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)

Abstract

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.

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References

  1. 1.
    Cohen, J.: A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20, 37–46 (1960)CrossRefGoogle Scholar
  2. 2.
    Crop Protection Compendium - Global Module, 5th edn. ©CAB International, Wallingford, UK (2003)Google Scholar
  3. 3.
    De Wolf, E., Francl, L.: Neural networks that distinguish infection periods of wheat tan spot in an outdoor environment. Phytopathology 87, 83–87 (1997)CrossRefGoogle Scholar
  4. 4.
    De Wolf, E., Francl, L.: Neural network classification of tan spot and Stagonospora blotch infection periods in a wheat field environment. Phytopathology 90, 108–113 (2000)CrossRefGoogle Scholar
  5. 5.
    Dimopulos, I., Chronopoulos, J., Chronopoulou-Sereli, A., Lek, S.: Neural network models to study relationships between lead concentration in grasses and permanent urban descriptors in Athens city (Greece). Ecological Modelling 120, 157–165 (1999)CrossRefGoogle Scholar
  6. 6.
    Flexer, A.: Statistical evaluation of neural network experiments: minimum requirements and current practice. In: Trappl, R. (ed.) Cybernetics and Systems 1996, Proceedings of the 13th European Meeting on Cybernetics and Systems Research, Austrian Society for Cybernetic Studies, pp. 1005–1008 (1996)Google Scholar
  7. 7.
    Gotelli, N., Entsminger, G.: EcoSim: Null models software for ecology. Version 7. Acquired Intelligence Inc. & Kesey-Bear. Jericho, VT 05465 (2006)Google Scholar
  8. 8.
    Gevrey, M., Worner, S.P.: Prediction of global distribution of insect pest species in relation to climate using an ecological informatics method. Environmental Entomology 99(3), 979–986 (2006)Google Scholar
  9. 9.
    Gotelli, N.J.: Null model analysis of species co-occurrence patterns. Ecology 81(9), 2606–2621 (2000)CrossRefGoogle Scholar
  10. 10.
    Joy, M.K., Death, R.G.: Predictive modelling and spatial mapping of freshwater fish and decapod assemblages using GIS and neural networks. Freshwater Biology 49, 1036–1052 (2004)CrossRefGoogle Scholar
  11. 11.
    Lek, S., Delacoste, M., Baran, P., Dimopoulos, I., Lauga, J., Aulagnier, S.: Application of neural networks to modelling nonlinear relationships in ecology. Ecological Modelling 90, 39–52 (1996)CrossRefGoogle Scholar
  12. 12.
    Olden, J.D., Jackson, D.A.: Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks. Ecological Modelling 154, 135–150 (2002)CrossRefGoogle Scholar
  13. 13.
    Olden, J.D., Joy, M.K., Death, R.G.: An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecological Modelling 178, 389–397 (2004)CrossRefGoogle Scholar
  14. 14.
    Paul, P.A., Munkvold, G.P.: Regression and artificial neural network modeling for the prediction of gray leaf spot of maize. Phytopathology 95, 388–396 (2005)CrossRefGoogle Scholar
  15. 15.
    Pietravalle, S., Shaw, M.W., Parker, S.R., van den Bosch, F.: Modeling of relationships between weather and Septoria tritici epidemics on winter wheat: A critical approach. Phytopathology 93, 1329–1339 (2003)CrossRefGoogle Scholar
  16. 16.
    Pivonia, S., Yang, X.: Assessment of the potential year-round establishment of soybean rust throughout the world. Plant Disease 88, 523–559 (2004)CrossRefGoogle Scholar
  17. 17.
    Prechelt, L.: A quantitative study of experimental evaluations of neural network learning algorithms: Current research practice. Neural Networks 9(3), 457–462 (1996)CrossRefGoogle Scholar
  18. 18.
    Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323, 533–536 (1986)CrossRefGoogle Scholar
  19. 19.
    Sutherst, R.W., Maywald, G.F.: A computerized system for matching climates in ecology. Agriculture Ecosystems Environment 13, 281–299 (1985)CrossRefGoogle Scholar
  20. 20.
    Watts, M.J., Worner, S.P.: Using artificial neural networks to determine the relative contribution of abiotic factors influencing the establishment of insect pest species. Ecological Informatics 3(1), 64–74 (2008)CrossRefGoogle Scholar
  21. 21.
    Worner, S.P., Gevrey, M.: Modelling global insect pest species assemblages to determine risk of invasion. Journal of Applied Ecology 43, 858–867 (2006)CrossRefGoogle Scholar

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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