Ontology-Driven Semantic IoT Framework for Precision Agriculture Using RDF and SPARQL

The integration of the Internet of Things (IoT) in agriculture enables real-time monitoring of environmental and soil parameters. However, heterogeneity of sensor data and lack of interoperability limit intelligent decision-making. This paper proposes an ontology-driven Semantic IoT framework for precision agriculture using Semantic Web technologies such as RDF, OWL, and SPARQL. The system semantically models agricultural sensor data and stores it in a triple store for intelligent querying and reasoning. The proposed framework enhances interoperability, scalability, and automated irrigation decision support. Experimental evaluation demonstrates improved semantic integration and efficient resource utilization compared to traditional IoT-based farming systems.