Application of neural networks to predict volume in eucalyptus

Bhering, Leonardo Lopes; Cruz, Cosme Damião; Rosado, Antônio Marcos; Laviola, Bruno Galvêas; Nascimento, Moysés

doi:10.1590/1984-70332015v15n3a23

Cited by 20 publications

(14 citation statements)

References 16 publications

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“…(Brasileiro et al, 2015); predizer o volume de eucalipto (Eucalyptus spp.) (Bhering et al, 2015); e quantificar a divergência genética de acessos de mamão (Carica papaya L.) (Barbosa et al, 2011). Contudo, sua aplicação para a seleção de genótipos em presença de interação genótipos x ambientes significativa ainda se restringe ao trabalho de Nascimento et al (2013); portanto, são necessárias mais pesquisas que comprovem a eficácia desse método.…”

Section: Redes Neurais Artificiais Para Identificar Genótipos De Feijunclassified

Redes neurais artificiais para identificar genótipos de feijão-caupi semiprostrado com alta adaptabilidade e estabilidade fenotípicas

Teodoro

Barroso

Nascimento

et al. 2015

Pesq. agropec. bras.

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Resumo -O objetivo deste trabalho foi verificar a concordância entre as redes neurais artificiais (RNAs) e o método de Eberhart & Russel na identificação de genótipos de feijão-caupi (Vigna unguiculata) com alta adaptabilidade e estabilidade fenotípicas. Utilizou-se o delineamento experimental de blocos ao acaso com quatro repetições. Os tratamentos consistiram de 18 linhagens experimentais e duas cultivares de feijão-caupi. Foram conduzidos quatro ensaios de valor de cultivo e uso nos municípios de Aquidauana, Chapadão do Sul e Dourados, no estado do Mato Grosso do Sul. Os dados de produtividade de grãos foram submetidos às análises de variância individual e conjunta. Em seguida, os dados foram submetidos às análises de adaptabilidade e estabilidade por meio dos métodos de Eberhart & Russell e de RNAs. Houve elevada concordância entre os métodos avaliados quanto à discriminação da adaptabilidade fenotípica dos genótipos de feijão-caupi semiprostrado, o que indica que as RNAs podem ser utilizadas em programas de melhoramento genético. Em ambos os métodos avaliados, os genótipos BRS Xiquexique, TE97-304G-12 e MNC99-542F-5 são recomendados para ambientes desfavoráveis, gerais e favoráveis, respectivamente, por apresentarem produtividade de grãos acima da média geral dos ambientes e alta estabilidade fenotípica.Termos para indexação: Vigna unguiculata, inteligência artificial, interação genótipos x ambientes.Artificial neural networks to identify semi-prostrate cowpea genotypes with high phenotypic adaptability and stabilityAbstract -The objective of this work was to verify the agreement between artificial neural networks (ANNs) and the Eberhart & Russel method in identifying cowpea (Vigna unguiculata) genotypes with high phenotypic adaptability and stability. The experimental design was in a randomized complete block with four replicates. The treatments consisted of 18 experimental lines and two cowpea cultivars. Four value for cultivation and use trials were conducted in the municipalities of Aquidauana, Chapadão do Sul, and Dourados, in the state of Mato Grosso do Sul, Brazil. Grain yield data were subjected to individual and joint variance analyses. Then, the data were subjected to adaptability and stability analyses through the methods of Eberhart & Russell and ANNs. There was a high agreement between the methods evaluated for discrimination of the phenotypic adaptability of semi-prostrate cowpea genotypes, indicating that ANNs can be used in breeding programs. In both evaluated methods, the BRS Xiquexique, TE97-304G-12, and MNC99-542F-5 genotypes are recommended for harsh, general, and favorable environments, respectively, for having grain yield above the overall average of environments and high phenotypic stability.Index terms: Vigna unguiculata, artificial intelligence, genotypes x environments interaction. IntroduçãoO feijão-caupi [Vigna unguiculata (L.) Walp.] é atualmente cultivado em três regiões do Brasil, o que torna importante investigar a magnitude da interação genótipos x ambientes para a escolha d...

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Section: Redes Neurais Artificiais Para Identificar Genótipos De Feijunclassified

Redes neurais artificiais para identificar genótipos de feijão-caupi semiprostrado com alta adaptabilidade e estabilidade fenotípicas

Teodoro

Barroso

Nascimento

et al. 2015

Pesq. agropec. bras.

Self Cite

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“…As a result, this method has been employed for breeding in crops such as alfalfa (Nascimento et al, 2013), sugarcane (Brasileiro et al, 2015), eucalyptus (Bhering et al, 2015), and papaya (Barbosa et al, 2011). The aim of the present study was to use ANNs to select common bean genotypes that have high adaptability and phenotypic stability, and to verify their consistency with the Eberhart and Russell (1966) method.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence in the selection of common bean genotypes with high phenotypic stability

Correa¹,

Teodoro

Gonçalves

et al. 2016

Genet. Mol. Res.

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ABSTRACT. Artificial neural networks have been used for various purposes in plant breeding, including use in the investigation of genotype x environment interactions. The aim of this study was to use artificial neural networks in the selection of common bean genotypes with high phenotypic adaptability and stability, and to verify their consistency with the Eberhart and Russell method. Six trials were conducted using 13 genotypes of common bean between 2002 and 2006 in the municipalities of Aquidauana and Dourados. The experimental design was a randomized block with three replicates. Grain yield data were submitted to individual and joint variance analyses. The data were then submitted to analysis of adaptability and stability through the Eberhart and Russell and artificial neural network methods. There was high concordance between the methodologies evaluated for discrimination of phenotypic adaptability of common bean genotypes, indicating that artificial neural networks can be used in breeding programs. Based on both approaches, the genotypes Aporé, Rudá, and CNFv 8025 are recommended for use in unfavorable, general and favorable environments, respectively by the grain yield above the overall average of environments and high phenotypic stability.

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“…Outputs: Volume increase and basal area." Bhering et al (2015) The main objective aimed at assessing the ANN methodology for the prediction of wood volume in eucalypt breeding programs and in the selection of families, comparing their results with those that the regression model found.…”

Section: Mlp Networkmentioning

confidence: 99%

“…The dissemination of the methodologies found in this field has achieved interesting results in different areas of knowledge. In forestry, the Artificial Neural Network (ANNs) technique is considered a promising and very efficient alternative for defining the best management of forest resources (Menzies et al, 2007;Bhering et al, 2015;Miguel et al, 2015;Ribeiro et al, 2016 andÇatal &Saplioğlu, 2018]).…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks Applied in Forest Biometrics and Modeling: State of the Art (January/2007 to July/2018)

Chiarello

Steiner

Oliveira

et al. 2019

CERNE

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Application of neural networks to predict volume in eucalyptus

Cited by 20 publications

References 16 publications

Redes neurais artificiais para identificar genótipos de feijão-caupi semiprostrado com alta adaptabilidade e estabilidade fenotípicas

Redes neurais artificiais para identificar genótipos de feijão-caupi semiprostrado com alta adaptabilidade e estabilidade fenotípicas

Artificial intelligence in the selection of common bean genotypes with high phenotypic stability

Artificial Neural Networks Applied in Forest Biometrics and Modeling: State of the Art (January/2007 to July/2018)

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