Applied Intelligence

, Volume 49, Issue 11, pp 3821–3833 | Cite as

A spatio-semantic approach to reasoning about agricultural processes

  • Henning DeekenEmail author
  • Thomas Wiemann
  • Joachim Hertzberg


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.


Semantic mapping Environment modeling Ontologies Agriculture 



Work by Deeken is supported by the German Federal Ministry of Education and Research in the SOFiA project (Grant No. 01FJ15028). Work by Wiemann is supported by the German Federal Ministry of Education and Research within the framework of the BonaRes-initiative, project SoilAssist2 (Grant No. 031B0684D). The DFKI Osnabrück branch is supported by the state of Niedersachsen (VW-Vorab).

The support is gratefully acknowledged.


  1. 1.
    Amiama C, Pereira JM, Castro A, Bueno J (2015) Modelling corn silage harvest logistics for a cost optimization approach. Comput Electron Agric 118:56–65CrossRefGoogle Scholar
  2. 2.
    Barbieri DF, Braga D, Ceri S, Della Valle E, Grossniklaus M (2009) C-sparql: Sparql for continuous querying. In: Proceedings of the 18th international conference on World wide web. ACM, pp 1061–1062Google Scholar
  3. 3.
    Batsakis S, Petrakis EG, Tachmazidis I, Antoniou G (2017) Temporal representation and reasoning in owl 2. Semant Web 8(6):981–1000CrossRefGoogle Scholar
  4. 4.
    Bechhofer S (2009) Owl: Web ontology language. In: Encyclopedia of database systems. Springer, pp 2008–2009Google Scholar
  5. 5.
    Borrmann A, Rank E (2008) 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)Google Scholar
  6. 6.
    Daniele L, Ferreira Pires L (2013) An ontological approach to logistics. In: Enterprise interoperability, research and applications in the service-oriented ecosystem, IWEI 13. ISTE Ltd. WileyGoogle Scholar
  7. 7.
    Deeken H, Wiemann T, Hertzberg J (2018) Grounding semantic maps in spatial databases. Robot Auton Syst 105:146–165CrossRefGoogle Scholar
  8. 8.
    Dury J, Garcia F, Reynaud A, Bergez JE (2013) Cropping-plan decision-making on irrigated crop farms: a spatio-temporal analysis. Eur J Agron 50:1–10CrossRefGoogle Scholar
  9. 9.
    Garbacz P, Trypuz R (2017) Representation of tensed relations in owl. In: Research conference on metadata and semantics research. Springer, pp 62–73Google Scholar
  10. 10.
    Hoxha J, Scheuermann A, Bloehdorn S (2010) An approach to formal and semantic representation of logistics services. In: 19th European conference on artificial intelligence (ECAI 2010) Proceedings of the Workshop on Artificial Intelligence and Logistics (AILog), Lisbon, pp 73–78Google Scholar
  11. 11.
    Kaloxylos A, Groumas A, Sarris V, Katsikas L, Magdalinos P, Antoniou E, Politopoulou Z, Wolfert S, Brewster C, Eigenmann R et al (2014) A cloud-based farm management system: Architecture and implementation. Comput Electron Agric 100:168–179CrossRefGoogle Scholar
  12. 12.
    Kostavelis I, Gasteratos A (2015) Semantic mapping for mobile robotics tasks: a survey. Robot Auton Syst 66:86–103CrossRefGoogle Scholar
  13. 13.
    Lauer J, Richter L, Ellersiek T, Zipf A (2014) Teleagro+: Analysis framework for agricultural telematics data. In: 7Th ACM SIGSPATIAL international workshop on computational transportation science, IWCTS ’14. ACM, pp 47–53Google Scholar
  14. 14.
    Mark TB, Whitacre B, Griffin T et al (2015) Assessing the value of broadband connectivity for big data and telematics: Technical efficiency. In: 2015 annual meeting. Southern Agricultural Economics Association, GeorgiaGoogle Scholar
  15. 15.
    Nüchter A, Hertzberg J (2008) Towards semantic maps for mobile robots. Robotics and Autonomous SystemsGoogle Scholar
  16. 16.
    Pfeiffer D, Blank S (2015) Real-time operator performance analysis in agricultural equipment. In: 73rd international conference on agricultural engineering (AgEng), pp 6–7Google Scholar
  17. 17.
    Sørensen CG, Nielsen V (2005) Operational analyses and model comparison of machinery systems for reduced tillage. Biosyst Eng 92(2):143–155CrossRefGoogle Scholar
  18. 18.
    Steinberger G, Rothmund M, Auernhammer H (2009) Mobile farm equipment as a data source in an agricultural service architecture. Comput Electron Agric 65(2):238–246CrossRefGoogle Scholar
  19. 19.
    Wolter D, Wallgrün JO (2010) Qualitative spatial reasoning for applications: New challenges and the sparq toolbox. IGI GlobalGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Knowledge-Based Systems GroupUniversity of OsnabrückOsnabrückGermany
  2. 2.CLAAS E-Systems GmbHDissen a.T.W.Germany
  3. 3.DFKI Robotics Innovation CenterOsnabrück BranchOsnabrückGermany

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