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

Embedding-based Retrieval with LLM for Effective Agriculture Information Extracting from Unstructured Data