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

Authors

  • Mariane Gonçalves Ferreira Federal University of Viçosa; Dept. of Crop Science
  • Alcinei Mistico Azevedo Federal University of Minas Gerais; Institute of Agricultural Sciences
  • Luhan Isaac Siman Federal University of Viçosa; Dept. of Crop Science
  • Gustavo Henrique da Silva Federal University of Viçosa; Dept. of Crop Science
  • Clebson dos Santos Carneiro Federal University of Viçosa; Dept. of Crop Science
  • Flávia Maria Alves Federal University of Viçosa; Dept. of Crop Science
  • Fábio Teixeira Delazari Federal University of Viçosa; Dept. of Crop Science
  • Derly José Henriques da Silva Federal University of Viçosa; Dept. of Crop Science
  • Carlos Nick Federal University of Viçosa; Dept. of Crop Science

DOI:

https://doi.org/10.1590/1678-992x-2015-0451

Keywords:

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.

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Published

2017-06-01

How to Cite

Ferreira, M. G., Azevedo, A. M., Siman, L. I., da Silva, G. H., Carneiro, C. dos S., Alves, F. M., Delazari, F. T., da Silva, D. J. H., & Nick, C. (2017). Automation in accession classification of Brazilian Capsicum germplasm through artificial neural networks. Scientia Agricola, 74(3), 203-207. https://doi.org/10.1590/1678-992x-2015-0451

Issue

Section

Genetics and Plant Breeding