International Journal of Science, Engineering and TechnologyAuthors: Mrs. G. Rohini Phaneendra Kumari, Thalla Vasavi Surya Prabha, Bandla Hima Varshini, Kilaru Vyshnav, Domathoti Nadiya
Abstract: One of the most important prerequisites for sustainable agricultural planning, food security, and proper resource management is being able to accurately predict crop yield. The nonlinear and intricate interactions between environmental variables and crop productivity are often overlooked by traditional statistical models based on limited historical data. This work is a machine learning exploration to develop a model that predicts food production given the soil, weather, and crop-related parameters. Our dataset has environmental and agricultural features that include soil nutrients like (N, P, K, pH), atmospheric variables (temperature, relative humidity, and rainfall) and crop-specific attributes. By mixing these factors together the research intends to deliver an intelligent predictive framework that would be very useful for farmers and policymakers in decision-making and agricultural planning. To get better prediction accuracy, various machine learning algorithms including Random Forest (RF), Support Vector Machine (SVM), and XG Boost were applied and compared. The quality of each model was measured by R² score, RMSE, and MAE metrics. Random Forest was the best performer and therefore had the best precision and stability in capturing nonlinear data patterns among the models that were tested. The findings serve as evidence of the potential of machine learning methods to revolutionize the Agri-ecosystem by turning the traditional farm practices into data-driven decision-making, which is a significant contributor to the implementation of sustainable crop management and enhanced yield forecasting.
