Artificial Intelligence and Growth Models: Enhancing Aquaculture Sustainability

The application of growth models in aquaculture enables the prediction of species development, feed optimisation, and better resource management throughout the production cycle, thereby enhancing the sustainability of the industry. One widely used model is the von Bertalanffy model, initially developed for livestock in 1934 and now broadly applied in aquaculture.

In contrast to methods like the Logistic or Gompertz Models, the von Bertalanffy approach is distinctive for its focus on anabolic and catabolic processes, providing a more precise description of individual growth over time, especially for species with asymptotic growth patterns.

With advancements in AI, the integration of machine learning with traditional growth models overcomes many of their previous limitations. By incorporating real-time data and dynamically adjusting growth predictions, these systems improve both accuracy and adaptability to changing conditions.

AI Optimises Growth Models

iRAS System by INNOVAQUA

AI has the capability to process vast amounts of data, identifying growth patterns and adjusting for variables such as temperature, water quality, feed, and stocking density. When combined with a growth model like von Bertalanffy, AI enables highly accurate growth predictions, which is especially valuable in aquaculture environments, where conditions can change rapidly, impacting species growth.

Feed is one of the main costs in aquaculture, and AI can optimise it significantly. Through machine learning algorithms, AI systems can automatically adjust feed amounts based on fish size, growth rates, and water conditions. This not only enhances growth but also reduces feed waste and, consequently, environmental impact.

AI can integrate with Internet of Things (IoT) technology, such as sensors and cameras, enabling real-time monitoring of environmental conditions and fish health. This allows aquaculturists to make immediate adjustments to feeding or management practices, increasing efficiency and reducing losses. Continuous monitoring also aids in detecting health or water quality issues before they become major threats to production.

Costs and Challenges of Implementing AI and Growth Models

Implementing AI and traditional growth models in aquaculture may entail initial costs and certain technical challenges. However, as technology advances, these costs will decrease, and ease of use will improve with more accessible solutions.

In the long term, the benefits in terms of optimisation, efficiency, and sustainability can make the investment worthwhile, yielding a positive return and helping to enhance the sector's competitiveness and sustainability.

Technically, implementing AI and traditional growth models requires appropriate infrastructure, such as sensors, cameras, and real-time monitoring systems (IoT). Moreover, integrating AI with growth models requires experts in data analysis, algorithm development, and aquaculture biology.

Developing or acquiring specialised AI software for analysing growth data can also involve costs, depending on whether customised solutions are developed or existing platforms are purchased. AI software solutions vary in price based on complexity and technical support, making this an important consideration.

One of the main challenges is integrating data from multiple sources, such as water quality, temperature, feeding, and fish growth. AI needs consistent, high-quality data to make accurate predictions. Establishing a robust data collection and management system may require time and effort, and if not done correctly, it can limit AI effectiveness.

Another important consideration is maintenance and updates. AI systems and growth models need regular maintenance and updates to ensure optimal performance. Updates may involve implementing new features or algorithm improvements as more data is gathered. This adds a layer of complexity and requires ongoing technical support.

At present, this kind of integration is particularly suitable for recirculating aquaculture systems, whether at hatchery, nursery, or grow-out stages, especially for species like Atlantic salmon, rainbow trout, turbot, sole, or amberjack.

An interesting integration could also be in aquaponic systems, as long as they are equipped with sensors. While more challenging due to difficulties in controlling parameters, these systems can also be adapted to floating nurseries and aquaculture ponds.