The main scientific and technological achievements include the development of a novel ontology-based visual analytics framework for agricultural big data. The multi-source and multi-scale data have been integrated within a visual analytics framework, which enabled efficient knowledge extraction. This framework helps extract information about the interaction of an apple variety testing program with the climate data. The main innovation outcomes of the project include the publication of a comprehensive ontology for apple traits, which standardizes and facilitates the understanding and analysis of apple-related data. Additionally, the project developed an advanced visual analytics interface that significantly enhances the interaction between the apple variety testing program and climate. This interface allows for more intuitive and efficient exploration and interpretation of agricultural data, leading to better-informed decision-making and optimization of agricultural practices. These innovations collectively contribute to advancing precision agriculture and integrating diverse data sources within the agricultural sector.
The OB-VISLY project achieved several key milestones across its work packages.
In WP1 Data Ecosystem, I collected data on fruit-growing activities and environmental parameters, created and enhanced ontologies for selected case studies, and harmonized data to establish FAIR ontologies.
In WP2 Data Analytics, I conducted a stakeholder workshop to define analytical requirements, linked structured data from various sources, and integrated data mining algorithms and predictive data analytics.
For WP3 Visual Analytics, I developed a visual analytics interface, realized data visualization, and established an iterative development workflow to ensure stakeholder satisfaction.
Finally, in WP4: Evaluation, I conducted a usability test to improve the developed tools and pave the path for future work.
