Cotton genotypes selection through artificial neural networks

Publication Overview
TitleCotton genotypes selection through artificial neural networks
AuthorsJúnior EGS, Cardoso DBO, Reis MC, Nascimento AFO, Bortolin DI, Martins MR, Sousa LB
TypeJournal Article
Journal NameGenetics and molecular research : GMR
Volume16
Issue3
Year2017
CitationJúnior EGS, Cardoso DBO, Reis MC, Nascimento AFO, Bortolin DI, Martins MR, Sousa LB. Cotton genotypes selection through artificial neural networks. Genetics and molecular research : GMR. 2017 Sep 27; 16(3).

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 was 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.

Properties
Additional details for this publication include:
Property NameValue
Publication ModelElectronic
ISSN1676-5680
eISSN1676-5680
Publication Date2017 Sep 27
Journal AbbreviationGenet. Mol. Res.
DOI10.4238/gmr16039798
Elocation10.4238/gmr16039798
LanguageEnglish
Language Abbreng
Publication TypeJournal Article
Journal CountryBrazil
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DatabaseAccession
PMID: PubMedPMID:28973775