## 1 Introduction  
Plant protection is a critical component of modern agricultural production, encompassing pest and disease management, soil health preservation, and crop yield optimization [1, 2]. The rapid advancement of agricultural practices has introduced complex challenges, including the emergence of novel plant pathogens, increasing pest resistance to chemical control measures, and the impacts of climate change on crop productivity [3, 4]. Accurate, timely plant protection knowledge is essential. This is particularly true for farmers, who are the primary field practitioners and often lack specialized expertise. We aim to provide them with practical, real-time recommendations. These guidelines are designed to address their daily challenges effectively. However, conventional knowledge acquisition methods, such as expert consultations, printed reference materials, and fragmented online resources, are often time-consuming, require specialized expertise, and may not provide real-time, context-specific recommendations. Moreover, variations in crop species, regional environmental conditions, and pest dynamics necessitate tailored solutions, making generic knowledge dissemination approaches insufficient to address the diverse and evolving needs of the agricultural sector. In recent years, large language models (LLMs) have demonstrated exceptional natural language understanding and generation capabilities, finding applications in diverse fields such as healthcare, finance, and legal services [5; 6; 7]. Their ability to process complex queries and generate contextually relevant responses makes them promising candidates for question-answering (QA) systems. However, applying LLMs to highly specialized domains such as plant protection presents significant challenges. One major limitation is their lack of domain-specific knowledge, as pre-trained models rely primarily on publicly available datasets that often lack high-quality, expert-reviewed agricultural content. Consequently, these models may generate incomplete, outdated, or misleading responses when addressing plant protection issues. Additionally, LLMs are prone to the well-documented hallucination problem, where they produce factually incorrect or unverifiable information. In plant protection, where accurate and scientifically validated knowledge is essential for effective decision-making, such hallucinations can result in incorrect diagnoses, improper pesticide applications, and ineffective pest management strategies, potentially leading to economic losses and environmental harm [8]. Furthermore, traditional LLMs struggle to adapt to region-specific agricultural conditions, as factors such as climatic variations, soil characteristics, and pest dynamics necessitate dynamic and context-aware responses that generic models fail to provide. To mitigate these challenges, fine-tuning has been widely explored to enhance LLM performance in specialized domains. However, conventional fine-tuning modifies a large number of parameters, requiring extensive computational resources and substantial storage, making it impractical for organizations with limited hardware capabilities. A more efficient alternative is low-rank adaptation (LoRA), a parameter-efficient fine-tuning method that significantly reduces computational overhead while preserving model performance. LoRA achieves this by introducing trainable low-rank matrices within specific layers of the pre-trained model instead of modifying the full parameter set, enabling efficient adaptation to new tasks [9; 10]. This approach not only enhances accessibility for domain-specific applications but also ensures that the model retains its core linguistic capabilities. In this study, we leverage LoRA to fine-tune an LLM for plant protection, equipping it with specialized knowledge while maintaining computational efficiency. In addition to fine-tuning, retrieval-augmented generation (RAG) has emerged as a powerful technique for enhancing the accuracy and reliability of LLM responses.