Agriculture plays a fundamental role in sustaining the global economy and ensuring food security, yet farmers often rely on intuition and traditional practices for crop selection, leading to inefficiencies in yield and resource utilization. This research proposes a machine learning-based system for smart crop prediction and recommendation, aimed at enhancing precision agriculture through data-driven decision-making. The study integrates historical datasets containing soil parameters (pH, nitrogen, phosphorus, potassium) and climatic factors (temperature, humidity, rainfall) with real-time environmental data fetched via APIs. Multiple machines learning models, including Decision Trees, Suppor... More >