The rise of AI in aquaculture

by Samuel Couture-Brochu, Chief Technology Officer, XpertSea

A few years ago, it was cloud, followed by the Internet of things; then blockchain took the centre stage; it seems like there’s always a new buzzword coming from the tech industry. This year, artificial intelligence and machine learning are everywhere, and aquaculture was not spared. The industry is taken by storm, but is it really worth the hype?
 

For clarification purposes, machine learning is an application of artificial intelligence (AI) that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. Let’s take a real-life example to demonstrate its application: say you have a young child, and you want to teach him colours.

First, you might want to teach him to recognise a blue car from a red one. Once he has achieved that, you could show him other objects that are blue or red. By increasing the number of examples of coloured objects, his brain will make sense of the colour concept, and soon learn that colours are valid for a panoply of objects. Machine learning is very similar, but with computers.

In other industries like human health, AI is improving disease diagnostics and doing so with greater accuracy than ever before. As an example, a recent study1 demonstrated that machine learning, in this case, deep learning, is more accurate at detecting lung tumours than radiologists.

This is only the beginning. By training AI models on an increasingly larger number of medical images, it is conceivable that those models would be able to detect cancer much earlier. Should radiologists be scared of such a technology? Not at all.

It will support their highly technical work and facilitate decision making. In this situation, an AI could only show the relevant cases to the expert. It could then suggest a diagnosis and let humans approve it, saving humans time while empowering radiologists.

Similarly, AI will not replace farmers in aquaculture; it will become a key asset in their day-to-day operations. Disease and feed management are two of the most important areas where AI could support farmers.

For animal health, models can be trained on thousands of images of a specific symptom, and be used to detect disease before the human eye can reliably do so, giving more options to farmers than just harvesting when it’s too late. From there, we can build on complexity, capturing treatments made to the pond, measuring the effectiveness of inputs, and adding variables such as feed usage, broodstock, and water quality.

Read the full article, HERE.

The Aquaculturists

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