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- Title
Selection in sugarcane families with artificial neural networks.
- Authors
Brasileiro, Bruno Portela; Marinho, Caillet Dornelles; de Almeida Costa, Paulo Mafra; Cruz, Cosme Damião; Peternelli, Luiz Alexandre; Pereira Barbosa, Márcio Henrique
- Abstract
The objective of this study was to evaluate Artificial Neural Networks (ANN) applied in an selection process within sugarcane families. The best ANN model produced no mistake, but was able to classify all genotypes correctly, i.e., the network made the same selective choice as the breeder during the simulation individual best linear unbiased predictor (BLUPIS), demonstrating the ability of the ANN to learn from the inputs and outputs provided in the training and validation phases. Since the ANN-based selection facilitates the identification of the best plants and the development of a new selection strategy in the best families, to ensure that the best genotypes of the population are evaluated in the following stages of the breeding program, we recommend to rank families by BLUP, followed by selection of the best families and finally, select the seedlings by ANN, from information at the individual level in the best families.
- Subjects
SUGARCANE; ARTIFICIAL neural networks; GENOTYPES; FORAGE; PLANT gene banks
- Publication
Crop Breeding & Applied Biotechnology, 2015, Vol 15, Issue 2, p72
- ISSN
1518-7853
- Publication type
Article
- DOI
10.1590/1984-70332015v15n2a14