Soil Classification From Large Imagery Databases Using a Neuro-Fuzzy Classifier

In this paper, we propose a neuro-fuzzy (NF) classification technique to determine various soil classes from large imagery soil databases. The technique looks at the feature-wise degree of belongings of the imagery databases to obtainable soil classes using a fuzzification method. The fuzzification method builds a membership matrix with an element count equal to the mathematical product of the number of data records and soil classes present. The elements of this matrix are the input to a neural network model. We apply our technique to three UCI databases, namely, Statlog Landsat Satellite, Forest Covertype, and Wilt for soil classification. The paper aims to find out soil classes using the proposed technique, and then compare its performance with four well-known classification algorithms, namely, radial basis function network, k-nearest neighbor, support vector machine, and adaptive NF inference system. Numerous measures, for example, root-mean-square error, kappa statistic, accuracy, false positive rate, true positive rate, precision, recall, F-measure, and area under the curve, are used for evaluating the quantitative analysis of the simulated results. All these evaluation measures approve the supremacy of the proposed NF method.