Research Article Colored fiber cotton in the Uberlândia region using artificial neural networks for yield assessment

Abstract

Cotton is the most widely utilized natural fiber in the world. Brazil is currently one of the world's largest cotton producers. Cotton crops are cultivated in all regions of the country, especially in the Cerrado biome. Studies of genotype x environment (GxE) interactions evaluate the adaptability and stability of cotton genotypes. Adaptability and stability evaluations help understand genotype responses to environmental stimuli and the predictability of genotypes in their response to environmental oscillations. We examined the effect of the genotype x environment interaction on cotton yield and fiber characteristics and compared artificial neural networks (ANNs) with conventional methods for assessing adaptability and stability of colored-fiber cotton genotypes. The experiment was conducted at the experimental farm of Universidade D.B.O. Cardoso et al. 2 ©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 18 (1): gmr18104 Federal de Uberlândia, during four crop years. Twelve genotypes of colored-fiber cotton were evaluated. The experimental design was randomized complete blocks with three replicates. Seed cotton yield was evaluated. The GxE interaction was analyzed by the F-test at α = 0.05. Adaptability, stability, and the factors of the decomposed GxE interaction were analyzed by the Eberhart and Russell, Centroid and ANN methods. The GxE interaction was significant for the variable seed cotton yield, demonstrating differences in genotype behavior among environments. The interactions were predominantly complex. There was concordance between Eberhart and Russsell and ANN analyses. Genotypes UFUJP-02 and UFUJP-17 were responsive to environmental stimuli; they had high predictability, in addition to high fiber yield. The ANN method reliably evaluated adaptability compared with Eberhartand Russel and Centroid methods.

Artificial neural networks discriminate lettuce seeds with different levels of thermoinhibition

“…Another relevant point is that ANNs are not only based on means and variances (Cruz and Nascimento 2018), but also increase the number of observations and reduce the apparent error rate, quantifying the weights between neurons (Cardoso et al, 2019).…”

Use of computational intelligence in the genetic divergence of colored cotton plants

“…Hu et al (2019), analyzing the cotton yarn quality prediction model based on the artificial recurrent neural network, found that the experimental results show better accuracy. Cardoso et al (2019), found greater efficiency of ARR for studies of adaptability and stability in cotton, when compared to conventional statistics.…”

“…This naturally colored cotton is important since the fiber does not need to be dyed, eliminating the use of water and reducing production costs (Dutt et al 2008). However, these fibers are of low quality when compared to white fibers, and therefore research into genetically improving the plants is required (Cardoso 2019).…”

“…Artificial neural networks (ANN's) simulate human behavior, with neurons and synapses transmitting information (estimating weights between them), making mistakes and getting it right, learning from experience and making decisions. In plant breeding, it is used to classify and group genotypes, in genetic diversity, prediction of genetic value, adaptability and stability, among other things (Haykin 2008;Nascimento et al 2013;Oliveira et al 2013;Bhering et al 2015;Cardoso et al 2019).…”

“…Cotton is grown in more than 72 countries on five continents with more than 90% of world's production is of the Gossypium hirsutum species, with a large part consisting of white fiber (Borém and Freire 2014). Cotton is considered the most important natural textile fiber in the world, as it is used to dress almost half of the global population (Cardoso et al 2019).…”