Flower Thinning Strategy of Flat Peach Inflorescence Based on RBCN-YOLO

Accurately identifying the morphology and spatial distribution of flat peach inflorescence is crucial for guiding precise flower thinning operations. In this study, based on the YOLOv8 framework, a flat peach inflorescence detection model (RBCN-YOLO) was developed for detecting all growth stages, from bud to initial flowering and full flowering. The model optimized the neck network architecture by incorporating RepBlock and BiFusion modules, integrating the CAFM module into the backbone network, and combining the NWD loss function with the CIoU loss function. The improved model showed better detection performance in remote viewing angles, backlight conditions, and complex scenarios. Moreover, it demonstrated good real-time performance on edge devices. Based on this model, a flower thinning strategy was designed by combining the density classification algorithm, inflorescence membership categorization, and interval flower-thinning requirements. The results showed that the RBCN-YOLO model achieved a mAP@0.5 of 82.9% and an F1 score of 78.9%. These scores represented improvements of 3.0% and 2.4%, respectively, compared to YOLOv8. Notably, the model performance in the initial flowering stage showed the most significant improvement, with the mAP@0.5 increasing from 65.1% to 70.7%. Additionally, the flower thinning strategy based on RBCN-YOLO achieved a flower thinning ratio of 54.55%, with a thinning accuracy of 78.84%. To further enhance the application of the research, a visualization system with integrated object detection and flower thinning functions was designed. This study provides a valuable reference for flower-thinning operations in flat peach orchards.