## 2 Literature Review
A review paper [23] provides a detailed analysis of the methods and challenges associated with using the YOLO algorithm for traffic sign detection and recognition. In agriculture, YOLO has been used to detect and classify crop diseases, helping farmers identify and address crop issues promptly, thereby improving crop yield and quality. A study combined MobileNetV3 and YOLOv7 to develop a new model specifically for detecting pests and diseases in rice crops. Additionally, research combining YOLO with SSD, such as [24], collected a dataset of tomato images taken by robots in greenhouses and conducted benchmark testing and evaluation on several deep learning models, including SSD and YOLO networks. The results showed that SSD MobileNet v2 was the best detection model, with high accuracy and fast inference speed. Moreover, the YOLOv4 Tiny model also performed well, with an inference time of about 5 milliseconds. Comparative analyses of SSD, MobileNet, and YOLO models [25; 26] indicate that each model has its unique strengths and limitations. The SSD model is renowned for its high accuracy and real-time processing capability, making it suitable for video streams and dynamic scenes, though it may be less precise than YOLO when dealing with small objects and has relatively higher computational costs. MobileNet is widely used in mobile and embedded devices due to its lightweight and efficient design, significantly reducing computational load and model size through depthwise separable convolutions, though it may sacrifice precision in some complex detection tasks. YOLO, celebrated for its rapid detection speed, can achieve high-accuracy object detection in a single pass, making it ideal for applications requiring real-time responses, such as traffic monitoring. However, YOLO may have limitations in accuracy when handling overlapping objects or small-sized objects. When choosing the appropriate model, it is crucial to consider the specific requirements of the application. For example, in applications where real-time processing is essential, YOLO might be the better choice. In recent years, AI, IoT, and edge computing technologies have been widely applied in smart agriculture. [PERSON]. [27] reviewed the application of AI and IoT in smart homes, focusing on how these technologies are integrated into various household devices, and analyzed their value in energy management and enhancing user convenience. This is relevant to smart agriculture, as both rely on sensor networks and real-time data processing. Ref. [28] explored how to optimize the YOLOv8 model for detecting tomatoes, particularly in enhancing features and recognition techniques in complex backgrounds, which closely aligns with our research methodology and serves as a valuable reference for applying algorithms in different scenarios. [PERSON] et al. [29] examined the application of AIoT in the architecture of smart healthcare systems, covering how AI can improve the efficiency of healthcare services. This technical framework can also inspire how environmental monitoring and data analysis can be implemented in smart aquaculture. In [30], [PERSON] et al. utilized Convolutional Neural Networks (CNNs) for fruit detection and counting, and provided a detailed comparison of different CNN architectures, which holds significant reference value for AI-based gender identification in aquaculture. Additionally, Ref. [31] introduced feature extraction methods for cocoa bean image classification, demonstrating how AI can be applied to image processing in smart agriculture, making it a suitable supplement to discussions on other application scenarios of AI in agriculture. These references collectively provide a solid technical foundation for this study and demonstrate the feasibility of applying AI technologies in various smart aquaculture environments. The intrusion detection system proposed by [32] addresses the issue of data imbalance with innovative techniques.