# Integrating AIoT Technologies in Aquaculture: A Systematic Review
## 5 Research Gaps  
Despite the extensive integration of AI applications in aquaculture, existing studies primarily focus on fish health monitoring, behaviour analysis, aquaculture area monitoring, and feeding management. Various AI techniques, including computer vision, deep learning, and machine learning, have been applied for disease detection, fish behaviour \begin{table}
\begin{tabular}{c c c c c} \hline
**Aquaculture** & **Specific Application** & **AI/Machine Learning Techniques** & **Data Sources and Methods** & **References** \\ \hline Wild fish monitoring & Deep learning (YOLOv4) & Underwater video, object detection & [163] \\ Underwater fish & Mask R-CNN, Gaussian mixture models (GMMs) & sensor network data, background subtraction & [84] \\ Crustacean larvae & Computer vision, YOLOv5s & Image processing, object detection & [164] \\ Fish counting & Improved YOLOv5, transformer module & Fish scale detection, model enhancement & [165] \\ Fish biomass & Deep learning & Real-time image detection and object extraction & [166] \\   estimation & (YOLOv5n), stereo vision & extraction & \\ Tuna egg quality & Vision-based CNN, R-CNN & Pre-trained images and object detection & [167] \\ Shrimp seed & Modified CSRNet & Smartphone images, crowded scene recognition & [11] \\ Fish weight & Mask R-CNN, regression & Image dimension extraction, regression learning & [72] \\ Slaughter trait & Image analysis, statistical modelling & Fish images, feature analysis & [168] \\ Decision support & Geospatial techniques, cloud computing, GIS & Landsat imagery, management, web-based applications & [169] \\  & SAMALNet, RADNet, DeepLabV3+, dense residual U-Net, U-Net, R3 Det, SegNet, U-Net++, marker-controlled watershed segmentation & Satellite image segmentation, feature fusion, model training, and accuracy assessment & [170] \\ Cage segmentation & Swin transformer, ensemble learning & Remote sensing images, feature extraction, segmentation & [171] \\  & Mask R-CNN, recursive feature pyramid (RFP), DCN & Computer vision, object detection & [172] \\ Seaweed farm & Deep learning (DCGAN, U-Net, DeepLabv3, SegNet) & Time series dataset, feature enhancement, sample amplification & [53] \\ Pond extraction & U2-Net & Remote sensing images, model training & [173] \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._analysis, satellite image processing, and feeding behaviour identification. However, notable gaps remain. 1. Limited AI Integration for Broodstock Management: While much attention has been given to monitoring fish health, growth, and environmental conditions during the grow-out phases of aquaculture production, there is a notable lack of research on AI-driven broodstock management. Broodstock management, the critical process of selecting and caring for parent fish for breeding, significantly influences aquaculture operations' overall success and sustainability. Effective broodstock selection, breeding optimization, and tracking physiological health are essential for improving aquaculture productivity. AI has the potential to revolutionise this area. For instance, AI could analyse extensive genetic datasets from broodstock to predict optimal breeding pairs, thereby accelerating selective breeding programs and enhancing desirable traits in offspring. Furthermore, AI can contribute to monitoring broodstock's physiological health by analysing sensor data related to hormone levels and activity patterns, enabling more precise timing of breeding interventions and improving reproductive success rates. The limited exploration of AI in broodstock management represents a significant opportunity to enhance aquaculture practices. 2. Lack of Real-Time, Multimodal AI Systems: Many existing AI solutions in aquaculture operate as isolated applications, focusing on data from single sources. For example, CNNs might be used for disease detection based on images, or YOLO models might be applied for biomass estimation.