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Registro Completo
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Biblioteca(s): |
Embrapa Meio Ambiente. |
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Data corrente: |
19/07/2021 |
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Data da última atualização: |
19/07/2021 |
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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: |
BIANCHINI, V. de J. M.; MASCARIN, G. M.; SILVA, L. C. A. S.; ARTHUR, V.; CARSTENSEN, J. M.; BOELT, B.; SILVA, C. B. da. |
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Afiliação: |
VITOR DE JESUS MARTINS BIANCHINI, CENA-USP; GABRIEL MOURA MASCARIN, CNPMA; LÚCIA CRISTINA APARECIDA SANTOS SILVA, CENA-USP; VALTER ARTHUR, CENA-USP; JENS MICHAEL CARSTENSEN, Technical University of Denmark; BIRTE BOELT, Aarhus University; CLÍSSIA BARBOZA DA SILVA, CENA-USP. |
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Título: |
Multispectral and X-ray images for characterization of Jatropha curcas L. seed quality. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Plant Methods, v. 17, n. 1, article 9, 2021. |
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ISSN: |
Multispectral and X-ray images for characterization of Jatropha curcas L. seed quality |
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DOI: |
https://doi.org/10.1186/s13007-021-00709-6 |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract: The use of non-destructive methods with less human interference is of great interest in agricultural industry and crop breeding. Modern imaging technologies enable the automatic visualization of multi-parameter for characterization of biological samples, reducing subjectivity and optimizing the analysis process. Furthermore, the combination of two or more imaging techniques has contributed to discovering new physicochemical tools and interpreting datasets in real time. We present a new method for automatic characterization of seed quality based on the combination of multispectral and X-ray imaging technologies. We proposed an approach using X-ray images to investigate internal tissues because seed surface profile can be negatively affected, but without reaching important internal regions of seeds. An oilseed plant (Jatropha curcas) was used as a model species, which also serves as a multi-purposed crop of economic importance worldwide. Our studies included the application of a normalized canonical discriminant analyses (nCDA) algorithm as a supervised transformation building method to obtain spatial and spectral patterns on different seedlots. We developed classification models using reflectance data and X-ray classes based on linear discriminant analysis (LDA). The classification models, individually or combined, showed high accuracy (> 0.96) using reflectance at 940 nm and X-ray data to predict quality traits such as normal seedlings, abnormal seedlings and dead seeds. Multispectral and X-ray imaging have a strong relationship with seed physiological performance. Reflectance at 940 nm and X-ray data can efficiently predict seed quality attributes. These techniques can be alternative methods for rapid, efficient, sustainable and non-destructive characterization of seed quality in the future, overcoming the intrinsic subjectivity of the conventional seed quality analysis. MenosAbstract: The use of non-destructive methods with less human interference is of great interest in agricultural industry and crop breeding. Modern imaging technologies enable the automatic visualization of multi-parameter for characterization of biological samples, reducing subjectivity and optimizing the analysis process. Furthermore, the combination of two or more imaging techniques has contributed to discovering new physicochemical tools and interpreting datasets in real time. We present a new method for automatic characterization of seed quality based on the combination of multispectral and X-ray imaging technologies. We proposed an approach using X-ray images to investigate internal tissues because seed surface profile can be negatively affected, but without reaching important internal regions of seeds. An oilseed plant (Jatropha curcas) was used as a model species, which also serves as a multi-purposed crop of economic importance worldwide. Our studies included the application of a normalized canonical discriminant analyses (nCDA) algorithm as a supervised transformation building method to obtain spatial and spectral patterns on different seedlots. We developed classification models using reflectance data and X-ray classes based on linear discriminant analysis (LDA). The classification models, individually or combined, showed high accuracy (> 0.96) using reflectance at 940 nm and X-ray data to predict quality traits such as normal seedlings, abnormal seedlings and... Mostrar Tudo |
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Thesagro: |
Controle de Qualidade; Jatropha Curcas; Pinhão de Purga; Semente. |
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Thesaurus NAL: |
Artificial intelligence; Jatropha; Radiography; Seed quality. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/224541/1/Mascarin-Multispectral-xray-2021.pdf
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Marc: |
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022 $aMultispectral and X-ray images for characterization of Jatropha curcas L. seed quality
024 7 $ahttps://doi.org/10.1186/s13007-021-00709-6$2DOI
100 1 $aBIANCHINI, V. de J. M.
245 $aMultispectral and X-ray images for characterization of Jatropha curcas L. seed quality.$h[electronic resource]
260 $c2021
520 $aAbstract: The use of non-destructive methods with less human interference is of great interest in agricultural industry and crop breeding. Modern imaging technologies enable the automatic visualization of multi-parameter for characterization of biological samples, reducing subjectivity and optimizing the analysis process. Furthermore, the combination of two or more imaging techniques has contributed to discovering new physicochemical tools and interpreting datasets in real time. We present a new method for automatic characterization of seed quality based on the combination of multispectral and X-ray imaging technologies. We proposed an approach using X-ray images to investigate internal tissues because seed surface profile can be negatively affected, but without reaching important internal regions of seeds. An oilseed plant (Jatropha curcas) was used as a model species, which also serves as a multi-purposed crop of economic importance worldwide. Our studies included the application of a normalized canonical discriminant analyses (nCDA) algorithm as a supervised transformation building method to obtain spatial and spectral patterns on different seedlots. We developed classification models using reflectance data and X-ray classes based on linear discriminant analysis (LDA). The classification models, individually or combined, showed high accuracy (> 0.96) using reflectance at 940 nm and X-ray data to predict quality traits such as normal seedlings, abnormal seedlings and dead seeds. Multispectral and X-ray imaging have a strong relationship with seed physiological performance. Reflectance at 940 nm and X-ray data can efficiently predict seed quality attributes. These techniques can be alternative methods for rapid, efficient, sustainable and non-destructive characterization of seed quality in the future, overcoming the intrinsic subjectivity of the conventional seed quality analysis.
650 $aArtificial intelligence
650 $aJatropha
650 $aRadiography
650 $aSeed quality
650 $aControle de Qualidade
650 $aJatropha Curcas
650 $aPinhão de Purga
650 $aSemente
700 1 $aMASCARIN, G. M.
700 1 $aSILVA, L. C. A. S.
700 1 $aARTHUR, V.
700 1 $aCARSTENSEN, J. M.
700 1 $aBOELT, B.
700 1 $aSILVA, C. B. da
773 $tPlant Methods$gv. 17, n. 1, article 9, 2021.
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