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* By Dimitris Pafras
Conventional methods of management have some what reliedon reactive approaches; however, climate change is bringing forth rapid and complex environmental dynamics and challenges that cannot be adequately managed through contemporary systems in aquaculture. There are lots of opportunities for keeping things sustainable, being productive, and adapting as fish farming goes toward a more tech-smart and climate-ready way. But there are big hurdles in the way of using such systems.
Aquaculture has rapidly evolved into a cornerstone of global food production, offering a vital solution to the growing need for sustainable protein sources in the face of escalating population growth and increasing nutritional awareness (Fiorella et al., 2021). Its expansion not only contributes significantly to food security but also supports the livelihoods of millions around the world. However, this accelerated growth brings with it a series of challenges, such as managing limited natural resources, controlling disease outbreaks, and ensuring water quality and environmental sustainability.
The situation is made worse by the escalation of climate change, which poses increasing risks to aquaculture systems through rising sea temperatures, acidification, and extreme weather (Stentiford et al., 2020). Conventional methods of management have somewhat relied on reactive approaches; however, climate change is bringing forth rapid and complex environmental dynamics and challenges that cannot be adequately managed through contemporary systems.
Therefore, rapid advancement, at the very least, towards sophisticated management practices that take proactive and data-driven approaches is crucial. Conventional management strategies, when intensified upon by climate change and its complexities, can no longer cope with the anticipated risks of aquaculture systems.
Mitchell, 2015; LeCun et al., 2015). Unlike other industries, AI in aquaculture must operate within biologically diverse and ecologically sensitive systems. Its applications range from automated feeding and disease diagnostics to water quality forecasting and fish behavior monitoring (Chiu et al., 2022; Islam et al., 2024; Zhao et al., 2021). The sector’s survival depends on adopting predictive systems that combine AI, IoT, and big data to create a flexible and resilient framework.
10 key applications of AIoT:
» Smart feeding systems optimize feeding quantity and time.
» Water quality management continuous monitoring with sensors and problem prediction.
» Disease detection & classification with biosensors, computer vision and AI models.
» Biomass assessment non-contact, computer vision or acoustic signals.
» Fish behavior detection for signs of stress or disease.
» Counting organisms automated, with AI and sensors.
» Species classification & identification in challenging underwater conditions.
» Growth & reproduction estimation with automated systems.
» Monitoring of individual fish for behavioral analysis.
» Robotics & automation with underwater vehicles and robotic systems.
ClimateVolatilityand AquacultureVulnerabilities
Climate change impacts aquaculture in a variety of ways, mainly through physical effects such as rising ocean temperatures, changes in water quality, and the increased frequency of extreme weather events. Higher water temperatures directly affect the physiology of farmed species, influencing their growth rates, feeding behavior, and immune responses.
Additionally, acidification of oceans and the degradation of water quality due to rising CO2 levels exacerbate the challenges faced by aquaculture systems, further compromising the viability of production.
Quantitative Insights: Linking Environmental Data to Fish Growth Dynamics
The predictive transformation of aquaculture hinges on the deep understanding of how environmental variables drive biological outcomes. As farms evolve into sensor-integrated ecosystems, the ability to interpret and act on physicochemical data becomes paramount. This section presents a set of key analytical visualizations that illustrate the relationships between core water parameters and fish growth. These diagrams serve both as evidence and as operational tools for data-driven aquaculture in a climate-uncertain world.
The bar chart (Figure 1) represents the relative importance of various physicochemical parameters — such as temperature, dissolved oxygen, pH, salinity, and turbidity — in predicting fish growth rates.
The heatmap-style matrix visualizes the Pearson correlation coefficients between key water quality parameters and observed fish growth (Figure 2). Strong positive correlations appear in dark blue, while negative or neutral correlations are shaded lighter or red.
The scatter plot with a fitted regression line (Figure 3) displays the observed relationship between average water temperature and fish growth rate across multiple production cycles.
Building a more effective and sustainable aquaculture system starts with an understanding of the intricate relationship between fish growth and the physicochemical characteristics of water. This chapter’s analytical visualizations emphasize how crucial it is to keep an eye on and control variables like temperature, turbidity, pH, and dissolved oxygen.
The data analysis’s conclusions highlight the importance of contemporary analytical techniques by enabling fish farmers to forecast and maximize fish growth using precise environmental data. In a world impacted by climate change and unpredictable environmental conditions, these insights are especially important. In particular, the relationship between temperature and dissolved oxygen highlights how crucial it is to always keep an eye on these variables in order to maximize output and reduce hazards.
With the integration of sensors and real-time data analysis, fish farmers can forecast and adjust environmental conditions, ensuring fish welfare and production efficiency. The next steps involve further improving predictive models and monitoring technology, while also fostering collaboration with the scientific community to continually enhance analytical methods.
Biological and Economic Consequences
The biological consequences of climate change are immediate, as higher temperatures and extreme weather events negatively impact the sustainability of farmed species. Disease outbreaks spread more rapidly in warmer waters, and the aquaculture sector loses approximately $10 billion annually due to climate-related disruptions (FAO, 2023). Moreover, climate change further destabilizes economic conditions, as production factors and market prices fluctuate due to external influences.
The Figure 4 illustrates the relationship between environmental variability (such as temperature fluctuations, pH, and oxygen levels) and their impact on production, including losses, costs, and disease risks. Each bubble represents a scenario, with the size of the bubble indicating the level of uncertainty or predicted risk. The colors of the bubbles reflect the severity of the risk: green for low risk, yellow for medium risk, and red for high or critical risk.
Example scenarios include Scenario A (Low O₂ + high temperatures, represented by a red bubble in the top right), Scenario B (Stable conditions, represented by a green bubble in the bottom left), and Scenario C (pH fluctuations + moderate heat, represented by a yellow-orange bubble in the middle zone).
The Data-Driven Producer
The challenge for producers in 2035 will be to develop a skillset that enables them to operate effectively in a world increasingly dominated by data and uncertainty. “Data literacy” will be crucial, as producers will need to interpret data from sensors, alerts from AI systems, and real-time information to make informed decisions. The ability to understand this data and translate it into strategic actions for farm development, animal health, and environmental sustainability will be essential for success.
Additionally, future producers will need systems thinking skills to balance ecological, technological, and economic factors. They must create an integrated strategy for production that considers environmental constraints, technological solutions, and the economics of aquaculture.
Tools of the trade
The tools for success in aquaculture by 2035 will revolve around real-time monitoring systems, AI platforms, and machine learning models. Realtime sensors will track essential parameters such as dissolved oxygen levels, pH, and biomass, providing immediate feedback to the producer. AI platforms will enable predictive models to optimize feeding strategies and alert producers to potential disease outbreaks before they spread, reducing the impact on production.
The use of Artificial Intelligence and machine learning will be pivotal in forecasting and early detection of issues in aquaculture. A notable example is the use of IBM’s Watson in Norwegian salmon farms, which predicts lice outbreaks with an accuracy of 92%. This predictive capacity allows farm managers to take proactive measures, preventing widespread damage to the fish population and improving overall farm productivity.
The concept of “digital twins” – virtual replicas of physical aquaculture systems – offers a powerful tool for simulating the potential impacts of climate change on farm operations. By using digital twins, producers can model farm responses to scenarios such as a 2°C rise in water temperature before making real-world adjustments. This allows them to test solutions in a controlled, simulated environment and implement strategies that are most likely to succeed under changing conditions.
The Figure 5 illustrates the architecture of a next-generation smart aquaculture system. At its foundation lies a dense network of environmental sensors that continuously monitor key water parameters such as temperature, oxygen levels, pH, and salinity.
These data are transmitted to a cloud-based AI engine, where machine learning models analyze both historical and real-time inputs to detect trends, predict risks, and suggest optimal interventions. The insights are visualized in a decision-making dashboard accessible by the operator, who can then adapt feeding schedules, aeration levels, or take proactive health measures.
The system is closed loop, learning over time and improving both accuracy and efficiency through continuous feedback. The transition to a more resilient and sustainable aquaculture sector requires active involvement from governments and the international community. Policy interventions will be crucial in facilitating this shift and supporting the adoption of cuttingedge technologies in the sector.
Subsidies for IoT adoption and real-time monitoring systems could provide the necessary financial incentives for producers to invest in smart technologies. Particularly for small and medium-sized enterprises, funding for infrastructure development and training in new technologies will be key to the success of the transition.
Future farmers will also be qualified data users and strategic managers thanks to the development of educational initiatives and certification pathways for producers. To manage sensor data and incorporate it into decision-making processes for sustainability, growth, and health, producers need to receive training.
Industry collaboration
Industry-wide collaboration and data-sharing networks will also be essential for creating a more resilient and sustainable aquaculture industry. Establishing regional data-sharing networks will allow producers to collaborate and exchange valuable information in real-time, reducing management costs and increasing production efficiency. These networks could include early-warning systems that enable producers to detect threats to their farms, such as diseases, extreme weather events, or changes in water quality, before they wreak havoc on their operations.
Such collaboration would extend to governmental bodies and agencies, which should create data platforms that support collective knowledge and action. Through this collaboration, a broader strategic goal can be achieved: the collection and analysis of data to develop joint programs for prevention and adaptation to climate risks.
Autonomous Swarm Systems: The Future of Smart Aquaculture
The rapid evolution of aquaculture due to real-time, predictive technologies is headed by Autonomous Swarm Systems. These AI-enabled underwater robotic units constitute cooperative networks designed for monitoring phytoplankton, diagnosing fish health, and providing resilience to the system.
Inspired by swarm behavior in animals, these robots work together, exchanging information and adapting their commands. They essentially operate as self-organizing mesh agents traversing through aquaculture systems in a game-like manner or possibly even as a living organism.
Swarm units equipped with biosensors, computer vision, and hydroacoustic tools to assess:
» Fish behavior and health (stress, erratic movement).
» Water quality (pH, temperature, salinity, oxygen).
» Integrity of infrastructure (net damage).
» AI-driven analysis on pathogen hotspots.
The units work in real-time coordination to identify areas of concern and share findings with human operators on-site.
The modalities include:
» Scalability across large farms
» Redundancy with distributed intelligence (if one unit fails, others can compensate)
» Low maintenance with auto docking
» Localized early warning system with good accuracy.
In the context of climate volatility, swarm systems can detect early changes in variables such as temperature or pH, providing timely alerts for emerging threats like heatwaves or acidification before they escalate. Such systems can operate with existing IoT and AI networks, sending data to cloud-based systems and quickening the decision-making processes of digital twins and dashboards. A hybrid model combining swarm agents with fixed sensors would cut costs and allow better access for smaller producers.
Swarm rings are being tested in Norway, Japan, and Chile, showing system performance improvements of up to 27%. In the future, the focus will be on standards for communication, better battery life, and edge AI for real-time decision-making. In conclusion, Autonomous Swarm Systems are redefining aquaculture, where interactions were limited to static observation, towards intelligent dynamic interactions with the environment.
There are lots of opportunities for keeping things sustainable, being productive, and adapting as fish farming goes toward a more techsmart and climate-ready way. But there are big hurdles in the way of using such systems. One big hurdle to advanced tech use is still the high costs at the start, especially for small and medium businesses.
Also, strong rules for data safety and privacy must be set up to gather store and analyze large amounts of data. The making of flexible AI models that can work well in different production types of an environment conditions is important too. It will need help from institutions, working together from different fields, and steady money for teaching, learning, and practical study to solve these problems.
References and sources consulted by the author on the elaboration of this article are available under previous request to our editorial staff.
* Dimitris Pafras. Ph.D. candidate in Marine Biology & Fisheries Dynamics. University of Thessaly (UTh), School of Agricultural Sciences. Department of Ichthyology and Aquatic Environment (DIAE).
The post Aquaculture 2035: Designing Resilience Through AI and Data in a Climate-UnstableWorld appeared first on Aquaculture Magazine.
Read more...
Conventional methods of management have some what reliedon reactive approaches; however, climate change is bringing forth rapid and complex environmental dynamics and challenges that cannot be adequately managed through contemporary systems in aquaculture. There are lots of opportunities for keeping things sustainable, being productive, and adapting as fish farming goes toward a more tech-smart and climate-ready way. But there are big hurdles in the way of using such systems.
Aquaculture has rapidly evolved into a cornerstone of global food production, offering a vital solution to the growing need for sustainable protein sources in the face of escalating population growth and increasing nutritional awareness (Fiorella et al., 2021). Its expansion not only contributes significantly to food security but also supports the livelihoods of millions around the world. However, this accelerated growth brings with it a series of challenges, such as managing limited natural resources, controlling disease outbreaks, and ensuring water quality and environmental sustainability.
The situation is made worse by the escalation of climate change, which poses increasing risks to aquaculture systems through rising sea temperatures, acidification, and extreme weather (Stentiford et al., 2020). Conventional methods of management have somewhat relied on reactive approaches; however, climate change is bringing forth rapid and complex environmental dynamics and challenges that cannot be adequately managed through contemporary systems.
Therefore, rapid advancement, at the very least, towards sophisticated management practices that take proactive and data-driven approaches is crucial. Conventional management strategies, when intensified upon by climate change and its complexities, can no longer cope with the anticipated risks of aquaculture systems.
Mitchell, 2015; LeCun et al., 2015). Unlike other industries, AI in aquaculture must operate within biologically diverse and ecologically sensitive systems. Its applications range from automated feeding and disease diagnostics to water quality forecasting and fish behavior monitoring (Chiu et al., 2022; Islam et al., 2024; Zhao et al., 2021). The sector’s survival depends on adopting predictive systems that combine AI, IoT, and big data to create a flexible and resilient framework.
10 key applications of AIoT:
» Smart feeding systems optimize feeding quantity and time.
» Water quality management continuous monitoring with sensors and problem prediction.
» Disease detection & classification with biosensors, computer vision and AI models.
» Biomass assessment non-contact, computer vision or acoustic signals.
» Fish behavior detection for signs of stress or disease.
» Counting organisms automated, with AI and sensors.
» Species classification & identification in challenging underwater conditions.
» Growth & reproduction estimation with automated systems.
» Monitoring of individual fish for behavioral analysis.
» Robotics & automation with underwater vehicles and robotic systems.
ClimateVolatilityand AquacultureVulnerabilities
Climate change impacts aquaculture in a variety of ways, mainly through physical effects such as rising ocean temperatures, changes in water quality, and the increased frequency of extreme weather events. Higher water temperatures directly affect the physiology of farmed species, influencing their growth rates, feeding behavior, and immune responses.
Additionally, acidification of oceans and the degradation of water quality due to rising CO2 levels exacerbate the challenges faced by aquaculture systems, further compromising the viability of production.
Quantitative Insights: Linking Environmental Data to Fish Growth Dynamics
The predictive transformation of aquaculture hinges on the deep understanding of how environmental variables drive biological outcomes. As farms evolve into sensor-integrated ecosystems, the ability to interpret and act on physicochemical data becomes paramount. This section presents a set of key analytical visualizations that illustrate the relationships between core water parameters and fish growth. These diagrams serve both as evidence and as operational tools for data-driven aquaculture in a climate-uncertain world.
The bar chart (Figure 1) represents the relative importance of various physicochemical parameters — such as temperature, dissolved oxygen, pH, salinity, and turbidity — in predicting fish growth rates.
The heatmap-style matrix visualizes the Pearson correlation coefficients between key water quality parameters and observed fish growth (Figure 2). Strong positive correlations appear in dark blue, while negative or neutral correlations are shaded lighter or red.
The scatter plot with a fitted regression line (Figure 3) displays the observed relationship between average water temperature and fish growth rate across multiple production cycles.
Building a more effective and sustainable aquaculture system starts with an understanding of the intricate relationship between fish growth and the physicochemical characteristics of water. This chapter’s analytical visualizations emphasize how crucial it is to keep an eye on and control variables like temperature, turbidity, pH, and dissolved oxygen.
The data analysis’s conclusions highlight the importance of contemporary analytical techniques by enabling fish farmers to forecast and maximize fish growth using precise environmental data. In a world impacted by climate change and unpredictable environmental conditions, these insights are especially important. In particular, the relationship between temperature and dissolved oxygen highlights how crucial it is to always keep an eye on these variables in order to maximize output and reduce hazards.
With the integration of sensors and real-time data analysis, fish farmers can forecast and adjust environmental conditions, ensuring fish welfare and production efficiency. The next steps involve further improving predictive models and monitoring technology, while also fostering collaboration with the scientific community to continually enhance analytical methods.
Biological and Economic Consequences
The biological consequences of climate change are immediate, as higher temperatures and extreme weather events negatively impact the sustainability of farmed species. Disease outbreaks spread more rapidly in warmer waters, and the aquaculture sector loses approximately $10 billion annually due to climate-related disruptions (FAO, 2023). Moreover, climate change further destabilizes economic conditions, as production factors and market prices fluctuate due to external influences.
The Figure 4 illustrates the relationship between environmental variability (such as temperature fluctuations, pH, and oxygen levels) and their impact on production, including losses, costs, and disease risks. Each bubble represents a scenario, with the size of the bubble indicating the level of uncertainty or predicted risk. The colors of the bubbles reflect the severity of the risk: green for low risk, yellow for medium risk, and red for high or critical risk.
Example scenarios include Scenario A (Low O₂ + high temperatures, represented by a red bubble in the top right), Scenario B (Stable conditions, represented by a green bubble in the bottom left), and Scenario C (pH fluctuations + moderate heat, represented by a yellow-orange bubble in the middle zone).
The Data-Driven Producer
The challenge for producers in 2035 will be to develop a skillset that enables them to operate effectively in a world increasingly dominated by data and uncertainty. “Data literacy” will be crucial, as producers will need to interpret data from sensors, alerts from AI systems, and real-time information to make informed decisions. The ability to understand this data and translate it into strategic actions for farm development, animal health, and environmental sustainability will be essential for success.
Additionally, future producers will need systems thinking skills to balance ecological, technological, and economic factors. They must create an integrated strategy for production that considers environmental constraints, technological solutions, and the economics of aquaculture.
Tools of the trade
The tools for success in aquaculture by 2035 will revolve around real-time monitoring systems, AI platforms, and machine learning models. Realtime sensors will track essential parameters such as dissolved oxygen levels, pH, and biomass, providing immediate feedback to the producer. AI platforms will enable predictive models to optimize feeding strategies and alert producers to potential disease outbreaks before they spread, reducing the impact on production.
The use of Artificial Intelligence and machine learning will be pivotal in forecasting and early detection of issues in aquaculture. A notable example is the use of IBM’s Watson in Norwegian salmon farms, which predicts lice outbreaks with an accuracy of 92%. This predictive capacity allows farm managers to take proactive measures, preventing widespread damage to the fish population and improving overall farm productivity.
The concept of “digital twins” – virtual replicas of physical aquaculture systems – offers a powerful tool for simulating the potential impacts of climate change on farm operations. By using digital twins, producers can model farm responses to scenarios such as a 2°C rise in water temperature before making real-world adjustments. This allows them to test solutions in a controlled, simulated environment and implement strategies that are most likely to succeed under changing conditions.
The Figure 5 illustrates the architecture of a next-generation smart aquaculture system. At its foundation lies a dense network of environmental sensors that continuously monitor key water parameters such as temperature, oxygen levels, pH, and salinity.
These data are transmitted to a cloud-based AI engine, where machine learning models analyze both historical and real-time inputs to detect trends, predict risks, and suggest optimal interventions. The insights are visualized in a decision-making dashboard accessible by the operator, who can then adapt feeding schedules, aeration levels, or take proactive health measures.
The system is closed loop, learning over time and improving both accuracy and efficiency through continuous feedback. The transition to a more resilient and sustainable aquaculture sector requires active involvement from governments and the international community. Policy interventions will be crucial in facilitating this shift and supporting the adoption of cuttingedge technologies in the sector.
Subsidies for IoT adoption and real-time monitoring systems could provide the necessary financial incentives for producers to invest in smart technologies. Particularly for small and medium-sized enterprises, funding for infrastructure development and training in new technologies will be key to the success of the transition.
Future farmers will also be qualified data users and strategic managers thanks to the development of educational initiatives and certification pathways for producers. To manage sensor data and incorporate it into decision-making processes for sustainability, growth, and health, producers need to receive training.
Industry collaboration
Industry-wide collaboration and data-sharing networks will also be essential for creating a more resilient and sustainable aquaculture industry. Establishing regional data-sharing networks will allow producers to collaborate and exchange valuable information in real-time, reducing management costs and increasing production efficiency. These networks could include early-warning systems that enable producers to detect threats to their farms, such as diseases, extreme weather events, or changes in water quality, before they wreak havoc on their operations.
Such collaboration would extend to governmental bodies and agencies, which should create data platforms that support collective knowledge and action. Through this collaboration, a broader strategic goal can be achieved: the collection and analysis of data to develop joint programs for prevention and adaptation to climate risks.
Autonomous Swarm Systems: The Future of Smart Aquaculture
The rapid evolution of aquaculture due to real-time, predictive technologies is headed by Autonomous Swarm Systems. These AI-enabled underwater robotic units constitute cooperative networks designed for monitoring phytoplankton, diagnosing fish health, and providing resilience to the system.
Inspired by swarm behavior in animals, these robots work together, exchanging information and adapting their commands. They essentially operate as self-organizing mesh agents traversing through aquaculture systems in a game-like manner or possibly even as a living organism.
Swarm units equipped with biosensors, computer vision, and hydroacoustic tools to assess:
» Fish behavior and health (stress, erratic movement).
» Water quality (pH, temperature, salinity, oxygen).
» Integrity of infrastructure (net damage).
» AI-driven analysis on pathogen hotspots.
The units work in real-time coordination to identify areas of concern and share findings with human operators on-site.
The modalities include:
» Scalability across large farms
» Redundancy with distributed intelligence (if one unit fails, others can compensate)
» Low maintenance with auto docking
» Localized early warning system with good accuracy.
In the context of climate volatility, swarm systems can detect early changes in variables such as temperature or pH, providing timely alerts for emerging threats like heatwaves or acidification before they escalate. Such systems can operate with existing IoT and AI networks, sending data to cloud-based systems and quickening the decision-making processes of digital twins and dashboards. A hybrid model combining swarm agents with fixed sensors would cut costs and allow better access for smaller producers.
Swarm rings are being tested in Norway, Japan, and Chile, showing system performance improvements of up to 27%. In the future, the focus will be on standards for communication, better battery life, and edge AI for real-time decision-making. In conclusion, Autonomous Swarm Systems are redefining aquaculture, where interactions were limited to static observation, towards intelligent dynamic interactions with the environment.
There are lots of opportunities for keeping things sustainable, being productive, and adapting as fish farming goes toward a more techsmart and climate-ready way. But there are big hurdles in the way of using such systems. One big hurdle to advanced tech use is still the high costs at the start, especially for small and medium businesses.
Also, strong rules for data safety and privacy must be set up to gather store and analyze large amounts of data. The making of flexible AI models that can work well in different production types of an environment conditions is important too. It will need help from institutions, working together from different fields, and steady money for teaching, learning, and practical study to solve these problems.
References and sources consulted by the author on the elaboration of this article are available under previous request to our editorial staff.
* Dimitris Pafras. Ph.D. candidate in Marine Biology & Fisheries Dynamics. University of Thessaly (UTh), School of Agricultural Sciences. Department of Ichthyology and Aquatic Environment (DIAE).
The post Aquaculture 2035: Designing Resilience Through AI and Data in a Climate-UnstableWorld appeared first on Aquaculture Magazine.
Read more...