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Registro Completo |
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Biblioteca(s): |
Embrapa Agroindústria Tropical. |
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Data corrente: |
21/08/1997 |
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Data da última atualização: |
23/04/2019 |
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Autoria: |
CARVALHO, J. F. de. |
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Título: |
Modelos lineares e sua avaliação. |
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Ano de publicação: |
1982 |
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Fonte/Imprenta: |
São Carlos: Instituto de Ciências Matemáticas de São Carlos, USP, 1982. |
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Páginas: |
95 p. |
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Idioma: |
Português |
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Notas: |
Tese Mestrado |
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Conteúdo: |
Modelos lineares e sua avaliacao. |
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Palavras-Chave: |
Avaliacao; Modelo linear. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 00458nam a2200157 a 4500
001 1421407
005 2019-04-23
008 1982 bl uuuu m 00u1 u #d
100 1 $aCARVALHO, J. F. de
245 $aModelos lineares e sua avaliação.
260 $aSão Carlos: Instituto de Ciências Matemáticas de São Carlos, USP$c1982
300 $a95 p.
500 $aTese Mestrado
520 $aModelos lineares e sua avaliacao.
653 $aAvaliacao
653 $aModelo linear
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Registro original: |
Embrapa Agroindústria Tropical (CNPAT) |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Arroz e Feijão. Para informações adicionais entre em contato com cnpaf.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Arroz e Feijão. |
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Data corrente: |
23/04/2019 |
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Data da última atualização: |
12/02/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
MORAIS, P. A. de O.; SOUZA, D. M. de; CARVALHO, M. T. de M.; MADARI, B. E.; OLIVEIRA, A. E. de. |
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Afiliação: |
PEDRO AUGUSTO DE OLIVEIRA MORAIS, UFG; DIEGO MENDES DE SOUZA, CNPAF; MARCIA THAIS DE MELO CARVALHO, CNPAF; BEATA EMOKE MADARI, CNPAF; ANSELMO ELCANA DE OLIVEIRA, UFG. |
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Título: |
Predicting soil texture using image analysis. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
Microchemical Journal, v. 146, p. 455-463, May 2019. |
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ISSN: |
0026-265X |
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DOI: |
10.1016/j.microc.2019.01.009 |
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Idioma: |
Inglês |
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Conteúdo: |
Laboratory analysis of soil texture is laborious and not environmentally friendly. After sampling, another 56 h are required for the final report and the laboratory procedure employs hydrogen peroxide and sodium hydroxide as chemical dispersion agents. Therefore a new analytical method to predict and classify soil texture is proposed using digital image processing of soil samples (image segmentation) and multivariate image analysis (MIA). Digital images of 63 soil samples, sieved to<2 mm, were acquired. Clay and sand contents determined by the pipette method were used as standard values and, after image processing, particle contents in the measured size fractions were correlated to image data using PLS2 multivariate regression. In order to statistically account for the sampling and validation dataset multivariate statistics was evaluated in conjunction with bootstrapping analysis. The computer vision approach adopted for the recognition of soil textures based on soil images matched 100% of the classification predicted according to the standard method. The new method is low-cost, environment-friendly, nondestructive, and faster than the standard method. |
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Thesagro: |
Textura do Solo. |
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Thesaurus NAL: |
Clay; Computer vision; Digital images; Sand; Soil texture. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 01899naa a2200265 a 4500
001 2108430
005 2020-02-12
008 2019 bl uuuu u00u1 u #d
022 $a0026-265X
024 7 $a10.1016/j.microc.2019.01.009$2DOI
100 1 $aMORAIS, P. A. de O.
245 $aPredicting soil texture using image analysis.$h[electronic resource]
260 $c2019
520 $aLaboratory analysis of soil texture is laborious and not environmentally friendly. After sampling, another 56 h are required for the final report and the laboratory procedure employs hydrogen peroxide and sodium hydroxide as chemical dispersion agents. Therefore a new analytical method to predict and classify soil texture is proposed using digital image processing of soil samples (image segmentation) and multivariate image analysis (MIA). Digital images of 63 soil samples, sieved to<2 mm, were acquired. Clay and sand contents determined by the pipette method were used as standard values and, after image processing, particle contents in the measured size fractions were correlated to image data using PLS2 multivariate regression. In order to statistically account for the sampling and validation dataset multivariate statistics was evaluated in conjunction with bootstrapping analysis. The computer vision approach adopted for the recognition of soil textures based on soil images matched 100% of the classification predicted according to the standard method. The new method is low-cost, environment-friendly, nondestructive, and faster than the standard method.
650 $aClay
650 $aComputer vision
650 $aDigital images
650 $aSand
650 $aSoil texture
650 $aTextura do Solo
700 1 $aSOUZA, D. M. de
700 1 $aCARVALHO, M. T. de M.
700 1 $aMADARI, B. E.
700 1 $aOLIVEIRA, A. E. de
773 $tMicrochemical Journal$gv. 146, p. 455-463, May 2019.
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Embrapa Arroz e Feijão (CNPAF) |
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