Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1133732
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dc.contributor.authorCOSTA, W. G. da
dc.contributor.authorBARBOSA, I. de P.
dc.contributor.authorSOUZA, J. E. de
dc.contributor.authorCRUZ, C. D.
dc.contributor.authorNASCIMENTO, M.
dc.contributor.authorOLIVEIRA, A. C. B. de
dc.date.accessioned2021-08-19T16:00:42Z-
dc.date.available2021-08-19T16:00:42Z-
dc.date.created2021-08-19
dc.date.issued2021
dc.identifier.citationPLoS One, v. 16, n. 1, : e0245298, 2021.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1133732-
dc.descriptionSeveral 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.
dc.language.isoeng
dc.rightsopenAccesseng
dc.titleMachine learning and statistics to qualify environments through multi-traits in Coffea arabica.
dc.typeArtigo de periódico
dc.subject.thesagroAnálise Estatística
dc.subject.thesagroGenótipo
dc.subject.thesagroProcessamento
dc.subject.thesagroPós-Colheita
dc.subject.thesagroCadeia Produtiva
dc.subject.thesagroCafé
dc.subject.nalthesaurusStatistical analysis
dc.subject.nalthesaurusStatistical models
dc.subject.nalthesaurusGenotype-environment interaction
dc.subject.nalthesaurusPostharvest systems
dc.subject.nalthesaurusCoffea arabica var. arabica
riaa.ainfo.id1133732
riaa.ainfo.lastupdate2021-08-19
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0245298
dc.contributor.institutionWEVERTON 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.
Appears in Collections:Artigo em periódico indexado (SAPC)

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