Ontology and semantic techniques in agricultural crop pest and disease management: A systematic literature review

Abstract

Agricultural crop pests and diseases significantly impact global food security, causing substantial crop losses and economic damage. Addressing these challenges requires innovative solutions that integrate modern technological advancements. This study presents a systematic literature review on using ontology and semantic techniques in crop pest and disease management. By leveraging ontology frameworks and semantic web technologies, the research identifies how structured knowledge representation and advanced reasoning tools enhance detection accuracy, resource optimisation, and sustainability in agriculture. Key findings highlight the potential of integrating data from IoT sensors, drones, and satellite imagery into ontology-based systems to enable real-time decision-making and predictive analytics. This review also discusses the strengths and limitations of ontology-driven approaches compared to traditional methods, with an emphasis on sustainability benefits such as reduced pesticide usage and improved ecological balance. Some of the improvements ontology and semantic techniques have offered in the agricultural sector include enhanced scalability, integrating deep learning, and developing multilingual agricultural ontologies for global applications.