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

• Autor(a) principal: Gomes,Danilo Araújo
• Data de Publicação: 2021
• Outros Autores: Maciel,Gabriel Mascarenhas, Siquieroli,Ana Carolina Silva, Oliveira,Camila Soares de, Finzi,Rafael Resende, Marques,Douglas José
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Bragantia
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0006-87052021000100246
• topic: Solanum lycopersicum; dwarfism; neural networks; Kohonen self-organizing maps

ABSTRACT The aim of this study was to estimate genetic divergence and select BC1F3 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 BC1F3 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.