# Integrating AIoT Technologies in Aquaculture: A Systematic Review
## 4 AIoT Applications in Aquaculture
### AI-Driven Approaches for Automated Counting in Aquaculture
However, accuracy declines in sparse populations due to reduced signal reflections, emphasising the need for advanced signal processing techniques to enhance detection capabilities in low-density settings. Expanding these methods to various aquaculture environments could improve their reliability and scalability for large-scale fish farming operations. Non-contact counting methods have also been applied to holduurian (sea cucumber) populations to minimise disturbances in aquaculture settings. AI-powered detection systems incorporating YOLOv3 and Faster R-CNN, combined with tracking algorithms, have achieved robust counting results even in turbid water. Nevertheless, underwater visibility and occlusion pose significant challenges, affecting model accuracy. Researchers are working to enhance these models by expanding underwater datasets and integrating more sophisticated tracking algorithms to improve counting performance across diverse conditions. Automated counting of phenotypic features, such as lateral line scales, is also gaining attention in fisheries management and breeding programs. AI-driven models like TRH-YOLOv5, which incorporate transformer mechanisms and small-target detection modules, have been developed to improve precision in scale counting, achieving high accuracy levels. These methods support phenotypic analysis for selective breeding and species identification. However, environmental factors such as underwater lighting variations and motion blur remain obstacles. Future improvements will focus on refining image preprocessing techniques and extending automated phenotypic feature detection to a broader range of aquaculture species. AI-driven solutions are revolutionising organism (highlighted in Table 2) counting in aquaculture by improving accuracy, reducing labour costs, and enabling real-time monitoring. While significant progress has been made, environmental variability, occlusion, and data scarcity still need to be addressed. Ongoing research aims to refine these models by incorporating multimodal sensing, enhancing dataset diversity, and optimising algorithms for deployment in real-world aquaculture settings. \begin{table}
\begin{tabular}{c c c c} \hline
**Aquaculture** & **Specific Application** & **AI/Machine Learning Techniques** & **Data Sources and Methods** & **References** \\ \hline \multirow{8}{*}{Fish health and behaviour} & & Computer vision, support vector machines (SVMs), convolutional neural networks (CNNs), attention mechanisms, online sequential extreme learning machine (OSELM), knowledge distillation, YOLOv7 & \multirow{8}{*}{Imaging systems, sonar, video, annotated datasets, feature extraction, model training, and classification} \\  & & & \\ \cline{1-1}  & Feeding behaviour & Gaussian mixture models (GMMs), k-nearest neighbour (KNN), CNNs & Sonar imaging, length and weight estimation, distribution analysis & [135] \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \end{tabular}
\end{table} 
Table 2: Applications of AI and machine learning in aquaculture: techniques, data sources, and methods.