Published Paper Details:

The rapid development of machine learning (ML) techniques has paved the way for innovative applications in agriculture, including the detection of leaf diseases. Leaf diseases can significantly impact crop yield and quality, making early detection crucial for effective disease management. This study presents a comprehensive approach to detect leaf diseases using ML algorithms. The methodology involves the collection of high-resolution images of diseased and healthy leaves, followed by data preprocessing, feature extraction, and the training of ML models. The results demonstrate the potential of ML in accurately identifying and classifying leaf diseases, enabling farmers to take timely preventive measures. The developed system provides a non-invasive and cost-effective solution, contributing to sustainable agriculture and food security. This research contributes to the advancement of precision agriculture and holds promise for real-time disease monitoring and management, ultimately leading to increased crop productivity and reduced environmental impact.