Enhancing Apple Variety Testing with Ontology-Enriched Visual Analytics: A Decision-Support Framework

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dc.contributor.authorChuprikova, Ekaterinaen_US
dc.contributor.authorGuerra, Walteren_US
dc.contributor.authorStocker, Roberten_US
dc.contributor.authorMejia-Aguilar, Abrahamen_US
dc.contributor.authorMonsorno, Robertoen_US
dc.contributor.editorFeige, Kathrinen_US
dc.contributor.editorNsonga, Baldwinen_US
dc.contributor.editorRink, Karstenen_US
dc.date.accessioned2025-05-26T06:27:55Z
dc.date.available2025-05-26T06:27:55Z
dc.date.issued2025
dc.description.abstractIn this work, we propose an ontology-driven visual analytics approach to support apple variety testing by integrating heterogeneous data sources, including climate records and both qualitative and quantitative agronomic observations. Our methodology includes the development of the Apple Trait Ontology, which standardizes trait definitions and enhances semantic interoperability during data integration, and the implementation of an interactive visual analytics system. This system offers an in-depth overview of apple variety performance over the years by examining key attributes-such as red over color, size, firmness, acidity, starch content, and sugar levels-along with other qualitative and quantitative characteristics. By leveraging ontology-enriched data structuring, the system enables expert-driven interpretation of variety performance, providing a comprehensive decision-support tool for agricultural domain experts. The apple variety-testing visual analytics system integrates spatiotemporal analytics, multimodal visual representations, and interactive filtering, allowing users to explore trait performance trends, climate resilience, and overall suitability under different environmental conditions. The findings demonstrate that combining semantic models with visual analytics enhances the accessibility and usability of complex agricultural data, ultimately improving breeding strategies and decision-making in apple variety selection. This research contributes to the broader field of agricultural informatics by showcasing the potential of knowledge-based systems to support precision farming.en_US
dc.description.sectionheadersSession I
dc.description.seriesinformationWorkshop on Visualisation in Environmental Sciences (EnvirVis)
dc.identifier.doi10.2312/envirvis.20251148
dc.identifier.isbn978-3-03868-287-5
dc.identifier.pages7 pages
dc.identifier.urihttps://doi.org/10.2312/envirvis.20251148
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/envirvis20251148
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCategories and Subject Descriptors (according to ACM CCS): H.2.8 [Information Systems]: Database Management-Database Applications
dc.subjectH.2.8 [Information Systems]
dc.subjectDatabase Management
dc.subjectDatabase Applications
dc.titleEnhancing Apple Variety Testing with Ontology-Enriched Visual Analytics: A Decision-Support Frameworken_US
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