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Registro Completo |
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Biblioteca(s): |
Embrapa Café. |
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Data corrente: |
19/08/2021 |
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Data da última atualização: |
19/08/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
COSTA, W. G. da; BARBOSA, I. de P.; SOUZA, J. E. de; CRUZ, C. D.; NASCIMENTO, M.; OLIVEIRA, A. C. B. de. |
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Afiliação: |
WEVERTON GOMES DA COSTA, UFV; IVAN DE PAIVA BARBOSA, UFV; JACQUELINE ENEQUIO DE SOUZA, UFV; COSME DAMIÃO CRUZ, UFV; MOYSÉS NASCIMENTO, UFV; ANTONIO CARLOS BAIAO DE OLIVEIRA, CNPCa. |
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Título: |
Machine learning and statistics to qualify environments through multi-traits in Coffea arabica. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
PLoS One, v. 16, n. 1, : e0245298, 2021. |
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DOI: |
https://doi.org/10.1371/journal.pone.0245298 |
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Idioma: |
Inglês |
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Conteúdo: |
Several factors such as genotype, environment, and post-harvest processing can affect the responses of important traits in the coffee production chain. Determining the influence of these factors is of great relevance, as they can be indicators of the characteristics of the coffee produced. The most efficient models choice to be applied should take into account the variety of information and the particularities of each biological material. This study was developed to evaluate statistical and machine learning models that would better discriminate environments through multi-traits of coffee genotypes and identify the main agronomic and beverage quality traits responsible for the variation of the environments. For that, 31 morpho-agronomic and post-harvest traits were evaluated, from field experiments installed in three municipalities in the Matas de Minas region, in the State of Minas Gerais, Brazil. Two types of post-harvest processing were evaluated: natural and pulped. The apparent error rate was estimated for each method. The Multilayer Perceptron and Radial Basis Function networks were able to discriminate the coffee samples in multi-environment more efficiently than the other methods, identifying differences in multi-traits responses according to the production sites and type of post-harvest processing. The local factors did not present specific traits that favored the severity of diseases and differentiated vegetative vigor. Sensory traits acidity and fragrance/aroma score also made little contribution to the discrimination process, indicating that acidity and fragrance/aroma are characteristic of coffee produced and all coffee samples evaluated are of the special type in the Mata of Minas region. The main traits responsible for the differentiation of production sites are plant height, fruit size, and bean production. The sensory trait "Body" is the main one to discriminate the form of post-harvest processing. MenosSeveral factors such as genotype, environment, and post-harvest processing can affect the responses of important traits in the coffee production chain. Determining the influence of these factors is of great relevance, as they can be indicators of the characteristics of the coffee produced. The most efficient models choice to be applied should take into account the variety of information and the particularities of each biological material. This study was developed to evaluate statistical and machine learning models that would better discriminate environments through multi-traits of coffee genotypes and identify the main agronomic and beverage quality traits responsible for the variation of the environments. For that, 31 morpho-agronomic and post-harvest traits were evaluated, from field experiments installed in three municipalities in the Matas de Minas region, in the State of Minas Gerais, Brazil. Two types of post-harvest processing were evaluated: natural and pulped. The apparent error rate was estimated for each method. The Multilayer Perceptron and Radial Basis Function networks were able to discriminate the coffee samples in multi-environment more efficiently than the other methods, identifying differences in multi-traits responses according to the production sites and type of post-harvest processing. The local factors did not present specific traits that favored the severity of diseases and differentiated vegetative vigor. Sensory traits acidity and fragrance/aroma sco... Mostrar Tudo |
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Thesagro: |
Análise Estatística; Cadeia Produtiva; Café; Genótipo; Pós-Colheita; Processamento. |
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Thesaurus Nal: |
Coffea arabica var. arabica; Genotype-environment interaction; Postharvest systems; Statistical analysis; Statistical models. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/225351/1/Machine-learning-and-statistics-to-qualify.pdf
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Marc: |
LEADER 02940naa a2200325 a 4500
001 2133732
005 2021-08-19
008 2021 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1371/journal.pone.0245298$2DOI
100 1 $aCOSTA, W. G. da
245 $aMachine learning and statistics to qualify environments through multi-traits in Coffea arabica.$h[electronic resource]
260 $c2021
520 $aSeveral factors such as genotype, environment, and post-harvest processing can affect the responses of important traits in the coffee production chain. Determining the influence of these factors is of great relevance, as they can be indicators of the characteristics of the coffee produced. The most efficient models choice to be applied should take into account the variety of information and the particularities of each biological material. This study was developed to evaluate statistical and machine learning models that would better discriminate environments through multi-traits of coffee genotypes and identify the main agronomic and beverage quality traits responsible for the variation of the environments. For that, 31 morpho-agronomic and post-harvest traits were evaluated, from field experiments installed in three municipalities in the Matas de Minas region, in the State of Minas Gerais, Brazil. Two types of post-harvest processing were evaluated: natural and pulped. The apparent error rate was estimated for each method. The Multilayer Perceptron and Radial Basis Function networks were able to discriminate the coffee samples in multi-environment more efficiently than the other methods, identifying differences in multi-traits responses according to the production sites and type of post-harvest processing. The local factors did not present specific traits that favored the severity of diseases and differentiated vegetative vigor. Sensory traits acidity and fragrance/aroma score also made little contribution to the discrimination process, indicating that acidity and fragrance/aroma are characteristic of coffee produced and all coffee samples evaluated are of the special type in the Mata of Minas region. The main traits responsible for the differentiation of production sites are plant height, fruit size, and bean production. The sensory trait "Body" is the main one to discriminate the form of post-harvest processing.
650 $aCoffea arabica var. arabica
650 $aGenotype-environment interaction
650 $aPostharvest systems
650 $aStatistical analysis
650 $aStatistical models
650 $aAnálise Estatística
650 $aCadeia Produtiva
650 $aCafé
650 $aGenótipo
650 $aPós-Colheita
650 $aProcessamento
700 1 $aBARBOSA, I. de P.
700 1 $aSOUZA, J. E. de
700 1 $aCRUZ, C. D.
700 1 $aNASCIMENTO, M.
700 1 $aOLIVEIRA, A. C. B. de
773 $tPLoS One$gv. 16, n. 1, : e0245298, 2021.
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Registro original: |
Embrapa Café (CNPCa) |
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| Registros recuperados : 3 | |
| 2. |  | OLIVEIRA, C. F. de; SOUZA, J. E. de; SIQUEIRA, M. J. da S.; SILVA JÚNIOR, A. C. da; FERREIRA, R. de P.; VILELA, D.; CRUZ, C. D. Selection of alfalfa genotypes for dry matter yield and persistence with repeated measures. Agronomy Science and Biotechnology, v. 9, p. 1-14, 2023.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 4 |
| Biblioteca(s): Embrapa Gado de Leite; Embrapa Pecuária Sudeste. |
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| 3. | | COSTA, W. G. da; BARBOSA, I. de P.; SOUZA, J. E. de; CRUZ, C. D.; NASCIMENTO, M.; OLIVEIRA, A. C. B. de. Machine learning and statistics to qualify environments through multi-traits in Coffea arabica. PLoS One, v. 16, n. 1, : e0245298, 2021.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Café. |
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| Registros recuperados : 3 | |
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