Relative Position Detection of Clustered Tomatoes Based on BlendMask-BiFPN

In robotic harvesting, maneuvering around obstacles to position manipulators is challenging, especially in unstructured environments. This study proposes a method to detect the relative position of tomato bunches to the main stem position using the BlendMask-BiFPN algorithm. Initial comparative tests between full-stem and partial-stem labelling strategies revealed that the latter produced more complete peduncle masks, which guided our choice for subsequent experiments. Significant modifications to the BlendMask algorithm included the integration of a ResNet-101-BiFPN backbone, which improved the feature fusion network of the model. The revised model demonstrated high efficiency in pinpointing the relative positions of clustered tomatoes, achieving 91.3 % ARmask 50 and 84.8 % APmask 50 for the detection of tomato bunches. Comparisons with Mask RCNN, YOLACT, YOLACT++, and YOLOv8 showed that the BlendMask-BiFPN model outperforms these alternatives, suggesting its potential for more effective robotic harvesting in complex agricultural scenarios.