## 2 Literature Review  
The development and application of technological advancements in aquaculture have significantly transformed traditional operations within the industry. Smart aquaculture technologies [1; 2; 3], such as artificial intelligence (AI), the Internet of Things (IoT), automation technologies, and big data analytics, have not only improved production efficiency but also enhanced the precision and sustainability of management practices [4; 5]. These technologies, including automated feeding systems, environmental monitoring, and fish behavior analysis, have markedly reduced the need for manual labor while improving the quality of aquaculture products. These advancements have not only optimized various stages of the production process but also played a crucial role in addressing environmental challenges and labor shortages. [PERSON] and [PERSON] [6] emphasized the role of technological innovation in fisheries and aquaculture in their study. They highlighted that smart aquaculture technologies, through the introduction of automation and digital technologies, have not only boosted productivity but also improved the sustainability of aquaculture practices. Similarly, [PERSON] et al. [7] explored the application of technological advancements in Cobia fish farming, particularly in water quality monitoring and automated feeding systems. They found that the integration of these technologies not only increased the precision of farming operations but also significantly enhanced the growth rate and yield of fish. The application of these technologies in Cobia fish farming not only reduced farming costs but also improved product consistency and market competitiveness. In traditional breeding operations worldwide, after pairing broodfish to produce fingerlings, farmers commonly use male hormones to treat the newly hatched fingerlings, forcing them to develop into males to ensure the production of single-sex fingerlings [8; 9]. However, the use of male hormones raises concerns about environmental pollution, causing significant impacts on the surrounding ecosystem [10]. In response, some hatcheries have adopted hybridization methods to produce single-sex fingerlings, thereby avoiding the reliance on male hormones. Additionally, the acquisition of hormone treatments has become increasingly restricted, with rising costs and declining purity and quality, further complicating the use of male hormones in breeding operations. Therefore, the fish gender identification tool developed in this study aims to provide immediate contributions to current aquaculture hatcheries. As it matures through the accumulation of sufficient data, it is expected to be further integrated into grow-out farms. Specifically, when juvenile fish grow to a stage where their gender can be identified through secondary sexual characteristics, this tool can be employed to separate male and female fish for rearing. This would allow mass production operations in aquaculture to break free from the dependence on obtaining single-sex fingerlings, achieving high-efficiency production--a potential future market with enormous promise. To achieve this goal, this study explores and analyzes three mainstream market models--SSD (Single Shot MultiBox Detector), MobileNet, and YOLO (You Only Look Once)--and examines their respective advantages and disadvantages. The Single Shot MultiBox Detector (SSD) has demonstrated excellent performance in visual recognition tasks [10; 11]. The primary advantages of SSD include its fast processing speed and high accuracy, particularly when handling large-scale datasets such as PASCAL VOC and COCO. However, SSD has some limitations. Firstly, its performance may decrease when detecting small objects due to the use of fixed-size and -ratio anchor boxes for predicting object locations [12]. Additionally, the SSD model requires a large amount of labeled data and computational resources for training, which may not align well with resource-constrained platforms where the model is eventually deployed.