Soil Quality Prediction and Classification Using Machine Learning Algorithms

Abstract

Soil quality assessment is critical for sustainable agriculture and land management, yet traditional methods lack scalability and efficiency. This study presents a novel Soil Quality Prediction and Classification (SQPC) framework that leverages advanced machine learning, including hybrid ensembles and deep learning models. Using a rich dataset of soil attributes—such as pH, nutrients, and texture—we apply automated feature engineering and dimensionality reduction (PCA, t-SNE) to enhance interpretability. Ensemble models like XGBoost and Stacked Generalization improve prediction accuracy, while a new Spatial-Aware Neural Network (SANN) incorporates geospatial data for localized insights. Our models achieve over 95% classification accuracy, with the SANN improving predictions by up to 10% in sparse data regions. Explainable AI tools (e.g., SHAP, LIME) enhance transparency, making outputs actionable for stakeholders. The framework also integrates transfer learning and adaptive algorithms for robust performance on small and evolving datasets. This research offers a scalable, interpretable approach to soil quality modelling, paving the way for real-time, data-driven decision support in agriculture and environmental policy.