1. ZHAW digitalcollection
  2. School of Engineering
  3. Publikationen
  4. Publikationen School of Engineering
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSchmid, Christian-
dc.contributor.authorLaurenzi, Emanuele-
dc.contributor.authorMichelucci, Umberto-
dc.contributor.authorVenturini, Francesca-
dc.date.accessioned2023-11-10T17:52:41Z-
dc.date.available2023-11-10T17:52:41Z-
dc.date.issued2023-
dc.identifier.isbn978-3-031-43125-8de_CH
dc.identifier.isbn978-3-031-43126-5de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/29045-
dc.description.abstractUnderstanding 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.de_CH
dc.language.isoende_CH
dc.publisherSpringerde_CH
dc.relation.ispartofseriesLecture Notes in Business Information Processingde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectFluorescence spectroscopyde_CH
dc.subjectOlive oilde_CH
dc.subjectQuality assessmentde_CH
dc.subjectKnowledge graphde_CH
dc.subjectComputer visionde_CH
dc.subjectFluorescent imagede_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc664: Lebensmitteltechnologiede_CH
dc.titleExplainable AI for the olive oil industryde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Angewandte Mathematik und Physik (IAMP)de_CH
zhaw.publisher.placeChamde_CH
dc.identifier.doi10.1007/978-3-031-43126-5_12de_CH
zhaw.conference.details22nd International Conference on Perspectives in Business Informatics Research, Ascoli Piceno, Italy, 13-15 September 2023de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end171de_CH
zhaw.pages.start158de_CH
zhaw.parentwork.editorHinkelmann, Knut-
zhaw.parentwork.editorLópez-Pellicer, Francisco J.-
zhaw.parentwork.editorPolini, Andrea-
zhaw.publication.statuspublishedVersionde_CH
zhaw.series.number493de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedingsPerspectives in Business Informatics Researchde_CH
zhaw.webfeedPhotonicsde_CH
zhaw.funding.zhawARES - AI for fluoREscence Spectroscopy in oilde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
Appears in collections:Publikationen School of Engineering

Files in This Item:
There are no files associated with this item.
Show simple item record
Schmid, C., Laurenzi, E., Michelucci, U., & Venturini, F. (2023). Explainable AI for the olive oil industry [Conference paper]. In K. Hinkelmann, F. J. López-Pellicer, & A. Polini (Eds.), Perspectives in Business Informatics Research (pp. 158–171). Springer. https://doi.org/10.1007/978-3-031-43126-5_12
Schmid, C. et al. (2023) ‘Explainable AI for the olive oil industry’, in K. Hinkelmann, F.J. López-Pellicer, and A. Polini (eds) Perspectives in Business Informatics Research. Cham: Springer, pp. 158–171. Available at: https://doi.org/10.1007/978-3-031-43126-5_12.
C. Schmid, E. Laurenzi, U. Michelucci, and F. Venturini, “Explainable AI for the olive oil industry,” in Perspectives in Business Informatics Research, 2023, pp. 158–171. doi: 10.1007/978-3-031-43126-5_12.
SCHMID, Christian, Emanuele LAURENZI, Umberto MICHELUCCI und Francesca VENTURINI, 2023. Explainable AI for the olive oil industry. In: Knut HINKELMANN, Francisco J. LÓPEZ-PELLICER und Andrea POLINI (Hrsg.), Perspectives in Business Informatics Research. Conference paper. Cham: Springer. 2023. S. 158–171. ISBN 978-3-031-43125-8
Schmid, Christian, Emanuele Laurenzi, Umberto Michelucci, and Francesca Venturini. 2023. “Explainable AI for the Olive Oil Industry.” Conference paper. In Perspectives in Business Informatics Research, edited by Knut Hinkelmann, Francisco J. López-Pellicer, and Andrea Polini, 158–71. Cham: Springer. https://doi.org/10.1007/978-3-031-43126-5_12.
Schmid, Christian, et al. “Explainable AI for the Olive Oil Industry.” Perspectives in Business Informatics Research, edited by Knut Hinkelmann et al., Springer, 2023, pp. 158–71, https://doi.org/10.1007/978-3-031-43126-5_12.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.