2024年第8期共收录43篇
1. Yield Estimation of Wheat Lines Based on UAV Hyperspectral Remote Sensing and Machine Learning
Accession number: 20243016769497
Title of translation: 基于无人机高光谱遥感与机器学习的小麦品系产量估测研究
Authors: Qi, Hao (1); Lu, Liangjie (2); Sun, Haifang (1); Li, Si (1); Li, Tiantian (1); Hou, Liang (1)
Author affiliation: (1) Institute of Agricultural Information and Economics, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang; 050051, China; (2) Institute of Cereal and Oil Crops, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang; 050035, China
Corresponding author: Hou, Liang(giantark@hotmail.com)
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 7
Issue date: July 2024
Publication year: 2024
Pages: 260-269
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: Rapid and accurate estimation of wheat yield can improve the efficiency of breeding. Yield data of wheat lines and hyperspectral data during grain filling period were collected. Firstly, the feature wavelengths were selected as model input variables by using recursive feature elimination method. Then three linear algorithms (ridge regression, partial least squares regression, multiple linear regression) and six nonlinear algorithms ( random forest, gradient boosting regression, eXtreme gradient boosting, Gaussian process regression, support vector regression, K-nearest neighbor) were employed to establish single algorithm yield estimation models for precision comparison. Finally, the Stacking algorithm was adopted to develop multi-model ensemble combinations, aiming to identify the optimal ensemble model. The results showed that the accuracy of yield estimation models, based on different algorithms, varied significantly, and that the nonlinear models were better than the linear models. The yield estimation model based on GBR performed best in the single models, with R of 0. 72, RMSE of 534. 49 kg/hm and NRMSE of 11. 10% in the training set, R2 of 0. 60, RMSE of 628. 73 kg/hm2, and NRMSE of 13. 88% in the testing set. The performance of the ensemble models based on Stacking algorithm was closely related to the selection of primary and secondary models. The model with KNN , RR, SVR as primary models and GBR as the secondary model effectively improved the yield estimation accuracy. Compared with the single model GBR, the training set R was increased by 1. 39% and the testing set R was increased by 3. 33%. The research result can provide an application reference for yield estimation of wheat lines based on hyperspectral technology. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 46
Main heading: Unmanned aerial vehicles (UAV)
Controlled terms: Forestry? - ?Least squares approximations? - ?Multiple linear regression? - ?Nearest neighbor search? - ?Remote sensing? - ?Support vector machines
Uncontrolled terms: Estimation models? - ?Gradient boosting? - ?HyperSpectral? - ?Machine-learning? - ?Model-based OPC? - ?Remote-sensing? - ?Stacking algorithms? - ?UAV hyperspectral? - ?Wheat line? - ?Yield estimation
Classification code: 652.1 Aircraft, General? - ?723 Computer Software, Data Handling and Applications? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?921.5 Optimization Techniques? - ?921.6 Numerical Methods? - ?922.2 Mathematical Statistics
Numerical data indexing: Mass 4.90E+01kg, Mass 7.30E+01kg, Percentage 1.00E+01%, Percentage 3.30E+01%, Percentage 3.90E+01%, Percentage 8.80E+01%
DOI: 10.6041/j.issn.1000-1298.2024.07.025
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
2. Accurate Detection and Localization Method of Citrus Targets in Complex Environments Based on Improved YOLO v5
Accession number: 20243516947196
Title of translation: 基于改进 YOLO v5 的复杂环境下柑橘目标 精准检测与定位方法
Authors: Li, Li (1, 2); Liang, Jiyuan (1, 2); Zhang, Yunfeng (1, 2); Zhang, Guanming (1, 2); Chun, Changpin (3)
Author affiliation: (1) College of Engineering and Technology, Southwest University, Chongqing; 400715, China; (2) Chongqing Key Laboratory of Agricultural Equipment for Hilly and Mountainous Regions, Chongqing; 400715, China; (3) Citrus Research Institute, Southwest University, Chinese Academy of Agricultural Sciences, Chongqing; 400700, China
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 8
Issue date: August 2024
Publication year: 2024
Pages: 280-290
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: Aiming at the challenges of mechanized citrus fruit harvesting in natural environments, such as complex environments and diverse fruit states, a multi-channel information fusion network (YOLO v5-citrus) was developed, to solve the problems of low accuracy of citrus fruit recognition, fuzzy fruit classification and low accuracy of localization. Different citrus targets were categorized into “ pickable” and “ hard-to-pick” by different occlusion conditions, and this classification strategy guided the robot to pick them sequentially in a real orchard, which improved the picking rate and reduced the damage rate of the robot body and end-effector. In YOLO v5-citrus, a multichannel information fusion module was inserted into the neck network to process the depth feature information of citrus to improve the recognition accuracy of the citrus picking state. At the same time, the splicing method of the neck network was modified to recognize the size of the target citrus. The clustering algorithm module was embedded in the recognition part after training to make the final distinction between the citrus targets blurred by the recognition in the training part. Pixel alignment of a depth image and a color image was performed after recognition and 3D coordinates of citrus targets were obtained by coordinate system transformation. In the dataset processed using multiple enhancement techniques, YOLO v5 - citrus improved mAP and precsion by 2. 8 percentage points and 3. 7 percentage points, respectively, compared with the original YOLO v5, respectively. It maintained higher detection accuracy and faster detection speed than other mainstream network architectures such as YOLO v7 and YOLO v8. Through the detection and localization test in the real orchard, the localization error of the 3D coordinate recognition localization system for the citrus target was obtained as (1. 97 mm, 0. 36 mm, 9. 63 mm), which satisfied the grasping condition of the end-effector. The experimental results showed that the model had strong robustness, meeting the requirements of citrus state recognition in complex environments, and can provide technical support for mechanical harvesting equipment in large-field citrus orchards. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 25
Main heading: Orchards
Controlled terms: Agricultural robots? - ?Citrus fruits? - ?Color image processing? - ?End effectors? - ?Industrial robots
Uncontrolled terms: 3d coordinate acquisition? - ?3D coordinates? - ?Citrus picking robot? - ?Complex environments? - ?Detection and localization? - ?Multi channel? - ?Objects detection? - ?Picking robot? - ?State differentiation? - ?YOLO v5
Classification code: 1106.3.1 ? - ?731.5 Robotics? - ?731.6 Robot Applications? - ?821.2 Agricultural Chemicals? - ?821.4 Agricultural Products? - ?821.5 Agricultural Wastes
Numerical data indexing: Size 3.60E-02m, Size 6.30E-02m, Size 9.70E-02m
DOI: 10.6041/j.issn.1000-1298.2024.08.025
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
3. Rice Disease Recognition in Natural Environment Based on RDN YOLO
Accession number: 20243516956056
Title of translation: 基于RDN-YOLO的自然环境下水稻病害识别模型研究
Authors: Liao, Juan (1); Liu, Kaixuan (1); Yang, Yuqing (1); Yan, Congkuan (1); Zhang, Aifang (2); Zhu, Dequan (1)
Author affiliation: (1) School of Engineering, Anhui Agricultural University, Hefei; 230036, China; (2) Institute of Plant Protection and Agricultural Product Quality and Safety, Anhui Academy of Agricultural Sciences, Hefei; 230031, China
Corresponding author: Zhu, Dequan(zhudequan@ahau.edu.cn)
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 8
Issue date: August 2024
Publication year: 2024
Pages: 233-242
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: Rice diseases such as brown spot, white leaf blight, bacterial blight and rice blast occur frequently during rice development stages, causing serious losses in rice production. Aiming at the challenges in accurately identifying rice diseases under natural conditions, where background is complex, and differences between disease classes are subtle, a rice disease detection network model (RDN - YOLO) was proposed to improve the accuracy of rice disease detection. Firstly, the YOLO v5 network was used as the basic framework, and the C2f module was embedded in the backbone network to enhance the model’s perception of disease features. Besides, the SPDConv was introduced in the backbone network to expand the model’s perception field and further improve the feature extraction ability of minor disease spots. Secondly, the SPDConv was embedded in the neck network, and the lightweight convolution GsConv was used to replace the standard convolution, which can improve the accuracy of positioning of the disease site and prediction of category information and inference speed, contributing to higher accuracy. The model was trained and tested on a dataset comprising images of five common rice diseases ; ear blast, leaf blast, leaf spot, smut, and bacterial blight, where the dataset were collected under natural environment. Experimental results showed that the proposed model achieved a disease detection accuracy of 94. 2% with mAP of 93. 5% and model parameters of 8. 1 MB. Compared with other models YOLO v5, Faster R - CNN, YOLO v7 and YOLO v8, the complexity of the proposed model was only slightly lower than that of YOLO v5, but the mAP was approximately 12. 2 percentage points than that of YOLO v5, which signified a notable advancement in rice disease detection, achieving high accuracy while reducing model complexity to a certain extent. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 26
Main heading: Plant diseases
Controlled terms: Fertilizers? - ?Security systems
Uncontrolled terms: Back-bone network? - ?Bacterial blight? - ?C2f? - ?Disease detection? - ?High-accuracy? - ?Lightweigh? - ?Natural environments? - ?Rice disease recognition? - ?SPDConv? - ?YOLO v5
Classification code: 103 ? - ?1502.1.1.3 ? - ?821.3 Agricultural Methods? - ?914.1 Accidents and Accident Prevention
Numerical data indexing: Percentage 2.00E+00%, Percentage 5.00E+00%
DOI: 10.6041/j.issn.1000-1298.2024.08.021
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
4. Optimal Feature Space Construction for Multispectral Vegetation Recognition Considering Endmember Variability
Accession number: 20243516956058
Title of translation: 考虑光谱变异性的多光谱植被识别最优特征空间构建
Authors: Lin, Yi (1, 2); Li, Lang (1); Yu, Jie (1, 2); Gao, Chen (1); Zhong, Daiqi (1); Chen, Xin (1); Yang, Yuxuan (1)
Author affiliation: (1) College of Surveying and Geo-informatics, Tongji University, Shanghai; 200092, China; (2) Research Center of Remote Sensing Technology and Application, Tongji University, Shanghai; 200092, China
Corresponding author: Yu, Jie(2011_jieyu@tongji.edu.cn)
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 8
Issue date: August 2024
Publication year: 2024
Pages: 225-232
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: Due to differences in data acquisition and vegetation growth periods, vegetation recognition on low- and medium-resolution remote sensing imagery widely suffers from endmember variability. The endmember variability directly leads to large vegetation unmixing errors. To increase the vegetation recognition accuracy on the multispectral imagery, an intra-inter distance genetic algorithm (IIDGA) that accounts for the endmember variability was proposed. IIDGA can decrease the intra-distance and increase the inter-distance simultaneously, which enhanced the distinguishability of the endmembers through an automatic feature selection method. An optimal feature space for vegetation unmixing was constructed on the medium resolution imagery to improve the vegetation recognition accuracy based on the Landsat imagery. The importance of optimal feature selection was demonstrated by comparing the linear unmixing model accuracy based on the classical band combination features, the spectral and textural feature set and the proposed IIDGA. The results verified that feature selection was beneficial to improve the unmixing accuracy. The RMSE of IIDGA equalled 0. 180 which was the lowest among the three methods. Meanwhile, the IID index-based Filter method, the standard GA-based Wrapper method and the proposed method were compared with their performances in automatic optimal feature selection. The results confirmed the superiority of the IIDGA in trading off accuracy and efficiency. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 27
Main heading: Genetic algorithms
Controlled terms: Feature Selection? - ?Image enhancement
Uncontrolled terms: Automatic feature selection? - ?Endmember variabilities? - ?Feature space? - ?Inter-distances? - ?Intra-inter distance genetic algorithm? - ?Multispectral remote sensing? - ?Recognition accuracy? - ?Spectral endmember variability? - ?Unmixing? - ?Vegetation recognition
Classification code: 1101.2 ? - ?1106 ? - ?1106.3.1 ? - ?1201.7
DOI: 10.6041/j.issn.1000-1298.2024.08.020
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
5. Estimation of Gross Primary Production of Paddy Field in Jiangxi Province Based on Remote Sensing Measured Sun-induced Chlorophyll Fluorescence and Its Correlation with Meteorological Factors
Accession number: 20243516947366
Title of translation: 基于遥感日光叶绿素荧光测算的江西省稻田初级生产总值及其与气象因素的相关性
Authors: Liu, Bo (1, 2); Xu, Tao (3); Xu, Qiangqiang (4); Li, Qilong (1); Liu, Fangping (3); Hou, Jiaji (1); Cui, Yuanlai (5)
Author affiliation: (1) College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou; 225009, China; (2) Modern Rural Water Resources Research Institute, Yangzhou University, Yangzhou; 225009, China; (3) Jiangxi Central Station of Irrigation Experiment, Nanchang; 330201, China; (4) Hanjiang Water Resources and Hydropower (Group) Co., Ltd., Wuhan; 430048, China; (5) State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan; 430072, China
Corresponding author: Hou, Jiaji(holiu@yzu.edu.en)
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 8
Issue date: August 2024
Publication year: 2024
Pages: 391-400
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: Gross primary productivity (GPP) is an indicator that reflects the absorption of atmospheric CO, by crops through photosynthesis and serves as an important starting point for crop yield formation. The remote sensing measured sun-induced chlorophyll fluorescence (SIF) had highly advantages in GPP estimation. However, there were few studies in estimating rice GPP using remote sensing measured SIF. The paddy field in Jiangxi Province was taken as research object, a non-linear model for estimating rice GPP was constructed based on remote sensing measured SIF and ground flux observation data, and then the GPP over paddy field in Jiangxi Province during 2001—2020 was estimated. The results showed that compared with MOD17 GPP and GOSIF GPP, the SIF-based non-linear model had higher GPP estimation accuracy. The model can better capture the seasonal variations of rice GPP in both the rice seasons and non-rice seasons, but it performed poorly during the period of early rice to late rice transition. During 2001—2020, the annual average rice GPP for the Jiangxi Province was (2 082. 8 ± 143. 2) g/(m” ? a), and generally exhibited lower values in the north and higher values in the south. The areas with low GPP values were mainly located in the Nanchang City and its surrounding areas, while high values were found in Ganzhou City and Jingdezhen City. During 2001—2020, the rice GPP in Jiangxi Province showed an overall increasing trend with an increasing rate of 24. 3 g/(m” - a). The regions with the largest increasing trend were located in the southern part of the Jiangxi Province, while areas with the smallest increasing or decreasing trend mainly located in Nanchang City and Jiujiang City, possibly related to the transition of “double cropping rice to single cropping rice”. The main influencing factor for inter-annual variations in rice GPP in Jiangxi Province was temperature, with contribution rate ranging from 28.3% to 44.2%. Solar radiation had a negative contribution to rice GPP, the wind speed had a positive contribution to rice GPP in some regions, while precipitation and relative humidity had little impact on rice GPP. The research findings can provide a theoretical basis for estimating rice GPP, as well as for assessing the carbon sequestration capacity and yield estimation of rice under climate change in Jiangxi Province. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 44
Main heading: Stockpile surveys
Controlled terms: Agricultural robots? - ?Air quality? - ?Time difference of arrival
Uncontrolled terms: Chlorophyll fluorescence? - ?Estimation models? - ?Gross primary production? - ?Gross primary productivity? - ?Jiangxi Province? - ?Paddy fields? - ?Productivity estimation? - ?Remote-sensing? - ?Spatial-temporal distribution? - ?Sun-induced chlorophyll fluorescence
Classification code: 1502.1.1.1.1 ? - ?1502.1.1.4.1 ? - ?405.3 Surveying? - ?716.1 Information Theory and Signal Processing? - ?731.6 Robot Applications? - ?742.1 Photography? - ?821.2 Agricultural Chemicals
Numerical data indexing: Mass 3.00E-03kg, Percentage 2.83E+01% to 4.42E+01%
DOI: 10.6041/j.issn.1000-1298.2024.08.036
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
6. Optimization Design of Inlet and Outlet Clearance between Stator and Rotor of Full Cross-flow Pump Unit Based on Doehlert Matrix
Accession number: 20243516952443
Title of translation: 基于Doehlert Matrix的全贯流泵装置定转子进出流间隙优化设计
Authors: Liu, Jianfeng (1); Xi, Wang (1); Lu, Weigang (1); Lu, Wen (2); Yang, Chenxia (2)
Author affiliation: (1) College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou; 225009, China; (2) Jiangsu Water Conservancy Survey and Design Institute Co., Ltd., Yangzhou; 225127, China
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 8
Issue date: August 2024
Publication year: 2024
Pages: 170-180
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: Full cross-flow pump is a new type of pump with its motor integrated. However, during its operation, the occurrence of clearance backflow between the stator and rotor can disrupt the flow field distribution inside the impeller, leading to energy loss, pressure fluctuations and noise in the pump unit, even affecting the normal operation of the pump station. To understand the influence mechanism of clearance flow on the pump unit, firstly, the hydraulic characteristics of the clearance flow between the stator and rotor of a full cross-flow pump unit was investigated through the methods of numerical simulation and model experiments. Secondly, combined with scheme design method of Doehlert Matrix -response surface optimization, the inlet and outlet clearance structure of the stator and rotor was optimized, with the overall operating efficiency of the unit, the axial velocity uniformity and the average vortex angle at the outlet section of the outlet channel selected as the evaluation indexes. Lastly, the influence mechanism of the inlet and outlet clearance structure of the stator and rotor on the performance of the pump unit was revealed, along with the final optimized scheme for the inlet and outlet clearance between the stator and rotor obtained, that was the outer extension section i, was 4. 921 r, the outer contraction section xt was 0. 624r, the inner extension section t2 was 3. 655r and the inner contraction section x2 was 1. 6r where r represented the width of the clearance between stator and rotor in which case, the head and the operating efficiency of full cross-flow pump can be improved by around 10. 3% and 5. 2%, respectively. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 20
Main heading: Vortex flow
Controlled terms: Dynamic programming? - ?Linear programming? - ?Machine design? - ?Matrix algebra? - ?Nonlinear programming? - ?Pumps? - ?Radial flow? - ?Radial flow turbomachinery? - ?Rotors? - ?Shape optimization ? - ?Structural dynamics? - ?Structural optimization
Uncontrolled terms: Clearance flow between stator and rotor? - ?Cross flows? - ?Doehlert matrix? - ?Doehlert matrix design? - ?Full cross-flow pump unit? - ?Influence mechanism? - ?Matrix design? - ?Operating efficiency? - ?Optimization design? - ?Pump units
Classification code: 1007 ? - ?1201.1 ? - ?1201.7 ? - ?301.1 ? - ?408 Structural Design? - ?601 Mechanical Design? - ?601.2 Machine Components? - ?609.2 ? - ?904
Numerical data indexing: Percentage 2.00E+00%, Percentage 3.00E+00%
DOI: 10.6041/j.issn.1000-1298.2024.08.015
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
7. Analysis of Surface and Topographic Features of Central Yunnan Ring Structure Based on UAV - SfM Digital Model
Accession number: 20243516947341
Title of translation: 基于UAV-SfM 数字模型的滇中环状构造 地表与地形特征分析
Authors: Luo, Weidong (1, 2); Gan, Shu (1, 2); Yuan, Xiping (2, 3); Chen, Cheng (1, 2); Li, Raobo (1, 2); Bi, Rui (1, 2); Zhu, Zhifu (1, 2)
Author affiliation: (1) School of Land and Resources Engineering, Kunming University of Science and Technology, Kunming; 650093, China; (2) Plication Engineering Research Center, Spatial Information Surveying and Mapping Technology in Plateau and Mountainous Areas Set by Universities in Yunnan Province, Kunming; 650093, China; (3) Key Laboratory of Mountain Real Scene Point Cloud Data Processing and Application for Universities, West Yunnan University of Applied Sciences, Dali; 671006, China
Corresponding author: Gan, Shu(nl480@qq.com)
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 8
Issue date: August 2024
Publication year: 2024
Pages: 361-373
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: Surface features are closely related to natural disasters and have a significant impact on maintaining the ecological environment and deeply understanding the evolutionary process of the earth’s surface and geological structural characteristics. High spatial resolution digital models constructed through unmanned aerial vehicle (UAV) aerial surveys and structure from motion (SfM) technology were used to analyze the distribution relationship between land cover information and terrain features in the circular tectonic landforms of central Yunnan. The results showed that in areas with a mix of bare rock, bare soil, and vegetation, DeepLabv3 + algorithm was found to have better extraction effectson land cover information in the experimental area compared with the RF algorithm, both qualitatively and quantitatively. Ground points obtained from filtered point clouds, and the Kriging algorithm with the smallest mean error and root mean square error in cross-validation was chosen to construct a 0. 1 m resolution digital elevation model (DEM) to interpret various terrain factors such as first-order slopes, second-order slopes, and compound slopes. Based on correlation analysis, six types of terrain factors were selected to construct a comprehensive terrain analysis model (CTAM). After analyzing the connection between the land cover information with the largest coverage area (bare soil and vegetation) and the terrain, within CTAM, the percentage of each grade’s pixel number to the total pixel number was the highest at 28. 87% for Grade II, with Grades I, IV, and V accounting for 18. 39%, 13. 82%, and 17. 29%, respectively. UAV ― SfM technology can effectively capture the surface features of circular structures and provide technical means and scientific basis for geological research and resource management in the region. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 38
Main heading: Unmanned aerial vehicles (UAV)
Controlled terms: Antenna grounds? - ?Research and development management? - ?Resource allocation? - ?Soil surveys
Uncontrolled terms: Aerial vehicle? - ?Central yunnan? - ?Digital modeling? - ?Land cover? - ?Ring landform? - ?Structure from motion? - ?Surface feature? - ?Terrain features? - ?Unmanned aerial vehicle ― structure from motion digital model? - ?Vehicle structures
Classification code: 405.3 Surveying? - ?483.1 Soils and Soil Mechanics? - ?652.1 Aircraft, General? - ?716.5.1 ? - ?901.3 Engineering Research? - ?912.2 Management
Numerical data indexing: Percentage 2.90E+01%, Percentage 3.90E+01%, Percentage 8.20E+01%, Percentage 8.70E+01%, Size 1.00E00m
DOI: 10.6041/j.issn.1000-1298.2024.08.033
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
8. Passion Fruit Rapid Detection Model Based on Lightweight YOLO v8s GD
Accession number: 20243516952440
Title of translation: 基于轻量化YOLO v8s-GD的自然环境下百香果快速检测模型
Authors: Luo, Zhicong (1, 2); He, Chentao (1); Chen, Dengjie (1); Li, Pengbo (1); Sun, Qiyan (3)
Author affiliation: (1) College of Mechanical and Electronic Engineering, Eujian Agriculture and Forestry University, Fuzhou; 350002, China; (2) Fujian Key Laboratory of Agricultural Information Sensoring Technology, Fuzhou; 350002, China; (3) College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou; 350002, China
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 8
Issue date: August 2024
Publication year: 2024
Pages: 291-300
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: In order to improve the accuracy of passion fruit detection and deploy the deep learning model on mobile platforms for rapid real-time inference, a lightweight passion fruit detection model was proposed based on an improved YOLO v8s. The model replaced the neck feature fusion network with a Gather-and -distribute mechanism (GD) to enhance cross-layer feature fusion and generalization capabilities for passion fruit images. Additionally, the model was pruned by using layer-adaptive sparsity for the magnitude-based pruning (LAMP), which traded off some accuracy to reduce model size and parameter count, facilitating rapid detection on embedded devices. Knowledge distillation was employed to compensate for the accuracy loss caused by pruning, further enhancing detection performance. Experimental results showed that for a passion fruit dataset collected in natural environments, the improved model reduced parameter count and memory usage by 63.88% and 62. 10%, respectively, compared with the original YOLO v8s baseline model. The precision and average precision (AP) of the improved model were increased by 0. 9 percentage points and 2.3 percentage points, respectively, outperforming other comparative models. Real-time detection frame rates (FPS) on Jetson Nano and Jetson Tx2 embedded devices were 5. 78 f/s and 19. 38f/s, respectively, which were 1.93 times and 1.24 times higher than that of the original model. Therefore, the proposed improved model effectively detects passion fruit in complex environments, providing theoretical and technical support for the deployment and application of mobile detection devices in scenarios such as automatic passion fruit harvesting. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 30
Main heading: Inference engines
Uncontrolled terms: Detection models? - ?Embedded device? - ?Features fusions? - ?Gather-and-distribute mechanism? - ?Lightweight? - ?Model-based OPC? - ?Passion fruits? - ?Percentage points? - ?Rapid detection? - ?YOLO v8s
Classification code: 1101.1
Numerical data indexing: Percentage 1.00E+01%, Percentage 6.388E+01%
DOI: 10.6041/j.issn.1000-1298.2024.08.026
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
9. Named Entity Recognition in Chinese Rice Breeding Questions Based on Text Data Augmentation
Accession number: 20243516952439
Title of translation: 基于文本数据增强的中文水稻育种问句命名实体识别
Authors: Niu, Peiyu (1); Hou, Chen (2, 3)
Author affiliation: (1) College of Information and Electrical Engineering, China Agricultural University, Beijing; 100083, China; (2) National Engineering Laboratory for Big Data Analysis and Applications, Peking University, Beijing; 100871, China; (3) PKU-Changsha Institute for Computing and Digital Economy, Changsha; 410205, China
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 8
Issue date: August 2024
Publication year: 2024
Pages: 333-343
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: Issues of low-level data management and high knowledge granularity exist in current rice breeding question answering systems. In addition, there is a lack of publicly available labeled data for named entity recognition in rice breeding, and manual annotation can be costly. To address these issues, an approach based on text data augmentation to the named entity recognition was proposed for rice breeding questions. The rice breeding knowledge graph was created to assist in subdividing larger named entity categories in rice breeding, such as rice characteristics entities, into smaller subcategories, such as resistance to abiotic stress and eating quality. It helped to enhance entity boundaries and reduce knowledge granularity. Responding to the challenge of high annotation costs for rice breeding data that results in suboptimal performance in named entity recognition, the DA — BERT — BILSTM — CRF model was presented by introducing a data augmentation layer into the BERT — BILSTM — CRF model. Using manually labeled rice breeding questions as training data, the proposed model was compared with three other baseline models. In the overall named entity recognition experiment under the small class entity division, the model achieved a precision of 93. 86%, a recall of 92. 82%, and an Fl score of 93. 34% . Compared with the best-performing BERT — BILSTM — CRF model among the three baseline models, the model outperformed by 4.98, 5.3 and 5. 15 percentages points, respectively. Meanwhile, it also performed better in the single-entity recognition metric, achieving a precision of 94. 26% and an Fl score of 93. 32% . The experiments showed that the proposed approach performed better in both overall named entity recognition and single-class named entity recognition tasks in rice breeding questions. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 28
Main heading: Knowledge graph
Controlled terms: Data reduction? - ?Labeled data? - ?Metadata? - ?Question answering
Uncontrolled terms: ‘current? - ?Baseline models? - ?Data augmentation? - ?Knowledge granularity? - ?Knowledge graphs? - ?Named entity recognition? - ?Question answering systems? - ?Rice breeding? - ?Text data? - ?Text data augmentation
Classification code: 1106.2 ? - ?1106.7 ? - ?903.1 Information Sources and Analysis? - ?903.3 Information Retrieval and Use
Numerical data indexing: Percentage 2.60E+01%, Percentage 3.20E+01%, Percentage 3.40E+01%, Percentage 8.20E+01%, Percentage 8.60E+01%
DOI: 10.6041/j.issn.1000-1298.2024.08.030
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
10. Kinematic Performance Analysis and Structural Parameter Optimization of Delta Parallel Robot
Accession number: 20243516929307
Title of translation: Delta并联机器人运动学性能分析与结构参数优化
Authors: San, Hongjun (1, 2); Yang, Xiaoyuan (1); Chen, Jiupeng (1, 2); Wu, Xingmei (1); Zhang, Haobin (1); Xu, Bei (1)
Author affiliation: (1) Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming; 650500, China; (2) Key Laboratory of Advanced Equipment Intelligent Manufacturing Technology of Yunnan Province, Kunming; 650500, China
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 8
Issue date: August 1, 2024
Publication year: 2024
Pages: 446-458
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: Delta parallel robot has the advantages of fast speed, simple structure and strong bearing capacity, and is widely used in pot seedling transplanting, product sorting and packaging. Aiming at the problems of the analysis of the influence of structural parameters on the kinematics performance of Delta parallel robot and the complete theoretical system for the optimization design of systematic structural parameters, the distribution of condition numbers and the distortion constraint conditions were obtained by analyzing the distribution law of the Jacobian matrix condition in accessible workspace, the constraint relation of structural parameters, and the change law and correlation of kinematic performance with structural parameters. On this basis, it was concluded that the increase of the radius difference of dynamic and static platform, the increase of the length of the driving arm and the decrease of the length of the driven rod can make the mechanism performance better. Then, given the design workspace, the parameters of the original structure were optimized. The optimization model was established combining the condition number distribution characteristics and distortion constraints, the envelope penalty function and the kinematic performance evaluation function that was obtained by using multivariate nonlinear fitting and linear weighted grouping method, and the genetic algorithm was used to optimize the structure. Compared with before optimization, while the volume of the accessible workspace was reduced by 14.26%, the mean value and standard deviation of the global condition number in the design workspace were reduced by 31. 20% and 11. 78% respectively after optimization. Moreover, the distribution law of the condition number in each section of the design workspace verified the reliability of the condition number distribution characteristics. It provided a reference for the structure optimization and performance analysis of Delta parallel robot. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 26
Main heading: Jacobian matrices
Controlled terms: Bearings (structural)? - ?Industrial robots? - ?Shape optimization? - ?Structural analysis? - ?Structural dynamics? - ?Structural optimization
Uncontrolled terms: Condition number distribution? - ?Condition number distribution law? - ?Constraint conditions? - ?Delta parallel robot? - ?Distortion constraint condition? - ?Distribution law? - ?Kinematic performance? - ?Parameter optimization? - ?Structural parameter? - ?Structural parameter optimization
Classification code: 1201.2 ? - ?1201.7 ? - ?408 Structural Design? - ?408.1 Structural Design, General? - ?731.6 Robot Applications
Numerical data indexing: Percentage 1.426E+01%, Percentage 2.00E+01%, Percentage 7.80E+01%
DOI: 10.6041/j.issn.1000-1298.2024.08.042
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
11. Design and Experiment of Self-propelled Six-row Harvester for Oilseed Rape Shoot
Accession number: 20243516947264
Title of translation: 油菜薹对行自走式收获机设计与试验
Authors: Shan, Yiyin (1); Liao, Qingxi (1, 2); Wan, Xingyu (1, 2); Yuan, Jiaeheng (1); Chen, Lei (1); Liao, Yitao (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
Corresponding author: Liao, Qingxi(liaoqx@mail.hzau.edu.cn)
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 8
Issue date: August 2024
Publication year: 2024
Pages: 93-104
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: Oilseed-vegetable-dual-purpose is the technology increasing to harvest shoot on the premise of rapeseed, as fresh vegetable, can effectively improve the benefit of rape. However, manual harvesting is inefficient and costly, and lacking of mechanized harvesting technology and equipment. Therefore, a mechanized row harvesting process plan was proposed, which included reciprocating cutting, clamping-transporting, throwing-placing, cross transporting and collecting. Further, a self-propelled six-row harvester was developed on the basis of the agronomic and mechanized harvesting requirements of oilseed rape shoot. The structure and working principle of the harvester were elaborated, and the key components such as cutting device, clamping and conveying device and cross transporting device were designed based on the theoretical analysis. In addition, the main structural and working parameters of the harvester were determined based on the geometric and kinematic conditions of the oilseed rape shoot migration along the transporting path. Field test results showed that when the speed ratio coefficient of the cutter machine and the harvester was 0. 8, the clamping-transporting speed was 0. 37 m/s, the speed of the cross belt was 0.5 m/s, the success rate of cutting was 100%, during the harvesting operation, and the clamping-transporting success rate was 93. 69%, the operation damage rate was 7. 4%, and the rate of production was 0. 17 hm /h : moreover, the key components of the harvester operated stably, and the harvester can complete the processes such as cutting, clamping-transporting, cross transporting and colleting at one time, and performance indicators met the requirements for mechanized harvesting for oilseed rape shoot. The research results can provide ways for the design and improvement of mechanized harvesting equipment for stem and leafy vegetables. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 27
Main heading: Vegetables
Controlled terms: Clamping devices? - ?Cutting tools? - ?Harvesters? - ?Seed
Uncontrolled terms: Clamping-transporting? - ?Cross transporting? - ?Fresh vegetables? - ?Mechanized harvesting? - ?Oil seed rape? - ?Oilseed rape shoot? - ?Process plan? - ?Reciprocating cutter? - ?Six-row harvester? - ?Technology and equipments
Classification code: 603.1 Machine Tools, General? - ?605.2 Small Tools, Unpowered? - ?821.2 Agricultural Chemicals? - ?821.5 Agricultural Wastes
Numerical data indexing: Percentage 1.00E+02%, Percentage 4.00E+00%, Percentage 6.90E+01%, Velocity 3.70E+01m/s, Velocity 5.00E-01m/s
DOI: 10.6041/j.issn.1000-1298.2024.08.008
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
12. Design and Experiment of Vertical Rotating Self-propelled Fixed-angle Straw Cleaning Machine in East China Rice and Wheat Crop Rotation Area
Accession number: 20243516947195
Title of translation: 华东稻麦轮作区立旋自走电驱动式定角度清秸机 设计与试验
Authors: Shi, Naiyu (1, 2); Chen, Haitao (3, 4); Ye, Junhong (1); Wang, Xing (3); Xu, Ting (1); Yang, Yongkang (1)
Author affiliation: (1) School of Aeronautical and Mechanical Engineering, Changzhou Institute of Technology, Changzhou; 213032, China; (2) School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang; 212013, China; (3) College of Engineering, Northeast Agricultural University, Harbin; 150030, China; (4) College of Mechanical and Electronic Engineering, East University of Heilongjiang, Harbin; 150066, China
Corresponding author: Chen, Haitao(htchen@neau.edu.cn)
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 8
Issue date: August 2024
Publication year: 2024
Pages: 127-137
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: The straw cleaning device plays a crucial role in advancing the adoption of mechanized conservation tillage technology. It is primarily categorized into horizontal and vertical rotary straw cleaning mechanisms. In the horizontal rotary configuration, the cutter shaft operates parallel to the seeding belt’s surface, leading to the co-disposal of soil during the cleaning process, which can result in the generation of fugitive dust or the accumulation on the equipment. On the other hand, the vertical rotary straw cleaning utilizes a cutter shaft perpendicular to the seeding belt, making it more suitable for damp clay conditions due to the absence of longitudinal velocity components during the cleaning process. However, this design tends to carry straw back onto the seeding belt, thereby reducing the cleaning efficiency. To address these practical challenges, a fixed-angle straw cleaning approach for vertical rotary mechanisms and an electric-powered, self-propelled device for sowing wheat with rice straw was proposed. This system was designed to maintain a constant cleaning-teeth angle through structural analysis and design, thereby preventing straw from being reintroduced to the seedbed. Additionally, the determination of key structural parameters was based on the analysis of the soil cutting distance by the cleaning teeth. The key structure parameters of the cleaning-tooth device were determined through the analysis of the soil cutting distance. The analysis applied a three-factors and three-levels orthogonal test method. The experimental factors included the operation speed, the coefficient of trajectory distance, and the cut depth, while the experimental evaluation indexes were the straw cleaning rate and power consumption per unit area. This process tested and optimized the relevant parameters that affected the working performance of the straw cleaning device. When the parameters consisted of operation speed ranging from 4 km/h to 8 km/h, the coefficient of trajectory distance of 2, and the cut depth of 10 mm, the straw cleaning rate was not less than 89. 1% and the power consumption per unit area was not more than 1. 84 W ? h/m2. Notably, there was no soil adhesion throughout the entire experiment. These research findings had the potential to transcend the limitations of sowing equipment operation in moist clay environments, thereby providing vital technical support for the mechanization development of rice-wheat rotation. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 26
Main heading: Crop rotation
Controlled terms: Belts? - ?Cleaning? - ?Cutting equipment? - ?Dynamic response? - ?Seed? - ?Shafts (machine components)? - ?Straw
Uncontrolled terms: Cleaning devices? - ?Cleaning machine? - ?Crop rotation? - ?Electric self-propelled? - ?Fixed angles? - ?Fixed-angle straw cleaning? - ?Rice and wheat crop rotation area? - ?Rice straws? - ?Straw cleaning machine? - ?Vertical rotary
Classification code: 1501 ? - ?601.2 Machine Components? - ?602.2 Mechanical Transmissions? - ?802.3 Chemical Operations? - ?821.5 Agricultural Wastes? - ?821.6 Farm Buildings and Other Structures? - ?942.2 Electric Variables Measurements
Numerical data indexing: Percentage 1.00E00%, Power 8.40E+01W, Size 1.00E-02m, Size 4.00E+03m, Size 8.00E+03m
DOI: 10.6041/j.issn.1000-1298.2024.08.011
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
13. Design and Test of Folding Cotton Wide Film Spreading Precision Seeder
Accession number: 20243516947280
Title of translation: 折叠式棉花宽幅铺膜精量播种机设计与试验
Authors: Shi, Zenglu (1, 2); Wang, Meijing (1); Zhang, Xuejun (1, 2); Zhang, Yan (1, 2); Li, Minghua (1); Lu, Dengming (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 Jintiancheng Machinery Equipment Co., Ltd., Aksu; 842008, China
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 8
Issue date: August 2024
Publication year: 2024
Pages: 53-62
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: In order to further improve the operational efficiency of film-laying precision seeding and solve the problems of inconvenient road access and difficult steering in the field, a parallel-folding wide-width film-laying precision seeding machine was improved and designed, which can complete the processes of seed bed shaping, furrowing and film-laying and belt laying, sowing and mulching in one operation. The structure and working principle of the seeding machine were described, and the key components such as profiling mechanism, wide frame, lifting device and hydraulic system were designed and analyzed, and the dimensions of each component and the key factors affecting the smoothness of the lifting process were determined : the rigid-flexible coupling analysis was carried out on the unfolding and lifting process of the whole machine through the ADAMS software to get the position of the maximum stress point of the wide frame and the deformation curve of the slide beam in the unfolding process. In order to verify the operational performance of the folded cotton wide film spreading planter, field tests were conducted on its film spreading, tape spreading and seeding performance, and the test results showed that the qualification rate of the width of the picking surface was 92. 1 %, the rate of single grain was 96. 4%, the qualification rate of the depth of seeding under the film was 95. 7%, the qualification rate of the distance between holes was 96. 3% and the rate of the longitudinal stretching of the drip irrigation belt was 0. 73%, which met the industry standards and agronomic requirements, and it can be used for the work of agricultural machinery in the work of a larger width. It can provide reference for the design and improvement of agricultural machinery with large working width and the need of parallel folding. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 24
Main heading: Hydraulic equipment
Controlled terms: Agricultural robots? - ?Belts? - ?Flexible couplings? - ?Seed
Uncontrolled terms: Design and tests? - ?Film spreading? - ?Foldings? - ?Lifting process? - ?Operational efficiencies? - ?Precision seeding? - ?Seeder? - ?Unfolding process? - ?Wide? - ?Wide films
Classification code: 1401.2 ? - ?601.2 Machine Components? - ?602.2 Mechanical Transmissions? - ?731.6 Robot Applications? - ?821.2 Agricultural Chemicals? - ?821.5 Agricultural Wastes
Numerical data indexing: Percentage 1.00E00%, Percentage 3.00E+00%, Percentage 4.00E+00%, Percentage 7.00E+00%, Percentage 7.30E+01%
DOI: 10.6041/j.issn.1000-1298.2024.08.004
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
14. Elimination Method of Asymmetric Hydraulic Cylinder Pressure Jump Based on Dual-spool Independent Metering Valve
Accession number: 20243516931360
Title of translation: 负载口独立控制阀控非对称缸压力跃变消除方法研究
Authors: Si, Guolei (1); Li, Binjie (1); Wei, Jinhua (2); Wei, Xiaoling (2); Wei, Liejiang (2)
Author affiliation: (1) Sichuan Aerospace Fenghuo Servo Control Technology Corporation, Chengdu; 611130, China; (2) Key Laboratory of Advanced Pumps, Valves and Fluid Control System, Ministry of Education, Lanzhou University of Technology, Lanzhou; 730050, China
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 8
Issue date: August 1, 2024
Publication year: 2024
Pages: 437-445
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: In hydraulic synchronous control system, the pressure jump generated by asymmetric cylinder during changing the direction results in the oscillation and even instability of the synchronous control system. Aiming at this problem, a method for eliminating pressure jump based on the dual-spool independent metering valve controlling asymmetric cylinder system was proposed. Firstly, the mechanisms of pressure jump generated by asymmetric cylinder were analyzed, in which the asymmetric cylinder was respectively controlled by traditional valve, asymmetric valve and dual-spool independent metering valve. Secondly, the models of traditional valve controlling asymmetric cylinder system and the dual-spool independent metering valve controlling asymmetric cylinder system were built in AMESim. Then the position following and the pressure jump in the traditional valve controlling asymmetric cylinder system and dual-spool independent metering valve controlling asymmetric cylinder system were compared and analyzed under different system pressures and different given signals. Finally, the experimental platform of the dual-spool independent metering valve controlling asymmetric cylinder system was built to verify the effectiveness of the proposed method. Simulation and experimental results showed that the asymmetric cylinder system controlled by the dual-spool independent metering valve can realize position following well through the fuzzy adaptive control algorithm. When the asymmetric cylinder changed direction, the pressure jump can be completely eliminated, reducing oscillation of the system, and made the system more stable. When the hydraulic cylinder was operated by square wave, the pressure impact was larger than that when the sine wave was operated. This method was beneficial to achieve good synchronous control effect. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 28
Main heading: Oscillating cylinders
Controlled terms: Adaptive control systems? - ?Crystallizers? - ?Feedback control? - ?Fuzzy control? - ?Hydraulic jump? - ?Hydraulic machinery? - ?Reels? - ?Valves (mechanical)
Uncontrolled terms: Asymmetric cylinder? - ?Asymmetric valve? - ?Cylinder pressures? - ?Cylinder systems? - ?Dual-spool independent metering valve? - ?Elimination method? - ?Hydraulic cylinders? - ?Pressure jumps? - ?Synchronous control? - ?Synchronous control systems
Classification code: 1401.1 ? - ?1401.2 ? - ?301.1 ? - ?601.2 Machine Components? - ?601.3 Mechanisms? - ?691.1 Materials Handling Equipment? - ?731 Automatic Control Principles and Applications? - ?731.1 Control Systems? - ?802.1 Chemical Plants and Equipment
DOI: 10.6041/j.issn.1000-1298.2024.08.041
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
15. Application of Image Enhancement Technology Based on EnlightenGAN in Apple Detection in Natural Scenes
Accession number: 20243516947375
Title of translation: 基于 EnlightenGAN 图像增强的自然场景下苹果检测方法
Authors: Song, Huaibo (1, 2); Yang, Hanru (1, 2); Su, Xiaowei (1, 2); Zhou, Yuhong (1, 2); Gao, Xinyi (1, 2); Shang, Yuying (1, 2); Zhang, Shujin (1, 2)
Author affiliation: (1) College of Mechanical and Electronic Engineering, Northwest A&F University, Shaanxi, Yangling; 712100, China; (2) Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Shaanxi, Yangling; 712100, China
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 8
Issue date: August 2024
Publication year: 2024
Pages: 266-279
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: Under natural light conditions, the presence of shadows reduced the accurate perception ability of apple harvesting robot towards apple targets, leading to low picking efficiency. Therefore, an EnlightenGAN algorithm for image enhancement was proposed, which effectively improved the accuracy of shadow removal and apple object detection. This algorithm first obtained a self-regularized attention map through image lighting standardization to achieve image shadow detection. Next, an attention-guided U — Net was used as the backbone network of the generator to obtain the enhanced image. Then, the information before and after enhancement was compared using a global-local discriminator, and image enhancement was ultimately achieved in the confrontation between the generator and discriminator. To further evaluate the effectiveness of the proposed method, EnlightenGAN, Zero _ DCE, Adaptive _ GAMMA, and RUAS algorithms were tested on the publicly available MinneApple dataset. Compared with Zero—DCE, Adaptive —GAMMA, and RUAS algorithms, the MSE of EnlightenGAN algorithm was decreased by 19.21%, 59.47%, and 67. 42%, respectively, while the PSNR was increased by 6. 26%, 34. 55%, and 47. 27%, respectively. The SSIM was increased by 2. 99%, 23. 21%, and 68. 29%, respectively. The detection P of EnlightenGAN algorithm before and after enhancement were 97. 38% and 98. 37%, respectively, with R of 74. 74% and 91. 37%. The Fl score were 84% and 94%, respectively. The precision, recall, and Fl score were improved by 1. 02%, 22. 25%, and 11. 90%, respectively. In order to verify the effectiveness of the model, different datasets were tested, and the results showed that the target detection precision, recall and Fl score after the enhancement of the EnlightenGAN algorithm were improved compared with the non enhanced algorithm, Zero _ DCE, Adaptive—GAMMA and RUAS algorithms. All results indicated that the proposed method can effectively improve the detection precision under uneven lighting conditions and provide reference for the visual system of apple harvesting robot. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 34
Main heading: Adaptive algorithms
Controlled terms: Image enhancement? - ?Photomapping? - ?Robots
Uncontrolled terms: Apple? - ?Detection precision? - ?Enlightengan? - ?Harvesting robot? - ?Image enhancement technologies? - ?Natural scenes? - ?Objects detection? - ?Shadow removal? - ?Technology-based? - ?YOLO v5m
Classification code: 1106.3.1 ? - ?405.3 Surveying? - ?731.5 Robotics? - ?742.1 Photography
Numerical data indexing: Percentage 1.921E+01%, Percentage 2.00E+00%, Percentage 2.10E+01%, Percentage 2.50E+01%, Percentage 2.60E+01%, Percentage 2.70E+01%, Percentage 2.90E+01%, Percentage 3.70E+01%, Percentage 3.80E+01%, Percentage 4.20E+01%, Percentage 5.50E+01%, Percentage 5.947E+01%, Percentage 7.40E+01%, Percentage 8.40E+01%, Percentage 9.00E+01%, Percentage 9.40E+01%, Percentage 9.90E+01%
DOI: 10.6041/j.issn.1000-1298.2024.08.024
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
16. Estimation Model of Soil Organic Carbon Content Based on Mid-infrared Spectral Characteristics Enhancement and Ensemble Learning
Accession number: 20243516947374
Title of translation: 基于中红外光谱特征增强和集成学习的土壤有机碳含量估算模型研究
Authors: Tang, Aohua (1, 2); Yang, Guijun (1, 2); Yang, Yue (2, 3); Chen, Weinan (1, 2); Xu, Xin’gang (2); Xu, Bo (2); Gao, Meiling (1); Zhang, Jing (1)
Author affiliation: (1) College of Geological Engineering and Geomatics, Chang’an University, Xi’an; 710054, China; (2) Key Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture and Rural Affairs, Beijing; 100097, China; (3) School of Land Engineering, Chang’an University, Xi’an; 710054, China
Corresponding author: Yang, Guijun(guijun.yang@163.com)
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 8
Issue date: August 2024
Publication year: 2024
Pages: 382-390
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: Mid-infrared spectral data holds immense potential for accurate, cost-effective, and rapid prediction of soil organic carbon (SOC) content. To enhance the universality of spectroscopic data estimation models, a spectroscopic feature enhancement strategy was employed and combined multiple machine learning methods by using the Stacking algorithm to construct a robust model for estimating SOC content. Various spectroscopic feature enhancement methods and their combinations were applied to enhance the features of mid-infrared soil spectra and select the optimal strategies. The Stacking algorithm was used in conjunction with multiple machine learning methods to build an ensemble model, aiming to improve the model’s generalization ability. The estimation performance of the ensemble model was compared with that of partial least squares regression (PLSR), gradient boosting trees (GBT), and 1-dimensional convolutional neural network (1D-CNN) models. The results demonstrated that the optimal spectral characteristics enhancement strategy can significantly improve the correlation between soil spectra and soil organic carbon content, and the optimal Pearson correlation coefficient reached -0.82. Compared with PLSR, GBT, and 1D-CNN models, the ensemble model exhibited higher estimation accuracy and robustness across various spectral datasets. In particular, under the spectral characteristic enhancement strategy of first derivative combined with multivariate scatter correction, the ensemble model demonstrated excellent estimation performance (R2=0.92, RMSE was 1.18g/kg, RPD was 3.52). The proposed method enabled timely and accurate estimation of SOC, which can provide a scientific basis for modern agricultural management. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 37
Main heading: Convolutional neural networks
Controlled terms: Adaptive boosting
Uncontrolled terms: Ensemble learning? - ?Ensemble models? - ?Estimation models? - ?Infrared spectral? - ?Mid-infrared spectroscopy? - ?Midinfrared? - ?Soil organic carbon? - ?Soil organic carbon content? - ?Spectral characteristic enhancement? - ?Spectral characteristics
Classification code: 1101.2.1 ? - ?1106
Numerical data indexing: Mass 1.18E-03kg
DOI: 10.6041/j.issn.1000-1298.2024.08.035
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
17. Sheep Instance Segmentation Method Based on Improved YOLO v8n - seg
Accession number: 20243516947201
Title of translation: 基于改进 YOLO v8n - seg 的羊只实例分割方法
Authors: Wang, Fushun (1, 2); Wang, Wang (1); Sun, Xiaohua (3); Wang, Chao (1, 2); Yuan, Wanzhe (4)
Author affiliation: (1) College of Information Science and Technology, Hebei Agricultural University, Baoding; 071001, China; (2) Hebei Key Laboratory of Agricultural Big Data, Baoding; 071000, China; (3) Department of Digital Transmit, Hebei Software Institute, Baoding; 071000, China; (4) College of Veterinary Medicine, Traditional Chinese Veterinary Medicine, Hebei Agricultural University, Baoding; 071000, China
Corresponding author: Yuan, Wanzhe(yuanwanzhe@126.com)
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 8
Issue date: August 2024
Publication year: 2024
Pages: 322-332
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: Sheep instance segmentation is an important prerequisite for sheep identification and tracking, behavior analysis and management, and disease monitoring. Aiming at the problem of false detection and missed detection of sheep instance detection caused by the occlusion of sheep individuals, dim light, and the similarity of individual color and background in the complex breeding environment of large-scale sheep farms, a sheep instance segmentation method based on improved YOLO v8n ― seg was proposed. The YOLO v8n ― seg network was used as the basic model for the individual sheep segmentation task. Firstly, the large separable kernel attention module was introduced to enhance the ability of the model to capture important feature information of the instance, which improved the representativeness of the features and the robustness of the model. Secondly, the bottleneck module in C2f was replaced by the expansion-wise residual module in DWR ― Seg, a hyperreal-time semantic segmentation model, to optimize the ability of the model to extract high-level network features, expanding the receptive field of the model, and enhanced the relationship between context semantics. Generate new feature maps with rich feature information. Finally, the dilated reparam block module was used to further improve C2f, and the feature information extracted from the high level of the network was fused several times to enhance theunderstanding ability of the model. The experimental results showed that the average segmentation accuracy of the improved YOLO v8n ― LDD ― seg for sheep cases reached 92. 08% at mAP5{ ) and 66. 54% at mAP50y. Compared with YOLO v8n ― seg, mAP50 and mAP5O 95 were improved by 3. 06 percentage points and 3. 96 percentage points, respectively. YOLO v8n ― LDD ― seg effectively improved the detection accuracy of individual sheep, improved the segmentation effect of sheep instances, and provided technical support for the detection and segmentation of sheep instances in complex breeding environments. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 32
Main heading: Semantic Segmentation
Uncontrolled terms: Behavior analysis? - ?Breeding environments? - ?Feature information? - ?Improved YOLO v8n ― LDD ― seg network? - ?Individual detection? - ?Instance segmentation? - ?Percentage points? - ?Segmentation methods? - ?Sheep? - ?Tracking behavior
Classification code: 1106.8
Numerical data indexing: Percentage 5.40E+01%, Percentage 8.00E+00%
DOI: 10.6041/j.issn.1000-1298.2024.08.029
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
18. Quantitative Method of Granular Agricultural Products Feeding Based on Volume Closed-loop Control
Accession number: 20243516955948
Title of translation: 基于容积闭环控制的颗粒农产品加料定量方法研究
Authors: Wang, Jiaen (1)
Author affiliation: (1) School of Mechanical and Electrical Engineering & Automation, Xiamen University Tan Kah Kee College, Zhangzhou; 363105, China
Corresponding author: Wang, Jiaen(1576471088@qq.com)
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 8
Issue date: August 2024
Publication year: 2024
Pages: 410-417
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: Aiming at the problems of low precision and low automation in quantitative feeding of granular agricultural products, the physical characteristics of common granular agricultural products were analyzed. A quantitative method based on volume estimation of mass was adopted, and a closed-loop control scheme was introduced into the control system. A dynamic quantitative feeding equipment for granular agricultural products was developed. The equipment mainly consisted of a quantitative device with a variable volume measuring cup structure, a transmission and distribution mechanism, and a reinspection weighing scale. Based on the explanation of the mechanical structure and working principle, a closed-loop control algorithm based on historical discrete data fitting prediction error was proposed. When the reinspection process detected a difference between the feeding quality and the target quality, the volume of the measuring cup in the quantitative feeding process would be compensated and corrected through a closed-loop control system, thereby reducing the error of quantitative feeding. The results of experiment showed that the equipment could achieve high-precision dynamic quantitative feeding of granular agricultural products with anti-interference and adaptive capabilities. Taking rice, soybeans, and kidney beans as the experimental object, the feeding quality error could be stably controlled within 1% after three sets of closed-loop feedback adjustments when the rotational speed of the turntable was 4 r/min. And the quantitative accuracy was far higher than that of the national requirement for the allowable shortage of quantitatively packaged goods. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 25
Main heading: Closed loop control systems
Controlled terms: Adaptive control systems? - ?Scales (weighing instruments)
Uncontrolled terms: Closed-loop control? - ?Control schemes? - ?Least-squares- methods? - ?Lower precision? - ?Physical characteristics? - ?Quantitative feeding? - ?Quantitative method? - ?Re-inspection? - ?Screw nut pair? - ?Volume estimations
Classification code: 731.1 Control Systems? - ?942.1.7 ? - ?961 Systems Science
Numerical data indexing: Angular velocity 6.68E-02rad/s, Percentage 1.00E00%
DOI: 10.6041/j.issn.1000-1298.2024.08.038
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
19. Calibration of Simulation Parameters for Roasted Green Tea Based on Discrete Element Method
Accession number: 20243516931428
Title of translation: 炒青绿茶离散元仿真参数标定研究
Authors: Wang, Xiaoyong (1, 2); Yu, Zhi (1, 2); Zhang, De (1, 2); Chen, Yuqiong (1, 2); Ni, Dejiang (1, 2)
Author affiliation: (1) College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan; 430070, China; (2) National Key Laboratory for Cermplasm Innovation, Utilization of Horticultural Crops, Wuhan; 430070, China
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 8
Issue date: August 1, 2024
Publication year: 2024
Pages: 418-427
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: Aiming to lack of accurate discrete element simulation parameters in the simulation analysis of roasted green tea refining equipment, taking roasted green tea as the research object, the discrete element simulation parameters of roasted green tea were calibrated. The contact parameters between roasted green tea and materials were measured by using the inclined plane method and the free fall method to determine the range of values for static friction coefficient, rolling friction coefficient, and collision recovery coefficient. The actual stacking angle of roasted green tea was obtained through bench tests of the cylinder lifting method. A discrete element model of roasted green tea was established, and the formation process of stacking angle was simulated. Using the actual stacking angle of roasted green tea as the response value, the Plackett - Burman experiment was used to screen out the parameters that had a significant impact on the stacking angle of roasted green tea. The steepest climb test was used to approximate the optimal response range, and finally the optimal combination of significant influencing parameters was obtained through Box - Behnken. The results showed that when the shear modulus was 2.930 MPa, the coefficient of static friction between tea particles was 0.771, the coefficient of rolling friction between tea particles was 0.133, and the coefficient of collision recovery between tea particles was 0.354, the simulated stacking angle of roasted green tea was 30.12°, and the error with the actual stacking angle was 1.59%. The optimized calibration parameters can be used to simulate the external contact characteristics of roasted green tea. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 30
Uncontrolled terms: Discrete elements method? - ?Discrete-element simulations? - ?Green tea? - ?Parameters calibrations? - ?Refining equipment? - ?Research object? - ?Roasted green tea? - ?Simulation analysis? - ?Simulation parameters? - ?Stacking angle
Numerical data indexing: Percentage 1.59E+00%, Pressure 2.93E+06Pa
DOI: 10.6041/j.issn.1000-1298.2024.08.039
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
20. Inversion of Leaf Area Index of Silage Corn Based on PROSAIL Model
Accession number: 20243516956009
Title of translation: 基于PROSAIL模型的青贮玉米叶面积指数反演
Authors: Wang, Yanlong (1); Wang, Jun (1); Cui, Ting (1)
Author affiliation: (1) College of Information Science and Technology, Gansu Agricultural University, Lanzhou; 730070, China
Corresponding author: Wang, Jun(julianwong82@163.com)
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 8
Issue date: August 2024
Publication year: 2024
Pages: 205-213
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: Accurately and efficiently estimating corn LAI data within a region is of crucial importance for field management decisions, predicting land yield, and implementing precision agriculture. In response to the problems of scale effect, low accuracy, and poor universality in multi-scale and large-scale remote sensing inversion, taking the silage corn experimental field in Minie County, Zhangye City as the research area, silage corn was selected as the research object, based on Landsat - 8 hyperspectral and Modis multispectral remote sensing images, combined with ground measured data. Through local and global sensitivity analysis of the input parameters of the PROSAIL model,the lookup table of canopy reflectance - LAI of silage corn in multiple growth periods and the inversion strategy of the minimum optimization cost function were constructed, and the optimal LAI inversion model for the study area was determined. The accuracy verification and linear fitting of the inversion results were completed by using the measured values in different growth periods of silage corn. The results showed that the inversion results of LAI were generally good, with high fitting accuracy and strong correlation with the measured values. The optimal determination coefficients R for the jointing stage, tasseling stage, and maturity stage were 0.85, 0.91, and 0.90, respectively. The root mean square error (RMSE) were 0.35, 0.58, and 0.51, respectively. Therefore, the inversion strategy based on multi-source hyperspectral remote sensing data combined with the PROSAIL model can provide scientific basis and methods for crop parameter inversion. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 30
Main heading: Sensitivity analysis
Controlled terms: Mean square error? - ?Table lookup
Uncontrolled terms: Field management? - ?Growth period? - ?Hyperspectral Data? - ?Inversion results? - ?Inversion strategy? - ?LAI? - ?Leaf Area Index? - ?Measured values? - ?PROSAIL model? - ?Silage corn
Classification code: 1106.1 ? - ?1201 ? - ?1202.2
DOI: 10.6041/j.issn.1000-1298.2024.08.018
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
21. Soil Volumetric Moisture Content Detection Based on Short-time Fourier Transform of Ultra-wide Band Radar Echo
Accession number: 20243516947339
Title of translation: 基于超宽带雷达回波短时傅里叶变换的土壤含水率检测
Authors: Wei, Pengliang (1, 2); Zhou, Yuhong (1); Wang, Ruozhen (1); Guo, Jiao (1, 2)
Author affiliation: (1) College of Mechanical and Electronic Engineering, Northwest A&F University, Shaanxi, Yangling; 712100, China; (2) Shaanxi Key Laboratory of Agriculture Information Perception and Intelligent Service, Shaanxi, Yangling; 712100, China
Corresponding author: Guo, Jiao(gjiao@nwafu.edu.cn)
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 8
Issue date: August 2024
Publication year: 2024
Pages: 352-360
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: Monitoring soil volumetric moisture content is crucial for enhancing agricultural production efficiency and devising reasonable soil management strategies. Ultra-wide band radar, due to its high resolution and strong penetration capabilities, is widely used in real-time monitoring of dynamic agricultural soil information. However, previous processing of ultra-wide band radar signals mainly focused on time-domain features, neglecting the equally informative frequency-domain characteristics. This oversight limited the utilization of echo signals in the inversion process of soil volumetric moisture content, thereby constraining the inversion accuracy. The soil echo signals obtained from ultra-wide band radar and extracts features related to soil volumetric moisture content were preprocessed. The signals were analyzed by using short-time Fourier transform (STFT) to investigate the time-frequency spectral characteristics related to soil volumetric moisture content variations over time. Furthermore, a soil volumetric moisture content classification and regression prediction algorithm model was established by combining these features with a convolutional neural network (CNN). Experimental results showed that based on data augmented with Gaussian white noise, the overall accuracy and Kappa coefficient for soil volumetric moisture content classification using time-frequency features combined with the CNN model were respectively 98. 69% and 0. 984 9. Compared with support vector machine (SVM) model built with ten time-domain features and the normalized difference vegetation index (NDVI), there was an increase in overall accuracy by 21. 78 percentage points and an improvement in the Kappa coefficient by 0. 251 5 -For soil volumetric moisture content regression prediction, combining time-frequency features with a convolutional neural network regression (CNNR) model, the coefficient of determination (R2) was 0. 987 2, the root mean square error (RMSE) was 0. 004 8 cm /cm, and the relative percent difference (RPD) was 6. 273 8. Compared with the CNNR model established with ten time-domain features and NDVI, there was an increase in R2 by 0. 231 6, a reduction in RMSE by 1. 337 7 cm3/cm3, and an improvement in RPD by 4. 271 4. Overall, the method proposed showed a clear advantage over traditional signal detection and processing methods in terms of classifying and predicting soil volumetric moisture content. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 38
Controlled terms: Benchmarking? - ?Binary images? - ?Forward error correction? - ?Fourier series? - ?Gaussian noise (electronic)? - ?Hemp? - ?Image acquisition? - ?Image analysis? - ?Image coding? - ?Image compression ? - ?Image segmentation? - ?Image texture? - ?Image thinning? - ?Soil moisture
Uncontrolled terms: Content classification? - ?Convolutional neural network? - ?Echo-signal? - ?Overall accuracies? - ?Regression predictions? - ?Short time Fourier transforms? - ?Soil volumetric moisture content? - ?Time domain features? - ?Ultra wideband radars? - ?Volumetric moisture content
Classification code: 1103.3 ? - ?1106 ? - ?1106.3 ? - ?1106.3.1 ? - ?1201.3 ? - ?483.1 Soils and Soil Mechanics? - ?716.1 Information Theory and Signal Processing? - ?821.5 Agricultural Wastes? - ?913.3 Quality Assurance and Control
Numerical data indexing: Percentage 6.90E+01%, Size 8.00E-02m, Volume 7.00E-06m3
DOI: 10.6041/j.issn.1000-1298.2024.08.032
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
22. Design and Experiment of Lyophyllum decastes Automatic Harvesting Device for Bottle Planting
Accession number: 20243516947217
Title of translation: 瓶栽鹿茸菇自动采收装置设计与试验
Authors: Wu, Yanqiang (1, 2); Hou, Xianwei (3); Yu, Junyuan (1); Shi, Haotong (1); Liu, Xia (4); Hou, Jialin (1, 2)
Author affiliation: (1) College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian; 271018, China; (2) Shandong Provincial Engineering Research Center for Intelligent Agricultural Equipment, Taian; 271018, China; (3) Shandong Agricultural Machinery Technology Extension Station, Ji’nan; 250014, China; (4) Shandong Nongfa Smart Biotechnology Group Co., Ltd., Dongying; 257200, China
Corresponding author: Hou, Jialin(jlhou@sdau.edu.cn)
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 8
Issue date: August 2024
Publication year: 2024
Pages: 105-116
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: Aiming to address the deficiency in mechanized harvesting equipment for cultivated bottle planting Lyophyllum decastes, an automated harvesting device was designed to achieve the transportation and positioning of the cultivation basket for bottle planting Lyophyllum decastes, the fixation of the cultivation bottle, low-loss and high-quality root cutting of Lyophyllum decastes, and flexible clamping and transportation. The overall structure and working principle of the device were explained. Through mechanical and kinematic analysis of the cultivation basket conveying process, the installation angle of the guide bar was determined to be 71°. Based on ANSYS LS ― DYNA, a simulation analysis was conducted on the root cutting process. Considering cutting speed, feeding speed, saw blade rake angle, and tooth pitch as influencing factors, and the cutting force was used as the response index. The response surface method was used to analyze the significant impact of each factor on the index. The regression model was optimized and validated through bench tests. When the cutting speed was 6. 49 m/min, the feeding speed was 0. 12 m/min, the front angle was 250, and the tooth pitch was 7 mm, the cutting force was the smallest. A flexible finger for harvesting Lyophyllum decastes was designed. Based on the Yeoh model theory, the rubber material parameters were fitted by using uniaxial tensile testing. Single factor experiments were conducted by using ABAQUS software, and combined with actual experiments, the optimal bending performance was obtained. The structural parameters of the flexible finger were air cavity thickness of 2 mm, 7 air cavities, air pressure at 25 kPa, and a restricting layer thickness of 3 mm. The overall machine trials demonstrated that the device operates smoothly, exhibiting excellent harvesting performance. The average net harvest rate, average loss rate, and average damage rate were recorded as 98. 18%, 3. 66%, and 2. 75%, respectively. These results met the actual harvesting requirements for bottle planting Lyophyllum decastes, ensuring efficient and effective harvesting. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 20
Main heading: Tensile testing
Controlled terms: ABAQUS? - ?Bending tests? - ?Bottles? - ?Clamping devices? - ?Cutting tools? - ?Rubber testing
Uncontrolled terms: Automatic harvesting device? - ?Bottle planting of lyophyllum decaste? - ?Cutting forces? - ?Cutting speed? - ?Feeding speed? - ?Flexible clamping? - ?Harvesting devices? - ?Plantings? - ?Root cuttings? - ?Rotary cutting
Classification code: 1106.5 ? - ?1201.5 ? - ?212 ? - ?215.1.2 ? - ?603.1 Machine Tools, General? - ?605.2 Small Tools, Unpowered? - ?694.1 Packaging, General
Numerical data indexing: Percentage 1.80E+01%, Percentage 6.60E+01%, Percentage 7.50E+01%, Pressure 2.50E+04Pa, Size 1.20E+01m, Size 2.00E-03m, Size 3.00E-03m, Size 4.90E+01m, Size 7.00E-03m
DOI: 10.6041/j.issn.1000-1298.2024.08.009
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
23. Optimization Design and Experiment of High Catching and Low Planting Type Differential Variable Attitude Planetary Gear System Planting Mechanism
Accession number: 20243516947279
Title of translation: 高接低栽式差速变姿态行星轮系栽植机构设计与试验
Authors: Xin, Liang (1); Sun, Mingyi (1); Li, Zeze (1); Zhu, Xuanwei (1); Feng, Yuchen (1); Li, Jiacheng (1)
Author affiliation: (1) College of Engineering, Northeast Agricultural University, Harbin; 150030, China
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 8
Issue date: August 2024
Publication year: 2024
Pages: 161-169 and 265
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: In response to the current problem that dryland vegetable pot seedling transplanting machine mostly applies to large plant spacing (more than 260 mm) transplanting, and there is a large height difference between the seedling catching and planting position, as well as a poor transplanting performance, the high catching and low planting type differential variable attitude planetary gear system planting mechanism was proposed. Based on the agronomic guidance for small and medium plant spacing of vegetable pot seedling transplanting and transplanting mechanism design requirements, the working principle of the double planetary carrier differential planetary gear system planting mechanism was analyzed and its kinematic theoretical model was established. A computer-aided analysis and optimization design software for planting mechanism based on Matlab GUI was developed by combining the objective function constructed with the proposed optimization objectives. A set of optimal mechanism design parameter combinations was obtained through human-computer interaction. The correctness and rationality of the mechanism were preliminarily verified through 3D modeling and assembly, as well as virtual simulation by ADAMS software. The experimental research on the physical prototype of planting mechanisms and the development of test bench systems were carried out. Through idle testing, the consistency of trajectory and attitude of planting mechanism among the actual operation, virtual simulation, and theoretical design was verified. The experimental research on the seedling catching and planting performance of planting mechanism was conducted. The results showed that the planting mechanism was excellent in all experimental indicators of seedling catching and planting, which could meet the expected design requirements of planting mechanism and the standards of dry land transplanting machinery, and it verified the feasibility and practicality of the planting mechanism. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 23
Main heading: MATLAB
Controlled terms: Epicyclic gears
Uncontrolled terms: Differential planetary gears? - ?Differential variables? - ?Double planetary carrier differential planetary gear system? - ?Non-circular gears? - ?Optimization design? - ?Planetary gear systems? - ?Planting mechanism? - ?Plantings? - ?Seedlings transplanting? - ?Vegetable pot seedling transplanting
Classification code: 1106.5 ? - ?1201.5 ? - ?601.2 Machine Components
Numerical data indexing: Size 2.60E-01m
DOI: 10.6041/j.issn.1000-1298.2024.08.014
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
24. Optimization Design and Experiment of Non-circular Gear - Linkage Combination Type Transplanting Mechanism of Rice Pot Seedling on Film
Accession number: 20243516952550
Title of translation: 非圆齿轮-连杆组合式水稻钵苗膜上移栽机构优化设计与试验
Authors: Xin, Liang (1); Wang, Mingcheng (1); Zhang, Hao (1); Sun, Guoyu (1); Wang, Hang (1); Zhuang, Zhiyuan (1)
Author affiliation: (1) College of Engineering, Northeast Agricultural University, Harbin; 150030, China
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 8
Issue date: August 2024
Publication year: 2024
Pages: 63-70
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: To achieve the mechanized integrated transplanting requirement of rice pot seedlings on film, a non-circular gear — linkage combined planetary gear system transplanting mechanism was proposed. This mechanism enabled a single set of components to achieve the coordinated operation of four processes: seedling picking, seedling delivery, film perforation and digging, and planting meeting the trajectory and attitude requirements of rice pot seedling transplanting on film. The transplanting mechanism was theoretically analyzed, and a kinematic model was established. Combining optimization objectives, a digital visualization optimization design software for the mechanism was developed based on the Matlab GUI platform, resulting in a set of mechanism parameters that met the transplanting requirements through human-computer interaction. Based on the optimized parameters, a comprehensive structural design of the mechanism was conducted, and a 3D model was established. Virtual prototype simulation was performed by using ADAMS software. A physical prototype of the rice pot seedling transplanting mechanism on film and a multifunctional test bench were developed. The kinematic characteristics and performance of the transplanting mechanism under idle and seedling-carrying states were studied. The results showed that the theoretical trajectory, virtual prototype simulation trajectory, and physical prototype test bench trajectory remained consistent within an acceptable error range, validating the consistency and correctness of the design of the transplanting mechanism. The performance test showed a seedling extraction success rate of 94%, a transplanting success rate of 92. 36%, and a coefficient of variation of planting spacing of 2. 67%, meeting the requirements of transplanting operation. This validated the rationality and feasibility of the design of the rice pot seedling transplanting mechanism on film. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 24
Main heading: MATLAB
Controlled terms: Agricultural robots? - ?Data visualization? - ?Digital elevation model? - ?Epicyclic gears? - ?Machine design? - ?Seed? - ?Software prototyping? - ?Structural dynamics? - ?Virtual prototyping
Uncontrolled terms: Combined mechanisms? - ?Gear — linkage combined mechanism? - ?Non-circular? - ?Non-circular planetary wheel system? - ?Optimization design? - ?Planetary wheel? - ?Rice pot seedling transplanting on film? - ?Seedlings transplanting? - ?Transplanting mechanisms? - ?Wheel system
Classification code: 1106.2 ? - ?1106.3.1 ? - ?1106.5 ? - ?1106.6 ? - ?1106.9 ? - ?1201.5 ? - ?408 Structural Design? - ?601 Mechanical Design? - ?601.2 Machine Components? - ?731.6 Robot Applications? - ?821.2 Agricultural Chemicals? - ?821.5 Agricultural Wastes? - ?904
Numerical data indexing: Percentage 3.60E+01%, Percentage 6.70E+01%, Percentage 9.40E+01%
DOI: 10.6041/j.issn.1000-1298.2024.08.005
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
25. Hyperspectral Non-destructive Detection of Nitrogen, Phosphorus and Potassium Content of Watermelon Seedling Leaves Based on Self - Attention - BiLSTM Network
Accession number: 20243516955957
Title of translation: 基于Self-Attention-BiLSTM网络的西瓜种苗叶片氮磷钾含量高光谱检测方法
Authors: Xu, Shengyong (1, 2); Liu, Zhengyi (1); Huang, Yuan (2, 3); Zeng, Yu (1); Bie, Zhilong (3); Dong, Wanjing (1)
Author affiliation: (1) College of Engineering, Huazhong Agricultural University, Wuhan; 430070, China; (2) Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen; 518000, China; (3) College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan; 430070, China
Corresponding author: Dong, Wanjing(dwj@mail.hzau.edu.cn)
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 8
Issue date: August 2024
Publication year: 2024
Pages: 243-252
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: Element content non-destructive testing technology can provide key real-time data for precise environmental regulation of plant growth and development. Taking watermelon seedlings as an example, a deep learning detection method based on graph feature fusion for nitrogen, phosphorus, and potassium content was proposed. Firstly, high-resolution hyperspectral images of watermelon seedling leaves were captured by using a hyperspectral image. The content of the three elements in the leaves was determined by using a continuous flow chemical analyzer. Then, the BOC - GF spectral preprocessing method and the RF algorithm were used to establish a prediction model. Based on the CARS and SPA algorithms, feature bands were preliminarily selected. Then, considering the number of bands and modeling accuracy, an optimal band evaluation method was designed to further reduce the number of bands to 3 ~ 4. Finally, the colour and texture features of the colour images segmented by using the U - Net network were extracted and used as inputs along with the spectral reflectance features to construct a prediction model for the three elemental contents based on the Self - Attention - BiLSTM network. The experimental results showed that the R2values for predicting nitrogen, phosphorus, and potassium content were 0. 961, 0. 954, and 0. 958, respectively, with corresponding RMSE values of 0. 294%, 0. 262%, and 0. 196% . These results indicated a high level of modeling accuracy. Using this model to test two other varieties of watermelon, the R2values exceeded 0. 899 and the RMSE values were less than 0. 498%, indicating that the model had excellent generalization ability. This hyperspectral modeling method achieved high accuracy detection with a small number of spectral bands, striking a good balance between precision and efficiency. It laied a solid theoretical foundation for the development of portable hyperspectral detection equipment in the future. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 30
Main heading: Phosphorus
Controlled terms: Ability testing? - ?Image texture? - ?Nitrogen? - ?Seed
Uncontrolled terms: BiLSTM? - ?Element contents? - ?HyperSpectral? - ?Modeling accuracy? - ?Nitrogen phosphorus? - ?Non destructive testing? - ?Nondestructive detection? - ?Prediction modelling? - ?Self-attention mechnism? - ?Watermelon seedling leaf
Classification code: 1106.3.1 ? - ?804 Chemical Products Generally? - ?821.5 Agricultural Wastes? - ?912.3 Operations Research
Numerical data indexing: Percentage 1.96E+02%, Percentage 2.62E+02%, Percentage 2.94E+02%, Percentage 4.98E+02%
DOI: 10.6041/j.issn.1000-1298.2024.08.022
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
26. Non-contact Core Body Temperature Detection Method for Caged Laying Hens
Accession number: 20243516947215
Title of translation: 非接触式笼养蛋鸡核心体温检测方法
Authors: Yan, Yu (1); Sheng, Zheya (2, 3); Gu, Yue (1); Heng, Yifan (1); Zhou, Haobo (2, 3); Wang, Shueai (1)
Author affiliation: (1) College of Engineering, Huazhong Agricultural University, Wuhan; 430070, China; (2) College of Animal Science and Technology, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan; 430070, China; (3) Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Wuhan; 430070, China
Corresponding author: Wang, Shueai(wsc01@mail.hzau.edu.cn)
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 8
Issue date: August 2024
Publication year: 2024
Pages: 312-321
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: Core body temperature (CBT) measurement of laying hens is very complex under cage breeding conditions. Meanwhile, traditional measurement methods also require handing the hens, can be stressful. Infrared thermography is an alternative means for assessing hens core temperature. A method was proposed for estimating the CBT of laying hens using infrared thermography and deep learning. A total of 10 994 infrared thermal images and corresponding CBT were collected through 172 hens. The hens facial were selected as region of interest (ROI). The YOLO v8s object detection algorithm was employed to automatically identify the ROI within the images. Additionally, the modified Res2Net50 network was used for regression training between ROI images and CBT values. Then the above two algorithms were combined to directly estimate the CBT of laying hens using infrared thermal images. Comparative experiments were conducted with four object detection algorithms (YOLO v4s, YOLO v5s, YOLO v7, YOLOX — s), and the results indicated that YOLO v8s achieved superior precision (99. 38%), mAP(99. 9%), and recall(99. 87%), compared with the other algorithms. Furthermore, seven algorithms (MobileNetV3, GhostNet, ShuffleNetV2, RegNet, ConvNeXt, Res2Net, MobileVIT) were compared with the modified Res2Net, and the results demonstrated that the modified Res2Net exhibited a higher coefficient of determination (R2) of 0. 973 64 and adjusted coefficient of determination (Rad-) of 0. 973 52 on the test images, surpassing the other algorithms. Finally, CBT estimation experiments were conducted by using the YOLO v8s ― Res2Net50 algorithm. Nine layers were randomly selected, and their infrared thermal images were input into the algorithm network. The results showed that the ROI could be fully identified, and the mean absolute error (MAE) of estimating CBT was 0. 153 °C. Thus the proposed deep learning model for CBT estimation can offer an effective automated detection method for assessing CBT in laying hens. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 35
Main heading: Deep learning
Controlled terms: Infrared heating? - ?Regression analysis? - ?Thermography (imaging)
Uncontrolled terms: Body temperature? - ?Caged laying hen? - ?Core body? - ?Core body temperature? - ?Detection methods? - ?Infrared thermal image? - ?Laying hens? - ?Region-of-interest? - ?Regions of interest? - ?YOLO v8s — res2net50
Classification code: 1101.2.1 ? - ?1202.2 ? - ?303.1 ? - ?746 Imaging Techniques
Numerical data indexing: Percentage 3.80E+01%, Percentage 8.70E+01%, Percentage 9.00E+00%, Temperature 4.26E+02K
DOI: 10.6041/j.issn.1000-1298.2024.08.028
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
27. Design and Testing of Field Disk Spreading Device Based on Improved YOLO v5n
Accession number: 20243516952445
Title of translation: 基于改进YOLO v5n的工厂化育秧田间铺盘装置设计与试验
Authors: J., Yu; Y., Li; Y., Zhou; W., Hu; S., Hao; L., Li
Author affiliation: (1) Zhejiang Institute of Mechanical and Electrical Engineering, Hangzhou; 310053, China; (2) Zhejiang Society for Agricultural Machinery, Hangzhou; 310003, China; (3) Zhejiang Institute of Industry and Information Technology, Hangzhou; 310003, China; (4) Zhengyang Technology Co., Ltd., Jinhua; 321300, China; (5) Zhejiang Modern Agricultural Equipment Design and Research Institute, Hangzhou; 310003, China; (6) School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou; 310018, China
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 8
Issue date: August 2024
Publication year: 2024
Pages: 71-80 and 116
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: Aiming at the problems of low automation and high cost of field spreading device for factory planting seedlings, a fully automated bilateral rail type field spreading device with a visual detection module for abnormal bumps in seedbed was designed. Firstly, the working principle of the tray spreading structure was analyzed, and then the structural design, force analysis and simulation analysis were carried out on the full-load operation condition of the tray spreading device. In order to prevent the abnormal bulge of seedbed from tilting the seedling tray when spreading the tray, which affected the survival rate of seedling refining, an abnormal bulge target recognition algorithm was proposed based on CBAM — YOLO v5n, and the improved YOLO v5n algorithm added the attention mechanism, and the average values of accuracy, recall, and average precision for the detection of the abnormal bulge target of the seedbed were respectively 98. 1%, 91. 7% and 94. 9%, which were 1. 2 percentage points, 1. 7 percentage points and 0. 9 percentage points higher than that of the original model, respectively. The developed tray-laying prototype was tested by orthogonal test method, and the test results showed that when the height of tray-laying was 90 mm, the rotational speed of tray-laying mechanism was 550 r/min, and the translational speed of tray-laying box was 0. 14 m/s, the highest tray-laying success rate was 96.4%, and after implanting the machine vision module, the tray-laying success rate can reach 99. 3% . The designed tray spreading device can effectively reduce the labor intensity of manual tray spreading and reduce the labor cost of tray spreading. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 14
Main heading: Machine vision
Controlled terms: Agricultural robots? - ?Cost reduction? - ?Seed? - ?Wages
Uncontrolled terms: Bilateral rail type? - ?Factory seeding? - ?Field spreading tray? - ?Fully automated? - ?High costs? - ?Machine-vision? - ?Percentage points? - ?Plantings? - ?Visual detection? - ?YOLO v5n
Classification code: 1106.8 ? - ?731.6 Robot Applications? - ?821.2 Agricultural Chemicals? - ?821.5 Agricultural Wastes? - ?911.2 Industrial Economics? - ?912.3 Operations Research
Numerical data indexing: Angular velocity 9.185E+00rad/s, Percentage 1.00E00%, Percentage 3.00E+00%, Percentage 7.00E+00%, Percentage 9.00E+00%, Percentage 9.64E+01%, Size 9.00E-02m, Velocity 1.40E+01m/s
DOI: 10.6041/j.issn.1000-1298.2024.08.006
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
28. Semi-supervised Network for Remote Sensing Crop Mapping Based on Improved AdvSemiSeg
Accession number: 20243516947197
Title of translation: 基于改进 AdvSemiSeg 的半监督遥感影像作物制图方法
Authors: Zhai, Xuedong (1); Han, Wenting (1, 2); Ma, Weitong (3); Cui, Xin (1); Li, Guang (4); Huang, Shenjin (5)
Author affiliation: (1) College of Mechanical and Electronic Engineering, Northwest A&F University, Shaanxi, Yangling; 712100, China; (2) Institute of Water-saving Agriculture in Arid Areas of China, Northwest A&F University, Shaanxi, Yangling; 712100, China; (3) College of Water Resources and Architectural Engineering, Northwest A&F University, Shaanxi, Yangling; 712100, China; (4) Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing; 400714, China; (5) Faculty of Computing, Harbin Institute of Technology, Harbin; 150001, China
Corresponding author: Han, Wenting(hanwt2000@126.com)
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 8
Issue date: August 2024
Publication year: 2024
Pages: 196-204
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: Crop precision remote sensing mapping holds significant importance for agricultural resource surveys and management. Deep learning provides technical support for achieving accurate and efficient crop mapping. To alleviate the dependency of deep learning on labeled samples, an improved semi-supervised remote sensing crop mapping method was proposed based on AdvSemiSeg. The proposed method introduced STMF ― DeepLabv3 + as the generator in the adversarial learning framework, enhancing the feature encoding and semantic expression capabilities of the generator through Swin Transformer (ST) and multi-scale fusion (MF) modules, thus improving the segmentation performance of remote sensing crop images. Additionally, the efficient channel attention (ECA) module was introduced after each convolutional layer of the discriminator to adaptively learn the representation information of different channel feature maps, enhancing the discriminator’s perception of different channel features. During the training process, the discriminator provided high-quality pseudo-labels and adversarial losses to the generator, effectively improving the generalization ability of the generator. Compared with several advanced semi-supervised semantic segmentation methods, the proposed method achieved optimal performance in extracting planting information from remote sensing images in the Hetao Irrigation District of Inner Mongolia. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 31
Main heading: Discriminators
Controlled terms: Adversarial machine learning? - ?Deep learning? - ?Generative adversarial networks? - ?Image enhancement? - ?Mapping? - ?Self-supervised learning? - ?Semantic Segmentation? - ?Semi-supervised learning
Uncontrolled terms: Adversarial networks? - ?Channel attention? - ?Crop mapping? - ?Features fusions? - ?Multi-scale feature fusion? - ?Multi-scale features? - ?Remote-sensing? - ?Semi-supervised? - ?Semi-supervised learning? - ?Supervised network
Classification code: 1101.2 ? - ?1101.2.1 ? - ?1106.3.1 ? - ?1106.8 ? - ?405.3 Surveying? - ?713.3 Modulators, Demodulators, Limiters, Discriminators, Mixers
DOI: 10.6041/j.issn.1000-1298.2024.08.017
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
29. Path Planning of Agricultural Robot Based on Improved A * and LM - BZS Algorithms
Accession number: 20243516947198
Title of translation: 基于改进A*算法+LM-BZS算法的 农业机器人路径规划
Authors: Zhang, Wanzhi (1, 2); Zhao, Wei (1, 2); Li, Yuhua (1, 3); Zhao, Lejun (1, 3); Hou, Jialin (1, 2); Zhu, Qian (1, 2)
Author affiliation: (1) College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian; 271018, China; (2) Shandong Provincial Engineering Research Center for Intelligent Agricultural Equipment, Taian; 271018, China; (3) Shandong Provincial Key Laboratory of Horticultural Machinery and Equipment, Taian; 271018, China
Corresponding author: Hou, Jialin(jlhou@sdau.edu.cn)
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 8
Issue date: August 2024
Publication year: 2024
Pages: 81-92
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: In order to solve the problems of low planning efficiency, many polyline segments of the planning path, large polyline angle and unstable operation of agricultural robots in the process of global path planning, a path planning method based on improved A * algorithm and low-order multisegment Bezier curve splicing (LM ― BZS) algorithms was proposed by taking the orchard crawler robot as the kinematic model. To begin with, the orchard environment information was obtained according to the prior map, the fruit trees and the obstacles were regarded as impassable regions, and the impassable regions were expanded and fitted according to the dimensions of the robot body. And then, the improved A algorithm was used to search for the path, and the tree row nodes were adjusted for the preliminary generation path. In the end, the LM ― BZS algorithm was used to optimize the adjusted path points to generate a driving path that meets the operation requirements of the orchard crawler robot. The simulation results manifested that compared with the traditional A * algorithm, the improved algorithm proposed reduced the path planning time by 76. 75% and 86. 40%, and the number of evaluation nodes by 36. 68% and 39. 37%, respectively in the barrier-free and obstacle environments. In the barrier-free environment, the average curvature of the path optimized by the LM ― BZS algorithm was reduced by 45. 81 % and 18. 94% compared with that of the traditional A” algorithm and the high-order Bezier curve algorithm, respectively, and the average curvature was reduced by 56. 98% and 27. 81 % compared with that of the traditional A * algorithm and the higher-order Bezier curve algorithm in the obstacle environment. The field test results manifested that in the barrier-free and obstacle environment, the maximum lateral error was 0. 428 m and 0. 491 m, the average lateral error was 0. 232 m and 0. 276 m, and the average course deviation was 11. 06° and 13. 76° respectively, which was in line with the autonomous driving conditions of the orchard crawler robot. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 31
Main heading: Orchards
Controlled terms: Agricultural robots? - ?Curve fitting? - ?Fertilizers? - ?Industrial robots? - ?Motion planning? - ?Robot programming? - ?Trees (mathematics)
Uncontrolled terms: A* algorithm? - ?Agricultural robot? - ?Barrier-free? - ?Bezier curve? - ?High-order? - ?Improved A * algorithm? - ?Low order? - ?Low-order multisegment bezy curve splicing algorithm? - ?Multi-segment? - ?Polyline
Classification code: 1101 ? - ?1106.1 ? - ?1201.8 ? - ?1201.9 ? - ?1502.1.1.3 ? - ?731.5 Robotics? - ?731.6 Robot Applications? - ?821.2 Agricultural Chemicals? - ?821.3 Agricultural Methods? - ?821.4 Agricultural Products
Numerical data indexing: Percentage 3.70E+01%, Percentage 4.00E+01%, Percentage 6.80E+01%, Percentage 7.50E+01%, Percentage 8.10E+01%, Percentage 9.40E+01%, Percentage 9.80E+01%, Size 2.32E+02m, Size 2.76E+02m, Size 4.28E+02m, Size 4.91E+02m
DOI: 10.6041/j.issn.1000-1298.2024.08.007
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
30. Online Detection Method of Corn Kernel Quality Based on FSLYOLO v8n
Accession number: 20243516952438
Title of translation: 基于FSLYOLO v8n的玉米籽粒收获质量在线检测方法研究
Authors: Zhang, Weiran (1, 2); Du, Yuefeng (1, 2); Li, Xiaoyu (1, 2); Liu, Lei (1, 2); Wang, Linze (1, 2); Wu, Zhikang (1, 2)
Author affiliation: (1) College of Engineering, China Agricultural University, Beijing; 100083, China; (2) Beijing Key Laboratory of Optimized Design for Modern Agricultural Equipment, China Agricultural University, Beijing; 100083, China
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 8
Issue date: August 2024
Publication year: 2024
Pages: 253-265
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: The broken rate and impurity rate of corn kernels are key indicators for evaluating the quality of corn harvest. Aiming at the demand for online detection of corn harvest quality in complex agricultural environments, a lightweight detection method for corn kernel broken rate and impurity rate suitable for small and large detection targets was proposed. Firstly, a quantity and quality regression model was established for complete kernels, broken kernels, corn cobs, and corn bracts, and an evaluation method for kernel broken rate and impurity rate was proposed. Secondly, an improved FSLYOLO v8n algorithm was proposed to address the characteristics of similar grain and impurity sizes, large number of detection objects, and small detection area. The algorithm improved the backbone network structure through FasterBlock module and small detection area and parameter free attention mechanism SimAM, and improved detection head by using shared convolution combined with scale module. In addition, the SlidLoss function was used to replace the original category classification loss function of YOLO v8n. The average accuracy of the improved FSLYOLO v8n model mAP@ 50 was 97.46%, FPS was 186.4 f/s, which was 6. 35% and 45 f/s higher than that of YOLO v8n. The network parameters and floating-point operations were compressed to 66.50% and 64.63% of YOLO v8n, respectively. The model size was only 4. 0 MB, and its performance was better than the commonly used lightweight models. The bench experiment showed that the proposed model can accurately detect the broken and impurity rate of corn kernels. The accuracy of the detection results was as high as 95. 33% and 96. 15% . The improved model was deployed on the Jetson TX2 development board and the device was installed on a corn combine harvester for field experiments. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 21
Main heading: Regression analysis
Controlled terms: Grain (agricultural product)? - ?Health risks
Uncontrolled terms: Broken rate? - ?Corn? - ?Corn kernels? - ?Direct kernel harvesting? - ?FSLYOLO v8n? - ?Harvest quality? - ?Impurity rates? - ?Key indicator? - ?On-line detection? - ?On-line detection method
Classification code: 102.1.2.1 ? - ?1202.2 ? - ?821.5 Agricultural Wastes
Numerical data indexing: Percentage 1.50E+01%, Percentage 3.30E+01%, Percentage 3.50E+01%, Percentage 6.463E+01%, Percentage 6.65E+01%, Percentage 9.746E+01%
DOI: 10.6041/j.issn.1000-1298.2024.08.023
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
31. Calibration Method of Soil Moisture Capacitance Sensor Based on Compensation of Electrical Conductivity to Relative Permittivity
Accession number: 20243516947194
Title of translation: 基于电导率对相对介电常数补偿的土壤含水率 电容传感器标定方法
Authors: Zhang, Xiliang (1); Xie, Feiyang (1); Sheng, Qingyuan (2); Ni, Mengyao (1); Zhang, Jiaqi (1); Xu, Yunfeng (1)
Author affiliation: (1) School of Mechanical Engineering, Jiangsu University, Zhenjiang; 212013, China; (2) School of Mechanical and Electrical Engineering and Transportation, Shaoxing Vocational and Technical College, Shaoxing; 312000, China
Corresponding author: Xu, Yunfeng(xuyunfeng@ujs.edu.cn)
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 8
Issue date: August 2024
Publication year: 2024
Pages: 344-351
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: Soil moisture capacitive sensors exhibited issues with low detection accuracy and limited applicability of calibration models to saline soils. In order to solve these problems, a calibration method for moisture capacitive sensors based on conductivity compensation of the relative permittivity was proposed. Firstly, a logarithmic model between soil relative permittivity and sensor output was established in the standard solution, and the R of the model was 0. 983. Furthermore, a regression calibration model of dielectric constant compensation for standard solution conductivity was established through the binary quadratic regression analysis, and the R of the model was 0.979. The calibration model was about relative permittirity, the output voltage and conductivity. Secondly, the third-order polynomial calibration model of the relationship between soil volume water content and relative permittivity was established according to the special calibration of soil sensor in soil sample, and the R of the model was 0. 996. Finally, the above two-step calibration equation was verified by soil measurement. When the soil conductivity ranged between 0 dS/m and 2 dS/m, the detection error range of soil volumetric moisture content was reduced from 0. 038 3 m3/m3 to 0. 012 7 m3/m3, and the maximum relative error was reduced from 12. 020 0% to 6. 224 1 %. The results indicated that, in similar soils with different conductivities, using the calibration method for moisture sensors based on conductivity compensation of the relative permittivity can significantly improve the accuracy of soil moisture detection and the applicability to loess soils with different conductivities. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 32
Main heading: Soil moisture
Controlled terms: Calibration? - ?Capacitance? - ?Error compensation? - ?Moisture meters? - ?Polynomial regression? - ?Polynomials? - ?Soil surveys? - ?Strain measurement? - ?Velocity measurement
Uncontrolled terms: %moisture? - ?Binary regression? - ?Binary regression analyze? - ?Calibration model? - ?Capacitance sensors? - ?Conductivity compensation? - ?Moisture capacitance sensor? - ?Relative permittivity? - ?Sensor calibration
Classification code: 1201.1 ? - ?1202.2 ? - ?405.3 Surveying? - ?483.1 Soils and Soil Mechanics? - ?701.1 Electricity: Basic Concepts and Phenomena? - ?731.1.1 ? - ?941.5?≠ ? - ?942.1.7 ? - ?942.1.8
Numerical data indexing: Percentage 0.00E00% to 6.00E+00%, Percentage 1.00E00%, Volume 3.00E+00m3, Volume 7.00E+00m3
DOI: 10.6041/j.issn.1000-1298.2024.08.031
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
32. Design and Flow Field Analysis of Bionic Blade of Banana Straw Crushing and Throwing Machine
Accession number: 20243516952444
Title of translation: 香蕉秸秆粉碎抛撒还田机仿生刀片设计与试验
Authors: Zhang, Ximi (1); Ni, Shilei (1); Liu, Junxiao (1); Hu, Xuhang (1); Zhang, Zhifu (1); Fu, Shaohua (2)
Author affiliation: (1) School of Mechanical and Electrical Engineering, Hainan University, Haikou; 570228, China; (2) School of Information and Communication Engineering, Hainan University, Haikou; 570228, China
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 8
Issue date: August 2024
Publication year: 2024
Pages: 138-151 and 160
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: To solve the problems of poor crushing effect and uneven scattering of banana straw crusher in Hainan Province during operation, a field returning machine was designed with a biomimetic serrated grinding knife with good straw crushing performance. Applying the principles of bionics to the design of crushing blades, a biomimetic serrated crushing knife was designed based on the serrated profile of blue shark teeth. The cutting ability of the biomimetic serrated knife on banana straw was verified through simulation, and a mixed use of the biomimetic serrated blade and traditional crushing knife was designed. An experimental plan was developed for machine simulation and field experiments. Using Fluent flow field analysis software, the effects of parameters such as the speed of the crushing blade shaft, the height of the crushing chamber from the ground, and the distance of the crushing blade tip from the casing on the pressure and velocity fields at different positions in the banana straw crushing and returning machine during operation were studied. When the spindle speed was 2 000 r/min, the feeding characteristics of the crushing chamber were the best. The higher the height of the crushing chamber from the ground was, the better the feeding characteristics of the straw were, but too high or too low of the height from the ground can lead to a decrease in the fluidity of the straw in the crushing chamber. Increasing the gap between the tip of the crushing knife and the casing can have a negative impact on the scattering of straw. Through simulation and field experiments, it can be concluded that the combination of biomimetic serrated blade and traditional crushing blade can ensure the straw picking ability while improving the crushing qualification and scattering uniformity of the entire machine. The research result can provide support for the design and optimization of banana straw returning machines. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 38
Main heading: Crushers
Controlled terms: Grinding (machining)
Uncontrolled terms: Banana straw? - ?Biomimetic blade? - ?Bionic blade? - ?Crushing and returning farmland? - ?Crushing chambers? - ?Field experiment? - ?Flow field characteristics? - ?Flow fields analysis? - ?Hainan Province? - ?Throwing machines
Classification code: 502 Mines and Quarry Equipment and Operations? - ?604.2 Machining Operations
Numerical data indexing: Angular velocity 0.00E00rad/s
DOI: 10.6041/j.issn.1000-1298.2024.08.012
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
33. Growth Monitoring of Spring Maize Using UAV Multispectral Imaging Based on Entropy Weight Fuzzy Comprehensive Evaluation Method
Accession number: 20243516955953
Title of translation: 基于熵权-模糊综合评价法的无人机多光谱春玉米长势监测模型研究
Authors: Zhao, Jinghua (1, 2); Ma, Shijiao (1, 2); Fang, Chengtai (3)
Author affiliation: (1) College of Water Conservancy and Civil Engineering, Xinjiang Agricultural University, Urumqi; 830052, China; (2) Xinjiang Key Laboratory of Hydraulic Engineening Safety and Water Disaster Prevention, Urumqi; 830052, China; (3) Corps Soil and Water Conservation and Water Resources Development Center, Urumqi; 830002, China
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 8
Issue date: August 2024
Publication year: 2024
Pages: 214-224
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: To achieve rapid monitoring of spring maize growth and gain real-time understanding of field crop conditions, focusing on spring maize planted in the Karamay region of Xinjiang, utilizing UAV multispectral imagery for growth monitoring of the spring maize, based on ground-collected data on spring maize leaf chlorophyll content, leaf area index, aboveground biomass, and plant height, comprehensive growth indicators CGMIEWM and CGMIFCEwere established by combining the entropy weight method (EWM) and fuzzy comprehensive evaluation (FCE) . Spectral indices were constructed by using UAV remote sensing imagery data, and the optimal input variables for the model were determined by using Pearson correlation analysis and the variance inflation factor. Partial least squares (PLS), random forest regression (RF), and particle swarm optimization (PSO) were used to optimize the RF model and establish a spring maize growth inversion model. By combining model accuracy evaluation metrics, the spatial distribution map of spring maize growth was ultimately determined. The results showed that the comprehensive growth indicators constructed using CGMIEWM and CGMIECE had higher correlations than single growth indicators. The growth indicators derived from CGMIECE, combined with the PSO - RF model, resulted in the best performance for inversion of spring maize growth. The coefficient of determination (R2) was 0.823, the root mean square error (RMSE) was 0.084%, and the relative percent deviation (RPD) was 2.345. The growth of spring maize in the study area was mostly concentrated in the normal growth (ZZ) category, indicating relatively stable growth across the region. The research results can provide a scientific basis for the field management of spring maize and offer a data foundation for the development of precision agriculture. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 34
Main heading: Mean square error
Controlled terms: Information management? - ?Leaf springs? - ?Regression analysis
Uncontrolled terms: Comprehensive growth indicator? - ?Entropy weight method? - ?Fuzzy-comprehensive evaluations? - ?Growth monitoring? - ?Multi-spectral? - ?Particle swarm? - ?Random forests? - ?Spring maize? - ?Swarm optimization? - ?UAV monitoring
Classification code: 1202.2 ? - ?601.2 Machine Components? - ?903 Information Science? - ?912.2 Management
Numerical data indexing: Percentage 8.40E-02%
DOI: 10.6041/j.issn.1000-1298.2024.08.019
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
34. Design and Experiment of Bionic Cutting Blades for Panax notoginseng Stem and Leaf Harvesting Machine
Accession number: 20243516947335
Title of translation: 三七茎叶采收机仿生切割刀片设计与试验
Authors: Zheng, Jiaxin (1, 2); Wang, Shishun (1); Ma, Long (1); Yang, Wencai (1, 2); Jin, Zhiwei (3); Yan, Yi (3); Zhu, Longtu (1)
Author affiliation: (1) Faculty of Mechanical and Electrical Engineering, Yunnan Agricultural University, Kunming; 650500, China; (2) Plateau-characteristic Modern Agricultural Equipment Engineering Research Center for Colleges, Universities in Yunnan, Kunming; 650500, China; (3) Kunming Haikou Forest Farm, Kunming; 650114, China
Corresponding author: Zhu, Longtu(zhult2020@163.com)
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 8
Issue date: August 2024
Publication year: 2024
Pages: 117-126
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: In response to the obvious shortcomings of traditional Panax notoginseng stem and leaf harvesting machine cutting blades in sliding cutting resistance reduction and blade edge sharpness, taking the upper jaw structure characteristics of leaf cutting ants as a biomimetic prototype, reverse engineering technology was used to extract the upper jaw contour curve of leaf cutting ants. Two different bionic cutting blades, A and B, were designed based on the sharp end of the cutting tooth tip and the upper jaw contour curve of leaf cutting ants : EDEM simulation and bench comparison experiments were conducted, and the simulation results showed that the average maximum shear forces of bionic blades A and B were reduced by 7. 74% and 3. 07% compared with that of traditional blades, respectively. The bench test results showed that the average maximum shear force of bionic blades A and B was reduced by 8. 84% and 2. 53% compared with that of traditional blades, respectively, and bionic blades A and B had a significant effect on improving the flatness of the transverse cutting surface of Panax notoginseng stem. The maximum shear force errors measured by the three blade simulation tests and bench tests were all not more than 3. 64%, and the simulation test results were basically consistent with the actual test results. Using blade shape, cutting angle, and cutting speed as experimental factors, an orthogonal experiment was conducted to determine the optimal parameter combination as bionic blade A, cutting angle 0°, and cutting speed 400 mm/min. Based on the optimal parameter combination, field experiments were conducted, and the results showed that the average intact rate of harvesting Panax notoginseng stems and leaves was 97. 37%, which was 2. 01 percentage points higher than thta of traditional blades. The average missed cutting rate was 2. 64%, which was 1. 46 percentage points lower than that of traditional blades. This indicated that the bionic blade designed with the sharp end of the cutting teeth on the upper jaw of the leaf cutting ant can effectively improve the operational performance of the Panax noto ginseng harvester. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 33
Main heading: Reverse engineering
Controlled terms: Contour followers
Uncontrolled terms: Bench tests? - ?Bionic blade? - ?Contour curves? - ?Cutting blades? - ?Cutting teeth? - ?Harvesting machine of panax noto ginseng stem and leaf? - ?Harvesting machines? - ?Leaf-cutting ants? - ?Maximum shear forces? - ?Panax notoginseng
Classification code: 601.3 Mechanisms? - ?603 Machine Tools? - ?901.3 Engineering Research
Numerical data indexing: Percentage 3.70E+01%, Percentage 5.30E+01%, Percentage 6.40E+01%, Percentage 7.00E+00%, Percentage 7.40E+01%, Percentage 8.40E+01%, Size 4.00E-01m
DOI: 10.6041/j.issn.1000-1298.2024.08.010
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
35. YSVD - Tea Algorithm for Tea Bud Object Detection Based on Few Annotated Samples
Accession number: 20243516947328
Title of translation: 基于少量标注样本的茶芽目标检测 YSVD-Tea 算法
Authors: Zheng, Ziqiu (1); Song, Yan (1, 2); Chen, Lin (1); Zhang, Hang (1); Ning, Jingming (3)
Author affiliation: (1) School of Engineering, Anhui Agricultural University, Hefei; 230036, China; (2) Anhui Provincial Engineering Research Center of Intelligent Agricultural Machinery, Hefei; 230036, China; (3) State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei; 230036, China
Corresponding author: Song, Yan(songyan@ahau.edu.cn)
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 8
Issue date: August 2024
Publication year: 2024
Pages: 301-311
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: Constructing a large-scale dataset for tea bud object detection is a time-consuming and intricate task. To mitigate the cost of dataset construction, exploring algorithms with a minimal number of annotated samples is particularly necessary. The YOLO singular value decomposition for tea bud detection (YSVD ― Tea) algorithm was introduced, which achieved the reconstruction of the YOLOX structure by replacing the basic convolution in the pre-trained model with three consecutive matrix structures. Through dimension transformation and singular value decomposition operations, pre-trained weights were converted into weights corresponding to the reconstructed algorithm structure, thereby separating the weights that require transfer learning from those that needed to be retained. This achieved the goal of preserving the general semantic information of the pre-trained model. Training and validation on three datasets of varying sizes were conducted. On the smallest 1/3 dataset, the YSVD ― Tea algorithm showed a 20. 3 percentage points improvement in mAP compared with the original YOLOX algorithm. Comparing performance metrics between the test and training sets, the mAP difference for the YSVD ― Tea algorithm was only 21. 9%, which was significantly lower than YOLOX’s 40. 6% and Faster R - CNN’s 55. 4%. In training with the largest complete dataset, the YOLOX algorithm achieved precision, recall, Fl score, and mAP of 86. 4%, 87. 0%, 86. 1%, and 88. 3%, respectively, surpassing the comparison algorithms. YSVD -Tea algorithm demonstrated superior suitability for the task of tea bud object detection, especially when confronted with a limited number of annotated samples. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 30
Main heading: Large datasets
Controlled terms: Network security
Uncontrolled terms: Detection algorithm? - ?Large-scale datasets? - ?Matrix structure? - ?Objects detection? - ?Singular values? - ?Small Sample Size? - ?TEA algorithms? - ?Tea bud? - ?Value decomposition? - ?YOLOX
Classification code: 1106 ? - ?1106.2
Numerical data indexing: Percentage 0.00E00%, Percentage 1.00E00%, Percentage 3.00E+00%, Percentage 4.00E+00%, Percentage 6.00E+00%, Percentage 9.00E+00%
DOI: 10.6041/j.issn.1000-1298.2024.08.027
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
36. State-of-the-art and Prospect on Sliding Identification and Control of Agricultural Machinery
Accession number: 20243416915598
Title of translation: 农业装备行驶滑动辨识与控制研究现状与展望
Authors: Du, Xiaoqiang (1, 2); Hong, Fangwei (1, 3); Ma, Zenghong (1, 4); Li, Yuechan (1); Zhao, Lijun (5)
Author affiliation: (1) School of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou; 310018, China; (2) Zhejiang Provincial Key Laboratory of Agricultural Intelligent Perception and Robotics, Hangzhou; 310018, China; (3) The Collaborative Innovation Center for Intelligent Production Equipment of Characteristic Forest Fruits in Hilly and Mountainous Areas of Zhejiang Province, Hangzhou; 310018, China; (4) Key Laboratory of Agricultural Equipment for Hilly and Mountainous Areas in Southeastern China (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Hangzhou; 310018, China; (5) College of Intelligent and Manufacturing Engineering, Chongqing University of Arts and Sciences, Chongqing; 402160, China
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 8
Issue date: August 2024
Publication year: 2024
Pages: 1-20
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: When agricultural machinery is driving in an agricultural environment, it is inevitable that common slip phenomena will occur and have obvious uncertainty. It has seriously hindered the development of agricultural machinery informatization and intelligence in planting, mid?tillage management and harvesting, which require precise operation. The current status of domestic and international research on sliding dynamics properties, sliding identification methods and path tracking control considering sliding were reviewed from three aspects, namely, sliding principle, sliding identification and traveling sliding control, respectively. Among them, researches about the sliding principle focused on the structural characteristics and driving ground environment of different steering mechanisms established by domestic and foreign scholars, and the models of various steering mechanisms and ground system were established. The sliding identification method was divided into two categories based on mathematical model and data?driven method, and the advantages and limitations of each method were discussed. Researches about the driving sliding control focused on the path following control method applied to agricultural machinery, and the limitations of the current driving sliding control were pointed out. Finally, it was pointed out that the research on driving slip detection was of great significance to the development of agricultural machinery automation, and it provided directional suggestions for the research on driving slip detection and control of agricultural machinery. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 112
Main heading: Steering
Controlled terms: Agricultural robots
Uncontrolled terms: Agricultural environments? - ?Agricultural equipment? - ?Driving sliding? - ?Identification method? - ?Path tracking control? - ?Sliding control? - ?Sliding identification? - ?Slip-detection? - ?State of the art? - ?Steering mechanisms
Classification code: 731.6 Robot Applications? - ?821.2 Agricultural Chemicals
DOI: 10.6041/j.issn.1000-1298.2024.08.001
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
37. Research Progress on Mechanized Harvesting Equipment and Technology for Peanuts
Accession number: 20243416915559
Title of translation: 花生机械化收获装备与技术研究进展
Authors: Shen, Haiyang (1); Luo, Weiwen (1); Wu, Feng (1); Gu, Fengwei (1); Yang, Hongguang (1); Hu, Zhichao (1)
Author affiliation: (1) Nanjing Instituteof Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing; 210014, China
Corresponding author: Hu, Zhichao(huzhichao@caas.cn)
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 8
Issue date: August 2024
Publication year: 2024
Pages: 21-38
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: Peanut harvesting is mainly characterized by strong seasonality, high labor intensity, low efficiency, and large harvesting losses. Therefore, peanut production needs mature mechanized harvesting technology. The mechanization level of China’s peanut harvest in the peanut production process is relatively low, significantly hindering the overall advancement of mechanization in China’s peanut industry. A systematic summary was provided based on three peanut harvesting operation modes (combined harvesting operation mode, two-stage harvesting operation mode, and three-stage harvesting operation mode). The study focused on the digging device and fruit-picking device utilized in China’s mechanized peanut harvesting. The performance and characteristics of various peanut harvesters were elaborated, including the conveyor chain peanut harvester, strip spreading peanut harvester, digging and tilling peanut harvester, as well as semi-feeding and full-feeding peanut combine harvester. Simultaneously, an analysis of mechanized peanut harvesting technology in the United States was carried out, accompanied by a brief description of the Indian peanut harvester. The characteristics of peanut mechanized harvesting equipment were summarized, providing insights into the challenges faced by peanut harvesting machinery in China. The analysis suggested that the future of peanut mechanization in China was poised to enter a new stage characterized by intelligence, refinement, and enhanced efficiency. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 117
Uncontrolled terms: Combined harvesting? - ?Harvesting operations? - ?Labour intensity? - ?Mechanisation? - ?Mechanized harvesting? - ?Operation mode? - ?Peanut? - ?Production process? - ?Seasonality? - ?Segmented harvesting
DOI: 10.6041/j.issn.1000-1298.2024.08.002
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
38. Design and Test of Spoon Chain Seed Guide Device for Grain Hole Seeder
Accession number: 20243416915611
Title of translation: 谷子穴播机勺链式导种装置设计与试验
Authors: Yi, Shujuan (1); Sun, Tinghan (1); Chen, Tao (1); Li, Yifei (1); Li, Yikai (1)
Author affiliation: (1) College of Engineering, Heilongjiang Bayi Agricultural University, Daqing; 163319, China
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 8
Issue date: August 2024
Publication year: 2024
Pages: 39-52
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: Aiming at the problem that grains bounce badly in the seed guide tube when sowing, resulting in poor hole formation after landing, a spoon chain seed guide device for grain hole seeder was designed. Its working principle was to improve the hole formation after landing by restraining the seed transport trajectory, the key components were designed, the motion analyses were carried out on the seed storage and seed casting process, and the important factors affecting the performance of the seed guide were determined. In order to obtain the best combination of parameters, three types of spoons, namely, round spoon, square spoon and square flat spoon, and three types of materials, namely, ABS, rubber and acrylic, were used as factors in the one-way test, and the test results showed that the square flat spoon and the rubber material were the most effective. On this basis, the angle of incorporation of seeds, height of incorporation seed and sprocket speed were taken as the incorporation of seed test factors, and the seeding qualification rate was taken as the evaluation index; the seeding height, seeding angle and sprocket speed were taken as the seeding test factors, and the qualification rate of hole diameter, qualification rate of the number of holes and the coefficient of variation of hole spacing were taken as the seeding evaluation indexes, and the three-factor, five-level quadratic rotational combination test was carried out for the seeding and the seeding process through the EDEM discrete element simulation software, respectively. The optimal parameter combinations for the seed-retention process were seed-retention angle of 45. 95°, seed-retention height of 74. 05 mm, and sprocket rotational speed of 1. 76 r / s; and the optimal parameter combinations for the seed-introduction process were seed-introduction angle of 53. 51°, seed-introduction height of 25. 96 mm, and sprocket rotational speed of 1. 98 r / s. In order to further validate the performance of seed-guiding device, bench-top validation test and comparative test were carried out under the optimal parameter combinations. In the test, the seed qualification rate was 95. 53%, the hole diameter qualification rate was 93. 29%, the hole number qualification rate was 94. 73%, and the coefficient of variation of hole spacing was 7. 46%, and the results of the bench validation test were basically the same as those of the simulation test, with the relative differences being less than 5%; and the results of the comparative test showed that the sowing effect of the installation of the spoon chain seed-guiding device was significantly better than that of the installation of the seed-guiding tube, and the hole diameter qualification rate. The qualified rate of hole diameter, qualified rate of hole number and coefficient of variation of hole spacing can be improved by 2. 75, 3. 43 and 3. 25 percentage points. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 27
Main heading: Chains
Controlled terms: Error compensation? - ?Grain (agricultural product)? - ?Splines? - ?Sprockets
Uncontrolled terms: Cereal hole planter? - ?Coefficients of variations? - ?Evaluation index? - ?Guide device? - ?Hole diameter? - ?Hole formation? - ?Optimal parameter combinations? - ?Performance? - ?Seed guide device? - ?Spoon chain
Classification code: 601.2 Machine Components? - ?602 Mechanical Drives and Transmissions? - ?602.1 Mechanical Drives? - ?731.1.1 ? - ?821.5 Agricultural Wastes
Numerical data indexing: Percentage 2.90E+01%, Percentage 4.60E+01%, Percentage 5.00E+00%, Percentage 5.30E+01%, Percentage 7.30E+01%, Size 5.00E-03m, Size 9.60E-02m
DOI: 10.6041/j.issn.1000-1298.2024.08.003
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
39. Design and Experiment of Pneumatic Feeding System for Freshwater Fish in Juanyang Mode
Accession number: 20243516947278
Title of translation: 淡水鱼圈养模式气送式投饲系统设计与试验
Authors: Kong, Xianrui (1, 2); Zhang, Qing (1); Li, Hongbo (1); Niu, Zhiyou (1, 2); Huang, Huang (1, 2); Liu, Meiying (1, 2); Liu, Jing (1, 2)
Author affiliation: (1) College of Engineering, Huazhong Agricultural University, Wuhan; 430070, China; (2) Key Laboratory of Smart Farming for Agricultural Animals, Ministry of Agriculture and Rural Affairs, Wuhan; 430070, China
Corresponding author: Liu, Jing(meliujing@mail.hzau.edu.cn)
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 8
Issue date: August 2024
Publication year: 2024
Pages: 152-160
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: Due to the problem of excessive waste accumulation and serious water pollution in traditional feeding mode, high-quality, high-yield and intensive feeding modes for freshwater fish were applied to meet the demand for water products in China, such as industrial aquaculture, containerized aquaculture and pond Juanyang mode. A pneumatic and automatic feeding system was designed, aiming at the problems of high intensity of labour and low degree of automation. The functions of directional, timing and quantitative feeding were realized, combined with Bluetooth control. The main content was summarized as follows : the specific four-way and three-way units were designed to ensure the directional delivery of feed. Based on Computational fluid dynamics ― Discrete element method (CFD ― DEM) coupling technology, the feeding speed was initially determined, and the fan was selected according to the feeding speed. The feeding system was developed with Arduino Mega 2560 processor : the time and weight information in this system were obtained by clock module and weighting sensor : the data of expected feeding time, feeding weight and feeding area was transmitted through Bluetooth module. Finally, the prototype was performed and tested. For two working conditoins, the error of average feeding distance between experiment and simulation were 5. 74% and 9. 54% - The directional, timing and quantitative feeding could be achieved accurately, with response time less than 1 s. The maximum error of quantified feeding was 5. 37%. The results showed that the automatic feeding system can meet the real feeding requirements with reliable structure and high-accuracy control system. This research can provide a solution for the feeding problem of freshwater fish farming. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 19
Main heading: Pneumatics
Controlled terms: Fish products? - ?Fisheries? - ?Lake pollution
Uncontrolled terms: Automatic feeding systems? - ?Computational fluid? - ?Computational fluid dynamic ― discrete element method? - ?Directional delivery? - ?Discrete elements method? - ?Feeding modes? - ?Fluid-dynamics? - ?Freshwater fishes? - ?Pneumatic feeding systems? - ?Timing and quantitative
Classification code: 1401.3 ? - ?1502.1.1.2 ? - ?471.5 Sea as Source of Minerals and Food? - ?822 Food Technology? - ?822.3 Food Products
Numerical data indexing: Percentage 3.70E+01%, Percentage 5.40E+01%, Percentage 7.40E+01%, Time 1.00E00s
DOI: 10.6041/j.issn.1000-1298.2024.08.013
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
40. Arbitrary Interactive Object Detection Method Based on Deep Normalization
Accession number: 20243516931743
Title of translation: 基于深度归一化的任意交互物体检测方法研究
Authors: Huang, Lingtao (1); Kong, Zijing (1); Yang, Fan (1); Zhang, Hongyan (1)
Author affiliation: (1) School of Mechanical and Aerospace Engineering, Jilin University, Changchun; 130022, China
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 8
Issue date: August 1, 2024
Publication year: 2024
Pages: 428-436
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: Detection and recognition of interactive objects is a key technology to realize human-computer interaction, and in order to solve the problem of limited types of interactive objects in the process of human-computer interaction, an arbitrary interactive object detection method was proposed based on image segmentation. Firstly, for the depth image, after filtering out the data outside the range of the original depth data, the min - max scale normalization method was used to improve the quality of the depth image. Secondly, the target area was segmented by using the image processing method based on saliency detection and the human pose-guided region growth algorithm for the operator’s side-to-side camera and front-facing camera posture, respectively. Then, the pixel set of the target object obtained by the above segmentation was input into the image processing functions, and the minimum external rectangle of the area point set was obtained, and the rotating bounding box of the target object was anchored. Then, for the depth image, after filtering out the data outside the range of the original depth data, the min - max scale normalization method was used to improve the quality of the depth image. Finally, the detection experiments of arbitrary interactive objects and the ranging and following experiments of different depth intervals were carried out. Experimental results showed that the proposed object detection method had a lower detection cost and a higher degree of freedom in the detection category of interactive objects, which can realize the detection of arbitrary interactive objects, and had wide applicability in the detection of interactive objects. The normalization of the small depth interval can effectively improve the depth image quality, make the object position error smaller, and improve the accuracy of the object detection distance and the following effect of the robot in the human-computer interaction experiment. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 25
Main heading: Image segmentation
Controlled terms: Gluing? - ?Image enhancement? - ?Image quality? - ?Network security
Uncontrolled terms: Computer interaction? - ?Depth image? - ?Depth normalization? - ?Images segmentations? - ?Interactive objects? - ?Normalisation? - ?Object detection method? - ?Objects detection? - ?Target object? - ?Target object detection
Classification code: 1106 ? - ?1106.3.1 ? - ?210 ? - ?214
DOI: 10.6041/j.issn.1000-1298.2024.08.040
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
41. Effect of Exogenous Biological Enhancement on Methane Production Characteristics of Aerobic-anaerobic Two-phase Fermentation of Rice Straw
Accession number: 20243516947289
Title of translation: 外源生物强化对稻草好氧-厌氧两相发酵产甲烷特性的影响
Authors: He, Dong (1, 2); Luo, Lina (2, 3); Xu, Minghan (4); Liu, Ying (5); Ding, Qinghua (6, 7); Sun, Yong (2, 8); Qin, Nan (2)
Author affiliation: (1) Key Laboratory of Research, Development and Application of New and Renewable Energy in Guangdong Province, Guangzhou; 510640, China; (2) School of Engineering, Northeast Agricultural University, Harbin; 150030, China; (3) Key Laboratory of Agricultural Renewable Resources Utilization Technology and Equipment in Cold Areas of Heilongjiang Province, Harbin; 150030, China; (4) State Key Laboratory of Agricultural Equipment Technology, Chinese Academy of Agricultural Mechanization Sciences Group Co., Ltd., Beijing; 100083, China; (5) Heilongjiang Agricultural Reclamation Management Cadre College, Harbin; 150090, China; (6) School of Modern Agriculture and Environment, Weifang Institute of Technology, Weifang; 262500, China; (7) Shandong Luxi Dasheng Environmental Protection Co., Ltd., Weifang; 262128, China; (8) Key Laboratory of Pig-breeding Facilities Engineering, Ministry of Agriculture and Rural Affairs, Harbin; 150030, China
Corresponding author: Luo, Lina(luolina@neau.edu.cn)
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 8
Issue date: August 2024
Publication year: 2024
Pages: 401-409
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: The aerobic hydrolysis process of rice straw was enhanced by biological reinforcement method, and the effects of green Trichoderma and its addition amount (3%, 5%, 7%, and 9% of the total feed mass fraction) on its fermentation characteristics were studied. The biological enhancement time in the aerobic hydrolysis reactor was 24 h, followed by anaerobic fermentation methane production potential testing under 35°C conditions. The results showed that compared with the control group, the addition of Trichoderma viride for biological enhancement resulted in varying degrees of improvement in the degradation rate of lignocellulose, production of volatile fatty acids (VFAs), and gas production rate in each group, with acetic acid being the main component of VFAs. The cumulative methane production was fitted by using Modified Gompertz, and the fitting results were good. The cumulative methane production of each pre-treatment experimental group with 3%, 5%, 7%, and 9% addition of Trichoderma viride was 198. 28 mL/g, 211. 351 mL/g, 228. 44 mL/g, and 234. 78 mL/g, respectively. Compared with that of the CK control group, the methane production was increased by 18.89%, 26.72%, 36.96%, and 40. 76%, respectively. The results showed that the comprehensive effect of adding 7% Trichoderma viride was the best. Under these conditions, the degradation rates of hemicellulose, cellulose and lignin were 36.86%, 31.57%, and 7.43%, respectively, and the methane production was increased by 36.96%. The dominant bacterial communities during aerobic hydrolysis were Firmicutes, Chloroflexi, Proteobacteria, and Bacteroidetes. Among them, the relative abundance of Firmicutes was decreased with the prolongation of hydrolysis time, while the relative abundance of Chloroflexi and Bacteroidetes was increased, indicating that the addition of microbial agents could change the structure of bacterial communities and promote the progress of aerobic hydrolysis reaction. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 30
Main heading: Volatile fatty acids
Controlled terms: Aerobic bacteria? - ?Antimicrobial agents? - ?Biodegradation? - ?Film preparation? - ?Quality assurance
Uncontrolled terms: Aerobic hydrolysis? - ?Anaerobic fermentation? - ?Biological reinforcement? - ?Green trichodermum? - ?Methane production? - ?Methane production characteristic? - ?Production characteristics? - ?Rice straws? - ?Trichoderma? - ?Trichoderma viride
Classification code: 101.7 ? - ?102.2.1 ? - ?103.1 ? - ?208.4 ? - ?801.1 Chemistry, General? - ?804.1 Organic Compounds? - ?913.3 Quality Assurance and Control
Numerical data indexing: Percentage 1.889E+01%, Percentage 2.672E+01%, Percentage 3.00E+00%, Percentage 3.157E+01%, Percentage 3.686E+01%, Percentage 3.696E+01%, Percentage 5.00E+00%, Percentage 7.00E+00%, Percentage 7.43E+00%, Percentage 7.60E+01%, Percentage 9.00E+00%, Specific volume 2.80E-02m3/kg, Specific volume 3.51E-01m3/kg, Specific volume 4.40E-02m3/kg, Specific volume 7.80E-02m3/kg, Temperature 3.08E+02K, Time 8.64E+04s
DOI: 10.6041/j.issn.1000-1298.2024.08.037
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
42. Land Quality Geochemical Evaluation Based on Catastrophe Theory
Accession number: 20243516947291
Title of translation: 基于突变理论的土地质量地球化学评价方法研究
Authors: Geng, Tingling (1); Liu, Jinwei (1); Song, Mian (1, 2); Bian, Chao (1); Cao, Yueting (1); Liu, Jiangtao (1); Zhang, Tao (1)
Author affiliation: (1) Center for Hydro geology and Environmental Geology, China Geological Survey, Baoding; 071051, China; (2) Hebei Province Collaborative Innovation Center for Sustainable Utilization of Water Resources and Optimization of Industrial Structure, Hebei GEO University, Shijiazhuang; 050031, China
Corresponding author: Song, Mian(906402436@qq.com)
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 8
Issue date: August 2024
Publication year: 2024
Pages: 374-381
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: According to the test results of 6 266 groups surface soil samples collected in Nankang area of southern Jiangxi, to achieve high-precision quantification of land quality geochemical evaluation coupling soil fertility and soil environmental quality, a cusp catastrophe model was established and applied to evaluate land quality geochemical evaluation. In this model, soil fertility index (Pf) including SOM, P, N, K, Mo, Mn, B, Cu, Zn in solid samples was the main variable, N erne row index (Pn), including As, Hg, Cd, Pb, Cr in solid samples was the secondary variable, for values greater than 3 of Pf, Pf were revised to 3. 1, and the influences of adjusting on the results were compared. The results showed that the average of Pf was 0. 75 and the overall level of soil fertility was III, the soil fertility was lower, the average of Pn was 0. 59 and the overall level of soil contamination was cleaner. The linear relationship between the boundary value of soil fertility index calculated by the adjusting Nemerow index and the land quality grade was better. The results of comparing with DZ/T 0295―2016 showed that 83. 57% of the evaluation grades were the same, and nearly 16% of the grade was increased by one grade, the range of grade increase or decrease did not exceed 1. The area of grade 2 was mainly distributed in the southeastern edge and the middle - northern part of the study area, the area of grade 3 was widely distributed, and the area of grade 4 was sporadically distributed. The research results showed that the method can be used for the comprehensive evaluation of land quality geochemistry, with more numerical and simpler evaluation process, it was a supplement to existing methods for comprehensive evaluation of land quality geochemistry which can provide effective reference. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 36
Main heading: Soil quality
Controlled terms: Soil pollution? - ?Soil testing
Uncontrolled terms: Catastrophe theory? - ?Comprehensive evaluation? - ?Fertility indices? - ?Geochemicals? - ?Land quality? - ?Land quality geochemical evaluation? - ?Nemerow index? - ?Soil fertility? - ?Soil fertility index? - ?Solid samples
Classification code: 1502.1.1.3 ? - ?1502.1.1.4.3 ? - ?483.1 Soils and Soil Mechanics
Numerical data indexing: Percentage 1.60E+01%, Percentage 5.70E+01%
DOI: 10.6041/j.issn.1000-1298.2024.08.034
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
43. Remote Sensing Identification for Seed Maize with Integrated Migration Learning and Unsupervised Classification
Accession number: 20243516947334
Title of translation: 基于迁移学习和非监督分类的制种玉米遥感识别方法
Authors: Chang, Wanqiu (1, 2); Yao, Yu (1, 2); Xi, Xiaojie (1, 2); Liu, Zhe (1, 2); Li, Shaoming (1, 2); Zhang, Xiaodong (1, 2); Zhao, Yuanyuan (1, 2)
Author affiliation: (1) College of Land Science and Technology, China Agricultural University, Beijing; 100193, China; (2) Key Laboratory of Remote Sensing for Agri-hazards, Ministry of Agriculture and Rural Affairs, Beijing; 100193, China
Corresponding author: Liu, Zhe(liuz@cau.edu.cn)
Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title: Nongye Jixie Xuebao
Volume: 55
Issue: 8
Issue date: August 2024
Publication year: 2024
Pages: 181-195
Language: Chinese
ISSN: 10001298
CODEN: NUYCA3
Document type: Journal article (JA)
Publisher: Chinese Society of Agricultural Machinery
Abstract: Crop classification studies generally focus on different types of crops, while there are fewer studies on the fine classification of different cropping patterns of the same crop. Research on the spatial distribution of seed production is essential to control the maize market, as private seed production and concealment of acreage are occurring in the maize seed market. Seed maize and common maize are the two modes of maize cultivation. Accurate identification of both and remote sensing mapping of the spatial distribution of seed maize are essential for maize seed industry and food security. Traditional crop remote sensing classification methods require a high number and distribution of samples, while visual interpretation of crop samples is difficult. How to improve the utilization of collected samples and at the same time reduce the dependence on samples in fine classification is a pressing issue nowadays. Based on this, combining migration learning with unsupervised classification methods, firstly, using the idea of transfer learning, Linze and Wuwei were used as the source domain, and the feature engineering in the source domain was constructed, including 8 original spectral bands BLUE, GREEN, RED, EDGE1, EDGE2, EDGE3, NIR, SWIR, and 18 vegetation indices NDVI, EVI, RVI, GNDVI, TVI, DVI, MSAVI, GCVI, RNDVI, NDRE, RRI1, RRI2, MSRRE, CLRE, IRECI, LSWI, GCI, SIPI. Then, the features that best characterized the differences in canopy spectra between seed maize and common maize and that differed least between seed maize in different source domains were extracted. Finally, it was used as prior knowledge in an unsupervised classification task in the target domain. The results showed that the near-infrared primordial band exhibited the strongest advantage among the many features. By comparing the classification accuracies of three time-series ranges, namely, before the removal of male ears from the seed maize females, after the removal of male ears from the seed maize females, and during the full-life span of maize growth, the NIR bands were best characterized after the removal of male ears from the seed maize females, i. e., when the DOY was 125?210. In order to further extract the NIR primary band information and enhance the performance of the features, the slope of the linear regression equation in the NIR band of the seed maize female after removal of male ears was used as a feature, and the K-means unsupervised method was used to classify the seed maize in the target domain of Shihezi and Kuitun. After comparative experiments on two target domains in 2019 and 2020, this method mostly showed different degrees of improvement over the K-means classification accuracies characterized by the near-infrared primitive bands in the same time period. In the two target domains Shihezi and Kuitun seed maize had Fl value of 74. 35% and 64. 97% in 2019 and 72. 50% and 75. 69% in 2020, respectively. This method effectively improved the utilization of samples by extracting prior knowledge and introducing an unsupervised classifier. It provided ideas for feature enhancement of the primary band by extracting the slope of the band regression as a feature, and provided a method for fine classification mapping of crops in sample-free scenarios. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.
Number of references: 53
Main heading: Crops
Controlled terms: Image annotation? - ?Seed? - ?Unsupervised learning
Uncontrolled terms: Feature engineerings? - ?Fine classification of crop interspecies? - ?K-mean unsupervised method? - ?K-means? - ?Prior-knowledge? - ?Remote sensing identification? - ?Remote-sensing? - ?Seed maize? - ?Target domain? - ?Unsupervised method
Classification code: 1101.2 ? - ?1106.3.1 ? - ?821.5 Agricultural Wastes
Numerical data indexing: Percentage 3.50E+01%, Percentage 5.00E+01%, Percentage 6.90E+01%, Percentage 9.70E+01%
DOI: 10.6041/j.issn.1000-1298.2024.08.016
Compendex references: YES
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2024 Elsevier Inc.
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