Machine learning improves fish farming with smarter decisions and monitoring
Machine Learning in Fish Farming
Machine Learning
Summary
Fish farmers face many challenges, from estimating how much fish they have to predicting water quality. The authors explain how machine learning, a type of artificial intelligence, helps by analyzing data to improve these tasks. They discuss different methods like neural networks and random forests that assist in identifying fish species, estimating fish biomass, and forecasting environmental changes. By combining machine learning with smart sensors, fish farms can monitor conditions in real time and make better decisions to keep fish healthy and farms productive.
What this means in practice
- •For fish farm managers: Use machine learning techniques to estimate fish biomass accurately, helping optimize feeding and improve harvest planning.
- •For environmental monitoring teams: Implement real-time environmental forecasting through ML and IoT data integration to predict and respond to water quality changes.
A survey. It maps existing work.
Authors
Fearghal O'Donncha, Nikos Papandroulakis, Jennie Korus, Abigail Langbridge, Alexander Timms, Konstantinos Topouzelis, Abdul Baseer Khan, Shree Rama Kamal Kumar Vegu, Mahtab Sarvmaili, Ryan Mowat, Rhanna Turberville, Tyler Sclodnick, Christopher Whidden
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.