Using interpretable fuzzy rule-based models for the estimation of soil organic carbon from VNIR/SWIR spectra and soil texture

In this paper, the use of a novel evolutionary fuzzy rule-based system (FRBS) for the prediction of Soil Organic Carbon from visible, near-infrared, and short-wave infrared (VNIR/SWIR) spectra and the textural information as additional predictor is examined. Compared to other techniques, the proposed model generates a compact set of rules with a high interpretation degree, mapping local input to local output regions. This is achieved through an evolutionary learning procedure which is applied to establish linguistic rules and assist in the interpretation of the association between spectra and the target property. The rule base may be also decomposed into texture-specific sets of rules, allowing a more detailed analysis on a per textural class basis. These intrinsic properties enable the development of spectral prototype signatures and sparse feature utilization histograms at different levels of aggregation, i.e. per textural class and/or output region. The proposed model is applied to the LUCAS topsoil database comprised of roughly 18,000 mineral samples across 23 European Union member-states. We first demonstrate the enhanced interpretation capabilities of our fuzzy approach, which can assist in the extraction of fruitful knowledge governing the association between soil properties and VNIR/SWIR spectra. The model is then compared with other contemporary approaches, namely PLS, SVM, and Cubist. The results indicate that our approach produced compact and interpretable results with fair prediction accuracies (equivalent with the best approach).