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In phytopathology quantitative measurements are rarely used to assess crop plant disease symptoms. Instead, a qualitative valuation by eye is often the method of choice. In order to close the gap between subjective human inspection and objective quantitative results, the development of an automated analysis system that is capable of recognizing and characterizing the growth patterns of fungal hyphae in micrograph images was developed. This system should enable the efficient screening of different host–pathogen combinations (e.g., barley—Blumeria graminis, barley—Rhynchosporium secalis) using different microscopy technologies (e.g., bright field, fluorescence). An image segmentation algorithm was developed for gray-scale image data that achieved good results with several microscope imaging protocols. Furthermore, adaptability towards different host–pathogen systems was obtained by using a classification that is based on a genetic algorithm. The developed software system was named HyphArea, since the quantification of the area covered by a hyphal colony is the basic task and prerequisite for all further morphological and statistical analyses in this context. By means of a typical use case the utilization and basic properties of HyphArea could be demonstrated. It was possible to detect statistically significant differences between the growth of an R. secalis wild-type strain and a virulence mutant.
Publications
Fungi cause severe diseases on a broad range of crop and ornamental plants, leading to significant economical losses. Plant pathogenic fungi exhibit a huge variability in their mode of infection, differentiation and function of infection structures and nutritional strategy. In this review, advances in understanding mechanisms of biotrophy, necrotrophy and hemibiotrophic lifestyles are described. Special emphasis is given to the biotrophy-necrotrophy switch of hemibiotrophic pathogens, and to biosynthesis, chemical diversity and mode of action of various fungal toxins produced during the infection process.