<![CDATA[The agricultural industry in Indonesia confronts the simultaneous task of augmenting food production to satisfy escalating demand while proficiently handling crop losses caused by pests and diseases. This study introduces a novel approach that leverages knowledge graphs to transform traditional, expert-based knowledge into a dynamic and interconnected system for addressing these agricultural challenges. The study delineates constructing a comprehensive knowledge graph, commencing with data extraction with SPARQL queries, and progressing to ontology design, object property and datatype property specification, and instance generation. The resultant knowledge graph not only serves as an organized archive for pest and disease information but also gives a systematic framework for the integration, analysis, and decision-making of data in agriculture. This knowledge graph adds to the broader junction of data science and agriculture by improving the diagnosis, prevention, and control of rice diseases.]]>
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