Real-Time Crop Health Monitoring Using AI-Based Drone Surveillance and YOLOv12

Abstract

Early crop disease detection remains challenging for precision agriculture. This research presents an AI-drone surveillance system using YOLOv12 deep learning model to automatically identify diseases for real-time monitoring in potato, banana, and cotton crops. The complete pipeline includes automated image acquisition, intelligent preprocessing, and real-time analysis. Compared to traditional manual inspection, this approach reduces diagnosis time from days to minutes while improving reliability. Key innovations include optimized model architectures for resource-limited environments and multi-spectral disease pattern recognition. Field tests confirm the system's robustness across varying weather conditions and growth stages. Proposed method processes the drone-captured images through Raspberry Pi edge computing, achieving 99.5%, 98.1%, and 89.7% detection accuracy of potato, banana, and cotton crops respectively. The lightweight YOLO-Nano variants enable efficient field deployment while maintaining precision. A merged dataset across 28 disease classes demonstrates 91.8% overall accuracy through comprehensive validation metrics. Farmers receive immediate alerts for targeted treatment, reducing pesticide use by 30-45% in trial implementations. This scalable solution outperforms existing methods in both speed (4.2ms per image) and accuracy. Results demonstrate practical potential for transforming global agricultural monitoring through accessible AI technology.