## 6 Conclusions  
This paper presents the development of an intelligent question-answering system tailored for the plant protection domain, leveraging large language model fine-tuning and retrieval-augmented generation (RAG). Extensive experiments demonstrate the efficacy of this approach in enhancing answer accuracy, professionalism, and interpretability. By employing low-rank adaptation (LoRA), the pre-trained model is efficiently infused with specialized domain knowledge without a significant increase in computational overhead. Simultaneously, the integration of RAG, which facilitates real-time retrieval of professional literature and agricultural data, markedly reduces the occurrence of \"hallucinated\" information, thereby bolstering the credibility of the responses. Comparative analyses reveal that post fine-tuning and real-time retrieval optimization, the system exhibits robust performance and delivers high-quality answers to challenges such as agricultural pest control, crop growth regulation, and regional agricultural promotion. Nonetheless, certain limitations persist within this research. The constructed plant protection dataset, while encompassing fundamental theories and specialized knowledge, requires further expansion in both breadth and depth, particularly concerning niche areas. The system's heavy reliance on external literature repositories means that the quality and update frequency of these sources directly influences response efficacy. Moreover, the current focus on textual data processing, without the integration of multimodal information like images and videos, may constrain the system's applicability in future agricultural contexts. Despite these challenges, this study offers a viable technical pathway for domain-specific question-answering systems, underscoring the significant role of combining large language model fine-tuning with real-time information retrieval in reducing \"hallucination\" issues and enhancing domain adaptability. Future endeavors should aim to expand the dataset's scope, enhance the authority of external knowledge bases, and explore the fusion of multimodal data. Such efforts will serve to refine system functionality and elevate overall performance. In essence, this work introduces innovative perspectives and practical insights for constructing intelligent question-answering systems in the plant protection sector, contributing positively to the advancement of agricultural informatization and informed decision-making. Conceptualization, J.X. ; methodology, J.X., L.P. and Y.L. ; software, J.X., L.P. and Y.L. ; validation, J.X., L.P. and Y.L. ; formal analysis, L.Z. ([PERSON]), L.Z. ([PERSON]) and S.T. ; investigation, L.P.; resources, J.X., [PERSON] ([PERSON]), L.Z. ([PERSON]) and S.T. ; data curation, Y.L. ; writing--original draft, J.X. ; writing--review and editing, J.X., L.P., Y.L., L.Z. ([PERSON]), L.Z. ([PERSON]), L.Z. ([PERSON]) and S.T. ; visualization, J.X. ; supervision, [PERSON] ([PERSON]), [PERSON] ([PERSON]) and S.T. ; project administration, S.T. ; funding acquisition, L.Z. ([PERSON]), L.Z. ([PERSON]) and S.T. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (Grant No. 62202163); the Research Foundation of Education Bureau of Hunan Province, China (Grant No. 22A0145); and the Research Foundation of Science and Technology Bureau of Hunan Province, China (Grant No. 2023 NK2011). Not applicable. The data presented in this study can be obtained upon request from the corresponding author. Other datasets can be obtained from their original papers. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: