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Registro Completo |
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Biblioteca(s): |
Embrapa Gado de Leite. |
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Data corrente: |
27/01/2014 |
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Data da última atualização: |
22/10/2014 |
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Tipo da produção científica: |
Orientação de Tese de Pós-Graduação |
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Autoria: |
SOUZA, R. C. S. N. P. |
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Afiliação: |
ROBERTO CARLOS SOARES NALON PEREIR SOUZA, UFJF. |
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Título: |
Algoritmos online baseados em vetores suporte para regressão clássica e ortogonal. |
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Ano de publicação: |
2013 |
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Fonte/Imprenta: |
Juiz de Fora: Universidade Federal de Juiz de Fora, 2013. |
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Idioma: |
Português |
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Notas: |
Tese (Mestrado em Ciência da Computação). Universidade Federal de Juiz de Fora. Juiz de Fora - MG. Co-orientador: Wagner Antonio Arbex, Embrapa Gado de Leite. |
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Palavras-Chave: |
Ciencia da computação; Inteligência artificial. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 00593nam a2200133 a 4500
001 1977582
005 2014-10-22
008 2013 bl uuuu m 00u1 u #d
100 1 $aSOUZA, R. C. S. N. P.
245 $aAlgoritmos online baseados em vetores suporte para regressão clássica e ortogonal.
260 $aJuiz de Fora: Universidade Federal de Juiz de Fora$c2013
500 $aTese (Mestrado em Ciência da Computação). Universidade Federal de Juiz de Fora. Juiz de Fora - MG. Co-orientador: Wagner Antonio Arbex, Embrapa Gado de Leite.
653 $aCiencia da computação
653 $aInteligência artificial
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Registro original: |
Embrapa Gado de Leite (CNPGL) |
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Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital; Embrapa Trigo. |
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Data corrente: |
28/09/2018 |
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Data da última atualização: |
28/09/2018 |
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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: |
BARBEDO, J. G. A.; GUARIENTI, E. M.; TIBOLA, C. S. |
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Afiliação: |
JAYME G.A. BARBEDO; ELIANA MARIA GUARIENTI, CNPT; CASIANE SALETE TIBOLA, CNPT. |
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Título: |
Detection of sprout damage in wheat kernels using NIR hyperspectral imaging. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Biosystems Engineering, v. 175, p. 124-132, 2018. |
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ISSN: |
1537-5110 |
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DOI: |
https://doi.org/10.1016/j.biosystemseng.2018.09.012 |
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Idioma: |
Inglês
Português |
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Conteúdo: |
The use of near-infrared (NIR) hyperspectral imaging (HSI) for detecting sprout damage in wheat kernels was investigated. Experiments were carried out to determine which spectral bands had the best potential for discriminating between sound and sprouted kernels. Two wavelengths were selected and combined into an index that was used to indicate the presence or absence of sprouting. Experiments with three wheat cultivars revealed that the proposed method is effective in identifying kernels for which the germination process has initiated, achieving 100% accuracy for the samples used in this study. It was also observed an imperfect correlation with the Falling Number (grain quality), making it challenging to accurately determine the degree of germination, especially if sprouts are not yet clearly visible. These results confirm the usefulness of the near-infrared spectral range for detecting chemical alterations in wheat kernels, as well as the fact that most information is usually contained in a few specific bands within such range. |
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Palavras-Chave: |
Germinação pré-colheita; Hyperspectral imaging; Image processing; Processamento de imagem; Sprout damage. |
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Thesagro: |
Germinação; Trigo. |
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Thesaurus NAL: |
Germination; Image analysis; Wheat. |
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Categoria do assunto: |
--
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/183708/1/ID44366-2018v175p124BiosystEng.pdf
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Marc: |
LEADER 01898naa a2200289 a 4500
001 2096599
005 2018-09-28
008 2018 bl uuuu u00u1 u #d
022 $a1537-5110
024 7 $ahttps://doi.org/10.1016/j.biosystemseng.2018.09.012$2DOI
100 1 $aBARBEDO, J. G. A.
245 $aDetection of sprout damage in wheat kernels using NIR hyperspectral imaging.$h[electronic resource]
260 $c2018
520 $aThe use of near-infrared (NIR) hyperspectral imaging (HSI) for detecting sprout damage in wheat kernels was investigated. Experiments were carried out to determine which spectral bands had the best potential for discriminating between sound and sprouted kernels. Two wavelengths were selected and combined into an index that was used to indicate the presence or absence of sprouting. Experiments with three wheat cultivars revealed that the proposed method is effective in identifying kernels for which the germination process has initiated, achieving 100% accuracy for the samples used in this study. It was also observed an imperfect correlation with the Falling Number (grain quality), making it challenging to accurately determine the degree of germination, especially if sprouts are not yet clearly visible. These results confirm the usefulness of the near-infrared spectral range for detecting chemical alterations in wheat kernels, as well as the fact that most information is usually contained in a few specific bands within such range.
650 $aGermination
650 $aImage analysis
650 $aWheat
650 $aGerminação
650 $aTrigo
653 $aGerminação pré-colheita
653 $aHyperspectral imaging
653 $aImage processing
653 $aProcessamento de imagem
653 $aSprout damage
700 1 $aGUARIENTI, E. M.
700 1 $aTIBOLA, C. S.
773 $tBiosystems Engineering$gv. 175, p. 124-132, 2018.
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Registro original: |
Embrapa Trigo (CNPT) |
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