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Adaptive Tracking and Cutting Control System for Tea Canopy: Design and Experimental Evaluation

Author

Listed:
  • Danzhu Zhang

    (College of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou 730070, China)

  • Ruirui Zhang

    (Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Liping Chen

    (College of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou 730070, China
    Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Linhuan Zhang

    (Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Tongchuan Yi

    (Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Quan Feng

    (College of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou 730070, China)

Abstract

Combined with the characteristic that tea is generally planted in hilly and mountainous areas and considering the existing problems of harvesting with current tea pickers, such as the inability to adjust their posture in real time, poor adaptability to the terrain, insufficient stability, and large differences in the harvesting lengths of tea. To address these issues, an adaptive canopy-following cutting control system has been designed for self-propelled tea harvesters in this study. Specifically, we developed a height-following control algorithm for tea canopy tracking and an adaptive header tilt angle control algorithm based on incremental PID control. Field experiments demonstrated that when the vehicle speed was 0.4 m/s, the height tracking errors for three harvesting lengths (20 mm, 30 mm, and 40 mm) remained within ±5 mm, with correlation coefficients exceeding 0.99. When the height differences between the two sides of the tea ridge were 10 cm, 15 cm, and 20 cm, the maximum uphill roll angles were measured at 1.7°, 2.3°, and 3.0°, respectively, and the time taken for the harvester to return to a horizontal position was around 1.7 s. During downhill movement, the maximum roll angles of the harvester were 1.3°, 2.0°, and 2.6°, respectively, and the time for the harvester to return to a horizontal position was around 2.1 s, demonstrating significant correction effectiveness. Quality assessments revealed that at the 30 mm harvesting length specification, the integrity rate of tea harvesting exceeded 79%, while the missed harvesting rate was below 1.1%. This system effectively enhances harvesting stability and quality, offering novel insights for efficient, high-volume tea production.

Suggested Citation

  • Danzhu Zhang & Ruirui Zhang & Liping Chen & Linhuan Zhang & Tongchuan Yi & Quan Feng, 2025. "Adaptive Tracking and Cutting Control System for Tea Canopy: Design and Experimental Evaluation," Agriculture, MDPI, vol. 15(5), pages 1-23, March.
  • Handle: RePEc:gam:jagris:v:15:y:2025:i:5:p:557-:d:1606372
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    References listed on IDEAS

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    1. Xiaoxing Weng & Dapeng Tan & Gang Wang & Changqing Chen & Lianyou Zheng & Mingan Yuan & Duojiao Li & Bin Chen & Li Jiang & Xinrong Hu, 2023. "CFD Simulation and Optimization of the Leaf Collecting Mechanism for the Riding-Type Tea Plucking Machine," Agriculture, MDPI, vol. 13(5), pages 1-21, April.
    2. Ruidong Yu & Yinhui Xie & Qiming Li & Zhiqin Guo & Yuanquan Dai & Zhou Fang & Jun Li, 2024. "Development and Experiment of Adaptive Oolong Tea Harvesting Robot Based on Visual Localization," Agriculture, MDPI, vol. 14(12), pages 1-22, December.
    3. Xiaolong Huan & Min Wu & Xianbing Bian & Jiangming Jia & Chenchen Kang & Chuanyu Wu & Runmao Zhao & Jianneng Chen, 2024. "Design and Experiment of Ordinary Tea Profiling Harvesting Device Based on Light Detection and Ranging Perception," Agriculture, MDPI, vol. 14(7), pages 1-23, July.
    4. Yingpeng Zhu & Chuanyu Wu & Junhua Tong & Jianneng Chen & Leiying He & Rongyang Wang & Jiangming Jia, 2021. "Deviation Tolerance Performance Evaluation and Experiment of Picking End Effector for Famous Tea," Agriculture, MDPI, vol. 11(2), pages 1-18, February.
    5. Haoxin Li & Tianci Chen & Yingmei Chen & Chongyang Han & Jinhong Lv & Zhiheng Zhou & Weibin Wu, 2025. "Instance Segmentation and 3D Pose Estimation of Tea Bud Leaves for Autonomous Harvesting Robots," Agriculture, MDPI, vol. 15(2), pages 1-23, January.
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