用多个AI智能体协作，让农作物病虫害管理更准更可靠
PestMA: LLM-based Multi-Agent System for Informed Pest Management
- 设计三个专精智能体：编辑、检索、验证，协同决策
- 初始准确率86.8%，经验证后提升至92.6%
- 适合农业决策系统开发者与智慧农业研究者
有效的病虫害管理因需精准、情境化的决策而复杂。大语言模型（LLMs）的进展为获取和推理复杂知识提供了新可能。然而，现有基于LLM的方法多依赖单一智能体，难以整合外部信息、系统性验证或处理阈值驱动决策。为此，我们提出PestMA——一种基于大语言模型的多智能体系统（MAS），用于生成可信、有证据支持的病虫害管理建议。该系统采用编辑范式，包含三类专用智能体：编辑器负责合成管理建议，检索器收集相关外部数据，验证器确保结果正确性。在真实病虫害场景下的评估显示，PestMA初始决策准确率达86.8%，经验证后提升至92.6%。结果表明，协作式智能体工作流在优化与验证决策方面具有显著价值，凸显了基于LLM的多智能体系统在自动化和增强病虫害管理中的潜力。
原文摘要 · Abstract (English)
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.
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