BIOdiversity, Machine Learning and Agriculture

Agricultural land accounts for more than half of the total area of Germany and can therefore have a significant impact on the country’s biodiversity. The BioMLAgrar project aims to use the data generated by sensors on current agricultural machinery for biodiversity monitoring. With the help of AI, biodiversity data from agricultural land and the associated agricultural operating data will be used to develop forecasting models for biodiversity management. One focus is on machine learning in order to model expert knowledge from the field of biodiversity and to obtain reliable forecast models despite the small-data situation. In the first phase, a system of indicators for the status of biodiversity will be developed. Parameters for biodiversity as well as supplementary parameters from agriculture and environmental monitoring will be considered. In addition, the further research concept will be developed in more detail and the application for the second phase will be submitted.

In a possible second phase, the developed indicator system will be tested in practice with the involvement of biological stations, municipalities and environmental associations and will be available for biodiversity monitoring. One focus here will be on the transferability of the indicator system to other regions. Once the second phase is complete, an interdisciplinary data pool will also be available for further research work. This will also be used to develop a forecasting tool for stakeholders. This will be used to support planning tasks and enable transparent recommendations for action.

Project lead: Prof. Dr. Burkhard Wrengler

OWL University of Applied Sciences and Arts