Improved YOLOv8-Based Segmentation Method for Strawberry Leaf and Powdery Mildew Lesions in Natural Backgrounds

This study addresses the challenge of segmenting strawberry leaves and lesions in natural backgrounds, which is critical for accurate disease severity assessment and automated dosing. Focusing on strawberry powdery mildew, we propose an enhanced YOLOv8-based segmentation method for leaf and lesion detection. Four instance segmentation models (SOLOv2, YOLACT, YOLOv7-seg, and YOLOv8-seg) were compared, using YOLOv8-seg as the baseline. To improve performance, SCDown and PSA modules were integrated into the backbone to reduce redundancy, decrease computational load, and enhance detection of small objects and complex backgrounds. In the neck, the C2f module was replaced with the C2fCIB module, and the SimAM attention mechanism was incorporated to improve target differentiation and reduce noise interference. The loss function combined CIOU with MPDIOU to enhance adaptability in challenging scenarios. Ablation experiments demonstrated a segmentation accuracy of 92%, recall of 85.2%, and mean average precision (mAP) of 90.4%, surpassing the YOLOv8-seg baseline by 4%, 2.9%, and 4%, respectively. Compared to SOLOv2, YOLACT, and YOLOv7-seg, the improved model’s mAP increased by 14.8%, 5.8%, and 3.9%, respectively. The improved model reduces missed detections and enhances target localization, providing theoretical support for subsequent applications in intelligent, dosage-based disease management.