activity relationships - Test sets
Classification code:723 Computer Software, Data Handling and Applications - 803 Chemical Agents and Basic Industrial Chemicals - 822.2 Food Processing Operations - 822.3 Food Products - 921.4 Combinatorial Mathematics, Includes Graph Theory, Set Theory - 921.5 Optimization Techniques - 921.6 Numerical Methods - 922.2 Mathematical Statistics - 931.3 Atomic and Molecular Physics - 961 Systems Science
Numerical data indexing:Percentage 2.00E+01%, Percentage 8.00E+01%
DOI:10.11975/j.issn.1002-6819.2021.19.035

Accession number:20215211376649
Title:Multi-target cow mouth tracking and rumination monitoring using Kalman filter and Hungarian algorithm
Title of translation:利用Kalman滤波和Hungarian算法的多目标奶牛嘴部跟踪及反刍监测
Authors:Mao, Yanru (1, 2, 3); Niu, Tong (1, 2); Wang, Peng (1, 3); Song, Huaibo (1, 2, 3); He, Dongjian (1, 2, 3)
Author affiliation:(1) College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling; 712100, China; (2) Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling; 712100, China; (3) Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling; 712100, China
Corresponding authors:He, Dongjian(hdj168@nwsuaf.edu.cn); He, Dongjian(hdj168@nwsuaf.edu.cn); He, Dongjian(hdj168@nwsuaf.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:192-201
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">Rumination enables cows to chew grass more completely for better digestion, thereby closely relating to the health, production, reproduction, and welfare of cows. To perceive ruminant behavior has widely been one of the most important steps in modern dairy farm management. However, the traditional monitoring ruminant behavior of cows depends mainly on human labor, time-consuming and laborious. In this study, feasible intelligent monitoring was proposed for multi-target automatic tracking mouth and ruminant behavior of cows in the complex environment of dairy farms using the Kalman filter and Hungarian algorithm. The upper and lower jaw regions of cow mouths were firstly recognized by the YOLOv4 model. Subsequently, the region of the upper jaw was tracked by the Kalman filter and Hungarian algorithm. The chewing curve of the mouth region was then obtained to match the upper and lower jaw regions of the same cow. Finally, the related rumination information was achieved to further realize the mouth tracking and ruminant behavior monitoring of multi-target cows. As such, the unmatched tracking boxes were remained and expanded to deal with the identity change of cows caused by rapid head swing or shed railing occlusion. 66 videos were collected for ruminant cows in the actual farm environment, where 58 videos were divided into frames to make the dataset for the YOLOv4 model, and the remaining 8 videos were used to verify the tracking and rumination behavior. Videos data was involved sunny, cloudy, and rainy days, in which the number of cows varied from 2 to 3. Lying or standing was included in the ruminant posture of cows. Additionally, there were some interference factors, such as the rapid head swing of ruminating cows, shed railing occlusion, and the movement of other cows. Two indexes were selected to evaluate the detection performance of the YOLOv4 model, including average precision and mean average precision. 6 400 images of the dataset were trained, and 800 images were tested. The results showed that the average precisions of the YOLOv4 model were 93.92% and 92.46% for the detection of the upper and lower jaw region, respectively. The mean average precision of YOLOv4 reached 93.19%, which was 1.04, 4.25, and 1.74 percentage points higher than that of YOLOv5, SSD, and Faster RCNN models, respectively. Four indexes were selected to verify the performance of tracking and rumination behavior under different environments, including the rate of identity switch, the rate of identity match, the detection rate of chewing times, and the tracking speed. It was found that the YOLOv4 model realized the stable multi-target tracking of mouth regions of cows in complex environments, while effectively alleviating the identity change of cows resulted from the rapid head swing and shed railing occlusion. The average rate of identity match was 99.89% for the upper and lower jaws, and the average tracking speed was 31.85 frames/s. The average detection rate of chewing times was 96.93% after the evaluation of rumination behavior, and the average error of rumination time was 1.48 s. This finding can provide a strong reference for the intelligent monitoring and ruminant behavior of multi-target cows (or moving parts of animals) in actual breeding.<br/></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:31
Main heading:Computer vision
Controlled terms:Cell proliferation - Clutter (information theory) - Consumer behavior - Farms - Image recognition - Image segmentation - Kalman filters - Mammals - Surface discharges - Target tracking
Uncontrolled terms:Cow - Hungarian algorithm - Kalman filter algorithms - Machine-vision - Mouth region - Multi-target-tracking - Multi-targets - Rapid head - Ruminant behavior - YOLOv4
Classification code:461.9 Biology - 701.1 Electricity: Basic Concepts and Phenomena - 716.1 Information Theory and Signal Processing - 723.5 Computer Applications - 741.2 Vision - 821 Agricultural Equipment and Methods; Vegetation and Pest Control - 931.3 Atomic and Molecular Physics
Numerical data indexing:Percentage 9.246E+01%, Percentage 9.319E+01%, Percentage 9.392E+01%, Percentage 9.693E+01%, Percentage 9.989E+01%, Time 1.48E+00s
DOI:10.11975/j.issn.1002-6819.2021.19.022

Accession number:20215211376617
Title:Agricultural drought evolution characteristics and driving mechanisms in the Yellow River Basin under climate and land use changes
Title of translation:气候和土地利用变化下黄河流域农业干旱时空演变及驱动机制
Authors:Li, Yunyun (1, 2); Chang, Jianxia (2); Fan, Jingjing (3); Yu, Bo (1)
Author affiliation:(1) Ecological Security and Protection Key Laboratory of Sichuan Province, Mianyang Normal University, Mianyang; 621000, China; (2) State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an; 710048, China; (3) School of Water Conservancy and Hydroelectric Power, Hebei University of Engineering, Handan; 056038, China
Corresponding author:Chang, Jianxia(chxiang@xaut.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:84-93
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">Agricultural drought has posed a serious threat to the national food security, social, environmental, and economic sustainable development in China over the last 20 years. The average annual disaster area has been accounted for more than 50% of natural disasters against climate warming and intensive human activities. Therefore, it is highly urgent to clarify the drought evolution and driving mechanisms for scientific drought prevention. More importantly, the Yellow River Basin provides the water supply for about 140 million people in the region, where about 15% of the total irrigated land was for agricultural irrigation in the country. However, the Yellow River Basin has historically been frequently experiencing serious droughts. For example, the disaster area caused by agricultural drought after 2000 was nearly six times that before 2000. Therefore, taking the Yellow River Basin as a study area, the main objective of this study is to comprehensively analyze the temporal and spatial evolution characteristics and driving mechanisms of agricultural drought. Six sub-basins were divided according to the climatic and topographic characteristics. The standard soil moisture index (SSMI) and threshold method were used to identify the duration, intensity, and drought events under different drought levels. A systematic analysis was also made on the agricultural drought characteristics and event frequencies in different zones in the study area on the annual and seasonal scales. A SWAT model in a simulation scheme was then selected to quantify the impacts of climate and land use land cover (LULC) change on agricultural drought in the study area. Results showed that: 1) The fewer frequencies occurred for the agricultural drought with longer duration, as the cumulative time was much longer. The duration of the agricultural drought was then remarked by SSMI-1, SSMI-6, and SSMI-18 corresponding to about 1-8, 1-12, and 1-22 months, respectively. The beginning and end time of agricultural drought was mainly concentrated in spring and summer. 2) The most serious agricultural drought occurred in the study area during 1981-1990, where that was greatly alleviated in most zones during 2001-2010. Specifically, Zone C and A were the most vulnerable to severe and extreme agricultural drought in the 1990s and 2000s, respectively. 3) Climate change was the main factor that caused the agricultural drought in the study area with a contribution rate of about 50%-90%, while the impact of LULC change was relatively weak with a contribution rate of about 10%-50%. Consequently, the greatest driving impacts of climate and land use were about 60%-90% and 10%-50%, respectively, on the frequency of agricultural drought in the study area. The findings can provide much more accurate information for actual management and disaster prevention of agricultural drought.<br/></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:29
Main heading:Land use
Controlled terms:Agriculture - Climate change - Climate models - Disasters - Drought - Economics - Food supply - Rivers - Soil moisture - Sustainable development - Water supply - Watersheds
Uncontrolled terms:Agricultural drought - Climate - Disaster areas - Driving mechanism - Evolution characteristics - Land-use land-cover changes - Soil moisture index - Study areas - The yellow river basin
Classification code:403 Urban and Regional Planning and Development - 443 Meteorology - 443.1 Atmospheric Properties - 443.3 Precipitation - 444 Water Resources - 444.1 Surface Water - 446.1 Water Supply Systems - 483.1 Soils and Soil Mechanics - 821 Agricultural Equipment and Methods; Vegetation and Pest Control - 822.3 Food Products - 921 Mathematics - 971 Social Sciences
Numerical data indexing:Age 2.00E+01yr, Age 8.33E-02yr to 1.8326E+00yr, Percentage 1.00E+01% to 5.00E+01%, Percentage 1.50E+01%, Percentage 5.00E+01%, Percentage 5.00E+01% to 9.00E+01%, Percentage 6.00E+01% to 9.00E+01%
DOI:10.11975/j.issn.1002-6819.2021.19.010

Accession number:20215211376496
Title:Lightweight object detection method for Lingwu long jujube images based on improved SSD
Title of translation:改进SSD的灵武长枣图像轻量化目标检测方法
Authors:Wang, Yutan (1); Xue, Junrui (1)
Author affiliation:(1) School of Mechanical Engineering, Ningxia University, Yinchuan; 750021, China
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:173-182
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">The complex working environment of picking robots has limited the picking speed and equipment memory resources in the intelligent harvesting of Lingwu long jujubes. Therefore, it is necessary to meet the requirements of lighter network structure and higher detection accuracy, particularly for the visual recognition system. A pre-train model has widely been loaded almost all the object detection at present, due to high initialization performance and convergence speed. However, two challenges are still remained: 1) The network structure cannot be changed on the limited memory resources of the device; 2) There may be great differences between the ImageNet dataset and the dataset to be trained, leading to the low training effect. Taking the SSD model as the basic framework, this research aims to propose a lightweight object detection for the images of Lingwu long jujubes. The excellent performance was achieved without loading the pre-train model. Firstly, data augmentation is performed on the collected 1 000 images to obtain 5 000 images. Data augmentation operations include random cropping, random vertical or horizontal flipping, random brightness adjustment, random contrast adjustment, and random saturation adjustment. Secondly, the Lingwu long jujube dataset was established, including 3 500 training images and 1 500 test images. The resolution of images consisted of 3 016×4 032, 4 068×3 456, and 2 448×3 264. The models of smartphones for image acquisition included HUAWEI TRT-AL00A, Vivo Y79A, and Xiaomi 2014501. The images were uniformly scaled to the resolution of 300×300, in order to meet the input requirements of image size in the SSD object detection. Data augmentation included random cropping, random vertical or horizontal flipping, as well as random adjustment of brightness, contrast, and saturation. The format of the PASCAL VOC dataset was also adopted. Labelling software was used to label the images, and then the marked images were stored in the label folder in XML format. Secondly, the improved DenseNet was utilized the Convolutional Block Attention Modules and two dense blocks with convolution groups of 6 and 8. Taking the improved DenseNet as the backbone network, the improved SSD model was obtained to combine with the multi-level fusion structure, where the first three additional layers were replaced in the SSD model with the Inception module. In the improved SSD model without loading the pre-train model, the mAP was 96.60%, the detection speed was 28.05 frames/s, and the number of parameters was 1.99×10<sup>6</sup>, particularly 2.02 percentage points and 0.05 percentage points higher than that of the SSD and SSD model (pre-train), respectively. Correspondingly, the parameter of the improved SSD model was 11.14×10<sup>6</sup> lower than the SSD model, fully meeting the requirements of the lightweight network without loading the pre-train model. This finding can provide a strong visual technical support for the intelligent harvesting of Lingwu long jujubes, even medical and multispectral images detection tasks.<br/></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:35
Main heading:Object detection
Controlled terms:Convolution - Horizontal wells - Image enhancement - Luminance - Object recognition
Uncontrolled terms:Data augmentation - Densenet - Images processing - Inception module - Lingwu long jujube - Memory resources - Network structures - Pre-train model - SSD model - Train model
Classification code:512.1.1 Oil Fields - 716.1 Information Theory and Signal Processing - 723.2 Data Processing and Image Processing
Numerical data indexing:Percentage 9.66E+01%
DOI:10.11975/j.issn.1002-6819.2021.19.020

Accession number:20215211376526
Title:Influence of guide vane length on the velocity characteristics of spiral flow in cross-sections between capsules
Title of translation:导叶长度对囊体间断面螺旋流流速特性的影响
Authors:Li, Yongye (1); Zhang, Qiwei (1); Song, Xiaoteng (1); Lu, Yifan (1); Yang, Xiaoni (1); Sun, Xihuan (1); Zhang, Xuelan (1); Pang, Yaqi (1)
Author affiliation:(1) College of Water Resource Science and Engineering, Taiyuan University of Technology, Taiyuan; 030024, China
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:48-56
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">Hydraulic capsule pipeline refers to the new type of transportation for goods in hollow containers in low carbon and environment-friendly way, particularly for agricultural products. Specifically, the farm products were sealed in airtight containers for point-to-point transportation through fixed pipelines, where the water pressure was taken as the power. Since the guide vanes are added around the capsule body, the water flow will generate circumferential velocity. Therefore, the length of guide vanes inevitably poses a great influence on the distribution and size of circumferential velocity. In this study, experimental and theoretical analysis was used to investigate the velocity characteristics of spiral flow in the cross section of the capsule with the length of the guide vane as the control variable. The results show that the axial velocity distribution of each cross-section was all the same with different lengths of guide vane. Specifically, the fluctuation of axial velocity decreased in each section between capsules, indicating the more uniform distribution of axial velocity, with the increase in the length of the guide vane. There was no major change in the axial velocity, but positive and the same as the water flow direction, with the increase in the length of the guide vane. Furthermore, the circumferential velocity gradient along each section between capsules first decreased, and then increased with different guide vane lengths, while the maximum and minimum circumferential velocity appeared near the upstream capsule. More importantly, the length of the guide vane presented the greatest influence on the circumferential flow velocity. The circumferential flow velocity increased with the increase of guide vane length, with a maximum of up to 1.2 m/s. The value of circumferential velocity was positive or negative, indicating that the circumferential velocity was divided into two directions, clockwise and counterclockwise along the circumference. The radial velocity gradient was the largest in the area near the upstream capsule, while smaller in the middle section between capsules under different guide vane lengths. There was a gradual decrease difference between the maximum and minimum radial velocity in the same section, where the radial velocity value was more uniform, with the increase of guide vane length. Compared with the circumferential velocity, the radial velocity was less affected by the length of the guide vane, and the radial velocity was positive or negative, indicating that the radial velocity was directed to the center of the circle and away from the center of the circle. The axial, circumferential, and radial velocity distribution on the same measuring ring was wavy under different guide vane lengths, ranging from -1.2 to 3.5 m/s, -0.6 to 1.2 m/s, and -1.6 to 1.2 m/s, respectively. The axial, circumferential, and radial velocity values were at the polar angle of 60°, 180°, and 300°. The peak value appeared at all the positions. The finding can provide a strong theoretical basis for the optimal design of capsule pipeline hydraulic transportation.<br/></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:25
Main heading:Flow velocity
Controlled terms:Agricultural products - Containers - Flow fields - Flow of water - Hydraulics - Pipelines - Pneumatic tubes - Velocity - Velocity distribution
Uncontrolled terms:Axial velocity - Capsule - Circumferential velocity - Flow velocity characteristics - Guide-vane - Hydraulic transportation - Length of guide vane - Radial velocity - Spiral flow - Velocity gradients
Classification code:619.1 Pipe, Piping and Pipelines - 631 Fluid Flow - 631.1 Fluid Flow, General - 631.1.1 Liquid Dynamics - 632.1 Hydraulics - 632.4 Pneumatic Equipment and Machinery - 821.4 Agricultural Products - 922.2 Mathematical Statistics - 943.2 Mechanical Variables Measurements
Numerical data indexing:Velocity 1.20E+00m/s, Velocity 1.20E+00m/s to 3.50E+00m/s, Velocity 1.60E+00m/s to 1.20E+00m/s, Velocity 6.00E-01m/s to 1.20E+00m/s
DOI:10.11975/j.issn.1002-6819.2021.19.006

Accession number:20215211376652
Title:Monocular distance measurement algorithm for pomelo fruit based on target pixels change
Title of translation:基于目标像素变化的柚果单目测距算法
Authors:Liu, Jie (1, 2, 3); Zhou, Dianzhuo (1); Li, Yan (1); Li, Dingke (1); Li, Yingqi (1); Rana, Rubel (1)
Author affiliation:(1) College of Engineering, Huazhong Agricultural University, Wuhan; 430070, China; (2) Key Laboratory of Agricultural Equipment in Mid-lower Yangtze River, Ministry of Agriculture and Rural Affairs, Wuhan; 430070, China; (3) Citrus Mechanization Research Base, Ministry of Agriculture and Rural Affairs, Wuhan; 430070, China
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:183-191
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">Accurate identification of target depth is the critical premise for the manipulator of fruit and vegetable picking in intelligent agriculture. However, the general ranging of fruit has posed a great challenge on that the orchard in the mountain areas, due mainly to the light change, as well as the branch and leaf occlusion. In this study, a novel imaging algorithm was proposed to detect the monocular distance between the pomelo fruits and camera using target pixels change. The pomelo fruit off the tree in the orchard was chosen as the samples for data collection. Multiple regression was also established to verify the number change of pixels in the target areas and imaging distance. Furthermore, the pomelo fruit on the tree was involved to test the applicability in the samples. Additionally, a systematic investigation was made to explore the influences of initial imaging distance and step interval on prediction accuracy. The specific procedure was as follows. A single camera was utilized to capture the imaging data of the sample fruit side within the imaging distance ranging from 25 to 150 cm, where the common range of picking operation was set at the step interval of 2.5 cm. Therefore, there were 51 images side for each fruit for one group of data. 20 pomelo fruits with the representative shape and size were selected for the imaging data collection, including 14 off-tree and 6 on-tree. In the pomelo fruits off the tree, the surface was equally divided into four sides, where one group of data was acquired from each side. As such, a total of 56 data groups were collected from the samples of the tree. Subsequently, the 40 data groups were randomly selected to establish the multiple regressions between the imaging distance and the number of pixels change in the target area on the image, while the rest 16 data groups were used to optimize the algorithm. In the pomelos fruits on the tree, only the side towards the outside of the canopy was shot as one sample side, where 6 data groups were collected for testing. The numbers of pixels were then measured for the target areas in the image using Photoshop software. MATLAB 2018 platform was finally utilized to calculate the regression and curve fitting. The results showed that the error of predicted distance decreased gradually, as the camera approached the fruit target from 150 to 120 cm. In the fruit samples off the tree, the accuracy of distance prediction was at the medium level closer than 130 cm at the high level of about 120 cm. The relative ranging errors of 16 samples off-tree were less than 5% within the imaging distance of 120 cm, when 150 cm was as the initial distance, indicating that an excellent detection performance of imaging distance between the target and camera. In the fruit samples on the tree, the ranging accuracies were set as 125-137.5 cm and 25.0-125.0 cm for the medium and high levels, respectively. Correspondingly, the relative ranging errors of 6 samples on the tree were less than 5% within 125 cm imaging distance, fully meeting the accuracy requirements of target positioning for the picking manipulator. In addition, there was a significant effect of initial imaging distance on the measurement accuracy. Consequently, the measurement method of monocular distance can widely be expected to realize the rapid prediction of the distance between the fruit target and camera in the complex orchard environment, especially in the hilly mountain areas. Meanwhile, the finding can also provide a feasible scheme for the accurate recognition of fruit targets for picking manipulators in modern orchards.<br/></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:25
Main heading:Fruits
Controlled terms:Cameras - Curve fitting - Data acquisition - Forestry - MATLAB - Orchards - Pixels - Regression analysis - Trees (mathematics)
Uncontrolled terms:Data collection - Data groups - Fruit samples - Identification - Imaging data - Imaging distances - Monocular distance measurement - Multiple regressions - Number of pixel - Orchard
Classification code:723.2 Data Processing and Image Processing - 723.5 Computer Applications - 742.2 Photographic Equipment - 821.0 Woodlands and Forestry - 821.3 Agricultural Methods - 821.4 Agricultural Products - 921 Mathematics - 921.4 Combinatorial Mathematics, Includes Graph Theory, Set Theory - 921.6 Numerical Methods - 922.2 Mathematical Statistics
Numerical data indexing:Percentage 5.00E+00%, Size 1.20E+00m, Size 1.25E+00m, Size 1.25E+00m to 1.375E+00m, Size 1.30E+00m, Size 1.50E+00m to 1.20E+00m, Size 1.50E+00m, Size 2.50E-01m to 1.25E+00m, Size 2.50E-01m to 1.50E+00m, Size 2.50E-02m
DOI:10.11975/j.issn.1002-6819.2021.19.021

Accession number:20215211376522
Title:Drag reduction mechanism of the 3D geometry of foreleg's claw toe of the mole cricket (Gryllotalpa orientalis)
Title of translation:蝼蛄前足爪趾三维几何构形的减阻机理
Authors:Zhang, Yan (1, 2); Qiao, Chaoxiong (1, 2); Wang, Tianqi (1, 2); Cao, Jiafeng (1, 2); Wang, Pengfei (1, 2); Shi, Lei (3)
Author affiliation:(1) Tianjin Key Laboratory of Integrated Design and On-line Monitoring for Light Industry & Food Machinery and Equipment, Tianjin; 300222, China; (2) College of Mechanical Engineering, Tianjin University of Science & Technology, Tianjin; 300222, China; (3) Tianjin Limin Condiment Co., Ltd., Tianjin; 300308, China
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:309-315
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">Biological plane geometry has been unable to meet the harsh requirements of bionic design in most soil contacting parts of tillage machinery in recent years, particularly on the operating speed, energy-saving, and emission reduction. In this study, a bionic investigation was performed on the toe of the foreleg claw in the mole cricket using the three-dimensional (3D) geometry. Projection and segmentation were also used to extract the 3D characteristic curves of claw toe in three orthogonal planes. The MATLAB platform was selected to determine the characteristic curves via the polynomial fitting and smoothing processing. An orthogonal experiment of bionic samples was carried out, where three plane configurations were taken as factors, while the different characteristics of configuration as levels. A total of 16 bionic specimens and 1 wedge-shaped comparison specimen were constructed by SolidWorks software and then fabricated using 3D printing (polylactic acid material). A test system of soil groove was utilized to evaluate the cutting resistance of each specimen, where the soil was assumed as the foamed phenolic plastics, the cutting speed was 10 mm/s, the cutting depth was 15 mm, and the cutting time was 20 s. The explicit dynamic Finite Element (FE) software ANSYS LS-DYNA was used to simulate the cutting process of the specimen, in order to determine the relationship between the 3D geometrical toes of the foreleg claw in the mole cricket and the drag reduction performance. It was found that the cutting process of the specimen was divided into the drag increase and stable phase. Furthermore, the drag reduction performance of specimens with 3D biological geometries was significantly better than that with one- and two-dimension, as well as the wedge shape. All configurations in the three planes also presented a significant impact on drag reduction. Correspondingly, the main influencing factor of drag reduction was the cross-sectional configuration perpendicular to the growth direction of claw toes. More importantly, the cutting resistance of the specimen was reduced up to 56.96% with 3D biological geometries. The FE analysis results showed that the 3D geometrical toes of the foreleg claw in the mole cricket effectively alleviated the accumulation of soil on the tip of the specimen. As such, the soil moved smoothly along the excavation surface, thereby avoiding the accumulation of pressure on the middle and back of specimens. This process was the reason for the reduction of cutting resistance. Furthermore, an optimal configuration of soil-contacting components was also achieved to reduce the cutting resistance, while effectively improving the working efficiency of machinery without the use of external energy and auxiliary devices. Nevertheless, the actual configuration was a 3D structure of soil-contacting parts in farming and engineering machinery, where many interrelated geometric parameters were involved during optimization. Consequently, the biological geometry can widely be expected to optimize soil-contacting parts, whether to project the main configuration of bionic objects in two dimensions, or to directly transplant the 3D biological geometry with 3D reverse. The characteristic curves of 3D biological geometry were also coupled to design bionic specimens. The feasible idea can also provide an insightful promising bionic design on soil cutting parts of tillage machinery, such as openers and subsoilers.<br/></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:26
Main heading:Bionics
Controlled terms:Agricultural machinery - Agriculture - Biomimetics - Curve fitting - Cutting - Cutting tools - Drag reduction - Energy conservation - Genetic algorithms - Geometry - Machine design - MATLAB - Software testing - Soils
Uncontrolled terms:Bionic design - Characteristic curve - Cutting process - Cutting resistance - Cutting resistance test - Mole cricket - Optimisations - Performance - Resistance tests - Two-dimensions
Classification code:461.1 Biomedical Engineering - 461.8 Biotechnology - 461.9 Biology - 483.1 Soils and Soil Mechanics - 525.2 Energy Conservation - 601 Mechanical Design - 603.2 Machine Tool Accessories - 723.5 Computer Applications - 821 Agricultural Equipment and Methods; Vegetation and Pest Control - 821.1 Agricultural Machinery and Equipment - 921 Mathematics - 921.6 Numerical Methods
Numerical data indexing:Percentage 5.696E+01%, Size 1.50E-02m, Time 2.00E+01s, Velocity 1.00E-02m/s
DOI:10.11975/j.issn.1002-6819.2021.19.036

Accession number:20215211376251
Title:Characteristics of water and nitrogen loss under subsurface pipe-open ditch controlled drainage in paddy fields
Title of translation:明沟-暗管组合控排下稻田水氮流失特征
Authors:Li, Yawei (1); Xu, Junzeng (1, 2); Liu, Wenhao (1); Jiao, Xiyun (1, 2); Zhou, Jiaoyan (3); Zhang, Jian (3)
Author affiliation:(1) College of Agricultural Science and Engineering, Hohai University, Nanjing; 211100, China; (2) State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing; 210098, China; (3) Urban Water Scheduling and Information Management Department of Kunshan, Suzhou; 215300, China
Corresponding authors:Xu, Junzeng(xjz481@hhu.edu.cn); Xu, Junzeng(xjz481@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:19
Issue date:October 1, 2021
Publication year:2021
Pages:113-121
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">Severe non-point source pollution has widely resulted from the nitrogen losses in paddy-field drainage in southern China, due mainly to excessive application of chemical fertilizers and unreasonable irrigation. The goal of this study was to improve the water use efficiency, while mitigating the reactive nitrogen losses in paddy fields. A controlled drainage system (CD) was designed to combine the subsurface pipe and open drainage ditches, with an open-ditch controlled drainage system (OD) as a control group. Specifically, the CD system consisted of a controlled drainage ditch and three field plots (CD1, CD2, and CD3) with a controlled subsurface pipe. By contrast, the three-field plots (OD1, OD2, and OD3) were free subsurface pipes in the OD system, where the field water freely drained into the drainage ditch through lateral infiltration. The drainage intensity and nitrogen concentration were monitored in various forms at the outlets of subsurface pipe and open ditch with a high frequency in six selected irrigation-induced drainage events, including three irrigation-drainage events with the fertilization (F1, F2, and F3) and three irrigation-drainage events without fertilization (D1, D2, and D3). The results showed that the drainage loss induced by six irrigation-drainage events accounted for 44.0% of the total amount of irrigation water in the OD system, indicating low water use efficiency. The combination of controlled drainage between the subsurface pipe and the open ditch greatly changed the drainage from the paddy field to the open ditch in the CD system. In all irrigation-drainage events except F3, the start time of open ditch drainage was later than that of subsurface pipe drainage, whereas, the peak of the intensity in the open ditch drainage was synchronized or significantly later than that of subsurface pipe drainage. Furthermore, the drainage peak and intensity of the open ditch in the CD system were much lower than those in the OD system among all six irrigation-drainage events. Specifically, the drainage peak of the latter was 1.3 to 8.2-fold that of the former, where the average drainage intensity was 1.5 to 4.4-fold. Compared with the OD, the CD decreased the drainage peak, intensity, duration time, as well as total drainage loss, where the proportion of drainage amount in the total amount of irrigation water dropped to 24.4%, indicating an effective role in drainage mitigation. In the irrigation-drainage event F1, F2, and F3, the concentrations of ammonium (NH<inf>4</inf><sup>+</sup>) and total nitrogen (TN) in the drainage from the outlets of subsurface pipe (CD1, CD2, and CD3) and open ditches (CD and OD) gradually increased over time until the end of the drainage. Nevertheless, the concentrations of NH<inf>4</inf><sup>+</sup>, nitrate (NO<inf>3</inf><sup>-</sup>), and TN in the drainage from these same outlets gradually increased over time in the irrigation-drainage events D1, D2, and D3. Furthermore, the average concentrations of NH<inf>4</inf><sup>+</sup>, NO<inf>3</inf><sup>-</sup> and TN in the drainage from F1, F2, and F3 were much higher than those from D1, D2, and D3, indicating that the nitrogen loss was effectively reduced during the drainage management in a certain period after fertilization. As such, the open ditch in the CD system significantly intercepted a large number of nitrogen loads from subsurface pipe drainages. The nitrogen losses in the forms of NH<inf>4</inf><sup>+</sup>, NO<inf>3</inf><sup>-</sup> and TN from open ditch drainage in the CD system greatly decreased by 42.6%, 70.7%, and 39.3%, respectively, compared with the OD system. Consequently, the CD system can be expected to significantly reduce drainage loss and control non-point source pollution. This finding can also provide promising drainage control for the paddy field in southern China.<br/></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:31
Main heading:Drainage
Controlled terms:Efficiency - Nitrogen fertilizers - Subirrigation - Water supply
Uncontrolled terms:Controlled-drainage - Drainage ditches - Drainage systems - Fertilisation - Nitrogen loss - Nonpoint-source pollution (NPS) - Open-ditch - Paddy fields - Subsurface pipe - Total nitrogen
Classification code:446.1 Water Supply Systems - 804 Chemical Products Generally - 821.2 Agricultural Chemicals - 821.3 Agricultural Methods - 913.1 Production Engineering
Numerical data indexing:Percentage 2.44E+01%, Percentage 3.93E+01%, Percentage 4.26E+01%, Percentage 4.40E+01%, Percentage 7.07E+01%
DOI:10.11975/j.issn.1002-6819.2021.19.013

Accession number:20215211376654
Title:Effects of soil bulk density and water content on the mechanical stability of soil structure using rheological method
Title of translation:基于流变学法研究容重和含水率对土壤结构力学稳定性的影响
Authors:Wang, Jinxiao (1, 2); Hu, Feinan (1, 2, 3); Xu, Chenyang (1); Zhao, Shiwei (2, 3); Liu, Jingfang (1, 2); Tu, Kun (1, 2); Song, Songsong (3)
Author affiliation:(1) College of Natural Resources and Environment, Northwest A&F University, Yangling; 712100, China; (2) State Key Laboratory of Soil Erosion and Dryland Farming on Loess Plateau, Northwest A&F University, Yangling; 712100, China; (3) Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling; 712100, China
Corresponding authors:Hu, Feinan(hufn@nwafu.edu.com); Hu, Feinan(hufn@nwafu.edu.com); Hu, Feinan(hufn@nwafu.edu.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:147-155
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">Mechanical stability of soil structure determined the scour resistance, erodibility, collapsibility, slope stability, and foundation stability of the soil, even the large-scale agricultural instruments, as well as irrigation and water conservancy projects. Alternatively, rheology has widely been one part of soil physical characterization under deformation. The rheological parameters can be utilized to clarify the highly complex soil process, including the most significant soil aggregation factors, such as soil bulk density and water content. In this study, the widely distributed Lou soil and loessal soil on the Loess Plateau were selected as the research objects. An amplitude scanning test was selected to simulate the oscillation load process. An investigation was made on the effect of soil bulk density and water content on the mechanical stability of soil structure under the oscillation load. The results show that: 1) The soil density increased the contact point between soil particles, leading to the increasing cohesion and friction between particles. The shear strength parameters were all increased, including the shear stress at the linear viscoelastic region, as the increase of soil bulk density, indicating the increase in the stability of soil structure. In terms of viscoelastic parameters, the shear strain at yield points of Loessal soil and integral zone of Lou soil increased first and then decreased, with the increase of soil bulk density. Soil particles were under the most stable way of organization and combination (1.3 g/cm<sup>3</sup>). Shear strength parameters with the change of soil bulk density were more sensitive than viscoelastic parameters. 2) As the increase of soil water content, the shear strength parameters presented the decreasing trends, including the shear stress at the linear viscoelastic region, the shear stress at yield point, the maximum shear stress, indicating the decreased stability of soil structure. In viscoelastic parameters, the shear strain at the linear viscoelasticity region of Lou soil increased with the increase of water content, but the shear strain at yield point of Lou soil and integral zone of Loessal soil decreased. It indicated that the cohesion and friction between soil particles decreased, with the increase of soil water content. The higher water content of soil particles decreased the relative sliding resistance between particles, leading to the deterioration of soil structural stability. 3) The elasticity and shear strength of Lou soil was higher than that of Loessal soil. This was mainly because Lou soil contained a higher content of clay, organic matter, cation exchange capacity, and specific surface area than those of Loessal soil, indicating the improved cementation strength between soil particles. Consequently, the rheological parameters from the amplitude scanning test in the rheometer can be used to quantitatively characterize the mechanical stability of soil structure, providing for rich evaluation parameters to further understand the micromechanical properties of soil. The finding can provide a shred of strong scientific evidence for agricultural water and soil engineering design, as well as the prevention and control of landslide and geological disasters in the Loess Plateau.<br/></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:40
Main heading:Aggregates
Controlled terms:Deformation - Deterioration - Elasticity - Friction - Irrigation - Mechanical stability - Shear strength - Shear stress - Slope protection - Slope stability - Soil moisture - Tribology - Viscoelasticity - Water management - Yield stress
Uncontrolled terms:Bulk water - Lou soils - Shear strength parameters - Shears strength - Soil bulk density - Soil particles - Soil structure - Soil-structure - Visco-elastic parameters - Yield points
Classification code:406 Highway Engineering - 406.2 Roads and Streets - 412.2 Concrete Reinforcements - 483.1 Soils and Soil Mechanics - 821.3 Agricultural Methods - 931 Classical Physics; Quantum Theory; Relativity - 931.2 Physical Properties of Gases, Liquids and Solids - 951 Materials Science
Numerical data indexing:Linear density 1.30E-01kg/m
DOI:10.11975/j.issn.1002-6819.2021.19.017

Accession number:20215211376663
Title:Seedling crop row extraction method based on regional growth and mean shift clustering
Title of translation:基于区域生长均值漂移聚类的苗期作物行提取方法
Authors:Wang, Aichen (1, 2); Zhang, Min (1, 2); Liu, Qingshan (1, 2); Wang, Lili (3); Wei, Xinhua (1, 2)
Author affiliation:(1) School of Agricultural Engineering, Jiangsu University, Zhenjiang; 212013, China; (2) Jiangsu Provincial Key Laboratory of Agricultural Equipment and Intelligent High Technology Research, Zhenjiang; 212013, China; (3) State Key Laboratory of Soil Plant Machine System Technology, Chinese Academy of Agricultural Mechanization Sciences, Beijing; 100083, China
Corresponding authors:Wei, Xinhua(wei_xh@126.com); Wei, Xinhua(wei_xh@126.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:202-210
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">Automatic navigation can be used to significantly improve the operation accuracy and efficiency of agricultural machinery. Particularly, machine vision-based automatic navigation can greatly contribute to crop row detection. In this study, a novel crop row extraction was proposed using regional growth and mean-shift clustering, especially for higher accuracy of crop row extraction under different crop types, the number of crop rows, and growing backgrounds. Firstly, the a and b components of an image were obtained in the Lab color space, and then the maximum entropy values of a and b components were calculated for the optimal segmentation threshold, after which the image was segmented by the threshold for the binarization image. Secondly, the vertical projection operation was performed on the top strip of the binary image, where the mean value of the vertical projection curve was calculated to distinguish crop and non-crop areas. The minimum distance between crop areas was selected as the bandwidth of the crop clustering window. The top center pixel of the whole image was selected as the initial center point of the clustering window. The clustering center point moved from the center to both sides of the top of the image with the iteration of crop row clustering, where the shift vector was calculated in the clustering window. The clustering center point moved along the shift vector in single row clustering, where the edge of the clustering window was used as the seed point for regional growth. As such, all crop rows were obtained by the movement of clustering window and regional growth, while, the clustering center points of each crop row were grouped into a cluster. Lastly, least-squares fitting was performed on these clustering center points to obtain crop row lines. A total of 170 seedling images of five crop varieties were obtained to verify the feasibility of the method, including garlic, corn, oilseed rape, rice, and wheat. Hough transform and projection-proximity classification were also used to extract crop rows for comparison. Experimental results showed that more satisfactory performance of segmentation was achieved for the images with less significant color difference between crops and growing background using the maximum entropy of a and b components in the Lab color space, compared with the conventional segmentation using an excess green index. Furthermore, the crop row extraction for tested five crops performed better than that of Hough transform and projection-proximity classification fitting, in terms of row recognition rate, mean error angle, and mean processing time. The mean row recognition rate for the 170 tested images was 98.18%, the mean error angle of extracted straight lines of all crop rows was 1.21°, and the mean processing time for each image was 0.48 s. This finding can provide a more robust for crop row extraction under the influence of multi factors in the field using machine vision, particularly on real-time embedded platforms in practical applications.<br/></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:24
Main heading:Least squares approximations
Controlled terms:Agricultural machinery - Binary images - Color - Color image processing - Colorimetry - Computer vision - Crops - Efficiency - Entropy - Extraction - Feature extraction - Hough transforms - Image segmentation - Iterative methods
Uncontrolled terms:Center points - Clustering centers - Clusterings - Crop rows - Images processing - Least-squares- methods - Machine-vision - Mean shift - Mean-Shift Clustering - Regional growth
Classification code:641.1 Thermodynamics - 723.2 Data Processing and Image Processing - 723.5 Computer Applications - 741.1 Light/Optics - 741.2 Vision - 802.3 Chemical Operations - 821.1 Agricultural Machinery and Equipment - 821.4 Agricultural Products - 913.1 Production Engineering - 921.3 Mathematical Transformations - 921.6 Numerical Methods - 941.4 Optical Variables Measurements
Numerical data indexing:Percentage 9.818E+01%, Time 4.80E-01s
DOI:10.11975/j.issn.1002-6819.2021.19.023

Accession number:20215211376523
Title:Design and experiment of double-storage turntable cotton vertical disc hole seeding and metering device
Title of translation:双仓转盘式棉花竖直圆盘穴播排种器设计与试验
Authors:Zhang, Xuejun (1, 2); Chen, Yong (1); Shi, Zenglu (1, 2); Jin, Wei (1); Zhang, Haitao (1); Fu, Hao (1); Wang, Duijin (3)
Author affiliation:(1) College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi; 830052, China; (2) Xinjiang Key Laboratory of Intelligent Agricultural Equipment, Urumqi; 830052, China; (3) Xinjiang Tiancheng Agricultural Machinery Manufacturing Limited Company, Tiemenguan; 841007, China
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:27-36
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">Current vertical disc hole seed-metering device has been limited to the low seed picking performance and high damage rate in recent years. In this study, a double-chamber turntable and vertical disc-hole seed-metering device was designed to combine the cotton precision hole sowing. First of all, the silo structure of seed picking and seeding was introduced to clarify the different work processes in the double warehouse, namely the completion of seed filling and clearing in the seed taking warehouse, then passing the cotton seed to the seeding warehouse through the warehouse transfer area, finally realizing the whole workflow of seeding in the seeding area. A double-bin separated structure was also designed for seed-carrying to reduce the damage of cotton seed. Secondly, a new equation was established for the time in a single seeding unit, thereby determining the way to take the side lying during precision hole-sowing. Specifically, the diameter of the seed disk was 220 mm, the length, width, and height of the seed hole was 9.2 mm×5.2 mm×4.7 mm, as well as the number of seed holes was 16. The seed tray was adopted the working mode of seeding from one side and different sides, in order to ensure that the seeding chamber was not interfering with the precision of seeding during the seed extraction stage. As such, the placement angles of the seed metering cavity and seed holes were staggered by a certain angle during design. Correspondingly, the cotton seeds were slid into the seeding chamber in time during the warehouse transfer stage, where the damage rate of cotton seeds was reduced significantly. Specifically, the steepest drop line was selected to calculate the falling angle of seeds, where the falling angle was determined to be 48°. Further, a mechanical model for the injury to the seed was established to analyze the force and movement state of cotton seeds in the warehouse transfer area, where the minimum seeding gap was 1.47 mm. As such, the optimal matching parameters of relevant components were determined to reduce the damage rate in the warehouse transfer stage. A mechanical model was also established to clarify the effect of seed hole offset angle and disc rotation speed on the seed picking performance in the seed collection area. Finally, Design Expert 8.0 software was used for the Box Benhnken design, where the rotating speed of the seed disk, the offset angle of the socket hole, and the seeding gap were the influencing factors. Subsequently, a three-factor three-level quadratic regression orthogonal test was carried out to optimize the single grain rate and the broken rate. Experiments show that the primary and secondary influencing factors of single-grain rate in the process of seeding were the offset angle of seed hole, the seeding gap, and the speed of seed disk. The better seeding performance was achieved in the combination of the seed disk rotation speed 23.9 r/min, seed hole offset angle of 31.7°, seed metering gap of 2.08 mm, At this time, the single-grain rate was 96%, and the damage rate was 0.13%. Field experiments were also performed to verify the optimal combination. Correspondingly, the single-grain rate was 94.3% and the breakage rate was 0.09%, fully meeting the agronomic requirement of cotton precision sowing. This finding can provide a strong reference for the structural design of vertical disc hole seeding and metering device for cotton.<br/></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:33
Main heading:Cotton
Controlled terms:Agricultural machinery - Optimization - Seed - Warehouses
Uncontrolled terms:Cotton seeds - Damage rate - Hole seeding device - Metering devices - Offset angle - Optimisations - Precision hole seeding - Precision holes - Seed-metering device - Single-grains
Classification code:694.4 Storage - 821.1 Agricultural Machinery and Equipment - 821.4 Agricultural Products - 921.5 Optimization Techniques
Numerical data indexing:Angular velocity 3.9913E-01rad/s, Percentage 1.30E-01%, Percentage 9.00E-02%, Percentage 9.43E+01%, Percentage 9.60E+01%, Size 1.47E-03m, Size 2.08E-03m, Size 2.20E-01m, Size 4.70E-03m, Size 5.20E-03m, Size 9.20E-03m
DOI:10.11975/j.issn.1002-6819.2021.19.004

Accession number:20215211376658
Title:Optimization of the drying process parameters for lentinus edodes in segment variable temperature infrared assisted spouted bed
Title of translation:香菇分段变温红外喷动床干燥工艺参数优化
Authors:Duan, Xu (1, 2); Xu, Yiming (1); Ren, Guangyue (1, 2); Li, Linlin (1); Hou, Zhiyun (1); Zhao, Mengyue (1)
Author affiliation:(1) College of Food and Biological Engineering, Henan University of Science and Technology, Luoyang; 471023, China; (2) Collaorative Innovation Center of Grain Storage Security, Henan Province, Zhengzhou; 450001, China
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 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">Lentinus edodes are favored by consumers, due mainly to the antitumor, immunoregulation, anti-aging, antioxidation, and anti-radiation. However, the drying process can be used to extend the shelf life, because the fresh lentinus edodes are not easy to store. In this experiment, a new infrared-assisted spouted bed drying equipment was used to investigate the drying process of lentinus edodes under the segment variable temperature. An infrared-assisted spouted bed was utilized to improve the high energy consumption and heat loss of infrared drying with uniform distribution. A single factor Box-Behnken Design was used to optimize the response surface experiment. The parameters included the early wind temperature, water content at conversion point, and late wind temperature on specific power consumption, crude polysaccharide content, brightness value L<sup>*</sup> and shrinkage ratio. The polynomial regression was derived by a weighted comprehensive score to optimize the drying process parameters of an infrared-assisted spouted bed. The results showed that the crude polysaccharide content of lentinus edodes dried by infrared spouted assisted bed gradually increased, with the increase of early wind temperature (A), where the retention rate increased to a maximum of 9.33 mg/g at 60°C. The materials were also heated evenly during drying. The specific power consumption decreased to reach the minimum of 160.95 kJ/g, while the drying time was shortened, with the increase of temperature, when the early wind temperature was 60°C. Brightness value L<sup>*</sup> and shrinkage ratio showed a trend of first increased and then decreased, with the increase of early wind temperature at the maximum of 55°C. Appropriate early wind temperature could effectively reduce the specific power consumption, thereby maintaining high nutrients for better economic benefits. The content of crude polysaccharide increased significantly, where the maximum was 10.44 mg/g at 70°C, particularly when the late wind temperature (C) was 60°C-70°C. Furthermore, there was an obvious downward trend, when the temperature continued to rise. Excessive temperature could damage the retention of nutrients during drying, due mainly to polysaccharide degradation produced oligosaccharides and partial caramel. The drying rate accelerated, but the specific power consumption decreased, with the increase of temperature. In addition, there was the most obvious shrinkage ratio at 75°C, with the maximum yellow/blue value b<sup>*</sup> of 19.93, while the brightness value L<sup>*</sup> value continued to decrease, with the increase of late wind temperature. Correspondingly, there was a significant difference (P<0.05), particularly slightly browning, and yellow color of Lentinula edodes. The specific power consumption decreased gradually, with the increase of water content at conversion point (B). There was also a trend of first increasing and then decreasing in the crude polysaccharide content. The shrinkage ratio and brightness value L<sup>*</sup> increased first and then decreased, with the increase of water content at the conversion point. The single factor interaction was ranked in the order of AC>AB>BC. The optimal drying parameters were achieved using the response surface method, where the early wind temperature was 56.00°C, the water content at the conversion point was 53.00%, and the late wind temperature was 72.00°C. In this case, the specific power consumption was 143.52 kJ/g, the crude polysaccharide content was 9.98 mg/g, the brightness value L<sup>*</sup> value was 68.11, the shrinkage ratio was 83.15%, the comprehensive score value was 35.37, and the fitting degree with the predicted value was 99.27%. Consequently, the infrared-assisted spouted bed drying can widely be expected to fully meet the development trend and demand of lentinus edodes products. The finding can also provide a theoretical foundation for the production and processing of dried lentinus edodes products.<br/></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:39
Main heading:Optimization
Controlled terms:Antioxidants - Drying - Electric power utilization - Nutrients - Shrinkage
Uncontrolled terms:Brightness values - Drying process - Infrared assisted spouted bed drying - Lentinus edodes - Polysaccharide contents - Shrinkage ratio - Specific power consumption - Spouted bed - Spouted bed drying - Wind temperature
Classification code:706.1 Electric Power Systems - 803 Chemical Agents and Basic Industrial Chemicals - 804 Chemical Products Generally - 804.1 Organic Compounds - 804.2 Inorganic Compounds - 921.5 Optimization Techniques - 951 Materials Science
Numerical data indexing:Percentage 5.30E+01%, Percentage 8.315E+01%, Percentage 9.927E+01%, Specific energy 1.4352E+08J/kg, Specific energy 1.6095E+08J/kg, Temperature 3.28E+02K, Temperature 3.29E+02K, Temperature 3.33E+02K, Temperature 3.33E+02K to 3.43E+02K, Temperature 3.43E+02K, Temperature 3.45E+02K, Temperature 3.48E+02K, null 1.044E+01null, null 9.33E+00null, null 9.98E+00null
DOI:10.11975/j.issn.1002-6819.2021.19.034

Accession number:20215211376648
Title:Feasibility of the air source heat pump system for heating swine houses in Beijing
Title of translation:北京猪舍空气源热泵供暖的可行性
Authors:Wang, Hua (1); Yi, Lu (1, 2); Wu, Zhonghong (1); Liu, Jijun (1); Wang, Meizhi (1)
Author affiliation:(1) College of Animal Science and Technology, China Agriculture University, Beijing; 100193, China; (2) People's Government of Miaoyu Town, Wushan County, Chongqing; 404100, China
Corresponding author:Wang, Meizhi(meizhiwang@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:19
Issue date:October 1, 2021
Publication year:2021
Pages:236-242
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">Coal has been widely used in many aspects in northern China. Nevertheless, urgent environmental protection has been required to prohibit the coal combustion for the heating source in winter since 2017. The application of clean energy has been the key issue need to be solved in recent years, particularly on the energy consumption of buildings. Air source heat pump (ASHP) as a piece of clean energy equipment has widely been used in civil and industrial buildings. But there is no application of ASHP in swine houses. It is an urgent need to conserve energy and reduce pollution emissions. In this study, an investigation of ASHP was carried out in the swine houses of Shunyi District, Beijing. The experimental size of the swine houses was 42 m long and 9.3 m wide. The ASHP heating system was installed in a swine barn. There were 6 pipes to supply water, and other 6 pipes to return water for the heating floor. The monitoring period was selected for the equipment performance: November 20th, 2016-April 16th, 2017. The first stage (January 3rd, 2017-January 8th, 2017): swine houses with pig test (two units). The second stage (January 9th, 2017-January 20th, 2017): swine houses with no pig test. The energy-saving rate of ASHP and direct electric heating systems were compared to measure the Coefficient of Performance (COP). ASHP, direct electric heating, Liquefied Natural Gas (LNG), and natural gas heating were compared by the unit energy operating price. The average supply water and return water temperature of ASHP was also measured. The results showed that the COP of the ASHP system was 2.86 when the average outdoor temperature was 0.1 ℃ during the heating period. The energy-saving rate of ASHP was 66%, compared with direct electric heating. The unit energy operating prices of ASHP, LNG, Natural Gas, direct electric, and ASHP were 0.62, 0.34, 0.37, and 0.64 yuan/ kW•h, respectively. The CO<inf>2</inf> emission was reduced by 8 636 kg during the experiment. The average supply water temperature and return water temperature of ASHP were (37.6±2.4) and (34.9±2.2) ℃, respectively. ASHP heating system presented the potential to reduce energy consumption and CO<inf>2</inf> emission, serving as an economical and clean alternative energy source for pig barns heating. Consequently, the air source heat pump system is suitable for swine houses with solid floors, but not with fully slatted floors.<br/></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:30
Main heading:Space heating
Controlled terms:Air source heat pumps - Ammonia - Carbon dioxide - Coal - Coal combustion - Costs - Emission control - Energy conservation - Energy utilization - Floors - Gas emissions - Geothermal heat pumps - Greenhouse gases - Heating equipment - Houses - Mammals - Natural gas - Ventilation
Uncontrolled terms:Air-source heat pump systems - Air-source heat pumps - Clean energy - Coefficient of Performance - Energy-saving and emission reductions - Heating system - Indoor temperature - Pig house - Return water - Supply waters
Classification code:402 Buildings and Towers - 402.3 Residences - 451.1 Air Pollution Sources - 451.2 Air Pollution Control - 521 Fuel Combustion and Flame Research - 522 Gas Fuels - 524 Solid Fuels - 525.2 Energy Conservation - 525.3 Energy Utilization - 616.1 Heat Exchange Equipment and Components - 641.2 Heat Transfer - 643.1 Space Heating - 643.5 Ventilation - 804.2 Inorganic Compounds - 911 Cost and Value Engineering; Industrial Economics
Numerical data indexing:Mass 6.36E+02kg, Percentage 6.60E+01%, Size 4.20E+01m, Size 9.30E+00m
DOI:10.11975/j.issn.1002-6819.2021.19.027

Accession number:20215211376471
Title:Thermal performance analysis and parameter optimization of a tractor exhaust waste heat plate-fin evaporator
Title of translation:拖拉机排气余热板翅式蒸发器热力性能分析与参数优化
Authors:Tu, Ming (1, 2); Zhang, Guotao (1, 3); Xia, Chen (1); Hu, Dawei (4); Zeng, Rong (1, 2); Zhou, Yong (1, 2)
Author affiliation:(1) College of Engineering, Huazhong Agricultural University, Wuhan; 430070, China; (2) Key Laboratory of Agricultural Equipment in Mid-lower Reaches of the Yangtze River, Ministry of Agriculture and Rural Affairs, Wuhan; 430070, China; (3) School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan; 430074, China; (4) Department of Mechanical Engineering, Michigan State University, East Lansing; 48824, United States
Corresponding authors:Zhou, Yong(zhyong@mail.hzau.edu.cn); Zhou, Yong(zhyong@mail.hzau.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:7-17
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:<div data-language="eng" data-ev-field="abstract">The fuel efficiency of the engine is only 15%-35% while the tractor is working in the field, and the exhaust energy accounts for 38%-45% of the energy released by the fuel. The recovery and reuse of exhaust heat energy could help improve fuel efficiency and reduce emissions. Studies have shown that the exhaust waste heat energy based on the Organic Rankine Cycle (ORC) is the highest. The evaporator is a key component of the ORC system, analyzing its thermal performance under limited space conditions of the tractor could provide a theoretical basis for the optimal design of evaporator parameters, thereby effectively improving the utilization of exhaust heat. This study according to the actual size of the tractor, a plate-fin evaporator was trial-produced to recover diesel exhaust waste heat. A numerical model of convective heat transfer between evaporator exhaust and working fluid based on moving boundary method was established and was verified the validity by combining with bench test data, the thermal performance of the evaporator under full operating conditions of the diesel engine was quantitatively analyzed; meanwhile in order to improve the heat transfer and scope of application of the evaporator, CFD simulation and BP neural network methods were used to further analyze the heat transfer characteristics of the evaporator under off-design conditions, the structure and working fluid parameters were optimized. The results showed that: 1) the evaporator had better thermal performance under medium and high speed load conditions, and the heat transfer reached a maximum of 69.89 kW under 4 000 r/min full load conditions, and the heat transfer of the evaporator would be unstable under medium and low speed load conditions due to the lower exhaust heat capacity flow rate, heat transfer coefficient, and a larger working fluid mass flow rate, resulting in the flow was difficult to ensure that the working fluid was transformed into superheated steam, so that the heat transfer in the two-phase zone and the superheat zone was zero within the evaporator. 2) in order to improve the distribution and turbulence of the fluid in the flow channel, increasing the pipe chamfer and adopting the corrugated fin shape to promote forced heat exchange, the CFD simulation showed the entire high-temperature area moved forward to the inlet of the nozzle to make the flow channel utilization rate higher and heat transfer more. With the optimized structure of the evaporator, the working fluid had a higher degree of overheating under the condition of the same overall size, the maximum heat transfer increased by 5.2%, the heat transfer area increased by 0.19 m<sup>2</sup>, and the volume only increased by 0.002 m<sup>3</sup>. 3) combined with the BP neural network algorithm, the evaporator flow channel length, working fluid flow and inlet temperature were optimized parameters, and the thermal performance of the evaporator under off-design working conditions was further analyzed, and the parameter range under the medium and low speed load conditions is determined. Thus, the selection range of the working fluid flow rate at different speeds was proposed, which effectively improving the thermal performance of the evaporator under low-to-medium speed load conditions, and providing a reference for the selection of the transmission ratio of the booster pump and the output speed of the diesel engine and the selection of the transmission device. For example, when 1 500 r/min was under a medium and high load, the flow rate could be changed from 0.03 kg/s to 0.08 kg/s and the maximum heat transfer up to 19.46 kW; at the same time, the transmission ratio could be set to 0.78-1.88 at 1 500 r/min. The results of the study are of great significance and present the fluid flow and heat transfer characteristics of the evaporator, which provide a reference for the actual use of the evaporator in tractors and matching with diesel engine operating conditions.<br/></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:29
Main heading:Waste heat
Controlled terms:Channel flow - Computational fluid dynamics - Diesel engines - Efficiency - Evaporation - Evaporators - Fins (heat exchange) - Fuels - Heat convection - Heat transfer coefficients - Neural networks - Nozzles - Numerical methods - Optimization - Rankine cycle - Specific heat - Tractors (truck) - Turbulence models - Waste heat utilization
Uncontrolled terms:Exhaust heat - Exhaust waste heat - Flow channels - Fuel efficiency - Load condition - Optimisations - Plate fins - Thermal Performance - Tractor - Working fluid
Classification code:525.3 Energy Utilization - 525.4 Energy Losses (industrial and residential) - 612.2 Diesel Engines - 616.1 Heat Exchange Equipment and Components - 631.1 Fluid Flow, General - 641.1 Thermodynamics - 641.2 Heat Transfer - 663.1 Heavy Duty Motor Vehicles - 723.5 Computer Applications - 802.1 Chemical Plants and Equipment - 802.3 Chemical Operations - 913.1 Production Engineering - 921.5 Optimization Techniques - 921.6 Numerical Methods - 931.1 Mechanics
Numerical data indexing:Angular velocity 0.00E00rad/s, Angular velocity 8.35E+00rad/s, Mass flow rate 3.00E-02kg/s to 8.00E-02kg/s, Percentage 1.50E+01% to 3.50E+01%, Percentage 3.80E+01% to 4.50E+01%, Percentage 5.20E+00%, Power 1.946E+04W, Power 6.989E+04W, Size 1.90E-01m, Size 2.00E-03m
DOI:10.11975/j.issn.1002-6819.2021.19.002

Accession number:20215211376554
Title:Coupling scheme optimization of Panax notoginseng considering yield, quality and water-fertilizer use efficiency
Title of translation:基于产量品质及水肥利用效率的三七水肥耦合方案优选
Authors:Liu, Yanwei (1); Zhou, Xiao (1); Han, Huanhao (1); Yang, Qiliang (1); Liu, Xiaogang (1)
Author affiliation:(1) Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming; 650500, China
Corresponding author:Yang, Qiliang(yangqilianglovena@163.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:139-146
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">Excessive irrigation and fertilization in the traditional planting of Panax notoginseng have often caused the direct waste of water and fertilizer resources, high incidence of diseases, the decline in quality and yield, even environmental pollution. These also limit the sustainable development of Panax notoginseng planting in modern agriculture. In this study, an optimal coupling scheme of water and fertilizer was therefore proposed to realize green production of Panax notoginseng using yield, quality, and use efficiency. Three irrigation levels (low water W1:0.5 of field capacity, medium water W2:0.7 of field capacity, high water W3:0.9 of field capacity) and four fertilization levels (Annual fertilizer application was 1 440 kg/hm<sup>2</sup>. According to the different fertilization ratios in each breeding period, set as F1 (the ratio of seedling period: flowering period: fruiting period: root weight gaining period is 25%: 25%: 25%: 25%), F2 (25%: 30%: 25%: 20%), F3 (30%: 30%: 25%: 15%) and F4 (40%: 20%: 30%: 10%) were set up with two-year-old Panax notoginseng as experiment materials in a field experiment. Each treatment was performed on two field blocks, each of which was about 15 m long and 2 m wide. The surface of the block was covered with 5mm thick pine needles, where the blocks were separated by plastic films buried underground to prevent the cross penetration of water and fertilizer. An analysis was made to clarify the effects of water and fertilizers, and their coupling effects on the yield, quality, and Water Use Efficiency (WUE) and Partial Factor Productivity of Fertilizer (PFP) of Panax notoginseng. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was also used to evaluate the comprehensive quality of saponins content. A scoring method was applied to evaluate the comprehensive performance in the growth and management model of each treatment. The results showed that the appropriate increases in the water stress and fertilizer during the seedling and fruit periods were beneficial to improve the yield, the content of notoginseng saponins, but reduce the incidence of disease. Nevertheless, the low-water treatment was not conducive to the yield, whereas, high-water treatment was not conducive to the quality of Panax notoginseng. More importantly, the greatest impact of irrigation presented on the WUE during the growth period, while the fertilization in different proportions performed the greatest impact on PFP. The effects of Panax notoginseng quality were ranked in the order of water and fertilizer coupling, moisture, as well as fertilizer. Additionally, the accumulation of notoginsenoside R1, ginsenoside Rg1, ginsenoside Re and ginsenoside Rd treated with W2F4 were significantly higher than others among the five saponins. Specifically, the content of notoginsenoside R1 treated with W2F4 was the highest, while the content of ginsenoside Rb1 treated with W1F4 was the highest. In comprehensive scoring, the W2F4 treatment presented the highest comprehensive score, where the incidence rate was 12.97 %, the yield was 2 976.42 kg/hm<sup>2</sup>, WUE was 1.65 kg/m<sup>3</sup>, and PFP was 1.09 kg/kg. The W2F4 treatment also scored the highest of 0.815 in the TOPSIS quality analysis. Consequently, the optimal mode of coupling between variable fertilization at different growth stages and irrigation was achieved, where the irrigation level of 0.5 FC, the fertilization ratio in the seedling, flowering, fruiting, and root gaining period of 40%: 20%: 30%: 10%. The finding can provide a strong theoretical basis and technical reference for the formulation of irrigation and fertilization system during Panax notoginseng planting.<br/></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:34
Main heading:Irrigation
Controlled terms:Efficiency - Fertilizers - Fruits - Productivity - Quality control - Sustainable development - Water pollution - Water supply
Uncontrolled terms:Fertilisation - Field capacity - Panax notoginseng - Partial factor productivity - Partial factor productivity of fertilizer - Plantings - Water and fertilizer coupling - Water use efficiency - Yield - Yield quality
Classification code:446.1 Water Supply Systems - 453 Water Pollution - 804 Chemical Products Generally - 821.2 Agricultural Chemicals - 821.3 Agricultural Methods - 821.4 Agricultural Products - 913.1 Production Engineering - 913.3 Quality Assurance and Control
Numerical data indexing:Linear density 1.65E+00kg/m, Mass 1.09E+00kg, Mass 4.40E+02kg, Mass 9.7642E+02kg, Percentage 1.00E+01%, Percentage 1.297E+01%, Percentage 1.50E+01%, Percentage 2.00E+01%, Percentage 2.50E+01%, Percentage 3.00E+01%, Percentage 4.00E+01%, Size 1.50E+01m, Size 2.00E+00m, Size 2.0701E-02m, Size 5.00E-03m
DOI:10.11975/j.issn.1002-6819.2021.19.016

Accession number:20215211376341
Title:Algorithm for the uniform petal carving of Hami melon based on three-dimensional reconstruction
Title of translation:基于三维重构的哈蜜瓜均瓣雕花算法
Authors:Zhao, Mingyan (1); Lin, Min (1); Xu, Peng (2); Wang, Yongjin (1); Song, Tianyue (1); Liang, Mingxuan (1); Hu, Jianhong (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:19
Issue date:October 1, 2021
Publication year:2021
Pages:276-283
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">Higher carving speed and uniform petal size of Hami melon are critical for the robot carving Hami melon. It is very necessary to plan the cutting path of the execution terminal (carving knife) in real-time, according to the three-dimensional coordinates of different processing objects. In this study, a uniform petal carving of Hami melon was proposed using point cloud splicing. The image features were extracted and reconstructed sparsely. The feature parameters of melon were firstly obtained by point cloud coordinates. Secondly, CMVS/PMVS algorithm was selected for dense reconstruction using the sparse points. Finally, the octree and Poisson surface reconstruction were used to obtain the accurate 3D spatial coordinates of melon. Different shapes of Hami melon was led to different reconstructions. Each piece of flesh presented the same volume after carving. The specific procedure was as follows. Firstly, the cutting height and depth of melon were determined to extract the point cloud. An arc function was then fitted to determine the center of the circle, according to the point cloud of the outermost circle of Hami melon. The number of carving petals was divided 360° to determine the pre carving start point, end point, and path. Specifically, the initial triangle was formed to search for the two closest points from any point in the numerous point clouds as the benchmark, and then to expand the triangle outward with the three sides of the triangle as the baseline, where the equal volume of each petal was taken as the objective function, while the equal cutting depth and cutting angle of each petal as the limiting conditions. Until all the point clouds were included in the three-dimensional triangle network, the area of the projected triangle was calculated by the Helen formula, where the average value for the Z coordinates of three projected points was taken as the height, and then to calculate the volume of the triangular pyramid. After depth-first and particle swarm optimization, the optimal solution was found in the coordinates of Hami melon point cloud through continuous recursive iteration. Finally, better cloud coordinates were stored as new datasets and then marked on the outside of Hami melon. As such, the manipulator was controlled to evenly carve the Hami melon. Specifically, the cutter first adjusted to the appropriate posture angle as posture point 2, then moved along the cutter ridge to a certain depth to posture point 1, and retreated to posture point 3, and finally, the cutter moved along the outer surface of Hami melon to the next adjacent posture point 2. These steps were repeated to complete the overall carving of the Hami melon. The regular and irregular models were also selected to verify the accuracy. The calculated volumes of cube, pyramid, and irregular body were compared with the real. 48 Hami melons (16 groups, 3 in each group) were divided, where the number of carved petals was 15-30, and the carving depth was 1.5, 2.0, and 2.5 cm. It was found that the precision of the group was the lowest with the number of cut petals N equal to 28. The maximum and minimum petal volumes were measured as 3.40 and 3.25 cm<sup>3</sup>, respectively, where the maximum volume difference was 0.15 cm<sup>3</sup>, and the error was less than 5%. Consequently, the melon petal carving using point cloud splicing presented a higher precision than before. The findings can provide strong technical support for robot carving Hami melon.<br/></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:30
Main heading:Particle swarm optimization (PSO)
Controlled terms:Cutting - Image reconstruction - Iterative methods - Surface reconstruction - Three dimensional computer graphics
Uncontrolled terms:3D reconstruction - Cutting depth - Cutting paths - Images processing - Point cloud splicing - Point-clouds - Real- time - Three-dimensional reconstruction - Triangular meshing - Uniform petal carving
Classification code:723 Computer Software, Data Handling and Applications - 723.2 Data Processing and Image Processing - 723.5 Computer Applications - 921.5 Optimization Techniques - 921.6 Numerical Methods
Numerical data indexing:Percentage 5.00E+00%, Size 1.50E-03m, Size 2.50E-02m, Size 3.25E-02m, Size 3.40E-02m, Size 7.62E-02m
DOI:10.11975/j.issn.1002-6819.2021.19.032

Accession number:20215211376393
Title:Prediction of tea drying moisture content based on PSO Elman algorithm
Title of translation:基于PSO-Elman算法的茶叶烘干含水率预测
Authors:Zhao, Liqing (1); Duan, Dongyao (1); Yin, Yuanyuan (1); Zheng, Yinghui (1); Xu, Xin (1); Sun, Ying (1); Xue, Yiwei (1)
Author affiliation:(1) College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao; 266109, China
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:284-292
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">Moisture content is critical in the process of tea hot air drying. Taking green tea as an example, an experiment was performed on the dynamic hot air drying of rolled tea, in order to monitor the dynamic change of moisture content of tea with drying time under different feeding amounts (800-1 200 g), drying temperatures (90-120 ℃) and drum speeds (20-30 r/min). Each significant factor was analyzed to explore the dynamic changes of the water content of tea under different drying conditions. The experimental results show that there were significant effects of temperature, rotational speed, and feeding rate on the drying of tea leaves. The influence was sorted in the descending order of temperature, feeding rate, and rotating speed. Among them, the temperature has posed the greatest influence on drying. In the feeding amount, it was appropriate to cover the drum wall with tea to form a perfect casting curtain. That was because too much feeding amount easily caused uneven heating of tea, and then appeared dry outside and wet inside, even focal point explosion. The decreasing rate of water content in tea leaves showed a trend of first increased and then decreased in the whole drying. As such, the water loss was less at the lower water content, and finally, the water change tended to be gentle. The water content of tea leaves was basically stable at 4%-5% at the end of drying, particularly for convenient transportation and preservation. A prediction experiment was carried out, where the water content of tea drying was taken as the output, while the structure parameters of the dryer, drying temperature, drum speed, drying initial water, and prediction time as the input. BP, Elman, and PARTICLE swarm optimization Elman neural network (PSO Elman) neural network were used to establish the dynamic prediction model of tea moisture content during drying. A comparison was also made on the traditional multiple linear regression fitting model. The results of verification and error analysis of the Linear fit, BP neural network, Elman neural network and PSO-Elman neural network models showed that their determination coefficients were 0.960 9, 0.998 0, 0.998 5, and 0.999 4, respectively. Compared with the traditional linear regression, the neural network was more accurately expressed the linear or nonlinear relationship in the complex system, showing better prediction for the tea drying. In three neural network models, the PSO-Elman model was more accurate than BP and Elman model, indicating better prediction on the change of water content during tea drying. The findings can provide a strong theoretical basis for the hot air drying of tea, therebyguiding tea processing and production for high efficiency and tea quality.<br/></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:31
Main heading:Neural networks
Controlled terms:Drying - Feeding - Forecasting - Linear regression - Moisture - Moisture determination - Particle swarm optimization (PSO) - Temperature
Uncontrolled terms:%moisture - Drum speed - Drying temperature - Dynamic changes - Feeding amount - Hot air drying - Neural-networks - Prediction modelling - Swarm optimization - Tea-leaves
Classification code:641.1 Thermodynamics - 691.2 Materials Handling Methods - 723 Computer Software, Data Handling and Applications - 921.5 Optimization Techniques - 922.2 Mathematical Statistics - 944.2 Moisture Measurements
Numerical data indexing:Angular velocity 3.34E-01rad/s to 5.01E-01rad/s, Mass 2.00E-01kg, Percentage 4.00E+00% to 5.00E+00%
DOI:10.11975/j.issn.1002-6819.2021.19.033

Accession number:20215211376510
Title:Effects of Vetiveria zizanioides hedgerow on the erosion of purple soil of slope land in the Three Gorges Reservoir Area of China
Title of translation:香根草植物篱对三峡库区坡地紫色土侵蚀的影响
Authors:Guo, Ping (1); Xia, Zhenyao (1, 2); Gao, Feng (1); Hu, Huan (1); Zhang, Qianheng (1); Yang, Yueshu (1, 2); Xiao, Hai (1, 2)
Author affiliation:(1) Key Laboratory of Geological Hazards on Three Gorges Reservoir Area (China Three Gorges University), Ministry of Education, Yichang; 443002, China; (2) Engineering Research Center of Eco-environment in Three Gorges Reservoir Region, Ministry of Education, China Three Gorges University, Yichang; 443002, China
Corresponding authors:Xiao, Hai(oceanshawctgu@163.com); Xiao, Hai(oceanshawctgu@163.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:105-112
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">Purple soil erosion of slope land has posed a serious threat to the operation safety of the Three Gorges Project in recent years. Fortunately, the hedgerow has widely been one of the most important measures for soil and water conservation on slope land. Most studies have also demonstrated that the hedgerows can effectively reduce the runoff and sediment of the slope. However, the specific reduction was still lacking on the aboveground and underground part of hedgerows. Taking the purple soil of slope land in the Three Gorges Reservoir Area as the research object, this study aims to clarify the effects of Vetiveriazizanioides L. hedgerow on the erosion of purple soil. Simulated rainfall experiments were also conducted at 2 slope gradients (15º and 25º), 2 rainfall intensities (60 and 120 mm/h), and 3 slope conditions (CK-Control check, P-Hedgerow, and R-Only hedgerow roots). The specific characteristics were analyzed for the initial runoff time, runoff, and sediment with rainfall duration under different slope conditions. The results indicated that the average contribution rates were 48.28% and 51.72% of the aboveground and underground parts to the increase of initial runoff time, respectively. The runoff production showed a trend of first increasing and then fluctuating, finally stable under different rainfall intensity and slope conditions. In sediment yield, the erosion rate of slope always kept a fluctuating and stable trend under the condition of P slope, whereas, there was a slow increase at first and a rapid increase under the condition of CK and R slope. The runoff reduction efficiency of hedgerows ranged from 11.20% to 26.19%, while the sediment reduction efficiency of hedgerows ranged from 71.54% to 83.63%. The average benefits of sediment reduction, underground and aboveground parts were 75.59%, 29.45%, and 46.13%, respectively, which were 4.79, 4.60, and 4.92 times of the average. The average contribution rates were 62.25% and 37.75% for the aboveground and underground part of hedgerows to the runoff reduction, respectively, while the average contribution rates were 60.44% and 39.56% to the sediment reduction, respectively. Correspondingly, the hedgerows greatly contributed to delay the runoff generation, where the contribution rates were not significant for the aboveground and underground part of hedgerows to the initial time of runoff generation. Meanwhile, the hedgerows can be expected to effectively reduce the runoff rate and erosion rate, where the effect of the aboveground part was greater than that of the underground part. Consequently, there was a more obvious effect of hedgerows on sediment reduction. More importantly, the effect of the aboveground part on runoff and sediment reduction was greater than that of the underground part. Anyway, Vetiveriazizanioides L. hedgerow can widely be expected to prevent the erosion process of purple soil in slope land. The finding can also provide a scientific basis for the prevention and control of soil and water loss on purple soil slopes in the Three Gorges Reservoir Area.<br/></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:39
Main heading:Soils
Controlled terms:Efficiency - Erosion - Rain - Reservoirs (water) - Runoff - Sediments - Soil conservation - Water conservation - Wooden fences
Uncontrolled terms:Aboveground part - Contribution rate - Purple soils - Reservoir area - Runoff and sediment reduction benefit - Runoff and sediments - Runoff reduction - Sediment reduction - Three Gorge reservoir - Underground part
Classification code:415.3 Wood Structural Materials - 441.2 Reservoirs - 442.1 Flood Control - 443.3 Precipitation - 444 Water Resources - 444.1 Surface Water - 483 Soil Mechanics and Foundations - 483.1 Soils and Soil Mechanics - 913.1 Production Engineering
Numerical data indexing:Size 1.20E-01m, Size 6.00E-02m, Percentage 1.12E+01% to 2.619E+01%, Percentage 2.945E+01%, Percentage 3.775E+01%, Percentage 3.956E+01%, Percentage 4.613E+01%, Percentage 4.828E+01%, Percentage 5.172E+01%, Percentage 6.044E+01%, Percentage 6.225E+01%, Percentage 7.154E+01% to 8.363E+01%, Percentage 7.559E+01%
DOI:10.11975/j.issn.1002-6819.2021.19.012

Accession number:20215211376252
Title:Monitoring and influencing factors of dune movement speed along the Yellow River using UAV technology
Title of translation:基于无人机技术黄河沿岸沙丘移动速度监测及影响因素分析
Authors:Li, Jinrong (1); Wang, Jian (1); Wang, Ru (2); Guo, Jianying (1); Luo, Xiangying (2); Li, Yingkun (2); Cui, Wanxin (2)
Author affiliation:(1) Yinshanbeilu Grassland Eco-hydrological National Observation and Research Station, China Institute of Water Resources and Hydropower Research, Beijing; 100038, China; (2) Institute of Desert Control Science and Engineering, Inner Mongolia Agricultural University, Hohhot; 010018, China
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:37
Issue:19
Issue date:October 1, 2021
Publication year:2021
Pages:57-64
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 highly urgent need has been concerned to more conveniently monitor the moving speed of dunes along the Yellow River in the Ulan Buh Desert. Taking the coastal dune in Ulan Buh Desert as the research object, the seasonal geomorphological process and influencing factors were investigated using image data acquisition through UAV in this study. Results showed that: 1) The annual movement speed of dune was 1.08-2.27 m/a, the multi-year average sand Drift Potential (DP) was 78.82 VU, and the annual Resultant Drift Potential (RDP) was 25.92 VU. It indicated that the study area was in a low wind energy environment. The 8-12 m/s level of sand DP accounted for 73.24% of annual sand DP. The intermediate directional wind variability (i.e. RDP/DP) was around 0.30-0.46. The average Resultant Drift Direction (RDD) was 57.83°-107.39°, which was consistent with the dune movement direction. The westerly wind contributed 52.09% of the variation of the annual sand DP, indicating the main driving force for the annual movement of dunes. 2) The dune movement speed presented obvious seasonal characteristics. Specifically, the fastest was observed in spring, followed by late winter to early spring, and the slowest in summer. In late autumn to late winter, spring and autumn, the sand DP was around 8.48-20.49 VU, the RDD was 90.02°-95.54°, and the RDP/DP was 0.3-0.8, indicating an intermediate rate of directional wind variability. The westerly wind presented a significant effect on dune movement, particularly compatible with the annual RDD and dune movement direction. In late winter to early spring and late spring to summer, the dune with a relatively low movement speed usually adapted to the wind direction through morphological changes under the northeasterly wind (NE) and southerly wind (SSE, S, and SSW). The seasonal sand DP was mainly composed of 8-10 m/s level wind speed, accounting for 40.76%-56.93% of the whole season. 3) Overall, the seasonal and inter-annual sand DP was linearly positively correlated with dune movement distance. The fitting equation was y=1.02+0.006 62x (R<sup>2</sup>=0.339, F=5.616, P=0.045). The fitting equation reached a significant level, indicating that the sand DP can be used to characterize the dune movement distance in the area. The comprehensive landscape was achieved for the dune movement monitored by UVA. Consequently, the wind speed above 8 m/s in the Westerly Group was the main driving force for dune movement in the study area. When the wind direction variability and the RDP were consistent with the movement direction of dunes, the dunes moved faster, otherwise, the dunes moved slowly. Anyway, UAVs can provide more convenient monitoring services for the movement of dunes on a larger scale. This finding can also provide a strong reference for UVA monitoring of dune movement in similar areas.<br/></div> © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:41
Main heading:Landforms
Controlled terms:Antennas - Data acquisition - Sand - Speed - Unmanned aerial vehicles (UAV) - Wind - Wind power
Uncontrolled terms:Drift direction - Drift potential - Dune movement - Movement speed - Sand drift - Sand-driving wind - Study areas - Ulan buh desert - Wind variabilities - Yellow river
Classification code:443.1 Atmospheric Properties - 481.1 Geology - 483.1 Soils and Soil Mechanics - 615.8 Wind Power (Before 1993, use code 611 ) - 652.1 Aircraft, General - 723.2 Data Processing and Image Processing
Numerical data indexing:Percentage 4.076E+01% to 5.693E+01%, Percentage 5.209E+01%, Percentage 7.324E+01%, Size 1.08E+00m to 2.27E+00m, Velocity 8.00E+00m/s to 1.00E+01m/s, Velocity 8.00E+00m/s to 1.20E+01m/s, Velocity 8.00E+00m/s
DOI:10.11975/j.issn.1002-6819.2021.19.007

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