The role of machine learning in precision agriculture

The field of precision agriculture is presently undergoing a transformative evolution as machine learning (ML) techniques are applied to optimize resource efficiencies and crop productivity. This chapter evaluated the use of several ML algorithms—namely, Random Forest, XGBoost, Support Vector Machine (SVM), and CNN—using various datasets that encompass soil nutrient content, meteorological data, crop health, and livestock metrics. These algorithms were trained and validated using real-world agricultural datasets to execute crop yield prediction, disease identification, irrigation scheduling, and fertilizer recommendation. The results reveal some models achieving 90% accuracy or greater and regression R² scores reaching up to 0.93. These results illustrate the considerable potential of ML to address some challenges at the heart of precision agriculture. This chapter provides a rigorous methodology, a justification for dataset selection, and detailed results presented through key quantitative measurements and visual analytics. The comprehensive findings presented establish a solid base for future research and real-time implementation to advance sustainable agriculture across diverse agro-climatic zones.