Value Generation with Computer Vision Techniques in the Olive Oil Industry - a Knowledge Graph Framework

Quality assessments of olive oils are labour-intensive and time-consuming tasks for olive oil producers. In addition to their own quality targets, producers have to meet extensive standardised quality criteria in order to be allowed to market high-quality olive oils. For the necessary chemical and organoleptic tests, various expensive measuring instruments and extensive procedures are necessary; producers usually only resort to some internal measurements and use their own industry knowledge for their decisions. Recent developments combine the measurement method of fluorescence spectroscopy with machine learning to analyse olive oil test samples in a applicable and fast way. Despite first promising prediction and classification results, producers are left with questions about how to interpret and use the results for their own production. To promote the explainability of AI systems and knowledge alignment with industry knowledge, knowledge graphs provide contextual understanding and improved data integration. Subsequently, this thesis investigated how image recognition techniques for fluorescence images of olive samples can be applied to enrich a domain-specific knowledge graph that ultimately supports quality assessment in the olive oil industry. For this purpose, using a design science research approach, results of fluorescent spectroscopy with the help of computer vision and machine learning results, as well as the domain characteristics in the quality assessment of olive oils were investigated. As a result, a domain-specific KG was created, which was enriched using object detection, colour analysis and image classification results based on a YOLOv8 and linear regression model. Olive oil producers receive explanations about the results of the fluorescence images through the reasoning of the knowledge graph and can identify dependencies to their own quality testing, production and market management. The proposed framework supports the traceability and overview of quality attributes and evaluation criteria by combining computer vision and knowledge graph technologies. The basic concept provides a practical starting point for industries relying on fluorescence spectroscopy and that are confronted with AI to foster knowledge management for quality assurance.

Schmid, Christian, 2023

Art der Arbeit Master Thesis
Betreuende Dozierende Laurenzi, Emanuele
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Studiengang: Business Information Systems (Master)
Vertraulichkeit: öffentlich
Art der Arbeit
Master Thesis
Autorinnen und Autoren
Schmid, Christian
Betreuende Dozierende
Laurenzi, Emanuele
Sprache der Arbeit
Business Information Systems (Master)
Standort Studiengang