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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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| Registros recuperados : 38 | |
| 6. |  | SOUSA, M. B. e; SILVA, K. J. D. e; ROCHA, M. de M.; NEVES, A. C. das. Estimativas de parâmetros genéticos em linhagens de feijão-caupi nos municípios de Balsas, MA e Primavera do Leste, MT. In: CONGRESSO NACIONAL DE FEIJÃO-CAUPI, 3., 2013, Recife. Feijão-Caupi como alternativa sustentável para os sistemas produtivos familiares e empresariais. Recife: IPA, 2013. CONAC 2012. Disponível em: http://www.conac2012.org/resumos/pdf/132a.pdf. Acesso em: 22 jul. 2013.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Meio-Norte. |
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| 7. | | SILVA, E. M. da; DINIZ, R. P.; SOUSA, M. B. e; BRAATZ, L. R.; OLIVEIRA, E. J. de. Geração de clones superiores de mandioca em progênies F1, S1 e S2 In: JORNADA CIENTÍFICA EMBRAPA MANDIOCA E FRUTICULTURA, 13., 2019. Foco e valor : resumos. Cruz das Almas, BA: Embrapa Mandioca e Fruticultura,2019. 119 p. il.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Mandioca e Fruticultura. |
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| 13. | | SOUSA, M. B. e; SILVA, K. J. D. e; ROCHA, M. de M.; MENEZES JUNIOR, J. A. de; LIMA, L. R. L. Adaptabilidade e estabilidade produtiva em linhagens elite de feijão-caupi de porte semiprostrado no Cerrado do Brasil. In: CONGRESSO NACIONAL DE FEIJÃO-CAUPI, 4., 2016, Sorriso. Feijão-caupi: avanços e desafios tecnológicos e de mercado: resumos. Brasília, DF: Embrapa, 2016. p. 133. Na publicação: Kaesel Jackson Damasceno-Silva.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Meio-Norte. |
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| 14. | | SOUSA, M. B. e; SILVA, K. J. D. e; ROCHA, M. de M.; MENEZES JUNIOR, J. A. de; LIMA, L. R. L. Adaptability and yield stability of cowpea elite lines of semi-prostrate growth habit in the cerrado biome. Revista Ciência Agronômica, v. 48, n. 5, Esp., p. 832-839, 2017.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
| Biblioteca(s): Embrapa Meio-Norte. |
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| 16. | | BISPO, V. R. de S.; SOUSA, M. B. e; SOUZA, G. D. de; CARVALHO, R. R. B. de; OLIVEIRA, L. A. de; OLIVEIRA, E. J. de. Classificação de genótipos de mandioca quanto à presença de compostos cianogênicos nas raízes via espectroscopia na região do visível (VIS) e infravermelho próximo (NIR). In: JORNADA CIENTÍFICA EMBRAPA MANDIOCA E FRUTICULTURA, 13., 2019. Foco e valor : resumos. Cruz das Almas, BA: Embrapa Mandioca e Fruticultura,2019. 119 p. il.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Mandioca e Fruticultura. |
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| 17. | | SILVA, P. P. dos S.; SOUSA, M. B. e; OLIVEIRA, E. J. de; MORGANTE, C. V.; OLIVEIRA, C. R. S. de; VIEIRA, S. L.; BOREL, J. C. Genome-wide association study of drought tolerance in cassava. Euphytica, v.217, n.60, 2021.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
| Biblioteca(s): Embrapa Mandioca e Fruticultura; Embrapa Semiárido. |
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| 18. | | SOUSA, M. B. e; SILVA, K. J. D. e; ROCHA, M. de M.; MENEZES JUNIOR, J. A. de; LIMA, L. R. L. Genotype by environment interaction in cowpea lines using GGE Biplot method. Revista Caatinga, Mossoró, v. 31, n. 1, p. 64-71, jan./mar. 2018.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
| Biblioteca(s): Embrapa Meio-Norte. |
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| 20. | | SOUSA, M. B. e; SILVA, K. J. D. e; ROCHA, M. de M.; MENEZES JUNIOR, J. A. de; LIMA, L. R. L. Interação genótipos x ambientes em linhagens-elite de feijão-caupi via método GGE Biplot. In: CONGRESSO NACIONAL DE FEIJÃO-CAUPI, 4., 2016, Sorriso. Feijão-caupi: avanços e desafios tecnológicos e de mercado: resumos. Brasília, DF: Embrapa, 2016. p. 179. Na publicação: KAESEL JACKSON DAMASCENO-SILVA.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Meio-Norte. |
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| Registros recuperados : 38 | |
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| Nenhum registro encontrado para a expressão de busca informada. |
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