MRDC-YOLO: A Lightweight Detector for Strawberry Growth-Stage and Defective Fruit Detection

Joint detection of strawberry growth stages and defective fruit is needed for harvest planning and quality screening, but field images make this task difficult because stage-related visual differences are subtle, flowers and early fruits are often small and densely distributed, and occlusion weakens localization reliability. This study develops Multi-Scale Refined Detection and Classification YOLO (MRDC-YOLO), a lightweight detector based on the YOLO11s framework, for this fine-grained detection scenario. The backbone, neck, and detection head are redesigned with three modules: a Multi-Scale Adaptive Edge Enhancement Module (MAEM), a Reparameterized Progressive Feature Aggregation (RPFA) module, and a Decoupled Cross-Scan Head (DCSH). MAEM strengthens boundary and texture responses for visually similar categories, RPFA reduces redundant multi-scale fusion while maintaining features for dense small targets, and DCSH introduces task-aware classification and regression branches with cross-scan-inspired spatial modeling for occlusion-sensitive localization. Experiments on a five-class strawberry dataset containing 5114 images show that MRDC-YOLO achieves 95.63% mAP@0.5 and 82.39% mAP@0.5:0.95. Over YOLO11s, the model yields a 2.06-percentage-point gain in precision and 1.34- and 1.53-percentage-point gains in mAP@0.5 and mAP@0.5:0.95, together with 10.7% fewer parameters and 8.9% lower GFLOPs. These results suggest that MRDC-YOLO improves fine-grained category discrimination and localization while retaining a smaller model size than the YOLO11s baseline.