A Spatio-Semantic Model for Agricultural Environments and Machines

Henning Deeken, Thomas Wiemann, Joachim Hertzberg

In: Proc. IEA/AIE 2018. International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA / AIE-2018) June 25-28 Montreal Canada Springer International Publishing International Publishing 2018.


Digitization of agricultural processes is advancing fast as telemetry data from the involved machines becomes more and more avail- able. Current approaches commonly have a machine-centric view that does not account for machine-machine or machine-environment relations. In this paper we demonstrate how to model such relations in the generic semantic mapping framework SEMAP. We describe how SEMAP’s core ontology is extended to represent knowledge about the involved machines and facilities in a typical agricultural domain. In the framework we com- bine different information layers – semantically annotated spatial data, semantic background knowledge and incoming sensor data – to derive qualitative spatial facts about the involved actors and objects within a harvesting campaign, which add to an increased process understanding.

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence