A Hybrid Machine Learning and Optimization Framework for Predictive Soil Health Maintenance and Fertilizer Recommendation
Soil health management plays a central role in achieving sustainable agricultural productivity; however, conventional soil testing and fertilizer advisory practices are often labor-intensive, costly, and limited in precision. To overcome these limitations, this work presents a Hybrid Machine Learning and Optimization Framework for predictive soil health maintenance. The proposed framework integrates multiple predictive tasks namely soil nutrient estimation using NPK regression, fertilizer recommendation through classification, Soil Health Index (SHI) computation, and crop suitability prediction within a unified and data-driven decision-support architecture. The predictive modeling layer employs an ensemble of Random Forest, XGBoost, and Deep Neural Network (DNN) models to capture complex nonlinear relationships between soil and environmental parameters. To enhance predictive performance and generalization, model hyperparameters are automatically tuned using a Genetic Algorithm (GA), enabling efficient exploration of the hyperparameter search space. Comprehensive experiments conducted on a real-world soil dataset demonstrate that the proposed optimization-enhanced models consistently outperform baseline approaches. For soil nutrient prediction, the Hybrid DNN + GA model achieved an approximate 20% reduction in RMSE compared to the baseline DNN, attaining a coefficient of determination R2 of 0.94. In the fertilizer recommendation task, the proposed approach achieved 94.2% classification accuracy with an ROC–AUC of 0.96, while crop suitability prediction reached an accuracy of 92.8%, representing a 4.6% improvement over conventional DNN-based models. The derived Soil Health Index exhibited a mean value of 0.78, with 58% of soil samples classified as Healthy, demonstrating the framework’s ability to translate complex predictions into an interpretable soil vitality indicator. The integration of predictive modeling, metaheuristic optimization, and interpretable soil health assessment enables reliable and actionable decision support for precision agriculture. The proposed framework offers a robust, scalable, and interpretable solution for proactive soil health management and efficient fertilizer utilization, contributing toward sustainable agricultural practices.