Comparison of machine learning algorithms for soil type classification

Abstract

Machine learning algorithm can be applied for automating soil type classification. This paper compares several machine learning algorithms for classifying soil type. Algorithms that involve support vector machine (SVM), neural network, decision tree, and naive bayesian are proposed and assessed for this classification. Soil dataset is taken from the real data. Simulation is run by using RapidMiner Studio. The performance observed is the accuracy. The result shows that SVM, with the use of linear function kernel, outperforms the others algorithms. The SVM best accuracy is 82.35%.