Yapay Zeka Kullanılarak Orta-Güney Anadolu Bölgesi Tarım Alanlarına Ait Topraklarının Toplam Mikro Element ve Toplam Ağır Metal İçeriklerinin Tahmin Edilmesi: Bir Pilot Çalışma

Abstract

Mutlak gerekli besin elementlerinden olan mikro besin element içerikleri ve doğal oluşum veya antropojenik etkiler sonucunda topraklarda bulunan ağır metal içeriklerinin Türkiye'nin Orta-Güney Anadolu Bölgesi'nden alınan toprak örnekleri üzerinde toplam mikro element ve toplam ağır metal konsantrasyonlarının tahmini, yapay zekâ modelleri kullanılarak yapılmıştır. Topraktan elde edilen mikro element ve ağır metal verilerinin doğru şekilde tahmin edilmesi, tarımsal üretkenlik ve çevre sağlığı açısından büyük önem taşımaktadır. Toplam 62 toprak numunesi üzerinde Bor (B), Demir (Fe), Çinko (Zn), Mangan (Mn), Bakır (Cu), Kadmiyum (Cd), Krom (Cr), Nikel (Ni) ve Kurşun (Pb) elementleri analiz edilmiştir. Yapay zekâ modelleri olarak Random Forest (RF), Gradient Boosting (GB) ve Support Vector Regressor (SVR) kullanılmıştır. Modellerin performansı, Ortalama Mutlak Hata (MAE), Ortalama Kare Hatası (MSE) ve R² skorları ile değerlendirilmiştir. En iyi performans, Bor (B) ve Bakır (Cu) elementleri için elde edilmiştir. Bor (B) tahmininde, GB modeli en iyi sonuçları vermiştir (MAE: 4.89, MSE: 28.01, R²: 0.55). Bakır (Cu) tahmininde ise RF modeli (MAE: 3.20, MSE: 16.80 ve R²: 0.75) en yüksek performansı sergilemiştir. Sonuçlar, kullanılan yapay zekâ modellerinin toprak numunelerinden mikro element ve ağır metal tahmininde umut verici bir potansiyel taşıdığını göstermektedir.

Keywords

• Tahmin
• Toprak mikro element içeriği
• toplam ağır metal içeriği
• Orta-Güney Anadolu Bölgesi toprakları
• yapay zeka

Supporting Institution

Selçuk üniversitesi ve YÖK

Project Number

MEV-2017-36, 18401058

Ethical Statement

Çalışmamızda herhangi bir etik unsuru bulunmamaktadır.

Thanks

Selçuk üniversitesi ve YÖK' e bu çalışmaya fon desteği sağladığı için teşekkür ederiz.

AI-Based Prediction of Microelement and Heavy Metal Contents in Central-Southern Anatolian Soils: A Pilot Study

Abstract

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.

Keywords

• Prediction
• Total micro element content
• Total heavy metal content
• Central Southern Anatolia Region soils
• Artificial Intelligence

Project Number

MEV-2017-36, 18401058

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