| 大语言模型在植物病虫害智能诊断中应用的研究进展 |
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| 引用本文：王惟实,张慧源,朱诗嫣,何雄奎.大语言模型在植物病虫害智能诊断中应用的研究进展.植物保护学报,2026,53(1):67-79 |
| DOI：10.13802/j.cnki.zwbhxb.2026.2026806 |
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| 中文摘要:植物病虫害是制约农业生产的重要因素，依赖人工目测和专家经验的传统诊断方式存在效率低、标准不一等问题。大语言模型凭借强大的语言理解、知识整合和推理能力为构建智能化病虫害诊断系统提供了新思路。该文重点阐述大语言模型的技术基础与发展现状，基于检索增强生成的病虫害诊断方法，多模态大语言模型的应用，基于智能体的诊断系统架构、提示工程与模型微调技术等研究进展，分析现有研究的优势与不足，并对未来发展方向进行展望，以期为相关研究和应用提供参考。 |
| 中文关键词:植物病虫害 大语言模型 检索增强生成 多模态学习 智能体 |
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| Research advances in the application of large language models for intelligent diagnosis of plant diseases and insect pests |
| Author Name | Affiliation | E-mail | | Wang Weishi | College of Science, China Agricultural University, Beijing 100193, China | | | Zhang Huiyuan | College of Science, China Agricultural University, Beijing 100193, China | | | Zhu Shiyan | College of Science, China Agricultural University, Beijing 100193, China | | | He Xiongkui | College of Science, China Agricultural University, Beijing 100193, China | xiongkui@cau.edu.cn |
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| Abstract:Plant diseases and pests are major constraints on agricultural production. Traditional diagnostic methods relying on manual visual inspection and expert experience are often characterized by low efficiency and inconsistent standards. Large language models (LLMs), with their powerful capabilities in language understanding, knowledge integration, and reasoning, offer new approaches for developing intelligent diagnostic systems for plant diseases and insect pests. This paper systematically reviews the technical foundations and recent developments of large language models, retrieval-augmented generation (RAG)-based diagnostic methods, applications of multimodal large language models, agent-based diagnostic system architectures, as well as prompt engineering and model fine-tuning techniques. The advantages and limitations of existing studies are analyzed, and future research directions are discussed, with the aim of providing a reference for related research and practical applications. |
| keywords:plant disease and insect pest large language model retrieval-augmented generation multimodal learning intelligent agent |
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