In the rapidly evolving world of digital agriculture, managing and interpreting vast amounts of data from diverse sources is a monumental challenge. Enter Anisa Kasim, a researcher from the V.M. Glushkov Institute of Cybernetics of the NAS of Ukraine, who has developed an ontological model that could revolutionize how we handle agricultural data. Published in the journal ‘Кібернетика та комп’ютерні технології’ (Cybernetics and Computer Technologies), Kasim’s work focuses on creating a knowledge base for intelligent Geographic Information Systems (GIS) that can seamlessly integrate and interpret heterogeneous data.
Kasim’s research addresses a critical gap in modern GIS technologies. While these systems excel at collecting, processing, and visualizing data, they often fall short in semantic information coordination and automated decision-making. “The ontological approach provides systematization, structuring, and interoperability of sensor data, formalization of domain knowledge, as well as intelligent extension of GIS functionality for solving applied tasks,” Kasim explains. This ontological model aims to formalize and integrate knowledge in the OWL (Web Ontology Language) format, enhancing the automation of agrotechnical process analysis and optimizing decision-making.
The model Kasim proposes is comprehensive, covering key entities such as soils, crops, climatic factors, technical means, and agro-technological operations. It consists of two main components: a four-component ontology that includes concepts, relations, interpretation functions, and axioms, and a separate set of instances that acts as a database. This structure allows for the integration of data from various sources like agrodrones, autonomous tractors, and cartographic services, each with different structures and levels of detail.
To validate the model, Kasim tested it in the Protege environment, which supports OWL notation. The research also involved generating SPARQL queries to interact with the ontological knowledge base, demonstrating its practical applicability. “The developed ontological model of the knowledge base for the intelligent geoinformation system of digital agriculture provides semantic integration and interpretation of heterogeneous data, automation of decision-making and, as a result, increasing the efficiency of agricultural production,” Kasim states.
The implications of this research are far-reaching. By creating a flexible and adaptive system, Kasim’s model can evolve by integrating new concepts and relations, making it a powerful tool for future agricultural technologies. The integration of SWRL (Semantic Web Rule Language) rules could further enhance the model’s decision-making capabilities, paving the way for more automated and efficient agricultural practices.
As digital agriculture continues to grow, the need for sophisticated data management and interpretation tools becomes ever more pressing. Kasim’s ontological model offers a promising solution, one that could significantly impact the efficiency and productivity of agricultural operations. With the potential to integrate new data sources and adapt to emerging technologies, this research could shape the future of digital agriculture, making it a cornerstone for the next generation of intelligent GIS systems.