Explainable AI for the olive oil industry
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Author (Corporation)
Publication date
2023
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Type
04B - Conference paper
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Parent work
Proceedings of the 22nd International Conference on Business Informatics Research, BIR 2023
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Series
Lecture Notes in Business Information Processing
Series number
493
Volume
Issue / Number
Pages / Duration
158-171
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Publisher / Publishing institution
Springer
Place of publication / Event location
Ascoli Piceno
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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.
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Event
22nd International Conference on Business Informatics Research
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ISBN
978-3-031-43125-8
978-3-031-43126-5
978-3-031-43126-5
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Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
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Peer review of the complete publication
Open access category
Closed
License
Citation
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