ARCHIVES

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, thisresearch proposes an AI-Powered Crop YieldPrediction and Agricultural Diagnostic System thatintegrates Machine Learning and Computer Vision technologies into a unified platform. The system utilizes a Random Forest regression model to predict crop yieldbased on rainfall, temperature, soilnutrients (NPK), crop type, and seasonal data, achieving a high predictiveaccuracy of 91.64%. Furthermore, a Convolutional Neural Network (CNN) model is incorporated for early plant disease detection and treatment recommendation. The system isdesignedwith a multi-layered architecture accessible via web and mobile interfaces, providing farmers with actionable insights to optimize resource management and agricultural productivity.