Forecasting Crop Yield using Machine Learning

Pothuraju V V Satyanarayana1 ,Bonu Jagadeesh2,Gokarla Sravani3,Duvvi Deepthi Sudha4, Chandapu
Kishore5,Althi Durga Prasad6
1 Associate Professor, Computer Science and Engineering, Visakha Institute of Engineering &
Technology(A), Narava, Visakhapatnam, India.
2,3,4,5,6 B.Tech Student, Computer Science and Engineering, Visakha Institute of Engineering &
Technology(A), Narava, Visakhapatnam, India

Abstract:Accurate prediction of crop yield plays an important role in improving agricultural efficiency and maintaining food security. Conventional forecasting approaches, such as crop cutting experiments, require significant time and provide results only after harvesting, which limits their usefulness for early agricultural planning. To overcome these limitations, this paper proposes a data-driven system titled “Forecasting Crop Yield Using Machine Learning.” The system predicts crop productivity at both state and district levels across India.The model utilizes a district-level agricultural dataset collected between 2019 and 2025, which includes environmental and soil-related parameters such as temperature, nitrogen, phosphorus, potassium, soil pH and cultivated area. A Random Forest Regressor algorithm is applied to analyze complex nonlinear relationships between these agricultural factors and crop yield. The model is trained using ten key attributes: State, District, Crop, Season,Area, Temperature, Nitrogen, Phosphorus, Potassium and pH. Experimental evaluation indicates that the model achieves more than 90% prediction accuracy, demonstrating its reliability for practical agricultural forecasting. Tomake the system user-friendly and accessible, it is deployed as an interactive web application using the Streamlit framework. This platform enables farmers, researchers and policymakers to input regional data and instantly obtain crop yield predictions. Overall, the system supports data-driven agricultural decision-making and encourages sustainable farming practices.
Keywords:Crop Yield Forecasting, Machine Learning, Random Forest Regressor, Agricultural Data Analysis, Soil Nutrients,Streamlit Application, District-Level Dataset, Predictive Modeling, Agricultural Decision Support, Sustainable Farming.