## 5 Discussion  
The research on question-answering systems in the field of plant protection has long attracted significant attention. Traditional agricultural information services have predominantly relied on manual expert consultation and the use of scattered literature resources. As a result, when farmers are confronted with challenges such as pest control, disease management, and crop cultivation, it is often difficult for them to obtain accurate and comprehensive information promptly. This issue becomes even more critical in the context of modern agriculture, where rapid changes in planting structures and production environments have led to an increasing demand for both specialized knowledge and real-time data. Consequently, the urgent task at hand is to harness the latest advancements in artificial intelligence to construct an intelligent question-answering system that is both efficient and accurate. When researchers began applying general large-scale pre-trained language models to specialized domains, they quickly discovered several shortcomings. On one hand, because the corpora used during pre-training are predominantly sourced from general domains, these models do not acquire sufficient specialized knowledge in plant protection. This lack of domain-specific understanding often results in misinterpretations when processing agricultural queries. On the other hand, these models are prone to a phenomenon known as \"hallucination\" during answer generation, whereby the content produced may not correspond to facts, thereby misleading users. Furthermore, agricultural information is inherently dynamic and exhibits pronounced regional differences. When dealing with diverse geographical environments and varying crop types, general-purpose models frequently fall short in providing personalized suggestions that are tailored to local conditions. The issue of data scarcity further exacerbates the problem; the limited availability of accurate plant protection question-answer pairs makes it challenging to fully capture the intricate domain characteristics during the model fine-tuning process, ultimately affecting overall performance and practical usability. In response to these challenges, this study proposes a method that combines large language model fine-tuning with RAG techniques. In terms of large language model fine-tuning, the study employs LoRA (low-rank adaptation). By performing a low-rank adaptation on a subset of the parameters within the pre-trained model, LoRA enables the efficient injection of specialized plant protection knowledge without incurring significant computational overhead. This parameter-efficient fine-tuning method is designed to retain the rich linguistic capabilities that the model has acquired from a general corpus while simultaneously enhancing its accuracy and professionalism in answering domain-specific questions. Moreover, to address the issues of knowledge loss and the \"hallucination\" phenomenon that may occur during the answer generation process, the proposed system incorporates RAG technology. During the answer generation phase, the system is capable of retrieving information relevant to the query from professional literature and agricultural databases in real time. It then integrates this external knowledge with the model's internal generation results. Such an approach not only mitigates the risk of generating erroneous content but also enhances the authority and credibility of the produced answers. To further support the fine-tuning process, this study has constructed a comprehensive plant protection dataset containing over 9000 question-answer pairs. This dataset encompasses both fundamental theoretical knowledge and rigorously validated professional content extracted from academic publications. It serves as a high-quality training resource that enables the system to deliver more precise responses when faced with a wide range of complex agricultural issues. In the experimental phase, the performance of multiple models was compared on plant protection question-answer tasks.