Leveraging Machine Learning for Soil Fertility Prediction and Crop Management in Agriculture

Abstract This study investigates how machine learning (ML) algorithms can be used in agriculture to forecast soil fertility and maximize crop yield. Machine learning (ML) models are created to predict soil nutrient levels, pH, and organic matter content across a range of geographical locations and land-use types with high accuracy by evaluating large datasets that include soil samples, environmental conditions, and agronomic methods. The research shows the advantages of nonlinear modeling approaches in capturing complex interactions inherent in agricultural systems through a comprehensive evaluation of several machine learning techniques, including ensemble methods like AdaBoost and Extra Tree Classifier. Furthermore, immediate insights and recommendations for improving agronomic decisions are made possible by the integration of real-time sensing technologies, such as proximate sensing, distant sensing, and Internet of Things (IoT) devices. Overall, this work highlights how machine learning (ML) can completely change crop management techniques and soil fertility prediction, enabling a more resilient and sustainable agriculture sector.