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 | Acesso ao texto completo restrito à biblioteca da Embrapa Solos. Para informações adicionais entre em contato com cnps.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Solos. |
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Data corrente: |
01/09/2021 |
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Data da última atualização: |
08/11/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
B - 1 |
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Autoria: |
SANTANA, F. B. de; OTANI, S. K.; SOUZA, A. M. de; POPPI, R. J. |
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Afiliação: |
FELIPE B. DE SANTANA, UNICAMP; SANDRO K. OTANI, UNICAMP; ANDRE MARCELO DE SOUZA, CNPS; RONEI J. POPPI, UNICAMP. |
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Título: |
Comparison of PLS and SVM models for soil organic matter and particle size using vis-NIR spectral libraries. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Geoderma Regional, v. 27, e00436, Dec. 2021. |
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DOI: |
https://doi.org/10.1016/j.geodrs.2021.e00436 |
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Idioma: |
Inglês |
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Conteúdo: |
In this study a systematic comparison was carried out to assess differences on the accuracy between partial least squares (PLS) and support vector machine (SVM) regression algorithms in soil organic matter and particle size determinations using vis-NIR spectroscopy. The comparison consisted in investigating the influence on the size of calibration set on the external validation set accuracy. For this purpose, three vis-NIR soil libraries containing 14,212, 15,330 and 42,471 soil samples were used to determine sand, clay, and SOM content, respectively. To increase the variability of the results obtained, each calibration subset was randomly generated 49 times and for each iteration a PLS, SVM-Linear and SVM-RBF (radial basis function) regression models were built. These calibration subsets were composed by 250, 1000, 2000, 5000 and 8000 or 10,000 samples. |
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Palavras-Chave: |
Carbono orgânico do solo; Machine learning; Molecular spectroscopy; Soil spectral library. |
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Thesagro: |
Textura do Solo. |
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Thesaurus NAL: |
Soil organic carbon; Soil texture. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 01674naa a2200253 a 4500
001 2133985
005 2021-11-08
008 2021 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.geodrs.2021.e00436$2DOI
100 1 $aSANTANA, F. B. de
245 $aComparison of PLS and SVM models for soil organic matter and particle size using vis-NIR spectral libraries.$h[electronic resource]
260 $c2021
520 $aIn this study a systematic comparison was carried out to assess differences on the accuracy between partial least squares (PLS) and support vector machine (SVM) regression algorithms in soil organic matter and particle size determinations using vis-NIR spectroscopy. The comparison consisted in investigating the influence on the size of calibration set on the external validation set accuracy. For this purpose, three vis-NIR soil libraries containing 14,212, 15,330 and 42,471 soil samples were used to determine sand, clay, and SOM content, respectively. To increase the variability of the results obtained, each calibration subset was randomly generated 49 times and for each iteration a PLS, SVM-Linear and SVM-RBF (radial basis function) regression models were built. These calibration subsets were composed by 250, 1000, 2000, 5000 and 8000 or 10,000 samples.
650 $aSoil organic carbon
650 $aSoil texture
650 $aTextura do Solo
653 $aCarbono orgânico do solo
653 $aMachine learning
653 $aMolecular spectroscopy
653 $aSoil spectral library
700 1 $aOTANI, S. K.
700 1 $aSOUZA, A. M. de
700 1 $aPOPPI, R. J.
773 $tGeoderma Regional$gv. 27, e00436, Dec. 2021.
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