| Publication type: | Conference paper |
| Type of review: | Peer review (publication) |
| Title: | Explainable AI for the olive oil industry |
| Authors: | Schmid, Christian Laurenzi, Emanuele Michelucci, Umberto Venturini, Francesca |
| et. al: | No |
| DOI: | 10.1007/978-3-031-43126-5_12 |
| Proceedings: | Perspectives in Business Informatics Research |
| Editors of the parent work: | Hinkelmann, Knut López-Pellicer, Francisco J. Polini, Andrea |
| Page(s): | 158 |
| Pages to: | 171 |
| Conference details: | 22nd International Conference on Perspectives in Business Informatics Research, Ascoli Piceno, Italy, 13-15 September 2023 |
| Issue Date: | 2023 |
| Series: | Lecture Notes in Business Information Processing |
| Series volume: | 493 |
| Publisher / Ed. Institution: | Springer |
| Publisher / Ed. Institution: | Cham |
| ISBN: | 978-3-031-43125-8 978-3-031-43126-5 |
| Language: | English |
| Subjects: | Fluorescence spectroscopy; Olive oil; Quality assessment; Knowledge graph; Computer vision; Fluorescent image |
| Subject (DDC): | 006: Special computer methods 664: Food technology |
| Abstract: | 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. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/29045 |
| Fulltext version: | Published version |
| License (according to publishing contract): | Licence according to publishing contract |
| Departement: | School of Engineering |
| Organisational Unit: | Institute of Applied Mathematics and Physics (IAMP) |
| Published as part of the ZHAW project: | ARES - AI for fluoREscence Spectroscopy in oil |
| Appears in collections: | Publikationen School of Engineering |
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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.
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