Developing an intelligent system for the prediction of soil properties with a portable mid-infrared instrument

Portable mid-infrared (MIR) technology is well suited for the provision of detailed and inexpensive information on key soil properties for optimum soil management. This technology requires prior complex multivariate modelling. In this manuscript, we propose an intelligent system approach based on portable MIR spectroscopy data modelled by machine learning techniques to predict total carbon (TC), nitrogen (TN), cation exchange capacity (CEC), clay, silt and exchangeable sodium (Na+) in 458 representative soil samples from Australia. To find the best performing algorithm to be incorporated into the intelligent system, we evaluated the performance of a number of machine learning techniques, including Gaussian process regression (GPR), Random Forest (RF), M5 rules, and Bagging and Decision Trees, which were compared with traditional Partial Least Squares Regression (PLSR). Generally, all the models showed a satisfactory performance. However, GPR stood out (according to statistical tests), especially for the predictions of clay, Na+ and CEC, obtaining root mean squared error differences between the GPR and PLSR techniques of -0.27%, -0.40% and -0.63 cmol(+) kg(-1), respectively. This study evidenced the feasibility of using the GPR technique to model data provided by a portable MIR instrument for the prediction of soil properties, providing the best, most stable and reliable results. (C) 2018 IAgrE. Published by Elsevier Ltd. All rights reserved.