AI-Powered Crop Health Monitoring System Using Drone Image Processing and Machine Learning for Disease and Deficiency Detection

Abstract

This paper presents an AI-powered crop health monitoring system that integrates drone imagery, advanced image processing, and machine learning for real-time agricultural diagnostics. The architecture employs an unmanned aerial vehicle (UAV) with high-definition (HD) or multispectral cameras for efficient wide-area data collection. Captured images are processed using MATLAB-based techniques to enhance quality and extract features indicative of plant health. A pre-trained machine learning model then classifies plant diseases and nutrient deficiencies with high accuracy. Upon detection, the system triggers real-time alerts, enabling timely farmer interventions to minimize crop loss. Experimental results demonstrate high classification performance across diverse crops and conditions. Comparative analysis with manual inspection and basic sensor-based systems highlights the proposed framework’s superior precision, scalability, and processing efficiency. This study showcases the potential of AI, remote sensing, and automation to transform agricultural practices and support sustainable farming.