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Registro Completo |
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Biblioteca(s): |
Embrapa Agroindústria de Alimentos. |
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Data corrente: |
16/02/1993 |
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Data da última atualização: |
21/02/2011 |
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Autoria: |
LORENZ, R. J.; KULP, K. ed. |
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Título: |
Handbook of cereal science and technology. |
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Ano de publicação: |
1991 |
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Fonte/Imprenta: |
New York: M.Dekker, 1991. |
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Páginas: |
882 p. |
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Série: |
(Food Science and Technology: aseries of monographs, textbooks, and references books, 41). |
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ISBN: |
0-8247-8358-1 |
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Idioma: |
Inglês |
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Conteúdo: |
Wheat. Corn: production, processing, and utilization. Barley. Oats. Sorghum. The millets. Rice: production, processing, and utilization. Rye. Triticale: production and utilization. Wild Rice: processing and utilization. Cereal proteins: composition of their major fractions and methods for identification. Carbohydrate composition of cereal grains. Cereal lipids. Minor constituents of cereals. Quality evaluation of cereals and cereal products. Breads and yeast-leavened barkery foods. soft wheat products. Breakfast cereals. Pasta: raw materials and processing. Cereal-based snack foods. Malted cereals: production and use. Cereal enrichment. Nutritional quality of cereals and cereal-based foods. Ethanol production from cereal grains. |
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Palavras-Chave: |
Cereais; Utilizacao. |
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Thesagro: |
Arroz; Milho; Processamento; Produção; Sorgo; Trigo; Triticale. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 01418nam a2200265 a 4500
001 1410826
005 2011-02-21
008 1991 bl uuuu 00u1 u #d
020 $a0-8247-8358-1
100 1 $aLORENZ, R. J.
245 $aHandbook of cereal science and technology.
260 $aNew York: M.Dekker$c1991
300 $a882 p.
490 $a(Food Science and Technology: aseries of monographs, textbooks, and references books, 41).
520 $aWheat. Corn: production, processing, and utilization. Barley. Oats. Sorghum. The millets. Rice: production, processing, and utilization. Rye. Triticale: production and utilization. Wild Rice: processing and utilization. Cereal proteins: composition of their major fractions and methods for identification. Carbohydrate composition of cereal grains. Cereal lipids. Minor constituents of cereals. Quality evaluation of cereals and cereal products. Breads and yeast-leavened barkery foods. soft wheat products. Breakfast cereals. Pasta: raw materials and processing. Cereal-based snack foods. Malted cereals: production and use. Cereal enrichment. Nutritional quality of cereals and cereal-based foods. Ethanol production from cereal grains.
650 $aArroz
650 $aMilho
650 $aProcessamento
650 $aProdução
650 $aSorgo
650 $aTrigo
650 $aTriticale
653 $aCereais
653 $aUtilizacao
700 1 $aKULP, K. ed.
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Registro original: |
Embrapa Agroindústria de Alimentos (CTAA) |
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Biblioteca |
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Tipo/Formato |
Classificação |
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Registro |
Volume |
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URL |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Pantanal. Para informações adicionais entre em contato com cpap.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Pantanal. |
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Data corrente: |
12/03/2018 |
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Data da última atualização: |
17/08/2020 |
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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: |
WEBER, F. de L.; WEBER, V. A. de M.; MENEZES, G. V.; OLIVEIRA JUNIOR, A. da S.; ALVES, D. A.; OLIVEIRA, M. V. M. de; MATSUBARA, E. T.; PISTORI, H.; ABREU, U. G. P. de. |
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Afiliação: |
FABRICIO DE LIMA WEBER, Universidade Estadual de Mato Grosso do Sul, Aquidauana; VANESSA APARECIDA DE MORAES WEBER, Universidade Católica Dom Bosco, Campo Grande; GEAZY VILHARVA MENEZES, Universidade Federal do Mato Grosso do Sul, Campo Grande; ADAIR DA SILVA OLIVEIRA JUNIOR, Universidade Federal do Mato Grosso do Sul, Campo Grande; DANIELA ARESTIDES ALVES, Universidade Estadual de Mato Grosso do Sul, Aquidauana; MARCUS VINICIUS MORAIS DE OLIVEIRA, Universidade Estadual de Mato Grosso do Sul, Aquidauana; EDSON TAKASHI MATSUBARA, Universidade Federal do Mato Grosso do Sul, Campo Grande; HEMERSON PISTORI, Universidade Católica Dom Bosco, Campo Grande; URBANO GOMES PINTO DE ABREU, CPAP. |
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Título: |
Recognition of Pantaneira cattle breed using computer vision and convolutional neural networks. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Computers and Electronics in Agriculture, v. 175, 105548, p. 1-9, 2020. |
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DOI: |
https://doi.org/10.1016/j.compag.2020.105548 |
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Idioma: |
Inglês |
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Conteúdo: |
The objective of this paper is to provide recognition for Pantaneira cattle breed using Convolutional Neural Networks (CNN). Fifty-one animals from the Aquidauana Pantaneira cattle Center (NUBOPAN) were studied. The center is located in the Midwest region of Brazil. Four monitoring cameras were distributed in the fences and took 27,849 images of Pantaneira cattle breed using different angles and positions. The following three CNN architectures were used for the experiment: DenseNet-201, Resnet50 and Inception-Resnet-V. All networks were submitted to 10-fold stratified cross-validation over 50 epochs. The results showed an accuracy of 99% in all networks, which is encouraging for future research. |
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Thesagro: |
Gado de Corte; Rede; Sistema de Informação. |
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Thesaurus NAL: |
Cattle; Computer vision; Neural networks. |
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Categoria do assunto: |
L Ciência Animal e Produtos de Origem Animal |
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Marc: |
LEADER 01634naa a2200301 a 4500
001 2088972
005 2020-08-17
008 2020 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.compag.2020.105548$2DOI
100 1 $aWEBER, F. de L.
245 $aRecognition of Pantaneira cattle breed using computer vision and convolutional neural networks.$h[electronic resource]
260 $c2020
520 $aThe objective of this paper is to provide recognition for Pantaneira cattle breed using Convolutional Neural Networks (CNN). Fifty-one animals from the Aquidauana Pantaneira cattle Center (NUBOPAN) were studied. The center is located in the Midwest region of Brazil. Four monitoring cameras were distributed in the fences and took 27,849 images of Pantaneira cattle breed using different angles and positions. The following three CNN architectures were used for the experiment: DenseNet-201, Resnet50 and Inception-Resnet-V. All networks were submitted to 10-fold stratified cross-validation over 50 epochs. The results showed an accuracy of 99% in all networks, which is encouraging for future research.
650 $aCattle
650 $aComputer vision
650 $aNeural networks
650 $aGado de Corte
650 $aRede
650 $aSistema de Informação
700 1 $aWEBER, V. A. de M.
700 1 $aMENEZES, G. V.
700 1 $aOLIVEIRA JUNIOR, A. da S.
700 1 $aALVES, D. A.
700 1 $aOLIVEIRA, M. V. M. de
700 1 $aMATSUBARA, E. T.
700 1 $aPISTORI, H.
700 1 $aABREU, U. G. P. de
773 $tComputers and Electronics in Agriculture$gv. 175, 105548, p. 1-9, 2020.
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Embrapa Pantanal (CPAP) |
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