# AQUA: A Large Language Model for Aquaculture & Fisheries

arXiv: https://arxiv.org/abs/2507.20520 · HTML: https://mlsanity.com/paper/2507.20520/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 40 references that are in the map.
- First posted on arXiv 28 Jul 2025.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining](https://mlsanity.com/paper/2210.10341/) (2022)
- [Docling Technical Report](https://mlsanity.com/paper/2408.09869/) (2024)
- [Deep learning for smart fish farming: applications, opportunities and challenges](https://mlsanity.com/paper/2004.11848/) (2020)
- [A Comparative Study of PDF Parsing Tools Across Diverse Document Categories](https://mlsanity.com/paper/2410.09871/) (2024)

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

- [THPL: A Vision-to-Language Decision Support Framework for Rainbow Trout Feeding Management in RAS](https://mlsanity.com/paper/2610.02378/) (2026)

## Abstract (arXiv)

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.