###### Abstract  
Precision aquaculture deploys multi-mode sensors on a fish farm to collect fish and environmental data and form a big collection of datasets to pre-train data-driven prediction models to fully understand the aquaculture environment and fish farm conditions. These prediction models empower fish farmers for intelligent decisions, thereby providing objective information to monitor and control factors of automatic aquaculture machines and maximize farm production. This paper analyzes the requirements of a digital transformation infrastructure consisting of five-layered digital twins using extensive literature reviews. Thus, the results help realize our goal of providing efficient management and remote monitoring of aquaculture farms. The system embeds cloud-based digital twins using machine learning and computer vision, together with sensors and artificial intelligence-based Internet of Things (AIoT) technologies, to monitor fish feeding behavior, disease, and growth. However, few discussions in the literature concerning the functionality of a cost-effective digital twin architecture for aquaculture transformation are available. Therefore, this study uses the modified analytical hierarchical analysis to define the user requirements and the strategies for deploying digital twins to achieve the goal of intelligent fish farm management. Based on the requirement analysis, the constructed prototype of the cloud-based digital twin system effectively improves the efficiency of traditional fish farm management. digital twin; digital transformation; AIoT technology; machine learning; big data analytics in aquaculture; analytic hierarchical process 2  
Outdoor ponds or sea cage farming can quickly fulfill these requirements compared with indoor tanks. However, in this environment, fish are exposed to natural fluctuations, which are essential factors in the production environment, e.g., water flow, temperature, salinity, and light intensity. However, with these exposures, additional challenges burden the farmers to control the production conditions because sea-based farming increases the chance of stressors such as pollutants, pathogens, and parasites in the population [1]. This study then analyzes the key requirement factors of intelligent fish farming to guide the production process and fully understand the aquaculture farm's environmental and fish conditions. The first step to the digital transformation of fish farm management is to deploy sensors on fish ponds or offshore sea cages to collect fish and environmental data to establish data-driven prediction models. The digital transformation of aquaculture is also defined as Aquaculture 4.0, which follows the concept of Industry 4.0 technologies [2; 3] to achieve the goal of precision aquaculture by supporting decision-making based on the utilization of artificial intelligence (AI) and the Internet of Things (IoT) [1; 4]. AI technologies, such as information-based management with big data and models, guide the production process and fully understand aquaculture farms' environmental and fish conditions. On the other hand, IoT technologies characterize intelligent fish farms with surveillance cameras, water inspecting devices, and working sensors to store and serve data to a fog or cloud system through interconnection networks. This digital transformation of aquaculture based on the implementation of the AIoT system facilitates fish farms to optimize operations of a precision aquaculture framework. Therefore, including sensor deployment, cloud big data management, and AI analytics in this framework enables real-time, data-driven decision-making [5]. In industrialized aquaculture, where many farms are situated on open seas, regular visits to the site for monitoring pose a big challenge, especially in its cost and the time requirement and safety of the workers.