Research on Tractor Condition Recognition Based on Neural Networks

Yahui Luo, Chen Li, Ping Jiang, Yixin Shi, Bin Li and Wenwu Hu ()
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Yahui Luo: College of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha 410128, China
Chen Li: College of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha 410128, China
Ping Jiang: College of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha 410128, China
Yixin Shi: College of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha 410128, China
Bin Li: College of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha 410128, China
Wenwu Hu: College of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha 410128, China

Agriculture, 2024, vol. 14, issue 4, 1-20

Abstract: 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.

Keywords: tractor; condition recognition; CNN; VGG-16 model; LVQ neural network (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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