CapsNet-Yolo: A Novel Deep Learning Approach for Real Time Tomato Disease Identification Synergised with Drone Technology and Pesticide Spraying

Precision agriculture is transforming crop management by leveraging technology to optimize yields and minimize environmental impact. This research presents a cutting-edge system, CapsNet-Yolo, which integrates Capsule Networks (CapsNet) and YOLO v8 models for real-time tomato disease identification and targeted pesticide application using drones. The approach employs drones equipped with advanced sensors to capture detailed aerial imagery of tomato crops. The captured images are processed by deep learning models, combining the strengths of CapsN et for robust feature extraction and YOLO v8 for efficient object detection. Upon identifying diseased plants, the system autonomously triggers precise pesticide spraying, ensuring efficient resource utilization and reducing environmental harm.