AGRI.AI: An AI-Powered Crop Recommendation and Fertilizer Suggestion and Plant Disease Detection System.

Agricultural productivity faces unprecedented challenges due to climate variability, increasing population demands, and resource constraints. Traditional farming methods often rely on empirical knowledge and lack precision in decision-making, leading to suboptimal crop yields and inefficient resource utilization. This research presents AGRI.AI, an integrated artificial intelligence system that addresses these challenges through intelligent crop recommendation, fertilizer optimization, and plant disease detection capabilities. The system employs a Model-View-Controller (MVC) architecture implemented using the Flask web framework, integrating multiple machine learning algorithms including Random Forest, Support Vector Machine, and Convolutional Neural Networks. The methodology incorporates comprehensive datasets encompassing soil parameters (nitrogen, phosphorus, potassium levels), environmental conditions (temperature, humidity, rainfall, pH), and plant pathology images. The crop recommendation module achieved 99.5% accuracy using ensemble methods, while the fertilizer prediction component demonstrated 97.3% precision in nutrient requirement estimation. The plant disease detection module, utilizing a custom CNN, achieved a classification accuracy of 98.2%. Results demonstrate that AGRI.AI provides a precise, scalable, and user-friendly solution that can significantly improve crop yields, optimize resource allocation, and support sustainable agricultural practices for a more resilient and productive future.