Kohonen's self-organizing maps for the study of genetic dissimilarity among soybean cultivars and genotypes

• Autor(a) principal: Ludimila Geiciane de Sá
• Data de Publicação: 2022
• Outros Autores: Alcinei Mistico Azevedo, Carlos Juliano Brant Albuquerque, Nermy Ribeiro Valadares, Orlando Gonçalves Brito, Ana Clara Gonçalves Fernandes, Ignacio Aspiazú
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UFMG
• Texto Completo: https://doi.org/10.1590/S1678-3921.pab2022.v57.02722; http://hdl.handle.net/1843/59798; https://orcid.org/0000-0002-6877-0656; https://orcid.org/0000-0001-5196-0851; https://orcid.org/0000-0003-2244-1336; https://orcid.org/0000-0001-7854-8111; https://orcid.org/0000-0001-6238-1644; https://orcid.org/0000-0002-8161-8130
• topic: Soja; Redes neurais (Computação); Análise multivariada; Plantas -- Melhoramento genético

The objective of this work was to evaluate the genetic dissimilarity between soybean cultivars and genotypes for the selection of parents, as well as to propose a new method for using Kohonen’s self-organizing maps (SOMs) and to test its efficiency through Anderson’s discriminant analysis. The morphoagronomic descriptors of soybean cultivars and genotypes were evaluated. For data analysis, SOM-type artificial neural networks were used. The proposed method allowed the determination of the best network architecture in a nonsubjective way. Furthermore, at the beginning of training, it was possible to mitigate the randomness effect of the synaptic weights on the formed clusters. Six dissimilar clusters were formed; therefore, there is genetic dissimilarity between soybean cultivars and genotypes. Cultivars C25, C8, and C13 can be combined with C36, C31, C32, and C33 because they show good yield-related attributes and high dissimilarity. The proposed methodology is advantageous in comparison with the use of traditional SOMs, besides being efficient due to clustering consistency according to Anderson’s discriminant analysis.