Evaluation of Novel Approaches for Early Detection of and Detailed Characterization of Maize Foliar Disease
Peter J. Balint-Kurti
Project runs from 02/01/2019 to 01/31/2020
We generally assess levels of foliar disease in corn by observation in the field using a visual scale. While this method is robust and gives reproducible data, it does not provide qualitative data such as lesion size or shape, nor does it give us good data on speed of disease progression or on timing of initial symptoms. We will rate a set of 50 genetically similar lines each of which has a different allele conferring resistance to the foliar disease southern leaf blight. We will use a variety of approaches to rate disease , including hyperspectral imaging and digital imaging. We will also rate them conventionally at very frequent intervals. Finally, we will measure yield.
This work will help to develop methods for the early detection of foliar disease in the field. It will also characterize disease progression and the relationship between symptoms and yield loss. This knowledge will aid in the development of predictive models to guide farmers in the decision of whether and when to apply fungicides.
The characterization of different mechanisms used by different resistance genes will guide breeders in the combination of resistance genes to produce more optimally-robust disease resistant lines.