Adaptive Tracking and Cutting Control System for Tea Canopy: Design and Experimental Evaluation
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.
Authors
- Tongchuan Yi
- Linhuan Zhang
- Danzhu Zhang
- Ruirui Zhang (ORCID: https://orcid.org/0000-0001-8928-4238)
- Quan Feng (ORCID: https://orcid.org/0000-0002-6368-3975)
- Liping Chen (ORCID: https://orcid.org/0000-0003-3384-9935)
Institutions
- Gansu Agricultural University (CN)
- Beijing Academy of Agricultural and Forestry Sciences (CN)
Publication Details
- Journal
- Agriculture
- Published
- 2025-03-06
- DOI
- https://doi.org/10.3390/agriculture15050557
- Citations
- 2
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 3.50
Funders
- National Natural Science Foundation of China