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Registro Completo |
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Biblioteca(s): |
Embrapa Soja. |
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Data corrente: |
02/12/2009 |
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Data da última atualização: |
20/04/2010 |
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Autoria: |
CARRÃO-PANIZZI, M. C.; PIPOLO, A. E.; ARIAS, C. A. A.; KASTER, M.; OLIVEIRA, M. F. de; OLIVEIRA, M. A. de; TOLEDO, J. F. F. de; MOREIRA, J. U. V.; CARNEIRO, G. E. de S. |
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Afiliação: |
MERCEDES CONCORDIA CARRAO PANIZZI, CNPSo; ANTONIO EDUARDO PIPOLO, CNPSo; CARLOS ALBERTO ARRABAL ARIAS, CNPSo; MILTON KASTER, CNPSo; MARCELO FERNANDES DE OLIVEIRA, CNPSo; MARCELO ALVARES DE OLIVEIRA, CNPSo; JOSE FRANCISCO FERRAZ DE TOLEDO, CNPSo; JOSE UBIRAJARA VIEIRA MOREIRA, CNPSo; GERALDO ESTEVAM DE SOUZA CARNEIRO, CNPSo. |
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Título: |
Breeding specialty soybean cultivars for processing and value-added utilization at Embrapa in Brazil. |
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Ano de publicação: |
2009 |
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Fonte/Imprenta: |
In: WORLD SOYBEAN RESEARCH CONFERENCE, 8., 2009, Beijing. Developing a global soy blueprint for a safe secure and sustainable supply: abstracts. Beijing: Chinese Academy of Agricultural Sciences: Institute of Crop Science, 2009. p. 113, ref. O221. WSRC 2009. Editado por Lijuan Qiu, Rongxia Guan, Jian Jin, Qijan Song, Shuntang Guo, Wenbin Li, Yuanchao Wang, Tianfu Han, Xiaobing Liu, Deyue Yu, Lianzhou Jiang, Deliang Peng. |
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Idioma: |
Inglês |
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Palavras-Chave: |
Lipoxigenase; Null lipoxygenase; Seed size. |
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Thesagro: |
Glycine Max; Melhoramento genético vegetal; Proteína; Soja. |
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Thesaurus Nal: |
Lipoxygenase; Plant breeding; Proteins; Soybeans. |
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Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
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Marc: |
LEADER 01329naa a2200337 a 4500
001 1576987
005 2010-04-20
008 2009 bl uuuu u00u1 u #d
100 1 $aCARRÃO-PANIZZI, M. C.
245 $aBreeding specialty soybean cultivars for processing and value-added utilization at Embrapa in Brazil.
260 $c2009
650 $aLipoxygenase
650 $aPlant breeding
650 $aProteins
650 $aSoybeans
650 $aGlycine Max
650 $aMelhoramento genético vegetal
650 $aProteína
650 $aSoja
653 $aLipoxigenase
653 $aNull lipoxygenase
653 $aSeed size
700 1 $aPIPOLO, A. E.
700 1 $aARIAS, C. A. A.
700 1 $aKASTER, M.
700 1 $aOLIVEIRA, M. F. de
700 1 $aOLIVEIRA, M. A. de
700 1 $aTOLEDO, J. F. F. de
700 1 $aMOREIRA, J. U. V.
700 1 $aCARNEIRO, G. E. de S.
773 $tIn: WORLD SOYBEAN RESEARCH CONFERENCE, 8., 2009, Beijing. Developing a global soy blueprint for a safe secure and sustainable supply: abstracts. Beijing: Chinese Academy of Agricultural Sciences: Institute of Crop Science, 2009. p. 113, ref. O221. WSRC 2009. Editado por Lijuan Qiu, Rongxia Guan, Jian Jin, Qijan Song, Shuntang Guo, Wenbin Li, Yuanchao Wang, Tianfu Han, Xiaobing Liu, Deyue Yu, Lianzhou Jiang, Deliang Peng.
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Registro original: |
Embrapa Soja (CNPSO) |
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Tipo/Formato |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Instrumentação. Para informações adicionais entre em contato com cnpdia.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Hortaliças; Embrapa Instrumentação. |
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Data corrente: |
09/05/2024 |
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Data da última atualização: |
09/05/2024 |
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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: |
FERREIRA, L. C.; CARVALHO, I. C. B.; JORGE, L. A. de C.; QUEZADO-DUVAL, A. M.; ROSSATO, M. |
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Afiliação: |
UNIVERSITY OF BRASILIA; UNIVERSITY OF BRASILIA; LUCIO ANDRE DE CASTRO JORGE, CNPDIA; ALICE MARIA QUEZADO DUVAL, CNPH; UNIVERSITY OF BRASILIA. |
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Título: |
Hyperspectral imaging for the detection of plant pathogens in seeds: recent developments and challenges. |
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Ano de publicação: |
2024 |
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Fonte/Imprenta: |
Frontiers in Plant Science, v. 15, 1387925, 2024. |
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Páginas: |
9 p. |
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DOI: |
10.3389/fpls.2024.1387925 |
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Idioma: |
Inglês |
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Conteúdo: |
Food security, a critical concern amid global population growth, faces challenges in sustainable agricultural production due to significant yield losses caused by plant diseases, with a multitude of them caused by seedborne plant pathogen. With the expansion of the international seed market with global movement of this propagative plant material, and considering that about 90% of economically important crops grown from seeds, seed pathology emerged as an important discipline. Seed health testing is presently part of quality analysis and carried out by seed enterprises and governmental institutions looking forward to exclude a new pathogen in a country or site. The development of seedborne pathogens detection methods has been following the plant pathogen detection and diagnosis advances, from the use of cultivation on semi-selective media, to antibodies and DNA-based techniques. Hyperspectral imaging (HSI) associated with artificial intelligence can be considered the new frontier for seedborne pathogen detection with high accuracy in discriminating infected from healthy seeds. The development of the process consists of standardization of methods and protocols with the validation of spectral signatures for presence and incidence of contamined seeds. Concurrently, epidemiological studies correlating this information with disease outbreaks would help in determining the acceptable thresholds of seed contamination. Despite the high costs of equipment and the necessity for interdisciplinary collaboration, it is anticipated that health seed certifying programs and seed suppliers will benefit from the adoption of HSI techniques in the near future. MenosFood security, a critical concern amid global population growth, faces challenges in sustainable agricultural production due to significant yield losses caused by plant diseases, with a multitude of them caused by seedborne plant pathogen. With the expansion of the international seed market with global movement of this propagative plant material, and considering that about 90% of economically important crops grown from seeds, seed pathology emerged as an important discipline. Seed health testing is presently part of quality analysis and carried out by seed enterprises and governmental institutions looking forward to exclude a new pathogen in a country or site. The development of seedborne pathogens detection methods has been following the plant pathogen detection and diagnosis advances, from the use of cultivation on semi-selective media, to antibodies and DNA-based techniques. Hyperspectral imaging (HSI) associated with artificial intelligence can be considered the new frontier for seedborne pathogen detection with high accuracy in discriminating infected from healthy seeds. The development of the process consists of standardization of methods and protocols with the validation of spectral signatures for presence and incidence of contamined seeds. Concurrently, epidemiological studies correlating this information with disease outbreaks would help in determining the acceptable thresholds of seed contamination. Despite the high costs of equipment and the necessity for interdis... Mostrar Tudo |
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Palavras-Chave: |
Machine learning; Nematode; Phytopathogen; Seedborne. |
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Thesagro: |
Segurança Alimentar; Semente. |
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Thesaurus NAL: |
Artificial intelligence; Plant pathogens; Seed quality. |
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Categoria do assunto: |
--
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 02547naa a2200301 a 4500
001 2164150
005 2024-05-09
008 2024 bl uuuu u00u1 u #d
024 7 $a10.3389/fpls.2024.1387925$2DOI
100 1 $aFERREIRA, L. C.
245 $aHyperspectral imaging for the detection of plant pathogens in seeds$brecent developments and challenges.$h[electronic resource]
260 $c2024
300 $a9 p.
520 $aFood security, a critical concern amid global population growth, faces challenges in sustainable agricultural production due to significant yield losses caused by plant diseases, with a multitude of them caused by seedborne plant pathogen. With the expansion of the international seed market with global movement of this propagative plant material, and considering that about 90% of economically important crops grown from seeds, seed pathology emerged as an important discipline. Seed health testing is presently part of quality analysis and carried out by seed enterprises and governmental institutions looking forward to exclude a new pathogen in a country or site. The development of seedborne pathogens detection methods has been following the plant pathogen detection and diagnosis advances, from the use of cultivation on semi-selective media, to antibodies and DNA-based techniques. Hyperspectral imaging (HSI) associated with artificial intelligence can be considered the new frontier for seedborne pathogen detection with high accuracy in discriminating infected from healthy seeds. The development of the process consists of standardization of methods and protocols with the validation of spectral signatures for presence and incidence of contamined seeds. Concurrently, epidemiological studies correlating this information with disease outbreaks would help in determining the acceptable thresholds of seed contamination. Despite the high costs of equipment and the necessity for interdisciplinary collaboration, it is anticipated that health seed certifying programs and seed suppliers will benefit from the adoption of HSI techniques in the near future.
650 $aArtificial intelligence
650 $aPlant pathogens
650 $aSeed quality
650 $aSegurança Alimentar
650 $aSemente
653 $aMachine learning
653 $aNematode
653 $aPhytopathogen
653 $aSeedborne
700 1 $aCARVALHO, I. C. B.
700 1 $aJORGE, L. A. de C.
700 1 $aQUEZADO-DUVAL, A. M.
700 1 $aROSSATO, M.
773 $tFrontiers in Plant Science$gv. 15, 1387925, 2024.
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Embrapa Instrumentação (CNPDIA) |
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