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2024年第3期

2024年第3期共收錄45篇

1. Dynamic Hysteresis Modeling and Control of Pneumatic Muscle Based on Prandtl Ishlinskii Model

Accession number: 20241515898154

Title of translation: 基于Prandtl - Ishlinskii模型的氣動肌肉遲滯特性動態(tài)建模與控制方法

Authors: Duan, Huiru (1); Xie, Shenglong (1); Wan, Yanjian (1); Chen, Dijian (1)

Author affiliation: (1) Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province, China Jiliang University, Hangzhou; 310018, China

Corresponding author: Xie, Shenglong(xieshenglong68@163.com)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 3

Issue date: March 2024

Publication year: 2024

Pages: 452-458

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Due to the use of offline parameter identification methods, the existing hysteresis models are difficult to characterize the time-varying and load-dependent properties from the hysteresis of pneumatic muscle (PM) , which was easy to generate significant modeling errors. In order to accurately characterize the hysteresis characteristics of PM, the Prandtl - Ishlinskii (PI) model was used to describe the length-pressure hysteresis characteristics of PM , and the forgetting factor recursive least squares ( FFRLS ) was used to identify parameters of the PI model online. Compared with offline identification, the online identification method can effectively improve the modeling accuracy of PI models. Then the feedforward online compensation controller was designed based on the PI inverse model, and a composite controller was established by combining with feedback control. This composite control approach was used to realize the motion control of PM. At the same time, corresponding experimental equipment was built and hysteresis modeling and motion control experiments of PM were conducted to compare and analyze the trajectory tracking effects of offline identification and online identification under different loads. The experimental results showed that the PI model using online parameter identification method can effectively describe the load-dependence of hysteresis and greatly reduce control errors caused by load variation. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 30

Main heading: Hysteresis

Controlled terms: Controllers - Inverse problems - Motion control - Muscle - Parameter estimation - Pneumatic control

Uncontrolled terms: Dynamic hysteresis modeling - Hysteresis characteristics - Hysteresis compensation - Hysteresis control - Hysteresis models - Offline - On-line identification - Parameter identification methods - Pneumatic muscle - Prandtl-Ishlinskii model

Classification code: 461.2 Biological Materials and Tissue Engineering - 632.3 Pneumatics - 731.1 Control Systems - 731.3 Specific Variables Control - 732.1 Control Equipment - 961 Systems Science

DOI: 10.6041/j.issn.1000-1298.2024.03.045

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

2. Survey of Research on Visual Odometry Technology for Mobile Robots

Accession number: 20241515891674

Title of translation: 移動機(jī) 器人視覺里程計技術(shù) 研究綜述

Authors: Chen, Mingfang (1); Huang, Liang (1); Wang, Sen (1); Zhang, Yongxia (1); Chen, Zhongping (1)

Author affiliation: (1) Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming; 650500, China

Corresponding author: Wang, Sen(wangsen0401@126.com)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 3

Issue date: March 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: With the continuous development of mobile robot technology, odometry technology has become a key technology for mobile robots to realize environmental perception, and its development level is of great significance to improving the autonomy and intelligence of robots. Firstly, the current development status of laser simultaneous localization and mapping (SLAM) and visual SLAM in simultaneous localization and mapping was systematically explained. The classic SLAM framework and its mathematical description were expounded, and the camera models of three common types of cameras and their mathematical description of visual odometry were briefly introduced. Secondly, the research progress of traditional visual odometry and deep learning odometry were systematically elaborated. The advantages and disadvantages of various mileage calculation methods in the past ten years were compared and analyzed. In addition, the performance of seven commonly used data sets was comparatively analyzed. Finally, the problems faced by odometry technology were summarized from the aspects of accuracy, robustness, data sets, and multi-modality, and five development trends of visual odometry were prospected from the aspects of improving the real-time performance and robustness of the algorithm. For the development of more intelligent and miniaturized new sensors, the integration with unsupervised learning, the improvement of semantic expression technology and the development of cluster robot collaboration technology were introduced. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 147

Main heading: Cameras

Controlled terms: Agricultural robots - Deep learning - Environmental technology - Intelligent robots - Mapping - Mobile robots - Robot vision - Semantics - Vision

Uncontrolled terms: Continuous development - Data set - Dataset - Deep learning - Direct method - Feature method - Mathematical descriptions - Odometry - Simultaneous localization and mapping - Visual odometry

Classification code: 405.3 Surveying - 454 Environmental Engineering - 461.4 Ergonomics and Human Factors Engineering - 731.5 Robotics - 731.6 Robot Applications - 741.2 Vision - 742.2 Photographic Equipment - 821.1 Agricultural Machinery and Equipment

DOI: 10.6041/j.issn.1000-1298.2024.03.001

Funding text: 國家自然科學(xué)基金項目（51965029)、國家重點研發(fā)計劃項目（2012YFB1306103)和云南省重大專項（202002AC020001)

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

3. Research on Cotton Chemical Topping System Based on Apical Bud Intelligent Recognition

Accession number: 20241515882249

Title of translation: 基于頂芽智能識別的棉花化學(xué)打頂系統(tǒng)研究

Authors: Xin, Han (1); Jin’ge, Han (1); Yunlin, Chen (1); Yubin, Lan (1); Jiankun, Li (2); Lihua, Cui (3)

Author affiliation: (1) College ofAgricultural Engineering and Food Science, Shandong University of Technology, Zibo; 255000, China; (2) Weichai Lovol Intelligent Agricultural Technology Co. , Ltd., Weifang; 261200, China; (3) Shandong Lufeng Agricultural Group Co. ,Ltd., Binzhou; 256600, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 3

Issue date: March 2024

Publication year: 2024

Pages: 145-152

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: A cotton chemical topping system based on top bud intelligent recognition was designed to achieve precise operation, rational and efficient use of cotton chemical topping agents, and reduce environmental pollution caused by excessive use of chemical topping agents. The system mainly consisted of cotton top bud recognition system, control system, and spraying system. A cotton top bud recognition model was constructed by using the YOLO v5s algorithm. The control system adopted STM32F407 microcontroller, which was responsible for receiving signals from the recognition system and controlling various cotton topping agent pipelines. At the same time, the display interface can display real-time parameters such as the driving speed of the equipment, the flow rate of the medicine, and the liquid level of the topping agent. The experimental results showed that in the field all day light experiment, the morning and afternoon time periods had the best recognition performance. At a speed of 0. 4 m/s, the average recognition rate was about 94% . When the signal transmission interval was 100 m m, the success rate of successfully sending signals to the lower computer reached 92% . The field target spraying experiment showed that the effective spraying rate was 94%, which met the spraying requirements. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 24

Main heading: Cotton

Controlled terms: Control systems

Uncontrolled terms: Apical bud recognition - Chemical topping - Display interfaces - Environmental pollutions - Intelligent recognition - Recognition models - Recognition systems - Spraying system - System control - YOLO v5s

Classification code: 731.1 Control Systems - 821.4 Agricultural Products

Numerical data indexing: Percentage 9.20E+01%, Percentage 9.40E+01%, Size 1.00E+02m, Velocity 4.00E+00m/s

DOI: 10.6041/j.issn.1000-1298.2024.03.014

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

4. Model Based Gait Transition Control for Quadruped Robots

Accession number: 20241515900280

Title of translation: 基于模型的四足機(jī)器人步態(tài)轉(zhuǎn)換控制研究

Authors: Chen, Jiupeng (1); Li, Chunlei (1); San, Hongjun (1); Kang, Wei (1); Ba, Guangyu (2); Yang, Xiaoyuan (1)

Author affiliation: (1) Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming; 650500, China; (2) Kunming Agricultural Machinery Technology Promotion Station, Kunming; 650500, China

Corresponding author: Kang, Wei(kmkangw@163.com)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 3

Issue date: March 2024

Publication year: 2024

Pages: 431-440 and 451

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In order to solve the problem of stable control in the gait transition process of quadruped robots by using model-based control methods, a quadruped robot prototype platform was designed based on bionics and mechanics, and the robot’s single leg kinematic model was derived. The robot’s leg height and step length were planned within the reachable workspace at the foot end. By using an ideal compound cycloid trajectory and controlling the gait period reasonably, a transition period variable control method was proposed, which achieved fixed speed control and variable step length control before and after gait transition, ensuring that the speed remained unchanged or variable before and after gait transition. In order to verify the correctness and stability of the proposed algorithm, single leg foot trajectory experiments and whole machine gait transformation experiments were conducted, respectively. On the basis of completing the overall motion control, the application of model-based control and central pattern generator based control in the gait transition process of quadruped robots was compared. The simulation and experimental results showed that under the model-based control algorithm, the quadruped robot can achieve smooth gait transformation, and the speed can be adjusted with changes in step size and period, meeting the walking requirements at different speeds, providing a reference for the motion control of quadruped robots. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 30

Main heading: Kinematics

Controlled terms: Motion control - Multipurpose robots - Process control

Uncontrolled terms: Gait-transition - Kinematics models - Model-based control methods - Model-based OPC - Quadruped Robots - Robot prototypes - Stable control - Step length - Transition control - Transition process

Classification code: 731.3 Specific Variables Control - 731.5 Robotics - 731.6 Robot Applications - 931.1 Mechanics

DOI: 10.6041/j.issn.1000-1298.2024.03.043

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

5. Construction of Bidirectional Mapping Model between Water Quality and Fish Behavior in Aquaculture

Accession number: 20241515891687

Title of translation: 水產(chǎn)養(yǎng)殖中水質(zhì)與魚類行為雙向映射模型研究

Authors: Wei, Tianjiao (1); Hu, Zhuhua (1); Fan, Xiyu (1)

Author affiliation: (1) School of Information and Communication Engineering, Hainan University, Haikou; 570228, China

Corresponding author: Hu, Zhuhua(eagler_hu@hainanu.edu.en)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 3

Issue date: March 2024

Publication year: 2024

Pages: 290-299

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In aquaculture, there is an inseparable mutual mapping relationship between water quality and fish behaviors. In the past, monitoring was more biased towards one-way mapping, which generally indicated the water quality through fish behaviors. In order to solve the problem of misjudgment and lag only by fish behaviors, a bidirectional mapping model between fish behaviors and water quality was constructed based on random forest. The bidirectional mapping model can not only provide more information to improve the accuracy of prediction, but also improve the reliability of the model through mutual verification. Firstly, YOLO v7 was improved by introducing a deformable convolution module, and the position of fish in the video was detected by using the improved model, and then the swimming parameters offish were quantified by the coordinates of the front and back frames. Then, the collected fish swimming parameters and the corresponding water quality parameters were taken as inputs, the random forest model was used for classification and regression, and the specific numerical values of fish swimming parameters and water quality parameters and the abnormal level of indicators were predicted respectively, so as to obtain a bidirectional mapping relationship. In order to show the generalization ability of the model, experiments were carried out under two data sets: Li’an Port and Xincun Port Fishing Ground. The experimental results showed that the proposed method can realize the bidirectional mapping between fish behaviors and water quality. Among them, the average accuracy of classification experiment can reach 90. 947% , and the average value of regression experiment determination coefficient R can reach 0. 801. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 30

Main heading: Water quality

Controlled terms: Deep learning - Farms - Fish - Fisheries - Forestry - Mapping

Uncontrolled terms: Bidirectional mapping - Bidirectional mapping model - Deep learning - Fish behavior - Fish swimming - Intelligent farming - Mapping modeling - Mapping relationships - Random forests - Water quality parameters

Classification code: 405.3 Surveying - 445.2 Water Analysis - 461.4 Ergonomics and Human Factors Engineering - 821 Agricultural Equipment and Methods; Vegetation and Pest Control

Numerical data indexing: Percentage 9.47E+02%

DOI: 10.6041/j.issn.1000-1298.2024.03.029

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

6. Detection Method for Implicit Apple Damages Based on SIRI and CNN

Accession number: 20241515898034

Title of translation: 基于SIRI和CNN的蘋果隱性損傷檢測方法

Authors: Wang, Yuwei (1); Yang, Lingling (1); Zhu, Haojie (1); Rao, Yuan (2); Liu, Lu (1); Hou, Wenhui (1)

Author affiliation: (1) School of Engineering, Anhui Agricultural University, Hefei; 230036, China; (2) Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs,, Hefei; 230036, China

Corresponding author: Hou, Wenhui(hwh303@ahau.edu.en)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 3

Issue date: March 2024

Publication year: 2024

Pages: 383-391

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: During the process from harvest to sales, apples are susceptible to mechanical damage, which can have detrimental effects on their quality and lead to rotting. Detecting and removing this damage in a timely manner is crucial to prevent further deterioration. However, early-stage mechanical damage in apples often manifests as subtle color changes, making it challenging to detect. To address this issue, an apple implicit damage detection method was presented based on structured-illumination reflectance imaging (SIRI ) and convolutional neural network ( CNN ) . By building an SIRI system to acquire modulated structured light images of the measured apples, and utilizing three-phase demodulation method to extract the alternating current component, the image contrast of the apple implicit damage can be enhanced. The dataset of apple implicit damages was produced by using the images of alternating current components. Several CNN-based semantic segmentation networks, including FCN, UNet, HRNet, PSPNet, DeepLabv3 + , LRASPP, and SegNet were employed to train the damage detection models, respectively. Several groups of experimental results demonstrated that these models can effectively detect the apple implicit damages in different situations. In contrast, the precision (P) , recall ( R) , Fl score, and mean intersection over union ( MIoU ) of the HRNet model were respectively 97. 96% , 97. 52% , 97. 74% and 97. 58% . However, its detection speed was only 60 frames per second. The PSPNet model had a faster detection speed, reaching up to 217 frames per second. However, it had slightly lower detection accuracy, with precision ( P) , recall ( R) , Fl score, and mean intersection over union (MIoU) of 97. 10% , 94. 57% , 95. 82% , and 95. 90% , respectively. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 35

Main heading: Damage detection

Controlled terms: Convolution - Demodulation - Deterioration - Fruits - Image enhancement - Phase modulation - Semantic Segmentation - Semantics

Uncontrolled terms: Apple - Convolutional neural network - Implicit damage detection - Phase demodulation - Semantic segmentation - Structured illumination - Structured-illumination reflection imaging - Three phase - Three phasis - Three-phase demodulation

Classification code: 716.1 Information Theory and Signal Processing - 723.4 Artificial Intelligence - 821.4 Agricultural Products - 951 Materials Science

Numerical data indexing: Percentage 1.00E+01%, Percentage 5.20E+01%, Percentage 5.70E+01%, Percentage 5.80E+01%, Percentage 7.40E+01%, Percentage 8.20E+01%, Percentage 9.00E+01%, Percentage 9.60E+01%

DOI: 10.6041/j.issn.1000-1298.2024.03.038

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

7. Small Sample Classification of Tea Diseases Based on SimAM-ConvNeXt-FL

Accession number: 20241515891672

Title of translation: 基于SimAM - ConvNeXt - FL 的茶葉病害小樣本分類方法

Authors: Tian, Tian (1); Cheng, Zhiyou (1); Ju, Wei (1); Zhang, Shuai (1)

Author affiliation: (1) School of Internet, Anhui University, Hefei; 230039, China

Corresponding author: Ju, Wei(20029@ahu.edu.cn)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 3

Issue date: March 2024

Publication year: 2024

Pages: 275-281

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In order to realize accurate classification of tea diseases, a disease image classification method based on SimAM — ConvNeXt — FL model of migration learning was proposed to address the small sample problem and uneven distribution of categories in tea disease image classification. Firstly, an SimAM module was added to the ConvNeXt model to enhance the extraction of complex features. Secondly, to address the problem of uneven sample distribution, the Focal Loss function was used as the loss function in the training process, and the effect of uneven sample distribution was reduced by increasing the weights of a smaller number of samples. Finally, the SimAM — ConvNeXt — F L m o d e l was used to train the Plant Village dataset, and the parameters obtained from the training were migrated to the measured tea leaf disease images and fine-tuned to reduce the impact of overfitting, and ablation experiments were set up to prove the validity of the model improvement, and comparison experiments were carried out with the different classification models AlexNet, VGG16, and ResNet34 models comparison experiments were conducted respectively. The experimental results showed that the SimAM — ConvNeXt — FL model had the best recognition effect, with an accuracy of 96. 48%, and the F1 values of the SimAM — ConvNeXt — FL model compared with the original ConvNeXt model for tea coal disease, tea phoma, tea anthracnose, healthy leaves, and tea white star disease were improved by 4. 46 percentage points, 3. 76 percentage points, 0.43 percentage points, 0.22 percentage points, and 5. 23 percentage points respectively. The results showed that the model proposed had high classification accuracy and strong generalizability, which can promote the development of tea disease classification. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 25

Main heading: Image classification

Controlled terms: Classification (of information) - Image enhancement - Plants (botany) - Transfer learning

Uncontrolled terms: Classification methods - Images classification - Loss functions - Percentage points - Sample classification - Sample distributions - Small samples - Tea disease - Transfer learning - Transfer learning: convnext

Classification code: 716.1 Information Theory and Signal Processing - 723.2 Data Processing and Image Processing - 723.4 Artificial Intelligence - 903.1 Information Sources and Analysis

Numerical data indexing: Percentage 4.80E+01%

DOI: 10.6041/j.issn.1000-1298.2024.03.027

Funding text: 國家自然科學(xué)基金項目,61672032)

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

8. Inverse Kinematics Analysis Method of Parallel Robot Based on Conformal Geometry Algebra

Accession number: 20241515898076

Title of translation: 基于共形幾何代數(shù)的并聯(lián)機(jī)器人逆運動學(xué)分析方法

Authors: Chai, Xinxue (1); Li, Xiangyi (1); Tang, Chenxin (1); Li, Qinchuan (1); Xu, Lingmin (2)

Author affiliation: (1) Faculty of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou; 310018, China; (2) State Key Laboratory of Mechanical Systems and Vibration, Shanghai Jiao Tong University, Shanghai; 200240, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 3

Issue date: March 2024

Publication year: 2024

Pages: 421-430

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Kinematics analysis is the basis of kinematics performance evaluation and structure size optimization of parallel mechanism. The existing kinematics analysis methods of parallel robots have the problem of separating geometric modeling from geometric calculation. A method for inverse kinematics analysis of parallel robots was proposed by taking advantage of conformal geometric algebra (CGA) , which integrated geometric representation and calculation. Firstly, the rigid body transformation of any point on the moving platform was realized by the geometric product under the framework of conformal geometry algebra, and then the conformal geometric expression of any point in the process of motion was obtained. Then the kinematic equation of the mechanism was established by using the inner product operation in combination with the dimension and geometry constraints of the mechanism. Finally, according to the kinematics equation, the inverse kinematics was calculated and the velocity was analyzed. A 3 - RPS parallel robot of three degrees of freedom and a 6 - UPS parallel robot of six degrees of freedom were taken as examples to verify the correctness of the proposed method, and the results of inverse kinematics were compared with those obtained by simulation software. This method combined the geometric objects such as space vector and rotation representation with the calculation methods such as matrix multiplication and vector outer product, so that the spatial geometry problems of parallel mechanism were handled in a unified algebraic system, so the analysis process was geometrically intuitive, and the analysis and calculation process of inverse kinematics were simplified. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 28

Main heading: Inverse kinematics

Controlled terms: Agricultural robots - Computer software - Degrees of freedom (mechanics) - Geometry - Inverse problems - Structural optimization - Vector spaces

Uncontrolled terms: Analysis method - Analysis process - Conformal Geometric Algebra - Conformal geometry - Geometric calculations - Inverse kinematic analysis - Kinematic Analysis - Kinematic equations - Parallel mechanisms - Parallel robots

Classification code: 723 Computer Software, Data Handling and Applications - 731.5 Robotics - 821.1 Agricultural Machinery and Equipment - 921 Mathematics - 921.5 Optimization Techniques - 931.1 Mechanics

DOI: 10.6041/j.issn.1000-1298.2024.03.042

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

9. Improved Pure Tracking Control of Four-wheel Synchronous Steering Agricultural Machinery Based on Variable Forward Looking Distance

Accession number: 20241515891676

Title of translation: 基于變前視距離的四輪同步轉(zhuǎn) 向農(nóng) 機(jī) 改進(jìn) 純跟蹤控制

Authors: Shen, Yue (1); Zhao, Sha (1); Zhang, Yafei (1); He, Siwei (1); Feng, Rui (1); Liu, Hui (1)

Author affiliation: (1) School of Electrical and Information Engineering, Jiangsu University, Zhenjiang; 212013, China

Corresponding author: Liu, Hui(alice@stmail.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: 3

Issue date: March 2024

Publication year: 2024

Pages: 21-28

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The autonomous navigation operation of agricultural machinery has become an inevitable trend in domestic and international development, and path tracking control is the key to improving the control accuracy of autonomous navigation systems. In response to the problem of low tracking accuracy of conventional pure tracking algorithms when turning in complex agricultural environments, an novel path tracking control algorithm for the four-wheel synchronous turning agricultural machinery was proposed based on the improved pure tracking model. A kinematic model and a pure tracking model based on four- wheel synchronous steering agricultural machinery were established. On this basis, an improved pure tracking model was obtained by considering heading error. RTK positioning coordinates were modified, and the optimal forward looking distance was derived from the optimal target points in the forward-looking area according to the evaluation function of the quantitative error. The proposed algorithm can obtain the appropriate forward looking distance in real-time for the improved pure tracking model of four-wheel synchronous steering agricultural machinery, which minimized heading and lateral errors and achieved adaptive optimization of target point. The simulation results showed that the average absolute lateral error of the method during turning was reduced to 0. 035 m, and the average absolute heading error was reduced to 0. 212°. The paddy field experiment showed that when the operating speed of the four-wheel synchronous steering agricultural machine was 3.6 km/h, the average absolute lateral error of the four- wheel steering agricultural machine trajectory tracking was reduced to 0. 109 m, the average absolute heading error was reduced to 2. 799°, and the turning tracking accuracy was significantly improved. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 29

Main heading: Errors

Controlled terms: Agricultural robots - Agriculture - Kinematics - Navigation - Navigation systems - Wheels

Uncontrolled terms: Adaptive optimization - Forward looking - Forward looking distance - Four-wheel synchronoi steering - Heading error - Improved pure tracking control - Paddy fields - Path tracking control - Tracking controls - Tracking models

Classification code: 601.2 Machine Components - 731.5 Robotics - 821 Agricultural Equipment and Methods; Vegetation and Pest Control - 821.1 Agricultural Machinery and Equipment - 931.1 Mechanics

Numerical data indexing: Size 1.09E+02m, Size 3.50E+01m, Size 3.60E+03m

DOI: 10.6041/j.issn.1000-1298.2024.03.002

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

10. Recognition and Localization Method for Pepper Clusters in Complex Environments Based on Improved YOLO v5

Accession number: 20241515891667

Title of translation: 基于改進(jìn)YOLO v5的復(fù)雜環(huán)境下花椒簇識別與定位方法

Authors: Huang, Hua (1); Zhang, Hao (1); Hu, Xiaolin (1); Nie, Xingyi (1)

Author affiliation: (1) School of Mechanical and Electrical Engineering, 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: 3

Issue date: March 2024

Publication year: 2024

Pages: 243-251

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Pepper trees yield is a substantial quantity of friuits, characterized by crisscrossed branches and dense foliage, resulting insignificant challenges for automated peppercorn picking. Therefore! a fast identification and localization metliod of pepper clusters in complex environment based on improved YOLO v5 was proposed. By adding efficient channel attention (E C A) after the CSPLayer of the backbone extraction network CSPDarknet and the upsampling layer of Neck to simplify the computation of the CSPLayer layer and improve the feature extraction capability. In the downsampling layer, coordinate attention (C A) was added to reduce the loss of information in the downsampling process, strengthen the spatial information of features, and cooperate with the heat map (Grad — CAM) and the depth map of the point cloud to complete the spatial localization of pepper clusters. The test results showed that the improved network over the original YOLO v5 reduced the residual computation to 1 time, which ensured the model was lightweight and the efficiency was improved. Under the same frame number interval, the accuracy of the improved network was 96. 27%, comparing with three similar feature extraction networks YOLO v5, YOLO v5 — tiny, and Faster R — CNN, the precision of the improved network was improved by 5 . 3 7 percentage points, 3. 35 percentage points, and 15. 37 percentage points, respectively, and the ability of separating and recognizing the pepper cliusters of the successive plants was greatly improved. The experimental results showed that the average checking accuracy of the system in the natural environment was 81. 60%, and the leakage rate was 18. 39%, which can satisfy the pepper cluster recognition, and build the foundation for mobile deployment. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 23

Main heading: Agricultural robots

Controlled terms: Deep learning - Extraction - Feature extraction - Signal sampling

Uncontrolled terms: Agricultural robot - Complex environments - Deep learning - Down sampling - Features extraction - Pepper cluster identification - Percentage points - Recognition methods - Targets detection - YOLO v5

Classification code: 461.4 Ergonomics and Human Factors Engineering - 723.2 Data Processing and Image Processing - 731.5 Robotics - 802.3 Chemical Operations - 821.1 Agricultural Machinery and Equipment - 922 Statistical Methods

Numerical data indexing: Percentage 2.70E+01%, Percentage 3.90E+01%, Percentage 6.00E+01%

DOI: 10.6041/j.issn.1000-1298.2024.03.024

Funding text: 現(xiàn)代絲路寒旱農(nóng)業(yè)發(fā)展資金項目（njyf2022-10)$和國家自然科學(xué)基金項目（51965037)

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

11. Dynamic Optimization Method of Spatial Parallel Mechanism Considering Lubrication Clearance Effect

Accession number: 20241515897959

Title of translation: 考慮潤滑間隙效應(yīng)的空間并聯(lián)機(jī)構(gòu)動力學(xué)優(yōu)化

Authors: Chen, Xiulong (1); Ju, Shuo (1); Jia, Yonghao (1)

Author affiliation: (1) College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao; 266590, China

Corresponding author: Ju, Shuo(jnsdjyh@163.com)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 3

Issue date: March 2024

Publication year: 2024

Pages: 441-451

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In order to improve the gradual deterioration of the dynamic performance of the spatial parallel mechanism caused by the lubrication clearance effect, a dynamic optimization method of the spatial parallel mechanism considering the lubrication clearance effect was proposed by taking the 3 - RRPaR redundant parallel mechanism as the research object. Firstly, the kinematic model of the lubrication clearance of the revolute pair and the oil film bearing capacity model were established. The transition force model of the contact state was derived, and the dynamic model of the 3 - RRPaR redundant parallel mechanism considering the lubrication clearance of the revolute pair was established. Then, the objective function was set up to optimize the dynamic response error of the end-effector and the constraint reaction force at the clearance joint. By optimizing the quality of the end-effector and the moment of inertia, the degradation effect caused by the clearance of the kinematic pair was alleviated, and the dynamic optimization model of the 3 - RRPaR redundant parallel mechanism considering the lubrication clearance effect was established. Finally, the effectiveness of the established dynamic model was experimentally verified, the influence of the two objective functions on the optimization effect was compared and analyzed to select the best optimization method, and the dynamic characteristics of the spatial parallel mechanism considering the lubrication clearance effect before and after optimization were analyzed. The results showed that the optimization reduced the peak value of the constraint reaction force at the lubrication clearance revolute joint by 16. 16% . The research result can provide theoretical support for improving the dynamic performance of spatial parallel mechanism by improving the clearance effect. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 25

Main heading: Lubrication

Controlled terms: Deterioration - Dynamic models - Dynamics - End effectors - Kinematics

Uncontrolled terms: Dynamic optimization - Dynamic performance - Lubrication / - Motion pair clearance - Optimal design - Optimization method - Pair clearances - Parallel mechanisms - Redundant parallels - Spatial parallel mechanism

Classification code: 607.2 Lubrication - 731.5 Robotics - 921 Mathematics - 931.1 Mechanics - 951 Materials Science

Numerical data indexing: Percentage 1.60E+01%

DOI: 10.6041/j.issn.1000-1298.2024.03.044

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

12. Lightweight Apple Recognition Method in Natural Orchard Environment Based on Improved YOLO v7 Model

Accession number: 20241515891680

Title of translation: 基于改進(jìn)YOLO v7輕量化模型的自然果園環(huán)境下蘋果識別方法

Authors: Zhang, Zhen (1); Zhou, Jun (1); Jiang, Zizhen (1); Han, Hongqi (1)

Author affiliation: (1) College of Engineering, Nanjing Agricultural University, Nanjing; 210014, China

Corresponding author: Zhou, Jun(zhoujun@njau.edu.en)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 3

Issue date: March 2024

Publication year: 2024

Pages: 231-242 and 262

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In the task of apple recognition in natural orchard environments, it is difficult for traditional object detection algorithms to achieve a balance between the accuracy, speed, and lightweight of the detection model. Therefore, a lightweight apple detection model based on improved YOLO v7 model was proposed. Firstly, partial convolution (PConv) was introduced in the multi branch stacking module to replace regular convolution, in order to reduce the parameter and computation of the model. Then a lightweight efficient channel attention ( ECA ) module was introduced to enhance the feature extraction ability and improve the problem of false and missed detection of occluded targets in complex environments. Finally, a learning rate optimization strategy based on sparrow search algorithm ( SSA) was adopted in model training to further increase the detection accuracy of the model. The experimental results showed that compared with the original YOLO v7 model, the precision, recall, and average accuracy of the improved model was raised by 4. 15 percentage points, 0. 38 percentage points and 1. 39 percentage points respectively; the number of parameters and computations were decreased by 22. 93% and 27.41% , respectively; and the average time to detect each image under GPU and CPU was decreased by 0.003 s and 0.014 s, respectively. The results indicated that the improved model can quickly and accurately detect apple fruits in natural orchard environments, and the number of parameters and computations were less, which was suitable to be deployed on the embedded devices of apple harvesting robots, and laying the foundation for unmanned intelligent apple picking. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 29

Main heading: Convolution

Controlled terms: Agricultural robots - Feature extraction - Fruits - Image enhancement - Intelligent robots - Learning algorithms - Object detection - Orchards - Parameter estimation

Uncontrolled terms: Apple recognition - Detection models - Efficient channel attention module - Efficient channels - Natural orchard environment - Partial convolution - Percentage points - Search Algorithms - Sparrow search algorithm - YOLO v7

Classification code: 716.1 Information Theory and Signal Processing - 723.2 Data Processing and Image Processing - 723.4.2 Machine Learning - 731.5 Robotics - 731.6 Robot Applications - 821.1 Agricultural Machinery and Equipment - 821.3 Agricultural Methods - 821.4 Agricultural Products

Numerical data indexing: Percentage 2.741E+01%, Percentage 9.30E+01%, Time 1.40E-02s, Time 3.00E-03s

DOI: 10.6041/j.issn.1000-1298.2024.03.023

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

13. Spatiotemporal Prediction Algorithm for Mushroom Growth Status Based on Improved LSTM

Accession number: 20241515891678

Title of translation: 基于改進(jìn)LSTM的蘑菇生長狀態(tài)時空預(yù)測算法

Authors: Yang, Shuzhen (1); Huang, Jie (1); Yuan, Jin (2)

Author affiliation: (1) School of Intelligent Manufacturing and Control Engineering, Shanghai Polytechnic University, Shanghai; 201209, China; (2) College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian; 271018, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 3

Issue date: March 2024

Publication year: 2024

Pages: 221-230

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Dense mushroom clusters can significantly impact mushroom quality and the success rate of automated harvesting. To address this issue, a spatiotemporal prediction algorithm for mushroom growth status based on historical time series growth images was proposed, which can facilitate early bud thinning to prevent the formation of dense mushroom clusters. The algorithm employed a sequence-to-sequence structure, comprising an encoder and a predictor. In the input, historical image sequences were transformed into 3D tensor sequences and sent to encoder. Within the encoder network, a three-layer long short term memory (LSTM) model was utilized. Here, convolution was fused into LSTM cell to extract spatiotemporal correlation features of mushroom growth. Meanwhile, a diffusion model was introduced into the predictor to address the blurriness issue in predicting images. Furthermore, a mushroom area difference loss function was designed and incorporated into the loss function to further reduce the shape and positional deviations between the predicted and actual mushrooms. The experimental results indicated that the proposed spatiotemporal prediction algorithm for mushroom growth status achieved a peak signal-to-noise ratio of 35.611 dB, a multiscale structure similarity of 0.927, and a high mushroom mean intersection over union of 0. 93 , which represented improvements of 36% , 33% and 24% , respectively, over that of the ConvLSTM ( Converlution LSTM) spatiotemporal prediction algorithm. This showed the proposed algorithm can effectively enhance the quality and accuracy of mushroom growth status image prediction, providing a approach for precise forecasting of edible mushroom growth. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 39

Main heading: Long short-term memory

Controlled terms: Clustering algorithms - Diffusion - Forecasting - Image enhancement - Signal encoding - Signal to noise ratio

Uncontrolled terms: Area difference loss function - Diffusion model - Grow status prediction - Loss functions - Mushroom - Prediction algorithms - Sequence structure - Spatio-temporal prediction - Thinnings - Times series

Classification code: 716.1 Information Theory and Signal Processing - 903.1 Information Sources and Analysis

Numerical data indexing: Decibel 3.5611E+01dB, Percentage 2.40E+01%, Percentage 3.30E+01%, Percentage 3.60E+01%

DOI: 10.6041/j.issn.1000-1298.2024.03.022

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

14. Identification of Producing Region at Eastern Foot of Helan Mountain Based on Electrical Parameters in Cabernet Sauvignon Wine

Accession number: 20241515898044

Title of translation: 基于電學(xué)參數(shù)的賀蘭山東麓赤霞珠葡萄酒子產(chǎn)區(qū)判別

Authors: Ma, Haijun (1, 2); Zhu, Juanjuan (1, 3); Zhou, Naishuai (1); An, Yajing (1); Hou, Lijun (1)

Author affiliation: (1) College of Biological Science and Engineering, North Minzu University, Yinchuan; 750021, China; (2) Ningxia Crape and Wine Innovation Center, Yinchuan; 750021, China; (3) North Minzu University, Innovation Team for Cenetic Improvement of Economic Forests,, Yinchuan; 750021, China

Corresponding author: Zhu, Juanjuan(zjj0954@163.com)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 3

Issue date: March 2024

Publication year: 2024

Pages: 375-382

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Spontaneous fermentation dry red wines of Cabernet sauvignon collected from five sub-producing areas (Yinchuan, Qingtongxia, Hongsibao, Shizuishan and Nongken) in the eastern foothills of Helan Mountain of Ningxia were selected as the materials. The basic physical and chemical indicators and electrical properties were measured, the differences of electrical properties among different producing regions were analyzed, and the characteristic frequency and effective electrical parameters to identify wines from different producing regions were screened. The aim was to explore the ability to identify wines from different sub-production areas in the eastern foothills of Helan Mountain in Ningxia based on electrical characteristics, in order to provide a method for simple, fast and effective identification of wine production regions. The results showed that there were significant differences in the physiochemical indicators of wines among the five sub-regions of Helan Mountain in Ningxia. Wine from Nongken region had the highest titratable acid content and the lowest reducing sugar content. The wine from Yinchuan had the lowest volatile acid sugar content, while the wine from Shizuishan had the highest alcohol content. Through correlation analysis, analysis of variance and multiple comparisons, the characteristic frequency of 0. 1 kHz and the effective electrical parameters Z, L , X, C and Q were selected to distinguish wines from different regions at 2 V. The results of principal component analysis and discriminant analysis showed that the five sub-production regions of the eastern foothills of Helan Mountains could be clearly distinguished by the electrical parameters of wine. The prediction model established by Fisher- LDS had the correct rates of return test and cross validation of 100% . Therefore, it was feasible to identify wine producing region by wine electrical characteristics. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 34

Main heading: Principal component analysis

Controlled terms: Discriminant analysis - Electric network parameters - Fermentation - Indicators (chemical) - Landforms - Wine

Uncontrolled terms: ‘Dry’ [ - Cabernet-Sauvignon - Characteristic-frequency - Electrical characteristic - Electrical parameter - Identification of wine-producing region - Principal-component analysis - Spontaneous fermentation - Spontaneous fermentation wine - Sugar content

Classification code: 481.1 Geology - 703.1 Electric Networks - 801 Chemistry - 804 Chemical Products Generally - 822.3 Food Products - 903.1 Information Sources and Analysis - 922 Statistical Methods - 922.2 Mathematical Statistics

Numerical data indexing: Frequency 1.00E+03Hz, Percentage 1.00E+02%, Voltage 2.00E+00V

DOI: 10.6041/j.issn.1000-1298.2024.03.037

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

15. Blockchain-based Multi-condition Query Optimization Method for Traceability Data of Agricultural Product Supply Chain

Accession number: 20241515897974

Title of translation: 基于區(qū)塊鏈的農(nóng)產(chǎn)品供應(yīng)鏈溯源數(shù)據(jù)多條件查詢優(yōu)化方法研究

Authors: Gao, Guanyue (1, 2); Sun, Chuanheng (2, 3); Luo, Na (2, 3); Xu, Darning (2, 3); Xing, Bin (2, 3)

Author affiliation: (1) College of Information Technology, Shanghai Ocean University, Shanghai; 201306, China; (2) National Engineering Research Center for Information Technology in Agriculture, Beijing; 100097, China; (3) National Engineering Laboratory for Agri-product Quality Traceability, Beijing; 100097, China

Corresponding author: Sun, Chuanheng(sunch@nercita.org.en)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 3

Issue date: March 2024

Publication year: 2024

Pages: 362-374

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: With the rapid development of blockchain-based agricultural product traceability systems, blockchain query capabilities face great challenges. For supply chain participants, most of the data stored in the blockchain are coded or serialized data, which makes the process of multi-condition query such as audit and supervision of supply chain participants very difficult. In general, native blockchains do not provide a query method to satisfy multi-condition queries. Therefore, in order to realize multi-condition query and improve query efficiency, an optimization method for agricultural product traceability data was proposed. Firstly, the method used an optimized Merkle tree structure ( n-Tree) to reconstruct the transaction information, so as to provide more efficient conditional verification ability. Secondly, the adaptive multi-condition block Bloom filter was used to judge the existence of query conditions in the transaction information, and then the blocks were quickly filtered. Finally, an index construction method using time weight and transaction number based heap structure was proposed, and the block number index list related to the main condition was constructed in the order of block weight. The process of querying product data included traversing the block index list, filtering irrelevant blocks, and validating specific query conditions to obtain conditional query results. The experimental results showed that the query method proposed can effectively solve the problem of conditional query in the supply chain of agricultural products. At the same time, the query time consumption was maintained at about 15 ms, and the query efficiency was improved by 60. 9% compared with Merkle semantic trie method and 87. 7% compared with original traverse method. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 30

Main heading: Blockchain

Controlled terms: Agricultural products - Data structures - Efficiency - Information filtering - Query processing - Search engines - Semantics - Supply chains - Trees (mathematics)

Uncontrolled terms: Agricultural product supply chain - Block-chain - Blockchain traceability - Bloom filters - Condition - Conditional query - N-tree - Optimization method - Product supply chains - Product traceability

Classification code: 723 Computer Software, Data Handling and Applications - 723.2 Data Processing and Image Processing - 723.3 Database Systems - 821.4 Agricultural Products - 903.1 Information Sources and Analysis - 911.3 Inventory Control - 912 Industrial Engineering and Management - 913 Production Planning and Control; Manufacturing - 913.1 Production Engineering - 921.4 Combinatorial Mathematics, Includes Graph Theory, Set Theory

Numerical data indexing: Percentage 7.00E+00%, Percentage 9.00E+00%, Time 1.50E-02s

DOI: 10.6041/j.issn.1000-1298.2024.03.036

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

16. Design and Test of Rock Wool Block Seedling Transplantation Mechanism in Plant Factory

Accession number: 20241515891679

Title of translation: 植物工廠巖棉塊種苗移植機(jī) 移植部件設(shè) 計與試驗

Authors: Tong, Junhua (1, 2); Liu, Ke (1); Liu, Nihong (3); Sun, Liang (1, 4); Wang, Xiaoqin (1); Norupiri, R. Obedience (1)

Author affiliation: (1) School of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou; 310018, China; (2) Zhejiang Provincial Key Laboratory of Transplanting, Hangzhou; 310018, China; (3) Guangdong Institute of Modern Agricultural Equipment, Guangzhou; 510630, China; (4) Key Laboratory of Agricultural Equipment for Southeastern Hilly and Mountainous Areas, Ministry of Agriculture and Rural Affairs, Hangzhou; 310018, China

Corresponding author: Sun, Liang(liangsun@zstu.edu.cn)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 3

Issue date: March 2024

Publication year: 2024

Pages: 106-114

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The plant factory rock wool block seedling transplantation equipment has a low penetration rate at present, and most of the operation still relies on manual labor, which is labor-intensive and inefficient. A high-speed transplantation mechanism combining secondary variable spacing for hydroponic leafy vegetable seedlings in plant factories was designed, and it was also equipped with a finger-type planting cup separation mechanism. The stress analysis of rock wool block seedlings in the process of transplanting hand picking seedlings was carried out to provide basis for the design of transplanting hand. The drop cup test of the finger-type planting cup separation mechanism was carried out to lay the foundation for the subsequent planting of seedlings to the planting cup of the cultivation slot. The transplanting mechanism test bench was built, and the transplanting success rate was tested by five-factor three-level orthogonal test with the grasping seedling depth, water content of rock wool block, overall traverse speed, lifting speed, and clamping spacing as the test factors. The influence of each factor on the success rate index was analyzed by variance analysis. The test showed that the seedling depth was 24 mm, rock wool block water content was 90%, overall traverse speed was 0. 8 s, lifting speed was 0. 24 s, clamping spacing was 14 mm, the transplantation success rate of the mechanism was 97. 9%, and the transplant speed reached 3 132 plants/h. which can meet the technical requirements of high-speed, high-efficiency and stable mechanized operation of rock wool block leaf vegetable seedling transplanting in plant factory. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 24

Main heading: Yarn

Controlled terms: Cultivation - Hydroponics - Rocks - Seed - Stress analysis - Vegetables - Wool

Uncontrolled terms: Design and tests - High Speed - Plant factory - Plantings - Rock wool - Rock wool block seedling - Separation mechanism - Transplanter - Traverse speed - Vegetable seedlings

Classification code: 819.4 Fiber Products - 821.3 Agricultural Methods - 821.4 Agricultural Products - 951 Materials Science

Numerical data indexing: Percentage 9.00E+00%, Percentage 9.00E+01%, Size 1.40E-02m, Size 2.40E-02m, Time 2.40E+01s, Time 8.00E+00s

DOI: 10.6041/j.issn.1000-1298.2024.03.010

Funding Details: Number: 2021YFD2000702, Acronym: CNTA, Sponsor: China National Tourism Administration;

Funding text: 國家重點研發(fā)計劃項目（2021YFD2000702)

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

17. Design and Experiment of Double Row Stem Clipping Type Automatic Picking and Throwing Device for Pepper Seedlings

Accession number: 20241515891663

Title of translation: 辣椒苗夾莖式雙排自動取投苗裝置設(shè) 計與試驗

Authors: Qiu, Shuo (1); Yu, Bo (1); Ji, Dong (1); Tian, Subo (1, 2); Zhao, Ping (1); Bai, Xiaohu (1)

Author affiliation: (1) College of Engineering, Shenyang Agricultural University, Shenyang; 110866, China; (2) Key Laboratory of Horticultural Equipment, Ministry of Agriculture and Rural Affairs, Shenyang; 110866, China

Corresponding author: Bai, Xiaohu(baixiaohu@syau.edu.cn)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 3

Issue date: March 2024

Publication year: 2024

Pages: 115-121 and 152

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Pepper is an important domestic economic crop that has a high economic benefit, and its large- scale mechanized planting can further reduce its production cost. However, as a crop planted in various regions in either field or greenhouse, pepper transplantation with the semi-automatic transplanter remains labour intensive and with low efficiency, and research on the automatic seedling picking and throwing device is in urgency. Hence, a double rowstem clipping type automatic picking and throwing device for commercial 2ZBX — 2 vegetable semi-automatic transplanter was designed. The cyclic picking and throwing assignments were realized for pepper pot seedlings through the two rows of clipping jaws that equipped on two face to face type mechanical arms, and orderly and smooth operation and the picking and throwing device was guaranteed by the PLC control system. The key components of clipping jaw were the soft materials and the spring steel sheet that fitted tightly, and accurate positioning of clipping jaw was realized with the horizontal and vertical motion mechanisms. Effect of soft material thickness, age of pepper seedling and cylinder pressure of clipping jaw on picking failure rate, delivering failure rate and throwing failure rate was analyzed by single test, and an ortiiogonal test was conducted, with the pickingthrowing success rate as an optimal object, to determine the optimal working parameters. Under the optimal working parameter combination that soft material thickness was 10. 0 mm, age of pepper seedling was 51 d, and cylinder pressure of clipping jaw was 0. 40 MPa, the test showed that the average real picking-throwing success rate reached 94. 6%, which basically met the technical requirements of vegetable transplanting operation. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 30

Main heading: Crops

Controlled terms: Costs - Cylinders (shapes) - Failure rate - Vegetables

Uncontrolled terms: Automatic picking - Clipping jaw - Cylinder pressures - Failure rate - Material thickness - Pepper seedling - Picking and throwing device - Semi-automatics - Softer materials - Transplanting

Classification code: 821.4 Agricultural Products - 911 Cost and Value Engineering; Industrial Economics - 913.1 Production Engineering

Numerical data indexing: Percentage 6.00E+00%, Pressure 4.00E+07Pa, Size 0.00E00m

DOI: 10.6041/j.issn.1000-1298.2024.03.011

Funding text: 國家重點研發(fā)計劃項目（2020YFD1000300)

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

18. Tea Disease Classification Method Based on Graph Neural Network with Dual Nodes-Dual Edges

Accession number: 20241515891669

Title of translation: 基于雙節(jié)點雙邊圖神經(jīng)網(wǎng)絡(luò)的茶葉病害分類方法

Authors: Zhang, Yan (1); Che, Xun (1); Wang, Peng (1); Wang, Yufeng (1); Hu, Gensheng (1)

Author affiliation: (1) School of Electronic and Information Engineering, Anhui University, Hefei; 230601, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 3

Issue date: March 2024

Publication year: 2024

Pages: 252-262

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The classification of traditional tea diseases mainly relies on manual categorization. Such methods are labor-intensive and time-consuming. Furthermore, insufficient availability of tea disease samples hampers the adequate training of existing machine learning models, resulting in decreased accuracy in disease classification. To address this problem, a tea disease classification method was proposed for four types of tea diseases, including tea anthracnose, tea black rot, and others. This method was based on a dual node - dual edge graph neural network. Firstly, RGB tea disease features and grayscale tea disease features were extracted by using two branches of convolutional neural networks, both branches employed ResNet12 as the backbone network, with independent parameters. The two types of features acted as two sub-nodes within the graph neural network, aiming to obtain disease information from different domains. Secondly, two types of edges, including relative metric edges and similarity edges, were created to improve the aggregation capability of disease features from neighboring nodes. Finally, with the dual node and dual edge feature updating modules, a dual-node and dual-edge alternate updating process was achieved. This process aimed to enhance the accuracy of edge features in measuring node distances. This resulted in achieving accurate classification of tea diseases, even when training samples were limited. Comparative experiments were conducted between the proposed methods, which were based on small-sample learning method. The results indicated that the proposed method achieved superior accuracy in tea disease classification. Specifically, on the minilmageNet and PlantVillage datasets, the proposed method achieved the accuracy of 69.30% and 88.42% in the 5way 1 shot, respectively. In the 5way 5 shot, the accuracy was improved to 82. 48% and 93. 04% on the minilmageNet and PlantVillage datasets. Furthermore j on the TeaD — 5 tea dataset j the accuracy of the proposed method reached 84. 74% in the 5way — 1shot and 86. 34% in the 5wa^ —5shot. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 33

Main heading: Graph neural networks

Controlled terms: Convolutional neural networks - Graph theory - Learning systems

Uncontrolled terms: Classification methods - Disease classification - Diseases features - Dual node - Edge features - Graph neural networks - Labor time - Labour-intensive - Relative metric edge - Similarity edge

Classification code: 723.4 Artificial Intelligence - 921.4 Combinatorial Mathematics, Includes Graph Theory, Set Theory

Numerical data indexing: Percentage 3.40E+01%, Percentage 4.00E+00%, Percentage 4.80E+01%, Percentage 6.93E+01%, Percentage 7.40E+01%, Percentage 8.842E+01%

DOI: 10.6041/j.issn.1000-1298.2024.03.025

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

19. Identification of Gannan Citrus Planting Area Based on Sentinel-2 and Temporal Sentinel-1 SAR Features

Accession number: 20241515882516

Title of translation: 基于Sentinel - 2 與時序Sentinel - 1 SAR特征的贛南柑橘種植區(qū)識別方法

Authors: Qi, Tang (1); Hengkai, Li (1); Yanbing, Zhou (2); Xiuli, Wang (3)

Author affiliation: (1) School of Civil and Surveying & Mapping Engineering, jiangxi University of Science and Technology, Ganzhou; 341000, China; (2) Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing; 100097, China; (3) School of Economics and Management, Jiangxi University of Science and Technology, Ganzhou; 341000, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 3

Issue date: March 2024

Publication year: 2024

Pages: 193-202

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In order to accurately obtain spatial distribution information of citrus orchards and achieve adjustments in citrus cultivation structure, yield estimation, and resource management, focusing on three main citrus-producing regions in southern Jiangxi ： Xinfeng County, Anyuan County, and Xunwu County, in addressing the challenge posed by frequent cloud cover and rainfall in the southern region, resulting in a scarcity of traditional optical images, Sentinel series data and the PIE 一 Engine platform were employed. Spectral features, vegetation water body index features, red edge band features, and texture features were constructed and optimized. Furthermore, the backscatter coefficients of time-series Sentinel - 1 synthetic aperture radar (SAR) data were incorporated to collectively explore the recognition and extraction effects of different feature combinations on citrus plantations. Based on the random forest algorithm and the fusion of Sentinel - 2 and temporal Sentinel - 1 SAR feature recognition, the citrus planting area in Gannan was extracted. The results indicated that the average backscatter coefficient separation between citrus plantations and other ground features was most pronounced in May, September, and November, which were the critical periods for citrus identification and extraction. The involvement of index features and texture features in classification proved advantageous for classification effectiveness and enhanced classification accuracy. In comparison with single SAR features, as well as index and texture features, the overall accuracy of the classification results with the inclusion of temporal SAR features was 90. 084%, with Kappa coefficient of 0. 863. misclassification and leakage errors were relatively small, aligning with the actual distribution of land objects, signifying the availability and practicality of temporal SAR features. The research result can provide reference for the identification and extraction of citrus orchards in the cloudy and rainy southern regions, and it had certain application potential. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 29

Main heading: Synthetic aperture radar

Controlled terms: Backscattering - Classification (of information) - Cultivation - Engines - Extraction - Forestry - Geometrical optics - Information management - Orchards - Radar imaging - Textures

Uncontrolled terms: Backscatter coefficients - Citrus orchards - Citrus; - PIE —engine - Plantation area identification - Planting areas - Sentinel satellite - Sentinel-1 - Temporal synthetic aperture radar - Texture features

Classification code: 716.1 Information Theory and Signal Processing - 716.2 Radar Systems and Equipment - 741.1 Light/Optics - 802.3 Chemical Operations - 821 Agricultural Equipment and Methods; Vegetation and Pest Control - 821.3 Agricultural Methods - 903.1 Information Sources and Analysis

Numerical data indexing: Percentage 8.40E+01%

DOI: 10.6041/j.issn.1000-1298.2024.03.019

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

20. Low Temperature Storage Tomato Maturity Recognition and Time Series Prediction Based on Swin Transformer-GRU

Accession number: 20241515882474

Title of translation: 基于Swin Transformer與 GRU的低溫貯藏番茄成熟度識別與時序預(yù)測研究

Authors: Xinting, Yang (1, 2); Tong, Liu (1); Jiawei, Han (2, 3); Xiangyang, Guo (1); Lin, Yang (4)

Author affiliation: (1) Department of Information, Shanghai Ocean University, Shanghai; 201306, China; (2) Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing; 100097, China; (3) National Engineering Laboratory for Agri-product Quality Traceability, Beijing; 100097, China; (4) Department of Information Science and Technology, Zhongkai University ofAgriculture and Engineering, Guangzhou; 510225, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 3

Issue date: March 2024

Publication year: 2024

Pages: 213-220

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Targeting the continuous ripening process of green mature tomatoes after harvest, timely temperature adjustment plays a pivotal role in meeting the appropriate storage and transportation temperature requirements for tomatoes at different stages of ripeness. Meanwhile, automatic recognition and dynamic prediction of fruit ripeness serve as fundamental prerequisites for achieving temperature control at the right time. A tomato ripeness recognition and temporal dynamic prediction model was proposed based on Swin Transformer and improved GRU. Firstly, by fusing the images of both sides of tomatoes, the overall redness proportion as a visual feature was obtained and a dataset of tomato images at different ripeness stages was constructed. Through transfer learning, the initial weight configuration of the Swin Transformer model was optimized to achieve tomato ripeness classification. Secondly, tomato image data at different storage temperatures (4工, 9 工 and 14 工) was periodically collected, and the initial color features of tomatoes were combined with storage environment information to build a tomato ripeness temporal prediction model based on Swin Transformer and GRU. Furthermore, a time attention module was incorporated to enhance the prediction accuracy of the model. Lastly, the prediction results of different models were compared and analyzed to validate the accuracy and superiority of the proposed model. The results demonstrated a correct recognition rate of 95. 783% for tomato ripeness classification, with respective improvements of 2. 83%, 3. 35%, and 12. 34% compared with that of the VGG16, AlexNet, and ResNet50 models. The mean square error (MSE) for tomato ripeness temporal prediction was 0. 225, representing a maximum reduction of 29. 46% compared with that of the original GRU, LSTM, and BiGRU models. The research result can provide a key theoretical basis for the flexible and timely regulation of storage temperature considering tomato maturity. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 23

Main heading: Mean square error

Controlled terms: Digital storage - Forecasting - Fruits - Long short-term memory - Temperature

Uncontrolled terms: Dynamic prediction - GRU - Low-temperature storage - Maturity recognition - Prediction modelling - Storage temperatures - Swin transformer - Time series prediction - Time series prediction model - Tomato

Classification code: 641.1 Thermodynamics - 722.1 Data Storage, Equipment and Techniques - 821.4 Agricultural Products - 922.2 Mathematical Statistics

Numerical data indexing: Percentage 3.40E+01%, Percentage 3.50E+01%, Percentage 4.60E+01%, Percentage 7.83E+02%, Percentage 8.30E+01%

DOI: 10.6041/j.issn.1000-1298.2024.03.021

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

21. Effects of Nitrogen Fertilizer Reduction and Rhizobia Inoculation on Physiological Growth, Nitrogen Use Efficiency and Yield of Soybean

Accession number: 20241515891686

Title of translation: 減施氮肥和接種根瘤菌對大豆生理生長與氮素利用效率及產(chǎn)量的影響

Authors: Xiang, Youzhen (1, 2); Zhang, Wei (1, 2); Tang, Zijun (1, 2); Fu, Junyu (1, 2); Li, Zhijun (1, 2); Zhang, Fucang (1, 2)

Author affiliation: (1) Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Northwest a and F University, Ministry of Education, Shaanxi, Yangling; 712100, China; (2) Institute of Water-saving Agriculture in Arid Areas of China, Northwest a and F University, 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: 3

Issue date: March 2024

Publication year: 2024

Pages: 340-351

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In agricultural production within arid and semi-arid regions, common practices involve rhizobium bacteria inoculation and nitrogen application to promote soybean growth and increase seed yields. However, there has been limited research on the interaction between rhizobium inoculation and nitrogen application and their impact on soybean growth and yield. This two-year field experiment aimed to address this gap by investigating four nitrogen application levels ( NO ; 0 kg/hm , Nl ; 60 kg/hm , N2 ; 120 kg/hm , N3 ; 180 kg/hm ) and two inoculation levels; rhizobium inoculation (R) and water mixed with no inoculation (unmarked). Various parameters related to soybean growth, including nodule number, nodule dry weight, leaf area index (LAI) , biomass accumulation and root characteristics, were measured at different growth stages. Additionally, physiological indicators such as chlorophyll content, photosynthetic parameters, fluorescence parameters and nitrogen uptake, along with nitrogen use efficiency calculations were assessed. The results demonstrated that soybean growth reached its optimum under the RN2 treatment, with maximum nodule number of 241.47, maximum nodule dry weight of 1. 30 g, maximum root length density of 15. 00 cm/cm , maximum LAI of 5.44 cm /cm , maximum biomass accumulation of 17 530.51 kg/hm , maximum chlorophyll content of 53.55, maximum net photosynthesis rate of 32. 75 |xmol/(m -s) , and maximum seed yield of 4 659. 4 kg/hm . In conclusion, reducing nitrogen fertilizer application ( N2 ) while concurrently inoculating with rhizobium ( R) was essential for enhancing the physiological growth of soybeans in the Guanzhong Plain, improving nitrogen use efficiency and increasing soybean yields. The research result can provide both theoretical underpinning and practical experience to elevate soybean production in arid and semi-arid regions. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 33

Main heading: Nitrogen fertilizers

Controlled terms: Arid regions - Chlorophyll - Ecology - Efficiency - Physiological models - Physiology - Plants (botany)

Uncontrolled terms: Arid and semi-arid regions - Biomass accumulation - Dry weight - Inoculation of rhizobium - Nitrogen application - Nitrogen-use efficiency - Physiological growth - Seed yield - Soybean - Yield

Classification code: 443 Meteorology - 444 Water Resources - 454.3 Ecology and Ecosystems - 461.9 Biology - 804 Chemical Products Generally - 804.1 Organic Compounds - 821.2 Agricultural Chemicals - 913.1 Production Engineering

Numerical data indexing: Mass 0.00E00kg, Mass 1.20E+02kg, Mass 1.80E+02kg, Mass 3.00E-02kg, Mass 4.00E+00kg, Mass 5.3051E+02kg, Mass 6.00E+01kg, Size 0.00E00m, Size 5.44E-02m

DOI: 10.6041/j.issn.1000-1298.2024.03.034

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

22. Risk Assessment of Nitrogen Agricultural Non-point Source Pollution and Identification of Critical Control Areas in Henan Province

Accession number: 20241515891689

Title of translation: 河南省氮素農(nóng)業(yè)面源污染風(fēng)險評價與關(guān)鍵管控區(qū)識別

Authors: Gao, Linlin (1); Wu, Yong (1); Yang, Shuhan (1); Liu, Xueke (1); Li, Ling (1); Li, Donghao (1)

Author affiliation: (1) College of Resources and Environment, Henan Agricultural University, Zhengzhou; 450002, China

Corresponding author: Wu, Yong(yong.wu@henau.edu.en)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 3

Issue date: March 2024

Publication year: 2024

Pages: 311-320

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Agricultural non-point source pollution is an important problem for water environmental protection in China, and risk assessment is of great significance for the prevention and control of agricultural non-point source pollution. Taking Henan Province as the research area, a multi factor comprehensive evaluation model for nitrogen agricultural non-point source pollution was constructed, the hierarchical assignment method of Analytic Hierarchy Process was used, the weights of each influencing factor were determined based on entropy method and expert scoring, the risk index of nitrogen agricultural non-point source pollution in Henan Province was calculated, and it was verified at the basin scale. The risk level of nitrogen agricultural non-point source pollution was divided and critical control areas were identified. The results showed that planting, breeding and domestic sources contributed 31.52% , 38. 47% and 30. 01% of the nitrogen loss load, respectively. The nitrogen loss load was low in the west and high in the middle and east of Henan Province. In Henan Province, 39 429 km area was at moderate risk, accounting for 23. 61% of the total area of Henan Province, and 17 318 km area was at high risk, accounting for 10. 37% of the total area of Henan Province. The medium and high-risk zones within 2 km from the river were classified as general control areas and key control areas, with the general control area of 10 982 km and the key control area of 9 285 km . Correlation analysis was carried out between the model and the data of automatic water quality monitoring stations in Henan Province in 2021 at the basin scale, and the fitting degree R =0.82, indicating that the model had a high accuracy. The results showed that the multi-factor comprehensive evaluation model established was scientific and accurate, which can be used for the identification of nitrogen agricultural non-point source pollution. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 43

Main heading: Nitrogen

Controlled terms: Agricultural pollution - Quality control - Risk assessment - River pollution - Water pollution control - Water quality

Uncontrolled terms: Agricultural non-point source pollutions - Basin scale - Comprehensive evaluation model - Control area - Henan Province - Multi-factor comprehensive evaluations - Nitrogen agricultural non-point source pollution - Nitrogen loss - Risk Identification - Risks assessments

Classification code: 445.2 Water Analysis - 453 Water Pollution - 453.2 Water Pollution Control - 454.2 Environmental Impact and Protection - 804 Chemical Products Generally - 913.3 Quality Assurance and Control - 914.1 Accidents and Accident Prevention

Numerical data indexing: Percentage 1.00E00%, Percentage 3.152E+01%, Percentage 3.70E+01%, Percentage 4.70E+01%, Percentage 6.10E+01%, Size 2.00E+03m, Size 2.85E+05m, Size 3.18E+05m, Size 4.29E+05m, Size 9.82E+05m

DOI: 10.6041/j.issn.1000-1298.2024.03.031

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

23. Design and Test of Automatic Deviation Adjustment System for No-tillage Sowing of Maize

Accession number: 20241515891677

Title of translation: 玉米免耕播種自動調(diào)偏系統(tǒng)設(shè)計與試驗

Authors: Zhang, Zhenguo (1, 2); Guo, Quanfeng (1, 2); Jiang, Guiju (3); Wang, Yunze (1, 2); Xing, Zhenyu (1, 2); Xu, Peng (1, 2)

Author affiliation: (1) College ofMeehanical and Eleetrieal Engineering, Xinjiang Agrieultural University, Urumqi; 830052, China; (2) Key Laboratory of Xinjiang Intelligent Agrieultural Equipment, Urumqi; 830052, China; (3) Agrieulture and Rural Bureau of Bole, Bole, 833400, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 3

Issue date: March 2024

Publication year: 2024

Pages: 40-52

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: To meet the no-till sowing pattern of wide and narrow rows in the drip irrigation area of Xinjiang’ the sowing of traditional large maize no-till planter has problems of touching stubble, failing to constrain path accurately and having a low qualified rate of maize kernel spacing. Combining the agronomic requirements of no-tillage seeding, the navigation and positioning technology was utilized to obtain information about the deviation between the current position of the seeder and the target path. The deviation automatic adjustment system was designed based on the no-tillage seeding stubble avoidance device. The system included a stubble avoidance device, a hydraulic actuating system, and a hydraulic steering control system. Through establishing the mechanical model of machine, stubble avoidance device and hydraulic actuating system were analyzed in terms of movement and force. The key structural parameters of stubble avoidance and hydraulic steering were determined. Meanwhile, the optimal hook-up length of stubble avoidance device and the maximum driving force of hydraulic actuating system were obtained. Moreover, the hydraulic steering control system was optimized to realize function of automatic adjustment for stubble avoidance devices and acceptance of feedback information. The results showed that the maximum steady state error of desired adjustment angle was 0.932° for neural network PID, the overshooting amount was less than 1 %, and the average response steady state error was less than 0. 9 °, which met expectations. When the tractor operating speed was no more than 1. 0 m/s and amount of straw was covered no less between than rows 85% was , vertical no more adjustment than 1. 0 distance kg/m 2 was the no field more test than showed 8.6 that m ? and the stubble coefficient avoidance of variation rate for qualifying grain spacing in maize was no more than 21. 63% . Seeder had the best effect of offsetting and stubble avoidance and met the agronomic index requirements of maize no-tillage planter. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 28

Main heading: Three term control systems

Controlled terms: Agronomy - Irrigation

Uncontrolled terms: Automatic adjustment - Automatic deviation adjustment - Maize - Neural network PID - Neural network PID control - No tillage - No-tillage seeding - No-tillage sowing - Steering control system - Stubble avoidance device

Classification code: 731.1 Control Systems - 821.3 Agricultural Methods

Numerical data indexing: Percentage 1.00E00%, Percentage 6.30E+01%, Percentage 8.50E+01%, Velocity 0.00E00m/s

DOI: 10.6041/j.issn.1000-1298.2024.03.004

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

24. Design and Test of Seed Ladle Tongue Type Flax Precision Burrow Planter

Accession number: 20241515891683

Title of translation: 舀種勺舌式胡麻精量穴播器設(shè) 計與試驗

Authors: Li, Hui (1); Zhao, Wuyun (1); Shi, Linrong (1); Dai, Fei (1); Rao, Gang (1); Wang, Zun (1)

Author affiliation: (1) College of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou; 730070, China

Corresponding author: Shi, Linrong(shilr@gsu.edu.cn)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 3

Issue date: March 2024

Publication year: 2024

Pages: 85-95

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming at the problems of large seed amount, large variation coefficient of sowing amount and uneven sowing, a spoon-tongue precise point planter for flax was designed based on the physical characteristics of fax seeds and the requirements of planting agronomy. By analyzing the working principle of the burrow planter, the composition of tlie burrow planter, the range of structural parameters and the number of installations were determined. The mechanical analysis of the process of spooning seed and clearing seed was carried out to determine the rotational speed range of the burrow planter. According to the EDEM simulation process, the pattern inner wall chute can not only improve the simulation efficiency, but also increase the seed fluidity, which was convenient for the scoop filling. With the rotation speed of the burrow planter, the radius of the transition angle at the top of the scoop and the height of the isolation plate of the seed chamber as the test factors, and the qualification rate of seed exclusion, the leakage rate and the replay rate of the burrow planter as the test indicators, the quadratic rotation orthogonal combination test was carried out by using EDEM discrete element simulation software. The results showed that the optimal parameter combination was as follows: the rotating speed of the burrow planter was 2.9 rad/s, the tip of the scoop was 2. 5 mm, and the height of the isolation plate was 6.8 mm. The seed discharge performance of the spoon was verified by 3D printing. The average pass rate, miss rate and repeat rate of the spoon were 87. 00%, 6. 33% and 6. 67%, respectively. Field experiments showed that the qualified rate of the spooning spoon was 88. 33%, the missed rate was 6. 67%, and the replay rate was 5. 00% . The average planting density of flax was 50 plants/m2 , the results of bench test and field test were basically consistent, and the performance met the agronomic requirements of fine sowing flax. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 24

Main heading: Flax

Controlled terms: 3D printing - Additives - Agronomy - Linen - Plates (structural components) - Seed - Software testing

Uncontrolled terms: Design and tests - EDEM - Mechanical analysis - Physical characteristics - Plantings - Precision burrow planter - Rotational speed - Seed ladle - Structural parameter - Variation coefficient

Classification code: 408.2 Structural Members and Shapes - 723.5 Computer Applications - 745.1.1 Printing Equipment - 803 Chemical Agents and Basic Industrial Chemicals - 819.1 Natural Fibers - 819.5 Textile Products and Processing - 821.3 Agricultural Methods - 821.4 Agricultural Products

Numerical data indexing: Angular velocity 2.90E+00rad/s, Percentage 0.00E00%, Percentage 3.30E+01%, Percentage 6.70E+01%, Size 5.00E-03m, Size 6.80E-03m

DOI: 10.6041/j.issn.1000-1298.2024.03.008

Funding text: 財政部和農(nóng)業(yè)農(nóng)村部:國家現(xiàn)代農(nóng)業(yè)產(chǎn)業(yè)技術(shù)體系項目（CARS-14-1-28),國家自然科學(xué)基金項目（52065004),甘肅省重大科技專項（21ZD4NA012), 農(nóng)業(yè)科學(xué)院重點研發(fā)計劃項目（2021GAAS23)和甘肅省教育廳高校創(chuàng)新平臺重大培育項目（2024CXPT- 15)

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

25. Hyperspectral Image Classification Based on Multi-attention Mechanism and Compiled Graph Neural Networks

Accession number: 20241515882370

Title of translation: 基于多注意力機(jī)制與編譯圖神經(jīng)網(wǎng)絡(luò)的高光譜圖像分類

Authors: Jie, Sun (1); Jing, Yang (1, 2); Shujie, Ding (2); Shaobo, Li (1, 2); Jianjun, Hu (3)

Author affiliation: (1) School of Mechanical Engineering, Guizhou University, Guiyang; 550025, China; (2) State Key Laboratory of Public Big Data, Guizhou University, Guiyang; 550025, China; (3) Department of Computer Science and Engineering, University of South Carolina, CO; 29208, United States

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 3

Issue date: March 2024

Publication year: 2024

Pages: 183-192 and 212

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In recent years, although some scholars have achieved satisfactory research results on hyperspectral image (HSI) classification, they often fail to achieve ideal classification results when facing small sample learning. Aiming at this problem, a hyperspectral image classification method was proposed by the organic combination of multi-attention mechanism fusion, compiled graph neural network and convolutional neural network. Firstly, a type of multiple mixed attention convolutional neural network (M C N N) and compiled graph neural network (C G N N) was designed, which can effectively retain the spectral and spatial information of HSI with limited learning samples; secondly, the introduced graph encoder and graph decoder can effectively map irregular HSI feature information ； finally, the designed multi-attention mechanism can focus on some important HSI feature categories. In addition, the effect of different training samples on different algorithms for learning example classification was also investigated. Experiments on the public dataset Botswana (B S) showed that the proposed method improved the overall classification accuracy (O A) by 2. 72 percentage points and 3. 86 percentage points compared with the current state-of-the-art algorithms (CNN-enhanced graph convolutional network, C E G C N ； weighted feature fusion of convolutional neural network, W F C G). Similarly, the experimental results on the IndianPines (IP) dataset with only 3 % of the training sample data showed that the method also improved the O A of the current state-of-the-art algorithms (C E G C N and W F C G) by 0.44 percentage points and 1.42 percentage points, respectively. This demonstrated that the proposed method not only had good spatial and spectral information perception for HSI, but also showed strong classification accuracy with small learning data. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 30

Main heading: Convolution

Controlled terms: C (programming language) - Classification (of information) - Convolutional neural networks - Graph neural networks - Image classification - Image segmentation - Learning algorithms - Sampling

Uncontrolled terms: Attention mechanisms - Classification accuracy - Convolutional neural network - Graph neural networks - HyperSpectral - Hyperspectral image classification - Percentage points - Spatial informations - Spectral information - Superpixel segmentations

Classification code: 716.1 Information Theory and Signal Processing - 723.1.1 Computer Programming Languages - 723.2 Data Processing and Image Processing - 723.4 Artificial Intelligence - 723.4.2 Machine Learning - 903.1 Information Sources and Analysis

Numerical data indexing: Percentage 3.00E+00%

DOI: 10.6041/j.issn.1000-1298.2024.03.018

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

26. Design and Experiment of Extensible Type of Picking Seedling Pot Mechanism for Tomato Pot Seedling Transplanting

Accession number: 20241515891666

Title of translation: 番茄缽苗移栽探出式取缽機(jī) 構(gòu) 設(shè) 計與試驗

Authors: Xin, Liang (1); Wang, Mingcheng (1); Sun, Guoyu (1); Zhang, Hao (1); Sun, Mingyi (1); Wang, Hang (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: 3

Issue date: March 2024

Publication year: 2024

Pages: 96-105

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In order to lower the damage to the root system and pot soil of the seedling caused by the picking seedling pot mechanism in the tomato pot seedling transplanting process, and avoid the optimization problem of special seedling-picking trajectory and posture design of the mechanical pot seedling transplanting mechanism, the extensible type of picking seedling pot mechanism that can be used in conjunction with a series of transplanting mechanisms was proposed. It was achievable that the seedling needles of the mechanism at key positions of the picking seedling completed the process of protruding into the seedling pot, moving and delivering seedlings, and retrieving and pushing seedlings at a fixed angle. Based on the analysis and design requirements of the process of transplanting and picking seedling pot, the mechanical analysis model of the picking seedling pot mechanism was established, and the factors affecting the minimum driving force on the driving rod when the seedling needles protruding into the seedling pot were obtained. A computer-aided analysis and design software for the picking seedling pot mechanism was developed based on the Matlab App Designer platform, obtaining a set of picking seedling pot mechanism design parameters that met the requirements of tomato pot seedling transplantation. Using the three-factor five-level quadratic regression orthogonal rotation center combination test method, taking the angle of inclined rods, moisture content of seedling pot and depth into the pot as the test factors, and taking the integrity rate of the pot and the success rate of taking seedlings as the evaluation indexes, the prototype was trial manufactured and the bench was built to implement the parameter combination optimization and verification test. The results showed that the extensible type of picking seedling pot mechanism could effectively cooperate with the seedling picking mechanism to complete the performance requirements. The results showed that the operation effect was the best when the combination parameters included angle of the driving inclined rods was 112°, the moisture content of the seedling pot was 57. 5%, and the depth into the pot was 28. 4 mm. The integrity rate of the seedling pot was 96. 44%, and the success rate of picking seedlings was 97. 06%, which met the performance of pot seedling transplanting. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 32

Main heading: Moisture

Controlled terms: Application programs - Computer aided analysis - Fruits - Machine design - MATLAB - Moisture determination - Needles - Plants (botany)

Uncontrolled terms: Extensible type - Mechanical - Mechanical analysis - Optimization problems - Picking seedling pot mechanism - Pot seedling transplanting - Root system - Seedlings transplanting - Tomato - Transplanting mechanisms

Classification code: 601 Mechanical Design - 723 Computer Software, Data Handling and Applications - 723.5 Computer Applications - 821.4 Agricultural Products - 921 Mathematics - 944.2 Moisture Measurements

Numerical data indexing: Percentage 4.40E+01%, Percentage 5.00E+00%, Percentage 6.00E+00%, Size 4.00E-03m

DOI: 10.6041/j.issn.1000-1298.2024.03.009

Funding text: 國家自然科學(xué)基金項目（52105247),黑龍江省自然科學(xué)基金優(yōu)秀青年項目（YQ2023E001),黑龍江省普通本科高等學(xué)校青年創(chuàng)新人才培養(yǎng)計劃項目（UNPYSCT-2020099),中央支持地方高校改革發(fā)展年和東北農(nóng)業(yè)大學(xué)”學(xué)術(shù)骨干”基金項目

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

27. Design and Experiment of Precision Seed Metering Device with Pneumatic Assisted Seed-filling for Peanut

Accession number: 20241515891690

Title of translation: 花生氣動輔助充種精密排種裝置設(shè)計與試驗

Authors: Guo, Peng (1); Zheng, Xiaoshuai (2); Wang, Dongwei (1, 3); Hou, Jialin (1); Zhao, Zhuang (1)

Author affiliation: (1) College ofMeehanical and Eleetrical Engineerings Shandong Agricultural University, Taian; 271018, China; (2) Yellow River Delta Intelligent Agricultural Machinery Equipment Industry Academy, Dongying; 257300, China; (3) College ofMechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao; 266109, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 3

Issue date: March 2024

Publication year: 2024

Pages: 64-74

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Abstract speed operation : In order in the to solve process the problem of peanut that planting peanut to seed precision filling effect and high is poor speed under? a the kind condition of pneumatic of high assisted seed-filling precision seed metering device was designed, specially, the structures of planter plate and pneumatic assisted seed-filling device were mainly designed. For peanut seeds with large particle size and mass, by analyzing the phenomenon of peanut seed accumulation in the seed metering device and the filling enhance time the , filling it was performance concluded that? so the as to peanut improve high-speed the filling seed efficiency. discharging Through and analyzing filling process the principle needed to of peanut seed filling, the relationship between the movement and force of seeds and seed metering device in the seed filling process of peanut seeds was clarified, and the influencing factors of the seed-filling process were analyzed. The key structural parameters of the seed suction holes and seed guide slot of the seed metering device and the parameters and arrangement of the auxiliary seed blowing holes were analyzed and calculated by designing the seed metering device with seed guide slot and the auxiliary seed filling rate of structure seed filling with as auxiliary indexes ? seed a three-factor guide holes. and Taking three-level the combination qualified rate test of was seed carried filling out and , and the leakage the test results were subjected to multivariate regression analysis to optimize with the optimal objective, and the optimal parameter combinations of seed planter plate were determined to be the negative pressure of seed suction in the seed metering device of 5. 156 kPa, the forward speed of the high-speed planter for peanut was 8. 007 km /h, and the positive pressure of disturbed blowing of seeds was 1. 149 kPa. In this case ^ the qualified rate of peanut seed filling was 95. 84%, and the leakage rate of peanut seed filling was 4.06%, which could realize the effective seed filling of peanut seeds. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 30

Main heading: Pneumatics

Controlled terms: Filling - Oilseeds - Particle size - Particle size analysis - Plates (structural components) - Regression analysis

Uncontrolled terms: Filling process - High Speed - Peanut - Peanut seeds - Pneumatic type - Precision seed-metering devices - Seed filling - Seed guide slot - Seed-metering device - Speed operation

Classification code: 408.2 Structural Members and Shapes - 632.3 Pneumatics - 691.2 Materials Handling Methods - 821.4 Agricultural Products - 922.2 Mathematical Statistics - 951 Materials Science

Numerical data indexing: Percentage 4.06E+00%, Percentage 8.40E+01%, Pressure 1.49E+05Pa, Pressure 1.56E+05Pa, Size 7.00E+03m

DOI: 10.6041/j.issn.1000-1298.2024.03.006

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

28. Simulation and Experiment on Ultrasonic Cutting Process of Clustered Tomato Stems

Accession number: 20241515881690

Title of translation: 簇生番茄果梗超聲切割過程仿真與試驗

Authors: Jun, Zhang (1); Di, Xin (1); Weike, Lan (1); Kehua, Dang (1); Zijie, Niu (1); Yongjie, Cui (1, 2)

Author affiliation: (1) College ofMechanical and Electronic Engineering, Northwest A&F University, Shaanxi, Yangling; 712100, China; (2) Key Laboratory ofAgricultural Internet of Things, Ministry ofAgriculture 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: 3

Issue date: March 2024

Publication year: 2024

Pages: 153-161

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In order to reveal the mechanism and advantages of ultrasonic technology in cutting and harvesting clustered tomatoes, the cutting force and removal characteristics of clustered tomato stem materials in conventional cutting and ultrasonic cutting were compared. Firstly, the parameters of clustered tomato fruit stalks and self-made ultrasonic cutting knife were measured, and then the fruit stalks were fiberized based on Abaqus simulation. The stress and removal mechanism during conventional cutting and ultrasonic cutting were compared in macro and micro simulation. Finally, the self-made test bench was used to measure the cutting force by changing the excitation frequency, input voltage, cutting speed and cutting angle of the ultrasonic cutting knife, and the Box - Behnken of response surface method was used to analyze the four factors and three levels, and then the cross-section morphology of the fruit stalk was observed. The results showed that within the working frequency (35 - 37 kHz) and voltage (340 - 380 V) of the self-made ultrasonic scalpel, the cutting speed and angle had the most significant influence on the cutting effect, and the cutting effect was the best when the excitation frequency and input voltage were near a specific value. Under the conditions of 36 kHz, 360 V, 0. 125 cm /s, and 00, the ultrasonic cutting time in the simulation was about 8 s, and the average maximum cutting force was 0. 635 N, which was 37. 7% lower than that of the conventional cutting (1.019 N) . In the experiment, the ultrasonic cutting took about 5 .3 s, and the maximum cutting force required was 0. 543 N, which was 46. 6% lower than that of the conventional maximum cutting force (1. 017 N), and the surface roughness was 20. 9% lower. The error between the experimental and finite element simulation results was 8. 9%, which was basically consistent. Ultrasonic cutting can reduce the cutting force, shorten the cutting time, improve the section quality, reduce the damage of fruit stem tissue and water loss, which was of certain significance to prolong the fruit preservation time. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 29

Main heading: Fruits

Controlled terms: Finite element method - Morphology - Surface roughness - Ultrasonic cutting - Ultrasonic effects - Ultrasonic waves

Uncontrolled terms: Cluster of tomato stem - Conventional cutting - Cutting forces - Cutting knives - Finite element - Maximum cutting force - Removal mechanism - Ultrasonic vibration cutting - Ultrasonic-vibration - Vibration cutting

Classification code: 604.1 Metal Cutting - 753.1 Ultrasonic Waves - 753.3 Ultrasonic Applications - 821.4 Agricultural Products - 921.6 Numerical Methods - 931.2 Physical Properties of Gases, Liquids and Solids - 951 Materials Science

Numerical data indexing: Force 1.019E+00N, Force 1.70E+01N, Force 5.43E+02N, Force 6.35E+02N, Frequency 3.50E+04Hz to 3.70E+04Hz, Frequency 3.60E+04Hz, Percentage 6.00E+00%, Percentage 7.00E+00%, Percentage 9.00E+00%, Time 3.00E+00s, Time 8.00E+00s, Velocity 1.25E+00m/s, Voltage 3.40E+02V to 3.80E+02V, Voltage 3.60E+02V

DOI: 10.6041/j.issn.1000-1298.2024.03.015

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

29. Optimized Design and Validation of Distributed Drive System for Electric Tractor Based on Multi-island Genetic Algorithm

Accession number: 20241515899888

Title of translation: 基于多島遺傳算法的電動拖拉機(jī)分布式驅(qū)動系統(tǒng) 優(yōu)化設(shè)計與試驗

Authors: Li, Xianzhe (1, 2); Zhang, Mingzhu (2, 3); Liu, Mengnan (2, 4); Xu, Liyou (1, 2); Yan, Xianghai (1, 2); Lei, Shenghui (1, 2)

Author affiliation: (1) College of Vehicle and Traffic Engineering, Henan University of Science and Technology, Luoyang; 471003, China; (2) State Key Laboratory of Intelligent Agricultural Power Equipment, Luoyang; 471039, China; (3) Engineering Department, Huanghe Science and Technology University, Zhengzhou; 450006, China; (4) YTO Croup Corporation R and D Center, Luoyang; 471039, China

Corresponding author: Liu, Mengnan(liumengnan27@163.com)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 3

Issue date: March 2024

Publication year: 2024

Pages: 401-411

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The distributed drive system allows for independent control of each wheel, providing greater maneuverability and adaptability to various terrains and working conditions. Additionally, when combined with electric technology, the distributed drive system can reduce emissions, decrease reliance on fossil fuels, and improve sustainability. These advantages position distributed drive electric tractor (DDET) as having broad potential for applications in agriculture and industry. Aiming at the low traction efficiency and high energy consumption of the DDET, a distributed drive system parameter optimization design and verification method based on the multi-island genetic algorithm (MIGA) was proposed. According to the working conditions of plowing operations, a 7-DOF coupled dynamics model of the tractor distributed drive system and a tire - soil interaction model were established. The parameter design and matching selection of key components in the drive system were completed. An MIGA-based optimization strategy for the front and rear wheel-side transmission ratios ( WTR) was proposed, taking WTR as the decision variable, minimizing energy losses in the drive system as the optimization objective, and with constraints on the power and speed of the drive motor. This effectively prevented the algorithm from prematurely falling into local optima during the optimization process, improving the efficiency and reliability in obtaining the globally optimal. A Matlab/Simulink - NI PXI joint simulation platform was built to verify the correctness and real-time executability of the parameter optimization strategy. The joint simulation results showed that the distributed drive system optimized based on MIGA achieved effective performance improvements. Under cyclic plowing conditions, the average traction of the tractor was 10 610 N with maximum traction power of 31. 25 kW. The average efficiency was increased by 0. 38% and energy consumption of the drive motor was decreased by 7. 53% . The research result can provide theoretical foundations and verification methodologies for the optimal design and system control of distributed drive electric tractors. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 26

Main heading: Wheels

Controlled terms: Design - Energy dissipation - Energy utilization - Fossil fuels - Genetic algorithms - Optimal systems - Simulation platform - Simulink - Tractors (agricultural) - Tractors (truck)

Uncontrolled terms: Condition - Distributed drive system - Drive motors - Drive systems - Electric tractors - Joint simulation - Multi island genetic algorithms - Optimal design - Optimization strategy - Transmission-ratio

Classification code: 525.3 Energy Utilization - 525.4 Energy Losses (industrial and residential) - 601.2 Machine Components - 663.1 Heavy Duty Motor Vehicles - 723.2 Data Processing and Image Processing - 723.5 Computer Applications - 821.1 Agricultural Machinery and Equipment - 961 Systems Science

Numerical data indexing: Force 6.10E+02N, Percentage 3.80E+01%, Percentage 5.30E+01%, Power 2.50E+04W

DOI: 10.6041/j.issn.1000-1298.2024.03.040

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

30. Energy-saving Control Method of Air Conditioning in Mushroom House Based on Model Predictive Control

Accession number: 20241515891691

Title of translation: 基于模型預(yù)測控制的菇房空調(diào)節(jié)能控制方法

Authors: Zhang, Xin (1, 2); Kong, Xiangshu (1, 2); Zheng, Wen’gang (2); Wang, Mingfei (2, 3); Shan, Feifei (2); Bao, Feng (2)

Author affiliation: (1) College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi; 830052, China; (2) Intelligent Equipment Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing; 100097, China; (3) North China Urban Agriculture Key Laboratory, Ministry of Agriculture and Rural Affairs, Beijing; 100096, China

Corresponding author: Wang, Mingfei(wangmf@nercita.org.en)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 3

Issue date: March 2024

Publication year: 2024

Pages: 352-361

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: At present, there are some problems such as low energy saving efficiency and large indoor temperature fluctuation in the control methods of mushroom air conditioning in factory production. An energy saving control method based on convolutional neural network (CNN) , gated recurrent unit neural network ( GRU ) and self-attention mechanism was proposed. The CNN - GRU - Attention combined neural network was used as the prediction model, and the prediction error compensation and the dynamic updating mechanism of the prediction model data set were combined to achieve accurate prediction of indoor temperature in mushroom houses. The control quantity of air conditioning was established as the objective function of state quantity, and the weight coefficient of the objective function was defined by entropy weight method and subjective method, respectively. The optimal control sequence of air conditioning in the control time domain was solved by non-dominated sorting genetic algorithm E ( NSGA - E ) , and the rolling optimization and feedback mechanism were integrated to realize the accurate and energy-saving control of the greenhouse environment. The experimental results showed that the CNN - GRU - Attention indoor temperature prediction model proposed in mushroom house showed that the previous 30 min data had the best effect in predicting the indoor temperature in the future 10 min. On a typical intra-day the maximum root mean square error of prediction accuracy was 0. 122°C , the minimum coefficient of determination was 0. 807 , and the maximum mean absolute percentage error was 0. 611% . The model predictive control method of mushroom air conditioning had a good anti-interference ability in weather fluctuation. Compared with threshold switching method and PID method, the energy consumption of air conditioning can be saved by 21% and 14% , respectively. In terms of temperature control accuracy, the root mean square error was decreased by 72% and 46% , respectively. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 38

Main heading: Houses

Controlled terms: Air conditioning - Energy utilization - Error compensation - Forecasting - Genetic algorithms - Mean square error - Model predictive control - Multiobjective optimization - Recurrent neural networks - Space heating - Time domain analysis

Uncontrolled terms: Control methods - Convolutional neural network - Energy-saving control - Indoor temperature - Model-predictive control - Multi-objectives optimization - Mushroom house - Neural-networks - Objective functions - Prediction modelling

Classification code: 402.3 Residences - 525.3 Energy Utilization - 643.1 Space Heating - 643.3 Air Conditioning - 921 Mathematics - 921.5 Optimization Techniques - 922.2 Mathematical Statistics

Numerical data indexing: Percentage 1.40E+01%, Percentage 2.10E+01%, Percentage 4.60E+01%, Percentage 6.11E+02%, Percentage 7.20E+01%, Temperature 3.95E+02K, Time 1.80E+03s, Time 6.00E+02s

DOI: 10.6041/j.issn.1000-1298.2024.03.035

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

31. Yield Estimation of Winter Wheat Based on Multiple Remotely Sensed Parameters and CNN-Transformer

Accession number: 20241515881843

Title of translation: 基于遙感多參數(shù)和CNN Transformer的冬小麥單產(chǎn)估測

Authors: Pengxin, Wang (1, 2); Jiangli, Du (1, 2); Yue, Zhang (1, 2); Junming, Liu (3); Hongmei, Li (4); Chunmei, Wang (5)

Author affiliation: (1) College of Information and Electrical Engineering, China Agricultural University, Beijing; 100083, China; (2) Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications, Ministry of Agriculture and Rural Affairs, Beijing; 100083, China; (3) College of Land Science and Technology, China Agricultural University, Beijing; 100193, China; (4) Shaanxi Provincial Meteorological Bureau, Xian; 710014, China; (5) Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing; 100094, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 3

Issue date: March 2024

Publication year: 2024

Pages: 173-182

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 winter wheat yield estimation and the phenomena of underestimation of high yield and overestimation of low yield that exist in yield estimation models, the Guanzhong Plain in Shaanxi Province, China was taken as the study area, and the vegetation temperature condition index (V T C I), leaf area index (L A I) and fraction of photosynthetically active radiation (F P A R) at the ten-day interval were selected as remotely sensed parameters, and a deep learning model was proposed for estimating winter wheat yield by combining the local feature extraction capability of convolutional neural network (C N N) and the global information extraction capability of Transformer network based on the mechanism of self-attention. Compared with the Transformer model (R 2 was 0. 64, R M S E was 465. 40 k g / h m 2, M A P E was 8. 0 4 %), the C N N - Transformer model had higher accuracy in estimating winter wheat yield (R 2 was 0. 70, R M S E was 420. 39 k g / h m 2, M A P E was 7. 6 5 %), which can extract more yield-related information from the multiple remotely sensed parameters, and improved the underestimation of high yield and overestimation of low yield which existed in the Transformer model. The robustness and generalization ability of the CNN - Transformer model were further validated based on the five-fold cross-validation method and the leave-one-out method. In addition, based on the CNN - Transformer model, the cumulative effect of the winter wheat growth process was revealed, the impact of gradually accumulating ten-day scale input information on yield estimation was analyzed, and the ability of the model to characterize the accumulation process of winter wheat at different growth stages was assessed. The results showed that the model can effectively capture the critical period of winter wheat growth, which was from late March to early May. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 25

Main heading: Convolution

Controlled terms: Convolutional neural networks - Crops - Deep learning - Parameter estimation - Remote sensing

Uncontrolled terms: Convolutional neural network - Estimation models - Extraction capability - Higher yield - Low-yield - Multiple remotely sensed parameter - Transformer modeling - Winter wheat - Winter wheat yields - Yield estimation

Classification code: 461.4 Ergonomics and Human Factors Engineering - 716.1 Information Theory and Signal Processing - 821.4 Agricultural Products

Numerical data indexing: Percentage 4.00E+00%, Percentage 5.00E+00%

DOI: 10.6041/j.issn.1000-1298.2024.03.017

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

32. Design and Experiment of Air-suction Double-row Staggered Precision Seed Metering Device for Maize Dense Planting

Accession number: 20241515891685

Title of translation: 氣吸雙行錯置式玉米密植精量排種器設(shè)計與試驗

Authors: Wang, Weiwei (1, 2); Song, Lanzhou (1); Shi, Wenbing (1); Wei, Dehua (1); Chen, Yongxin (1, 2); Chen, Liqing (1, 2)

Author affiliation: (1) School of Engineering, Anhui Agricultural University, Hefei; 230036, China; (2) Anhui Province Engineering Research Center of Intelligent Agricultural Machinery, Hefei; 230036, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 3

Issue date: March 2024

Publication year: 2024

Pages: 53-63

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: A air-suction double-row staggered precision seed metering device was designed to address the problems of short filling time and poor airflow stability caused in the high-speed operation of the traditional air-suction seederâ€™s single row seed tray, which made it difficult to achieve high-speed and precise seeding in the soybean corn compound dense planting mode. The structure and working principle of the seeder were explained, theoretical analysis of the seeding process and key components, construction of seed mechanics models for filling and seeding stages, determination of key structural parameters such as the arrangement of inner and outer ring holes in the seeding disc, seeding wheel, and air chamber, and analysis of negative pressure distribution and airflow characteristics inside single and double air passages were done. Based on the DEM CFD coupling method, the seeding process of the seeder was simulated and analyzed with operating speed, air chamber structure and negative pressure as experimental factors, and filling qualification rate, refilling rate and leakage rate as evaluation indicators, the optimal chamber structure was selected. A comparative test was conducted on the seeding performance of different air-suction seeders through bench tests. The test results showed that the air-suction double row staggered dense planting precision seeder had a seeding qualification index greater than 88.7% under high-speed dense seeding conditions with operating speeds of 5 1-^ 10 km/h. Moreover, compared with the commonly used single ring air-suction seeder at operating speed of 10 km /h, the qualification index was increased by 5. 5 percentage points and the missed seeding indexwas decreased by 5. 6 percentage points. 95. The 7% field, the test replanting results showed index that was under 1. 6% the ? and working the missed condition sowing of 5 km index /h, was the qualified 2.8% . The sowing proposed index was air-suction double-row staggered corn dense planting precision seeder had good seeding performance in high speed operation and could meet the requirements of high-speed precision seeding of soybeans and maize. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 25

Main heading: Filling

Controlled terms: Seed - Structural optimization

Uncontrolled terms: Air chambers - Air suction - Air-suction seed metering device - DEM CFD air chamber structure - Double rows - Double-row misplacement - High Speed - Maize - Plantings - Seed-metering device

Classification code: 691.2 Materials Handling Methods - 821.4 Agricultural Products - 921.5 Optimization Techniques

Numerical data indexing: Percentage 2.80E+00%, Percentage 6.00E+00%, Percentage 7.00E+00%, Percentage 8.87E+01%, Size 1.00E+04m, Size 5.00E+03m

DOI: 10.6041/j.issn.1000-1298.2024.03.005

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

33. Characteristics of Dry Ice Jet Quick-frozen Blueberries with Multi-outlet Fan-shaped Nozzle

Accession number: 20241515891682

Title of translation: 多出口扇形噴嘴干冰噴射速凍藍(lán)莓特性研究

Authors: Ning, Jinghong (1); Song, Zhipeng (1); Yang, Xin (1); Ren, Ziliang (1); Wang, Nuanhou (1); Bao, Xiang (1)

Author affiliation: (1) School of Mechanical Engineering, Tianjin University of Commerce, Tianjin; 300134, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 3

Issue date: March 2024

Publication year: 2024

Pages: 392-400

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In order to improve the atomization characteristics of quick-frozen blueberries, a fan-shaped nozzle with multi-outlet was designed and optimized. It can spray a fan-shaped dry ice particle jet, which can ultimately freeze blueberries more evenly and quickly. A physical model of a multi-outlet fan-shaped nozzle and a computational model of the dry ice jet flow field for quick-frozen blueberries were established. Using Fluent software and the gas - solid two-phase dynamics model DPS, Realizable, k-£ turbulence model, a numerical simulation study was conducted on the process of quick-freezing blueberries by using a multi-outlet fan-shaped nozzle for dry ice particle spraying. The different V-shaped groove angles (60°, 70°, 80° and 90°) at the outlet of the fan-shaped nozzle were investigated, the effects of different angles on the flow field distribution of dry ice particles in quick-freezing chamber, as well as the freezing rate of blueberry and the freezing uniformity were studied under the same inlet flow rate and outlet aperture. The results showed that as the angle of the V-shaped groove was increased, the width of the fan-shaped impinging jet was decreased, and the flow velocity in the core region of the impinging jet was increased. When the V-shaped groove angle of the multi-outlet fan-shaped nozzle outlet was 70°, compared with 60°, 80°, and 90°, the freezing completion time distribution of the whole plate of blueberries in the quick-freezing chamber was the most concentrated, the overall freezing speed was fast, and the flow field was the most uniform. Therefore, it was the optimal outlet parameter for this nozzle model (inlet diameter of 30 mm, inlet velocity of 0. 25 m/s, outlet was a combination of circularly arranged diameter of 5. 2 mm X 6 ( there were six outlets with a hole diameter of 5. 2 mm ) and centrally arranged diameter of 2 mm x4) . The optimal result of the simulation was then tested experimentally. The results showed that the whole plate of blueberries completed quick-freezing in 119 s, with a freezing rate of 0. 50 cm/min. The error between the experimental and simulated cooling curves was 4. 3% . Tests were conducted on anthocyanins content, soluble solids, mass fraction, and water loss rate of frozen blueberries after quick freezing. The results showed that the sensory quality of dry ice quick-frozen blueberries during storage was better than that of national standard quick-frozen blueberries. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 24

Main heading: Freezing

Controlled terms: Atomization - Computer software - Flow fields - Flow velocity - Ice - Phase diagrams - Spray nozzles - Turbulence models

Uncontrolled terms: Atomization characteristics - Dry ice jets - Dry ice particles - Fan-shaped nozzle - Freezing rate - Groove angle - Impinging jet - Quick-frozen blueberry - Simulate - V-shaped grooves

Classification code: 631 Fluid Flow - 631.1 Fluid Flow, General - 723 Computer Software, Data Handling and Applications - 802.3 Chemical Operations - 943.2 Mechanical Variables Measurements

Numerical data indexing: Percentage 3.00E+00%, Size 2.00E-03m, Size 3.00E-02m, Size 5.00E-01m, Time 1.19E+02s, Velocity 2.50E+01m/s

DOI: 10.6041/j.issn.1000-1298.2024.03.039

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

34. High-speed No-till Seeder Seeding Depth Monitoring System Based on IWHO-EKF

Accession number: 20241515891664

Title of translation: 基于IWHO EKF的高速免耕播種機(jī)播種深度監(jiān)測系統(tǒng)研究

Authors: Wang, Song (1); Yi, Shujuan (1); Zhao, Bin (1, 2); Li, Yifei (1, 3); Tao, Guixiang (1); Mao, Xin (1)

Author affiliation: (1) College of Engineering, Heilongjiang Bayi Agricultural University, Daqing; 163319, China; (2) Heilongjiang Provincial Key Laboratory of Intelligent Agricultural Machinery Equipment, Daqing; 163319, China; (3) College of Engineering, NortheastAgricultural 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: 3

Issue date: March 2024

Publication year: 2024

Pages: 75-84

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: A high-speed no-tillage seeder seeding depth monitoring system based on the improved wild horse optimizer — extended Kalman filter (IWHO EKF) was proposed. The system addressed the mechanical vibration issues caused by uneven terrain during operation, which led to a decrease in accuracy of the seeding depth monitoring. Additionally, it improved the poor reliability of a single monitoring sensor. Firstly, a mathematical model for monitoring seeding depth was established by using laser, ultrasonic, and angle sensors as the multi-sensor monitoring unit. Secondly f a Kalman filtering algorithm was implemented to filter the measurements from the three individual sensors separately. Lastly, the IWHO proposed the use of the Levy flight and Gaussian mutation algorithms to optimize the key parameters of the EKF for data fusion. Qsigma f Rsigma1 f Rsigma2 f and Rsigma3 were the parameters that were optimized for the fusion of filtered measurements from the three sensors. Technical term abbreviations such as EKF were explained when first used. The aim was to reduce interference from mechanical vibration, decrease sensor measurement errors and ensure accurate and reliable real-time of seeding the proposed depth monitoring method, during simulation high-speed experiments operation and of the field no-till validation seeder. experiments To ascertain were the effectiveness conducted ? comparing the IWHO EKF with original sensor measurements, filtered seeding depth values and the WHO — EKF. The results from simulation experiments demonstrated that the IWHO — EKF algorithm had a mean absolute error (MAE) and root mean squared error (RMSE) of 0.073 cm and 0.090 cm, respectively, with a high correlation coefficient (R) of 0.983. This suggested a high level of accuracy and significant improvements in precision compared with measurements from the original sensor and filtered values, as well as the WHO EKF. Technical term abbreviations were explained when it was firstly used. Field validation tests confirmed that the IWHO —EKF for seeding depth monitoring system in high-speed no-till seeders reduced the average MAE and RMSE by 0. 063 cm and 0.067 cm, respectively, when compared with data from the three sensors. Additionally, the average R was increased by 0. 027. This system offerred improved, accurate, and dependable monitoring values for seeding depth. The research result can provide lessons and references for high precision seeding depth monitoring during high-speed seeding. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 27

Main heading: Extended Kalman filters

Controlled terms: Agriculture - Errors - Mean square error - Parameter estimation - Sensor data fusion - Vibrations (mechanical)

Uncontrolled terms: High Speed - High-speed no-till seede - Improved wild horse optimizer - Monitoring system - No-till - Optimizers - Seeding depth - Seeding depth monitoring - Technical terms - Three sensors

Classification code: 723.2 Data Processing and Image Processing - 821 Agricultural Equipment and Methods; Vegetation and Pest Control - 922.2 Mathematical Statistics - 931.1 Mechanics

Numerical data indexing: Size 6.30E-01m, Size 6.70E-04m, Size 7.30E-04m, Size 9.00E-04m

DOI: 10.6041/j.issn.1000-1298.2024.03.007

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

35. High-speed Transmission System for Agricultural Monitoring Video Based on PCIe Cascaded Network Interface

Accession number: 20241515881763

Title of translation: 基于 PC Ie級聯(lián)網(wǎng)口的農(nóng)業(yè)監(jiān)測視頻高速傳輸系統(tǒng)研究

Authors: Ruifeng, Duan (1, 2); Yan, Chen (3); Kai, Hong (1); Jiu, Zhang (1); Haiyan, Zhang (1, 2)

Author affiliation: (1) School of Information Science and Technology, Beijing Forestry University, Beijing; 100083, China; (2) Engineering Research Center for Forestry-oriented, Intelligent Information Processing of National Forestry and Grassland Administration, Beijing; 100083, China; (3) School of Software, Beihang University, Beijing; 100191, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 3

Issue date: March 2024

Publication year: 2024

Pages: 203-212

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The intelligence of agricultural monitoring requires real-time, efficient and reliable video data processing and transmission solutions. In order to solve the problems of low rate and poor real-time performance of traditional systems based on C P U and G P U architecture, a high-speed transmission system based on Zynq architecture with PCIe cascaded network interfaces was designed. For the development of PCIe interface, at the hardware level, X D M A IP core parameters were optimized, the interface data transceiver engine was designed, and D D R cache area was optimized based on M I G 7 IP core. At the software level, the PCIe driver was used to schedule V L C software for video data read operations, enabling fast data transmission and smooth playback between the board and the host computer. For the implementation of the network interface protocol stack, the A R M programmable feature was utilized to schedule the lightweight L W I P protocol stack and develop the T C P protocol, achieving fast data transmission for the network interface and avoiding the delay and computational overhead of the host computer C P U directly processing network protocols. Additionally, the A X I protocol was scheduled to establish high-speed connectivity between the PCIe interface and the network interface. Moreover, the rate and reliability of the system were also measured by transmitting video files on Zynq M Z 7 0 3 0 F A platform. The results showed that the transmission rate of the network interface was 800 M b / s , that w a s , gigabit ethernet was basically realized; the maximum transmission rate of PCIe interface was 816 MB/s, which was close to the maximum speed of hardware PCIe 2.0x2, and the whole system achieved reliable transmission at the application layer. The research result can provide an efficient and reliable solution for the application of agricultural monitoring video transmission, and the system had good scalability and generalization. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 31

Main heading: Ethernet

Controlled terms: Agriculture - C (programming language) - Computer architecture - Computer hardware - Data acquisition - Data handling - Data transfer - Field programmable gate arrays (FPGA) - Image communication systems - Internet protocols - Network architecture - Real time systems - Transceivers - Video recording

Uncontrolled terms: Agricultural monitoring - Cascaded networks - Gigabit Ethernet - High speed transmission - High speed transmission systems - High-speed interfaces - IP-cores - PCIe high-speed interface - Video data - Zynq

Classification code: 716.3 Radio Systems and Equipment - 716.4 Television Systems and Equipment - 721.2 Logic Elements - 722 Computer Systems and Equipment - 722.3 Data Communication, Equipment and Techniques - 722.4 Digital Computers and Systems - 723 Computer Software, Data Handling and Applications - 723.1.1 Computer Programming Languages - 723.2 Data Processing and Image Processing - 821 Agricultural Equipment and Methods; Vegetation and Pest Control

Numerical data indexing: Capacitance 0.00E00F

DOI: 10.6041/j.issn.1000-1298.2024.03.020

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

36. Water Competition and Economic Relationship between Energy and Food Production in Sichuan Province

Accession number: 20241515891681

Title of translation: 四川省能源和糧食生產(chǎn)用水競爭及與經(jīng)濟(jì)關(guān)系研究

Authors: Kang, Yinhong (1); He, Shuai (1); Wang, Jiachi (1); Ni, Tiefeng (1); Wang, Junqin (2)

Author affiliation: (1) College of Water Conservancy and Hydropower Engineering, Sichuan Agricultural University, Ya’an; 625014, China; (2) Sichuan Water Resources Scientific Research Institute, Chengdu; 610072, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 3

Issue date: March 2024

Publication year: 2024

Pages: 300-310

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: There is a complex relationship of interdependence and mutual constraint in the water - energy - food system, and quantifying the interrelationships of the water - energy - food system is of great significance for the rational allocation and coordinated utilization of water, energy and food resources, as well as for promoting sustainable socio-economic development in the region. The models of energy and food production water footprint, competition index, decoupling theory and Theil index were applied to calculate and analyze the spatiotemporal variation characteristics of the water footprint of energy and food production in Sichuan Province from 2009 to 2019. It also explored the competition relationship between energy and food production for water and investigated the inherent connection between water footprint and gross domestic product ( GDP) . The research findings were as follows: both the water footprint of food production and the water footprint of energy production showed an increasing trend. The annual average blue water footprint of energy production was 5. 45 X 10 m , accounting for 93. 76% of the water footprint of energy production. The annual average blue water footprint of food production was 1. 06 X 10 m , accounting for 26. 02% of the water footprint of food production, and the contribution of the green water footprint exceeded the sum of blue water footprint and grey water footprint, accounting for 53. 06% . The competition index of water for energy and food production in Sichuan Province was on the rise, with an increase of 42. 69% . Due to the consideration of both food and hydropower production in areas such as the Ecological Demonstration Zone in Northwest Sichuan and the Panxi Economic Zone, their competition index was significantly higher than that in other areas. The decoupling relationship between the water footprint and GDP of Sichuan Province was weakly decoupling and in a state of relatively coordinated development, indicating that economic development had a relatively good state of control over water consumption. The total difference in water footprint intensity showed an expanding trend, indicating that the unevenness of water use efficiency in Sichuan Province was gradually expanding, but it declined in the later period of the study, with a gradual convergence of development. The research result can provide suggestions for water resource allocation, energy development and food production in Sichuan Province, as well as for optimizing the economic development approach and the coordinated development of water use efficiency. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 38

Main heading: Economics

Controlled terms: Coordination reactions - Economic and social effects - Water resources - Water supply

Uncontrolled terms: Blue water - Competition index - Decoupling analysis - Decouplings - Energy productions - Food production - Sichuan province - Theil’s indices - Water energy - Water footprint

Classification code: 444 Water Resources - 446.1 Water Supply Systems - 802.2 Chemical Reactions - 971 Social Sciences

Numerical data indexing: Percentage 2.00E+00%, Percentage 6.00E+00%, Percentage 6.90E+01%, Percentage 7.60E+01%, Size 1.00E+01m

DOI: 10.6041/j.issn.1000-1298.2024.03.030

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

37. Characteristics of Drainage and Nitrogen Losses from Rice-wheat Rotation Fields in Plain River Network Area of Eastern China

Accession number: 20241515899883

Title of translation: 平原河網(wǎng)地區(qū)稻麥輪作農(nóng)田排水與氮素流失特征研究

Authors: Zou, Jiarong (1); Jia, Zhonghua (1); Zhu, Weibin (2); Liu, Wenlong (1); Ding, Shihong (2); Luo, Wan (1)

Author affiliation: (1) College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou; 225009, China; (2) Administrative Office of Yanyun Irrigated Area in Jiangdu District, Yangzhou; 225261, China

Corresponding author: Luo, Wan(luowan@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: 3

Issue date: March 2024

Publication year: 2024

Pages: 321-330

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Farmland drainage is the main contributor to regional non-point source pollution in the rice-wheat rotation area in the lower reaches of the Yangtze River, China, it is of great importance to examine the characteristics of farmland drainage and nitrogen ( N ) losses for sustainable development of regional agricultural production and ecological environment protection. In the existing studies on drainage and nitrogen losses from rice-wheat rotation fields, nitrogen losses were generally estimated by observing surface runoff and deep percolation with lysimeters or soil column test, which was different from the actual situation that soil moisture and nitrogen entered the drainage system mainly through the lateral flow path. A simulation study on nitrogen losses with field drainage was presented by using the DRAINMOD - N II model; drainage and nitrogen losses from rice-wheat rotation fields under different rainfall conditions were predicted after validating the model with 4-year field monitoring data. The results showed that the predicted average annual nitrogen loss in drainage was 28. 4 kg/hm , accounting for 6. 0% of the fertilizer application rate; most N losses occurred in the rice growing season with the average value of 25. 6 kg/hm . The predicted nitrogen losses in the wheat growing season was only 2. 8 kg/hm . When compared with the report values in the existing literature, the total drainage volume was 35. 4% higher, and the total nitrogen loss was 44. 6% lower. The differences were mainly from wheat growing season, the average reported N losses (31. 8 kg/hm ) was 11 times of this research. The predicted field drainage and nitrogen losses were significantly correlated to the rainfall pattern ( coefficient of determination R > 0. 5 ) , the probabilities of the relative increment of the three variables were distributed nearly the same; the predicted nitrogen losses were relatively stable and maintained at 0. 8 ~ 1. 2 times of the average value in normal years with the rainfall return period of less than five years. In the drier or wetter years (i.e. , with rainfall return period greater than five years) , the predicted nitrogen losses appeared to be more variable. Hence, drainage reduction during the rice growing period through proper control measures was critical to overall reduction of drainage and nitrogen losses from the rice-wheat rotation fields. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 45

Main heading: Rain

Controlled terms: Agricultural pollution - Crop rotation - Farms - River pollution - Rotation - Soil moisture - Solvents - Sustainable development

Uncontrolled terms: % reductions - Average values - DRAINMOD-N - DRAINMOD-N H model - Farmland drainage - Growing season - N loss - Nitrogen loss - Rainfall return periods - Rice-wheat rotations

Classification code: 443.3 Precipitation - 453 Water Pollution - 454.2 Environmental Impact and Protection - 483.1 Soils and Soil Mechanics - 803 Chemical Agents and Basic Industrial Chemicals - 821 Agricultural Equipment and Methods; Vegetation and Pest Control - 931.1 Mechanics

Numerical data indexing: Age 4.00E+00yr, Mass 4.00E+00kg, Mass 6.00E+00kg, Mass 8.00E+00kg, Percentage 0.00E00%, Percentage 4.00E+00%, Percentage 6.00E+00%

DOI: 10.6041/j.issn.1000-1298.2024.03.032

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

38. Design and Test of Crushing and Kneading Device for Machine Harvested Cotton Field Residual Film Mixture

Accession number: 20241515881715

Title of translation: 機(jī)收棉田殘膜混合物粉碎揉絲裝置設(shè)計與試驗

Authors: Jianhua, Xie (1, 2); Qinghe, Meng (1); Jia, Zhang (1, 3); Wang, Liu (1); Yakun, Du (1); Yuanze, Li (1); Xin, Shi (4)

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) College of Mechanical and Electrical Engineering, Xinjiang Institute of Engineering, Urumqi; 830023, China; (4) Research Institute of Agricultural Mechanization, Xinjiang Academy of Agricultural Sciences, Urumqi; 830091, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 3

Issue date: March 2024

Publication year: 2024

Pages: 133-144

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In response to the difficulties in resource utilization of machine-harvested residual film mixtures, and the fact that the residual film mixtures processed by the existing shredding and kneading devices do not satisfy the palatability of larvae of white-starred golden tortoise, a residual film mixture crushing and kneading device was designed, processing of residual film mixtures to meet the palatability of white-starred golden tortoise larvae using crushing and kneading techniques. The device was mainly composed of crushing device, conveying device and silk kneading device, etc. The structural and working parameters of each component were determined through kinematic and dynamic analysis of the operation process of the residual film mixture crushing and silk kneading device. In order to verify the operational performance of the residual film mixture crushing and kneading device, a three-factor, three-level quadratic regression response surface experiment was conducted by using the grinding roller speed, kneading roller speed, and kneading roller gap as experimental factors, and the residual film crushing qualification rate, cotton stem crushing length qualification rate, and cotton stem kneading rate as experimental indicators. A regression model was established to analyze the impact of each factor on the operational performance of the residual film mixture crushing and kneading device, and parameter optimization and experimental verification were carried out. The experimental results showed that the main and secondary factors affecting the qualified rate of residual film crushing and the qualified rate of cotton straw crushing length were the speed of the crushing roller, the gap between the kneading rollers, and the speed of the kneading roller. The main and secondary order of factors affecting the cotton straw kneading rate was the gap between kneading rollers, speed of kneading rollers, and speed of crushing rollers. The optimized optimal working parameters were: crushing roller speed of 13. 0 r/min, kneading roller speed of 60. 0 r/min, kneading gap of1.6 m m, and the average values of residual film crushing qualification rate, cotton stalk crushing length qualification rate and cotton stalk kneading rate were 90. 4 %, 92. 6 % and 92. 2 %, respectively, which were the same as the theoretical optimization value, with the relative error of no more than 2. 0 %, the research results can provide reference for the design of residual film mixture crushing and kneading device. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 31

Main heading: Contact angle

Controlled terms: Cotton - Crushing - Grinding (machining) - Regression analysis - Rollers (machine components) - Silk

Uncontrolled terms: Contact with sharp angle - Cotton straws - Crushing - Kneading - Operational performance - Residual film mixture - Residual films - Roller speed - Roller type - Working parameters

Classification code: 601.2 Machine Components - 604.2 Machining Operations - 819.1 Natural Fibers - 821.4 Agricultural Products - 922.2 Mathematical Statistics - 931.2 Physical Properties of Gases, Liquids and Solids

Numerical data indexing: Angular velocity 0.00E00rad/s, Percentage 0.00E00%, Percentage 2.00E+00%, Percentage 4.00E+00%, Percentage 6.00E+00%

DOI: 10.6041/j.issn.1000-1298.2024.03.013

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

39. Design and Experiment of Reciprocating Furrowing and Loosening Machine for Tea Garden

Accession number: 20241515891675

Title of translation: 茶園仿生往復(fù) 式開溝松土機(jī) 設(shè) 計與試驗

Authors: Qin, Kuan (1, 2); Lang, Xutao (1, 2); Shen, Zhougao (3, 4); Wu, Zhengmin (3, 4); Bi, Haijun (3, 4); Cao, Chengmao (1, 2); Sun, Yan (1, 2); Ge, Jun (1, 2); Fang, Liangfei (1, 2)

Author affiliation: (1) School of Engineering, Anhui Agricultural University, Hefei; 230036, China; (2) Anhui Intelligent Agricultural Machinery Equipment Engineering Laboratory, Hefei; 230036, China; (3) College of Tea and Food Science and Technology, Anhui Agricultural University, Hefei; 230036, China; (4) State Key Laboratory of Tea Plant Biology and Resource Utilization, Hefei; 230036, China

Corresponding author: Cao, Chengmao(caochengmao@sina.com)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 3

Issue date: March 2024

Publication year: 2024

Pages: 29-39

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Hilly mountainous areas of tea plantation soil crust, gravel is more, the use of traditional rotary or mobile furrowing machine will appear to hit the stone jump knife, the knife can not enter the soil, the furrow is not deep, the operation has the problem of high resistance. In view of the above problems, according to the artificial shoveling has the spontaneity to complete the optimal operating path with the lowest power consumption characteristics, the design of crank linkage mechanism device imitating the action of artificial shoveling, and the development of small-scale tea plantation reciprocating trenching and loosening machine were done. Through the analysis of artificial shovel shoveling action, the establishment of the soil into the soil, cut the soil, throw the soil movement model was carried out, based on Matlab software analysis to get the artificial shovel tip trajectory fitting equation, taking this equation as a benchmark, the crank linkage mechanism of the objective function, combined with the constraints of the crank rocker mechanism was established to solve the structural parameters, and at the same time on the trenching shovel for the analysis of the resistance to trenching, to determine the structural parameters of the trenching shovel. Coupled Recurdyn and EDEM furrowing shovel-soil interaction simulation model was established, and a three-factor, three-level orthogonal test was conducted to optimize the operational and structural parameters, and the optimal parameter combinations were obtained as follows: the forward speed v of the implement was 0.06 m/s, the rotational speed of the crank was 42 r/min, and the inclination angle of the soil entry φ was 80°. The field test showed that the average furrowing depth of reciprocating furrowing and loosening machine operation in tea plantation was 211.5 mm, the power consumption of furrowing was 0. 119 kW, and the coefficient of stability of trench depth was 90. 9%, which reduced the power consumption of furrowing by 6. 3% and the coefficient of stability of furrow depth was increased by 3. 1 percentage points and the quality of the whole machine operation satisfied the agronomic requirements. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 27

Main heading: Soils

Controlled terms: Bionics - Compressive strength - Electric power utilization - MATLAB - Structural optimization - Trenching

Uncontrolled terms: Crank linkage mechanism - EDEM - Furrowing and loosening machine - Linkage mechanism - Machine operation - Recurdyn - Recurdyn; - Structural parameter - Tea gardens - Tea plantations

Classification code: 461.1 Biomedical Engineering - 483.1 Soils and Soil Mechanics - 619.1 Pipe, Piping and Pipelines - 706.1 Electric Power Systems - 723.5 Computer Applications - 921 Mathematics - 921.5 Optimization Techniques

Numerical data indexing: Angular velocity 7.014E-01rad/s, Percentage 3.00E+00%, Percentage 9.00E+00%, Power 1.19E+05W, Size 2.115E-01m, Velocity 6.00E-02m/s

DOI: 10.6041/j.issn.1000-1298.2024.03.003

Funding text: 國家自然科學(xué)基金青年基金項目（52105239, 52075003, 52205509), 茶樹生物學(xué)與資源利用國家重點實驗室開放基金項目 (SKLTOF20210121) 和安徵省自然科學(xué)基金項目（2308085ME160)

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

40. Segmentation Model of Cow Body Parts Based on YOLO v8n-seg-FCA-BiFPN

Accession number: 20241515891668

Title of translation: 基于YOLO v8n - seg - FCA - BiFPN的奶牛身體分割方法

Authors: Zhang, Shujin (1, 2); Xu, Xingshi (1, 2); Deng, Hongxing (1, 2); Wen, Yuchen (1, 2); Song, Huaibo (1, 2)

Author affiliation: (1) College of Mechanical and Electronic Engineering, Northwest a and F University, Shaanxi, Yangling; 712100, China; (2) Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Shaanxi, Yangling; 712100, China

Corresponding author: Song, Huaibo(songyangfeifei@163.com)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 3

Issue date: March 2024

Publication year: 2024

Pages: 282-289 and 391

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The fine segmentation of cow body parts has significant applications in research fields such as cow body condition scoring, posture estimation, behavior recognition, and body measurement. Due to the limited practicality of existing segmentation methods for different cow body parts, an improved YOLO v8n - seg model named YOLO v8n - seg - FCA - BiFPN was proposed for cow body part segmentation tasks. The improved model added FCA channel attention mechanism to the YOLO v8n backbone feature extraction network to better extract the geometric feature information of small targets, and used repeated weighted bidirectional features in the network feature fusion layer. The BiFPN was used to achieve the purpose of increasing the coupling of features at each scale. In order to validate the model performance, side-view images of cows at the channel were collected for network training. To ensure the quality of the dataset, the structural similarity algorithm was used to remove similar redundant images, resulting in a total of 1 452 images. LabelMe software was used to label the target cows, which were divided into eight parts , forelimbs , hindlimbs , udders , tails , belly , head , neck , and trunk , and was sent to the training model. The test results showed that the precision was 96. 6% , the recall was 94. 6% and the mean average precision was 97. 1% , the parameters number was 3. 3 X 10 , and the detection speed was 6. 2 f/s. The precision of each part was from 90. 3% to 98. 2% , and the mean average precision was 96. 3% . The YOLO v8n - seg - FCA - BiFPN network could realize accurate segmentation of various parts of dairy cows. Compared with the original YOLO v8n, the precision, recall and mean average precision of YOLO v8n~ seg - FCA - BiFPN were 3. 2 percentages points, 2. 6 percentages points and 3. 1 percentages points higher than that of YOLO v8n - seg, respectively. The precision under occlusion was 93. 8% , the recall value was 91. 67% , and the mean average precision was 93. 15% . The volume of the improved model remained unchanged and had strong robustness. Under occlusion, the precision was 93. 8% , the recall was 91. 67% , and the mean average precision was 93. 15% . The overall results showed that the research can provide necessary technical support for precise segmentation of dairy cows’ body parts. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 29

Main heading: Semantics

Controlled terms: Behavioral research - Feature extraction - Network layers - Semantic Segmentation

Uncontrolled terms: Body part segmentation - Body parts - Dairy cow - Fcabasicblock - Fine segmentations - Part based - Percentage points - Segmentation models - Semantic segmentation - YOLO v8n

Classification code: 461.4 Ergonomics and Human Factors Engineering - 723 Computer Software, Data Handling and Applications - 723.4 Artificial Intelligence - 971 Social Sciences

Numerical data indexing: Percentage 1.00E00%, Percentage 1.50E+01%, Percentage 2.00E+00%, Percentage 3.00E+00%, Percentage 3.00E+00% to 9.80E+01%, Percentage 6.00E+00%, Percentage 6.70E+01%, Percentage 8.00E+00%

DOI: 10.6041/j.issn.1000-1298.2024.03.028

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

41. Image Segmentation of Apple Orchard Feathering Pest Adhesion Based on Shape-Color Screening

Accession number: 20241515891688

Title of translation: 基于形色篩選的蘋果園羽化害蟲粘連圖像分割方法

Authors: Liu, Shuangxi (1, 2); Wang, Yunfei (1); Zhang, Hongjian (1, 3); Sun, Linlin (1, 3); Ma, Bo (1); Mu, Junlin (1); Ren, Zhuo (1); Wang, Jinxing (1, 3)

Author affiliation: (1) College of Meehanieal and Eleetronie Engineering 9, Shandong Agrieultural University, Taian; 271018 9, China; (2) Shandong Agrieultural Equipment Intelligent Engineering Laboratory9, Taian; 271018 9, China; (3) Shandong Provineial Key Laboratory of Hortieultural Maehinery and Equipment 9, Taian; 271018 9, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 3

Issue date: March 2024

Publication year: 2024

Pages: 263-274

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming at the adhesion problem in the process of apple orchard pest identification, a pest adhesion image segmentation method was proposed based on shape and color screening. Firstly, the apple orchard pest images were collected 9 focusing on the feathered pests. Pests have completed most of their growth and development during the feathering process, and their external morphology, color, and texture are more stable and significant. Therefore 9 based on the analysis of the shape and color feature information of different kinds of pests 9 the pest HSV segmentation threshold and template outline were obtained. Secondly 9 the shape factor was used to determine the segmentation of adherent regions 9 and the segmentation of non-inter-species and inter-species adherent pests was achieved by the color segmentation method and the contour localization segmentation method. Finally, the collected pest images of apple orchard were experimentally analyzed 9 and the segmentation method based on shape —color screening was used to segment individual pests 9 and the results showed that the average segmentation rate 9 average segmentation error rate, and average segmentation efficiency of the proposed method were 101% , 3. 14% and 96. 86% 9 respectively 9 and the segmentation effect was superior to that of traditional image segmentation methods. In addition, with predefined color thresholds 9 the method achieved accurate classification of cotton bollworm, peach borer and corn borer, with average classification accuracies of 97. 77%, 96. 75% and 96. 83%, respectively. At the same time, the Mask R —CNN model was used as the recognition model, and the average recognition accuracy was used as the evaluation index, and the recognition test was carried out on the pest images that were segmented by the proposed method and those thatwere not segmented by the proposed method, respectively. The results showed that the average recognition accuracies of cotton bollworm, peach borer and corn borer pest images that were segmented with the proposed method were 96. 55% ? 94.80% and 95.51% ? respectively, and the average recognition accuracies were improved by 16. 42, 16. 59 and 16. 46 percentage points, respectively, which indicated that the proposed method can provide a theoretical and methodological basis for accurate identification of orchard pests. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 29

Main heading: Adhesives

Controlled terms: Adhesion - Color - Cotton - Fruits - Image segmentation - Orchards - Textures

Uncontrolled terms: Adhesive image - Apple orchards - Color characteristics - Contour positioning - Feathering pest - Form and color characteristic - Images segmentations - Pests images - Precise segmentation - Segmentation methods

Classification code: 741.1 Light/Optics - 821.3 Agricultural Methods - 821.4 Agricultural Products - 951 Materials Science

Numerical data indexing: Percentage 1.01E+02%, Percentage 1.40E+01%, Percentage 5.50E+01%, Percentage 7.50E+01%, Percentage 7.70E+01%, Percentage 8.30E+01%, Percentage 8.60E+01%, Percentage 9.48E+01%, Percentage 9.551E+01%

DOI: 10.6041/j.issn.1000-1298.2024.03.026

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

42. Electrokinetic-assisted Phytoremediation of Soil Cadmium Pollution in Paddy Fields under Polarity Exchange Technology

Accession number: 20241515891671

Title of translation: 極性交換電場輔助植物修復(fù)稻田土壤鎘污染研究

Authors: Luan, Yajun (1, 2); Xu, Junzeng (1, 2); Li, Yawei (1, 2); Hu, Zhewei (1, 3); Wang, Haiyu (1, 2); Wang, Yonghong (4); Xu, Xihua (5)

Author affiliation: (1) College of Agricultural Science and Engineering, Hohai University, Nanjing; 211100, China; (2) National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing; 210098, China; (3) Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing; 210042, China; (4) Kunshan Water Affairs Bureau, Kunshan; 215300, China; (5) Caoyou Water Resources Bureau, Caoyou, 225600, China

Corresponding author: Xu, Junzeng(xjz481@hhu.edu.en)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 3

Issue date: March 2024

Publication year: 2024

Pages: 331-339

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The feasibility of electrokinetic-assisted phytoremediation ( EKPR) with polarity reversal to remove cadmium (Cd) contaminants from paddy soils under controlled irrigation was investigated. The experiments were carried out in cuboid pots powered by solar energy. The rice crops were planted near the electrodes under controlled irrigation, and the Scirpus tabernaemontani was planted as hyperaccumulator between two rows of rice plants. The results showed that H and OH produced by electrolysis reactions could be directly neutralized by the polarity reversal operation, thus effectively avoiding the polarization of soil pH value. The soil current was varied from 0. 08A to 0. 36A, indicating that polarity exchange and high soil water content effectively ensured the number and mobility of free-moving ions in soils, which could drive heavy metal migration. Compared with CK treatment, EKPR treatment significantly increased the root dry biomass of Scirpus tabernaemontani by 34.93%. EKPR treatment significantly reduced the root and brown rice dry biomasses by 17.21% ~ 30. 16% and 16. 18% ~ 22. 28% respectively, while significantly increased the leaf and stem dry biomasses by 3. 82% ~ 13. 17% and 7. 59% ~ 30. 91% , respectively. EKPR treatment increased the Cd content in root and ground parts by 15. 49% ~ 22. 45% and 33. 30% ~ 35. 45% , respectively. The content of Cd in rice root and brown rice was decreased by 14.48% ~ 35. 06% and 39. 04% ~ 57.43% , respectively. The electrokinetic-assisted phytoremediation technology could increase the Cd enrichment of Scirpus tabernaemontani and decrease the Cd enrichment of rice crops. Compared with CK treatment, the bioenrichment of Cd in Scirpus tabernaemontani under EKPR treatment was significantly increased by 46. 48% , and that in rice crops was significantly decreased by 24. 75% . At the end of EKR experiments, the soil Cd content near rice roots was decreased by 16. 33% ~ 19. 14% . The results showed that under controlled irrigation, the electrokinetic-assisted phytoremediation with polarity reversal was a feasible method for cadmium contaminants in paddy soils. It had a good application prospect and practical significance to use electrokinetic-assisted phytoremediation to achieve soil purification in crop production. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 27

Main heading: Irrigation

Controlled terms: Biomass - Bioremediation - Cadmium - Crops - Electrodynamics - Soil moisture - Soil pollution - Soil pollution control - Solar energy

Uncontrolled terms: Assisted phytoremediation - Control irrigation - Controlled irrigations - Electro-kinetics - Electrokinetic remediation - Phytoremediation - Polarity reversal - Rice - Rice crops - Scirpus

Classification code: 454.2 Environmental Impact and Protection - 483.1 Soils and Soil Mechanics - 549.3 Nonferrous Metals and Alloys excluding Alkali and Alkaline Earth Metals - 657.1 Solar Energy and Phenomena - 701 Electricity and Magnetism - 821.3 Agricultural Methods - 821.4 Agricultural Products - 931.1 Mechanics

Numerical data indexing: Electric current 3.60E+01A, Electric current 8.00E+00A to 0.00E00A, Percentage 1.40E+01%, Percentage 1.448E+01%, Percentage 1.60E+01%, Percentage 1.70E+01%, Percentage 1.721E+01%, Percentage 1.80E+01%, Percentage 2.80E+01%, Percentage 3.00E+01%, Percentage 3.30E+01%, Percentage 3.493E+01%, Percentage 4.00E+00%, Percentage 4.50E+01%, Percentage 4.80E+01%, Percentage 4.90E+01%, Percentage 5.743E+01%, Percentage 5.90E+01%, Percentage 6.00E+00%, Percentage 7.50E+01%, Percentage 8.20E+01%, Percentage 9.10E+01%

DOI: 10.6041/j.issn.1000-1298.2024.03.033

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

43. Rotary Tillage Stability of Greenhouse Electric Tractor Based on Time Series Analysis and Feedforward PID

Accession number: 20241515897976

Title of translation: 溫室電動拖拉機(jī)旋耕穩(wěn)定性時序分析與前饋PID 控制方法研究

Authors: Yang, Hangxu (1, 2); Zhou, Jun (1); Qi, Zezhong (1); Sun, Chenyang (1); Lai, Guoliang (1)

Author affiliation: (1) College of Engineering, Nanjing Agricultural University, Nanjing; 210031, China; (2) College of Mechanical and Electrical Engineering, Jinhua Polytechnic,, Jinhua; 321017, China

Corresponding author: Zhou, Jun(zhoujun@njau.edu.en)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 3

Issue date: March 2024

Publication year: 2024

Pages: 412-420

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In response to the sensitivity of greenhouse small agricultural machinery to ground flatness, small ground undulations can cause the equipment to pitch. Based on the greenhouse electric tractor developed by the research group, the method based on time series analysis was introduced into angle prediction and feedforward PID control ( APF - PID ) to solve the problems of poor responsiveness, unstable tillage depth, and sudden power changes caused by equipment pitch in greenhouse rotary tillage operations. Firstly, a power model for greenhouse electric tractor rotary tillage operation was established, and a conversion matrix between pitch angle and tillage depth was established to obtain the conversion value of the actual tillage depth of the rotary tillage system. Secondly, time series analysis was used to predict the pitch angle of the aircraft body and serve as disturbance input for the rotary tillage system. Then, combining the conversion value of tillage depth and the predicted disturbance, the APF - PID controller was used to adjust the lifting mechanism of the rotary tillage system, and the rotary tiller was maintained at the target tillage depth. Finally, actual vehicle experiments were conducted on two types of plots in a greenhouse, one without rotary tillage and the other with rotary tillage. The results showed that the correlation coefficient of the pitch angle time series prediction model can reach 0. 983 2 , the control performance of APF - PID control was superior to that of PID control. In the test road surface with a target tillage depth of 6 cm , the average tillage depth of APF - PID on two test plots was 6. 47 cm and 6. 44 cm , with root mean square errors of 0. 80 cm and 0. 72 cm, absolute average errors of 0. 67 cm and 0. 58 cm, and tillage depth stability coefficients of 89. 95% and 91. 30% , respectively. The total energy consumption was reduced by 4. 18% and 19. 13% compared with that of PID control, which effectively achieved the stability control of greenhouse electric tractor rotary tillage and met the requirements of greenhouse operations. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 25

Main heading: Tillage

Controlled terms: Energy utilization - Forecasting - Greenhouses - Harmonic analysis - Mean square error - Three term control systems - Time series analysis - Tractors (agricultural) - Tractors (truck)

Uncontrolled terms: Angle prediction - Electric tractors - Feed forward - Feedforward PID - Pitch angle - Rotary tillages - Small agricultural machineries - Stability control - Tillage system - Time-series analysis

Classification code: 525.3 Energy Utilization - 663.1 Heavy Duty Motor Vehicles - 731.1 Control Systems - 821.1 Agricultural Machinery and Equipment - 821.3 Agricultural Methods - 821.6 Farm Buildings and Other Structures - 921.6 Numerical Methods - 922.2 Mathematical Statistics

Numerical data indexing: Percentage 1.30E+01%, Percentage 1.80E+01%, Percentage 3.00E+01%, Percentage 9.50E+01%, Size 4.40E-01m, Size 4.70E-01m, Size 5.80E-01m, Size 6.00E-02m, Size 6.70E-01m, Size 7.20E-01m, Size 8.00E-01m

DOI: 10.6041/j.issn.1000-1298.2024.03.041

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

44. Analysis of Energy Dissipation Mechanism of Multistage Hydraulic Turbine Based on Entropy Production Theory

Accession number: 20241515881936

Title of translation: 基于嫡產(chǎn)理論的多級液力透平能量耗散機(jī)理分析

Authors: Xiaohui, Wang (1, 2); Huzhong, Jiang (1); Senchun, Miao (1); Xiaobang, Bai (3); Bing, Qi (1)

Author affiliation: (1) School of Energy and Power Engineering, Lanzhou University of Technology, Lanzhou; 730050, China; (2) Key Laboratory of Fluid Machinery, System of Gansu Province, Lanzhou University of Technology, Lanzhou; 730050, China; (3) Chongqing Pump Industry Co. , Ltd., Chongqing; 404100, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 3

Issue date: March 2024

Publication year: 2024

Pages: 163-172

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: As a liquid residual pressure energy recovery device, hydraulic turbine is widely used in the field of small hydropower construction and energy recovery, but its internal energy loss characteristics are unclear. The two-stage radial hydraulic turbine was taken as the research object. Based on the entropy production theory, the energy loss in each flow component was quantitatively analyzed, and the energy dissipation mechanism in the turbine was further revealed by combining the Omega vortex identification criterion and flow field distribution. The results showed that velocity pulsation and wall effect were the primary sources of energy dissipation. The total proportion of the two was 98. 03% under the design condition. The impeller and the guide vane were the main areas of energy dissipation in the turbine; the impeller loss accounted for a higher percentage in the small flow condition, while the guide vane loss accounted for a higher percentage in the large flow condition. The energy loss in the impeller originated from the unstable flow phenomena such as vortex separation at the leading edge of the blade, return vortex at the suction surface, and vortex at the trailing edge of the blade, and the matching of the relative liquid flow flow angle in the and impeller the angle ； in the of placement guide vane of I the and inlet the of guide the blade vane was n _ the anti-guide fundamental vane, reason the factors for the leading unstable to the dissipation of their energy at different flow rates were basically the same, and the poor flow such as the stagnation vortex at the leading edge of the blade and the flow separation. The momentum exchange caused by the blade leading edge stall vortex and flow separation was the main cause of energy loss. Due to the asymmetry of the flow inside the annular suction chamber, the entropy yield distribution in each channel of the guide vane I was not uniform, while the guide vane I - anti-guide vane reduced the shock effect through the rectification of the positive guide vane, and the entropy yield distribution in each channel was uniform and the high entropy area was small. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 31

Main heading: Energy dissipation

Controlled terms: Entropy - Flow separation - Hydraulic machinery - Hydraulic motors - Hydraulic turbines - Impellers - Vortex flow

Uncontrolled terms: Entropy production - Entropy production rates - Entropy production theory - Guide-vane - Identification criterion - Multi-stages - Multistage hydraulic turbine - Omega vortex identification criteria - Production theory - Vortex identifications

Classification code: 525.4 Energy Losses (industrial and residential) - 601.2 Machine Components - 617.1 Hydraulic Turbines - 631 Fluid Flow - 631.1 Fluid Flow, General - 632.2 Hydraulic Equipment and Machinery - 641.1 Thermodynamics

Numerical data indexing: Percentage 3.00E+00%

DOI: 10.6041/j.issn.1000-1298.2024.03.016

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

45. Design and Test of Automatic Tray Stacking Device for Vegetable Foam Seedling Trays

Accession number: 20241515891665

Title of translation: 蔬菜泡沫育苗盤多適應(yīng)性自動疊盤裝置設(shè)計與試驗

Authors: Li, Xu (1, 2); Wu, Shuoxiang (1); Kuang, Minqiu (1); Liu, Qing (1); Liu, Dawei (1, 3); Xie, Fangping (1, 3)

Author affiliation: (1) College ofMeehanical and Eleetrical Engineering, Hunan Agricultural University, Changsha; 410128, China; (2) Hunan Key Laboratory of Intelligent Agricultural Machinery Equipment, Changsha; 410128, China; (3) Key Laboratory of Southern Intelligent Seedling, Ministry of Agriculture and Rural Affairs, Yiyang; 413055, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 3

Issue date: March 2024

Publication year: 2024

Pages: 122-132

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In order to improve the transfer efficiency of seedling tray in vegetable seedling raising line, and aiming at the problems of high labor intensity and low uniformity that exist in manual tray stacking, a multi-adaptive automatic tray stacking device for vegetable floating seedling was designed. The device was composed of frame, seedling tray conveying mechanism, tray stacking mechanism and the control system. The device took 200Smart PLC as the control core, and used the main and auxiliary conveyor belts to realize the transportation of the seedling trays, and combined the photoelectric sensor to realize the positioning of the seedling trays, which can complete the automatic stacking of foam seedling trays of different sizes, and the designed horizontal adjustment mechanism and vibration reduction mechanism can achieve the horizontal position adjustment of the seedling trays on the stacking mechanism and reduce the vibration impact of the stacking process. The test results showed that under the production rate of 450 plates/h of seedling line, the best effect of automatic stacking device was achieved when the wire diameter of the damping spring was 1. 5 mm, the speed gap between the main and auxiliary conveyor belts was 0. 1 m/s and the rising and falling speed of the electric cylinder was 0. 13 m/s for tray of 200 holes, and the success rate of tray stacking was 100%, and the deviation variance of tray stacking uniformity was 2. 32 mm2. At the same time, the amplitudes of X axis, Y axis and Z axis of the seedling trays did not exceed 0. 8 mm in the vibration detection test. And when 135 holes and 160 holes of foam seedling trays were replaced in the test, the success 2 rate of stacking 2 were 100%, and the deviation variance of tray stacking uniformity were 3. 94 mm and 5. 98 mm, which showed that the device met the requirements of multi-adaptability. Under the optimal operating parameters, when the productivity of seedling line was increased to 900 plates/h, the maximum amplitude of the seedling trays was 0. 709 mm, and there was no significant change in the amplitude, which indicated that the device had good operational stability. The research results can provide reference for improving the stacking effect and reducing the vibration of the seedling line. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 20

Main heading: Damping

Controlled terms: Belt conveyors - Electric lines - Foams - Plates (structural components) - Vegetables

Uncontrolled terms: Automatic stacking tray device - Conveyor belts - Design and tests - Dimensional foam seedling adaptability tray - Floating stack uniformity seedling - Labour intensity - Stackings - Transfer efficiency - Vegetable seedlings - Vibration-damping

Classification code: 408.2 Structural Members and Shapes - 692.1 Conveyors - 706.2 Electric Power Lines and Equipment - 821.4 Agricultural Products - 931.1 Mechanics

Numerical data indexing: Area 3.20E-05m2, Percentage 1.00E+02%, Size 5.00E-03m, Size 7.09E-01m, Size 8.00E-03m, Size 9.40E-02m, Size 9.80E-02m, Velocity 1.00E00m/s, Velocity 1.30E+01m/s

DOI: 10.6041/j.issn.1000-1298.2024.03.012

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village