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| 11. |  | SILVA, B. A. da; NOGUEIRA, J. L.; VIEIRA, E. S. N.; PANOBIANCO, M. Critérios para condução do teste de tetrazólio em sementes de araucária. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 51, n. 1, p. 61-68, jan. 2016.| Biblioteca(s): Embrapa Florestas; Embrapa Unidades Centrais. |
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| 16. | | SÁ, F. P. de; BELNIAKI, A. C.; PANOBIANCO, M.; GABIRA, M. M.; KRATZ, D.; LIMA, E. A. de; WENDLING, I.; MAGALHAES, W. L. E. Peach palm residue compost as substrate for Bactris gasipaes self-sustaining seedlings production. International Journal of Recycling of Organic Waste in Agriculture, v. 9, n. 2, p. 183-192, 2020.| Biblioteca(s): Embrapa Florestas. |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Florestas. Para informações adicionais entre em contato com cnpf.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
04/12/2023 |
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Data da última atualização: |
08/12/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 3 |
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Autoria: |
MICHELON, T. B.; VIEIRA, E. S. N.; PANOBIANCO, M. |
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Afiliação: |
THOMAS B. MICHELON, FEDERAL UNIVERSITY OF PARANÁ; ELISA SERRA NEGRA VIEIRA, CNPF; MARISTELA PANOBIANCO, FEDERAL UNIVERSITY OF PARANÁ. |
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Título: |
Spectral imaging and chemometrics applied at phenotyping in seed science studies: a systematic review. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Seed Science Research, v. 33, p. 9-22, 2023. |
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DOI: |
https://doi.org/10.1017/ S0960258523000028 |
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Idioma: |
Português |
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Conteúdo: |
Abstract The evaluation of the genetic quality of a seed lot is crucial for the quality control process in its production and commercialization, as well as in the identification of superior genotypes and the verification of the correct crossing in plant breeding programmes. Current techniques, based on the identification of seed morphological characteristics, require skilled analysts, while biochemical methods are time-consuming and costly. The application of spectral imaging analysis, which combines digital imaging with spectroscopy, is gaining ground as a fast, accurate and non-destructive method. The success of this technique is closely linked to chemometric techniques, which use statistical and mathematical tools in data processing. The aim of the work was to evaluate the main procedures in terms of spectral image analysis and chemometric procedures applied in seed phenotyping and its practical application. A systematic review was conducted using the PRISMA methodology, in which a total of 1304 articles were identified and screened to the inclusion of 44 articles pertaining to the scope. It was concluded that spectral image analysis has a high ability to classify seeds of different genotypes (93.33%) in a range of situations: between cultivars; hybrids and progenitors; and hybrids and lines, as well as in the separation of coated seeds. Accurate classification can be obtained by different strategies, such as the choice of the equipment type, the spectrum range and extra features, guided by the characteristics of the species, as well as in the choice of algorithms and dimensionality reduction procedures for the optimization of models when there is a large amount of data. Despite the fact that the practical application of this technique in seed phenotyping still needs to be developed for use in laboratories with large volumes of analyses, lots, genotypes and harvests. Research has been accelerated to overcome the practical challenges of this method, as seen in works using model update algorithms, online classification systems, and real-time classification maps. Thus, there are strong indications that the application of multispectral image analysis will reach the routine of seed analysis laboratories. MenosAbstract The evaluation of the genetic quality of a seed lot is crucial for the quality control process in its production and commercialization, as well as in the identification of superior genotypes and the verification of the correct crossing in plant breeding programmes. Current techniques, based on the identification of seed morphological characteristics, require skilled analysts, while biochemical methods are time-consuming and costly. The application of spectral imaging analysis, which combines digital imaging with spectroscopy, is gaining ground as a fast, accurate and non-destructive method. The success of this technique is closely linked to chemometric techniques, which use statistical and mathematical tools in data processing. The aim of the work was to evaluate the main procedures in terms of spectral image analysis and chemometric procedures applied in seed phenotyping and its practical application. A systematic review was conducted using the PRISMA methodology, in which a total of 1304 articles were identified and screened to the inclusion of 44 articles pertaining to the scope. It was concluded that spectral image analysis has a high ability to classify seeds of different genotypes (93.33%) in a range of situations: between cultivars; hybrids and progenitors; and hybrids and lines, as well as in the separation of coated seeds. Accurate classification can be obtained by different strategies, such as the choice of the equipment type, the spectrum range and extra ... Mostrar Tudo |
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Palavras-Chave: |
Classificação; Deep learning; Espectroscopia; Hyperspectral imaging; Imagem hiperespectral; Machine learning; Multispectral imaging; Seed classification. |
|
Thesagro: |
Semente. |
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Thesaurus NAL: |
Spectroscopy. |
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Categoria do assunto: |
K Ciência Florestal e Produtos de Origem Vegetal |
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Marc: |
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001 2159100
005 2023-12-08
008 2023 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1017/ S0960258523000028$2DOI
100 1 $aMICHELON, T. B.
245 $aSpectral imaging and chemometrics applied at phenotyping in seed science studies$ba systematic review.$h[electronic resource]
260 $c2023
520 $aAbstract The evaluation of the genetic quality of a seed lot is crucial for the quality control process in its production and commercialization, as well as in the identification of superior genotypes and the verification of the correct crossing in plant breeding programmes. Current techniques, based on the identification of seed morphological characteristics, require skilled analysts, while biochemical methods are time-consuming and costly. The application of spectral imaging analysis, which combines digital imaging with spectroscopy, is gaining ground as a fast, accurate and non-destructive method. The success of this technique is closely linked to chemometric techniques, which use statistical and mathematical tools in data processing. The aim of the work was to evaluate the main procedures in terms of spectral image analysis and chemometric procedures applied in seed phenotyping and its practical application. A systematic review was conducted using the PRISMA methodology, in which a total of 1304 articles were identified and screened to the inclusion of 44 articles pertaining to the scope. It was concluded that spectral image analysis has a high ability to classify seeds of different genotypes (93.33%) in a range of situations: between cultivars; hybrids and progenitors; and hybrids and lines, as well as in the separation of coated seeds. Accurate classification can be obtained by different strategies, such as the choice of the equipment type, the spectrum range and extra features, guided by the characteristics of the species, as well as in the choice of algorithms and dimensionality reduction procedures for the optimization of models when there is a large amount of data. Despite the fact that the practical application of this technique in seed phenotyping still needs to be developed for use in laboratories with large volumes of analyses, lots, genotypes and harvests. Research has been accelerated to overcome the practical challenges of this method, as seen in works using model update algorithms, online classification systems, and real-time classification maps. Thus, there are strong indications that the application of multispectral image analysis will reach the routine of seed analysis laboratories.
650 $aSpectroscopy
650 $aSemente
653 $aClassificação
653 $aDeep learning
653 $aEspectroscopia
653 $aHyperspectral imaging
653 $aImagem hiperespectral
653 $aMachine learning
653 $aMultispectral imaging
653 $aSeed classification
700 1 $aVIEIRA, E. S. N.
700 1 $aPANOBIANCO, M.
773 $tSeed Science Research$gv. 33, p. 9-22, 2023.
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