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

• Autor(a) principal: Sá, Ludimila Geiciane de
• Data de Publicação: 2022
• Outros Autores: Azevedo, Alcinei Mistico, Albuquerque, Carlos Juliano Brant, Valadares, Nermy Ribeiro, Brito, Orlando Gonçalves, Fernandes, Ana Clara Gonçalves, Aspiazú, Ignacio
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Pesquisa Agropecuária Brasileira (Online)
• Texto Completo: https://seer.sct.embrapa.br/index.php/pab/article/view/27089
• topic: Glycine max; artificial neural networks; multivariate analysis; plant breeding; Glycine max; redes neurais artificiais; análise multivariada; melhoramento genético vegetal

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.