AN OPTIMIZED WHEAT DISEASE DETECTION FRAMEWORK USING YOLOV10

Abstract

Wheat leaf diseases, including rust, powdery mildew, and leaf blight, significantly impact crop yield and quality, making early detection essential for effective disease management. This project presents an advanced wheat disease detection framework using YOLOv10, a cutting-edge object detection model that combines high accuracy, real-time processing, and efficient feature extraction. YOLOv10’s enhanced architecture and anchor-free detection capabilities allow it to accurately identify multiple disease types from wheat leaf images, even under varying lighting conditions, leaf orientations, and field environments. By training on a diverse and augmented dataset, the model achieves strong generalization and robustness, enabling rapid and reliable disease detection through mobile devices, drones, or field cameras. The system not only provides precise classification and localization of diseased regions but also supports timely intervention and precision agriculture, helping farmers make data-driven decisions to protect crops. With its scalability, speed, and adaptability, YOLOv10 represents a powerful tool for automated plant disease management and sustainable farming practices.