AI-Based Prediction of Microelement and Heavy Metal Contents in Central-Southern Anatolian Soils: A Pilot Study
Makaleler > Tam Makale · 2025
Yazarlar: Noyan EKEN, Enes Efe, KAMER YAZAR, Mehmet Hamurcu, Fatma Gökmen Yılmaz, Sait Gezgin, Erdoğan Eşref Hakkı
ÇOMÜ Ziraat Fakültesi Dergisi, Hakemli Üniversite Dergisi, Uluslararası, TR Dizin (Ulakbim), 2025, cilt 13, ss 12-31
DOI: 10.33202/comuagri.1586063
Özet
The estimation of total microelement and heavy metal concentrations in soil samples taken from the Central-Southern Anatolian Region of Turkiye was conducted using artificial intelligence models. The accurate prediction of microelement and heavy metal contents obtained from the soil is of great importance for agricultural productivity and environmental health. A total of 62 soil samples were analyzed for Boron (B), Iron (Fe), Zinc (Zn), Manganese (Mn), Copper (Cu), Cadmium (Cd), Chromium (Cr), Nickel (Ni), and Lead (Pb). The artificial intelligence models used in this study were Random Forest (RF), Gradient Boosting (GB), and Support Vector Regressor (SVR). Model performance was evaluated based on Mean Absolute Error (MAE), Mean Squared Error (MSE), and R² scores. The best performance was achieved for Boron (B) and Copper (Cu). In the case of Boron (B), the GB model provided the best results (MAE: 4.89, MSE: 28.01, R²: 0.55), while the RF model showed the highest performance for Copper (Cu) predictions (MAE: 3.20, MSE: 16.80, R²: 0.75). The results indicate that the artificial intelligence models used in this study hold promising potential for the prediction of microelement and heavy metal concentrations in soil samples.