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Registro Completo |
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Biblioteca(s): |
Embrapa Mandioca e Fruticultura. |
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Data corrente: |
08/12/2022 |
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Data da última atualização: |
08/12/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
CARVALHO, R. R. B. de; CORTES, D. F. M.; SOUSA, M. B. e; OLIVEIRA, L. A. de; OLIVEIRA, E. J. de. |
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Afiliação: |
RAVENA ROCHA BESSA DE CARVALHO, UNIVERSIDADE FEDERAL DO RECÔNCAVO DA BAHIA; DIEGO FERNANDO MARMOLEJO CORTES; MASSAINE BANDEIRA E SOUSA; LUCIANA ALVES DE OLIVEIRA, CNPMF; EDER JORGE DE OLIVEIRA, CNPMF. |
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Título: |
Image-based phenotyping of cassava roots for diversity studies and carotenoids prediction. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
PLoS One, v.17, n.1, e0263326, January, 2022. |
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ISSN: |
1932-6203 |
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DOI: |
https://doi.org/10.1371/journal.pone.0263326 |
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Idioma: |
Inglês |
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Conteúdo: |
Phenotyping to quantify the total carotenoids content (TCC) is sensitive, time-consuming, tedious, and costly. The development of high-throughput phenotyping tools is essential for screening hundreds of cassava genotypes in a short period of time in the biofortification program. This study aimed to (i) use digital images to extract information on the pulp color of cassava roots and estimate correlations with TCC, and (ii) select predictive models for TCC using colorimetric indices. Red, green and blue images were captured in root samples from 228 biofortified genotypes and the difference in color was analyzed using L*, a*, b*, hue and chroma indices from the International Commission on Illumination (CIELAB) color system and lightness. Colorimetric data were used for principal component analysis (PCA), correlation and for developing prediction models for TCC based on regression and machine learning. A high positive correlation between TCC and the variables b* (r = 0.90) and chroma (r = 0.89) was identified, while the other correlations were median and negative, and the L* parameter did not present a significant correlation with TCC. In general, the accuracy of most prediction models (with all variables and only the most important ones) was high (R2 ranging from 0.81 to 0.94). However, the artificial neural network prediction model presented the best predictive ability (R2 = 0.94), associated with the smallest error in the TCC estimates (root-mean-square error of 0.24). The structure of the studied population revealed five groups and high genetic variability based on PCA regarding colorimetric indices and TCC. Our results demonstrated that the use of data obtained from digital image analysis is an economical, fast, and effective alternative for the development of TCC phenotyping tools in cassava roots with high predictive ability. MenosPhenotyping to quantify the total carotenoids content (TCC) is sensitive, time-consuming, tedious, and costly. The development of high-throughput phenotyping tools is essential for screening hundreds of cassava genotypes in a short period of time in the biofortification program. This study aimed to (i) use digital images to extract information on the pulp color of cassava roots and estimate correlations with TCC, and (ii) select predictive models for TCC using colorimetric indices. Red, green and blue images were captured in root samples from 228 biofortified genotypes and the difference in color was analyzed using L*, a*, b*, hue and chroma indices from the International Commission on Illumination (CIELAB) color system and lightness. Colorimetric data were used for principal component analysis (PCA), correlation and for developing prediction models for TCC based on regression and machine learning. A high positive correlation between TCC and the variables b* (r = 0.90) and chroma (r = 0.89) was identified, while the other correlations were median and negative, and the L* parameter did not present a significant correlation with TCC. In general, the accuracy of most prediction models (with all variables and only the most important ones) was high (R2 ranging from 0.81 to 0.94). However, the artificial neural network prediction model presented the best predictive ability (R2 = 0.94), associated with the smallest error in the TCC estimates (root-mean-square error of 0.24). The st... Mostrar Tudo |
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Thesagro: |
Mandioca. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1149383/1/journal.pone.0263326.pdf
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Marc: |
LEADER 02501naa a2200205 a 4500
001 2149383
005 2022-12-08
008 2022 bl uuuu u00u1 u #d
022 $a1932-6203
024 7 $ahttps://doi.org/10.1371/journal.pone.0263326$2DOI
100 1 $aCARVALHO, R. R. B. de
245 $aImage-based phenotyping of cassava roots for diversity studies and carotenoids prediction.$h[electronic resource]
260 $c2022
520 $aPhenotyping to quantify the total carotenoids content (TCC) is sensitive, time-consuming, tedious, and costly. The development of high-throughput phenotyping tools is essential for screening hundreds of cassava genotypes in a short period of time in the biofortification program. This study aimed to (i) use digital images to extract information on the pulp color of cassava roots and estimate correlations with TCC, and (ii) select predictive models for TCC using colorimetric indices. Red, green and blue images were captured in root samples from 228 biofortified genotypes and the difference in color was analyzed using L*, a*, b*, hue and chroma indices from the International Commission on Illumination (CIELAB) color system and lightness. Colorimetric data were used for principal component analysis (PCA), correlation and for developing prediction models for TCC based on regression and machine learning. A high positive correlation between TCC and the variables b* (r = 0.90) and chroma (r = 0.89) was identified, while the other correlations were median and negative, and the L* parameter did not present a significant correlation with TCC. In general, the accuracy of most prediction models (with all variables and only the most important ones) was high (R2 ranging from 0.81 to 0.94). However, the artificial neural network prediction model presented the best predictive ability (R2 = 0.94), associated with the smallest error in the TCC estimates (root-mean-square error of 0.24). The structure of the studied population revealed five groups and high genetic variability based on PCA regarding colorimetric indices and TCC. Our results demonstrated that the use of data obtained from digital image analysis is an economical, fast, and effective alternative for the development of TCC phenotyping tools in cassava roots with high predictive ability.
650 $aMandioca
700 1 $aCORTES, D. F. M.
700 1 $aSOUSA, M. B. e
700 1 $aOLIVEIRA, L. A. de
700 1 $aOLIVEIRA, E. J. de
773 $tPLoS One$gv.17, n.1, e0263326, January, 2022.
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Registro original: |
Embrapa Mandioca e Fruticultura (CNPMF) |
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Registro Completo
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Biblioteca(s): |
Embrapa Trigo. |
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Data corrente: |
29/02/2016 |
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Data da última atualização: |
21/12/2017 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
DOSSA, A. A.; FERREIRA, P. E. P.; TOMM, G. O. |
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Afiliação: |
ALVARO AUGUSTO DOSSA, CNPT; PAULO ERNANI PERES FERREIRA, CNPT; GILBERTO OMAR TOMM, CNPT. |
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Título: |
O que dificulta o aumento da área de canola no Brasil? |
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Ano de publicação: |
2014 |
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Fonte/Imprenta: |
In: SIMPÓSIO LATINO AMERICANO DE CANOLA, 1., 2014, Passo Fundo. Anais... Brasília, DF: Embrapa, 2014. |
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Páginas: |
5 p. |
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Descrição Física: |
Poster 3. |
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Idioma: |
Português |
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Palavras-Chave: |
SLAC. |
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Thesagro: |
Brassica Napus; Transferência de Tecnologia. |
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Thesaurus NAL: |
canola. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/140291/1/ID43574-2014SLACPoster3.pdf
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Marc: |
LEADER 00580nam a2200181 a 4500
001 2038948
005 2017-12-21
008 2014 bl uuuu u00u1 u #d
100 1 $aDOSSA, A. A.
245 $aO que dificulta o aumento da área de canola no Brasil?$h[electronic resource]
260 $aIn: SIMPÓSIO LATINO AMERICANO DE CANOLA, 1., 2014, Passo Fundo. Anais... Brasília, DF: Embrapa$c2014
300 $a5 p.$cPoster 3.
650 $acanola
650 $aBrassica Napus
650 $aTransferência de Tecnologia
653 $aSLAC
700 1 $aFERREIRA, P. E. P.
700 1 $aTOMM, G. O.
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Embrapa Trigo (CNPT) |
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