A smart fishery represents a deviation from traditional and conventional methods of aquaculture, using IoT devices, AI and machine learning (ML) to predict fish behaviours, optimise feeding schedules and detect diseases early.
Modern day aquaculture is facing a range of challenges. Major challenges that are posing risks to even the future of aquaculture, include antimicrobial resistance to disease treatments, mitigating the impacts of climate change as rising ocean temperatures affect the survival of fish and addressing food safety, all while the industry looks to feed a growing world population with an appetite for seafood.
The Institute of Marine and Antarctic Studies (IMAS) conducted a study which investigated how rising ocean temperatures may potentially be impacting salmon physiology, as it showed a salmon’s liver function changed and its metabolic rate rose in response to temperatures of 21 degrees Celsius.
In an announcement about the study, Co-Author and IMAS Professor Chris Carter said: “This research is part of an ongoing program that aims to understand how salmon and other aquaculture animals manage increased temperature and to build a picture about a sequence of physiological changes that occur over periods of exposure to elevated temperature.”
Setting the stage for the challenges aquaculture faces emphasises the value of applying these technologies including IoT, AI and ML.
How AI and ML can be applied
Understanding fish behaviour is crucial for any fish farmer. AI and ML algorithms can sort through huge amounts of data collected from sensors and cameras to better understand behavioural patterns.
As an example, cameras installed in fish tanks or pens capture continuous footage which can be then analysed by AI models to identify abnormal behaviours that might indicate stress or illness. These models can be trained on data sets to detect changes in fish movement and activity levels, which can be a sign of illness. Ill fish can become lethargic and only swim when they’re disturbed.
AI systems can monitor swimming patterns, feeding habits and social interactions among fish. This information can then be used to adjust environmental conditions and feeding strategies, for instance, to promote good health and growth.
Optimising feeding schedules can help to address operational costs in aquaculture. Traditional feeding methods often lead to overfeeding or underfeeding, both of which can be detrimental to fish health and farm profitability. AI and ML offer a solution by optimising feeding schedules based on real-time data.
Smart feeders equipped with AI capabilities can dispense feed based on detected activity of the fish. bAI systems can draw data from sensors that monitor parameters such as water quality, temperature and fish behaviour in order to determine the optimal feeding quantities and times. ML algorithms can continuously learn from this data and inform feeding strategies.
Disease outbreaks can devastate fish farms, causing significant financial losses and threatening food security. Early detection is vital to prevent the spread of diseases and safeguard fish populations. AI and ML can prove instrumental in identifying diseases at an early stage.
AI-powered diagnostic tools are being developed to detect pathogens. These tools use advanced algorithms to analyse biological samples in order to be able to identify the presence of harmful microorganisms. These technologies enable more precise and rapid diagnosis, enabling early treatment and reducing the risk of widespread outbreaks.
The annual fish health report put out by the Norwegian Veterinary Institute showed that in 2023, 37.7 million farmed salmon and 2.4 million rainbow trout died in land-based hatcheries, reflecting an urgent need to address and contain outbreaks.
The evolution of AI and ML technologies are sure to see the expansion of their applications in aquaculture, to offer great benefits and support the aquaculture industry as the challenges grow.
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