Heterotic Group Formation in Psidium Guajava L. By Artificial Neural Network and Discriminant Analysis

Abstract

abstract-the present study aimed at evaluating the heterotic group formation in guava based on quantitative descriptors and using artificial neural network (ANN). For such, we evaluated eight quantitative descriptors. Large genetic variability was found for the eight quantitative traits in the 138 genotypes of guava. The artificial neural network technique determined that the optimal number of groups was three. The grouping consistency was determined by linear discriminant analysis, which obtained classification percentage of the groups, with a value of 86 %. It was concluded that the artificial neural network method is effective to detect genetic divergence and heterotic group formation. index terms: guava, genetic variability, multivariate analysis, heterotic group.formação de grupos Hetróticos em Psidium guajava L.peLas redes neurais artificiais resumo-o presente estudo teve como objetivo avaliar a formação de grupos heteróticos na cultura da goiabeira com base em descritores quantitativos e utilizando uma técnica de bioinformática conhecida como redes neurais artificiais (RNA). Para tanto, foram avaliados oito descritores quantitativos. Foi encontrada ampla variabilidade genética para os oito caracteres quantitativos em 138 genótipos de goiabeira. a técnica de redes neurais artificiais determinou que o número ideal de grupos foi três. A consistência do agrupamento foi determinada via Análise Discriminante linear, obtendo-se assim percentagem de classificação dos grupos, com um valor de 86%. Foi concluído que a metodologia de redes neurais artificiais é eficiente para detectar a divergência genética e na formação de grupos heteróticos. termos para indexação: goiaba, variabilidade genética, análise multivariada, grupos heteróticos.1

Digital phenotyping for quantification of genetic diversity in inbred guava (Psidium guajava) families

“…The State University of Northern Rio de Janeiro (UENF) has been developing a guava (P. guajava L.) breeding program for approximately nine years, generating promising results regarding the composition of several types of study populations (Pessanha et al, 2011;Campos et al, 2013;Oliveira et al, 2013;Campos et al, 2016). In these studies, the production of inbred families was determined as the possibility of forming new types of populations.…”

“…To measure genetic variability, morphological descriptors of several parts of the plant can be used, such as those related to the leaf, flower, fruit, seeds, and DNA markers. In the case of guava, many variables and methodologies have been adopted successfully to determine genetic diversity among genotypes (Pessanha et al, 2011;Campos et al, 2013;Oliveira et al, 2013;Campos et al, 2016).…”

Genotypic superiority of Psidium Guajava S1 families using mixed modeling for truncated and simultaneous selection

“…Guava tree cultivation has been expanding in the state of Rio de Janeiro, especially in the north and northwest regions, as it presents conditions favorable to its cultivation. Under this scenario, the Universidade Estadual do Norte Fluminense Darcy Ribeiro (UENF) has been developing a genetic breeding program of guava (P. guajava L.) over the last nine years, achieving promising results (Pessanha et al, 2011;Campos et al, 2013;Oliveira et al, 2014;Campos et al, 2016;Quintal et al, 2017a, b;Gomes et al, 2017). This study demonstrates that obtaining inbred families is a feasible way of forming superior populations.…”

Self-organized map: the new aproach for study of genetic divergence in kale

“…The efficiency of the use of neural networks in breeding programs has been presented in several studies, such as the selection of more productive genotypes of sugarcane (Saccharum spp.) [19] and the evaluation of genetic divergence (Vitis vinifera L.) [20], papaya (Carica papaya L.) [1] and guava (Psidium guajava L.) [9].…”

“…Often genetic diversity studies have been performed using traditional multivariate techniques such as dendrograms, major components, and canonical variables. However, there is the possibility of carrying out these studies through computational intelligence using artificial neural networks (ANNs) [1,9]. The main advantages of RNAs are their non-parametric approach, tolerance to data loss, and the need for detailed information about the modeling system as a design and genealogies [10,11].…”