The YOLO-OBB-Based Approach for Citrus Fruit Stem Pose Estimation and Robot Picking

Lei Ye, Junjun Ma, Yuanhua Lv, Zhipeng Guo, Zhihao Lai, Chuhong Ou, Jin Li () and Fengyun Wu ()
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Lei Ye: School of Intelligent Engineering, Shaoguan University, Shaoguan 512000, China
Junjun Ma: School of Intelligent Engineering, Shaoguan University, Shaoguan 512000, China
Yuanhua Lv: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Zhipeng Guo: School of Intelligent Engineering, Shaoguan University, Shaoguan 512000, China
Zhihao Lai: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Chuhong Ou: School of Intelligent Engineering, Shaoguan University, Shaoguan 512000, China
Jin Li: School of Intelligent Engineering, Shaoguan University, Shaoguan 512000, China
Fengyun Wu: School of Modern Information Industry, Guangzhou College of Commerce, Guangzhou 511363, China

Agriculture, 2025, vol. 15, issue 22, 1-22

Abstract: Precise localization of the fruit stem picking point is crucial for robots to achieve efficient harvesting operations. However, in unstructured orchard environments, citrus fruit stems are easily obscured by branches and leaves and affected by factors such as overlapping fruits. This leads to poor picking localization accuracy for robots, impacting their autonomous picking efficiency. Therefore, this paper proposes a method for estimating the posture of citrus fruit stems and performing picking operations under environmental occlusion, based on the YOLO-OBB algorithm. First, the YOLOv5s algorithm detects the ROI of citrus, combined with depth information to obtain their 3D point clouds. Second, the OBB algorithm constructs oriented point cloud bounding boxes to determine stem orientation and picking point locations. Finally, through hand–eye pose transformation of the robotic arm, the end-effector is controlled to achieve precise picking operations. Experimental results indicate that the average picking success rate of the YOLO-OBB algorithm reaches 82%, representing a 50% improvement over approaches without fruit stem estimation. This conclusively shows that the proposed algorithm provides precise fruit stem pose estimation, effectively enhancing robotic picking success rates under constrained fruit stem detection conditions. It offers crucial technical support for autonomous robotic harvesting operations.

Keywords: picking robot; fruit stem pose estimation; YOLOv5s; OBB algorithm (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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