Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1133732
Title: Machine learning and statistics to qualify environments through multi-traits in Coffea arabica.
Authors: COSTA, W. G. da
BARBOSA, I. de P.
SOUZA, J. E. de
CRUZ, C. D.
NASCIMENTO, M.
OLIVEIRA, A. C. B. de
Affiliation: 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.
Date Issued: 2021
Citation: PLoS One, v. 16, n. 1, : e0245298, 2021.
Description: 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.
Thesagro: Análise Estatística
Genótipo
Processamento
Pós-Colheita
Cadeia Produtiva
Café
NAL Thesaurus: Statistical analysis
Statistical models
Genotype-environment interaction
Postharvest systems
Coffea arabica var. arabica
Language: Ingles
DOI: https://doi.org/10.1371/journal.pone.0245298
Type of Material: Artigo de periódico
Access: openAccess
Appears in Collections:Artigo em periódico indexado (SAPC)

Files in This Item:
File Description SizeFormat 
Machine-learning-and-statistics-to-qualify.pdf1,95 MBAdobe PDFThumbnail
View/Open

FacebookTwitterDeliciousLinkedInGoogle BookmarksMySpace