发表机构
IBM Research Europe; Hellenic Centre for Marine Research; Innovasea Marine Systems Canada; Imperial College London; University of the Aegean; Dalhousie University; RS Aqua; Scottish Sea Farms; DeepSense（IBM欧洲研究院; 希腊海洋研究中心; Innovasea加拿大海洋系统公司; 伦敦帝国理工学院; 爱琴大学; 达尔豪斯大学; RS Aqua公司; 苏格兰海产养殖公司; DeepSense）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本章综述机器学习在水产养殖中的应用，涵盖随机森林、CNN、RNN及GNN、LLM等新兴技术，用于生物量估算、物种识别、行为分析和环境预测，并与物联网协同实现实时监控，以提升养殖效率与可持续性。
AI 中文摘要
本章探讨了机器学习（ML）如何变革水产养殖业，特别关注于增强决策过程和提升运营效率。本章结构首先介绍水产养殖中的挑战及人工智能的作用，然后概述机器学习技术在水产养殖背景下的应用，随后介绍应用案例、新兴趋势、未来方向及案例研究。重点在于机器学习技术的实际应用，包括随机森林、卷积神经网络（CNN）和循环神经网络（RNN），以及图神经网络（GNN）和大型语言模型（LLM）等新兴技术。关键应用包括生物量估算、物种识别、行为分析和环境预测。本章还强调了机器学习与物联网（IoT）在实时监控和决策支持方面的协同作用。最终，机器学习驱动的创新有潜力彻底改变鱼类养殖，引领水产养殖业走向更高效、可持续和高产的生产实践。
英文摘要
This chapter explores how machine learning (ML) is transforming aquaculture, with a particular focus on enhancing decision-making processes and improving operational efficiency. The chapter is structured to first introduce the challenges in aquaculture and the role of AI and then provide an overview of ML techniques in the context of aquaculture, followed by applications, emerging trends, future directions, and case studies. The focus is on real-world applications of ML techniques, including Random Forest, Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs), as well as emerging technologies such as Graph Neural Networks (GNNs) and large language models (LLMs). Key applications include biomass estimation, species recognition, behavioural analysis, and environmental forecasting. The chapter also highlights the synergy between ML and the Internet of Things (IoT) for real-time monitoring and decision support. Ultimately, ML-driven innovations have the potential to revolutionise fish farming, leading to more efficient, sustainable, and productive practices in the aquaculture industry.
Journal refO'Donncha, F. et al. (2026). Machine Learning in Fish Farming. In: O'Donncha, F., Fore, M., Grant, J. (eds) Digital Twin for Marine Fish Farming. Aquatic Production Systems. Springer, Cham
DOI：10.1007/978-3-032-10362-8_3
