Effective pest management is complex due to the need for accurate, context-specific decisions. Recent advancements in large language models (LLMs) open new possibilities for addressing these challenges by providing sophisticated, adaptive knowledge acquisition and reasoning. However, existing LLM-based pest management approaches often rely on a single-agent paradigm, which can limit their capacity to incorporate diverse external information, engage in systematic validation, and address complex, threshold-driven decisions. To overcome these limitations, we introduce PestMA, an LLM-based multi-agent system (MAS) designed to generate reliable and evidence-based pest management advice. Building on an editorial paradigm, PestMA features three specialized agents, an Editor for synthesizing pest management recommendations, a Retriever for gathering relevant external data, and a Validator for ensuring correctness. Evaluations on real-world pest scenarios demonstrate that PestMA achieves an initial accuracy of 86.8% for pest management decisions, which increases to 92.6% after validation. These results underscore the value of collaborative agent-based workflows in refining and validating decisions, highlighting the potential of LLM-based multi-agent systems to automate and enhance pest management processes.

本研究针对现有基于智能体的害虫管理方法在多样性信息整合和复杂决策上的局限性，提出了一种新颖的多智能体系统PestMA。该系统通过编辑、检索和验证三个专门代理的协作，提供基于证据的害虫管理建议，实验证明其决策准确率从86.8%提升至92.6%，展示了协作智能体工作流程在优化害虫管理中的巨大潜力。

基于大型语言模型的多智能体系统PestMA：用于智能害虫管理