Crop Yield Prediction using Machine Learning

Agricultural productivity is significantly influenced by soil features and climate variability, although traditional yield estimation methods generally depend on manual soil testing and historical averages that may not reflect real-time field circumstances. This paper proposes an intelligent crop yield forecast system that incorporates soil image analysis, machine learning algorithms, and geo-weather data to enhance data-driven agricultural decision-making. The proposed framework gathers soil pictures from farmers and processes them using image enhancement techniques and supervised learning models to estimate soil pH and classify soil type. Simultaneously, the system gets location-specific environmental characteristics such as temperature, rainfall, humidity, and sun radiation through weather API integration based on geographic coordinates These soil and meteorological variables are paired with historical agricultural records to train a Random Forest Regression model for forecasting crop production per hectare. Experimental evaluation reveals that merging image-derived soil properties with real-time meteorological data enhances prediction accuracy compared to standard single-source models. The technology further gives comparative yield visualization and advises the best suitable crops for particular field conditions The results indicate that the proposed approach can aid farmers in optimizing crop choices, enhancing productivity, and lowering dependent on laboratory soil testing, thereby contributing to sustainable and technology-enabled agriculture.