Title:	Artificial neural networks classify cotton genotypes for fiber length.
Authors:	CARVALHO, L. P. de TEODORO, P. E. BARROSO, L. M. A. FARIAS, F. J. C. MORELLO, C. de L. NASCIMENTO, M.
Affiliation:	LUIZ PAULO DE CARVALHO, CNPA; PAULO EDUARO TEODORO, UFMS - CHAPADÃO DO SUL, MS; LAÍS MAYARA AZEVEDO BARROSO, UFV; FRANCISCO JOSE CORREIA FARIAS, CNPA; CAMILO DE LELIS MORELLO, CNPA; MOYSÉS NASCIMENTO, UFV.
Date Issued:	2018
Citation:	Crop Breeding and Applied Biotechnology, v. 18, p. 200-204, 2018.
Description:	Fiber length is the main trait that needs to be improved in cotton. However, the presence of genotypes x environments interaction for this trait can hinder the recommendation of genotypes with greater length fibers. The aim of this study was to evaluate the adaptability and stability of the fibers length of cotton genotypes for recommendation to the Midwest and Northeast, using artificial neural networks (ANNs) and Eberhart and Russell method. Seven trials were carried out in the states of Ceará, Rio Grande do Norte, Goiás and Mato Grosso do Sul. Experimental design was a randomized block with four replications. Data were submitted to analysis of adaptability and stability through the Eberhart & Russell and ANNs methodologies. Based on these methods, the genotypes BRS Aroeira, CNPA CNPA 2009 42 and CNPA 2009 27 has better performance in unfavorable, general and favorable environment, respectively, for having fiber length above the overall mean of environments and high phenotypic stability.
Thesagro:	Algodão Gossypium Hirsutum Gossypium Hirsutum Marie Galante Genótipo
NAL Thesaurus:	Cotton Artificial intelligence Genotype-environment interaction
Keywords:	Inteligência artificial
ISSN:	1518-7853
DOI:	10.1590/1984-70332018v18n2n28
Type of Material:	Artigo de periódico
Access:	openAccess
Appears in Collections:	Artigo em periódico indexado (CNPA)