2016
DOI: 10.4238/gmr.15028230
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Artificial intelligence in the selection of common bean genotypes with high phenotypic stability

Abstract: 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 Doura… Show more

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Cited by 10 publications
(8 citation statements)
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References 19 publications
(41 reference statements)
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“…As for the phenotypic stability, there was similarity of 81.82% in the classification of genotypes, probably because in the ANN the stability is based on the concept of Finlay and Wilkinson (1963), which differs from the Eberhart and Russel (1966) methodology, which considers stability as invariance and no predictability. Similar results were obtained by Nascimento et al (2013), Teodoro et al (2015) and Correa et al (2016), who verified agreement over 80% between Eberhart and Russell (1966) and ANNs for the adaptability and phenotypic stability of alfalfa, cowpea and common bean genotypes, respectively. Due to the high concordance rates between the evaluated methodologies, ANNs can be considered an effective alternative to measure the adaptability and phenotypic stability of genotypes in breeding programs.…”
Section: Practical Classificationsupporting
confidence: 85%
“…As for the phenotypic stability, there was similarity of 81.82% in the classification of genotypes, probably because in the ANN the stability is based on the concept of Finlay and Wilkinson (1963), which differs from the Eberhart and Russel (1966) methodology, which considers stability as invariance and no predictability. Similar results were obtained by Nascimento et al (2013), Teodoro et al (2015) and Correa et al (2016), who verified agreement over 80% between Eberhart and Russell (1966) and ANNs for the adaptability and phenotypic stability of alfalfa, cowpea and common bean genotypes, respectively. Due to the high concordance rates between the evaluated methodologies, ANNs can be considered an effective alternative to measure the adaptability and phenotypic stability of genotypes in breeding programs.…”
Section: Practical Classificationsupporting
confidence: 85%
“…The deep learning framework was based on convolutional neural networks (CNNs), which were used to predict the quantitative traits from SNPs and achieved more accurate results. Similarly, artificial neural networks (ANNs) have been employed for GS-based prediction modeling in common bean [ 235 ]. The genotype Aporé, which was studied using ANNs, was recommended for use in unfavorable environments because of its grain yield and high phenotypic stability even under unfavorable conditions.…”
Section: Smart Farming: Artificial Intelligence (Ai)-based Pulse Breeding For Climate Resiliencementioning
confidence: 99%
“…The ANNs have been used for plant breeding, including use in the investigation of genotype x environment interactions. Correa et al (2016) have developed an ANN method as an effective alternative to measure the adaptability and phenotypic stability of genotypes in breeding programs in Common bean (Phaseolus vulgaris L.). In this regard, the simulated genotypes are used to train and validate neural networks.…”
Section: Crop Management Systemsmentioning
confidence: 99%