## 4 Results  
While this study demonstrates high efficiency in the gender identification of fingerlings under specific environmental conditions, we acknowledge that broader validation across various conditions (such as different lighting, water quality, and fish species) has not yet been conducted. However, the primary goal of this research was to develop a feasible technological solution for specific aquaculture operational scenarios. To ensure accurate performance evaluation during the early development phase, we opted for controlled experimental conditions to validate the model. Future research will expand to more diverse conditions to test the model's generalization. For now, the results of this study should be discussed within the scope of the designed experimental framework. In this study, a relatively small sample size was used for the training and validation of the YOLO model. While a larger dataset could further enhance the model's generalization ability, YOLO is renowned for its efficiency in real-time object detection and can achieve high accuracy even with smaller datasets. The experimental design was optimized for specific fish species and environmental conditions, allowing for good recognition performance despite the limited sample size. Future research will consider expanding the dataset and implementing more rigorous data augmentation techniques to further improve the model's robustness and adaptability. This study adopted a typical hardware configuration as a reference:  
* CPU: Intel Core i7-10700K @ 3.80 GHz;
* GPU: NVIDIA GeForce RTX 2080 or NVIDIA A100;
* RAM: 16 GB;
* Storage: 512 GB SSD;
* Operating System: Ubuntu 18.04 or Windows 10. This configuration efficiently handles the inference process of deep learning models and achieved reasonable inference times during the experiments. This study leverages the capability of the recognition module to perform instant identification, achieving a real-time identification rate of 12 fish per minute with a gender recognition accuracy of over 95%. This significantly reduces the workload of laborers in the aquaculture industry and enhances sorting efficiency. The study focused on Taiwan's aquaculture industry, specifically using tilapia (Oreochromis spp.) as the experimental subject. The secondary sexual characteristics of the cloaca of male and female tilapia were input into the YOLO AI model. Through annotation, scaling, and parameter adjustments, the study overcame challenges such as diverse external characteristics and suboptimal identification environments (e.g., significant light variations, the presence of bubbles, excrement, and gravel mixed with the fish). During the experiment, the system was able to instantly identify the gender of Taiwan tilapia at a speed of 12 fish per minute, with an accuracy rate exceeding 95%. Breeders need only use a mobile phone as the identification platform, allowing them to capture images of fish cloacal characteristics in any setting and quickly display AI judgment results. This operation greatly enhances the operational outlook of tilapia breeding, transforming it from traditional manual methods to a more automated operational mode. This Table 1 summarizes the data used in the first test, categorized by sample type, source, and content. First Training Sample Conclusion: The source of image capture significantly impacts the learning ability of the recognition model. After initial sample evaluation, it became clear that the reproductive traits of different fish vary significantly, and the number of machine learning samples affects accuracy. In addition to the recognition of sexual characteristics, other parameters can be adjusted to improve identification accuracy. The genital characteristics of farmed fish are quite small, and when the fish are taken out of the breeding pond, bubbles or excrement often contaminate the environment. The visual recognition model frequently misidentifies bubbles or excrement as genital features, leading to incorrect identification results.