Aquaculture offers a viable solution for food production, but it is not without challenges: diseases, inefficient resource management, environmental impact, and the need to optimize production are constant obstacles. In this context, the use of artificial intelligence (AI) in the aquaculture industry emerges not only as an innovative tool but as a transformative force capable of addressing these challenges and leading the industry towards a more efficient, sustainable, and productive future. The synergy between artificial intelligence and aquaculture is redefining traditional practices and opening new frontiers in the management of aquatic farms.
This article will explore in-depth the role of Artificial Intelligence in modern aquaculture, detailing its diverse applications, the tangible benefits it offers, the challenges inherent in its implementation, and the future prospects of this exciting technological convergence. We will analyze how the use of AI in water management, precision feeding, health monitoring, and other key areas are optimizing operations.
What is Artificial Intelligence and how is it applied in aquaculture?
Essentially, artificial intelligence refers to the ability of machines and computer systems to perform tasks that typically require human intelligence. This includes learning from experience (machine learning), recognizing patterns (computer vision), understanding natural language, and making data-driven decisions.
In the realm of aquaculture, AI presents itself as a transformative force in the aquaculture industry (Yang et al., 2025), offering tools such as the Internet of Things (IoT), machine learning, cameras, and algorithms to reduce human intervention and improve productivity (Ashraf et al., 2024); in this sense, AI works by processing large volumes of data collected from various sources within an aquatic farm. This data can come from:
- IoT Sensors (Internet of Things): Various sensors are used to collect essential data on water parameters such as pH, temperature, dissolved oxygen (DO), salinity (SL), nitrite (NO2), nitrate (NO3), and ammonia (Capetillo-Contreras et al., 2024); these devices are linked via the Internet of Things (IoT). In this regard, Huang and Khabusi (2025) report that the integration of artificial intelligence (AI) and the internet of things (IoT) is known as the artificial intelligence of things (AIoT).
- Underwater and Aerial Cameras: Capture images and videos of the fish or seafood, allowing analysis of their size, behavior, health, and population density.
- Automated Feeding Systems: Record the amount of feed dispensed and sometimes use acoustic or visual sensors to detect feeding activity.
- Historical Records: Data on previous production cycles, growth rates, disease incidences, treatments applied, and environmental conditions.
AI algorithms analyze this ‘flood’ of data to identify complex patterns, predict future trends, and automate decisions or actions. For example, an AI system could predict an imminent drop in oxygen levels based on the time of day, water temperature, and current biomass, automatically activating aeration systems before a critical level is reached. Artificial intelligence and aquaculture combine to create ‘precision aquaculture’ or ‘smart aquaculture’ systems, where every aspect of production is managed with an unprecedented level of detail and efficiency.
Key Applications of Artificial Intelligence in Aquaculture
The versatility of AI allows its application in virtually all stages of the aquaculture production cycle. Below are some of the most impactful areas:
Feeding Optimization: Precision for Growth
Feed represents one of the largest operational costs in aquaculture (often between 40% and 60%). Overfeeding not only wastes costly resources but also contributes to water pollution through uneaten nutrients. Underfeeding, on the other hand, slows growth and reduces productivity.
Huang and Khabusi (2025) report that smart feeding systems using AIoT optimize feeding schedules, minimize waste, and improve feed efficiency, leading to significantly better growth rates. In this regard, artificial intelligence in aquaculture offers sophisticated solutions for precision feeding:
- Feeding Behavior Analysis: Computer vision systems and acoustic sensors monitor the activity of fish or shrimp during feeding. AI algorithms analyze the intensity with which the fish consume feed, determining when they are satiated and automatically adjusting the amount dispensed to minimize waste. Lim (2023) highlights that in smart feeding, acoustic and vibration sensors are used to distinguish between hungry and well-fed fish; and cites shrimp farming as an example, where AI is applied in smart feeders based on appetite, monitoring of swimming patterns, real-time water quality surveillance, and monitoring of injuries and size using machine learning and cameras.
- Predictive Appetite Models: AI can integrate data on water temperature, time of day, fish size, and oxygen levels to predict optimal feeding times and the required amount, ensuring that fish or shrimp receive adequate nutrition when their metabolism is most receptive.
- Customized Diet Formulation: In the long term, AI could analyze growth and health data to help optimize the formulation of specific diets for different species, life stages, or even individual batches, maximizing feed conversion ratio (FCR). According to Mandal and Ghosh (2024) and Ashraf et al. (2024), AI can calculate ideal feeding schedules, adjust amounts in real-time based on fish behavior, and develop personalized feeding plans to optimize growth and reduce feed waste.
Health Monitoring and Early Disease Detection
Diseases can devastate aquaculture populations, causing massive economic losses. Early detection is crucial for implementing effective treatments and preventing large-scale outbreaks; in this sense, AI has become a powerful tool to address fish health challenges, enabling early identification of disease symptoms and prediction of potential health risks (Yang et al., 2025), which allows for rapid disease diagnosis and targeted treatment, reducing the need for excessive use of antibiotics and chemicals, while improving fish welfare (Mandal y Ghosh, 2024).
AI is revolutionizing health surveillance:
- Automated Visual Diagnosis: Ashraf et al. (2024) report that AI can analyze data from sensors and cameras to identify early signs of disease or stress in fish; in this way, computer vision algorithms, trained with thousands of images, can analyze videos or photos of fish to detect early signs of diseases or parasites (such as skin lesions, color changes, eroded fins, or the presence of sea lice) long before they are apparent to the human eye.
- Anomalous Behavior Analysis: AI can monitor swimming patterns, activity levels, and social behaviors. Subtle changes, such as lethargy, erratic swimming, or isolation from the group, can be early indicators of stress or disease, triggering alerts for operators.
- Disease Risk Prediction: By correlating environmental data (water quality), operational data (population density), and historical health data, AI models can predict periods of higher risk for specific disease outbreaks, allowing for proactive preventive measures.
Use of AI in Water Management: Quality and Sustainability
Maintaining optimal water quality is fundamental for the health and growth of aquatic organisms. This involves constant monitoring and precise adjustments of multiple interrelated parameters; in this scenario, AI-based water quality management has proven fundamental for maintaining optimal conditions in aquaculture through real-time monitoring and proactive adjustments (Yang et al., 2025).
The use of AI in water management brings intelligence and automation to this critical process:
- Continuous and Predictive Monitoring: Internet of Things (IoT) sensors collect real-time data on dissolved oxygen, pH, temperature, ammonia, nitrites, nitrates, salinity, and turbidity; in this way, AI can analyze sensor data to detect patterns and anomalies in parameters such as temperature, dissolved oxygen, pH, and ammonia levels, allowing for early corrective actions and the development of predictive models (Ashraf et al., 2024). Er-rousse and Qafas (2024) report that in Morocco, aquaculture farms use smart sensors to collect real-time data on parameters such as temperature, salinity, water quality, and oxygen level.AI analyzes sensor data not only to detect current deviations but also to predict future changes based on historical patterns and current conditions (e.g., predicting an oxygen drop overnight or an ammonia spike after feeding).
- Automated System Control: Based on predictions and real-time data, AI can automatically control equipment such as aerators, water pumps, filtration systems, or chemical dosers to maintain parameters within optimal ranges proactively, not just reactively. In this regard, Roy et al. (2024) highlight that IoT-based systems provide instant feedback for making timely decisions and maintaining ideal water quality.
- Resource Use Optimization: In recirculating aquaculture systems (RAS) or open-flow systems, AI can optimize the use of water and energy, for example, by adjusting water exchange rates or pump operation according to actual need, instead of operating on fixed schedules.
Biomass Estimation and Population Counting
Accurately knowing the number of individuals and their average weight (total biomass) is essential for inventory management, feed planning, harvest scheduling, and farm performance evaluation. Traditional methods (manual sampling) are laborious, stressful for the fish, and often inaccurate.
AI offers non-invasive and accurate alternatives:
- Computer Vision for Counting and Measurement: Underwater cameras (often stereo for depth perception) capture images of the fish. Machine Learning and computer vision algorithms provide non-invasive methodologies (Roy et al., 2024) that can identify, count, and estimate the individual size and weight of fish based on their length and shape, using models specifically trained for the cultivated species.
- Acoustic Systems: Sonar technology can also be used, with AI interpreting the acoustic signals to estimate the density and average size of fish in a cage or tank.
- Real-Time Biomass Estimation: By integrating count and size data over time, AI systems can provide updated estimates of total biomass, allowing for dynamic management adjustments. Ashraf et al. (2024) reported that AI, combined with machine learning and computer vision, allows for more accurate estimation of the size, weight, number, age, and sex of fish non-invasively. Cristea et al. (2024) reported, in the case of sturgeon aquaculture, significant advantages of using an AI system for biomass monitoring, including reduced unit costs for labor and feed, improved water quality, and active optimization of breeding conditions.
Reproduction and Genetic Improvement Programs
Genetic improvement programs are important for the success of the aquaculture industry. According to Ashraf et al. (2024), AI has the potential to improve broodstock management, predict performance, and accelerate the analysis of genetic data.
- Reproduction: AI can improve fish reproduction management by creating breeding plans and identifying optimal conditions for egg production. Likewise, AI can be employed to improve the selection of breeding pairs based on genomic data (Fernandes & DMello, 2025).On the other hand, Kao and Chen (2024) demonstrated the feasibility and high accuracy of an intelligent system based on YOLO for gender identification in tilapia fry, offering an efficient and less laborious alternative to traditional manual methods in smart city aquaculture.
- Genetic Improvement Programs: AI can analyze genomic data to create predictive models of fish performance, enabling more effective breeding programs for traits such as disease resistance and growth rate.
- Fish Genome, Sequencing, and Genomic Editing: AI has the potential to revolutionize the study of fish genomes, accelerating genetic data analysis, genome sequencing and assembly, and the design and refinement of genomic editing technologies like CRISPR-Cas9.
Behavior Analysis and Animal Welfare
Animal welfare is a growing concern in animal production, including aquaculture. Understanding how fish behave under different conditions can help optimize their environment and management. According to Huang and Khabusi (2025), monitoring fish behavior using motion sensors, computer vision, and acoustics provides valuable insights into fish health, animal welfare, and activity levels.
- Behavioral Monitoring: AI, using computer vision, can track swimming patterns, speed, distribution in the tank or cage, social interactions, and responses to stimuli (such as feeding or environmental changes). The research by Zhao et al. (2025) on behavior recognition focused on identifying stressors that cause abnormal responses in fish and on analyzing appetite and feeding status; they analyzed individual and group behavior, using qualitative and quantitative indicators.
- Stress Detection: Anomalous behaviors (such as rubbing against walls, rapid breathing at the surface, excessive aggression) can be automatically detected by AI as indicators of environmental, social, or health-related stress.
- Environment Optimization: By correlating behavioral data with environmental and operational parameters, AI can help identify optimal conditions that promote welfare and minimize stress (e.g., ideal population densities, suitable lighting regimes).
Post-Harvest Processing
According to Yang et al. (2025), the application of artificial intelligence (AI) in post-harvest processing in aquaculture has significantly improved the efficiency, precision, and sustainability of these operations. AI technologies optimize various processing stages, from quality assessment to packaging, reducing labor needs and minimizing post-harvest losses while maintaining high product quality standards.
- Quality Assessment: Scientific literature reports that machine learning (ML) algorithms are used for real-time analysis of fish or shrimp quality, evaluating parameters such as size, weight, and physical condition. This ensures that only fish meeting specific criteria advance in the processing line. This approach improves product quality consistency and reduces reliance on manual inspection, thereby minimizing human error.
- Automated Sorting and Grading: Advanced computer vision systems (CVS) further automate sorting and grading processes, allowing for precise fish classification based on standardized criteria. This automation supports uniformity in processed products, meeting market demands more efficiently.
- Optimization of Logistics and Processing Schedules: AI-driven predictive models optimize processing schedules and logistics by analyzing market trends, supply chain constraints, and processing capacity. These models facilitate efficient resource allocation, ensuring timely packaging, storage, and transport. By improving resource utilization and reducing waste, AI-driven processing technologies contribute to the economic viability and environmental sustainability of aquaculture, ensuring an efficient and effective post-harvest process.
Automation and Robotics in Aquaculture Farms
AI is the ‘brain’ driving advanced automation and robotics in aquaculture.
- Underwater Robots (ROVs) and Autonomous (AUVs): Equipped with cameras and sensors, and guided by AI, these underwater robots can perform tasks such as inspecting nets and cages, cleaning biofouling, repairing structures, and even removing mortalities. Likewise, AI-equipped unmanned aquatic vehicle systems are used to monitor water quality, collecting data such as electrical conductivity, water depth, turbidity, and pH (Lim, 2023).On the other hand, Er-rousse and Qafas (2024) report that AI-equipped underwater robots can perform tasks such as monitoring water quality, dispensing feed, cleaning pools or cages, sorting fish, and detecting contaminants.
- Robotic Feeding Systems: Beyond automatic dispensing, robots could move to distribute feed more uniformly or target specific areas based on fish detection.
- Automated Harvesting: AI-guided robotic systems are being explored to perform selective harvesting of fish that reach market size, reducing labor and stress on the remaining fish.
Spatial Planning and Site Selection for Aquaculture
Er-rousse and Qafas (2024) highlight that artificial intelligence (AI) technologies have improved spatial planning and site selection for aquaculture operations. The availability of satellite imagery and the ability to access oceanographic, hydrological, and meteorological data (water temperature, precipitation patterns, salinity levels, storm frequency) thanks to long-term remote sensing, combined with the use of digital drone imagery, have allowed for planning that is not only more efficient and faster but also enables a more comprehensive application of the Ecosystem Approach to Aquaculture (EAA).
In this sense, AI facilitates more informed, data-driven planning for aquaculture, contributing to optimizing facility location and minimizing the environmental impact of these activities.
Tangible Benefits of Artificial Intelligence and Modern Aquaculture
The integration of artificial intelligence and aquaculture is generating a series of measurable benefits that drive the economic and environmental viability of the sector:
- Improved Efficiency and Productivity: Optimization of feed use, reduction of production cycles, increased survival rates, and better harvest planning, all leading to higher yields.
- Environmental Sustainability: Reduction of feed waste, more efficient use of water and energy, less need for chemicals and antibiotics, and minimization of pollution from effluents.
- Improved Animal Welfare: Continuous monitoring of health and behavior, maintenance of optimal environmental conditions, and reduction of handling-related stress, contributing to more ethical farming practices.
- Risk and Cost Reduction: Early detection of diseases and environmental problems preventing massive losses, optimization of costly inputs (feed, energy), and reduction of labor costs through automation.
- Data-Driven Decision Making: Transforms aquaculture management from an approach based on intuition and experience to one grounded in objective data analysis and reliable predictions, enabling more precise and proactive management.
- Improved Traceability and Transparency: Data collected by AI systems can be used to document the entire production cycle, improving end-product traceability for consumers and regulators.
Challenges and Considerations for AI Implementation in Aquaculture
Despite the enormous potential, the widespread adoption of artificial intelligence in aquaculture faces several obstacles; These challenges include restricted access to representative data, prohibitive costs, technical complexities, lack of social acceptance, and concerns about data privacy and security (Aung et al., 2025 and Fernandes & DMello, 2025).
Below is a brief description of each challenge:
- Initial Investment Cost: Acquiring sensors, cameras, processing hardware, AI software, and the necessary infrastructure can require a significant initial investment, especially for small and medium-sized producers.
- Need for High-Quality, High-Volume Data: AI algorithms require large amounts of accurate, well-labeled data to be trained effectively. Collecting reliable data in aquatic environments (turbid water, biofouling on sensors, variable lighting conditions) can be technically challenging.
- Requirement for Technical Expertise: Qualified personnel are needed not only in aquaculture but also in data science, AI, IoT management, and technology maintenance, a combination of skills that can be difficult to find.
- Harsh Environmental Conditions: Electronic equipment (sensors, cameras) must be robust and resistant to withstand saltwater corrosion, pressure, biofouling, and extreme weather conditions in many aquaculture operations.
- Integration with Existing Systems: Incorporating new AI technologies into existing farm infrastructures and workflows can be complex and require significant adaptations.
- Connectivity: Especially on remote or offshore farms, ensuring reliable internet connectivity for real-time data transmission can be a challenge.
- Standardization and Compatibility: The lack of common data standards and interoperability between different systems and providers can hinder integration.
- Ethical and Regulatory Aspects: As AI makes more autonomous decisions, questions arise about accountability, algorithm transparency, and the need for adapted regulatory frameworks.
The Future of Artificial Intelligence in Aquaculture: Emerging Trends
The future of artificial intelligence in aquaculture is promising, with several trends likely to shape the next generation of smart aquatic farms:
- Deep AIoT Integration: The combination of AI and IoT will become even closer, creating cyber-physical ecosystems where sensors collect data, AI analyzes it, and actuators respond automatically in a continuous optimization cycle.
- Hyper-Personalized Predictive Models: Algorithms capable of predicting the growth, health, and needs of specific batches of fish or even individuals, enabling ultra-precise management.
- Advanced Autonomous Robotics: More sophisticated robots capable of performing complex tasks autonomously, such as individualized vaccination, selective harvesting, or detailed environmental monitoring within large cages.
- AI for Genetics and Selective Breeding: Use of AI to analyze genomic and performance data to accelerate selective breeding programs, developing strains that are more disease-resistant, faster-growing, or better adapted to specific conditions.
- Cloud-Based Integrated Management Platforms: Software solutions that integrate all farm data (environmental, feed, health, biomass, financial) into a single remotely accessible platform, with powerful AI-driven analysis and visualization tools.
- Democratization of Technology: Development of more affordable and user-friendly AI solutions, possibly based on subscription models or open-source platforms, so that benefits also reach small producers.
- Focus on Holistic Sustainability: Use of AI not only to optimize production but also to monitor and minimize the operation’s complete environmental footprint, including energy use, carbon emissions, and impact on surrounding ecosystems.
Conclusion
Artificial intelligence in aquaculture is no longer a futuristic vision, but a present reality that is driving significant improvements in the sector’s efficiency, sustainability, and profitability. From optimizing the exact amount of feed to detecting diseases before they spread and proactively ensuring water quality, AI offers powerful tools to address the challenges inherent in aquatic farming.
While implementation presents challenges related to costs, data, and technical expertise, the potential benefits are immense. The ability to make decisions based on accurate data, automate complex tasks, and predict problems before they occur is transforming the management of aquatic farms. The convergence of artificial intelligence and aquaculture is fundamental to meeting the growing global demand for seafood responsibly and sustainably.
As technology continues to evolve and become more accessible, we can expect to see even greater adoption of AI, leading the aquaculture industry towards an unprecedented era of precision, intelligence, and resilience. The application of artificial intelligence in fisheries and aquaculture, although different in their specific approaches, shares the common goal of using technology to ensure a more sustainable future for our aquatic resources and the communities that depend on them. Continued investment in research, development, and adoption of these technologies will be key to unlocking the full potential of smart aquaculture.
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