Prediction of Soil Health and Nutrient Deficiency in the Soil using Advanced Machine Learning Techniques

Predicting soil health is essential for advancing precision agriculture and promoting sustainable land management practices. This study investigates the effectiveness of various machine learning approaches—such as Logistic Regression (LR), Random Forest (RF), Support Vector Machines (SVM), XGBoost, LightGBM, and hybrid models incorporating Voting Classifier and Stacking techniques in classifying soil health. Experimental findings reveal that Random Forest, the Hybrid Model, and the Stacking Model demonstrate superior performance compared to other classifiers, achieving higher accuracy in evaluating soil quality. Notably, the Stacking Model efficiently combines multiple algorithms to enhance prediction capabilities. The study highlights the importance of ensemble learning in soil classification, providing datadriven insights that can optimize agricultural productivity and support sustainable soil management strategies.