A unified ontology-based and semantic interoperability framework for smart agriculture systems

Modern agriculture is evolving with the integration of IoT, drones, machine learning, and semantic technologies. However, interoperability across heterogeneous agricultural systems remains a critical barrier, leading to fragmented decision-making. This paper proposes a unified Semantic Interoperability framework for smart crop management, which leverages ontologies, reasoning engines, and real-time data integration platforms to standardize heterogeneous data and support holistic decisions. To show its relevance, a Semantic Crop Management Framework (SCMF) for pumpkin farming is implemented as a case study. SCMF integrates deep learning (YOLOv8) for ripeness detection, ontology modeling through RDF and OWL, and semantic querying through SPARQL to produce precise recommendations for soil, irrigation, and harvest management. Experimental results indicate 15–20% yield improvement, 20–30% in water savings, and 10–15% in chemical use reduction. This research emphasizes the significance of semantic interoperability and ontology-based reasoning as a scalable and generalizable solution for smart farming in various agricultural sectors.