Emerging Technologies in Aquaculture – Aquaculture 4.0

Aquaculture is one of the most promising forms of animal protein production. According to the FAO’s 2024 report, aquaculture production has, for the first time, surpassed wild captures, becoming the main source of fish worldwide and impacting the lives of thousands of people.

Despite this success, the aquaculture industry faces challenges such as intensive labour, environmental pollution, disease outbreaks and a lack of product traceability. However, the arrival of automation systems and emerging technologies from Industry 4.0 promises to have a profound impact and significantly improve the sector’s performance as it moves into a future where demand for its products will presumably continue to increase.

Industry 4.0, a concept that first appeared in Germany in 2011, is related to strategies of digitalisation and automation of the manufacturing process, integrating disruptive technologies and leading to intelligent, autonomous and decentralised plants – “smart factories”-, that communicate and cooperate with humans in real-time. The concept is based on a group of emerging technologies such as the Internet of Things (IoT), Smart Production, Cloud Computing (CC) and Artificial Intelligence (AI). These technologies are also revolutionising the way we produce fish, algae, molluscs and crustaceans, leading the industry towards Aquaculture 4.0, the fourth industrial revolution applied to the sector.

The term Precision Fish Farming (PFF) is associated with Aquaculture 4.0 and combines knowledge from engineering and computer science with multisensor systems in aquaculture. The integration of online servers or workstations with suitable software, to manage and control the system, helps improve production standards, energy efficiency and product quality control, and reduces overall costs. Disruptive approaches include advanced robotics combined with innovative sensors, IoT, and smart information processing models.

Continuous data collection is possible through Big Data Analytics and AI, which form the core of IoT, and are essential to achieve accuracy for aquaculture control. This type of intelligent system typically involves gathering data on water quality and other relevant information, through sensors and cameras. This data is sent to a control centre where processing and decision-making occur, with the help of AI, particularly machine learning (ML) on Cloud platforms. The feedback is sent to equipment, which performs operations intelligently and automatically. Such equipment enables various aquaculture operations, including water treatment, feeding, monitoring, net cleaning, counting, fishing, sorting, and evaluating animals.

In recent years, the combination of Big Data Analytics with Cloud platforms has been used in aquaculture to process and analyse large volumes of data, providing useful insights to producers and supporting decision-making. These technologies can offer predictions, early warning solutions, disease diagnostics, detection and analysis of abnormal behaviours, quality control, and traceability. However, precise monitoring, detection, and control in aquaculture remain challenging due to the complex environment and numerous influencing factors.

Finally, Digital Twin technology could be key in integrating and managing the data from physical and biological processes, throughout production phases. This technology aims to create a virtual representation of a physical system or object by assimilating real-time data from sensors. Recent advances in mathematical modelling, artificial intelligence, data processing capabilities, and visualisation methods have driven this trend. The Digital Twin concept emerges as a natural framework to take the next steps towards closed-loop control in aquaculture through Precision Aquaculture. It is particularly useful for early problem identification in animal groups or individuals and assessing the respective impacts. However, while most applications in aquaculture are inherently transdisciplinary, developing comprehensive solutions specifically for aquaculture remains necessary.

Advances in the digitisation and automation of aquaculture have been remarkable, however, several challenges remain, such as inadequate development or even the absence of intelligent and specific analysis models and technologies, restricting the maximisation of the benefits of digitisation. Another significant challenge is the complexity of correlating and integrating data analysis throughout the industry’s value chain, which can limit efficiency and informed decision-making.

The digitalisation and automation of aquaculture can bring great benefits, but the democratisation of technology is crucial so that all producers, regardless of their size, can benefit from it. For small producers, the challenges include high costs, a lack of labour with technical knowledge and even limited infrastructure. Solutions such as developing more accessible technologies, training, better infrastructure and incentives are essential. It’s worth emphasising that research and development into innovative solutions adapted to the specific needs of small-scale aquaculture can also be key. This could imply developing low-cost technologies, robust and easy-to-use sensors, intuitive software and effective communication platforms. These challenges must be overcome to realise the full potential of aquaculture and guarantee a sustainable and prosperous future for the whole sector while contributing even more effectively to its sustainability and productivity.