Soil microbial dynamics prediction using machine learning regression methods

Soil microbial dynamics is significant for the soil productivity. The present study explores the application of machine learning based regression methods in the prediction of selected soil microbial dynamics, including bacterial population (BP), phosphate solubilization (PS), and enzyme activities. An experiment was designed in a salt medium with rock phosphate inoculated with the solubilizing microorganism to measure the PS, BP, and 1-Aminocyclopropane-1-carboxylate (ACC) deaminase activity at a different temperature, pH, and incubation period. The artificial neural network (ANN), support vector regression (SVR), Wang and Mendel's (WM) - fuzzy inference systems (FIS), and subtractive clustering (SC)-FIS methods have been applied in the estimation of PS, BP, and ACC deaminase activity using the experimental conditions. The performance of four regression methods has been evaluated in the terms of the coefficient of determination (R2), root mean square error (RMSE), and correlation coefficient (ρ). The SC-FIS method has better performance than the rest three methods in the prediction of each of the soil microbial dynamics (R2 of 0.99 in the prediction of PS).