PoultryTalk: A Multi-modal Retrieval-Augmented Generation (RAG) System for Intelligent Poultry Management and Decision Support

2025-12-08

AI Summary

Small- and medium-scale poultry farmers face practical challenges including delayed disease diagnosis, suboptimal nutritional management, and inadequate mitigation of climate- and market-related risks. To address these issues, this study proposes the first vertical-domain multimodal RAG system tailored for poultry farming. The system accepts both textual and image inputs, integrating a domain-specific poultry knowledge base, a multimodal alignment retrieval mechanism, and GPT-4o’s generative capabilities to deliver precise, real-time support for avian disease identification, nutritional recommendations, and farm management decisions. Compared with general-purpose models, our architecture significantly enhances domain-specific semantic understanding and contextual adaptability. Evaluation on 200 expert-annotated queries yields a semantic similarity score of 84.0% and an average response latency of 3.6 seconds. User validation demonstrates an accuracy rate of 89.9%, with 95.6% of users rating responses as “always correct” or “mostly correct.”

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

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.

Problem

• Provides real-time expert guidance for poultry management via text and image interactions
• Addresses lack of timely expert support for disease diagnosis and nutrition planning
• Delivers accurate, data-driven consultation to aid quick, informed decision-making

Innovation

• Multi-modal RAG system for poultry management
• Uses OpenAI embeddings and GPT-4o for advice
• Real-time expert guidance via text and images