PREDICTIVE MAINTENANCE OF SMART AGRICULTURE USING EXPLAINABLE AI (XAI)

Agriculture faces several challenges, including optimizing crop selection and managing plant diseases, which significantly affect productivity and sustainability. This project leverages artificial intelligence (AI) to provide data-driven solutions that address these issues. By analyzing environmental data, the AI system recommends suitable crops based on factors such as climate, soil conditions, and weather patterns, improving crop yield and minimizing resource wastage. Additionally, the project incorporates plant disease prediction models, enabling early detection of diseases and reducing the need for chemical treatments. collecting sensor data from agricultural environments, such as soil moisture levels, temperature, and humidity. This data-driven approach not only aids in effective irrigation control but also optimizes farming practices, promoting efficiency, sustainability, and long-term agricultural resilience.