用图文交互的智能系统，帮养鸡户快速做疾病诊断和管理决策。
PoultryTalk: A Multi-modal Retrieval-Augmented Generation (RAG) System for Intelligent Poultry Management and Decision Support
- 结合文本与图像，通过检索增强生成技术提供实时建议。
- 问答准确率达89.9%，响应速度仅3.6秒，语义相似度达84.0%。
- 适合中小养殖户、农业技术人员，尤其在信息滞后地区实用。
家禽业对全球粮食安全至关重要，但中小规模养殖户常难以及时获取专家级支持，面临疾病诊断、营养规划和管理决策难题。随着气候压力上升、饲料价格波动及疫病威胁加剧，养殖户亟需数据驱动的智能系统实现快速决策。本文提出 PoultryTalk，一种多模态检索增强生成（RAG）系统，通过文本与图像交互提供实时专家指导。系统采用 OpenAI 的 text-embedding-3-small 和 GPT-4o 模型，从文本、图像或提问中生成上下文感知的建议。通过 200 个专家验证查询和 34 名参与者提交的 267 条查询进行评估。专家基准测试显示，系统达到 84.0% 的语义相似度，平均响应延迟为 3.6 秒。相比 GPT-4o，PoultryTalk 在家禽相关问题上表现更准确可靠。用户评价显示，95.6% 的反馈认为回答“始终正确”或“大多正确”，响应准确率达 89.9%，约 9.1% 的回答被判定错误。82.6% 参与者表示愿意推荐该工具，17.4% 表示“可能”。结果表明，PoultryTalk 不仅提供精准且情境相关的建议，还具备高用户满意度与可扩展潜力。
原文摘要 · Abstract (English)
The Poultry industry plays a vital role in global food security, yet small- and medium-scale farmers frequently lack timely access to expert-level support for disease diagnosis, nutrition planning, and management decisions. With rising climate stress, unpredictable feed prices, and persistent disease threats, poultry producers often struggle to make quick, informed decisions. Therefore, there is a critical need for intelligent, data-driven systems that can deliver reliable, on-demand consultation. This paper presents PoultryTalk, a novel multi-modal Retrieval-Augmented Generation (RAG) system designed to provide real-time expert guidance through text and image-based interaction. PoultryTalk uses OpenAI's text-embedding-3-small and GPT-4o to provide smart, context-aware poultry management advice from text, images, or questions. System usability and performance were evaluated using 200 expert-verified queries and feedback from 34 participants who submitted 267 queries to the PoultryTalk prototype. The expert-verified benchmark queries confirmed strong technical performance, achieving a semantic similarity of 84.0% and an average response latency of 3.6 seconds. Compared with OpenAI's GPT-4o, PoultryTalk delivered more accurate and reliable information related to poultry. Based on participants' evaluations, PoultryTalk achieved a response accuracy of 89.9%, with about 9.1% of responses rated as incorrect. A post-use survey indicated high user satisfaction: 95.6% of participants reported that the chatbot provided "always correct" and "mostly correct" answers. 82.6% indicated they would recommend the tool, and 17.4% responded "maybe." These results collectively demonstrate that PoultryTalk not only delivers accurate, contextually relevant information but also demonstrates strong user acceptance and scalability potential.
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