Use of VIS-NIRS for land management classification with a support vector machine and prediction of soil organic carbon and other soil properties

The objective of this research was to investigate the effects of a long-term experiment on soil spectral properties and to develop prediction models of these properties (soil organic carbon (SOC), N, pH, Hh, P2O5, K2O, Ca, Mg, K, and Na content) from texturally homogeneous samples (loamy sand). To t...

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Autores principales:	Debaene,Guillaume, Pikuła,Dorota, Niedźwiecki,Jacek
Lenguaje:	English
Publicado:	Pontificia Universidad Católica de Chile. Facultad de Agronomía e Ingeniería Forestal 2014
Materias:	Manure near-infrared spectroscopy nitrogen fertilizer partial least square regression soil organic carbon Support Vector Machine
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-16202014000100003
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spelling	oai:scielo:S0718-162020140001000032014-10-10Use of VIS-NIRS for land management classification with a support vector machine and prediction of soil organic carbon and other soil propertiesDebaene,GuillaumePikuła,DorotaNiedźwiecki,Jacek Manure near-infrared spectroscopy nitrogen fertilizer partial least square regression soil organic carbon Support Vector Machine The objective of this research was to investigate the effects of a long-term experiment on soil spectral properties and to develop prediction models of these properties (soil organic carbon (SOC), N, pH, Hh, P2O5, K2O, Ca, Mg, K, and Na content) from texturally homogeneous samples (loamy sand). To this aim, chemometric techniques, such as partial least square (PLS) regression and support vector machine (SVM) classification, were used. Our results show that visible and near infrared spectroscopy (VIS-NIRS) is suitable for the prediction of properties of texturally homogeneous samples. The effects of fertilizer applications were sufficient to modify the soil chemical composition to a range suitable for using VIS-NIRS for calibration and modeling purposes. The best results were obtained for SOC and N content prediction using the full dataset with cross-validation (r² = 0.76, RMSECV = 0.04, RPD = 2.02 and r² = 0.81, RMSECV = 0.01, RPD = 2.20, respectively) and with an independent validation dataset (r² = 0.70, RMSEP = 0.04, RPD = 1.80 and r² = 0.73, RMSEP = 0.03, RPD = 1.22, for SOC and N content, respectively). The use of fertilizers and the type of crop rotation appear to have a significant impact on soil spectral properties; the SVM methodology with a linear kernel function was able to classify soil samples as functions of the applied doses of organic and inorganic fertilizers with 75% accuracy with cross-validation and the type of crop rotation with more than 90% accuracy with full validation of separate datasets.info:eu-repo/semantics/openAccessPontificia Universidad Católica de Chile. Facultad de Agronomía e Ingeniería ForestalCiencia e investigación agraria v.41 n.1 20142014-04-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-16202014000100003en10.4067/S0718-16202014000100003
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	Manure near-infrared spectroscopy nitrogen fertilizer partial least square regression soil organic carbon Support Vector Machine
spellingShingle	Manure near-infrared spectroscopy nitrogen fertilizer partial least square regression soil organic carbon Support Vector Machine Debaene,Guillaume Pikuła,Dorota Niedźwiecki,Jacek Use of VIS-NIRS for land management classification with a support vector machine and prediction of soil organic carbon and other soil properties
description	The objective of this research was to investigate the effects of a long-term experiment on soil spectral properties and to develop prediction models of these properties (soil organic carbon (SOC), N, pH, Hh, P2O5, K2O, Ca, Mg, K, and Na content) from texturally homogeneous samples (loamy sand). To this aim, chemometric techniques, such as partial least square (PLS) regression and support vector machine (SVM) classification, were used. Our results show that visible and near infrared spectroscopy (VIS-NIRS) is suitable for the prediction of properties of texturally homogeneous samples. The effects of fertilizer applications were sufficient to modify the soil chemical composition to a range suitable for using VIS-NIRS for calibration and modeling purposes. The best results were obtained for SOC and N content prediction using the full dataset with cross-validation (r² = 0.76, RMSECV = 0.04, RPD = 2.02 and r² = 0.81, RMSECV = 0.01, RPD = 2.20, respectively) and with an independent validation dataset (r² = 0.70, RMSEP = 0.04, RPD = 1.80 and r² = 0.73, RMSEP = 0.03, RPD = 1.22, for SOC and N content, respectively). The use of fertilizers and the type of crop rotation appear to have a significant impact on soil spectral properties; the SVM methodology with a linear kernel function was able to classify soil samples as functions of the applied doses of organic and inorganic fertilizers with 75% accuracy with cross-validation and the type of crop rotation with more than 90% accuracy with full validation of separate datasets.
author	Debaene,Guillaume Pikuła,Dorota Niedźwiecki,Jacek
author_facet	Debaene,Guillaume Pikuła,Dorota Niedźwiecki,Jacek
author_sort	Debaene,Guillaume
title	Use of VIS-NIRS for land management classification with a support vector machine and prediction of soil organic carbon and other soil properties
title_short	Use of VIS-NIRS for land management classification with a support vector machine and prediction of soil organic carbon and other soil properties
title_full	Use of VIS-NIRS for land management classification with a support vector machine and prediction of soil organic carbon and other soil properties
title_fullStr	Use of VIS-NIRS for land management classification with a support vector machine and prediction of soil organic carbon and other soil properties
title_full_unstemmed	Use of VIS-NIRS for land management classification with a support vector machine and prediction of soil organic carbon and other soil properties
title_sort	use of vis-nirs for land management classification with a support vector machine and prediction of soil organic carbon and other soil properties
publisher	Pontificia Universidad Católica de Chile. Facultad de Agronomía e Ingeniería Forestal
publishDate	2014
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-16202014000100003
work_keys_str_mv	AT debaeneguillaume useofvisnirsforlandmanagementclassificationwithasupportvectormachineandpredictionofsoilorganiccarbonandothersoilproperties AT piku322adorota useofvisnirsforlandmanagementclassificationwithasupportvectormachineandpredictionofsoilorganiccarbonandothersoilproperties AT nied378wieckijacek useofvisnirsforlandmanagementclassificationwithasupportvectormachineandpredictionofsoilorganiccarbonandothersoilproperties
_version_	1714202154225369088

Use of VIS-NIRS for land management classification with a support vector machine and prediction of soil organic carbon and other soil properties

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