Selection of BC1F3 populations of Santa Cruz type dwarf tomato plant by computational intelligence techniques

Abstract

The aim of this study was to estimate genetic divergence and select BC 1 F 3 populations of dwarf tomato plant within the Santa Cruz segment by computational intelligence techniques. The experiment was conducted in a greenhouse in the Vegetable Crop Experimental Station of the Universidade Federal de Uberlândia (UFU), Monte Carmelo, MG, Brazil. A randomized block experimental design was used with 17 treatments and four replications. The genetic material evaluated comprised thirteen dwarf tomato plant populations obtained by a backcross and two self-fertilizations, plus both parents (recurrent and donor), and two commercial check varieties. The traits evaluated were mean fruit weight (MFW), soluble solids content (SSC), fruit diameter (FD), fruit length (FL), fruit shape (FS), pulp thickness (PT), number of locules (NL), distance between internodes, and acylsugar, β-carotene, and lycopene content. The data were analyzed by means testing, and genetic divergence was measured using Mahalanobis generalized distance by the unweighted pair group method with arithmetic mean (UPGMA) and through computational intelligence using Kohonen self-organizing maps (SOM). Genetic dissimilarity in relation to the donor parent could be confirmed through both methodologies. However, the SOM was able to detect differences and organize the similarities among the populations in a more consistent manner, resulting in a larger number of groups. In addition, the computational intelligence techniques allow the weight of each variable in formation of the groups to be ascertained. Among the BC 1 F 3 populations, UFU-SC#3 and UFU-SC#5 stood out for agronomic traits, and UFU-SC#10 and UFU-SC#11 stood out for quality parameters.

Artificial neural networks and genetic dissimilarity among saladette type dwarf tomato plant populations

“…It has been reported that tomato plant characteristics should be prioritized to obtain future cultivars, especially with regard to the various biotic ( Ferrero et al, 2020 , Zanin et al, 2021 ) and abiotic stressors ( Oliveira et al, 2021 , Wen et al, 2021 ), as well as the nutritional quality of the fruit ( Londoño-Giraldo et al, 2020 , Gomes et al, 2021 , Oliveira et al, 2022 ) for a healthy diet ( Asensio et al, 2019 ). A major obstacles to this is the narrow genetic base of the tomato germplasm ( Hassan et al, 2021 ).…”

“…In order to conduct the introgression of genes of interest for different types of biotic stress and fruit nutritional quality, and promote higher productivity, research with dwarf tomato plants has been intensified ( Maciel et al, 2015 , Finzi et al, 2017 , Finzi et al, 2020 , Cavasin et al, 2021 , Gomes et al, 2021 , Oliveira et al, 2022 ). The use of dwarf plants has provided multiple advantages, and obtaining saladette/roma dwarf tomato populations is a promising technology for the future development of higher yielding hybrids, similar to that observed with cherry/grape tomatoes ( Finzi et al, 2017 ).…”

“…The hexagonal topology used in this study allowed for a better arrangement of the neurons and a minimization of possible errors in the classification process ( Kohonen, 2014 ). The organization of the topological structure reflects the similarity among the genotypes under study, allowing for classification by approximation ( Santos et al, 2019 , Gomes et al, 2021 ).…”

“…The better capacity of the SOM in discriminating the genotypes in relation to the UPGMA method was also reported by Cardoso et al (2021) , who compared the two methods in the evaluation of genetic dissimilarity of colored cotton genotypes. Gomes et al (2021) classified salad-type dwarf tomato populations and also observed a greater number of groups by the SOM method than the UPGMA method, and determined that this methodology was more efficient for studying the genetic dissimilarity in dwarf tomato germplasm.…”

“…One of the advantages of using the Kohonen network in relation to other multivariate techniques is the ability to determine the weights of the variables in the formation of the groups and visualize the related correlations ( Cardoso et al, 2021 , Gomes et al, 2021 ). The similarity in the patterns of colors observed for the MW, LD, and TD, and PT traits emphasizes their correlations, showing that these traits are closely related to fruit size ( Tijskens et al, 2020 ).…”