Towards Intelligent: A Robust Attention-Enhanced YOLO Framework for Oudemansiella raphanipes Detection in Factory Cultivation Systems

Accurate and efficient counting of Oudemansiella raphanipes plays a key role in intelligent cultivation management, yield estimation, and unmanned production monitoring. However, automated detection of Oudemansiella raphanipes remains challenging due to dense fruiting body distribution, morphological variations, complex soil backgrounds, and illumination fluctuations in practical cultivation environments. To address these challenges, this study proposes OR-YOLO, an enhanced deep learning-based detection framework for in situ fruiting body recognition and counting. The proposed model integrates Bi-level Routing Attention (BRA) and Coordinate Attention (CA) modules to improve multi-scale feature representation and spatial localization capability, while an FEIoU-VFL optimization strategy is introduced to enhance bounding-box regression and confidence estimation for difficult samples. Experimental results demonstrated that OR-YOLO achieved Precision, Recall, mAP50, mAP75, mAP50–95, and F1-score values of 88.2%, 84.5%, 89.5%, 72.2%, 65.8%, and 86.3%, respectively. Compared with the baseline model, OR-YOLO effectively reduced missed detections caused by dense growth and target occlusion. Furthermore, the model maintained stable performance under different soil backgrounds and illumination conditions. The predicted fruiting body counts showed strong agreement with manual annotations, with a coefficient of determination (R2) of 0.945 and a correlation coefficient (r) of 0.972, demonstrating the reliability of OR-YOLO for automated quantitative phenotypic analysis. Collectively, this study provides an efficient and robust solution for intelligent mushroom monitoring and offers a detection approach for automated phenotyping in complex agricultural production environments.