# Crop GraphRAG: pest and disease knowledge base Q&A system for sustainable crop protection

## TL;DR

This paper introduces Crop GraphRAG, a system that improves question answering about crop pests and diseases by combining knowledge graphs with AI, leading to more accurate and reliable answers.

## Contribution

The novel contribution is a framework that integrates knowledge graphs with RAG to enhance accuracy and reduce hallucinations in agricultural QA systems.

## Key findings

- Crop GraphRAG outperforms baselines in answer accuracy and coverage for agricultural questions.
- The framework effectively suppresses hallucinated content in generated answers.
- The system is verified as a practical tool for intelligent question answering in crop protection.

## Abstract

Intelligent prevention and control of crop diseases and pests is a critical link in safeguarding food security. However, agricultural practitioners often face fragmented information and low retrieval efficiency when seeking accurate, actionable knowledge. Furthermore, general-purpose large language models (LLMs) are prone to providing inaccurate or erroneous answers when applied to these specialized domains. To address these challenges, we assembled a large-scale corpus of knowledge on crop diseases and pests. Via entity and relation extraction, we constructed a multi-relational knowledge graph covering crops, diseases, pests, symptoms, and control measures. We subsequently designed Crop GraphRAG, a new framework that integrates knowledge graphs with retrieval-augmented generation (RAG). This system enables local knowledge-base question answering by retrieving adjacency subgraphs for relevant entities alongside summary-based passage retrieval. To evaluate performance, we curated a domain-specific test suite of question–answer pairs and conducted comparative and ablation experiments. Our experiments demonstrate that the Crop GraphRAG framework offers distinct advantages in answer accuracy and coverage compared to baselines. Crucially, the framework effectively suppresses hallucinated content, a common issue in generative models. These results verify the practical utility of the Crop GraphRAG framework for vertical-domain question answering. By mitigating the limitations of large language models in specialized agricultural contexts, this study provides a pragmatic tool for intelligent QA in the agricultural domain and advances the application of AI in crop protection.