Enhanced YOLOv5 with ECA Module for Vision-Based Apple Harvesting Using a 6-DOF Robotic Arm in Occluded Environments

Yan Xu, Xuejie Qiao, Li Ding, Xinghao Li, Zhiyu Chen and Xiang Yue ()
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Yan Xu: College of Engineering, Shenyang Agricultural University, Shenyang 110866, China
Xuejie Qiao: College of Engineering, Shenyang Agricultural University, Shenyang 110866, China
Li Ding: College of Engineering, Shenyang Agricultural University, Shenyang 110866, China
Xinghao Li: College of Engineering, Shenyang Agricultural University, Shenyang 110866, China
Zhiyu Chen: College of Engineering, Shenyang Agricultural University, Shenyang 110866, China
Xiang Yue: College of Engineering, Shenyang Agricultural University, Shenyang 110866, China

Agriculture, 2025, vol. 15, issue 17, 1-19

Abstract: Accurate target recognition and localization remain significant challenges for robotic fruit harvesting in unstructured orchard environments characterized by branch occlusion and leaf clutter. To address the difficulty in identifying and locating apples under such visually complex conditions, this paper proposes an improved YOLOv5-based visual recognition algorithm incorporating an efficient channel attention (ECA) module. The ECA module is strategically integrated into specific C3 layers (C3-3, C3-6, C3-9) of the YOLOv5 network architecture to enhance feature representation for occluded targets. During operation, the system simultaneously acquires apple pose information and achieves precise spatial localization through coordinate transformation matrices. Comprehensive experimental evaluations demonstrate the effectiveness of the proposed system. The custom-designed six-degree-of-freedom (6-DOF) robotic arm exhibits a wide operational range with a maximum working angle of 120°. The ECA-enhanced YOLOv5 model achieves a confidence level of 90% and an impressive in-range apple recognition rate of 98%, representing a 2.5% improvement in the mean Average Precision (mAP) compared to the baseline YOLOv5s algorithm. The end-effector positioning error is consistently controlled within 1.5 mm. The motion planning success rate reaches 92%, with the picking completed within 23 s per apple. This work provides a novel and effective vision recognition solution for future development of harvesting robots.

Keywords: robotic apple harvesting; YOLOv5 enhancement; efficient channel attention (ECA); occlusion handling; 6-DOF robotic arm (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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