For a more comprehensive understanding of cause-effect relationships, different scales regarding both geographical distances and the periods of time considered must be integrated. This is the only way to identify and describe local and remote as well as direct and delayed effects in the biosphere.
This purpose requires the collection of comprehensive data. However, information needed to better understand interrelationships is frequently missing. With appropriate sensors and cognitive analytics, it will be possible to close this gap in the future. In the context of this projects, sensor concepts will therefore be developed further in accordance with the requirements in agricultural technology and raw data will be processed for use in application processes.
This includes, for example, the use of agricultural sensor technology for:
- seismic imaging of soil compaction,
- analysis of soil nitrogen content,
- classification of soil areas and vegetation by hyper- and multi-spectral optical methods.
This will lead to the development of methods for soil parameters that cannot be measured yet to date, but which are essential for the planning of agricultural activities.
The data of these airborne and ground-based sensors will be combined with data from existing measurement systems as well as with remote sensing, weather, and yield data. This will provide a comprehensive amount of data for automated interpretation and decision support for different time horizons.