An Efficient Approach to Beetle Damage Detection of Banana Using Deep Learning

This study develops a lightweight YOLOv8n-based framework for instance-level detection of visible banana surface damage using a locally collected and manually annotated dataset from Bangladesh. The model distinguishes healthy and unhealthy banana instances and achieves 87.04% mAP@0.50 on the held-out test set while maintaining a compact 5.96 MB model size and fast inference, making it suitable as a practical baseline for automated banana surface-quality screening.