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Computational Ecology and Software, 2012, 2(1): 70-79
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Article

Using artificial neural networks to predict the distribution of bacterial crop diseases from biotic and abiotic factors

Michael J. Watts 1, Susan P. Worner 2
1School of Earth and Environmental Sciences, University of Adelaide, North Terrace, SA 5005, Australia
2Bio-Protection Research Centre, PO Box 84, Lincoln University, Lincoln 7647, New Zealand

Received 6 November 2011;Accepted 10 December 2011;Published online 1 March 2012
IAEES

Abstract
Constructing accurate computational global distribution models is an important first step towards the understanding of bacterial crop diseases and can lead to insights into the biology of disease-causing bacteria species. We constructed artificial neural network models of the geographic distribution of six bacterial diseases of crop plants. These ANN modelled the distribution of these species from regional climatic factors and from regional assemblages of host crop plants. Multiple ANN were combined into ensembles using statistical methods. Tandem ANN, where an ANN combined the outputs of individual ANN, were also investigated. We found that for all but one species, superior accuracies were attained by methods that combined biotic and abiotic factors. These combinations were produced by both ensemble and cascaded ANN. This shows that firstly, ANN are able to model the geographic distribution of bacterial crop diseases, and secondly, that combining abiotic and biotic factors is necessary to achieve high modelling accuracies. The work reported in this paper therefore provides a basis for constructing models of the distribution of bacterial crop diseases.

Keywords artificial neural network;biotic and abiotic factors;bacterial crop disease;prediction.



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