AI-Based Plant Disease Detection Using Drone Imaging And Yolov8

Author(s)	Mr. Shathrugna Dhiru J M, Mr. Adharsh S, Mr. Abhishek R, Mr. Laalu Prasad S K, Prof. Kasiviswanath N J
Country	India
Abstract	Agriculture is a crucial sector in India, and crop health significantly impacts farmers’ productivity and income. Delayed detection of plant diseases can lead to severe yield loss and economic damage. This study proposes a drone-based automated system for plant disease detection using the YOLOv8 deep learning model. The system captures crop images using a smartphone-mounted drone and processes them through a backend server for disease identification and classification. The model detects multiple disease categories, including Rust, Virus, Leaf Curl, Shot Hole, Powdery Mildew, and Healthy leaves, with high accuracy. The results are visualized through a web-based dashboard, enabling real-time monitoring and decision-making. The proposed approach reduces manual effort, improves detection speed, and provides a scalable solution for precision agriculture.
Keywords	YOLOv8, Drone, Plant Disease Detection, Deep Learning, Agriculture
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-03-27

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International Journal For Multidisciplinary Research