# Integrating AIoT Technologies in Aquaculture: A Systematic Review
## 1 Introduction
IoT devices such as cameras, sensors, and automated monitoring systems continuously collect data on key environmental parameters, fish behaviour, and feeding patterns, which AI algorithms process to generate actionable insights [26; 27; 28]. These advancements reduce reliance on manual intervention, enhance decision-making, and improve resource efficiency [29; 30]. The IoT framework in aquaculture consists of interconnected sensors, communication gateways, and cloud computing platforms, enabling real-time data collection, analysis, and transmission [31]. This infrastructure monitors critical water quality parameters, including temperature, pH, dissolved oxygen, and salinity, ensuring optimal conditions for aquatic organisms. Cloud computing enhances AI-driven solutions by providing scalable processing capabilities to handle large volumes of data. AIoT applications in aquaculture include automated breeding, disease prediction, biomass estimation, water quality management, intelligent feeding systems, and behaviour analysis [32; 33]. AI-powered feeding systems utilise sensor data and underwater imaging to optimise feeding schedules, improving fish growth rates, reducing environmental impact, and minimizing feed waste [34; 35; 36]. A fundamental aspect of AIoT-driven aquaculture is the management of water quality. IoT sensors continuously monitor crucial water parameters such as temperature, pH, dissolved oxygen, and ammonia, while AI-based models analyse trends and suggest proactive interventions [37]. For example, deep learning models like long short-term memory (LSTM) networks process time-series data to predict oxygen depletion, allowing timely aeration adjustments [38; 39]. Similarly, reinforcement learning (RL) algorithms optimise water management strategies, balancing environmental stability and energy efficiency. AIoT is also key in early disease detection, as outbreaks can cause significant financial losses. Deep learning models such as U-Net and YOLO analyse high-resolution images from IoT cameras, detecting lesions and abnormal swimming patterns indicative of disease. Additionally, biosensors integrated with AI models analyse biological markers, enabling early disease detection and mitigation before outbreaks escalate [40; 41]. Beyond health monitoring, AIoT systems contribute to species identification, population tracking, and selective breeding optimization. Convolutional neural networks (CNNs), including VGG and ResNet, facilitate species segmentation and organism counts, ensuring compliance with regulations and biodiversity preservation [42; 43]. AI-driven selective breeding programs leverage genetic and environmental data to predict desirable traits such as disease resistance and growth efficiency, improving sustainability and productivity. Robotics and automation are redefining aquaculture operations. Autonomous underwater vehicles (AUVs) are equipped with sensors and cameras that perform feed distribution, fish inspections, and tank cleaning with minimal human supervision. These systems are enhanced by reinforcement learning algorithms, enabling adaptive decision-making based on real-time environmental feedback. Despite recent review papers on aquaculture [31; 44; 45; 46; 47; 48], this review contributes a comprehensive synthesis of current AIoT applications in aquaculture, providing a structured overview of the diverse technologies and methodologies employed and systematically examining advancements in areas such as water quality management, automated counting, smart feeding, and disease detection. This paper identifies critical research gaps and outlines future directions for AIoT development. Specifically, it highlights the need for integrated multimodal systems, enhanced predictive analytics, and tailored solutions for crustacean aquaculture, offering a valuable resource for researchers, aquaculture experts, and practitioners seeking to advance sustainable and efficient aquaculture practices through AIoT integration.