Research on an Intelligent Agricultural Machinery Unmanned Driving System

Ren, Haoling; Wu, Jiangdong; Lin, Tianliang; Yao, Yu; Liu, Chang

doi:10.3390/agriculture13101907

Cited by 9 publications

(6 citation statements)

References 28 publications

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“…During the decelerated stop condition, the vehicle speed decreased from 0.18 m/s to 0 m/s until the tractor stopped, and the angular velocity approached 0. The acceleration of the tractor decreased from 0 to −0.27 m/s 2 , and the roll angle, pitch angle, and heading angle changed in a consistent trend. The roll angle and pitch angle were both 0, and the heading angle remained at 110 • .…”

Section: Feature Generationmentioning

confidence: 78%

“…After completing model training, it is necessary to quantify the recognition accuracy using the test set. The specific formula is shown in Equation (2).…”

Section: Calculation Of Recognition Accuracymentioning

confidence: 99%

“…Unmanned and intelligent agricultural machinery has become an inevitable trend. Through the intelligent transformation of tractors [2], agricultural machinery can better meet the development needs of Chinese agriculture. In terms of tractor condition recognition, there are few relevant studies, and most of them are based on the use of tractor spatial position information [3,4] data to study tractor conditions.…”

Section: Introductionmentioning

confidence: 99%

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Research on Tractor Condition Recognition Based on Neural Networks

Luo,

Li,

Jiang

et al. 2024

Agriculture

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Tractor condition recognition has important research value in helping to understand the operating status of tractors and the trend of tillage depth changes in the field. Therefore, this article presents a method for recognizing tractor conditions, providing the basis for establishing the relationship between tractor conditions and the tillage depth of the attached agricultural machinery. This study designed a tractor condition recognition method based on neural networks. Using real-world vehicle data to establish a data set, K-means clustering analysis was used to label the data set based on four conditions: “accelerated start”, “constant speed”, “decelerated stop” and “turning”. The learning vector quantization (LVQ) neural network and the VGG-16 model of a CNN were selected for use recognizing the tractor conditions. The results showed that both the neural networks had good recognition effects. The average accuracy rates of the VGG-16 model of CNN and LVQ neural network were 90.25% and 79.7%, respectively, indicating that these models could be applied to tractor condition recognition and provide theoretical support for the correction of angle detection errors.

show abstract

Section: Feature Generationmentioning

confidence: 78%

“…After completing model training, it is necessary to quantify the recognition accuracy using the test set. The specific formula is shown in Equation (2).…”

Section: Calculation Of Recognition Accuracymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Research on Tractor Condition Recognition Based on Neural Networks

Luo,

Li,

Jiang

et al. 2024

Agriculture

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“…If an autonomous vehicle seeks to navigate independently across diverse orchard types, such as unmanned and precision agriculture orchards, a significant bottleneck hindering robotic autonomy and automation within these settings is their lack of human-like intuition in understanding and perceiving objects within point cloud maps [12,13]. Consequently, there is a pressing need to devise methodologies that enhance robots' perceptual capabilities in orchard point cloud maps [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

ODN-Pro: An Improved Model Based on YOLOv8 for Enhanced Instance Detection in Orchard Point Clouds

Pan,

Xiao,

et al. 2024

Agronomy

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In an unmanned orchard, various tasks such as seeding, irrigation, health monitoring, and harvesting of crops are carried out by unmanned vehicles. These vehicles need to be able to distinguish which objects are fruit trees and which are not, rather than relying on human guidance. To address this need, this study proposes an efficient and robust method for fruit tree detection in orchard point cloud maps. Feature extraction is performed on the 3D point cloud to form a two-dimensional feature vector containing three-dimensional information of the point cloud and the tree target is detected through the customized deep learning network. The impact of various feature extraction methods such as average height, density, PCA, VFH, and CVFH on the detection accuracy of the network is compared in this study. The most effective feature extraction method for the detection of tree point cloud objects is determined. The ECA attention module and the EVC feature pyramid structure are introduced into the YOLOv8 network. The experimental results show that the deep learning network improves the precision, recall, and mean average precision by 1.5%, 0.9%, and 1.2%, respectively. The proposed framework is deployed in unmanned orchards for field testing. The experimental results demonstrate that the framework can accurately identify tree targets in orchard point cloud maps, meeting the requirements for constructing semantic orchard maps.

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“…The future farmland work will require tractors to perform challenging unmanned operation tasks in hilly and mountainous terrains to ensure the safety of drivers [1][2][3]. The task objective includes crossing rugged terrain with high autonomy.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Soil Characteristic Parameters for Electric Mountain Tractor Based on Gauss–Newton Iteration Method

Xi,

Feng,

Liu

et al. 2024

WEVJ

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Future field work tasks will require mountain tractors to pass through rough terrain with limited human supervision. The wheel–soil interaction plays a critical role in rugged terrain mobility. In this paper, an algorithm for the estimation of soil characteristic parameters based on the Simpson numerical integration method and Gauss–Newton iteration method is presented. These parameters can be used for passability prediction or in a traction control algorithm to improve tractor mobility and to plan safe operation paths for autonomous navigation systems. To verify the effectiveness of the solving algorithm, different initial values and soils were selected for simulation calculations of soil characteristic parameters such as internal friction angle, settlement index, and the joint parameter of soil cohesion modulus and friction modulus. The results show that the error was kept within 2%, and the calculation time did not exceed 0.84 s, demonstrating high robustness and real-time performance. To test the applicability of the algorithm model, further research was conducted using different wheel parameters of electric mountain tractors under wet clay conditions. The results show that these parameters also have high accuracy and stability with only a few iterations. Thus, the estimation algorithm can meet the requirements of quickly and accurately identifying soil characteristic parameters during tractor operation. A criterion for the passability of wheeled tractors through unknown terrain is proposed, utilizing identified soil parameters.

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Research on an Intelligent Agricultural Machinery Unmanned Driving System

Cited by 9 publications

References 28 publications

Research on Tractor Condition Recognition Based on Neural Networks

Research on Tractor Condition Recognition Based on Neural Networks

ODN-Pro: An Improved Model Based on YOLOv8 for Enhanced Instance Detection in Orchard Point Clouds

Estimation of Soil Characteristic Parameters for Electric Mountain Tractor Based on Gauss–Newton Iteration Method

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