The data-driven approach towards developing a crop yield prediction system intends to predict the yields of crops in agriculture based on real-time data from farms with the aid of computational techniques. The factors considered by this system include soil characteristics, weather, rainfall, temperature, fertilizer usage, and irrigation that greatly affect the crop yield. In terms of data analysis, the advanced models employed are the Gaussian Process Regression (GPR) for probabilistic prediction and the Autoregressive Integrated Moving Average (ARIMA) for time-series forecasting. The models learn from previous data in agriculture to accurately predict yields. The proposed solution is provided as a web-based application, developed with Flask, with a frontend interface designed through HTML, CSS, and JavaScript. Users can input agricultural data and receive live yield predictions, thus improving decision-making, resource allocation, and agricultural production efficiency.
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