A soft-computing approach to estimate soil electrical conductivity

Soil apparent electrical conductivity (ECa) is an indirect and rapid measurement for soil salinity, but because of its dependency on some physical and chemical properties of soil in addition to salinity, consideration of the soil extract EC is preferred for monitoring soil salinity, especially in semi-arid areas, though its measurement needs laboratory processes. This study, therefore, sought to develop a multivariable model to estimate the soil ECe from soil ECa, temperature, moisture content, bulk density, and clay percentage, using radial basis function (RBF) artificial neural network (ANN). In the first step, a set of tests was performed in laboratory in Box-Behnken design (BBD) to train the RBF-ANN. The developed RBF estimated the soil ECe with R-2 = 0.99 and RMSE = 0.005 dS.m(-1). Moreover, a quadratic response surface model (RSM) was also developed to compare with the RBF model. The sensitivity analysis revealed that ECa, moisture, bulk density, and temperature had the maximum to minimum effect on the estimation of soil ECe, respectively. In the second step, the RBF and RSM models were validated by another dataset obtained from three sites located in a semi-arid area. They were applied in-field with a multi-sensor portable device. The R-2 and RMSE of the estimation of ECe by the RBF were equal to 0.801 and 0.350 dS.m(-1), respectively. While, R-2 and RMSE of the RSM model were 0.735 and 0.439 dS.m(-1), respectively. The results of the study indicated excellent ability of the RBF-ANN in the rapid and precise estimation of soil ECe. (C) 2021 IAgrE. Published by Elsevier Ltd. All rights reserved.

Automated summary

This study aimed to develop a multivariable model to estimate soil ECe using radial basis function (RBF) artificial neural network (ANN). Tests were conducted in the laboratory to train and validate the RBF-ANN, showing excellent ability in the rapid and precise estimation of soil ECe.