Prediction of soil macronutrients using fractal parameters and artificial intelligence methods

• Zolfaghari, Ali A. ^[1] ; Abolkheiryan, Meysam ^[1] ; Soltani-Toularoud, Ali A. ^[2] ; Taghizadeh-Mehrjardi, Ruhollah ^[4] ; Weldeyohannes, Amanuel O. ^[3]
  1. [1] Semnan University

     Semnan University

     Irán

     
  2. [2] University of Mohaghegh Ardabili

     University of Mohaghegh Ardabili

     Irán

     
  3. [3] University of Alberta

     University of Alberta

     Canadá

     
  4. [4] Ardakan University, Faculty of Agriculture and Natural Resources, Ardakan, Iran University of Tübingen, Dept. of Geosciences, Soil Science and Geomorphology, Tübingen, Germany

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• Localización: Spanish journal of agricultural research, ISSN-e 2171-9292, ISSN 1695-971X, Vol. 18, Nº. 2, 2020
• Idioma: inglés
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• Resumen

  • Aim of study: To evaluate artificial neural networks (ANN), and k-Nearest Neighbor (k-NN) to support vector regression (SVR) models for estimation of available soil nitrogen (N), phosphorous (P) and available potassium (K).Area of study: Two separate agricultural sites in Semnan and Gorgan, in Semnan and Golestan provinces of Iran, respectively.Material and methods: Complete data set of soil properties was used to evaluate the models’ performance using a k-fold test data set scanning procedures. Soil property measures including clay, sand and silt content, soil organic carbon (SOC), electrical conductivity (EC), lime content as well as fractal dimension (D) were used for the prediction of soil macronutrients. A Gamma test was utilized for defining the optimum combination of the input variables.Main results: The sensitivity analysis showed that OC, EC, and clay were the most significant variables in the prediction of soil macronutrients. The SVR model was more accurate compared to the ANN and k-NN models. N values were estimated more accurately than K and P nutrients, in all the applied models.Research highlights: The accuracy of models among the test stages illustrated that using a single data set for investigation of model performance could be misleading. Therefore, the complete data set would be necessary for suitable evaluation of the model.