Explainable AI for the olive oil industry
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Autor:in (Körperschaft)
Publikationsdatum
2023
Typ der Arbeit
Studiengang
Typ
04B - Beitrag Konferenzschrift
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
Proceedings of the 22nd International Conference on Business Informatics Research, BIR 2023
Themenheft
DOI der Originalpublikation
Link
Reihe / Serie
Lecture Notes in Business Information Processing
Reihennummer
493
Jahrgang / Band
Ausgabe / Nummer
Seiten / Dauer
158-171
Patentnummer
Verlag / Herausgebende Institution
Springer
Verlagsort / Veranstaltungsort
Ascoli Piceno
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
Understanding Machine Learning results for the quality assessment of olive oil is hard for non-ML experts or olive oil producers. This paper introduces an approach for interpreting such results by combining techniques of image recognition with knowledge representation and reasoning. The Design Science Research strategy was followed for the creation of the approach. We analyzed the ML results of fluorescence spectroscopy and industry-specific characteristics in olive oil quality assessment. This resulted in the creation of a domain-specific knowledge graph enriched by object recognition and image classification results. The approach enables automatic reasoning and offers explanations about fluorescence image results and, more generally, about the olive oil quality. Producers can trace quality attributes and evaluation criteria, which synergizes computer vision and knowledge graph technologies. This approach provides an applicable foundation for industries relying on fluorescence spectroscopy and AI for quality assurance. Further research on image data processing and on end-to-end automation is necessary for the practical implementation of the approach.
Schlagwörter
Fachgebiet (DDC)
Veranstaltung
22nd International Conference on Business Informatics Research
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
Enddatum der Konferenz
Datum der letzten Prüfung
ISBN
978-3-031-43125-8
978-3-031-43126-5
978-3-031-43126-5
ISSN
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Veröffentlicht
Begutachtung
Peer-Review der ganzen Publikation
Open Access-Status
Closed
Lizenz
Zitation
Schmid, C., Laurenzi, E., Michelucci, U., & Venturini, F. (2023). Explainable AI for the olive oil industry. In K. Hinkelmann, F. J. López-Pellicer, & A. Polini (Eds.), Proceedings of the 22nd International Conference on Business Informatics Research, BIR 2023 (pp. 158–171). Springer. https://doi.org/10.1007/978-3-031-43126-5_12