Danvirutai Pobporn, Charoenwattanasak Siripavee, Tola Siriporn, Thaiso Kampon, Yuangsoi Bundit, Minh Hoang Trong, Srichan Chavis
College of Computing, Khon Kaen University, Khon Kaen, Thailand.
Faculty of Agriculture, Khon Kaen University, Khon Kaen, Thailand.
Sci Rep. 2025 Jul 1;15(1):21377. doi: 10.1038/s41598-025-05892-3.
The fish farming industry is advancing by adopting technologies designed to enhance efficiency, productivity, and sustainability. This study investigates integrating a Retrieval-Augmented Generation Large Language Model (RAG-LLM) with a Deep Q-Network (DQN) in autonomous aquaculture. It compares their performance to traditional expert-led methods and other AI-based systems. The developed autonomous system employs ensemble learning of RAG-LLM and DQN, incorporating IoT devices to thoroughly monitor feeding schedules, disease management, growth, and water quality parameters. This integration allows the system to generate optimal policies through majority voting, leveraging pre-trained LLM knowledge to improve initialization conditions and accelerate learning convergence. The hybrid approach of RAG-LLM and DQN demonstrates superior growth rates and rapid stabilization of automation policies. This highlights its potential to enable non-experts to manage fish farms and efficiently scale production for global food sustainability.
养鱼业正通过采用旨在提高效率、生产力和可持续性的技术不断发展。本研究探讨了在自主水产养殖中，将检索增强生成大语言模型（RAG-LLM）与深度Q网络（DQN）相结合的情况。研究将它们的性能与传统的专家主导方法以及其他基于人工智能的系统进行了比较。所开发的自主系统采用了RAG-LLM和DQN的集成学习，并结合物联网设备对投喂计划、疾病管理、生长情况和水质参数进行全面监测。这种集成使系统能够通过多数投票生成最优策略，利用预训练的大语言模型知识改善初始化条件并加速学习收敛。RAG-LLM和DQN的混合方法展现出更高的生长速度以及自动化策略的快速稳定。这凸显了其使非专业人员能够管理养鱼场并有效扩大生产规模以实现全球粮食可持续性的潜力。
