OPTIMIZATION OF DAMAGED CORN KERNEL RECOGNITION ALGORITHM BASED ON A DUAL-LIGHT SYSTEM

To enhance real-time detection of corn breakage rate under dim conditions, this study designed a dual-light (top/backlight) sampling system. By comparing four datasets (top-scattered, top-clustered, backlight-scattered, backlight-clustered), the algorithm optimized with backlight-scattered data achieved optimal accuracy (79.6%). A lightweight YOLOv8n_gcd model was proposed, integrating Ghost convolution in the backbone to reduce redundancy, attention mechanisms for feature enhancement, and depthwise separable convolutions in the neck. The optimized model reduced FLOPs by 24% and increased FPS by 165%, offering an efficient, lowcost solution for agricultural quality inspection with theoretical and practical value.