Corn Leaf Disease Detection using YOLOv5

This research introduces an autonomous system employing YOLOv5 for the precise detection of corn leaf diseases. Through the integration of GoPro cameras, the system achieves instantaneous object detection without relying on drones, leveraging high-resolution imagery. The utilization of YOLOv5 facilitates ongoing adaptation through real-time object detection and unsupervised learning. Fixed GoPro cameras contribute to a robust dataset, ensuring the system delivers prompt results via real-time data transfer to farmers' smartphones. By offering actionable insights for timely disease management, the proposed system strives to enhance crop yield and implement sustainable farming practices. It stands as a cost-effective tool, revolutionizing the evaluation of corn leaf health in agriculture.