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A Spatio-Semantic Model for Agricultural Environments and Machines

  • Henning Deeken
  • Thomas Wiemann
  • Joachim Hertzberg
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10868)

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.

Keywords

Semantic mapping Environment modeling Ontologies Agriculture 

Notes

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.

References

  1. 1.
    Deeken, H., Wiemann, T., Lingemann, K., Hertzberg, J.: SEMAP-a semantic environment mapping framework. In: 2015 European Conference on Mobile Robots (ECMR), pp. 1–6. IEEE (2015)Google Scholar
  2. 2.
    Steinberger, G., Rothmund, M., Auernhammer, H.: Mobile farm equipment as a data source in an agricultural service architecture. Comput. Electron. Agric. 65(2), 238–246 (2009)CrossRefGoogle Scholar
  3. 3.
    Pfeiffer, D., Blank, S.: Real-time operator performance analysis in agricultural equipment. In: 73rd International Conference on Agricultural Engineering (AgEng), pp. 6–7 (2015)Google Scholar
  4. 4.
    Steckel, T., Bernardi, A., Gu, Y., Windmann, S., Maier, A., Niggemann, O.: Anomaly detection and performance evaluation of mobile agricultural machines by analysis of big data. In: 73rd International Conference on Agricultural Engineering (AgEng), pp. 6–7 (2015)Google Scholar
  5. 5.
    Dury, J., Garcia, F., Reynaud, A., Bergez, J.E.: Cropping-plan decision-making on irrigated crop farms: a spatio-temporal analysis. Eur. J. Agron. 50, 1–10 (2013)CrossRefGoogle Scholar
  6. 6.
    Lauer, J., Richter, L., Ellersiek, T., Zipf, A.: TeleAgro+: analysis framework for agricultural telematics data. In: 7th ACM SIGSPATIAL International Workshop on Computational Transportation Science, IWCTS 2014, pp. 47–53. ACM (2014)Google Scholar
  7. 7.
    Sørensen, C.G., Nielsen, V.: Operational analyses and model comparison of machinery systems for reduced tillage. Biosyst. Eng. 92(2), 143–155 (2005)CrossRefGoogle Scholar
  8. 8.
    Amiama, C., Pereira, J.M., Castro, A., Bueno, J.: Modelling corn silage harvest logistics for a cost optimization approach. Comput. Electron. Agric. 118, 56–65 (2015)CrossRefGoogle Scholar
  9. 9.
    Kaloxylos, A., Groumas, A., Sarris, V., Katsikas, L., Magdalinos, P., Antoniou, E., Politopoulou, Z., Wolfert, S., Brewster, C., Eigenmann, R., et al.: A cloud-based farm management system: architecture and implementation. Comput. Electron. Agric. 100, 168–179 (2014)CrossRefGoogle Scholar
  10. 10.
    Mark, T.B., Whitacre, B., Griffin, T., et al.: Assessing the value of broadband connectivity for big data and telematics: technical efficiency. In: 2015 Annual Meeting, 31 January–3 February 2015, Atlanta, Georgia. Southern Agricultural Economics Association (2015)Google Scholar
  11. 11.
    Nüchter, A., Hertzberg, J.: Towards semantic maps for mobile robots. Rob. Auton. Syst. 56, 915–926 (2008)CrossRefGoogle Scholar
  12. 12.
    Kostavelis, I., Gasteratos, A.: Semantic mapping for mobile robotics tasks: a survey. Rob. Auton. Syst. 66, 86–103 (2015)CrossRefGoogle Scholar
  13. 13.
    Wolter, D., Wallgrün, J.O.: Qualitative spatial reasoning for applications: new challenges and the SparQ toolbox. IGI Global (2010)Google Scholar
  14. 14.
    Bechhofer, S.: Owl: web ontology language. In: Liu, L., Özsu, M.T. (eds.) Encyclopedia of Database Systems, pp. 2008–2009. Springer, Boston (2009).  https://doi.org/10.1007/978-0-387-39940-9CrossRefGoogle Scholar
  15. 15.
    Borrmann, A., Rank, E.: Topological operators in a 3D spatial query language for building information models. In. Proceedings of the 12th International Conference on Computing in Civil and Building Engineering (ICCCBE) (2008)Google Scholar
  16. 16.
    Daniele, L., Ferreira Pires, L.: An ontological approach to logistics. In: Enterprise Interoperability, Research and Applications in the Service-Oriented Ecosystem, IWEI 2013, ISTE Ltd., Wiley (2013)Google Scholar
  17. 17.
    Barbieri, D.F., Braga, D., Ceri, S., Della Valle, E., Grossniklaus, M.: C-SPARQL: SPARQL for continuous querying. In: Proceedings of the 18th International Conference on World Wide Web, pp. 1061–1062. ACM (2009)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Henning Deeken
    • 1
    • 2
    • 3
  • Thomas Wiemann
    • 1
  • Joachim Hertzberg
    • 1
    • 3
  1. 1.Knowledge-Based Systems GroupUniversity of OsnabrückOsnabrückGermany
  2. 2.CLAAS E-Systems KGaA mbH & Co KGDissen a.T.WGermany
  3. 3.DFKI Robotics Innovation Center, Osnabrück BranchOsnabrückGermany

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