According to a news article published by misPeces, the most mature applications of AI aquaculture are currently focused on computer vision technologies capable of analysing feeding behaviour and automatically adjusting feed distribution. These systems help reduce feed waste and improve production efficiency, particularly in recirculating aquaculture systems (RAS) and other controlled environments.

The article explains that AI-powered cameras can detect uneaten pellets, changes in swimming patterns and feeding intensity. This data allows real-time adjustments, contributing to lower operational costs and reduced environmental impact. As a result, AI aquaculture is becoming an important driver of sustainability in fish farming.

According to a scientific study published on ScienceDirect, the integration of machine learning, deep learning and smart sensor networks enables farmers to process large volumes of environmental data. Parameters such as temperature, dissolved oxygen, pH and turbidity can now be continuously monitored and analysed.

Researchers cited by ScienceDirect explain that AI models can identify complex patterns invisible to the human eye, supporting faster and more accurate management decisions. This data-driven approach improves animal welfare, optimises farm performance and reduces production risks.

However, as misPeces reports, AI aquaculture still faces important technical limitations. In real farming environments, poor water visibility, changing light conditions and biofouling on cameras significantly reduce the accuracy of computer vision systems. These challenges make it difficult to deploy AI solutions consistently across different aquaculture systems.

Another major barrier to the widespread adoption of AI aquaculture is the need for large, high-quality datasets. According to researchers referenced by ScienceDirect, training reliable AI models requires years of historical production data, which many small and medium-sized farms do not have. In addition, the cost of installing sensors, cameras and data infrastructure remains a significant obstacle.

More advanced applications of AI aquaculture, such as early disease detection, animal welfare assessment and mortality prediction, are still largely experimental. As reported by misPeces, these technologies are mainly being tested in laboratory environments or pilot projects and are not yet widely implemented at commercial scale.

Experts agree that the future of AI aquaculture depends on improved data availability, reduced technology costs and stronger collaboration between researchers, technology providers and producers.

In conclusion, both misPeces and scientific literature agree that AI aquaculture should be seen as a complementary tool to human expertise, enhancing decision-making rather than replacing technical knowledge in the field. While full automation is still far away, AI is already playing a key role in shaping a more efficient, sustainable and data-driven aquaculture sector.

This growing relevance of artificial intelligence in the blue economy will be at the heart of Blue Wink-E 2026 | Ocean AI Futures, an international event taking place on 20 March 2026 at the Porto Cruise Terminal, in Portugal. The conference will bring together researchers, industry leaders and decision-makers to explore how AI and digital transformation are reshaping ocean-based sectors, including aquaculture, marine biotechnology and environmental monitoring, fostering dialogue between science, innovation and real-world impact.