AI-Based Soil Nutrient Prediction Using Machine Learning and Data Integration

Abstract

The accurate prediction of soil nutrient levels is essential for optimizing fertilizer management, improving crop productivity, and ensuring sustainable agricultural practices. This study presents an artificial intelligence-based framework for soil nutrient prediction using machine learning and data integration techniques. The proposed methodology incorporates multi-source datasets, including soil properties such as nitrogen, phosphorus, and potassium levels, pH, moisture content, organic matter, and environmental variables such as temperature, rainfall, and land-use patterns. These data are preprocessed through cleaning, normalization, and feature engineering techniques to ensure consistency and reliability. Advanced machine learning models, including Random Forest, Support Vector Machines, Gradient Boosting, and Artificial Neural Networks, are employed to capture complex nonlinear relationships between soil characteristics and nutrient dynamics. The integration of heterogeneous data sources enhances model robustness and improves prediction accuracy. The system is trained and validated using region-specific datasets to ensure adaptability across diverse soil types and agro-climatic conditions. Experimental results demonstrate that the proposed approach significantly outperforms traditional laboratory-based and empirical methods in predicting soil nutrient levels. Additionally, the framework provides actionable insights for optimized fertilizer application, reducing input costs and minimizing environmental impact. The findings highlight the effectiveness of combining artificial intelligence with integrated data sources for precision agriculture applications. The study concludes that AI-based soil nutrient prediction systems offer a scalable, efficient, and data-driven solution for improving soil fertility management, enhancing crop yield, and promoting sustainable farming practices.