Tomato Ripening Detection in Complex Environments Based on Improved BiAttFPN Fusion and YOLOv11-SLBA Modeling - Yan Hao; Lei Rao; Xueliang Fu; Hao Zhou; Honghui Li (Agriculture, 2025)
Several pressing issues have been revealed by deep learning-based tomato ripening detection technology in intricate environmental applications: The ripening transition stage distinction is not accurate enough, small target tomato detection is likely to miss, and the detection technology is more susceptible to variations in light. Based on the YOLOv11 model, a YOLOv11-SLBA tomato ripeness detection model was presented in this study. First, SPPF-LSKA is used in place of SPPF in the backbone section, greatly improving the model’s feature discrimination performance in challenging scenarios including dense occlusion and uneven illumination. Second, a new BiAttFPN hierarchical progressive fusion is added in the neck area to increase the feature retention of small targets during occlusion. Lastly, the feature separability of comparable categories is significantly enhanced by the addition of the auxiliary detection head DetectAux. In this study, comparative experiments are carried out to confirm the model performance. Under identical settings, the YOLOv11-SLBA model is compared to other target detection networks, including Faster R-CNN, SSD, RT-DETR, YOLOv7, YOLOv8, and YOLOv11. With 2.7 million parameters and 10.9 MB of model memory, the YOLOv11-SLBA model achieves 92% P, 83.5% R, 91.3% mAP50, 64.6% mAP50-95, and 87.5% F1-score. This is a 3.4% improvement in accuracy, a 1.5% improvement in average precision, and a 1.6% improvement in F1-score when compared to the baseline model YOLOv11. It outperformed the other comparison models in every indication and saw a 1.6% improvement in score. Furthermore, the tomato-ripeness1public dataset was used to test the YOLOv11-SLBA model, yielding model p values of 78.6%, R values of 91.5%, mAP50 values of 93.7%, and F1-scores of 84.6%. This demonstrates that the model can perform well across a variety of datasets, greatly enhances the detection generalization capability in intricate settings, and serves as a guide for the algorithm design of the picking robot vision system.