###### Abstract
Traditional data collection approaches involve using pen and paper to manually write the observations made by the workers and farm owners on their observations of the current status and welfare of the aquatic animals on the site. These written observation data are then transferred to spreadsheet applications for data analysis. However, such a mechanism is often incomplete, inconsistent, and poorly formatted. The presence of big data and big data analytics infrastructure transformed the aquaculture industry for data-driven decision-making. More and more aquaculture farmers are turning to data to support their farm management and operation procedures. Many are now investing in new technologies for sustainability and to increase profitability. With these influences, automatic collection, processing, and the advanced analysis of data can be translated into an accessible and readable format that the fish farmers can understand. Low-cost sensors allow them to collect data remotely from far and hard-to-reach locations. Unlimited data acquisition can now be sent via wireless network technologies to the cloud for storage and data analytics. Such mechanisms provide automatic and higher-quality data collection. Intelligent fish farm management aims to optimize and automate the fish farm computerization by the intelligent networking of smart sensors, aquaculture machines, and AI processes using an AIoT system. The implementation of an AIoT system integrates previously existing concepts such as the Internet of things (IoT), cloud computing, artificial intelligence (AI), machine learning (ML), big data analytics, and cognitive services and applies them to fish production. Figure 1 shows the AIoT system design for intelligent fish farming based on the fusion of three vital design principles, i.e., interconnection, information transparency, and decentralized decisions. The underlying IoT system facilitates the interconnection among machines, devices, sensors, and people so they can communicate via the Internet. Information transparency's relevance relies on implementing an AI-empowering cloud system that can automatically collect, manage, and organize the vast amount of data from connected machines, devices, and sensors. These virtualized technologies represent the physical objects of the AIoT system as logical objects or digital twins (DTs). Each object is described as a formal digital representation of some asset, process, or system that captures attributes and behaviors of that entity suitable for communication, storage, interpretation, or processing within a specific context [6]. The AIoT system is also required to incorporate AI and ML farm operations to meet the requirements of decentralized decisions. Thus, it facilitates DTs to provide intelligent services, enabling better decision-making and increasing automation and overall productivity. DTs with enhanced capabilities are called cognitive twins (CT) [7]. This study aims to construct an AIoT system that addresses all the key operations of fish farm management. As shown in Figure 2, the system's functionality can be implemented using a five-layer digital twin architecture to facilitate the digital transformation for precision aquaculture. The first step of the digital transformation of fish farm management is the virtualization of physical sensors, machinery controllers, and fish ponds or offshore cages. In this phase, every physical object is transformed into the corresponding logic object, also defined as a digital twin. The features, states, prediction models, and action sequences of the physical object are stored in this stage. However, the characteristics of individual physical objects might be very different and thus very difficult to be represented by a generic digital twin template. For instance, both the optical and the sonar cameras could be used to automatically acquire environmental or fish data for monitoring the status of the fish. However, different machine learning tools are often used to analyze their respective captured videos.