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* By Anil Singh, Shashank Singh, Arya Singh, Suman Dey, Harshit Singh and Vikash Kushwaha
Aquaculture is a rapidly expanding sector crucial to global food security, nutrition, employment,economy, environmental sustainability, healthcare and providing livelihoods. Traditional farming methods face various limitations in aquaculture production while Artificial Intelligence (AI) is transforming aquaculture by improving efficiency, accuracy and sustainability. AI integration is poised to make aquaculture more sustainable and productive, empowering fish farmers with smart tools to meet growing global demands efficiently and responsibly.
Introduction
In recent times, the world faces critical challenges such as hunger, malnutrition, and nutrient- related diseases. Aquaculture has emerged as a key solution, especially as the growing global population increases demand for sustainable food sources (FAO, 2022). With a history of over 4,000 years, aquaculture now supplies 15% of global animal protein and 6% of total protein intake (FAO, 2024). However, traditional fish farming methods are not very reliable. They can cause problems like fish diseases, water pollution, over- fishing, high labor required, time taking and damage to the environment.
These issues can also lead to lower profits, financial losses and a lack of information about market risks for farmed fish. Proper planning is needed to solve social health, environmental and economic problems related to fish farming both national and international level. To solve these challenges, recent advancements have been driven by Artificial Intelligence (AI), which is revolutionizing the sector by improving efficiency, sustainability and productivity.
AI’s role in aquaculture was first recognized in 2000 when the University of Texas Medical Branch in Galveston developed a fuzzy logic- based control system for denitrification in a closed recirculating system (Lee et al., 2000). This system used real-time sensor data such as dissolved oxygen, oxidation-reduction potential and pH to autonomously adjust pumping rates and carbon feed, optimizing bioreactor performance. Since then, AI has become a vital tool in modern aquaculture research and operations.
Work Flow of AI
These systems utilize cameras (similar to human eyes), sensors (sensitive devices) and computers (acting as the brain) to understand language, recognize objects, make decisions and respond effectively (Xu et al., 2021). Therefore, to ensure the sustainable growth of aquaculture, the use of AI in existing resources can help create favorable conditions (Figure 1).
AI Powered Innovations in Aquaculture
To overcome the challenges of traditional aquaculture farming, AI-powered innovations are being adopted to enhance efficiency, sustainability and productivity across the industry without impact on the environmental conditions, water quality and fish health. AI helps optimize resources and streamline operations (Chrispin et al., 2020).
Key applications include automated feeding, site monitoring, growth tracking, temperature and water quality control, aeration, disease detection, fish classification, harvesting, biomass estimation, supply chain management, and aquatic vegetation control, making aquaculture more efficient and sustainable (Figure 2).
AI in water quality management
Effective water quality monitoring is crucial for aquaculture, as fish health depends on stable environmental conditions (Lindholm-Lehto, 2023). AI enhances monitoring by analyzing real-time sensor data such as temperature, dissolved oxygen, pH, and ammonia to detect anomalies and predict issues (Zhao et al., 2022). It enables timely action, reducing fish mortality and improving farm efficiency (Javaid et al., 2022).
AI can also forecast water quality changes using historical data, weather and feeding patterns (Saeed et al., 2022), and tailor conditions to specific fish species (Chiu et al., 2022). Models like NARNET, LSTM, SVM, and K-NN have proven effective in predicting and classifying water quality (Aldhyani et al., 2020). Overall, AI provides fish farmers with accurate insights, early warnings and data-driven decision-making tools for sustainable aquaculture.
AI in disease detection and management
Several studies highlight AI’s growing role in fish health monitoring, disease detection, and control in aquaculture. AI systems use data from underwater cameras and sensors to analyze fish behavior, appearance, and swimming patterns, helping identify signs of stress or illness early (Li et al., 2023). These tools detect clinical signs like lesions or discoloration, enabling timely treatment and reducing antibiotic use (Chen et al., 2023). Overall, AI enhances disease management and promotes healthier, more sustainable aquaculture systems.
AI in biomass monitoring
Biomass is a vital indicator of fish health and growths during cultivation (Li et al., 2020). Traditional methods like netting and weighing are labor-intensive, stressful for fish and prone to errors (Aung et al., 2025). To address these issues, researchers have turned to AI-based approaches to more efficient and accurate for biomass estimation.
Technologies such as machine learning and computer vision can estimate fish size, weight and count using image analysis (Monkman et al., 2019). CNNs have proven effective in predicting fish weight from images, while other models use infrared cameras and classifiers to automate weight and length estimation (Lopez-Tejeida et al., 2022). These AI tools reduce stress on fish, improve accuracy, and support sustainable aquaculture practices.
AI in size, sex and age determination
Fish body length is vital for resource management, but traditional manual measurement is slow and inaccurate. AI models, using datasets like Image Net, have improved efficiency; for example, Monkman et al. (2019) used R-CNN and OpenCV to estimate European bass length with just a 2.2% error. Traditional sex identification methods were often harmful and error-prone.
Machine learning combined with machine vision offers a non-invasive, accurate alternative by analyzing morphology, as explained by Barulin (2019) using Boruta and Random Forest algorithms on starlet sturgeon. This highlights AI’s strong potential in fish sex determination. Fish age is commonly determined by analyzing otolith images, a process enhanced by deep learning. Moen et al. (2018) applied transfer learning with CNNs for expert-level age estimation, though accuracy was lower for the youngest fish.
AI in feed optimization
Fish feed costs make up above 40- 50% of aquaculture expenses, with about 60% of feed wasted, leading to water pollution and reduced fish growth (Mattos et al., 2022). Accurately matching feed to fish appetite is essential but challenging. AI helps optimize feeding by analyzing factors like water temperature, oxygen levels and feed composition to predict ideal feeding times and amounts (Hu et al., 2022).
AI also monitors fish behavior via sensors and cameras to adjust feeding in real-time. Personalized feeding strategies based on genetics, age, and weight further enhance growth while minimizing waste and environmental impact, supporting sustainable aquaculture (Chen et al., 2025).
AI in promoting growth
Optimal fish growth depends on maintaining species-specific temperatures, with deviations harming growth rates (Mandal et al., 2024). AI supports aquaculture by monitoring growth through technologies like stereoscopic and sonar cameras, even in challenging environments (Li et al., 2020).
AI-driven models use environmental data temperature, oxygen, nutrients to predict growth rates, optimize feeding and improve yield forecasts. Studies show machine learning can create individualized feeding plans, enhancing growth and feed efficiency (Quispesivana et al., 2022). Overall, AI improves aquaculture efficiency, growth, and sustainability.
AI in fish behavior monitor
Fish behavior, including feeding and social interactions, is closely influenced by temperature. Warmer water often increases aggression in species like tilapia, largemouth bass and Atlantic salmon, raising stress and disease risks. Conversely, colder temperatures slow metabolism and activity, prompting species like trout, catfish, carp, pike and walleye to seek warmer or deeper waters to conserve energy.
These temperature-driven behaviors impact fish health and growth, making thermal management vital for aquaculture. An example of AI use is Umitron Corporation’s Tokyo-based system that monitors fish swimming behavior in real-time to optimize feeding, reducing waste and improving feed efficiency (Rather et al., 2024).
AI in fish reproduction
AI is transforming fish reproduction and breeding by optimizing environmental conditions and using predictive models to enhance outcomes. In reproduction, AI helps manage temperature, feeding, water quality, and disease control, improving spawning success and aligning breeding with ideal conditions (Prapti et al., 2022). In breeding, AI analyzes genomic data to predict traits like growth and disease resistance, enabling faster, more accurate selection and healthier fish stocks (Mandal & Ghosh, 2023). These AI-driven approaches support more efficient, sustainable, and profitable aquaculture.
AI in fish processing
Demand for processed seafood is rising, AI-powered robots have transformed fish and shrimp processing by performing tasks like cutting, filleting, cleaning, grading, packing, and transporting with high accuracy and speed (Ravishankar et al., 2024). These automated systems reduce labor costs and require minimal supervision. Iceland’s company Marel produces AI-based robots that handle the entire processing workflow efficiently.
AI in aquatic animal conservation
Human activities have rapidly reduced aquatic animal populations, making conservation challenging, especially in open seas. AI drones equipped with vision sensors and cameras can quickly track endangered fish and analyze their habitats more efficiently than humans. Larger species like sharks and humpback whales are monitored using transmitters on their fins, aiding behavioral studies and improving conservation efforts (Isabelle et al., 2024).
Merits and Demerits of AI in Aquaculture
AI significantly enhances aquaculture productivity by improving efficiency, accuracy and disaster prediction across all stages, from hatcheries to processing. It automates key advantages such as water monitoring, feeding optimization, early disease detection, enabling farmers to manage larger operations with fewer resources. By adjusting environmental conditions in real time, AI boosts growth rates, feed efficiency and sustainability through reduced waste and environmental impact (Alshater et al., 2023).
Despite its benefits, AI adoption faces several challenges. High initial and maintenance costs can be a major barrier, especially for small-scale farmers. Automation may also lead to job losses in the fishing industry (Li et al., 2022). Furthermore, AI systems require high-quality, domain-specific data, which is often scarce in remote areas. The need for advanced equipment and trained personnel further limits accessibility (Mustapha et al., 2021).
Future Opportunities of AI in Aquaculture
The future of AI in fisheries and aquaculture is highly promising and full of potential. With the help of AI, fish farmers can reduce labor costs, diseases, feed wastage and mortality rates. AI holds great importance in aquaculture, offering applications in weather forecasting, livestock assessment, fish resource management, hatchery operations, water quality monitoring, disease detection, and highly efficient management. In the future, there is a strong possibility that these tasks will be carried out fully automatically without human intervention (Wang et al., 2021).
Conclusion
AI is reshaping the aquaculture industry by offering innovative solutions that enhance productivity, sustainability and operational efficiency. From real-time data monitoring to disease detection and feed optimization, AI enables smarter, data-driven decision-making across the entire aquaculture value chain. While challenges such as high costs, data limitations and skill requirements remain, the benefits of AI such as reduced labor, improved fish health and minimized environmental impact highlight its transformative potential. As technologies continue to advance, AI will play an increasingly vital role in achieving sustainable aquaculture, supporting global food security, and ensuring long-term economic and environmental resilience.
References and sources consulted by the author on the elaboration of this article are available under previous request to our editorial staff.
Anil Singh, Shashank Singh*, Harshit Singh and Vikash Kushwaha Department of Aquaculture, College of Fisheries. Corresponding author*: drssaqua@gmail.com
Arya Singh Department of Aquatic Animal Health Management, College of Fisheries.
Suman Dey Department of Fisheries Extension, College of Fisheries.
Acharya Narendra Deva University of Agriculture and Technology, Kumarganj, Ayodhya-224229, (U.P.), India.
The post AI- Driven Innovations in Aquaculture appeared first on Aquaculture Magazine.
Read more...
Aquaculture is a rapidly expanding sector crucial to global food security, nutrition, employment,economy, environmental sustainability, healthcare and providing livelihoods. Traditional farming methods face various limitations in aquaculture production while Artificial Intelligence (AI) is transforming aquaculture by improving efficiency, accuracy and sustainability. AI integration is poised to make aquaculture more sustainable and productive, empowering fish farmers with smart tools to meet growing global demands efficiently and responsibly.
Introduction
In recent times, the world faces critical challenges such as hunger, malnutrition, and nutrient- related diseases. Aquaculture has emerged as a key solution, especially as the growing global population increases demand for sustainable food sources (FAO, 2022). With a history of over 4,000 years, aquaculture now supplies 15% of global animal protein and 6% of total protein intake (FAO, 2024). However, traditional fish farming methods are not very reliable. They can cause problems like fish diseases, water pollution, over- fishing, high labor required, time taking and damage to the environment.
These issues can also lead to lower profits, financial losses and a lack of information about market risks for farmed fish. Proper planning is needed to solve social health, environmental and economic problems related to fish farming both national and international level. To solve these challenges, recent advancements have been driven by Artificial Intelligence (AI), which is revolutionizing the sector by improving efficiency, sustainability and productivity.
AI’s role in aquaculture was first recognized in 2000 when the University of Texas Medical Branch in Galveston developed a fuzzy logic- based control system for denitrification in a closed recirculating system (Lee et al., 2000). This system used real-time sensor data such as dissolved oxygen, oxidation-reduction potential and pH to autonomously adjust pumping rates and carbon feed, optimizing bioreactor performance. Since then, AI has become a vital tool in modern aquaculture research and operations.
Work Flow of AI
These systems utilize cameras (similar to human eyes), sensors (sensitive devices) and computers (acting as the brain) to understand language, recognize objects, make decisions and respond effectively (Xu et al., 2021). Therefore, to ensure the sustainable growth of aquaculture, the use of AI in existing resources can help create favorable conditions (Figure 1).
AI Powered Innovations in Aquaculture
To overcome the challenges of traditional aquaculture farming, AI-powered innovations are being adopted to enhance efficiency, sustainability and productivity across the industry without impact on the environmental conditions, water quality and fish health. AI helps optimize resources and streamline operations (Chrispin et al., 2020).
Key applications include automated feeding, site monitoring, growth tracking, temperature and water quality control, aeration, disease detection, fish classification, harvesting, biomass estimation, supply chain management, and aquatic vegetation control, making aquaculture more efficient and sustainable (Figure 2).
AI in water quality management
Effective water quality monitoring is crucial for aquaculture, as fish health depends on stable environmental conditions (Lindholm-Lehto, 2023). AI enhances monitoring by analyzing real-time sensor data such as temperature, dissolved oxygen, pH, and ammonia to detect anomalies and predict issues (Zhao et al., 2022). It enables timely action, reducing fish mortality and improving farm efficiency (Javaid et al., 2022).
AI can also forecast water quality changes using historical data, weather and feeding patterns (Saeed et al., 2022), and tailor conditions to specific fish species (Chiu et al., 2022). Models like NARNET, LSTM, SVM, and K-NN have proven effective in predicting and classifying water quality (Aldhyani et al., 2020). Overall, AI provides fish farmers with accurate insights, early warnings and data-driven decision-making tools for sustainable aquaculture.
AI in disease detection and management
Several studies highlight AI’s growing role in fish health monitoring, disease detection, and control in aquaculture. AI systems use data from underwater cameras and sensors to analyze fish behavior, appearance, and swimming patterns, helping identify signs of stress or illness early (Li et al., 2023). These tools detect clinical signs like lesions or discoloration, enabling timely treatment and reducing antibiotic use (Chen et al., 2023). Overall, AI enhances disease management and promotes healthier, more sustainable aquaculture systems.
AI in biomass monitoring
Biomass is a vital indicator of fish health and growths during cultivation (Li et al., 2020). Traditional methods like netting and weighing are labor-intensive, stressful for fish and prone to errors (Aung et al., 2025). To address these issues, researchers have turned to AI-based approaches to more efficient and accurate for biomass estimation.
Technologies such as machine learning and computer vision can estimate fish size, weight and count using image analysis (Monkman et al., 2019). CNNs have proven effective in predicting fish weight from images, while other models use infrared cameras and classifiers to automate weight and length estimation (Lopez-Tejeida et al., 2022). These AI tools reduce stress on fish, improve accuracy, and support sustainable aquaculture practices.
AI in size, sex and age determination
Fish body length is vital for resource management, but traditional manual measurement is slow and inaccurate. AI models, using datasets like Image Net, have improved efficiency; for example, Monkman et al. (2019) used R-CNN and OpenCV to estimate European bass length with just a 2.2% error. Traditional sex identification methods were often harmful and error-prone.
Machine learning combined with machine vision offers a non-invasive, accurate alternative by analyzing morphology, as explained by Barulin (2019) using Boruta and Random Forest algorithms on starlet sturgeon. This highlights AI’s strong potential in fish sex determination. Fish age is commonly determined by analyzing otolith images, a process enhanced by deep learning. Moen et al. (2018) applied transfer learning with CNNs for expert-level age estimation, though accuracy was lower for the youngest fish.
AI in feed optimization
Fish feed costs make up above 40- 50% of aquaculture expenses, with about 60% of feed wasted, leading to water pollution and reduced fish growth (Mattos et al., 2022). Accurately matching feed to fish appetite is essential but challenging. AI helps optimize feeding by analyzing factors like water temperature, oxygen levels and feed composition to predict ideal feeding times and amounts (Hu et al., 2022).
AI also monitors fish behavior via sensors and cameras to adjust feeding in real-time. Personalized feeding strategies based on genetics, age, and weight further enhance growth while minimizing waste and environmental impact, supporting sustainable aquaculture (Chen et al., 2025).
AI in promoting growth
Optimal fish growth depends on maintaining species-specific temperatures, with deviations harming growth rates (Mandal et al., 2024). AI supports aquaculture by monitoring growth through technologies like stereoscopic and sonar cameras, even in challenging environments (Li et al., 2020).
AI-driven models use environmental data temperature, oxygen, nutrients to predict growth rates, optimize feeding and improve yield forecasts. Studies show machine learning can create individualized feeding plans, enhancing growth and feed efficiency (Quispesivana et al., 2022). Overall, AI improves aquaculture efficiency, growth, and sustainability.
AI in fish behavior monitor
Fish behavior, including feeding and social interactions, is closely influenced by temperature. Warmer water often increases aggression in species like tilapia, largemouth bass and Atlantic salmon, raising stress and disease risks. Conversely, colder temperatures slow metabolism and activity, prompting species like trout, catfish, carp, pike and walleye to seek warmer or deeper waters to conserve energy.
These temperature-driven behaviors impact fish health and growth, making thermal management vital for aquaculture. An example of AI use is Umitron Corporation’s Tokyo-based system that monitors fish swimming behavior in real-time to optimize feeding, reducing waste and improving feed efficiency (Rather et al., 2024).
AI in fish reproduction
AI is transforming fish reproduction and breeding by optimizing environmental conditions and using predictive models to enhance outcomes. In reproduction, AI helps manage temperature, feeding, water quality, and disease control, improving spawning success and aligning breeding with ideal conditions (Prapti et al., 2022). In breeding, AI analyzes genomic data to predict traits like growth and disease resistance, enabling faster, more accurate selection and healthier fish stocks (Mandal & Ghosh, 2023). These AI-driven approaches support more efficient, sustainable, and profitable aquaculture.
AI in fish processing
Demand for processed seafood is rising, AI-powered robots have transformed fish and shrimp processing by performing tasks like cutting, filleting, cleaning, grading, packing, and transporting with high accuracy and speed (Ravishankar et al., 2024). These automated systems reduce labor costs and require minimal supervision. Iceland’s company Marel produces AI-based robots that handle the entire processing workflow efficiently.
AI in aquatic animal conservation
Human activities have rapidly reduced aquatic animal populations, making conservation challenging, especially in open seas. AI drones equipped with vision sensors and cameras can quickly track endangered fish and analyze their habitats more efficiently than humans. Larger species like sharks and humpback whales are monitored using transmitters on their fins, aiding behavioral studies and improving conservation efforts (Isabelle et al., 2024).
Merits and Demerits of AI in Aquaculture
AI significantly enhances aquaculture productivity by improving efficiency, accuracy and disaster prediction across all stages, from hatcheries to processing. It automates key advantages such as water monitoring, feeding optimization, early disease detection, enabling farmers to manage larger operations with fewer resources. By adjusting environmental conditions in real time, AI boosts growth rates, feed efficiency and sustainability through reduced waste and environmental impact (Alshater et al., 2023).
Despite its benefits, AI adoption faces several challenges. High initial and maintenance costs can be a major barrier, especially for small-scale farmers. Automation may also lead to job losses in the fishing industry (Li et al., 2022). Furthermore, AI systems require high-quality, domain-specific data, which is often scarce in remote areas. The need for advanced equipment and trained personnel further limits accessibility (Mustapha et al., 2021).
Future Opportunities of AI in Aquaculture
The future of AI in fisheries and aquaculture is highly promising and full of potential. With the help of AI, fish farmers can reduce labor costs, diseases, feed wastage and mortality rates. AI holds great importance in aquaculture, offering applications in weather forecasting, livestock assessment, fish resource management, hatchery operations, water quality monitoring, disease detection, and highly efficient management. In the future, there is a strong possibility that these tasks will be carried out fully automatically without human intervention (Wang et al., 2021).
Conclusion
AI is reshaping the aquaculture industry by offering innovative solutions that enhance productivity, sustainability and operational efficiency. From real-time data monitoring to disease detection and feed optimization, AI enables smarter, data-driven decision-making across the entire aquaculture value chain. While challenges such as high costs, data limitations and skill requirements remain, the benefits of AI such as reduced labor, improved fish health and minimized environmental impact highlight its transformative potential. As technologies continue to advance, AI will play an increasingly vital role in achieving sustainable aquaculture, supporting global food security, and ensuring long-term economic and environmental resilience.
References and sources consulted by the author on the elaboration of this article are available under previous request to our editorial staff.
Anil Singh, Shashank Singh*, Harshit Singh and Vikash Kushwaha Department of Aquaculture, College of Fisheries. Corresponding author*: drssaqua@gmail.com
Arya Singh Department of Aquatic Animal Health Management, College of Fisheries.
Suman Dey Department of Fisheries Extension, College of Fisheries.
Acharya Narendra Deva University of Agriculture and Technology, Kumarganj, Ayodhya-224229, (U.P.), India.
The post AI- Driven Innovations in Aquaculture appeared first on Aquaculture Magazine.
Read more...