Data/hora: 25/06/2024 12:03:54

Biblioteca(s): | Embrapa Café; Embrapa Unidades Centrais.
Data corrente: | 15/05/2017
Data da última atualização: | 15/12/2017
Autoria: | SILVA, G. N.; NASCIMENTO, M.; SANT'ANNA, I. de C.; CRUZ, C. D.; CAIXETA, E. T.; CARNEIRO, P. C. S.; ROSADO, R. D. S.; PESTANA, K. N.; ALMEIDA, D. P. de; OLIVEIRA, M. da S.
Afiliação: | GABI NUNES SILVA, UFV-DE; MOYSÉS NASCIMENTO, UFV-DE; ISABELA DE CASTRO SANT'ANNA, UFV-DBG; COSME DAMIÃO CRUZ, UFV-DBG; EVELINE TEIXEIRA CAIXETA, SAPC; PEDRO CRESCENCIO SOUZA CARNEIRO, UFV-DBG; RENATO DOMICIANO SILVA ROSADO, UFV-DBG; KÁTIA NOGUEIRA PESTANA, CNPMF; DÊNIA PIRES DE ALMEIDA, UFV-IBAA; MARCIANE DA SILVA OLIVEIRA, UFV-DBG.
Título: | Artificial neural networks compared with Bayesian generalized linear regression for leaf rust resistance prediction in Arabica coffee.
Ano de publicação: | 2017
Fonte/Imprenta: | Pesquisa Agropecuária Brasileira, Brasília, DF, v. 52, n. 3, p. 186-193, mar. 2017.
Idioma: | Inglês
Notas: | Título em português: Redes neurais artificiais comparadas com modelos lineares generalizados sob o enfoque bayesiano para predição de resistência à ferrugem em café arábica.
Conteúdo: | The objective of this work was to evaluate the use of artificial neural networks in comparison with Bayesian generalized linear regression to predict leaf rust resistance in Arabica coffee (Coffea arabica). This study used 245 individuals of a F2 population derived from the self-fertilization of the F1 H511-1 hybrid, resulting from a crossing between the susceptible cultivar Catuaí Amarelo IAC 64 (UFV 2148-57) and the resistant parent Híbrido de Timor (UFV 443-03). The 245 individuals were genotyped with 137 markers. Artificial neural networks and Bayesian generalized linear regression analyses were performed. The artificial neural networks were able to identify four important markers belonging to linkage groups that have been recently mapped, while the Bayesian generalized model identified only two markers belonging to these groups. Lower prediction error rates (1.60%) were observed for predicting leaf rust resistance in Arabica coffee when artificial neural networks were used instead of Bayesian generalized linear regression (2.4%). The results showed that artificial neural networks are a promising approach for predicting leaf rust resistance in Arabica coffee.
Palavras-Chave: | Inteligência artificial; Predição.
Thesagro: | Coffea Arábica; Hemileia Vastatrix; Marcador molecular.
Thesaurus Nal: | Artificial intelligence; Genetic markers; Prediction.
Categoria do assunto: | --

URL:

http://ainfo.cnptia.embrapa.br/digital/bitstream/item/159883/1/Artificial-neural-networks.pdf
http://ainfo.cnptia.embrapa.br/digital/bitstream/item/159878/1/Artificial-neural-networks.pdf

Registro original: | Embrapa Café (CNPCa)

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