A Practical Guide to Using AI in Aquaculture

Artificial intelligence (AI) is already making huge improvements to the efficiency and sustainability of global aquaculture, as this practical guide to some of the best systems currently available shows.

Things have changed drastically since 2010 and AI is already a part of the daily lives of millions of people. No need to drive to the big city to pick up that book or tool you’ve been drooling over – just a few clicks and it’ll be delivered to your doorstep. You can avoid the indignity of asking for driving directions because of the magic of Waze and Google Maps. Even the ads that pop-up, when you’re scrolling through social media, are customized and catered to each individual or persona.

By providing a variety of services AI has become a potent tool in strengthening various industries – especially aquaculture.

Reducing waste feed

Feeding represents the biggest cost to fish farmers, so optimization in this area always means better profitability. But feeding strategies are often arbitrary and dependent on people who constantly watch how much the farmed stocks are gobbling up. Pellet dispersal is based on observation or, very often, intuition.

Various companies offer a plug-and-play AI and data processing system to track measurable patterns when stocks are feeding. Their goal is to provide farmers empirical and objective guidance on how much to feed. Besides, the system is also being developed which uses sensors to detect hunger levels in shrimp and fish, controlling dispensers, which release the right amounts of food; it is claimed that this can reduce feed costs by up to 21 percent.

A smart fish feeder that can be controlled remotely also allows farmers to gain data-driven decision-making advice to optimize feeding schedules. This reduces waste, improves both profitability and sustainability while offering users a better work-life balance by eliminating the need to be out in the water in dangerous conditions. These systems consider weather events like storms or the sapping glare of hot summer months, helping farmers produce more seafood with fewer resources, ultimately increasing profits significantly.

Preventing diseases and tracking prices

Diseases are the next big cost driver and something AI can readily address. Programs can predict disease outbreaks before they happen by annotating collected data, presenting it, and applying preventive measures.

The main aim of the system is to help fish-health managers and researchers deal with sea lice, predicting or even preventing lice development in sea cages to reduce dependency on expensive medical treatments, thus minimizing stock mortality. Many agree that smart technology is the key to better disease management and productivity.

Sensor-equipped drones and robots are also being developed to collect data such as water pH, salinity, dissolved oxygen levels, turbidity, pollutants, and even the heart rates of stock – all accessible via a smartphone. Through AI, farmers can remotely switch pumps, motors, aerators, or diffusers on or off. Production and demand can also be forecasted by altering program parameters that will further improve farm efficiency and monitoring ability.

Even optimizing economics during harvesting, which most farmers gauge based on educated guesswork, can be dictated by machines. There is already a computer vision and AI to calculate the growth of shrimp, helping farmers predict the most profitable harvest periods. Advanced AI techniques like deep learning are used to pinpoint timeframes by continuously using machine learning on historical growth cycle data.

Conclusions

Though great strides in mechanized aquaculture are being made, full automation is still a long way off. We probably won’t see fish farms that can manage entirely without the sure hands of humans any time soon. But fully embracing and investing in AI plus automation can significantly produce more seafood to feed the growing world population, while reducing the cost and environmental footprint of aquaculture operations.

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