SOFTWARE BASED CROP DISEASE DETECTION USING IMAGE PROCESSING AND ARTIFICIAL INTELLIGENCE

Abstract

Software-based crop disease detection uses image processing and artificial intelligence to identify plant infections from leaf, stem, fruit, and field images. The system captures images through smartphones, cameras, drones, or agricultural monitoring devices and analyzes visual features such as colour changes, spots, lesions, texture, and abnormal growth. Machine learning and deep learning models classify crop diseases and estimate their severity according to trained image datasets. Real-time alerts help farmers detect infections early and receive recommendations regarding treatment, pesticide use, or expert consultation. Cloud-based dashboards can monitor disease occurrence across fields, crops, and seasons. Integration with farm management and weather systems improves disease-risk assessment. Overall, the software can reduce crop losses, improve treatment accuracy, support timely intervention, and strengthen precision agriculture.