Artificial neural networks and linear discriminant analysis in early selection among sugarcane families

dc.creatorPeternelli, Luiz Alexandre
dc.creatorMoreira, Édimo Fernando Alves
dc.creatorNascimento, Moysés
dc.creatorCruz, Cosme Damião
dc.date2019-09-24T12:31:58Z
dc.date2019-09-24T12:31:58Z
dc.date2017-10
dc.date.accessioned2023-09-27T21:00:24Z
dc.date.available2023-09-27T21:00:24Z
dc.identifier1984-7033
dc.identifierhttp://dx.doi.org/10.1590/1984-70332017v17n4a46
dc.identifierhttps://locus.ufv.br//handle/123456789/27114
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8952678
dc.descriptionOne of the major challenges in sugarcane breeding programs is an efficient selection of genotypes in the initial phase. The purpose of this study was to compare modelling by artificial neural networks (ANN) and linear discriminant analysis (LDA) as alternatives for the selection of promising sugarcane families based on the indirect traits number of sugarcane stalks (NS), stalk diameter (SD) and stalk height (SH). The analysis focused on two models, a full one with all predictors, and a reduced one, from which the variable SH was excluded. To compare and assess the applied methods, the apparent error rate (AER) and true positive rate (TPR) were used, derived from the confusion matrix. Modeling with ANN and LDA can be used successfully for selection among sugarcane families. The reduced model may be preferable, for having a low AER, high TPR and being easier to obtain in operational terms.
dc.formatpdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherCrop Breeding and Applied Biotechnology
dc.relationv. 17 n. 04, p. 299- 305, out./ dez. 2017
dc.rightsOpen Access
dc.subjectPlant breeding
dc.subjectArtificial intelligence
dc.subjectStatistical learning
dc.titleArtificial neural networks and linear discriminant analysis in early selection among sugarcane families
dc.typeArtigo


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