Aquaculture plays a vital role in global food security and coastal economies by providing sustainable protein sources. As the industry expands to meet rising demand, it faces growing challenges such as disease outbreaks, inefficient feeding practices, rising labor costs, logistical inefficiencies, and critical hatchery issues, including high mortality rates and poor water quality control. Although artificial intelligence has made significant progress, existing machine learning methods fall short of addressing the domain-specific complexities of aquaculture. To bridge this gap, we introduce AQUA, the first large language model (LLM) tailored for aquaculture, designed to support farmers, researchers, and industry practitioners. Central to this effort is AQUADAPT (Data Acquisition, Processing and Tuning), an Agentic Framework for generating and refining high-quality synthetic data using a combination of expert knowledge, largescale language models, and automated evaluation techniques. Our work lays the foundation for LLM-driven innovations in aquaculture research, advisory systems, and decision-making tools.
@article{arxiv.2507.20520,
title = {AQUA: A Large Language Model for Aquaculture & Fisheries},
author = {Praneeth Narisetty and Uday Kumar Reddy Kattamanchi and Lohit Akshant Nimma and Sri Ram Kaushik Karnati and Shiva Nagendra Babu Kore and Mounika Golamari and Tejashree Nageshreddy},
journal= {arXiv preprint arXiv:2507.20520},
year = {2025}
}