• DocumentCode
    2675889
  • Title

    Do-it-Yourself Digital Agriculture applications with semantically enhanced IoT platform

  • Author

    Jayaraman, Prem Prakash ; Palmer, Doug ; Zaslavsky, Arkady ; Georgakopoulos, Dimitrios

  • Author_Institution
    DP&S Flagship, CSIRO, Canberra, ACT, Australia
  • fYear
    2015
  • fDate
    7-9 April 2015
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    Internet of Things (IoT) enables various applications (crop growth monitoring and selection, irrigation decision support, etc) in Digital Agriculture domain. Semantic enhancements to IoT platforms address challenges of interoperability, data fusion, integration of heterogeneous IoT silos, annotation of data streams, just to name a few. This paper discusses the recently developed OpenIoT platform which demonstrated its applicability and efficiency in a number of use cases, including a Digital Agriculture use case (Phenonet). An ontology to represent Phenonet domain concepts in order to facilitate smart collection, annotation, validation, processing and storing of data streams from sensors in the field has been proposed and the results of experimental study, related semantic queries and reasoning using the ontology are presented. A Do-It-Yourself principle-driven zero-programming enabling Phenonet user interface demonstrates benefits, novelty and efficiency of the approach.
  • Keywords
    Internet of Things; agriculture; crops; ontologies (artificial intelligence); semantic Web; sensor fusion; OpenloT platform; Phenonet; crop growth monitoring; crop growth selection; data fusion; data streams; do-it-yourself digital agriculture applications; internet of things; interoperability; irrigation decision support; ontology; principle-driven zero programming; semantically enhanced loT platform; Clouds; Moisture; Ontologies; Semantics; Sensors; Soil; Soil measurements; do-it-yourself; internet of things; semantic digital agriculture; semantic web;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 2015 IEEE Tenth International Conference on
  • Conference_Location
    Singapore
  • Print_ISBN
    978-1-4799-8054-3
  • Type

    conf

  • DOI
    10.1109/ISSNIP.2015.7106951
  • Filename
    7106951