Abstract

The crucial importance of agriculture for survival is complemented by the transformational capacity of machine learning (ML) in addressing agricultural yield concerns. Conventional techniques, such as manual enumeration and satellite imagery, often lack precision. This article centres on the use of machine learning (ML) to predict millet output. This allows farmers to enhance their crop yields by considering parameters such as land area and irrigation. Millets, known for their nutritional robustness, provide a substantial contribution to global food security. Precise yield forecasts are crucial for the long-term viability of agriculture. The research utilises regression models like AdaBoost Regressor, XGBoost Regressor, Decision Tree, Support Vector Machine and Random Forest. Among these models, AdaBoost Regressor demonstrates the best level of accuracy. The combination of machine learning and modern technologies improves the accuracy of yield estimate and highlights the connection between agricultural practices and state-of-the-art technology.