Data Mining Algorithms for Prediction of Soil Organic Matter and Clay Based on Vis-NIR Spectroscopy

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Data Mining Algorithms for Prediction of Soil Organic Matter and Clay Based on Vis-NIR Spectroscopy

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dc.contributor.author Teixeira, Sandro
dc.contributor.author Guimarães, Alaine Margarete
dc.contributor.author Proença, Carlos A.
dc.contributor.author Rocha, José Carlos Ferreira da
dc.contributor.author Caires, Eduardo Fávero
dc.date.accessioned 2020-08-13T17:10:07Z
dc.date.available 2020-08-13T17:10:07Z
dc.date.issued 2014-04-01
dc.identifier.issn 2165-8846
dc.identifier.uri http://hdl.handle.net/123456789/947
dc.description.abstract Organic matter (OM) amount and clay content in the soil are important constituents in the sustainability of agricultural systems. The methods used for OM and clay analyses in laboratories are laborious, time consuming and use require reagents that pollute the environment. The use of reflectance in the visible and near infrared (Vis-NIR) can be highly viable in soil analysis identifying the attributes contents in a cleaner and quicker way. There is still no general model specifying the wavelengths to be used for neither each variable being analyzed nor a well-defined methodology to be applied. The aim of this study was to apply all the classification algorithms available in the Weka software trying to find the best correlations between spectral data in the Vis and NIR spectrums, separately, and OM and clay content in the soil. As result, the clay prediction had a strong correlation with both Vis and NIR spectrum. OM prediction presented a determination coefficient greater than 0.7 but brought an error that cannot be overlooked. Lazy KStar algorithm showed to be more adequate to mine the data presenting the higher determination coefficients and the lower errors. The best results for both OM and clay were obtained when correlated with the Vis spectrum. This suggests that it is possible to predict OM and clay using only the Vis spectrum. pt_BR
dc.language.iso en pt_BR
dc.rights Attribution-NoDerivatives 4.0 International *
dc.rights.uri http://creativecommons.org/licenses/by-nd/4.0/ *
dc.subject Soil properties pt_BR
dc.subject Vis-NIR pt_BR
dc.subject Spectral reflectance pt_BR
dc.subject Classifier algorithms pt_BR
dc.title Data Mining Algorithms for Prediction of Soil Organic Matter and Clay Based on Vis-NIR Spectroscopy pt_BR
dc.type Article pt_BR


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