| Journal: | Scientia Agricola |
| Database: | PERIÓDICA |
| System number: | 000455467 |
| ISSN: | 0103-9016 |
| Authors: | Inocente, Gabriela1 Garbuglio, Deoclécio Domingos2 Ruas, Paulo Maurício3 |
| Institutions: | 1Universidade Estadual de Londrina, Departamento de Agronomia, Londrina, Parana. Brasil 2Instituto de Desenvolvimento Rural do Parana, Londrina, Parana. Brasil 3Universidade Estadual de Londrina, Centro de Ciencias Biologicas, Londrina, Parana. Brasil |
| Year: | 2022 |
| Volumen: | 79 |
| Number: | 3 |
| Country: | Brasil |
| Language: | Inglés |
| Document type: | Artículo |
| Approach: | Experimental, analítico |
| English abstract | In the last decades, a new trend to use more refined analytical procedures, such as artificial neural networks (ANN), has emerged to be most accurate, efficient, and extensively applied for mining and data prediction in different contexts, including plant breeding. Thus, this study was developed to establish a new classification proposal for targeting genotypes in breeding programs to approach classical models, such as a complete diallel and modern prediction techniques. The study was based on the standard deviation values of an interpopulation diallel and it also verified the possibility of training a neural network with the standardized genetic parameters for a discrete scale. We used 12 intercrossed maize populations in a complete diallel scheme (66 hybrids), evaluated during the 2005/2006 crop season in three different environments in southern Brazil. The implemented MLP architecture and other associated parameters allowed the development of a generalist model of genotype classification. The MLP neural network model was efficient in predicting parental and interpopulation hybrid classifications from average genetic components from a complete diallel, regardless of the evaluation environment |
| Disciplines: | Agrociencias, Biología |
| Keyword: | Gramíneas, Genética, Redes neuronales, Selección genética, Parámetros genéticos, Maíz, Dialelos, Genotipos |
| Keyword: | Gramineae, Genetics, Maize, Genetic selection, Neural networks, Genetic parameters, Dialleles, Genotypes |
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