Monocular Vision-Based Clamping-Point Determination via Pose Estimation for Walnut Vibration Harvesting

Efficient vibration based walnut harvesting relies on the accurate determination of clamping-points on tree trunks. Properly selected clamping points can significantly enhance vibration transmission efficiency while minimizing mechanical damage to trees. However, most existing studies focus on developing generalized vibration models applicable to multiple trees, often overlooking the structural uniqueness of individual trees in clamping-point determination. This study proposes a monocular vision-based method for clamping-point determination in walnut vibration harvesting. Robustness and applicability in complex orchard environments are enhanced by introducing three keypoint annotation strategies with varying levels of structural constraints, namely 5-key-part annotation (5-KAS), 2-key-part annotation (2-KAS), and single-key-part annotation (1-KAS). Pose estimation models based on the YOLO architecture, including YOLOv8-pose, YOLO11-pose, and YOLOv12-pose, were evaluated to examine the effect of structure assisted annotation, and the results show that introducing structural constraints improves detection accuracy, training stability, and robustness. The YOLOv12-pose model combined with the 5-KAS achieves the best performance, with a precision of 95.8% and mean average precision (mAP) of 95.5%. Field harvesting experiments demonstrate that clamping-point prediction incorporating structural information achieves higher and more stable net harvesting rates. Overall, the proposed method offers a reliable and deployable solution for clamping-point determination using monocular RGB images, facilitating intelligent vibration harvesting in walnut orchards.