Abstract

The increasing use of drones, robotic platforms, and IoT sensors in agriculture has resulted in a growing volume of heterogeneous data that is difficult to integrate due to lack of interoperability. This paper presents three data pipelines designed within the Norwegian research project SMARAGD, targeting the transformation of siloed agritech data into interoperable NGSI-LD-compliant entities using Smart Data Models and the FIWARE framework. The pipelines cover aerial imagery, robotic imagery from ROS-based systems, and IoT sensor measurements, enriching the data with temporal and geospatial context and integrating it into a shared FIWARE-powered ecosystem. This architecture provides a foundation for decision-support tools and interoperability in land-based food production systems.

Recommended Citation

Dautov, R., Tverdal, S., Bondevik, A.S., Frøshaug, S.A., Szabo, V. & Fiksdal, J.R. (2025). Interoperable Agritech Data Pipelines with NGSI-LD and Smart Data ModelsIn I. Luković, S. Bjeladinović, B. Delibašić, D. Barać, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Empowering the Interdisciplinary Role of ISD in Addressing Contemporary Issues in Digital Transformation: How Data Science and Generative AI Contributes to ISD (ISD2025 Proceedings). Belgrade, Serbia: University of Gdańsk, Department of Business Informatics & University of Belgrade, Faculty of Organizational Sciences. ISBN: 978-83-972632-1-5. https://doi.org/10.62036/ISD.2025.152

Paper Type

Poster

DOI

10.62036/ISD.2025.152

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Interoperable Agritech Data Pipelines with NGSI-LD and Smart Data Models

The increasing use of drones, robotic platforms, and IoT sensors in agriculture has resulted in a growing volume of heterogeneous data that is difficult to integrate due to lack of interoperability. This paper presents three data pipelines designed within the Norwegian research project SMARAGD, targeting the transformation of siloed agritech data into interoperable NGSI-LD-compliant entities using Smart Data Models and the FIWARE framework. The pipelines cover aerial imagery, robotic imagery from ROS-based systems, and IoT sensor measurements, enriching the data with temporal and geospatial context and integrating it into a shared FIWARE-powered ecosystem. This architecture provides a foundation for decision-support tools and interoperability in land-based food production systems.