PestMA: LLM-based Multi-Agent System for Informed Pest Management

2025-04-14

AI Summary

Current LLM-based pest management approaches predominantly employ single-agent architectures, which struggle to integrate heterogeneous external knowledge, perform cross-validation, and execute threshold-sensitive decisions. To address these limitations, this paper proposes the first editing-paradigm multi-agent system (MAS) specifically designed for agricultural pest and disease management. The MAS comprises three specialized agents—editing, retrieval, and verification—that collaboratively generate context-aware, evidence-driven decisions. It synergistically integrates large language models (LLMs), retrieval-augmented generation (RAG), and rule-guided verification. Evaluated in real-world agricultural scenarios, the system achieves a decision accuracy of 92.6%, outperforming baseline methods by 5.8 percentage points. This advancement significantly improves the reliability, interpretability, and threshold adaptability of management recommendations.

Technology Category

• Multiagent Systems: Other Foundations of Multi Agent Systems
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

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.

Problem

• Enhancing pest management accuracy with multi-agent LLM systems
• Overcoming single-agent limitations in pest decision-making
• Integrating diverse data for evidence-based pest advice

Innovation

• LLM-based multi-agent system for pest management
• Editor, Retriever, Validator agents for decision-making
• Validation improves accuracy from 86.8% to 92.6%