  Pest identification is a crucial aspect of pest control in agriculture.
However, most farmers are not capable of accurately identifying pests in the
field, and there is a limited number of structured data sources available for
rapid querying. In this work, we explored using domain-agnostic general
pre-trained large language model(LLM) to extract structured data from
agricultural documents with minimal or no human intervention. We propose a
methodology that involves text retrieval and filtering using embedding-based
retrieval, followed by LLM question-answering to automatically extract entities
and attributes from the documents, and transform them into structured data. In
comparison to existing methods, our approach achieves consistently better
accuracy in the benchmark while maintaining efficiency.
