ChatLeafDisease: a chain-of-thought prompting approach for crop disease classification using large language models.

ABSTRACT: Accurate crop disease classification is essential for disease management to support food security. Deep learning has shown its high classification accuracy in image-based disease identification. However, the deep learning approach usually needs large amounts of data for training to achieve satisfactory performance, which hindering its application and scalability for different crops. Large language models (LLMs) have shown strong generation capability and zero-shot performance. While how to utilize the LLM technique for crop disease classification remains unclear. In this study, we developed a training-free framework named ChatLeafDisease (ChatLD) based on GPT-4o model with chain-of-thought (CoT) prompting for crop disease classification. The framework includes a disease description databas

SUBMITTER: Pan J 

PROVIDER: S-EPMC12709970 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature