Imaging with spatio-temporal modelling to characterize the dynamics of plant-pathogen lesions

Abstract

Within-host spread of pathogens is an important process for the study of plant-pathogen interactions. However, the development of plant-pathogen lesions remains practically difficult to characterize and quantify beyond the common traits such as lesion area. We tackle the spatio-temporal dynamics of interactions by combining image-based phenotyping with mathematical modelling. We consider the spread of Peyronellaea pinodes on pea stipules that were monitored daily with visible imaging. We assume that pathogen propagation on host-tissues can be described by the Fisher-KPP model where lesion spread depends on both a logistic local growth and an homogeneous diffusion. Model parameters are estimated using a variational data assimilation approach on sets of registered images. This modelling framework is used to compare the spread of an aggressive isolate on two pea cultivars with contrasted levels of partial resistance. We show that the expected slower spread on the most resistant cultivar is actually due to a decrease of diffusion and, to a lesser extent, local growth. These results demonstrate that spatial models with imaging allows one to disentangle the processes involved in host-pathogen interactions. Hence, promoting model-based phenotyping of interactions would allow a better identification of quantitative traits thereafter used in genetics and ecological studies.

SYMPATHIQUE: Image-based tracking of Symptoms and monitoring of Pathogenesis to decompose Quantitative disease resistance in the field

“…However, an image registration process capable of aligning sequential images without the need for arti cial marks could reduce the workload associated with our current method while at the same time increasing the analyzable leaf area present in each image. Though this may be very challenging for wheat leaves which do not have many distinct features on the leaf, nor a feature-rich contour that could guide a marker-free alignment [36,37], novel deep-learning based approaches could offer signi cant potential for resolving this issue. At the level of image processing, a more explicit consideration of time differences between images during inference will allow re ning leaf-level segmentation and detection results.…”

“…More detailed investigations of symptoms or pathogenesis that require a high spatial precision across the entire leaf also demand careful image registration, e.g. [36,37].…”