Automation in accession classification of Brazilian Capsicum germplasm through artificial neural networks

• Autor(a) principal: Ferreira,Mariane Gonçalves
• Data de Publicação: 2017
• Outros Autores: Azevedo,Alcinei Mistico, Siman,Luhan Isaac, da Silva,Gustavo Henrique, Carneiro,Clebson dos Santos, Alves,Flávia Maria, Delazari,Fábio Teixeira, da Silva,Derly José Henriques, Nick,Carlos
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Scientia Agrícola (Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162017000300203
• topic: Capsicum spp.; Garson’s method; artificial intelligence; taxonomy; germplasm bank

ABSTRACT Germplasm classification by species requires specific knowledge on/of the culture of interest. Therefore, efforts aimed at automation of this process are necessary for the efficient management of collections. Automation of germplasm classification through artificial neural networks may be a viable and less laborious strategy. The aims of this study were to verify the classification potential of Capsicum accessions regarding/ the species based on morphological descriptors and artificial neural networks, and to establish the most important descriptors and the best network architecture for this purpose. Five hundred and sixty-four plants from 47 Brazilian Capsicum accessions were evaluated. Neural networks of multilayer perceptron type were used in order to automate the species identification through 17 morphological descriptors. Six network architectures were evaluated, and the number of neurons in the hidden layer ranged from 1 to 6. The relative importance of morphological descriptors in the classification process was established by Garson's method. Corolla color, corolla spot color, calyx annular constriction, fruit shape at pedicel attachment, and fruit color at mature stage were the most important descriptors. The network architecture with 6 neurons in the hidden layer is the most appropriate in this study. The possibility of classifying Capsicum plants regarding/ the species through artificial neural networks with 100 % accuracy was verified.