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

arXiv: https://arxiv.org/abs/2512.08995 · HTML: https://mlsanity.com/paper/2512.08995/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Time series and agricultural RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 9 of its 35 references that are in the map.
- First posted on arXiv 8 Dec 2025.

## Placed in

- Retrieval-augmented generation / [Time series and agricultural RAG](https://mlsanity.com/rag/time-series-and-agricultural-rag/)

## Builds on (most cited first, 9 of 9)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models](https://mlsanity.com/paper/2401.01313/) (2024)
- [RAGCache: Efficient Knowledge Caching for Retrieval-Augmented Generation](https://mlsanity.com/paper/2404.12457/) (2024)
- [A Survey on Retrieval-Augmented Text Generation for Large Language Models](https://mlsanity.com/paper/2404.10981/) (2024)
- [Re-ViLM: Retrieval-Augmented Visual Language Model for Zero and Few-Shot Image Captioning](https://mlsanity.com/paper/2302.04858/) (2023)
- [Style-Aware Contrastive Learning for Multi-Style Image Captioning](https://mlsanity.com/paper/2301.11367/) (2023)
- [Multi-RAG: A Multimodal Retrieval-Augmented Generation System for Adaptive Video Understanding](https://mlsanity.com/paper/2505.23990/) (2025)
- [A Hybrid Approach to Information Retrieval and Answer Generation for Regulatory Texts](https://mlsanity.com/paper/2502.16767/) (2025)
- [SimInterview: Transforming Business Education through Large Language Model-Based Simulated Multilingual Interview Training System](https://mlsanity.com/paper/2508.11873/) (2025)

## Built on by (most cited first, 1 of 1)

- [Hidden in the Metadata: Stealth Poisoning Attacks on Multimodal Retrieval-Augmented Generation](https://mlsanity.com/paper/2603.00172/) (2026)

## Abstract (arXiv)

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.