Predictive Analytics for Soil Productivity Using Machine Learning

Abstract

A major factor in agricultural production is soil productivity, which is impacted by soil fertility as well as the compatibility of the crops cultivated there. In order to improve agricultural efficiency and sustainability, farmers must make educated judgments on crop selection and fertilizer use, which requires an accurate estimate of soil productivity. This study offers a predictive analytics method that uses machine learning and K-Means clustering and crop recommendation models to evaluate soil productivity. The K-Means clustering algorithm divides soil into three classes: Fertile, Highly Fertile, and Less Fertile, depending on the amount of nitrogen (N), phosphorus (P), and potassium (K) it contains. A Random Forest Regression model is used to forecast the best crop because soil productivity is influenced by both crop selection and fertility. In order to suggest crops that maximize production potential, this model examines a variety of soil attributes and environmental factors. Through precise soil fertility prediction and crop recommendation, this study offers farmers useful information that helps them maximize soil utilization, increase crop yields, and engage in sustainable agriculture. In the end, this data-driven strategy benefits both agricultural output and environmental preservation by enhancing farm productivity and resource management.