A spatio-semantic approach to reasoning about agricultural processes


Digitization of agricultural processes is advancing fast as telemetry data from the involved machines becomes more and more available. 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 combine different information layers – semantically annotated spatial data, semantic background knowledge and incoming sensor data – to derive qualitative spatial facts and continuously track them to generate process states and events about the ongoing logistic process of a harvesting campaign, which adds to an increased process understanding.

Applied Intelligence 2019