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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. |
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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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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: |
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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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| Registros recuperados : 6 | |
| 2. |  | REZENDE, C. C.; FRASCA, L. L. de M.; SILVA, M. A.; PIRES, R. A. C.; LANNA, A. C.; FILIPPI, M. C. C. de; NASCENTE, A. S. Physiological and agronomic characteristics of the common bean as affected by multifunctional microorganisms. Semina: Ciências Agrárias, v. 42, n. 2, p. 599-618, mar./abr. 2021.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 1 |
| Biblioteca(s): Embrapa Arroz e Feijão. |
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| 3. | | REZENDE, C. C.; FRASCA, L. L. de M.; SILVA, M. A.; PIRES, R. A. C.; FILIPPI, M. C. C. de; LANNA, A. C.; FERREIRA, E. P. de B.; NASCENTE, A. S. Influência de microrganismos multifuncionais na produtividade do feijoeiro-comum. In: SEMINÁRIO JOVENS TALENTOS, 15., 2021, Santo Antônio de Goiás. Resumos... Brasília, DF: Embrapa; Santo Antônio de Goiás: Embrapa Arroz e Feijão, 2021. p. 59. Evento online.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Arroz e Feijão. |
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| 4. | | SILVA, M. A.; FRASCA, L. L.; REZENDE, C. C.; PIRES, R. A. C.; DUARTE, J. R. de M; LACERDA, M. C.; FILIPPI, M. C. C. de; LANNA, A. C.; FERREIRA, E. P. de B.; NASCENTE, A. S. Mix de plantas de cobertura e coinoculação de microrganismos multifuncionais para aumento da produtividade de soja. In: SEMINÁRIO JOVENS TALENTOS, 15., 2021, Santo Antônio de Goiás. Resumos... Brasília, DF: Embrapa; Santo Antônio de Goiás: Embrapa Arroz e Feijão, 2021. p. 76. Evento online.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Arroz e Feijão. |
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| 5. | | REZENDE, C. C.; NASCENTE, A. S.; SILVA, M. A.; FRASCA, L. L. de M.; PIRES, R. A. C.; FILIPPI, M. C. C. de; LANNA, A. C.; SILVA, J. F. A. e. Physiological and agronomic performance of common bean treated with multifunctional microorganisms. Revista Brasileira de Ciências Agrárias, v. 16, n. 4, e838, 2021.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 1 |
| Biblioteca(s): Embrapa Arroz e Feijão. |
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| 6. | | ANTUNES, A. C.; MONTANARIN, A.; GRÄBIN, D. M.; MONTEIRO, E. C. dos S. M.; PINHO, F. F. de; ALVARENGA, G. C.; AHUMADA, J.; WALLACE, R. B.; RAMALHO, E. E.; BARNETT, A. P. A.; BAGER, A.; LOPES, A. M. C.; KEUROGHLIAN, A.; GIROUX, A.; HERRERA, A. M.; CORREA, A. P. de A.; MEIGA, A. Y.; JÁCOMO, A. T. de A.; BARBAN, A. de B.; ANTUNES, A.; COELHO, A. G. de A.; CAMILO, A. R.; NUNES, A. V.; GOMES, A. C. dos S. M.; ZANZINI, A. C. da S.; CASTRO, A. B.; DESBIEZ, A. L. J.; FIGUEIREDO, A.; THOISY, B. de; GAUZENS, B.; OLIVEIRA, B. T.; LIMA, C. A. de; PERES, C. A.; DURIGAN, C. C.; BROCARDO, C. R.; ROSA, C. A.; ZÁRATE CASTAÑEDA, C.; MONTEZA MORENO, C. M.; CARNICER, C.; TRINCA, C. T.; POLLI, D. J.; FERRAZ, D. da S.; LANE, D. F.; ROCHA, D. G. da; BARCELOS, D. C.; AUZ, D.; ROSA, D. C. P.; SILVA, D. A.; SILVÉRIO, D. V.; EATON, D. P.; NAKANO OLIVEIRA, E.; VENTICINQUE, E.; JUNIOR, E. C.; MENDONÇA, E. N.; VIEIRA, E. M.; ISASI CATALÁ, E.; FISCHER, E.; CASTRO, E. P.; OLIVEIRA, E. G.; MELO, F. R. de; MUNIZ, F. de L.; ROHE, F.; BACCARO, F. B.; MICHALSKI, F.; PAIM, F. P.; SANTOS, F.; ANAGUANO, F.; PALMEIRA, F. B. L.; REIS, F. da S.; AGUIAR SILVA, F. H.; BATISTA, G. de A. B.; ZAPATA RÍOS, G.; FORERO MEDINA, G.; NETO, G. de S. F.; ALVES, G. B.; AYALA, G.; PEDERSOLI, G. H. P.; EL BIZRI, HANI R.; PRADO, H. A.; MOZERLE, H. B.; COSTA, H. C. M.; LIMA, I. J.; PALACIOS, J.; ASSIS, J. de R.; BOUBLI, J. P.; METZGER, J. P.; TEIXEIRA, J. V.; MIRANDA, J. M. D.; POLISAR, J.; SALVADOR, J.; BORGES ALMEIDA, K.; DIDIER, K.; PEREIRA, K. D. de L.; TORRALVO, K.; GAJAPERSAD, K.; SILVEIRA, L.; MAIOLI, L. U.; MARACAHIPES SANTOS, L.; VALENZUELA, L.; BENAVALLI, L.; FLETCHER, L.; PAOLUCCI, L. N.; ZANZINI, L. P.; DA SILVA, L. Z.; RODRIGUES, L. C. R.; BENCHIMOL, M.; OLIVEIRA, M. A.; LIMA, M.; DA SILVA, M. B.; SANTOS JUNIOR, M. A. dos; VISCARRA, M.; COHN HAFT, M.; ABRAHAMS, M. I.; BENEDETTI, M. A.; MARMONTEL, M.; HIRT, M. R.; TÔRRES, N. M.; CRUZ JUNIOR, O. F.; ALVAREZ LOAYZA, P.; JANSEN, P.; PRIST, P. R.; BRANDO, P. M.; PERÔNICO, P. B.; LEITE, R. do N.; RABELO, R. M.; SOLLMANN, R.; BELTRÃO MENDES, R.; FERREIRA, R. A. F.; COUTINHO, R.; OLIVEIRA, R. da C.; ILHA, R.; HILÁRIO, R. R.; PIRES, R. A. P.; SAMPAIO, R.; MOREIRA, R. da S.; BOTERO ARIAS, R.; MARTINEZ, R. V.; NÓBREGA, R. A. de A.; FADINI, R. F.; MORATO, R. G.; CARNEIRO, R. L.; ALMEIDA, R. P. S.; RAMOS, R. M.; SCHAUB, R.; DORNAS, R.; CUEVA, RUBÉN; ROLIM, S.; LAURINDO, S.; ESPINOSA, S.; FERNANDES, T. N.; SANAIOTTI, T. M.; ALVIM, T. H. G.; DORNAS, TIAGO TEIXEIRA; PIÑA, T. E. N.; ANDRADE, V. L. C.; SANTIAGO, W. T. V.; MAGNUSSON, W. E.; CAMPOS, Z.; RIBEIRO, M. C. Amazonia Camtrap: a data set of mammal, bird, and reptile species recorded with camera traps in the Amazon forest. Ecology, v. 103, n. 9, p. e3738, 2022. Datar Paper.| Biblioteca(s): Embrapa Pantanal. |
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| Registros recuperados : 6 | |
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