Precision irrigation necessitates decision support systems that are both efficient and comprehensible to farmers and agronomists. Numerous current solutions depend on Internet of Things (IoT) frameworks or black-box artificial intelligence (AI) models, which frequently encounter challenges related to interoperability, transparency, and reproducibility. To overcome these constraints, this research developed an ontology-driven expert system that formalizes knowledge of crops, soil, and climate into a modular framework capable of generating explicable irrigation recommendations. The system was developed using the Web Ontology Language (OWL) and augmented with Semantic Web Rule Language (SWRL) rules, employing a hybrid development methodology that integrates the structured precision of the V-cycle with the data-driven focus of the Cross-Industry Standard Process for Data Mining (CRISP-DM) and the semantic precision of the NeOn (Networked Ontologies) methodology. Competency questions guided the development of numerous agricultural concepts pertaining to irrigation. Scenario-based assessments validated that the ontology accurately deduced crop coefficient (Kc), integrated AI-predicted ET₀ with sensor-derived assertions, and produced consistent irrigation or non-irrigation recommendations across diverse situations. All reasoning processes performed in under one second, showcasing computational efficiency. Thus, the research provides a replicable and adaptable foundation for ontology-based irrigation decision support, facilitating future implementation using real-time sensor data and field validation.