Aquaculture is one of the most promising forms of animal protein production. According to the FAO’s 2024 biennial report, aquaculture production surpassed wild catches for the first time, becoming the main source of fish worldwide.

Despite this success, the aquaculture industry still faces some challenges such as labor intensity, environmental pollution, disease outbreaks, and the difficulty in tracking these products from origin to consumer. However, with the arrival of automation systems and emerging Industry 4.0 technologies, a profound impact and a significant improvement in the sector’s performance are expected for the future, where demand for its products will continue to increase.

Industry 4.0, a concept that first emerged in Germany in 2011, relates to the digitalization and automation strategies of the production process, integrating disruptive, autonomous, and decentralized technologies – “smart factories” – that communicate and cooperate with each other and with humans in real time. The concept is based on a group of emerging technologies, such as: Internet of Things (IoT), Smart Production, Cloud Computing (CC), and Artificial Intelligence (AI). These technologies are also revolutionizing the way we produce fish, algae, mollusks, 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, along with multi-sensor schemes, in aquaculture systems. The association of online servers and/or workstations with the most suitable software to manage and control the system contributes to improving production standards, energy efficiency, product quality control, and reducing overall production costs. The most disruptive approaches include the integration of advanced robotics with innovative sensors, the Internet of Things (IoT), and intelligent information processing models.

Big Data Analytics (continuous data collection) and Artificial Intelligence are at the core of IoT, being essential for achieving precision in control. This type of intelligent system generally involves collecting data on water quality and other relevant information through sensors and cameras. This data is sent to a control center, where processing and decision-making are performed with the help of AI, especially machine learning (ML) on the cloud platform. Feedback is sent to the equipment, which in turn performs operations intelligently and automatically. This type of equipment allows for various operations in aquaculture, such as water treatment, feeding, monitoring, net washing, counting, fishing, classification, and animal welfare assessment.

In recent years, the combination of big data analytics technologies with cloud platforms has been used in aquaculture to process and analyze large amounts of data, presenting useful results to producers and supporting decision-making. These technologies can provide aquaculture systems with forecasts, early warning solutions, disease diagnostics, detection and analysis of abnormal behaviors, quality control, and traceability. However, accurate monitoring, detection, and control in aquaculture have proven difficult to achieve due to its complex environment and the many factors that influence it.

Finally, Digital Twin technology can be a key element in integrating data from physical and biological processes in various stages of production. This technology aims to create a virtual representation of a physical system or object by assimilating data from sensors in real time. This trend has been driven by recent advances in technologies such as mathematical modeling, artificial intelligence, data processing capabilities, and visualization methods. The concept of Digital Twins emerges as a natural framework for taking the next steps towards closed-loop aquaculture control through Precision Aquaculture. It stands out for its usefulness in the early identification of problems in groups of animals or individual animals, as well as their respective impact. However, although most aquaculture applications are inherently transdisciplinary, it will still be necessary to develop comprehensive solutions specifically for this sector.

Advances in the digitalization and automation of aquaculture have been notable, but several challenges persist, such as inadequate development or even the absence of intelligent and specific analysis models and technologies, hindering the maximization of the benefits of digitalization. Another significant challenge is the difficulty…