AgroPredictX: Machine Learning for Precision Farming

Agriculture is the primary source of livelihood for a massive population in India, particularly in states like Maharashtra. However, farmers face mounting challenges including unpredictable climate conditions, soil degradation, and increasing instances of crop diseases. To combat these issues, this research proposes an AI-Powered Crop Yield Prediction and Agricultural Diagnostic System that integrates Machine Learning and Computer Vision technologies into a unified platform. The system utilizes a Random Forest regression model to predict crop yield based on rainfall, temperature, soil nutrients (NPK), crop type, and seasonal data, achieving a high predictive accuracy of 91.64%. Furthermore, a Convolutional Neural Network (CNN) model is incorporated for early plant disease detection and treatment recommendation. The system is designed with a multi-layered architecture accessible via web and mobile interfaces, providing farmers with actionable insights to optimize resource management and agricultural productivity.