# Integrating AIoT Technologies in Aquaculture: A Systematic Review
## 1 Introduction  
By 2050, it is expected that there will be nearly 10 billion people on the planet, putting tremendous strain on food systems to supply the growing demand for diets high in protein [1]. When addressing global food security, aquatic foods are an essential source of nutrition, especially in areas where they are the primary source of protein. The fastest-growing industry in food production, aquaculture has become a vital tool for balancing aquatic foods' supply-and-demand gap, contributing over 50% of the world's fish forhuman consumption [1]. However, the rapid expansion of aquaculture has also brought challenges, including rising production costs, environmental degradation, and resource inefficiencies, which threaten its long-term sustainability [2]. The rising costs of aquaculture production, driven by factors such as feed expenses, disease outbreaks, and labour shortages, have further exacerbated the industry's challenges [3; 4]. Traditional practices often rely on manual monitoring and decision-making, which are prone to inefficiencies and errors, leading to suboptimal yields and increased waste. In this context, there is an urgent need for innovative, sustainable solutions that can enhance productivity while minimising environmental and economic costs. AI, or artificial intelligence, has become a game-changing technology that can revolutionise aquaculture by enabling precision farming, optimising resource use, and improving decision-making processes [5]. By integrating AI technologies with the Internet of Things (IoT), the aquaculture industry can address critical challenges, reduce production costs, and sustainably contribute to global food security [6; 7; 8]. Aquaculture, a vital contributor to global food systems, encompasses diverse species tailored to meet varying market demands and ecological roles. Finfish, spearheaded by commercially significant species such as catfish, seagrass, trout, carp, salmon, and tilapia, represent the most prominent group because of their high market value and ability to adapt to new aquatic environments [9; 10]. Crustacean aquaculture, particularly shrimp farming, has emerged as a cornerstone of international trade, especially in regions with high consumer demand [11; 12]. Molluscs, such as mussels, scallops, and oysters, are frequently farmed in marine habitats because of their nutritional and commercial value. Interestingly, the production of pearls from specific oyster species is one example of how aquaculture intersects with high-value luxury markets. Coral culturing is becoming increasingly important in decorative trade and ecological restoration initiatives, especially when restoring damaged coral reefs [13; 14]. Additional species diversifying the aquaculture landscape include aquatic macroinvertebrates, bioindicators in environmental health assessment [15; 16]. Phytoplankton, fundamental to aquatic food webs, are also cultured for their ecological and nutritional value [17; 18]. Aquatic plants, such as algae and seaweed, expand aquaculture's range, with applications ranging from direct human consumption to producing biofuels and cosmetics [1; 19]. This wide range of farmed aquatic species highlights aquaculture's exceptional versatility and crucial role in addressing several complex global issues, including environmental sustainability, food security, and economic development. The integration of artificial intelligence (AI) and the Internet of Things (IoT) is transforming industries by enhancing sustainability, efficiency, and innovation [20; 21; 22]. AI enables the analysis of large, complex datasets, identifies patterns, and automates decision-making using deep learning (DL), machine learning (ML), computer vision (CV), and natural language processing (NLP). In aquaculture, these AI-driven solutions facilitate real-time monitoring, optimise operations, and contribute to sustainable practices [23; 24; 25].