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| 1. |  | SILVA, L. K. X.; BARROS, D. V.; LOURENÇO JUNIOR, J. de B.; SILVA, A. O. A. da; SOUSA, J. S. de; FATURI, C.; PANTOJA, M. H. de A.; BOTTA, D.; GARCIA, A. R. Temperaturas de superfície escrotal avaliadas por termografia de infravermelho e qualidade seminal de touros bubalinos em condições de clima tropical úmido. In: REUNIÃO DA ASSOCIAÇÃO BRASILEIRA DE ANDROLOGIA ANIMAL, 2., 2017, Uberlândia, MG. Anais... Corumbá: Embrapa Pantanal, 2017. p. 187-190. (Embrapa Pantanal. Documentos, 146)| Biblioteca(s): Embrapa Pecuária Sudeste. |
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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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Circulação/Nível: |
A - 1 |
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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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| 161. | | 5153958, ANNALS OF MATHEMATICAL STATISTICS, Institute of Mathematical Statistics, Ann Arbor, Mich., US
Biblioteca(s): Catálogo Coletivo de Periódicos Embrapa; Embrapa Unidades Centrais. | |
| 162. | | 0905093, ANUARIO ESTATISTICO DO AMAPA, Secretaria de Planejamento e Coordenacao, Macapa, AP
Biblioteca(s): Catálogo Coletivo de Periódicos Embrapa; Embrapa Amapá; Embrapa Amazônia Oriental. | |
| 163. | | 0350129, ARCHIV FUR TIERZUCHT, Akademi Verlag, Dummerstof-Alemanha
Biblioteca(s): Catálogo Coletivo de Periódicos Embrapa; Embrapa Suínos e Aves. | |
| 164. | | 0600633, AUSTRALIAN SEPTORIA NEWSLETTER, Wheat Industry Research Council of Australia, Australia
Biblioteca(s): Catálogo Coletivo de Periódicos Embrapa; Embrapa Trigo. | |
| 165. | | 0900360, BAHIA RURAL, Cooperativa Central Instituto de Pecuaria da Bahia, Salvador, BA
Biblioteca(s): Catálogo Coletivo de Periódicos Embrapa; Embrapa Semiárido. | |
| 167. | | 2450182, BIOTECHNOLOGY AND DEVELOPMENT MONITOR, Dept. of International Relations and Public International Law of University Amsterdam, Amsterdam-Holanda
Biblioteca(s): Catálogo Coletivo de Periódicos Embrapa; Embrapa Amapá; Embrapa Meio Ambiente; Embrapa Soja; Embrapa Trigo. | |
| 169. | | 0900808, BRASIL MOAGEIRO, Acao Moageira, Porto Alegre-RS
Biblioteca(s): Catálogo Coletivo de Periódicos Embrapa; Embrapa Trigo. | |
| 171. | | 2000160, CAHIERS DE MEDECINE VETERINAIRE, Societe Parisiense d'Expansion Chimique, Paris-Franca
Biblioteca(s): Catálogo Coletivo de Periódicos Embrapa; Embrapa Amazônia Oriental. | |
| 172. | | 5151638, CALIFORNIA TOMATO GROWER, California Tomato Growers Association, California-CA
Biblioteca(s): Catálogo Coletivo de Periódicos Embrapa; Embrapa Hortaliças. | |
| 174. | | 0901626, SUÍNOS & CIA, Campinas - SP
Biblioteca(s): Catálogo Coletivo de Periódicos Embrapa; Embrapa Suínos e Aves. |  |
| 175. | | 2000090, CHIMIE ANALYTIQUE, Societe de Productions Documentaires, Malmaison-Franca
Biblioteca(s): Catálogo Coletivo de Periódicos Embrapa; Embrapa Agroindústria de Alimentos. | |
| 176. | | 5150982, COLEOPTERISTS BULLETIN, Coleopterists Society, Chicago-IL
Biblioteca(s): Catálogo Coletivo de Periódicos Embrapa; Embrapa Acre; Embrapa Algodão; Embrapa Amazônia Oriental; Embrapa Arroz e Feijão; Embrapa Cerrados; Embrapa Gado de Corte; Embrapa Meio Norte / UEP-Parnaíba; Embrapa Semiárido; Embrapa Soja; Embrapa Suínos e Aves; Embrapa Trigo. MenosCatálogo Coletivo de Periódicos Embrapa; Embrapa Acre; Embrapa Algodão; Embrapa Amazônia Oriental; Embrapa Arroz e Feijão; Embrapa Cerrados; Embrapa Gado de Corte; Embrapa Meio Norte / UEP-Parnaíba; Embrapa Semiárido; Embrapa Soja... Mostrar Todas | |
| 177. | | 3600041, COMERCIO EXTERIOR. MEXICO, Banco Nacional de Comercio Exterior, Mexico-DF
Biblioteca(s): Catálogo Coletivo de Periódicos Embrapa; Embrapa Semiárido. | |
| 179. | | 0600031, CSIRO FOOD RESEARCH QUARTERLY, CSIRO, Melbourne-Australia
Biblioteca(s): Catálogo Coletivo de Periódicos Embrapa; Embrapa Agroindústria de Alimentos; Embrapa Cerrados. | |
| 180. | | 2650380, DAIRY SCIENCE ABSTRACTS, Commonwealth of Dairy Science Technology, Farnham Royal-Inglaterra
Biblioteca(s): Catálogo Coletivo de Periódicos Embrapa; Embrapa Pantanal; Embrapa Semiárido. | |
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