## 2 Related Work  
The development of question-answering systems can be traced back to the 1960s when early systems were primarily designed for specific domains and structured data [12]. For instance, in 1961, [PERSON] developed the BASEBALL [13] system to answer questions about a single season of Major League Baseball; the LUNAR [14] system was created to provide geological analyses of lunar rock samples from the Apollo moon landing program. These pioneering systems relied on early artificial intelligence techniques to deliver accurate answers within narrowly defined fields. In the 1970s, advances in computational linguistics shifted the research focus toward employing linguistic technologies to reduce the costs of system construction. Although these systems remained confined to specific domains--for example, the Unix Consultant [15] system, which answered questions about the Unix operating system using a manually designed domain knowledge base--these early efforts laid a solid foundation for the evolution of question-answering technologies. The 1990s marked a significant expansion as question-answering systems began to tackle open-domain challenges. In 1999, the introduction of the TREC-8 (Text Retrieval Conference) question-answering competition heralded the birth of open-domain systems capable of addressing a broader array of topics, thus breaking free from the constraints of earlier, domain-specific approaches. Entering the 21 st century, IBM's Watson [16] system famously defeated human contestants on the television quiz show \"Jeopardy!\", vividly demonstrating the potent capabilities of open-domain question-answering systems. More recently, the advent of deep learning has further propelled the development of question-answering systems. Models built on the Transformer architecture, such as BERT [17] and GPT-3 [18], have showcased outstanding natural language understanding and generation abilities through pre-training on vast corpora. These models not only manage complex language phenomena but have also spurred the rise in generative artificial intelligence, leading to systems like ChatGPT and LLaMA. These advanced models generate fluent, natural content and are adept at addressing intricate, cross-domain challenges and finding applications in customer service, education, and content creation. Driven by a rich historical evolution, new applications tailored to specific fields continue to emerge, for example, FarmChat, developed by [PERSON] et al. [19]. In 2018, it is a dialog agent that supports both text and voice inputs by leveraging call center logs from the Kisan system and insights from local agricultural experts to deliver agricultural information to rural Indian farmers. Similarly, the crop protection information system proposed by [PERSON] et al. [20] employs SMS and web to disseminate plant protection advice to Tanzanian rural farmers, enabling timely responses to pests and diseases. In 2024, [PERSON] et al. [21] introduced AgAsk, an intelligent agent that retrieves scientific literature to provide farmers with reliable agricultural information, thereby aiding in informed planting decisions, while the Template-based Chatbot for Agriculture Related FAQs by [PERSON] et al. [22] utilizes predefined templates and rules to rapidly generate answers, significantly enhancing the efficiency of information retrieval. [PERSON] et al. [23] integrated generative adversarial networks for image enhancement, convolutional neural networks for precise disease detection, and large language models for interactive farmer support, thereby revolutionizing crop monitoring and management. Similarly, [PERSON] et al. [24] demonstrated that generative AI techniques can offer accurate and scalable diagnostic solutions for plant diseases. Moreover, [PERSON] et al. [25] proposed a framework that combines natural language processing with conventional machine learning to deliver context-aware and tailored disease detection.