# Integrating AIoT Technologies in Aquaculture: A Systematic Review
## 4 AIoT Applications in Aquaculture
### AI-Driven Approaches for Automated Counting in Aquaculture  
Accurate counting of aquatic organisms is crucial for efficient stock management, health assessment, and optimising feeding practices. Conventional approaches, which typically involve manual counting or invasive sampling, are time-consuming and susceptible to inaccuracies, especially in high-density aquaculture environments. Recent progress in artificial intelligence, computer vision, and sensor technologies has facilitated the development of automated and precise counting techniques for various species, such as fish, shrimp, and holothurians. Numerous studies have highlighted the success of deep learning models in this field. For example, ShrimpseedNet, a smartphone-based model, has been created to count shrimp seeds, offering a convenient and effective tool for hatchery operators. A deep learning approach based on density estimation has been used to enhance the accuracy of shrimp larvae population assessments. In controlled environments, computer vision methods have been applied for fish counting, and echo-sounder-based algorithms have been developed to estimate fish populations within farming nets. Advances in technology include an enhanced YOLOv5 model for detecting underwater fish and multi-scale context-aware convolutional neural networks (CNNs) for estimating fish density. Additionally, attention-based deep learning techniques have been implemented to count fry in high-density cultures, and non-contact systems have been created to evaluate holothurian populations in aquaculture settings. Counting shrimp larvae and seeds is crucial in hatcheries to ensure accurate stock estimation and maintain optimal stocking densities. AI-based models such as Shrimp-CountNet, which uses density estimation, have achieved over 98% accuracy in counting shrimp larvae. Despite its high accuracy, the model faces challenges when applied to highly dense populations, where overlapping individuals can hinder precise detection. Similarly, ShrimpeedNet has been optimised for smartphone deployment, making it a practical tool for aquaculture workers. However, lighting conditions and image quality variations can impact its performance, highlighting the need for further refinement. Future research aims to improve model robustness under diverse environmental conditions and extend applications to additional aquatic species. Fish counting in controlled and natural environments remains complex due to occlusion, movement, and varying environmental factors. Advanced deep learning models such as YOLOv5 have been employed to detect and count fish while analysing their movement for stress detection. A multi-scale context-enhanced CNN has been developed to improve fish density estimation, addressing occlusion and perspective distortion challenges. This model has demonstrated superior accuracy in crowded environments, though its performance decreases in low-density populations. An alternative approach involves using a super-resolution GAN-based network for fry counting in high-density settings, achieving a 97.57% accuracy rate. While these models show promise, limitations remain in adapting to real-time video processing and integrating multimodal data sources for enhanced precision. Research has also investigated the use of deep learning in marine ecology and fisheries management, such as employing YOLOv2 for detecting scallops in low-contrast underwater imagery and utilising generative models to estimate jellyfish swarm populations. Nonetheless, these methods encounter difficulties adapting to varied environmental conditions, highlighting the need for advancements in data augmentation and model adaptation strategies. Echo-sounder technology provides a non-invasive method for assessing fish populations, especially in aquaculture nets where visual enumeration is difficult. Using acoustic signal processing techniques, fish populations can be estimated with an accuracy margin of under 10%, effectively accounting for disruptions caused by net structures.