Unmanned aerial vehicles (UAVs) are foreseen to significantly change the world by supplying various types of services that are complex, expensive, or dangerous to provide otherwise. In particular, UAVs assist with automation in several industrial domains such as agriculture. In the context of this project, UAVs serve as moving base-stations to enhance wireless connectivity over the agriculture site at different levels, for example, between agricultural machinery and sensors. To optimize control and resource allocation of UAV networks, artificial intelligence and machine learning are of great importance.  The core contribution of this project encompasses developing resource-efficient, accurate, and interpretable algorithms for federated- and transfer learning that can be applied by UAVs to optimize the performance of the agriculture site in different perspectives, and in particular, regarding wireless communications.

The project receives funding from the German Ministry of Education and Research (BMBF). It is a multi-party project in collaboration with several partners, including Fraunhofer Heinrich-Hertz-Institute, University of Kaiserlautern, John Deere ETIC, Welotec GmbH, and CiS GmbH. The project runtime is 05.2020-04.2023.