# Integrating AIoT Technologies in Aquaculture: A Systematic Review
## 4 AIoT Applications in Aquaculture
### Fish Behaviour Detection  
The continuous monitoring of fish behaviour is essential for maintaining fish health, welfare, and overall aquaculture productivity. Behavioural changes in fish often serve as early signs of environmental stress, disease onset, and feeding responses, enabling timely interventions to prevent potential losses. Recent advancements in artificial intelligence, computer vision, acoustic monitoring, and IoT-based sensor networks have significantly improved the accuracy and reliability of behaviour analysis. These technologies facilitate real-time and non-invasive monitoring in complex aquaculture systems, reducing the need for manual observation while enhancing decision-making processes. Deep learning models have been widely applied to detect abnormalities in fish behaviours, such as lethargy, rapid movement changes, and erratic swimming, which are often associated with a disease or suboptimal water conditions. A ResNeXt 3\(\times\)1D convolutional network was implemented in one study, achieving 95.3% accuracy in identifying abnormal fish behaviours [128]. The model improves computational efficiency and monitoring effectiveness in large-scale aquaculture systems. Another study [61] used Faster R-CNN for object detection, dynamic time warping for behaviour analysis, and directed cycle graphs for posture classification, obtaining a 92.8% accuracy in detecting abnormal fish behaviours. These hybrid models provide a strong foundation for monitoring behaviour in real time, though they can be affected by external factors like lighting and water clarity. Additionally, a dual-flow deep network combined with a multi-instance learning framework has been employed to study swarm behaviour in densely populated fish groups, offering important insights into how these groups react to stress and disease [129]. Analysis of locomotion and swimming speed are also crucial indicators of fish health and metabolic activity. Acoustic telemetry has been employed to measure swimming speeds in marine cages, demonstrating a root mean square error of 7.85 cm/s across different velocity ranges. This non-invasive technique allows for continuous monitoring of fish movement; however, signal interference from environmental noise and reflections can affect accuracy [130]. In another approach, computer vision-based deformable models have been applied to analyse swimming patterns, capturing species-specific movement characteristics. Using convolutional neural networks (CNNs) to analyse fish locomotion has further improved classification accuracy, with studies reporting up to 96% accuracy in predicting activity patterns based on movement data [131]. Behavioural analysis has also proven valuable for early disease detection, as abnormal swimming patterns and reduced activity levels often precede visible symptoms. A transformer-based model, EchoBERT, has been introduced to analyse echograms for detecting early-stage pancreatic disease in Atlantic salmon, achieving a Matthews correlation coefficient of 0.694. This model demonstrated superior performance compared to traditional long short-term memory (LSTM)-based methods, allowing for disease detection up to one month earlier [132]. The potential for AI-driven behavioural monitoring to enhance disease management in aquaculture is significant, with ongoing research focusing on improving generalisation across different species and environments. Shoaling and social behaviour modelling provide additional insights into fish group dynamics and welfare. CNN-based models have been developed to classify different shoaling states, such as feeding activity and stress responses, with an accuracy of 82.5% using spatiotemporal fusion images [133]. By integrating spatial and optical flow data, these models improve behavioural classification but may still be affected by variations in environmental lighting and water conditions.