## 3 Materials and Methods  
### Plant Protection Dataset  
In this study, we constructed a high-quality, comprehensive plant protection dataset comprising 7000 professional papers and books [30]. During the data screening process, to ensure the quality and consistency of the QA data, we de-weighted the data, standardized the format, and filtered the low-quality entries. First, among 7000 agriculture-related documents, we performed de-weighting using DOI, title similarity, and abstract matching to eliminate duplicates or highly similar documents and used MinHash to calculate text similarity to remove redundant or similarly expressed QA pairs to ensure the diversity and independence of the data. In the process of format standardization, we conducted text cleanup, including the removal of HTML tags, and special characters, the unification of full-angle and half-angle symbols, and the standardization of the use of punctuation. In addition, we standardized the sentence format to maintain the consistency of the question, for example, \"How to control rice diseases?\" to \"What are the methods of controlling rice diseases?\", and ensured the consistency of dosage expressions, such as \"2 g/L\" to \"2 g/L\". In addition, to reduce colloquial expressions, we changed \"dosing\" to \"pesticide spraying\" to standardize the language. In terms of low-quality data filtering, we used Perplexity (PPL) to detect language fluency and excluded pairs with high PPL values (>100) to ensure the readability of the text. At the same time, we filtered QA with answer lengths of less than 10 words or more than 500 words to avoid content with too little information or too long redundancy. Through the above data cleaning and quality control, we finally constructed 9000 high-quality agricultural QA datasets, which provide reliable data support for fine-tuning the QA system [31]. Experimental analysis demonstrates that this dataset significantly enhances the F1 score of our QA system. It aggregates papers from multiple academic databases and professional books from agricultural research institution libraries, covering core areas such as pest and disease management, crop pathology, and pesticide application. These materials offer not only rich theoretical knowledge but also experimental data and practical application cases, thereby providing a robust scientific foundation for the system's knowledge base. To further broaden the dataset and enhance the model's generalization, we employed large language model to generate a series of question-answer pairs using designed prompt templates [32; 33]. These templates address fundamental issues in plant protection, including crop disease prevention and treatment, as well as strategies to optimize the growth environment and reduce disease incidence. The generated question-answer pairs were rigorously screened and cleaned to ensure scientific validity and practical relevance, effectively supplementing gaps in the original dataset and deepening the model's understanding of core issues. All generated pairs underwent manual refinement to ensure high quality and reliability, thereby enhancing the model's capacity to address fundamental questions and bolstering its generalization in real-world applications. Since the original dataset comprised files and text in various formats, we implemented a series of preprocessing steps to ensure efficient processing and learning. These steps involved clearing sensitive information and redundant content, removing low-quality data, performing deduplication to ensure dataset independence, and reformatting the data into the question-answer format required for training. Additionally, we built a local vector database to convert the processed data into vector embeddings, facilitating efficient retrieval during answer generation.