Optimizing Machine Learning Models for Soil Fertility Analysis: Insights from Feature Engineering and Data Localization

Soil fertility is a critical determinant of agricultural productivity, yet traditional assessment methods often fall short in providing timely and precise recommendations. This study explores the potential of machine learning (ML) models to predict soil fertility, leveraging localized soil data and advanced feature engineering techniques. A comprehensive methodology was employed, involving data preprocessing, feature selection, and the implementation of six ML algorithms: Random Forest Regressor, Gradient Boosting Regressor, XGBoost Regressor, K-Nearest Neighbours Regressor, and Neural Network (MLP). The models were evaluated using robust metrics such as RMSE, R², and K-Fold Cross-Validation. Results demonstrate that engineered features significantly enhanced model performance, with Random Forest Regressor consistently outperforming other models across multiple soil nutrient parameters, achieving a testing R² of up to 0.99 and minimal RMSE. Exploratory Data Analysis (EDA) revealed key insights into soil nutrient dynamics, emphasizing the importance of pH, nitrogen, and organic matter as predictors. Feature engineering techniques, such as polynomial generation and scaling, further improved model accuracy and stability. This study highlights the transformative potential of ML in optimizing soil management practices. By integrating localized data and advanced predictive models, the findings provide actionable insights for farmers and agronomists, fostering sustainable agricultural practices and informed decision-making. This approach underscores the value of data-driven methods in addressing soil fertility challenges, paving the way for scalable and cost-effective solutions in precision agriculture.