Increasing challenges for agricultural production such as climate change, environmental concerns, energy demands, and growing expectations from consumers triggered the necessity for innovation using data-driven approaches such as visual analytics. OB-VISLY extends the visual analytics approach with a structural way of data organization (ontologies), data mining, and visualization techniques to retrieve knowledge from the agricultural monitoring data. The latest advances in data visualization and analytics made it possible to fully exploit the potential of the proposed approach and gain insights into high complexity datasets (multi-source, multi-scale, and different stages).
In OB-VISLY, I will carry out state-of-the-art research that unites two strands of recent, significant inquiry: Big Data analytics in the agricultural sector and visual methods. OB-VISLY aims to (1) establish a regionally significant dataspace enabled to synthesize information about fruit-growing apple orchards and vineyards and derive insight from massive, dynamic, and often conflicting data by providing up-to-date, consistent, and credible assessments; (2) create a single visual analytics user interface that can turn the data into knowledge for users of different information retrieval proficiency.
OB-VISLY will establish and implement an innovative visual analytics-enabled dataspace within the European agricultural sector. The findings will contribute to European priority in building a digital single market and tackle obstacles that hinder the exploitation of big data and digital tools. Thus OB-VISLY will serve social and environmental well-being by uncovering hidden patterns from big agricultural data for future sustainable and environmentally friendly development. Such an endeavor aims to pave a way towards strengthening precision and conservation agriculture methods and create an added value to sustain under competitive conditions and increase agricultural potential in Europe.
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