CatBoost-based Soil Health Monitoring and Fertilizer Application Optimization

Abstract

The aim of this research is to develop soil health prediction model and to compare the performance of XGBoost with the proposed algorithm, CatBoost, in enhancing fertilizer application decisions. The two groups evaluated the dataset. Group 1 used XGBoost tuned for its hyperparameters, while Group 2 used CatBoost that featured ordered boosting to resolve categorical soil attributes. A Kaggle dataset containing more than 10,000 soil samples was refined, prepared, and split into training, validation, and testing sets for evaluating the model. The XGBoost achieved an average accuracy of 85.87%, whereas CatBoost significantly outperformed it, achieving accuracy between 94.80% and 96.70%, with a total average of 95.55%. Statistical evaluation validated that this discrepancy is extremely significant (t = –60.275, p = 0.000), CatBoost’s enhanced predictive performance This means that CatBoost yields more stable and reliable predictions of soil health. CatBoost outperformed and showed greater stability than XGBoost in modeling soil nutrients for optimizing fertilizer use. Its high and consistent performance allows it to be considered a reliable tool in precision agriculture for making rational decisions on soil health assessment.