Abstract
In 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.