teste1

Abstract

Considered a quality indicator, carbon constitutes an important attribute in the productive capacity of the soil. However the traditional methodologies used for determining carbon cause environmental problems due to the use of chemical reagents. The replacement of this procedure by others that generate little or no amount of toxic waste has been considered important. Spectroscopy is one of the promising techniques in Precision Agriculture for soil analysis and can be used to estimate carbon content. Among its benefits, highlights the sample preservation, no consumption of reagents, and their efficiency acquiring data from a large number of samples. The aim of this work was to contribute to determine a regression model able to predict the carbon content in soil samples using spectroscopy in the visible and near infrared region. The Machine Learning SVM technique available in the WEKA software was used to create the model. Because of their generalization ability SVM has been considered a better alternative than the other methods of multivariate regression. Two sets of soil samples collected in the Campos Gerais region were used to the experiments. The results evaluation was based on the forecast errors and the correlation coefficients between the values carbon content predicted by the model. Correlation coefficients ranging from 0.84 to 0.90 were found. It was concluded that the NIRS-vis spectroscopy combined with SVM technique can be recommended as an alternative to conventional methods for carbon analysis in the soil.