Plucking Point and Posture Determination of Tea Buds Based on Deep Learning

Chengju Dong, Weibin Wu (), Chongyang Han, Zhiheng Zeng, Ting Tang and Wenwei Liu
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Chengju Dong: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Weibin Wu: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Chongyang Han: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Zhiheng Zeng: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Ting Tang: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Wenwei Liu: The Fifth Electronics Research Institute of Ministry of Industry and Information Technology, Guangzhou 511370, China

Agriculture, 2025, vol. 15, issue 2, 1-27

Abstract: Tea is a significant cash crop grown widely around the world. Currently, tea plucking predominantly relies on manual work. However, due to the aging population and increasing labor costs, machine plucking has become an important trend in the tea industry. The determination of the plucking position and plucking posture is a critical prerequisite for machine plucking tea leaves. In order to improve the accuracy and efficiency of machine plucking tea leaves, a method is presented in this paper to determine the plucking point and plucking posture based on the instance segmentation deep learning network. In this study, tea images in the dataset were first labeled using the Labelme software (version 4.5.13), and then the LDS-YOLOv8-seg model was proposed to identify the tea bud region and plucking area. The plucking points and the central points of the tea bud’s bounding box were calculated and matched as pairs using the nearest point method (NPM) and the point in range method (PIRM) proposed in this study. Finally, the plucking posture was obtained according to the results of the feature points matching. The matching results on the test dataset show that the PIRM has superior performance, with a matching accuracy of 99.229% and an average matching time of 2.363 milliseconds. In addition, failure cases of feature points matching in the plucking posture determination process were also analyzed in this study. The test results show that the plucking position and posture determination method proposed in this paper is feasible for machine plucking tea.

Keywords: plucking point; plucking posture; tea buds; deep learning; feature points matching (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: 2025
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