Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/33349
metadata.artigo.dc.title: Neuronal networks to describe epidemics of cocoa witches’ broom
metadata.artigo.dc.creator: Pozza, Edson Ampélio
Maffia, Luiz Antônio
Silva, Carlos Arthur Barbosa da
Alves, Marcelo de Carvalho
Braga, José Luis
Costa, João de Cássia do Bonfim
metadata.artigo.dc.subject: Soft computing
Epidemiology
Forecast
Plant disease
Computação suave
Epidemiologia
Doença vegetal
metadata.artigo.dc.publisher: Universidade Federal de Lavras
metadata.artigo.dc.date.issued: 2018
metadata.artigo.dc.identifier.citation: POZZA, E. A. et al. Neuronal networks to describe epidemics of cocoa witches’ broom. Theoretical and Applied Engineering, [S. l.], v. 2, n. 3, p. 1-12, 2018.
metadata.artigo.dc.description.abstract: Artificial Neural networks (ANN) were evaluated as tools to describe epidemics of cocoa’s witche's broom and as a potential method to forecast the disease. The ANN were built with data collected in Altamira-PA-Brazil, between January 1986 and December 1987, and were compared by regression analysis. The variables studied were basidiocarp production, disease intensity, and 16 climatic variables. Seven climatic variables were selected at 1 to 10 weeks before basidiocarp production and 11 variables at the 8th and 9th weeks before evaluation of disease intensity. Temporal series were also analyzed. A total of 37 regression models were tested and 100 ANN built. Neuronal networks could forecast disease intensity more efficiently than regression equations. The best ANN used 11 climatic variables, in the 9th week before disease occurrence. The best ANN, with two intermediary layers of artificial neurons, and regression equation to describe basidiocarp production included the variable rainfall duration, in hours.
metadata.artigo.dc.identifier.uri: http://www.taaeufla.deg.ufla.br/index.php/TAAE/article/view/8
http://repositorio.ufla.br/jspui/handle/1/33349
metadata.artigo.dc.language: en_US
Appears in Collections:DFP - Artigos publicados em periódicos

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