Machine Learning in Fish Farming

Fearghal O'Donncha; Nikos Papandroulakis; Jennie Korus; Abigail Langbridge; Alexander Timms; Konstantinos Topouzelis; Abdul Baseer Khan; Shree Rama Kamal Kumar Vegu

cs.LGSep 12, 2026

Machine Learning in Fish Farming

Authors: Fearghal O'Donncha, Nikos Papandroulakis, Jennie Korus, Abigail Langbridge, Alexander Timms, Konstantinos Topouzelis, Abdul Baseer Khan, Shree Rama Kamal Kumar Vegu,

Organizations: IBM Research Europe, Dublin, Ireland · Hellenic Centre for Marine Research, Crete, Greece · Innovasea Marine Systems Canada, Nova Scotia, Canada · Imperial College London, United Kingdom ·

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.

Figures & tables

Figure 1: This diagram illustrates the key components and data flows in a large language model (LLM)-driven agent architecture. The central agent coordinates interactions between tools (e.g., calculators, code interpreters, APIs), memory systems (short-term and retrieval-augmented), and action modules. Cognitive processes such as planning, task decomposition, self-critique, and reflection support goal-oriented behavior and adaptive reasoning. Guardrails ensure system robustness through safety, factual reasoning, feedback, and monitoring. The architecture enables scalable, flexible execution of complex tasks in dynamic environments.

Figure 2: Mean average error (MAE) of 24-hour prediction for various tests of the model on the same cage it was trained on, a different cage from the same farm, a cage from a different farm within the same region and a cage on a farm in a different region.

Figure 3: Actual raw oxygen concentration and model output of a 24-hour prediction using 7 quantiles that indicate a confidence interval for the forecasted values. Data presented shows the output of the model on a single cage that was trained on the entire farm dataset.

Figure 4: Flowchart of the implemented methodology. Reproduced from Chatziantoniou et al. (2022) , https://doi.org/10.1016/j.rsase.2022.100865 , under the CC BY-NC-ND 4.0 licence.

Panel of Figure 5 — Figure 5: The spatial distribution of (a) SST (b) Chl-a and (c) DO around Lesvos Island for four representative dates (fall, winter, spring, summer). Reproduced from Chatziantoniou et al. (2022) , https://doi.org/10.1016/j.rsase.2022.100865 , under the CC BY-NC-ND 4.0 licence.

Figure 6: The DT4GS Agent acts as a central orchestrator that coordinates specialized sub-agents and tools to support key operational tasks. It includes a planning module that decomposes tasks and delegates them to appropriate agents (e.g., anomaly detection, asset health, ship assistant). The memory and reflection modules track context and assess system performance. On the left, agent resources such as a data space, knowledge graph, and edge/cloud platforms provide contextual information. On the right, agent tools perform domain-specific functions such as anomaly detection, FMEA, and reporting. This modular, language-driven framework enables flexible, autonomous interaction with complex ship systems.

Figure 7: Schematic of the agentic workflow using a planner–router architecture. The Planning Agent parses the initial user prompt and assigns subtasks to specialized agents (e.g., Navigation, FMEA, Ship Exploration). The Re-router coordinates execution flow by determining whether further steps are needed, enabling iterative multi-agent collaboration.

Figure 8: Sample trajectory illustrating the step-by-step process of responding to a user query regarding anomalies in a ship engine. It demonstrates the integration of multiple tools—such as data retrieval, anomaly detection, and failure mode analysis—to detect, visualize, and recommend maintenance strategies. The process culminates in generating a work order based on detected anomalies, filtered by external factors like speed and ambient pressure, ensuring informed decision-making for condition-based maintenance.

ML Technique	Application in Aquaculture	Benefits	TRL
CNNs	Fish behavior and health monitoring via computer vision	Non-invasive, continuous visual monitoring	6–8
GNNs	Modeling interactions among environmental and biological systems	Captures system connectivity and spatial relationships	2–4
SVR	Forecasting dissolved oxygen using satellite or sensor data	Site-specific, accurate environmental predictions	7–8
LLMs	Natural language interfaces, log analysis, document summarization	Scalable knowledge extraction across diverse sources	2–4
AI Agents	Autonomous task execution and adaptive response agents	Decision automation and reduced manual intervention	2–4
Random Forests / Decision Trees	Classification and anomaly detection in water quality or sensor data	Robust predictions with explainable outputs	7–9

Table 1: Key machine learning techniques applied in aquaculture, including typical use cases, benefits, and estimated TRLs based on current research and deployments.

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LakeFM: Toward a Foundation Model for Aquatic Ecosystems Using Irregular Multivariate Multi-depth Time Series Data

Abhilash Neog, Sepideh Fatemi, Medha Sawhney, Kazi Sajeed Mehrab+8

Virginia Tech Blacksburg, VA, USA · Grand Valley State University Muskegon, MI, USA · University of Wisconsin - Madison Madison, WI, USA+2

Irregular Time-Series Modeling Multivariate Time Series Forecasting Time Series Representation Learning Time-Series Foundation Models

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Understanding and forecasting lake dynamics is critical for monitoring water quality and ecosystem health across lakes and reservoirs. While machine learning methods have been recently applied to ecological time-series data, existing works assume regular sampling in time and depth, and struggle to generalize across lakes with heterogeneous variables, depths, and observation patterns. To address these limitations, we introduce LakeFM, a foundation model for aquatic systems, pre-trained on large-scale ecological datasets comprising both simulated and observed lakes. Through extensive empirical evaluation, we show that LakeFM learns meaningful representations spanning broader lake-level characteristics, and achieves competitive or often superior-forecasting performance compared to existing time-series foundation and non-foundation models, while producing physically plausible predictions consistent with real-world lake dynamics.

Abstract

Aquaculture

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