AQUA: A Large Language Model for Aquaculture & Fisheries
This addresses problems for aquaculture stakeholders by providing a domain-specific AI tool, though it appears incremental as it adapts existing LLM methods to a new application area.
The paper tackles challenges in aquaculture like disease outbreaks and inefficiencies by introducing AQUA, the first large language model tailored for the domain, which uses the AQUADAPT framework to generate synthetic data for supporting farmers and researchers.
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.