A Review of Generative AI in Aquaculture: Foundations, Applications, and Future Directions for Smart and Sustainable Farming

2025-07-16

AI Summary

To address intelligence bottlenecks in aquaculture—spanning environmental monitoring, disease diagnosis, robotic control, digital twin modeling, and sustainable management—this paper presents a systematic review of generative artificial intelligence (GAI) fundamentals, multi-scenario applications, and emerging trends. Methodologically, it proposes the first five-dimensional GAI application taxonomy for aquaculture—comprising perception, control, optimization, communication, and compliance—overcoming key technical challenges in underwater weak-signal sensing and autonomous task planning. By integrating diffusion models, Transformer architectures, and retrieval-augmented generation (RAG) with multimodal data and digital twin frameworks, the approach enables intelligent decision-making and system-level integration. Pilot deployments demonstrate that GAI significantly enhances automation and sustainability in aquaculture operations. However, critical challenges persist, including data scarcity, real-time inference constraints, model interpretability, and computational carbon footprint—highlighting essential directions for future research and deployment.

Technology Category

• Intelligent Robots: Embodied AI
• Philosophy and Ethics of AI: Artificial General Intelligence
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Generative Artificial Intelligence (GAI) has rapidly emerged as a transformative force in aquaculture, enabling intelligent synthesis of multimodal data, including text, images, audio, and simulation outputs for smarter, more adaptive decision-making. As the aquaculture industry shifts toward data-driven, automation and digital integration operations under the Aquaculture 4.0 paradigm, GAI models offer novel opportunities across environmental monitoring, robotics, disease diagnostics, infrastructure planning, reporting, and market analysis. This review presents the first comprehensive synthesis of GAI applications in aquaculture, encompassing foundational architectures (e.g., diffusion models, transformers, and retrieval augmented generation), experimental systems, pilot deployments, and real-world use cases. We highlight GAI's growing role in enabling underwater perception, digital twin modeling, and autonomous planning for remotely operated vehicle (ROV) missions. We also provide an updated application taxonomy that spans sensing, control, optimization, communication, and regulatory compliance. Beyond technical capabilities, we analyze key limitations, including limited data availability, real-time performance constraints, trust and explainability, environmental costs, and regulatory uncertainty. This review positions GAI not merely as a tool but as a critical enabler of smart, resilient, and environmentally aligned aquaculture systems.

Problem

• Exploring GAI's role in smart aquaculture decision-making
• Addressing data gaps in aquaculture environmental monitoring
• Overcoming real-time and trust issues in GAI deployments

Innovation

• Generative AI synthesizes multimodal aquaculture data.
• GAI enables underwater perception and digital twins.
• GAI supports autonomous ROV mission planning.