Abstract
The objective of this work was to analyze the genetic diversity using conventional methods and artificial neural networks among 12 colored fiber cotton genotypes, using technological characteristics of the fiber and productivity in terms of cottonseed and cotton fiber yield. The experiment was conducted in an experimental area located at Fazenda Capim Branco, belonging to the Federal University of Uberlândia, in the city of Uberlândia, Minas Gerais. Twelve genotypes of colored fiber cotton were evaluated, 10 from the Cotton Genetic Improvement Program (PROMALG): UFUJP - 01, UFUJP - 02, UFUJP - 05, UFUJP - 08, UFUJP - 09, UFUJP - 10, UFUJP - 11, UFUJP - 13, UFUJP - 16, UFUJP - 17 and two commercial cultivars: BRS Rubi (RC) and BRS Topázio (TC). The experimental design used was complete randomized block (CRB) with three replications. The following evaluations were carried out at full maturation: yield of cottonseed (kg ha-1) and the technological characteristics, which include, fiber length, micronaire, maturation, length uniformity, short fiber index, elongation and strength, using the HVI (High volume instrument) device. Genetic dissimilarity was measured using the generalized Mahalanobis distance and after obtaining the dissimilarity matrix, the genotypes were grouped using a hierarchical clustering method (UPGMA). A discriminant analysis and the Kohonen Self-Organizing Map (SOM) by Artificial Neural Networks (ANN’s) were performed through computational intelligence. SOM was able to detect differences and organize the similarities between accesses in a more coherent way, forming a larger number of groups, when compared to the method that uses the Mahalanobis matrix. It was also more accurate than the discriminant analysis, since it made it possible to differentiate groups more coherently when comparing their phenotypic behavior. The methods that use computational intelligence proved to be more efficient in detecting similarity, with Kohonen's Self-Organizing Map being the most adequate to classify and group cotton genotypes.
Highlights
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)
The methods that use computational intelligence proved to be more efficient in detecting similarity, with Kohonen's SelfOrganizing Map being the most adequate to classify and group cotton genotypes
Use of computational intelligence in the genetic divergence of colored cotton plants In Brazil, it is an important commodities in the agriculture
Summary
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). In Brazil, it is an important commodities in the agriculture It is the fourth producer in the world and the second in export volume, with emphasis on Mato Grosso, Bahia and Minas Gerais as the largest producers in the country (ABRAPA 2020). Cotton plants produce colored fibered cotton naturally, which has a small niche market 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). These fibers are of low quality when compared to white fibers, and research into genetically improving the plants is required (Cardoso 2019)
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