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Registro Completo |
Biblioteca(s): |
Embrapa Hortaliças; Embrapa Instrumentação. |
Data corrente: |
09/05/2024 |
Data da última atualização: |
11/06/2024 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
FERREIRA, L. C.; CARVALHO, I. C. B.; JORGE, L. A. de C.; QUEZADO-DUVAL, A. M.; ROSSATO, M. |
Afiliação: |
UNIVERSITY OF BRASILIA; UNIVERSITY OF BRASILIA; LUCIO ANDRE DE CASTRO JORGE, CNPDIA; ALICE MARIA QUEZADO DUVAL, CNPH; UNIVERSITY OF BRASILIA. |
Título: |
Hyperspectral imaging for the detection of plant pathogens in seeds: recent developments and challenges. |
Ano de publicação: |
2024 |
Fonte/Imprenta: |
Frontiers in Plant Science, v. 15, 1387925, 2024. |
Páginas: |
9 p. |
DOI: |
10.3389/fpls.2024.1387925 |
Idioma: |
Inglês |
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 |
Palavras-Chave: |
Machine learning; Nematode; Phytopathogen; Seedborne. |
Thesagro: |
Segurança Alimentar; Semente. |
Thesaurus Nal: |
Artificial intelligence; Plant pathogens; Seed quality. |
Categoria do assunto: |
-- X Pesquisa, Tecnologia e Engenharia |
Marc: |
LEADER 02547naa a2200301 a 4500 001 2164150 005 2024-06-11 008 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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Registro original: |
Embrapa Instrumentação (CNPDIA) |
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| Acesso ao texto completo restrito à biblioteca da Embrapa Soja. Para informações adicionais entre em contato com valeria.cardoso@embrapa.br. |
Registro Completo
Biblioteca(s): |
Embrapa Soja. |
Data corrente: |
29/12/2016 |
Data da última atualização: |
11/01/2017 |
Tipo da produção científica: |
Capítulo em Livro Técnico-Científico |
Autoria: |
O'HARA, G. W.; ZILLI, J. E.; POOLE, P. S.; HUNGRIA, M. |
Afiliação: |
MARIANGELA HUNGRIA DA CUNHA, CNPSO. |
Título: |
Taxonomy and physiology of rhizobia. |
Ano de publicação: |
2016 |
Fonte/Imprenta: |
In: HOWIESON, J. G.; DILWORTH, M. J. (Ed.). Working with rhizobia. Canberra: Australian Centre for International Agricultural Research, 2016. |
Páginas: |
p. 125-144. |
Idioma: |
Inglês |
Thesagro: |
Fixação de nitrogênio. |
Thesaurus NAL: |
Nitrogen fixation. |
Categoria do assunto: |
S Ciências Biológicas |
Marc: |
LEADER 00596naa a2200181 a 4500 001 2059626 005 2017-01-11 008 2016 bl uuuu u00u1 u #d 100 1 $aO'HARA, G. W. 245 $aTaxonomy and physiology of rhizobia.$h[electronic resource] 260 $c2016 300 $ap. 125-144. 650 $aNitrogen fixation 650 $aFixação de nitrogênio 700 1 $aZILLI, J. E. 700 1 $aPOOLE, P. S. 700 1 $aHUNGRIA, M. 773 $tIn: HOWIESON, J. G.; DILWORTH, M. J. (Ed.). Working with rhizobia. Canberra: Australian Centre for International Agricultural Research, 2016.
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