Predicting the Distribution of Fungal Crop Diseases from Abiotic and Biotic Factors Using Multi-Layer Perceptrons

  • Michael J. Watts
  • Sue P. Worner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5506)


Predictions of the distribution of fungal crop diseases have previously been made solely from climatic data. To our knowledge there has been no study that has used biotic variables, either alone or in combination with climate factors, to make broad scale predictions of the presence or absence of fungal species in particular regions. The work presented in this paper used multi-layer perceptrons (MLP) to predict the presence and absence of several species of fungal crop diseases across world-wide geographical regions. These predictions were made using three sets of variables: abiotic climate variables; biotic variables, represented by host plant assemblages; And finally the combination of predictions of the climate and host assemblage MLP using a cascaded MLP architecture, such that final predictions were made from both abiotic and biotic factors.


Hide Neuron Biotic Variable Gray Leaf Spot Training Epoch Cascade Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Michael J. Watts
    • 1
  • Sue P. Worner
    • 2
  1. 1.School of Biological SciencesUniversity of SydneyAustralia
  2. 2.Bio-Protection Research CentreLincoln UniversityLincolnNew Zealand

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