Lightweight Harvest-Window Recognition of Pleurotus ostreatus for Robotic Picking Using YOLO26s-Generated ROIs and a Stage-Guided Boundary Expert Network

Harvest-window recognition of facility-grown Pleurotus ostreatus is challenged by clustered fruiting bodies, occlusion, and subtle differences between near-mature and mature stages. We developed a two-stage framework combining YOLO26s detection with a lightweight dual-view classifier, RepViT-RSG-BEHarvestNet. YOLO26s was selected from four candidate detectors using the validation subset of a 2946-image dataset. Matched detections produced 3547 paired target and context regions of interest. The classifier combined stage-guided expert routing with boundary soft labels and teacher distillation. On the independent test set, YOLO26s achieved a Precision of 0.9681, Recall of 0.9117, and mAP50 of 0.9787. Across ten paired runs on matched test regions, the final classifier achieved a Macro F1 of 0.8827 ± 0.0044, compared with 0.8589 ± 0.0054 for the dual-view baseline, while adding 0.071 M parameters. On the near-mature–mature subset, Macro F1 increased from 0.7994 ± 0.0115 to 0.8563 ± 0.0105. On all 296 test images containing 404 annotated targets, the complete pipeline achieved target-level Precision of 0.7770, Recall of 0.7941, a false harvest rate of 0.1007, and a missed harvest rate of 0.2059. Mean end-to-end latency was 94.7 ms per image. The results show improved boundary classification while identifying detection errors as a remaining constraint on complete-image harvest decisions.