Development of an Expert System for Precision Irrigation: Knowledge Modeling Approach

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.