Instance Segmentation and 3D Pose Estimation of Tea Bud Leaves for Autonomous Harvesting Robots

Haoxin Li, Tianci Chen, Yingmei Chen, Chongyang Han, Jinhong Lv, Zhiheng Zhou () and Weibin Wu ()
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Haoxin Li: National Key Laboratory of Agricultural Equipment Technology, College of Engineering, South China Agricultural University, Guangzhou 510642, China
Tianci Chen: National Key Laboratory of Agricultural Equipment Technology, College of Engineering, South China Agricultural University, Guangzhou 510642, China
Yingmei Chen: National Key Laboratory of Agricultural Equipment Technology, College of Engineering, South China Agricultural University, Guangzhou 510642, China
Chongyang Han: National Key Laboratory of Agricultural Equipment Technology, College of Engineering, South China Agricultural University, Guangzhou 510642, China
Jinhong Lv: National Key Laboratory of Agricultural Equipment Technology, College of Engineering, South China Agricultural University, Guangzhou 510642, China
Zhiheng Zhou: School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510640, China
Weibin Wu: National Key Laboratory of Agricultural Equipment Technology, College of Engineering, South China Agricultural University, Guangzhou 510642, China

Agriculture, 2025, vol. 15, issue 2, 1-23

Abstract: In unstructured tea garden environments, accurate recognition and pose estimation of tea bud leaves are critical for autonomous harvesting robots. Due to variations in imaging distance, tea bud leaves exhibit diverse scale and pose characteristics in camera views, which significantly complicates the recognition and pose estimation process. This study proposes a method using an RGB-D camera for precise recognition and pose estimation of tea bud leaves. The approach first constructs an for tea bud leaves, followed by a dynamic weight estimation strategy to achieve adaptive pose estimation. Quantitative experiments demonstrate that the instance segmentation model achieves an mAP@50 of 92.0% for box detection and 91.9% for mask detection, improving by 3.2% and 3.4%, respectively, compared to the YOLOv8s-seg instance segmentation model. The pose estimation results indicate a maximum angular error of 7.76°, a mean angular error of 3.41°, a median angular error of 3.69°, and a median absolute deviation of 1.42°. The corresponding distance errors are 8.60 mm, 2.83 mm, 2.57 mm, and 0.81 mm, further confirming the accuracy and robustness of the proposed method. These results indicate that the proposed method can be applied in unstructured tea garden environments for non-destructive and precise harvesting with autonomous tea bud-leave harvesting robots.

Keywords: YOLOv8s-seg model; adaptive pose estimation; RGB-D camera; precise harvesting (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
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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