Mango Leaf Disease Detection using YOLO v1 to YOLO v10

This study explores and compares the performance of various YOLO (You Only Look Once) object detection models— ranging from YOLOv1 to YOLOv10—for identifying diseases in mango leaves. A dataset of 4,000 mango leaf images was prepared, covering eight distinct classes, including common diseases such as Anthracnose, Bacterial Canker, Powdery Mildew, Sooty Mould, Leaf Spot, Dieback, Algal Leaf Spot, along with healthy leaves. Each image was manually annotated to highlight the affected areas using bounding boxes. To ensure fairness in evaluation, all YOLO versions were trained on the same dataset under consistent conditions. The models were assessed based on standard performance metrics such as mean Average Precision (mAP), precision. The comparative results offer valuable insights into how YOLO has progressed over its different versions, revealing the strengths and weaknesses of each in terms of detection accuracy and computational efficiency. This work aims to guide researchers and developers in choosing the most effective YOLO version for real-time disease detection in agricultural settings.