TARAG: A time-aware retrieval-augmented generation framework for supporting precision crop pest and disease management through large language models

Plant diseases and pests cause significant annual crop losses, severely threatening global food security. To mitigate crop losses, large language models (LLMs) demonstrate the ability to alleviate challenges in accessing precise farming support by acting as intelligent assistants that provide farmers with timely and precise decision support services. However, existing efforts still fall short in precision due to the neglect of time-sensitive nature in agricultural practices. They implicitly treat domain knowledge as time-irrelevant, ignoring the impact of crop phenology and pest life stages in their generated recommendations. To address this challenge, we propose a Time-Aware Retrieval-Augmented Generation framework (TARAG), comprising a time-aware knowledge base construction module, a hybrid retrieval module, and a time-based generation module to provide precise pest management suggestions. Firstly, the time-aware knowledge base construction module constructs a time-annotated knowledge base from unstructured documents to provide supplementary agricultural knowledge. Secondly, the hybrid retrieval module performs coarse-grained sparse retrieval to ensure relevance, with a time-sensitive re-ranking stage that refines the results to achieve both semantic relevance retrieval and time alignment with user queries. Finally, the time-aware generation module leverages the top-k retrieved documents and an instruction prompt to produce the final suggestion. To validate the effectiveness of the proposed framework, we contribute TAQA, the first bilingual, time-annotated agricultural question-answering dataset. Experiments demonstrate that TARAG significantly outperforms state-of-the-art RAG frameworks in retrieval precision and suggestion quality, with 99.14% retrieval recall and an F1 score of 66.85% for generated suggestions. The implementation and datasets for this work are available in https://github.com/lee-hash1/agri_rag