A Semantic Meta-Model for Data Integration and Exploitation in Precision Agriculture and Livestock Farming

Tracking #: 2920-4134

This paper is currently under review
Dimitris Zeginis
Evangelos Kalampokis
Raúl Palma
Rob Atkinson1
Konstantinos A. Tarabanis

Responsible editor: 
Guest Editors Global Food System 2021

Submission type: 
Full Paper
At the domains of agriculture and livestock farming a huge amount of data are produced through numerous heterogeneous sources including sensor data, weather/climate data, statistical and government data, drone/satellite imagery, video, and maps. This plethora of data can be used at precision agriculture and precision livestock farming in order to provide predictive insights in farming operations, drive real-time operational decisions, and redesign business processes. The predictive power of the data can be further boosted if data from diverse sources are integrated and processed together, thus providing more unexplored insights. However, the exploitation and integration of agricultural data is not straightforward since they: i) cannot be easily discovered across the numerous heterogeneous sources and ii) use different structural and naming conventions hindering their interoperability. The aim of this paper is to firstly study the characteristics of agricultural data and the user requirements related to data modeling and processing from nine real cases at the agriculture, livestock farming and aquaculture domains and then propose a semantic meta-model that is based on W3C standards (DCAT, PROV-O and QB vocabulary) in order to enable the definition of metadata that facilitate the discovery, exploration, integration and accessing of data in the domain.
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