Intelligent Wild Mushroom Recognition Using YOLOv11n: A YOLOv11n-Based System for Accurate and Real-Time Identification

Accurate identification of wild mushrooms remains a persistent challenge due to the high morphological similarity between edible and toxic species. Traditional manual methods are labor-intensive, subjective, and error-prone, while existing computational approaches often prioritize algorithmic performance over practical, user-friendly solutions. This study presents an integrated intelligent recognition system designed to bridge this gap. A dataset of over 8,000 images representing 40 common wild