# Machine Learning in Fish Farming

arXiv: https://arxiv.org/abs/2609.13919 · HTML: https://mlsanity.com/paper/2609.13919/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 139 references that are in the map.
- First posted on arXiv 12 Sep 2026.

## Builds on (most cited first, 29 of 29)

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- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [Long-term Recurrent Convolutional Networks for Visual Recognition and Description](https://mlsanity.com/paper/1411.4389/) (2014)
- [GraphCast: Learning skillful medium-range global weather forecasting](https://mlsanity.com/paper/2212.12794/) (2022)
- [Tackling Climate Change with Machine Learning](https://mlsanity.com/paper/1906.05433/) (2019)
- [Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere](https://mlsanity.com/paper/2306.03838/) (2023)
- [Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models](https://mlsanity.com/paper/1708.08296/) (2017)
- [ETA Prediction with Graph Neural Networks in Google Maps](https://mlsanity.com/paper/2108.11482/) (2021)
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- [Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge](https://mlsanity.com/paper/1711.07970/) (2017)
- [Physics informed deep learning for computational elastodynamics without labeled data](https://mlsanity.com/paper/2006.08472/) (2020)
- [Recurrent Neural Networks (RNNs): A gentle Introduction and Overview](https://mlsanity.com/paper/1912.05911/) (2019)
- [Deep Learning for Time-Series Analysis](https://mlsanity.com/paper/1701.01887/) (2017)
- [AI Foundation Models for Weather and Climate: Applications, Design, and Implementation](https://mlsanity.com/paper/2309.10808/) (2023)
- [70 years of machine learning in geoscience in review](https://mlsanity.com/paper/2006.13311/) (2020)
- [A SWAT-based Reinforcement Learning Framework for Crop Management](https://mlsanity.com/paper/2302.04988/) (2023)
- [Using Deep Learning to Extend the Range of Air-Pollution Monitoring and Forecasting](https://mlsanity.com/paper/1810.09425/) (2018)
- [A spatio-temporal LSTM model to forecast across multiple temporal and spatial scales](https://mlsanity.com/paper/2108.11875/) (2021)
- [Transfer learning to improve streamflow forecasts in data sparse regions](https://mlsanity.com/paper/2112.03088/) (2021)
- [Managing a blockchain-based platform ecosystem for industry-wide adoption: The case of TradeLens](https://mlsanity.com/paper/2209.04206/) (2022)
- [Causal Temporal Graph Convolutional Neural Networks (CTGCN)](https://mlsanity.com/paper/2303.09634/) (2023)
- [Statistical and machine learning ensemble modelling to forecast sea surface temperature](https://mlsanity.com/paper/1909.08573/) (2019)
- [Forecasting Soil Moisture Using Domain Inspired Temporal Graph Convolution Neural Networks To Guide Sustainable Crop Management](https://mlsanity.com/paper/2212.06565/) (2022)
- [Attention-based Domain Adaptation Forecasting of Streamflow in Data-Sparse Regions](https://mlsanity.com/paper/2302.05386/) (2023)

## Abstract (arXiv)

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.