# Integrating AIoT Technologies in Aquaculture: A Systematic Review
## 3 Components of AIoT in Aquaculture
### AI Algorithms in Aquaculture
#### 3.2.2 Deep Learning (DL)  
Deep learning (DL) [81; 82; 83], a branch of machine learning (ML), employs artificial neural networks (ANNs) with multiple layers to identify significant patterns from large and intricate datasets. Inspired by the neural architecture of the human brain, deep learning models are particularly effective for tasks such as classification, segmentation, and prediction in aquaculture [4]. These models can process structured and unstructured data, making them highly versatile for various applications [84]. At the heart of deep learning lies the concept of artificial neural networks (ANNs), which serve as the foundation for various architectures designed to handle specific data types and tasks. The most basic form of neural network is the feedforward neural network (FNN) [85], where data flow in a single direction--from the input layer, through hidden layers, to the output layer. FNNs are primarily utilised for tasks such as classification and regression, mainly when dealing with structured numerical data. In aquaculture, for instance, FNNs can process historical data to forecast essential water quality parameters like temperature and pH levels, enabling proactive farm management. Convolutional neural networks (CNNs) [86; 87] are commonly used for tasks involving unstructured data, such as images. CNNs leverage convolutional layers to extract spatial features from images, making them highly effective for object detection, image classification, and segmentation applications. For instance, CNN architectures such as residual network (ResNet) and visual geometry group network (VGG) have demonstrated significant success in aquaculture. VGG is well known for its deep, yet uniform architecture and is commonly used to identify different fish species and detect abnormalities in aquatic environments. ResNet, which incorporates skip connections to address the vanishing gradient problem, allows for the development of deeper networks. This capability makes it particularly suitable for monitoring fish movements and assessing their health condition through high-resolution imagery [88; 89; 90]. Beyond traditional CNN models, more specialised deep learning architectures have been developed to address complex tasks in aquaculture. U-Net [91], a fully convolutional network, is particularly effective in photograph segmentation tasks, such as separating fish outlines from underwater video footage to facilitate automated fish population monitoring [29]. Another widely used model, You Only Look Once (YOLO), is an algorithm for detecting real-time objects that excel in identifying fish behaviour patterns and external threats, such as predatory species in aquaculture systems [34]. These advanced architectures enable real-time analysis of high-resolution images and videos, making them necessary for decision-making in aquaculture operations. In addition to image-based tasks, deep learning models are optimised for analysing temporal and sequential data. Long Short-Term Memory (LSTM) networks [92; 93], an advanced form of Recurrent Neural Networks (RNNs), are especially well suited for time-series analysis. LSTMs are designed to handle long-term dependencies, making them highly effective in predicting water quality parameters and fish-feeding patterns by analysing historical data. For example, an LSTM model can analyse real-time sensor inputs to forecast dissolved oxygen levels for the upcoming 24 h, enabling farmers to optimise their aeration systems in advance. Autoencoders [94; 95] are widely used for feature extraction and dimensionality reduction for unsupervised learning tasks. In aquaculture, these models can analyse sensor data to detect anomalies in water quality, such as irregular pH levels or fluctuations in dissolved oxygen, which may indicate malfunctions of equipment or disturbances in the environment.