Abstract
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 about the involved actors and objects within a harvesting campaign, which add to an increased process understanding.
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Acknowledgment
Work by Deeken is supported by the German Federal Ministry of Education and Research in the SOFiA project (Grant No. 01FJ15028). The DFKI Osnabrück branch is supported by the state of Niedersachsen (VW-Vorab). The support is gratefully acknowledged.
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Deeken, H., Wiemann, T., Hertzberg, J. (2018). A Spatio-Semantic Model for Agricultural Environments and Machines. In: Mouhoub, M., Sadaoui, S., Ait Mohamed, O., Ali, M. (eds) Recent Trends and Future Technology in Applied Intelligence. IEA/AIE 2018. Lecture Notes in Computer Science(), vol 10868. Springer, Cham. https://doi.org/10.1007/978-3-319-92058-0_57
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DOI: https://doi.org/10.1007/978-3-319-92058-0_57
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