Machine Learning in Fish Farming

2026-09-12

AI Summary

本文探讨了机器学习如何通过提高决策过程和运营效率来解决水产养殖中的挑战，使用了随机森林、CNNs、RNNs等技术，并结合物联网实现实时监控。

Abstract

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.

Problem

• aquaculture
• machine learning
• decision-making
• operational efficiency
• biomass estimation

Innovation

• Random Forest
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Graph Neural Networks (GNNs)
• large language models (LLMs)