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

Teodoro, Paulo Eduardo; Barroso, Laís Mayara Azevedo; Nascimento, Moysés; Torres, Francisco Eduardo; Sagrilo, Edvaldo; Santos, Adriano dos; Ribeiro, Larissa Pereira

doi:10.1590/s0100-204x2015001100008

Cited by 26 publications

(23 citation statements)

References 12 publications

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“…For phenotypic stability, there was 85% similarity in the classification of genotypes, probably because stability in the ANN method is based on the method of Finlay and Wilkinson (1963), who consider stability as an invariance and therefore not predictable, which differs from the Eberhart and Russell method (1966). Similar results were obtained by Nascimento et al (2013) and Teodoro et al (2015), who showed an average agreement of 93 and 85% between the Eberhart and Russell (1966) and ANN methods regarding adaptability and phenotypic stability of alfalfa and cowpea genotypes, respectively.…”

Section: Resultssupporting

confidence: 85%

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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Section: Resultssupporting

confidence: 85%

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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“…Similar results were obtained by Rocha et al (2007), Barros et al (2013), Nunes et al (2014), Teodoro et al (2015), Santos et al (2015Santos et al ( , 2016 who also found significant differences in the genotype, environments and genotypes x environments interaction effects, evaluating semi-prostate cowpea genotypes in multienvironments trials in the Mid-North and Midwest Brazilian region. The existence of significant genotypes x environments interaction for grain yield indicates that the adaptability and stability analyzes are appropriate because the edaphoclimatic factors are those that most influence the adaptability and stability of genotypes.…”

Section: Resultssupporting

confidence: 76%

Selection of cowpea genotypes for Mato Grosso do Sul viaGGE Biplot and linear regression

Santos¹,

Ceccon²,

Rodrigues³

et al. 2017

Biosci. J.

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ABSTRACT:The aim of this study was to investigate the association between GGE Biplot and Eberhart and Russell methodologies and to select cowpea genotypes that meet both high grain yield, adaptability and stability for Mato Grosso do Sul environments. The trials were carried out from February to July of 2010, 2011 and 2012 in the municipalities of Dourados, Aquidauana and Chapadão do Sul. The trials in Chapadão do Sul were conducted only in the years 2010 and 2011, totaling eight environments. After detecting significant GE interaction, adaptability and phenotypic stability of cowpea genotypes were analyzed by GGE Biplot and linear regression methods. Eberhart and Russell and GGE biplot methodologies discriminate differently the best cowpea genotypes and it can be used in a complementary way. MNCO1-649F-2-11 and MNCO2-675-4-9 genotypes are the closest to the ideal in terms of high grain yield and phenotypic stability, being so suitable for cultivation in the state of Mato Grosso do Sul.

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“…In study on adaptability and stability of cowpea, Teodoro et al (2015) found similarity of the parameters obtained by the traditional method of Eberhart & Russel (1966) and the use of artificial neural networks.…”

Section: Resultsmentioning

confidence: 84%

Artificial intelligence in seeding density optimization and yield simulation for oat

Dornelles

Kraisig

Silva

et al. 2018

Rev. bras. eng. agríc. ambient.

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A B S T R A C TArtificial intelligence may represent an efficient strategy for simulation and optimization of important processes in agriculture. The main goal of the study is to propose the use of artificial intelligence, namely artificial neural networks and genetic algorithms, respectively, in the simulation of oat grain yield and optimization of seeding density, considering the main succession systems of southern Brazil. The study was conducted in a randomized complete block design with four replicates, following a 4 x 2 factorial scheme, for seeding densities (100, 300, 600 and 900 seeds m -2 ) and oat cultivars (Brisasul and URS Taura), in succession systems of corn/oats and soybean/oats. A multi-layered artificial neural network and a genetic algorithm were implemented in Java programming language, and the results obtained from this implementation were compared with traditional polynomial regression. The use of artificial intelligence through neural networks and genetic algorithms allows the efficient simulation of oat grain yield and better optimization of seeding density in comparison to polynomial regression, considering the main succession systems in southern Brazil.Inteligência artificial na otimização da densidade de semeadura e simulação da produtividade da aveia R E S U M O O uso de inteligência artificial pode representar uma estratégia eficiente de simulação e otimização de processos importantes na agricultura. O objetivo deste estudo é propor o uso de inteligência artificial via redes neurais artificiais e algoritmos genéticos, respectivamente, na simulação da produtividade de grãos de aveia (Avena sativa) e na otimização da densidade de semeadura, nos principais sistemas de sucessão do sul do Brasil. O estudo foi conduzido em blocos ao acaso com quatro repetições em esquema fatorial 4 x 2, para as densidades de semeadura (100, 300, 600 e 900 sementes m -2 ) e cultivares de aveia (Brisasul e URS Taura), nos sistemas de sucessão milho/aveia e soja/aveia. Implementou-se uma rede neural artificial de múltiplas camadas e um algoritmo genético, em linguagem de programação Java, e comparou-se os resultados obtidos desta implementação com análises tradicionais de regressão polinomial. O uso de inteligência artificial via redes neurais artificiais e algoritmos genéticos permite simular com eficiência a produtividade de grãos de aveia e melhor otimização da densidade de semeadura na comparação com regressão polinomial, considerando os principais sistemas de sucessão no sul do Brasil.

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

Cited by 26 publications

References 12 publications

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

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

Selection of cowpea genotypes for Mato Grosso do Sul viaGGE Biplot and linear regression

Artificial intelligence in seeding density optimization and yield simulation for oat

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