# Integrating AIoT Technologies in Aquaculture: A Systematic Review
## 3 Components of AIoT in Aquaculture
### AI Algorithms in Aquaculture
#### 3.2.1 Machine Learning (ML) Approaches in Aquaculture  
In predictive analytics and pattern recognition, machine learning (ML) [65; 66] is essential, especially when processing structured data gathered from Internet of Things (IoT) sensors in aquaculture. ML algorithms are classified into three main types--supervised learning, unsupervised learning, and reinforcement learning--each providing distinct functionalities for analysing intricate datasets and enhancing farm management strategies [67]. In supervised learning [68; 69; 70], models are trained using labelled datasets to perform tasks such as regression and classification. Classification models are useful for organising data into predefined categories, supporting decision-making processes in areas like water quality evaluation and disease identification. Techniques such as logistic regression (LR), k-nearest neighbour (KNN), decision trees (DTs), random forests (RFs), and support vector machines (SVMs) are commonly employed to classify water conditions as either'safe' or 'unsafe', facilitating prompt actions to address potential hazards. Likewise, classification methods are extensively applied in disease detection, where they analyse factors like fish behaviour or image data to enable early diagnosis and effective disease management. The performance of these models is assessed using metrics like accuracy, precision, and recall, which gauge their ability to differentiate between various conditions accurately. In contrast, regression models [71; 72] predict continuous values, making them suitable for forecasting environmental conditions such as water temperature or fish growth rates. Techniques like linear regression and neural networks analyse historical trends to provide predictive insights into key aquaculture parameters. Performance metrics (e.g., root mean square error (RMSE) and mean absolute error (MAE)) are commonly used to assess the accuracy of these models in predicting continuous variables like oxygen levels or pH fluctuations. Unsupervised Learning for Pattern Discovery  
Unsupervised learning [73; 74; 75] methods identify patterns and relationships in datasets by analysing unlabelled data. Clustering algorithms, such as hierarchical clustering, density-based spatial clustering of applications with noise (DBSCAN), and k-means are widely used in aquaculture to detect behavioural anomalies, segment fish populations based on feeding behaviour, and monitor changes in water quality. For instance, clustering techniques can identify groups of fish showing unusual movement patterns, potentially signalling stress, disease, or environmental disturbances. The effectiveness of clustering models is often measured using metrics like the Jaccard index, silhouette coefficient, Davies-Bouldin index (DBI), and normalised mutual information (NMI). Reinforcement learning (RL) [76] is especially well suited for dynamic and adaptive decision-making processes in aquaculture. RL models operate through a trial-and-error learning mechanism, where an agent interacts with an environment and learns an optimal strategy based on rewards and penalties. This approach is highly applicable in optimising feeding schedules, aeration control, and water quality management [77]. Q-learning, a value-based RL algorithm, enables agents to determine optimal policies by estimating cumulative rewards for different actions. Q-learning can be applied in aquaculture to optimise aeration systems by adjusting oxygen levels based on real-time water quality data. By combining deep learning and Q-learning, Deep Q-Networks (DQNs) [78] extend these capabilities to complicated, high-dimensional environments. For instance, a DQN-based system can analyse real-time sensor and video feed data to model collective fish behaviour, improving monitoring and management decisions. Policy gradient methods [79; 80] offer another RL approach by directly optimising an agent's decision-making policy, making them suitable for tasks requiring continuous adjustments, such as fine-tuning feeding routines.