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| Registros recuperados : 63 | |
| Registros recuperados : 63 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Algodão. |
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Data corrente: |
21/11/2018 |
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Data da última atualização: |
21/11/2018 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
CARVALHO, L. P. de; TEODORO, P. E.; BARROSO, L. M. A.; FARIAS, F. J. C.; MORELLO, C. de L.; NASCIMENTO, M. |
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Afiliação: |
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. |
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Título: |
Artificial neural networks classify cotton genotypes for fiber length. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Crop Breeding and Applied Biotechnology, v. 18, p. 200-204, 2018. |
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ISSN: |
1518-7853 |
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DOI: |
10.1590/1984-70332018v18n2n28 |
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Idioma: |
Inglês |
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Conteúdo: |
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. |
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Palavras-Chave: |
Inteligência artificial. |
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Thesagro: |
Algodão; Genótipo; Gossypium Hirsutum; Gossypium Hirsutum Marie Galante. |
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Thesaurus NAL: |
Artificial intelligence; Cotton; Genotype-environment interaction. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/186613/1/Artificial-neural-networks.pdf
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Marc: |
LEADER 01921naa a2200301 a 4500
001 2099791
005 2018-11-21
008 2018 bl uuuu u00u1 u #d
022 $a1518-7853
024 7 $a10.1590/1984-70332018v18n2n28$2DOI
100 1 $aCARVALHO, L. P. de
245 $aArtificial neural networks classify cotton genotypes for fiber length.$h[electronic resource]
260 $c2018
520 $aFiber 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.
650 $aArtificial intelligence
650 $aCotton
650 $aGenotype-environment interaction
650 $aAlgodão
650 $aGenótipo
650 $aGossypium Hirsutum
650 $aGossypium Hirsutum Marie Galante
653 $aInteligência artificial
700 1 $aTEODORO, P. E.
700 1 $aBARROSO, L. M. A.
700 1 $aFARIAS, F. J. C.
700 1 $aMORELLO, C. de L.
700 1 $aNASCIMENTO, M.
773 $tCrop Breeding and Applied Biotechnology$gv. 18, p. 200-204, 2018.
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Registro original: |
Embrapa Algodão (CNPA) |
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