Data/hora: 12/06/2024 10:14:08

Provedor de dados: | 102
País: | Brazil
Título: | Description of epidemics of coffee rust with neural networks
Autores: | PINTO,AUGUSTO CARLOS S.; POZZA,EDSON A.; SOUZA,PAULO E. DE; POZZA,ADÉLIA A. A.; TALAMINI,VIVIANE; BOLDINI,JULIANA M.; SANTOS,FLORISVALDA S.
Data: | 2002-09-01
Ano: | 2002
Palavras-chave: | Inteligência artificial; Epidemiologia; Hemileia vastatrix; Coffea arabica.
Resumo: | The objective of this paper was to evaluate the potential of neural networks (NN) as an alternative method to the basic epidemiological approach to describe epidemics of coffee rust. The NN was developed from the intensities of coffee (Coffea arabica) rust along with the climatic variables collected in Lavras-MG between 13 February 1998 and 20 April 2001. The NN was built with climatic variables that were either selected in a stepwise regression analysis or by the Braincel® system, software for NN building. Fifty-nine networks and 26 regression models were tested. The best models were selected based on small values of the mean square deviation (MSD) and of the mean prediction error (MPE). For the regression models, the highest coefficients of determination (R²) were used. The best model developed with neural networks had an MSD of 4.36 and an MPE of 2.43%. This model used the variables of minimum temperature, production, relative humidity of the air, and irradiance 30 days before the evaluation of disease. The best regression model was developed from 29 selected climatic variables in the network. The summary statistics for this model were: MPE=6.58%, MSE=4.36, and R²=0.80. The elaborated neural networks from a time series also were evaluated to describe the epidemic. The incidence of coffee rust at four previous fortnights resulted in a model with MPE=4.72% and an MSD=3.95.
Tipo: | Info:eu-repo/semantics/article
Idioma: | Inglês
Identificador: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-41582002000500013
Editor: | Sociedade Brasileira de Fitopatologia
Relação: | 10.1590/S0100-41582002000500013
Formato: | text/html
Fonte: | Fitopatologia Brasileira  v.27 n.5 2002
Fonte: | info:eu-repo/semantics/openAccess