Soil type classification and estimation of soil properties using support vector machines

Abstract

Quantitative techniques for prediction and classification in soil survey are developing rapidly. The paper introduces application of Support Vector Machines in the estimate of values of soil properties and soil type classification based on known values of particular chemical and physical properties in sampled profiles. Comparison of proposed approach with other linear regression models shows that Support Vector Machines are the model of choice for estimation of values of physical properties and pH value when using only chemical data inputs. They are also the model of choice in the cases where chemical data inputs are not strongly correlated to the estimated property. However, in classification task, their performance is similar to that of the other compared methods, with an increasing advantage when a data set consists of a small number of training samples per each soil type.