Agrivision: AI-Powered Solutions for Sustainable Farming Publications

This research presents a machine learning (ML) and deep learning (DL) powered web application aimed at supporting farmers by recommending optimal crops to grow, suggesting suitable fertilizers, and detecting diseases in crops. The system leverages advanced ML and DL algorithms to process environmental data, soil properties, and crop health records, providing actionable insights for sustainable agricultural practices. It integrates methodologies such as hybrid feature selection, SARIMAX predictive models for climate forecasting, and convolutional neural networks (CNNs) for accurate crop disease detection. By analyzing key factors like soil pH, nitrogen content, and seasonal climate data, the system delivers personalized, real-time recommendations that enhance farming productivity and sustainability. This solution empowers farmers with an intuitive, scalable platform, bridging the gap between modern technology and traditional farming practices. The system's adaptability and precision aim to reduce resource waste, increase yield, and address challenges related to global food security and environmental impact.