Tomato Leaf Disease Detection Using ML, IP and Fabrication of Pesticides Spraying Prototype

This report explores cutting-edge methods for improving tomato cultivation through efficient disease detection and precise pesticide application. Employing Machine Learning (ML) and Image Processing (IP) techniques, we enhance the speed and accuracy of disease identification on tomato leaves. Furthermore, a novel pesticide spraying prototype isintroduced, designed to target only diseased areas, reducing pesticide usage and environmental impact. By integrating thesetechnologies, we aim to revolutionize tomato farming practices, enabling early disease intervention, minimizing crop damage, and promoting sustainable agriculture. The report underscoresthe significance of adopting advanced techniques to address agricultural challenges, ensuring the health and productivity oftomato crops while minimizing environmental harm.