Enterprise AI Analysis
Artificial Intelligence in Aquaculture Risk Management: A Systematic Review by PRISMA
The aquaculture industry is growing rapidly. It is the fastest growing food industry in the world, with production expanding 16-fold between 1985 and 2018, according to the Food and Agriculture Organization FAO. The industry operates in an environment of high uncertainty, as the management of biological and environmental risks is critical. The aim of this research is to identify machine learning (ML) algorithms applied to quantify risks, categorize applications by sector, and evaluate data linkage to the extent that they feed into formal risk management protocols. A systematic review was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. This search was conducted in Scopus and Science Direct for publications up to January 2026. Initially, 134 records were identified, of which 38 studies were ultimately included in the analysis. The results showed that artificial intelligence (AI) and ML offer new predictive capabilities. Integrating Internet of Things (IoT) sensors, AI methods and ML algorithms improve risk mitigation. However, there is a significant disconnection between algorithmic predictions and operational action. Only 3 of 38 studies demonstrated integration with standardized risk management frameworks (e.g., ISO 31000). The study concludes that while AI tools provide predictive efficiency, interdisciplinary frameworks are required to filter predictions through economic and ethical criteria. Strengthening this connection will bring the use of AI as a tool for proactive and standardized risk mitigation.
Executive Impact & Key Takeaways
This review highlights the critical role of AI in transforming aquaculture risk management. Here’s what you need to know to drive strategic decisions.
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Studies Analyzed
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Deep Learning Usage
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Hybrid Model Adoption
Core Insights from the Analysis:
- AI & ML are mature for aquaculture risk management, improving prediction and detection.
- Models accurately predict oxygen depletion, disease outbreaks, biomass, and monitor welfare.
- A significant management gap exists: algorithms are not automatically translated into action.
- The next leap is integrating existing algorithms into hybrid decision-making systems using ISO frameworks.
- A standardized data handshake protocol between AI developers and risk managers is needed.
- Industry moving from "Risk as Emotions" to "Risk as Data," farms to "digital twins," reactive to proactive.
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Environmental Risk Applications
Three studies focused on environmental and water quality parameters. The prediction of critical chemical-physical parameters, such as dissolved oxygen (DO), temperature, pH and ammonia, was in the spotlight. Long-Short-Term Memory (LSTM) networks and their variants dominated these applications. LSTMs achieved high accuracy in predicting environmental variables... A common finding in environmental studies is that models often go beyond simple prediction and reach the stage of automation. While technical accuracy has been high, few studies linked prediction to automated decision making.
Biological Risk Applications
Seventeen studies were related to biological hazards, mainly fish diseases and mortality. Early diagnosis of diseases in aquaculture is of great importance... Two main approaches came forward: image/video processing to detect visible symptoms, and analysis of environmental/production data to predict outbreak probabilities. CNN and other deep learning models were used for the first, Random Forest and Gradient Boosted Trees for the second. Integration into daily management varies, with challenges like lack of real-time data, cost of sensors, and trust in automated recommendations.
Operational Risk Applications
Fifteen studies fall into this category. It covers biomass estimation and feeding control, supply chain risks. Computer vision techniques are used for biomass estimation... ML models predict market prices... Decision support systems optimize harvest planning... Research links technical risk with financial risk, calculating value-at-risk. Forecasting is not enough; integration into business planning and defining physical boundaries are required. Benefits of AI are real, impacting cost and profit.
Management Frameworks Integration
Three studies explicitly addressed the connection between ML and standard risk management frameworks. This is the most under-represented area... Theodorou and Tzovenis (2024) adapted ISO 31000 to mussel farming, integrating ML tools at specific stages... Luna et al. (2023) provides a conceptual framework for risks, acting as a terminology connector... Stewart-Koster et al. (2017) used Bayesian Belief Networks to quantify risk factors... These studies indicate how integration could be accomplished through hybrid frameworks.
70%
Recent Research Activity (2023-2025)
Approximately 70% of the included studies have been published in the last three years (2023–2025), indicating a rapidly emerging field for AI in aquaculture.
Enterprise Process Flow
Sensors & Data (IoT, historical farm records)
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AI/ML Models (DL, LSTM/CNN; Ensembles, RF/XGBoost; CV pipelines)
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Model Outputs (predictions/estimates: water quality, disease, biomass/feeding)
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Risk Indicators (KPIs)
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Thresholds / Decision Rules
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Alerts & Recommended Actions
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Identify
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Analyse
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Evaluate
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Treat
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Monitor & Review
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AI-Powered Early Warning for Cryptocaryoniasis Outbreaks
Xie et al. (2025) developed an early warning system for cryptocaryoniasis, a parasitic marine ich disease. By incorporating 7 years of outbreak data and environmental factors into a Random Forest model, the system achieved remarkable predictive capabilities. Its pilot application in commercial cages provided producers with a crucial two-week warning period.
Key Result: 98.6% Sensitivity, 2-week Advance Warning
Calculate Your Potential AI-Driven ROI
Estimate the significant operational efficiency and cost savings your enterprise could achieve by integrating AI into risk management, based on industry benchmarks.
Estimated Annual Savings
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Reclaimed Hours Annually
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Your AI Implementation Roadmap
A strategic, phased approach is essential for successful AI integration into your risk management framework. Here's a suggested roadmap.
Adopt a Contextualization Layer
Translate complex ML outputs into actionable business terms. Implement an intermediate layer to filter AI predictions through financial and operational constraints, providing tangible options for managers.
Standardize Data Handshake Protocols
Establish clear communication protocols between AI developers and risk managers. Integrate ML models directly into a unified risk management platform connected to sensors, control mechanisms, and action protocols, moving beyond mere forecasts.
Invest in Explainable AI (XAI)
Prioritize AI models that can explain their predictions. This builds trust, especially in critical decision-making, and uncovers new knowledge, helping managers understand the 'why' behind AI recommendations.
Leverage Big Data & Collaborative Models
Overcome data scarcity by exploring techniques for collective risk modeling. This allows for shared learning and improved predictions across units without compromising raw data privacy.
Secure Regulatory & Policy Support
Advocate for and integrate AI adoption through certifications, incentives, or mandatory risk management plans, fostering a supportive environment for industry-wide transformation.
Embrace Hybrid Decision-Making Systems
Combine the computational power of ML models with the invaluable experience and judgment of human managers within a clear decision framework for maximum efficiency and transparency.
Ready to Transform Your Aquaculture Operations with AI?
Strengthening the connection between AI predictions and operational action is crucial for proactive, standardized risk mitigation. Let's discuss how your enterprise can lead this transformation.
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