Brief description of the project
Increasing globalised trade and tourism favour the introduction and spread of harmful organisms to other countries. Heat-loving pathogens are increasingly favoured by climate change in Central Europe, where they spread and establish themselves. Such pathogens include phytoplasmas – cell wall-less bacteria – such as flavescence dorée or black wood disease. As a long-term permanent crop in climatically favourable regions, vines are particularly at risk. The consequences of a pest infestation are serious: high yield losses, reduced wine quality and lower vitality of infected vines. The joint project »PhytoMo« therefore aims to develop a monitoring procedure to identify phytoplasmosis-infested vines at an early stage.
Project goals
The »PhytoMo« project aims to recognise infected vines and prevent further infection of the vines in good time. To make this possible, a systematic monitoring procedure is being developed. The required data is collected using a combination of hyper- and multispectral cameras and drones. Differences in the leaf reflectance of stressed and non-stressed plants are measured. In stressed, which means infected plants, biochemical and biophysical properties change. These have an influence on leaf reflectance and can therefore provide information in a spectral analysis as to whether a vine is infected.
Project results
In June 2024, the last series of measurements took place in the vineyards at the Julius Kühn Institute (JKI) at Geilweilerhof in Siebeldingen. The SPR department of the Fraunhofer Institute IOSB was on site with its camera technology. The multispectral and depth cameras were attached to a JKI quad bike and recorded images while driving along the vines. The exact position was also recorded via GPS. The drone from project partner Staatliche Lehr- und Versuchsanstalt für Wein- und Obstbau (LVWO) in Weinsberg generated image data while flying over the vineyard. The University of Bonn, which was also involved in the project, was present with geodesy experts and provided the required accuracy for the next step – merging the image data collected by the quad and the drone – with precisely measured markers that were visible from both the ground and the air.