Spatio-temporal modeling of host-pathogen interactions using level set method

Abstract

Phenotyping host-pathogen interactions is crucial for understanding infectious diseases in plants. Traditionally, this process has relied on visual assessments or manual measurements, which can be subjective and labor-intensive. Recent advances in image processing and mathematical modeling enable precise and high-throughput phenotyping. In this study, we propose an innovative approach in plant pathology by combining image processing techniques with the level set method. This integrated approach leverages the strengths of both methodologies to provide accurate, robust, and detailed analysis of leaf and lesion evolution. By employing this combination, we achieve precise delineation of lesion boundaries and track their progression over time, offering clear visual feedback. This enhances the ability of the method to monitor plant health status comprehensively. The results, which track the growth of Peyronellaea pinodes on the stipules of two pea cultivars and the associated leaf deformation, provide an accurate visual representation of disease progression. This model represents a significant advancement in plant disease phenotyping, offering precise and detailed insights that can enhance our understanding of host-pathogen interactions.