Tomato Pedicel Picking-Point Localization via Improved YOLOv8n-EED-Seg and RGB-D Fusion

Accurate and rapid localization of tomato pedicel picking points presents a significant challenge for automated harvesting, due to factors such as occlusion by dense foliage, overlapping fruits, variable lighting conditions, and the slender morphology of pedicels. To address these, we propose an integrated picking decision system combining enhanced instance segmentation with RGB-D fusion. In this study, a lightweight detection model named YOLOv8n-EED-seg is introduced. An optimized EfficientRep backbone is integrated to enhance computational efficiency, while the EMAttention mechanism and a refined DynamicHead module strengthen multi-scale feature representation for slender pedicels. The model further incorporates the Zhang–Suen algorithm for skeleton extraction and a large-neighborhood mean method for depth restoration, enabling precise 3D localization. Experiments are conducted on a dataset of 3310 images collected in a greenhouse environment. Compared with the baseline YOLOv8n-seg, our model improves precision, recall, F1 score, and mAP50 by 5.09%, 2.78%, 3.63%, and 4.31%, respectively. The system achieves an inference speed of 4.8 ms per frame, enabling real-time performance, while attaining a 93.88% success rate in 3D picking-point localization. Furthermore, the proposed model demonstrates superior robustness in complex environments compared with common segmentation models, effectively balancing accuracy, speed, and model complexity. This study provides a reliable technical pathway for high-precision, vision-based tomato-harvesting robots.