An evolutionary fuzzy rule-based system applied to the prediction of soil organic carbon from soil spectral libraries

Abstract

The recent efforts for obtaining vast soil spectral libraries covering a significant part of the spatial and compositional variability of soils have underscored the need for accurate and interpretable models. Herein, the application of an evolutionary Fuzzy Rule-based System (FRBS) named DECO3RUM (Differential Evolution based Cooperative and Competing learning of Compact Rule-based Models) for the prediction of soil properties from visible, near-infrared, and shortwave-infrared (VNIR–SWIR) laboratory spectral data obtained from the LUCAS topsoil database is investigated. FRBSs model the input–output relation with fuzzy logic statements, offering an enhanced interpretability degree for the experts over classical rule-based systems and other black box models. The proposed algorithm was statistically compared with other state of the art approaches and was found to outperform other global models, while being statistically similar with local approaches that offer lower interpretation capabilities.