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KEYNOTE LECTURE SESSION 5: DISEASE CONTROL AND FORECASTING
MODELS
Plant disease prediction using data mining and
machine learning: a case study on Fusarium head
blight and deoxynivalenol content in winter wheat
S. Landschoot 1,2 , K. Audenaert 1,3 , W. Waegeman 2 , B. De Baets 2 , G.
Haesaert 1,3
1 Faculty of Applied Bioscience Engineering, University College Ghent, Valentin Vaerwyckweg 1, BE-
9000 Gent, Belgium; 2 KERMIT, Department of Mathematical Modelling, Statistics and Bioinformatics,
Ghent University, Coupure links 653, BE-9000 Gent, Belgium; 3 Department of Crop Protection,
Laboratory of Phytopathology, Ghent University, Coupure links 653, BE-9000 Gent, Belgium.
E-mail: sofie.landschoot@hogent.be
Crop diseases are one of the major causes of yield loss and hence timely
application of remedial measures may combat yield loss to a great extent.
Prediction models help in providing prior knowledge of the time and severity of the
outbreak of diseases and allow growers to underpin decision making on
cultivation techniques and the application of fungicides. Existing prediction models
are based on the combined effect of host susceptibility, inoculum strength and
meteorological conditions on disease development. The main objective for
developing a useful predictive model may be timely accurate predictions, but this
must be accomplished using the most simple, easily acquired, and inexpensive
data. If the data required to run the model is too expensive to acquire, it is unlikely
that the model will be deployed in practice.
Fusarium head blight (FHB) results in yield loss and quality loss due to
mycotoxins. Given these severe consequences throughout the world, various
models for FHB have been developed. Although of great interest, these models
cannot directly be transported to a Belgian context. The lack of fit of these models
could be explained by regional factors. Therefore, the objective of this research
was to implement machine learning techniques to generate field-specific
predictions of FHB and deoxynivalenol in winter wheat. To reach this goal various
regression techniques were compared. Additionally, the influence of a correct
cross-validation strategy, to gain insight in the performance of the models, was
studied. In order to deliver the models to growers and industry a web tool was
developed. This tool will lead to an integrated pest management and a more
sustainable agriculture since the optimal time and dose of fungicides is
recommended. It will also have an economic impact by reducing the input of
fungicides and a more efficient sampling strategy to guarantee food and feed
safety.
Keywords: disease forecasting, Fusarium, web tool, winter wheat
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