pdKGraph: A Novel Approach to Constructing Plant Disease Knowledge Graphs Using Large Language Models

Alakananda Mitra, Dinh Quy Duong, David H. Fleisher, Vangimalla R. Reddy, Chittaranjan Ray

Abstract: Plant diseases pose a persistent challenge to global agriculture, resulting in significant economic losses and threatening food security. Traditional diagnostic approaches and static literature reviews are increasingly inadequate in keeping pace with rapidly evolving pathogens and emerging detection technologies. In this initial study, we present a prototype artificial intelligence framework that integrates Retrieval-Augmented Generation (RAG) with Knowledge Graphs (KGs) to support the intelligent exploration and structuring of plant disease literature. The system enables semantic retrieval, natural language querying, and automated extraction of key entities—including diseases, diagnostic methods, datasets, and performance metrics—into structured KGs. Achieving a Top-1 accuracy of 70.3 percent, with an average query latency of 13–16 seconds and a hallucination rate of 3.84 percent, the framework demonstrates the feasibility of combining RAG and KGs for dynamic, evidence-grounded information access. This work provides a scalable basis for future research into more advanced reasoning capabilities, including temporal trend analysis and knowledge gap identification in plant disease diagnostics.

External IDs:dblp:journals/access/MitraDFRR26