Efficient YOLO-Based Deep Learning Model for Real-Time Potato Disease Detection

Abstract

Crop diseases pose a significant threat to global food security, requiring efficient and accurate detection methods to support sustainable agriculture. This study focuses on evaluating YOLO-based deep learning models for detecting potato leaf diseases, leveraging the Potato Leaves Disease Detection Dataset from publicly available repository. The dataset contains 1,398 images categorized into Fungus, Pest, and Healthy classes. Four YOLO model configurations YOLOv8 (large and medium) and YOLOv9 (medium and small) were experimented onto the Potato leaf disease dataset. The YOLOv8 (large) model outperformed all other configurations, achieving the highest mAP50 (97.2%) and mAP50-95 (92.4%), along with the lowest training and validation losses, demonstrating robust generalization and precise object detection capabilities. YOLOv9 models, particularly YOLOv9 (small), exhibited lower and more variable performance, highlighting challenges in distinguishing complex or overlapping disease features despite their compact design. This study concludes that YOLOv8 (large) is the most reliable model for applications requiring high precision and robust performance in real-time agricultural disease detection. The findings underscore the potential of YOLO-based models to transform crop disease management by improving early detection and intervention, contributing to enhanced productivity and sustainable farming practices.