Cluster of Excellence
Research for the Future of Agriculture
Crops are the cornerstones of sustainable food production. However, crop production is currently under immense pressure for several reasons. First, the growing world population demands more food, and simultaneously more renewable resources for non-food products. Second, arable land is limited, thus an increase in space is not an option. The same is true for some essential nutrients for plant production and, at the same time, climate change increases the severity of plant stress. Third, the environmental footprint needs to be reduced by limiting the use of agro-chemicals and minimizing soil degradation, water consumption, and pollution. Finally, society increasingly demands high-quality and organic food. Addressing such conflicting demands requires drastic changes in the way we produce crops. To increase crop yields while minimizing the environmental footprint under adverse conditions and in an economically reasonable way, substantial progress in our scientific understanding of novel technologies is required.
Based on successful interdisciplinary research, the Cluster of Excellence PhenoRob is moving toward sustainable crop production, spanning from monitoring and understanding to assessment and identification of promising solutions. The research in the cluster will be organized along six core projects, accompanied by further exploratory research activities.
PhenoRob takes a technology-driven approach to address the challenging scientific objectives. We foresee novel ways of growing crops and managing fields, and aim at reducing the environmental footprint of crop production, maintaining the quality of soil and arable land, and analyzing the best routes to improve the adoption of technology. The novel approach of PhenoRob is characterized by the integration of robotics, digitalization, and machine learning on one hand, and modern phenotyping, modeling, and crop production on the other.
We systematically monitor all essential aspects of crop production using sensor networks as well as ground and aerial robots. These various in-field monitoring activities will generate large amounts of heterogeneous data on plants, crop stands, soil, and the environment such as weather or vegetation biodiversity. This enables a more targeted management of inputs (genetic resources, crop protection, fertilization) for optimizing outputs (yield, growth, environmental impact).
We develop novel technologies to enable real-time and automated control of weeds and selective spraying and fertilization of individual plants in field stands. This helps to reduce the environmental footprint by lowering the amount of applied chemicals, improving water and nutrient use efficiency, and minimizing soil degradation and erosion.
We apply modern machine learning techniques to analyze large amounts of acquired crop data. Doing so, we improve our understanding and our models of plant growth, and of nutrient and water use efficiency, and identify correlations between inputs and outputs.
We investigate the requirements for technology adoption and socioeconomic and environmental impact of the innovations. And we predict the expected impacts of novel approaches on management decisions at the farm level.