2024年第12期

2024年第12期

2024年第12期共收录50篇

1. Accurate Inversion of Rice Chlorophyll Content by Integrating Multispectral and Texture Features Derived from UAV Multispectral Imagery

Accession number: 20245217600944

Title of translation: 基于无人机多光谱植被指数与纹理特征的水稻叶绿素含量反演

Authors: Zhu, Qingzhen (1); Zhu, Yanqiu (1); Wang, Aichen (1); Zhang, Liyuan (1)

Author affiliation: (1) School of Agricultural Engineering, Jiang.su Universitj, Zhenjiang; 212013, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 12

Issue date: December 2024

Publication year: 2024

Pages: 287-293

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The emerging unmanned aerial vehicle (UAV) remote sensing technology has gradually become a popul?r approach to achieve precise management of field crops. Some researches have been conducted on high spatiotemporal resolution, low-cost and accurate monitoring of crop growth. However, there is relatively little research about the estimation of rice leaf green content by integrating UAV multispectral Vegetation index and texture features. UAV multispectral remote sensing images and ground measured Chlorophyll content of rice were collected during tillering, flowering, and filling growth stages. A total of 50 features, 15 Vegetation indices and 35 texture features, were calculated from multispectral images. The max-relevance and min-redundancy (mRMR) algorithm was applied to screen ten Vegetation indices and ten texture features from these features. Three modeling strategies were adopted, namely based solely on Vegetation indices, based solely on texture features, and based on the combination of Vegetation indices and texture features. Four regression modeling algorithms, including artificial neural network (ANN), random forest (RF), support vector machine (SVM), and multiple linear regression (MLR), were used to establish the rice Chlorophyll content estimation models. The results showed that both the Vegetation indices and texture features were highly correlated with the rice Chlorophyll content. Among them, the NGBDI index and the B_M texture feature had the highest correlation, with Pearson coefficients of 0. 77 and 0. 73, respectively. The fusion of Vegetation indices and texture features can effectively improve the estimation accuracy of rice Chlorophyll content. Compared with the ANN model based on Vegetation indices, the R was improved by 0.08 when adding texture features to the models. Among the four regression algorithms, the artifieial neural network had the best regression estimation accuracy with R of 0. 72 and RMSE of 1. 52. Therefore, the fusion of Vegetation indices and texture features derived from UAV multispectral images can accurately estimate rice Chlorophyll content, providing Information support for the refined management of rice in the field. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 37

Main heading: Vegetation

Controlled terms: Agricultural robots - Image enhancement - Image texture - More electric aircraft - Multiple linear regression - Support vector regression - Time difference of arrival - Unmanned aerial vehicles (UAV)

Uncontrolled terms: Aerial vehicle - Chlorophyll contents - Inversion - Multi-spectral - Multispectral images - Neural-networks - Rice - Texture features - Unmanned aerial vehicle multispectral image - Vegetation index

Classification code: 103 - 1101.2 - 1106.3.1 - 1202 - 652.1 Aircraft, General - 652.1.1 Commercial Aircraft - 716.1 Information Theory and Signal Processing - 731.6 Robot Applications - 821 Agricultural Equipment and Methods; Vegetation and Pest Control - 821.2 Agricultural Chemicals

DOI: 10.6041/j.issn.1000-1298.2024.12.027

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

2. General Model Building and Experiment on Traction Performance Prediction Based on APSO Algorithm

Accession number: 20245217600955

Title of translation: 基于APSO算法的拖拉机牵引性能预测通用模型建立与试验

Authors: Zhao, Jinghui (1, 2); Zhao, Tenglong (1, 2); Xu, Liyou (1, 2); Li, Yanying (1, 2); Zhang, Jingyun (1, 2); Liu, Yonghong (3); Sun, Li (3)

Author affiliation: (1) College of Vehicle and Traffic Engineering, Henan University qf Science and Technology, Luoyang; 471003, China; (2) State Key Laboratory of Intelligent Agricultural Power Equipment, Luoyang; 471039, China; (3) Cama (Luoyang) Electromechanic Co., Ltd., Luoyang; 471003, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 12

Issue date: December 2024

Publication year: 2024

Pages: 519-529

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming at the problems of poor generality and low prediction accuracy of existing models for traction Performance of wheeled tractors, a set of general model for traction Performance prediction of four-wheel drive and two-wheel drive tractors was proposed, which covered the whole process of System modeling, prediction optimization and case verification. By analyzing the interaction of many physical fields such as soil mechanics, tire mechanics and transmission System, the tractor traction Performance was abstracted into four basic models, namely wheel - soil model, driving force model, slip rate model and tractive force model, in order to establish a general model for the whole machine traction Performance prediction of four-wheel drive and two-wheel drive tractors. In order to improve the prediction accuracy, the traction Performance prediction optimization algorithm based on adaptive particle swarm optimization (APSO) was established with the overall machine slip rate as the optimization objective. Through on-line optimization, the accuracy and universality of the model were verified. In order to further verify its superiority and engineering practicability, a 105 kW tractor of YTO was used as a test prototype to complete the offline test in the whole field test site. The experimental results showed that compared with the existing prediction models, the error of slip rate and rolling resistance of the APSO-based prediction method was 1.9% and 0. 18 kN, respectively. For two-wheel drive tractors, the corresponding errors were 2. 7% and 0. 25 kN, respectively, and the accuracy was greatly improved. The general model of traction Performance prediction for four-wheel drive and two-wheel drive tractors was studied, which had certain research significance in the fields of traction control and Performance of wheeled tractors. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 20

Main heading: Tractors (truck)

Controlled terms: All wheel drive vehicles - Prediction models - Rolling resistance - Soil testing - Traction control - Tractors (agricultural) - Vehicle wheels

Uncontrolled terms: Adaptive particle swarm optimization algorithm - Four-wheel drive tractor - Four-wheel drives - General model - Performance prediction - Prediction modelling - Traction performance - Tractive performance - Two-wheel drive tractor - Two-wheel-drive

Classification code: 1101 - 1502.1.1.4.3 - 212.3 - 483.1 Soils and Soil Mechanics - 601.2 Machine Components - 662.1 Automobiles - 663.1 Heavy Duty Motor Vehicles - 731.2 Control System Applications - 821.2 Agricultural Chemicals

Numerical data indexing: Force 1.80E+04N, Force 2.50E+04N, Percentage 1.90E+00%, Percentage 7.00E+00%, Power 1.05E+05W

DOI: 10.6041/j.issn.1000-1298.2024.12.049

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

3. Design and Experiment of Vertical Screw-feeding Cultivator-hiller for Sugarcane in Wide and Narrow Planung Mode

Accession number: 20245217600980

Title of translation: 立式螺旋送土甘蔗宽窄行中耕培土机设计与试验

Authors: Wu, Tao (1); Luo, Xiaowei (1); Jiang, Jiaoli (2); Liu, Qingting (1); Zou, Xiaoping (1); Huang, Junjie (1)

Author affiliation: (1) College of Engineering, South China Agricultural University, Guangzhou; 510642, China; (2) Guangdong Agricultural Technology Extension Center, Guangzhou; 530104, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 12

Issue date: December 2024

Publication year: 2024

Pages: 71-80

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The quality of intertillage and ridging Operations in sugarcane has a significant impact on promoting the growth of sugarcane plants, lodging resistance and increasing yield. Traditional sugarcane intertillage ridges with equal row spacing have poor ridging effects on sugarcane planted in the wide-narrow row pattern, which is prone to causing the “ volcano mouth” phenomenon in the narrow-row sugarcane ridges. In response to the above problems, a vertical screw-feeding culticator-hiller for sugarcane in wide-narrow row was designed; according to the ridging agronomic requirements under the wide-narrow row planting pattern, the structure of key components and the r?nge of Operation parameters were determined through theoretical calculations; in the context of using EDEM Software, the test factors included the rotary tillage depth, screw-feeding device rotation speed, and travel speed, with the ridging height and Operation power consumption as the test indicators. The Simulation results showed that the rotary tillage depth, screw-feeding machine rotation speed, and travel speed had significant effects on the ridging height and Operation power consumption; when the travel speed was 4 km/h, the interaction between the rotary tillage depth and the screw-feeding device rotation speed did not significantly affect either the ridging height or the Operation power consumption. The optimal Operation parameters obtained were as follows: the rotary tillage depth was 274 mm and the screw-feeding device rotation speed was 245 r/min. Through field tests of the prototype, the accuracy and reliability of the Simulation results were verified. The field Performance test results were as follows: the average ridging height was 134.4 mm, the qualified rate of ridging height was 100%, the sugaroane injury rate was 5%, the weeding rate was 90%, and the narrow rows of canes formed a turtlebaek ridge at the base after cultivation, without crater shape, which met the ridging Operation requirements under the wide-narrow row planting pattern. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 15

Main heading: Screws

Controlled terms: Agricultural robots - Shims - Sugar cane

Uncontrolled terms: Discrete elements method - Eultivator-hille - Feeding devices - Narrow lines - Rotary tillages - Rotation speed - Sugarcane - Travel speed - Vertieal screw-feeding device - Wide and narrow line

Classification code: 601.2 Machine Components - 605.2 Small Tools, Unpowered - 731.6 Robot Applications - 821.2 Agricultural Chemicals - 821.5 Agricultural Wastes

Numerical data indexing: Angular velocity 4.0915E+00rad/s, Percentage 1.00E+02%, Percentage 5.00E+00%, Percentage 9.00E+01%, Size 1.344E-01m, Size 2.74E-01m, Size 4.00E+03m

DOI: 10.6041/j.issn.1000-1298.2024.12.006

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

4. Citrus Yield Estimation by Integrating UAV Imagery and Machine Learning

Accession number: 20245217600935

Title of translation: 基于无人机影像与机器学习的柑橘产量估测研究

Authors: Wu, Lifeng (1); Xu, Wenhao (1); Pei, Qingbao (1)

Author affiliation: (1) School of Soll and Water Conservation, Nanchang Institute of Technology, Nanchang; 330099, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 12

Issue date: December 2024

Publication year: 2024

Pages: 294-305

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In order to acourately and rapidly predict citrus yield to precisely guide orchard production management, remote sensing image data of citrus fruit ripening stage was obtained by DJI multispectral Version of UAV, and visible and multispectral band indices were extracted as feature variables from the images. The eXtreme gradient boosting (XGB), random forest (RF) and support vector machine (SVM) model were used to construct citrus fruit presence and absence Classification model, fruit number and quality estimation model, respectively. The results showed that the excess red index was the most important in the Classification of citrus fruit presence and absence while the modified excess green index was the most important in the estimation of number and quality through the Screening analysis of feature variables by the XGB model. All three models in combination modeling had better accuracy in combination 4. For the Classification model, the optimal model was the SVM model with AUC of 0. 969 and accuracy of 0. 919. While the XGB model was the best model for estimating both number and quality, with the number estimation model’s R value being 0. 79 and RMSE being 466, and the quality estimation model’s R value being 0.79 and RMSE being 19.51 kg. Finally, the Shapley additive explanations (SHAP) method was utilized to reveal the importance of Vegetation index features in the construction of the yield estimation model and to elucidate the interaction effects of the features with the top four SHAP values. The research results can provide an application reference and theoretical basis for the research of UAV remote sensing in citrus yield. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 55

Main heading: Fruits

Controlled terms: Adaptive boosting - Information management - Orchards - Quality control - Support vector machines

Uncontrolled terms: Citrus yield - Classification models - Estimation models - Feature variable - Machine-learning - Multi-spectral - Shapley - Shapley additive explanation - Support vector machine models - Yield estimation

Classification code: 1101.2 - 1106 - 821.4 Agricultural Products - 821.5 Agricultural Wastes - 903 Information Science - 913.3 Quality Assurance and Control

Numerical data indexing: Mass 1.951E+01kg

DOI: 10.6041/j.issn.1000-1298.2024.12.028

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

5. Infrared Performance Monitoring System of Belt-type High-speed Seed Guide Device for High-speed Precision Seeder

Accession number: 20245217600937

Title of translation: 高速精量播种机带式高速导种装置导种性能红外监测系统研究

Authors: Wang, Song (1); Yi, Shujuan (1); Zhao, Bin (1, 2); Li, Yifei (1, 3); Wang, Guangyu (1); Sun, Wensheng (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, 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: 12

Issue date: December 2024

Publication year: 2024

Pages: 160-168

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In order to solve the problem that the existing seed performance monitoring method is not available for the belt-type high-speed seed guide device, and the seed belt carrier and seed particle cannot be distinguished during the seed casting process, making seed performance difficult to monitor, a monitoring method and system for a belt device based on infrared sensors was studied and designed. The two-sided pulse comparison method was proposed, and the hardware circuit and software process of the monitoring module for the belt-type high-speed guide device were independently designed. At the same time, a belt-speed device monitoring algorithm ( bilateral pulse value analysis and energy masking smoothing algorithm, BPV - EMSA) was developed. It reduced the noise and random fluctuations of the original pulse, made the data smoother and more stable, and highlighted the main trends and patterns of the data, while suppressed transient pulse interference and improved the data interpretability and analysis accuracy. The accuracy test results of the monitoring system showed that the monitoring accuracy of the designed belt-type high-speed seed guide device monitoring system was above 95. 9% at different operating speeds, with the highest accuracy of 97. 65% and the lowest of 95. 99% , proving that the system can accurately collect the pulse changes of seed particles through the monitoring point. The results of performance evaluation test of monitoring system showed that the average monitoring error of seeding qualification rate was 2. 00 percentage points, the average monitoring error of seeding missed seeding rate was 1.45 percentage points, and the average monitoring error of seeding reseeding rate was 0.56 percentage points. The relative error of seeding pass rate was not more than 2. 23 percentage points, the relative error of seeding missed seeding rate was not more than 1. 78 percentage points, and the relative error of seeding reseeding rate was not more than 1. 00 percentage points. This monitoring method can accurately monitor the seed guide performance of the belt type high-speed seed guide device. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 25

Main heading: Infrared devices

Controlled terms: Belt conveyors - Belts - Precision casting

Uncontrolled terms: Belt-type high-speed seed guide device - Guide device - High Speed - High-speed precision seeder - Infra-red sensor - Infrared sensor - Performance-monitoring - Row performance monitoring - Signal processing algorithms

Classification code: 201.4.2 - 601.2 Machine Components - 602.2 Mechanical Transmissions - 692.1 Conveyors - 741.3 Optical Devices and Systems

Numerical data indexing: Percentage 6.50E+01%, Percentage 9.00E+00%, Percentage 9.90E+01%

DOI: 10.6041/j.issn.1000-1298.2024.12.014

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

6. Belt-type High-speed Seed Guide Device Control System Based on FSMC Kaiman

Accession number: 20245217600964

Title of translation: 基于FSMC-Kalman的带式高速导种装置控制系统研究

Authors: Wang, Song (1); Yi, Shujuan (1); Zhao, Bin (1, 2); Li, Yifei (1, 3); Wang, Guangyu (1); Li, Shuaifei (1); Sun, Wensheng (1)

Author affiliation: (1) College of Engineering, Heilongjiang Bayi Agrieultural University, Daqing; 163319, China; (2) Heilongjiang Provincial Key Laboratory of Intelligent Agrieultural Machinery Equipment, Daqing; 163319, China; (3) College of Engineering, Northeast Agrieultural 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: 12

Issue date: December 2024

Publication year: 2024

Pages: 169-179 and 332

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 low synchronization rate and poor stability of the speed of the driving motor and the belt high-speed guide device, which results in the increase of seeding distance coeffieient and poor sowing uniformity, a control System of belt high-speed guide device based on fuzzy sliding mode control— Kaiman (FSMC — Kaiman) algorithm was studied. Through the analysis of the System movement process, the relationship between the train and guide motors and the Operation speed was established to establish the mathematical model of the two motors. The proposed FSMC — Kaiman algorithm used the approximation coeffieient and rate coeffieient in the fuzzy algorithm, and added the Kaiman filter algorithm in the feedback link, so as to enhance the robustness and adaptability of the control System. The Simulation test showed that the speed of guide motor based on FSMC — Kaiman algorithm had no overshoot, the adjustment time was 0. 22 s, and the steady State error was 4. 68 r/min; the speed of type motor based on FSMC — Kaiman algorithm had no overshoot, the adjustment time was 0. 23 s, and the steady State error was 1. 96 r/min. The bench test showed that the average qualified plant spacing coeffieient of Variation of the four Operation speeds of FSMC — Kaiman algorithm was 7. 98%. The FSMC — Kaiman algorithm reduced the average coeffieient of Variation by 4. 67 percentage points, which was 3. 36 pereentage points lower than that of the FSMC algorithm, and 2.06 percentage points lower than the average coefficient of Variation of the SMC — Kaiman algorithm. The eontrol System of belt high-speed guide device based on FSMC — Kaiman can make the guide motor and the seed type drive motor work stably with high synchronization rate, thus improving the sowing uniformity. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 25

Main heading: Sliding mode control

Controlled terms: Belt drives - Feedback - Fuzzy filters - Fuzzy inference - Guides (mechanical) - Kalman filters - Robust control - Robustness (control systems) - Traction motors

Uncontrolled terms: Belt-type high-speed seed guide device - Eontrol system - Fuzzy algorithms - Fuzzy sliding mode control - Fuzzy-sliding mode controls - Guide device - High Speed - Kaiman filter - SMC - Synchronization rate

Classification code: 1101 - 601 Mechanical Design - 602.2 Mechanical Transmissions - 705.3 Electric Motors - 716.1 Information Theory and Signal Processing - 731 Automatic Control Principles and Applications - 731.1 Control Systems

Numerical data indexing: Angular velocity 1.1356E+00rad/s, Angular velocity 1.6032E+00rad/s, Percentage 9.80E+01%, Time 2.20E+01s, Time 2.30E+01s

DOI: 10.6041/j.issn.1000-1298.2024.12.015

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

7. Load Pressure Prediction Model for Sugarcane Harvester Base-cutting System

Accession number: 20245217600968

Title of translation: 甘蔗收获机根部切割系统负载压力预测模型研究

Authors: Ma, Fanglan (1); Luo, Yiming (1); Li, Jiacheng (1); Miao, Jinze (1); Ye, Fengzi (1); Chen, Bin (1)

Author affiliation: (1) College of Mechanical Engineering, Guangxi University, Nanjing; 530004, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 12

Issue date: December 2024

Publication year: 2024

Pages: 81-89

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to enhance the applicability and accuracy of the cutting depth control system for sugarcane harvesters, a load pressure prediction model was established to address the problem that the current reference pressure setting could not be automatically adjusted according to soil parameters and locomotive parameters. The relationship between the load pressure and the cutting depth into the soil, the feeding volume, the soil moisture content and the soil firmness was collected by orthogonal test methods, and the test data were used as the training samples and test samples of the load pressure prediction model. Based on the training samples, load pressure prediction models using extreme learning machine (ELM) and ELM based on sparrow search algorithm optimization ( SSA - ELM) were established. Performance of the prediction model was evaluated by the test samples, and the results showed that compared with the ELM model, the mean absolute error, mean relative error and root-mean-square error of the SSA - ELM prediction model were reduced by 50. 00% , 44. 14% and 44. 44% under the yellow soil condition, and reduced by 58. 33% , 56. 98% and 57. 14% under red soil conditions. To verify the applicability of the load pressure prediction model in actual harvesting processes, various working conditions encountered in the cane field were simulated on the test platform, and the prediction model was applied to the existing control system for testing. The results showed that the prediction model met the setting requirements of the reference pressure when the cutting depth into the soil was 20 mm, the operating speed was 0. 34 m/s, and the rotational speed of the cutter disc was 700 r/min, and the maximum error between the cutting depth and the target depth was no more than 5 mm, which met the actual requirements of sugarcane harvesting production. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 24

Main heading: Soil testing

Controlled terms: Harvesters - Model predictive control - Prediction models - Sugar cane

Uncontrolled terms: Extreme learning machine - Learning machines - Load pressure - Prediction modelling - Predictive models - Pressure predictions - Search Algorithms - Sparrow search algorithm - Sugarcane harvesters - Under-the-ground base-cutting

Classification code: 1101 - 1201.7 - 1502.1.1.4.3 - 483.1 Soils and Soil Mechanics - 731.1 Control Systems - 821.2 Agricultural Chemicals - 821.5 Agricultural Wastes

Numerical data indexing: Angular velocity 1.169E+01rad/s, Percentage 0.00E00%, Percentage 1.40E+01%, Percentage 3.30E+01%, Percentage 4.40E+01%, Percentage 9.80E+01%, Size 2.00E-02m, Size 5.00E-03m, Velocity 3.40E+01m/s

DOI: 10.6041/j.issn.1000-1298.2024.12.007

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

8. Design and Experiment of Point Tracking Automatic Sampling Control System for Farmland Soil Sampling Vehicle

Accession number: 20245217600990

Title of translation: 农田土壤采样车点跟踪自动取土控制系统设计与试验

Authors: Luo, Chengming (1, 2); Zhu, Xingyu (1); Wang, Ning (1); Xie, Yongjin (1); Zhong, Jing (1); Xia, Junfang (1, 2)

Author affiliation: (1) College of Engineering, Huazhong Agrieultural Univerdty, Wuhan; 430070, China; (2) Key Laboratory of Agrieultural Equipment in Mid-lower Yangtze River, Ministry of Agrieulture and Rural Affairs, Wuhan; 430070, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 12

Issue date: December 2024

Publication year: 2024

Pages: 180-190

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: To tackle the problems in current farmland soil sampling Operation, ineluding high labor demand, poor sampling accuracy, low automation level etc., a farmland soil sampling vehicle with its automatic Operation control System was designed based on an electric drive tracked chassis. The structure components, working principle and control System scheme were elaborated. The control strategy for the sampling vehicle Operation process was designed based on the finite State machine method. To realize the goal of performing soil sampling at a series of target points in the field automatically, a continuous point tracking process was designed, and the point tracking model for the sampling vehicle was established. A point tracking control algorithm was developed based on the method of vector field. From the on-line Performance test results, the point tracking algorithm designed based on vector field method had good deviation correction ability. When the initial lateral deviation was 2 m and the initial heading deviation was 0°, the on-line time consumed by the sampling vehicle at 0. 3 m/s, 0. 6 m/s and 0. 9 m/s were 15.7 s, 11. 8 s and 11.9 s, respectively, and the on-line distance travelled by the sampling vehicle at 0. 3 m/s, 0. 6 m/s and 0. 9 m/s were 4. 72 m, 7. 10 m and 10. 74 m, respectively. From the continuous point tracking test results, when the Operation speed of the sampling vehicle was set at 0. 3 m/s, 0. 6 m/s and 0. 9 m/s, the maximum absolute lateral deviations with respect to the reference path were 0. 081 m, 0. 107 m and 0. 210 m, respectively, the mean absolute lateral deviations were 0. 018 m, 0. 022 m and 0. 050 m, respectively, the Standard deviations were 0. 026 m, 0. 027 m and 0. 064 m, respectively, and the mean absolute distance errors of the sampling vehicle with respeet to the target points were 0. 068 m, 0.081 m and 0. 141 m, respectively. The Performance test of the soil sampling device revealed that its mechanisms could work smoothly together, the consumed time at different locations for the same sampling depth was consistent, and the coefficient Variation of sample masses was small. The sampling vehicle could realize accurate continuous point tracking to a series of target points under the control of designed control process and point tracking algorithm, which permitted that the requirements for automatic farmland soil sampling could be met. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 32

Controlled terms: Agricultural robots - Invariance - Leaf springs - Magnetic couplings - Root loci - Vector control (Electric machinery)

Uncontrolled terms: Farmland soils - Lateral deviation - Point tracking control - Point-tracking - Soil sampling - Soil sampling vehicle - Target point - Tracked chassi - Tracking controls - Vector fields

Classification code: 601.1 Mechanical Devices - 601.2 Machine Components - 705.1 Electric Machinery, General - 731 Automatic Control Principles and Applications - 731.1 Control Systems - 731.6 Robot Applications - 821.2 Agricultural Chemicals

Numerical data indexing: Size 1.00E+01m, Size 1.07E+02m, Size 1.41E+02m, Size 1.80E+01m, Size 2.00E+00m, Size 2.10E+02m, Size 2.20E+01m, Size 2.60E+01m, Size 2.70E+01m, Size 5.00E+01m, Size 6.40E+01m, Size 6.80E+01m, Size 7.20E+01m, Size 7.40E+01m, Size 8.10E+01m, Size 8.10E-02m, Time 1.19E+01s, Time 1.57E+01s, Time 8.00E+00s, Velocity 3.00E+00m/s, Velocity 6.00E+00m/s, Velocity 9.00E+00m/s

DOI: 10.6041/j.issn.1000-1298.2024.12.016

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

9. Identification of Rapeseed Seedling Number Based on YC YOLO v7 Model

Accession number: 20245217600977

Title of translation: 基于YC-YOLO v7模型的油菜幼苗株数识别方法

Authors: Li, Zhaodong (1, 2); Zhang, Yanfang (1); Wang, Yunhong (1); Zhao, Qianhua (1); Liu, Lichao (1, 2); Zhang, Tian (1, 2); Chen, Yongxin (1, 2)

Author affiliation: (1) School of Engineering, Anhui Agrieultural Vniversity, Hefei; 230036, China; (2) Anhui Provineial Engineering Research Center of Intelligent Agrieultural 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: 12

Issue date: December 2024

Publication year: 2024

Pages: 322-332

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In response to problems such as different graphics, densely distributed, and difficult to identify in the field environment, the study of the number of rapeseed seedlings based on the YC — YOLO v7 algorithm was carried out. Introduce the depth-separated convolutional module in the ELAN of the original model YOLO v7 to improve the extraetion ability of the model on small features. By adding the CBAM attention mechanism module to the feature layer Output by the main network, the model of the model’s identification of small targets is enhanced. Replace the loss function CIOU to WIOU, which improves the quality of the anchor frame. In order to expand the model of the model for the goal, the SPPF space pyramid structure was constructed. The test results show that the average accuracy of the improved YC — YOLO v7 model was 94. 0%, the accuracy was 89. 8%, the recall rate was 91. 2%, the reasoning speed increased by 16. 1 f/s, and the floating-point Computing volume was reduced by 2. 56 x 10. Compared with the other phase model YOLO v5s, SSD, and second-stage model Faster R — CNN, the average accuracy increased by 12. 8 percentage points, 17. 8 percentage points, and 20. 3 percentage points, respectively. The improved YC - YOLO v7 model was deployed to the PC, and an oilseed rape seedling detection and identification System was constructed using the PYQT5 framework, with the average accuracy of the System detection being greater than 90%, which can provide technical support for the accurate counting of oilseed rape seedlings in the field environment, and provide effective support for the farmers to judge the quality of the breeding and the effect of sowing. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 26

Controlled terms: Feature extraction - Object detection - Object recognition - Oilseeds

Uncontrolled terms: Attention mechanisms - Model identification - Objects detection - Oil seed rape - Original model - Percentage points - Plant identification - Rape seedling - Small features - YOLO v7

Classification code: 1101.2 - 1106.3.1 - 1106.8 - 821.5 Agricultural Wastes

Numerical data indexing: Percentage 0.00E00%, Percentage 2.00E+00%, Percentage 8.00E+00%, Percentage 9.00E+01%

DOI: 10.6041/j.issn.1000-1298.2024.12.031

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

10. Aerodynamic Characteristics and Parameter Optimization of Sugarcane Chopper Harvester Extractor Impeller on Transverse

Accession number: 20245217600969

Title of translation: 横置式甘蔗收获机排杂风机叶轮气动特性分析与参数优化

Authors: Li, Weiqing (1); Ma, Shaochun (1, 2); Li, Wenzhi (1); Zhou, Baocheng (1); Huo, Peng (1)

Author affiliation: (1) College of Engineering, China Agricultural University, Beijing; 100083, China; (2) Sanya Institute of China Agricultural University, Sanya; 572025, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 12

Issue date: December 2024

Publication year: 2024

Pages: 90-99 and 109

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The extraotor is one of the core components of the sugarcane chopper harvester, and its Performance plays a vital role in the impurity rate of the sugarcane harvester, while the impeller is the core component of the extractor, and its aerodynamic characteristics is closely related to the effect of the exhaust. Aiming at the problem of insufficient optimization of the Performance of the impeller of the miscellaneous fan of the sugarcane harvester, which affects the miscellaneous effect, taking the impeller of the miscellaneous fan of the transverse sugarcane harvester as the object, and the influences of the type of hub, mounting angle of the impeller ?, number of the impeller Nb, dimensionless area of the impeller G, angle of the impeller y on the aerodynamic characteristics of the fan and its mechanism were researched. With the objective of increasing the füll pressure of the fan, the best impeller structure Parameters were obtained by the response surface optimization method, which were the blade mounting angle of 23.34°, the impeller dimensionless area of 0.43 and the blade angle of 14.56°. Field experiments were conducted at different fan speeds (1 050 r/min, 1 350 r/min and 1 650 r/min), different cane growth conditions (good, poor and severe collapse) and different traveling speeds (1 km/h, 2 km/h and 3 km/h). The results showed that for sugarcane with good growth, the optimized turbines reduced the impurity rate by 1. 06, 1. 99 and 3. 28 percentage points at different driving speeds when the turbine rotational speed was 1 050 r/min; when the turbine rotational speed was 1 350 r/min, the optimized turbines reduced the impurity rate by 2. 5 percentage points at most; when the turbine rotational speed was increased to 1 650 r/min, the optimized fan did not show obvious differences in the impurity rate at each driving speed; for the sugarcane with poor growth and serious collapse, the optimized fan could reduce the impurity rate by up to 5. 45 and 2. 1 percentage points, respectively. The optimized fan improved the ability of the sugarcane harvester to remove impurities in complex field environments, and the obtained data can provide theoretical support for subsequent fan research. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 21

Main heading: Impellers

Controlled terms: Dynamic programming - Energy efficiency - Linear programming - Mountings - Nonlinear programming - Turbine components - Turbomachine blades

Uncontrolled terms: Aerodynamic characteristics - Core components - Extractor - Full pressure - Impurity rates - Parameter optimization - Percentage points - Performance - Rotational speed - Sugarcane harvesters

Classification code: 1007 - 1007.1 - 1009 - 1201.7 - 601.2 Machine Components - 609.2

Numerical data indexing: Angular velocity 1.0855E+01rad/s, Angular velocity 5.845E+00rad/s, Angular velocity 8.35E-01rad/s, Size 1.00E+03m, Size 2.00E+03m, Size 3.00E+03m

DOI: 10.6041/j.issn.1000-1298.2024.12.008

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

11. Method for Locating Missing Ratoon Sugarcane Seedlings Based on RGB Images from Unmanned Aerial Vehicles and Improve YOLO v5s

Accession number: 20245217600948

Title of translation: 基于无人机RGB图像与改进YOLO v5s的宿根蔗缺苗定位方法

Authors: Li, Shangping (1, 2); Zheng, Chuangrui (1, 2); Wen, Chunming (1, 2); Li, Kaihua (1, 2)

Author affiliation: (1) College of Electronic Information, Guangxi Minzu University, Nanjing; 530006, China; (2) Key Laboratory of Intelligent Unmanned System and Intelligent Equipment, Nanjing; 530006, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 12

Issue date: December 2024

Publication year: 2024

Pages: 57-70

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In response to the lack of specific missing seedling data for the transverse replanting machine of pre-cut double bud sugarcane segments, resulting in poor replanting efficiency, a method for locating missing ratoon sugarcane seedlings based on UAV RGB images was proposed. Firstly, high-resolution images of ratoon sugarcane seedlings in the field were rapidly captured by using UAVs, which were then segmented into multiple sub-images and subjected to data augmentation to construct a dataset. Secondly, enhancements to the YOLO v5s model involved the introduction of P2 small target feature layers and DyHead modules to improve the detection accuracy of small seedling targets. Additionally, an image weighting strategy was employed during training to address sample imbalance issues and further improve detection accuracy, especially for occluded seedlings. Subsequently, a framework incorporating sliced -assisted inference facilitated the detection of ratoon sugarcane seedlings in large-scale field images by using the trained model. Finally, a row recognition algorithm based on an improved DBSCAN clustering algorithm and PC A fitting algorithm was developed to locate missing seedling positions along crop rows. Experimental results demonstrated that the improved ratoon sugarcane seedling detection model achieved an average detection accuracy of 96. 8% on sub-images and recognition precision and recall rates of 94. 5% and 91. 8% , respectively, on large-scale images, with a detection time of 0. 32 s. Utilizing the detection coordinates, the row recognition algorithm achieved 100% clustering accuracy, with an average angular error of 0. 245 5° for fitted row angles, and precision and recall rates of 91. 9% and 97. 1% , respectively, for missing seedling detection along rows. This method can be applied to intelligent missing seedling localization in large-scale, complex field images of ratoon sugarcane, providing technical support for replanting operations and holding significant implications for extending ratoon lifespan and increasing sugarcane yield. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 28

Main heading: Image enhancement

Controlled terms: Fruits - Image segmentation - Scales (weighing instruments) - Seed - Sugar cane

Uncontrolled terms: Crop rows detection - DBSCAN - Detection accuracy - Large-scales - Localisation - Missing seedling localization - Ratoon sugarcane seedling - RGB images - UAV RGB image - YOLO v5s

Classification code: 1106.3.1 - 821.5 Agricultural Wastes - 942.1.7

Numerical data indexing: Percentage 1.00E+02%, Percentage 1.00E00%, Percentage 5.00E+00%, Percentage 8.00E+00%, Percentage 9.00E+00%, Time 3.20E+01s

DOI: 10.6041/j.issn.1000-1298.2024.12.005

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

12. Method for Measuring Heading Angle of Agricultural Machinery Based on Two-position Method and Improved STEKF

Accession number: 20245317608712

Title of translation: 基于两位置法与改进STEKF的农机航向角测量方法

Authors: He, Jie (1, 2); Wei, Zhenghui (1); Hu, Lian (1, 3); Wang, Pei (1, 3); Huang, Peikui (1, 3); Ding, Shuaiqi (1)

Author affiliation: (1) College of Engineering, South China Agricultural University, Guangzhou; 510642, China; (2) Huangpu Innovation Research Institute, South China Agricultural University, Guangzhou; 510700, China; (3) Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, South China Agricultural University, Guangzhou; 510642, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 12

Issue date: December 2024

Publication year: 2024

Pages: 365-372

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: To solve the problems of low accuracy in heading measurement for single-antenna GNSS agricultural machinery navigation System at low speed and the difficulty of starting up, research on agricultural machinery heading measurement technology was conducted based on two-position method and improved STEKF. A suitable heading measurement method for GNSS/IMU fusion in single-antenna agricultural machinery was designed. The heading of high-speed agricultural machinery was measured by the two-position method, and the heading of low-speed agricultural machinery was measured by the improved STEKF algorithm based on the 9-axis data Output by the IMU and the position and speed Information Output by the GNSS. A one-dimensional Kaiman filter fusion method based on the two-position method and the improved STEKF heading was designed. The M — 1204 tractor produced by Lovol was used as the experimental platform to verify the heading measurement accuracy and stability under speed gradient change conditions. The results showed that during the process of starting up from a stationary State and traversing mixed routes containing straight lines and curves at a speed of 0. 36 km/h to 5. 40 km/h, the heading angle obtained by the proposed fusion heading measurement method was within 0. 5° of the heading angle obtained by the dual-antenna GNSS, indicating that the proposed method was suitable for stable heading acquisition of agricultural machinery in the State of starting up and low-speed movement, which improved the precision and stability of single-antenna agricultural machinery navigation and low-speed driving control and provided technical basis for high-precision agricultural machinery autonomous unmanned Operation. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 28

Main heading: Global positioning system

Controlled terms: Agricultural robots - Automatic guidance (agricultural machinery) - Tachometers - Tractors (agricultural)

Uncontrolled terms: Data output - Heading angles - Heading measurement - High Speed - Improved STEKF - Low speed - Measurement methods - Measurement technologies - Single antenna - Two position method

Classification code: 435.1 - 731.6 Robot Applications - 821.2 Agricultural Chemicals - 942.1.7

Numerical data indexing: Size 3.60E+04m, Size 4.00E+04m

DOI: 10.6041/j.issn.1000-1298.2024.12.035

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

13. Few-shot Crop Disease Recognition Based on Progressive Learning and Enhanced Prototype Metrie

Accession number: 20250117619760

Title of translation: 基于渐进式学习和增强原型度量的小样本农作物病害识别方法

Authors: Du, Haishun (1, 2); An, Wenhao (1); Zhang, Chunhai (1, 2); Zhou, Yi (1, 2)

Author affiliation: (1) School of Artificial Intelligence, Henan University, Zhengzhou; 450046, China; (2) International Joint Laboratory for Cooperative Vehieular Networks of Henan, Zhengzhou; 450046, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 12

Issue date: December 2024

Publication year: 2024

Pages: 344-353

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: At present, crop disease recognition is mostly realized based on convolutional neural network. However, due to the lack of training data in actual agricultural produetion, these crop disease recognition methods based on convolutional neural network often have limited applications and perform poorly. In Order to carry out the low-cost, general and flexible crop disease recognition, a few-shot crop disease recognition network based on progressive learning and enhanced prototype metric was proposed. Specifically, an enhanced prototype metric module was firstly designed to compute the enhanced prototype that can accurately represent the category center, and make füll use of its rieh category Information to recognize the crop disease. Then, a progressive learning strategy was designed to train the model to help it better adapt to the crop disease recognition, and further improve the few-shot crop disease recognition aecuraey. On the self-made few-shot crop disease datasets FSCD — Base, FSCD — Complex and the cross-domain setting from FSCD — Base to FSCD — Complex, the 5 — way 1 — shot average recognition aecuraey of the FPE — Net reached 70. 65%, 53. 47% and 49. 58%, and the 5 — way 5 — shot average recognition aecuraey of the FPE — Net reached 83. 02%, 66. 15% and 64. 21%, respectively. These experimental results showed that the FPE — Net was significantly better than other few-shot crop disease recognition models, which can recognize crop diseases more accurately when the training data was insufficient. ? 2024 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 31

Main heading: Convolutional neural networks

Controlled terms: Zero-shot learning

Uncontrolled terms: Convolutional neural network - Crop disease - Disease recognition - Enhanced prototype metric - Few-shot learning - Learning strategy - Progressive learning - Progressive learning strategy - Recognition methods - Training data

Classification code: 1101.2 - 1101.2.1

Numerical data indexing: Percentage 1.50E+01%, Percentage 2.00E+00%, Percentage 2.10E+01%, Percentage 4.70E+01%, Percentage 5.80E+01%, Percentage 6.50E+01%

DOI: 10.6041/j.issn.1000-1298.2024.12.033

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

14. Apple Leaf Spot Segmentation Model Based on Consistency Semi-supervised Learning

Accession number: 20245317614397

Title of translation: 基于一致性半监督学习的苹果叶片病斑分割模型研究

Authors: Ding, Yongjun (1); Yang, Wentao (1); Zhao, Yilong (1)

Author affiliation: (1) College of Computer Science and Engineering, Northwest Normal University, Lanzhou; 730070, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 55

Issue: 12

Issue date: December 2024

Publication year: 2024

Pages: 314-321

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Rapid and accurate lesion segmentation was essential for assessing disease severity and ensuring precise pesticide application. Deep learning-based semantic segmentation offered the technical foundation necessary for developing high-precision disease detection models. However, the annotation of apple leaf spots was both time-consuming and labor-intensive. To address this issue, a model for apple leaf spot segmentation was proposed based on a lightweight consistency semi-supervised learning framework, using Longdong apples as the research subject. Firstly, following the Mean Teacher semi-supervised learning framework, two lightweight DeepLabV3 + models were utilized to build the lesion semantic segmentation model, which improved its abili

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