Using Different Regression Tree Algorithms to Predict Soil Organic Matter with Digital Color Parameters in Soil Profile Wall

Abstract

Soil organic matter has a critical role for the physical, chemical and biological properties of the soil and for sustainable soil and agriculture. Quick and cost-effective prediction of soil organic matter can provide basic data support for precision agriculture. The study area is located in the Muttalip pasture of Tepebaşı, Eskişehir. The soil profile wall (1x1 m) was dug and divided into 10x10 cm raster cell. A total of 100 soil samples were taken from center of each raster cell of the soil profile wall. The field-based and lab-based digital color parameters (CIE Lab) were measured depending on the grid sampling model. The ordinary Kriging interpolation method was used in geostatistical distribution maps of the amount of organic matter (OM) and field-based and lab-based CIE Lab values. CHAID, Ex-CHAID, and CART regression tree algorithms were used to predict the OM with field-based and lab-based CIE Lab values. The OM in the soil profile wall varies between 4.65-10.54% in the topsoils, while it varies between 0.01-0.41% in the subsoils. According to the results, lab-based CIE Lab values obtained high predicting performance and more effective than field-based CIE Lab values. It concluded that the CART algorithm can be used rapidly and economically in prediction OM with high prediction performance (R2=0.89) with lab-based digital color parameters.

Keywords

• Data mining algoritms
• interpolations
• pasture soils
• CIE Lab
• soil color

Toprak Profil Duvarında Farklı Regresyon Ağacı Algoritmaları Kullanılarak Sayısal Renk Parametreleri ile Organik Maddenin Tahmin Edilmesi

Abstract

Toprak organik maddesi toprağın fiziksel, kimyasal ve biyolojik özellikleri ile sürdürülebilir toprak ve tarım için oldukça kritik bir role sahiptir. Toprak organik maddesinin çabuk ve düşük maliyetle tahmin edilmesi hassas tarım için temel veri desteği sağlayabilir. Çalışma alanı Eskişehir ili Muttalıp merası sınırları içerisinde yeralmaktadır. Toprak profil duvarı (1x1m) kazılmış ve 10x10 cm'lik grid hücrelere bölünmüştür. Toprak profil duvarından herbir grid hücreden grid yöntemi ile toplam 100 adet toprak örneği alınmıştır. Toprak örneklerinde sayısal renk parametrelerinin belirlenmesi grid örnekleme modeline bağlı olarak hem arazi hem de lobaratuvar koşullarında gerçekleştirilmiştir. Profil duvarından arazi ve laboratuvar koşullarında elde edilen CIE Lab değerleri ve organik madde miktarının jeoistatistiksel olarak dağılım haritalarında Ordinary Kriging interpolasyon metodu kullanılmıştır. Sayısal renk parametreleri ile organik madde miktarının tahmin edilmesinde CHAID, Ex-CHAID ve CART regresyon ağacı algoritmaları kullanılmıştır. Toprak profil duvarında organik madde miktarı yüzey topraklarda %4.65-10.54 arasında değişirken yüzey altı topraklarda %0.01-0.41 arasında değişmektedir. Araştırma sonuçlarına göre, OM miktarının tahmin edilmesinde laboratuvar koşullarında elde edilen CIE Lab değerlerinin laboratuvar koşullarında elde edilen CIE Lab değerlerinden daha etkilidir. Araştırma, CART algoritmasının laboratuvar koşullarında elde edilen sayısal renk parametreleri ile OM miktarının yüksek başarı performansı (R2=0.89) ile tahmin edilmesinde hızlı ve ekonomik olarak kullanılabileceğini ortaya çıkarmıştır.

Keywords

• Veri madenciliği algoritmaları
• interpolasyon
• mera toprakları
• CIE Lab
• toprak rengi

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