Integrated Application for Paddy Disease Detection and Remedy Suggestion

In India, agriculture is the backbone of rural communities, with the majority of villagers relying on it as their primary livelihood. However, challenges such as disease management, soil quality assessment, and weed control often limit productivity. While Kisan call centers provide essential support, their effectiveness is constrained by communication barriers and the limitations of verbal explanations. This paper proposes an innovative solution through a machine learning-based mobile application designed specifically for farmers. The app enables users to upload images of paddy disease symptoms, soil test results, and weeds for analysis. Leveraging advanced algorithms, it provides automated, data-driven recommendations for disease control, crop management, and soil improvement. The app also offers local language interfaces, making it accessible to less educated farmers. By continuously refining its decision-making capabilities through user feedback, the app evolves to meet the specific needs of different regions and farming practices. This approach not only enhances efficiency and productivity but also empowers farmers with immediate, reliable expert advice, bridging the gap between traditional practices and modern technology. Ultimately, the application aims to significantly improve agricultural outcomes, contributing to the sustainability and growth of the farming sector in India.