The University of Cologne, founded in 1388, is among Germany’s leading research universities and a member of the German U15 group. With students from around 180 countries, it fosters a diverse and international academic community. The University comprises six faculties and 16 cross-faculty research and teaching centers, encouraging interdisciplinary collaboration. Among its internationally recognized research units is the Cluster of Excellence on Plant Sciences (CEPLAS), funded within the German Excellence Strategy, which advances fundamental and applied research in plant biology and biotechnology. Our research group, AG Kopriva, is part of CEPLAS, contributing to discoveries in plant sulfur metabolism, nutrient signalling, and plant-microbe interactions. The University of Cologne is also a founding member of the European University for Well-Being (EuniWell) and currently hosts approximately 45,000 undergraduate students and 4,000 doctoral candidates.
plant health monitoring, plant–microbe interactions, plant disease early detection, root‑associated microbiome, sustainable crop protection, VOCs,
Within the Cluster of Excellence on Plant Sciences (CEPLAS) at the University of Cologne, our research group AG Kopriva focuses on plant nutrition, mineral metabolism and plant–microbe interactions in the rhizosphere, with a strong emphasis on sulfur metabolism and nutrient signalling. We study how nutrient status and root exudation shape plant–microbe relationships and modulate plant responses to abiotic and biotic stress, which can inform early‑warning indicators of plant health and stress tolerance.
For Topic 2.2.1-2026 (RIA), we propose a collaboration where AG Kopriva provides biological and physiological expertise on plant nutrient status, root‑microbiome interactions and plant stress responses, to be integrated with novel remote and non‑invasive ICT monitoring and control systems (e.g., imaging, sensors, AI‑based early‑detection tools) developed by engineering and ICT partners. Our group can contribute trait‑measuring protocols, controlled‑environment phenotyping pipelines, and metabolomic / physiological readouts that can be linked to remote sensing data, to help design and validate non‑invasive strategies for early warning and control of disease and pest infestation in crops.
