link to homepage

Institute of Bio- and Geosciences

Navigation and service ZS1

Phenotyping of the infection dynamics of leaf pathogens with non-invasive sensor technology

The aim is to develop a non-invasive sensor system for detection and quantification of pathogens (Cercospora beticola) in sugar beet leaves, which allows the selection of resistant strains in the breeding process. Because of the green colour and the fluorescence emission of chlorophyll, plants can be recorded separately from the background by the means of image processing. Thus, camera systems can be used as non-invasive sensors for the measurement of growth and morphology of plants (GROWSCREEN and GROW SCREEN fluoro).

On infected plants leaf pathogens evoke symptoms in the leaf tissue. Thus, the infected tissue differs optically from the healthy environment in colour and texture. This will be used to determine the pathogen of sugar beet leaves and the dynamics of infection by sensors. Suitable combinations of cameras and filters should help to detect the typical disease symptoms in plant tissues. The use of sensors allows non-invasive and objective diagnoses of infection severity and thus a distinction between healthy and diseased plants, as well as a quantification of the degree of disease.

One advantage of sensor-based diagnostics of leave infection is the independence of rating methods, which always depend on a subjective assessment of the rating person.
Thus, the use of sensors is an important contribution to quality management.
The identification of resistant strains requires not only a reliable, objective assessment of the pathogen, but also a standardized cultivation and inoculation of the test plants. This way it is ensured that the detected differences of the strains are attributed to resistance properties of the plants but not to differences in handling.


Additional Information


Opens new window


sponsorship no.

BMBF Fz. 0315531C

Networking sensor technology R&D for crop breeding and management

Opens new window



project team


research area