ChatLeafDisease:使用大型语言模型进行作物疾病分类的思维链提示方法
Jiandong Pan1, Renhai Zhong2, Fulin Xia1
1College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang, 310058, China.
Plant phenomics (Washington, D.C.)
|December 19, 2025
概括
一个新的没有培训的框架,ChatLeafDisease (ChatLD),使用大型语言模型 (LLMs) 进行准确的作物疾病分类. 这种方法提供了一个可扩展的解决方案,用于识别植物疾病,而无需广泛的数据培训.
科学领域:
- 农业科学 农业科学
- 人工智能的人工智能
- 植物病理学 植物病理学
背景情况:
- 准确的作物疾病分类对于粮食安全和有效的疾病管理至关重要.
- 深度学习模型需要大量的训练数据,这限制了它们对各种作物的应用.
- 大型语言模型 (LLM) 提供了零射击学习的潜力,但它们在作物疾病分类中的应用尚未得到充分探索.
研究的目的:
- 开发一种新的,不需要培训的框架,用于使用LLMs对作物疾病进行分类.
- 评估拟议框架的有效性与现有的番茄疾病识别模型相比.
- 评估新作物疾病框架的可扩展性和性能.
主要方法:
- 开发了ChatLeafDisease (ChatLD),这是一个基于GPT-4o的框架,具有思维链 (CoT) 提示.
- 整合了疾病描述数据库和由Cot提示指导的分类剂.
- 与GPT-4o,Gemini和对比语言图像预训练 (CLIP) 模型相比,比较了ChatLD的性能.
主要成果:
- 在六种番茄疾病中,ChatLD的准确率达到88.9%,明显超过GPT-4o (45.9%),Gemini (56.1%) 和CLIP (64.3%).
- 在Cot提示符中的评分规则对于捕捉疾病特异性差异和提高准确性至关重要.
- 缩的疾病描述提高了分类性能,而ChatLD对新作物疾病的准确性很高,表明了可扩展性.
结论:
- 聊天LD框架为作物疾病分类提供了基于LLM的高度准确和可扩展的解决方案.
- 这种无培训的方法,利用文本描述,克服了传统深度学习方法的数据限制.
- 聊天LD为农业疾病识别提供了一个有希望的替代方案,支持全球粮食安全工作.
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