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Development and Field Evaluation of a Robotic Harvesting System for Plucking High-quality Tea by Yatao Li; Shunkai Wu; Leiying He; Junhua Tong; Runmao Zhao; Jiangming Jia; Jianneng Chen; Chuanyu Wu is a Agricultural and Biological Sciences article available to read on EtoBox.

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The critical issue of the robotic harvesting high-quality tea is to realize tea shoot detection, plucking point localization, and motion planning. In addition, the accuracy and efficiency of the robotic plucking of high-quality tea in the field are essential. Therefore, a robotic harvesting system is proposed in this paper by combining deep learning, point cloud processing, and spatial path planning. First, the deep learning method and the compressed YOLOv3 network are used to quickly and accurately identify tea shoots. Second, an efficient point cloud processing-based 3D localization algorithm for high-quality tea plucking points was proposed. The genetic algorithm is then used to shorten the end-effector's motion path by optimizing the plucking sequences. Eventually, a harvester robot with a parallel manipulator was developed to conduct field plucking experiments and evaluate the effectiveness of the proposed harvesting system. All experimental results demonstrate that the success rates of detection, localization, and motion plucking are 85.16 %, 78.90 %, and 80.23 %, respectively. Furthermore, the overall process harvesting success rate is 53.91 %, and the average plucking time

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Author

Yatao Li; Shunkai Wu; Leiying He; Junhua Tong; Runmao Zhao; Jiangming Jia; Jianneng Chen; Chuanyu Wu

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Agricultural and Biological Sciences (Physical Sciences)