Artificial neural networks for adaptability and stability evaluation in alfalfa genotypes

Nascimento, Moysés; Peternelli, Luiz Alexandre; Cruz, Cosme Damião; Nascimento, Ana Carolina Campana; Ferreira, Reinaldo de Paula; Bhering, Leonardo Lopes; Salgado, Caio Césio

doi:10.1590/s1984-70332013000200008

Cited by 49 publications

(90 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The ANN allowed for a clear separation of the different occurrences of geophytes and variations in the water. Nascimento et al (2013) used ANNs to classify alfalfa genotypes, confirming their superiority over commonly used methodologies.…”

Section: Dsmentioning

confidence: 60%

Superiority of artificial neural networks for a genetic classification procedure

et al. 2015

View full text Add to dashboard Cite

ABSTRACT. The correct classification of individuals is extremely important for the preservation of genetic variability and for maximization of yield in breeding programs using phenotypic traits and genetic markers. The Fisher and Anderson discriminant functions are commonly used multivariate statistical techniques for these situations, which allow for the allocation of an initially unknown individual to predefined groups. However, for higher levels of similarity, such as those found in backcrossed populations, these methods have proven to be inefficient. Recently, much research has been devoted to developing a new paradigm of computing known as artificial neural networks (ANNs), which can be used to solve many statistical problems, including classification problems. The aim of this study was to evaluate the feasibility of ANNs as an evaluation technique of genetic diversity by comparing their performance with that of traditional methods. The discriminant functions were equally ineffective in discriminating the populations, with error rates of 23-82%, thereby preventing the correct discrimination of individuals between populations. The ANN was effective in classifying populations with low and high differentiation, such as those derived from a genetic design established from backcrosses, even in cases of low differentiation of the data sets. The ANN appears to be a promising technique to solve classification problems, since the number of individuals classified incorrectly by the ANN was always lower than that of the discriminant functions. We envisage the potential relevant application of this improved procedure in the genomic classification of markers to distinguish between breeds and accessions.

show abstract

Section: Dsmentioning

confidence: 60%

Superiority of artificial neural networks for a genetic classification procedure

et al. 2015

View full text Add to dashboard Cite

show abstract

“…ANNs are an alternative, which is based on a computation concept that aims to work with data processing in similar way to human brain, acquiring knowledge through experience, predicting and recognizing patterns or establishing groups (Haykin, 2008;Braga et al, 2011). Genetic breeding applies ANNs in genetic diversity studies (Barbosa et al, 2011), genetic value prediction (Silva et al, 2014;Carneiro, 2015), as well as adaptability and stability analysis (Barroso et al, 2013;Nascimento et al, 2013). Cotton prediction studies have used some traits related to textile industry wiring (Jackowska-Strumillo et al, 2004;Ghosh et al, 2005;Ureyen and Kadoglu, 2007;Gharehaghaji et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Research Article Cotton genotypes selection through artificial neural networks.

Gonçalves¹,

Cardoso²,

Reis³

et al. 2017

Genet. Mol. Res.

View full text Add to dashboard Cite

ABSTRACT. Breeding programs currently use statistical analysis to assist in the identification of superior genotypes at various stages of a cultivar's development. Differently from these analyses, the computational intelligence approach has been little explored in genetic improvement of cotton. Thus, this study was carried out with the objective of presenting the use of artificial neural networks as auxiliary tools in the improvement of the cotton to improve fiber quality. To demonstrate the applicability of this approach, this research was carried out using the evaluation data of 40 genotypes. In order to classify the genotypes for fiber quality, the artificial neural networks were trained with replicate data of 20 genotypes of cotton evaluated in the harvests of 2013/14 and 2014/15, regarding fiber length, uniformity of length, fiber strength, micronaire index, elongation, short fiber index, maturity index, reflectance degree, and fiber quality index. This quality index 2 E.G. Silva Júnior et al. Genetics and Molecular Research 16 (3): gmr16039798was estimated by means of a weighted average on the determined score (1 to 5) of each characteristic of the HVI evaluated, according to its industry standards. The artificial neural networks presented a high capacity of correct classification of the 20 selected genotypes based on the fiber quality index, so that when using fiber length associated with the short fiber index, fiber maturation, and micronaire index, the artificial neural networks presented better results than using only fiber length and previous associations. It was also observed that to submit data of means of new genotypes to the neural networks trained with data of repetition, provides better results of classification of the genotypes. When observing the results obtained in the present study, it was verified that the artificial neural networks present great potential to be used in the different stages of a genetic improvement program of the cotton, aiming at the improvement of the fiber quality of the future cultivars.

show abstract

“…Because artificial neural networks (ANNs) are efficient to model complex problems (Barbosa et al 2011;Nascimento et al 2013;Azevedo et al 2015;Brasileiro et al 2015), they may also be effective in the indirect phenotyping of vitamin A content by using colorimetric data. The ANNs are computational models of the human brain that can recognize patterns and regularities of the data, becoming an alternative as universal approximator of complex functions (Gianola et al 2011).…”

Section: Introductionmentioning

confidence: 99%

High-efficiency phenotyping for vitamin A in banana using artificial neural networks and colorimetric data

2016

View full text Add to dashboard Cite

show abstract

Artificial neural networks for adaptability and stability evaluation in alfalfa genotypes

Abstract: Abstract

Cited by 49 publications

References 14 publications

Superiority of artificial neural networks for a genetic classification procedure

Superiority of artificial neural networks for a genetic classification procedure

Research Article Cotton genotypes selection through artificial neural networks.

High-efficiency phenotyping for vitamin A in banana using artificial neural networks and colorimetric data

Contact Info

Product

Resources

About