## 5 Discussion  
The evaluation of this study's technology is primarily based on the specific application scenario, designed and tested to meet the practical needs of aquaculture. Given the significant differences in environmental conditions, datasets, and testing parameters across various studies, we believe that direct data comparisons may not be consistent. Therefore, we opted not to perform direct data comparisons, but instead focused on the system's performance under variable lighting and complex environmental conditions. Future research may explore the feasibility of horizontal comparisons of technologies under similar conditions. This study focuses on the application of smart aquaculture within the framework of smart cities, specifically investigating the potential of an AI-based intelligent fish fingering separation system for use in aquaculture, particularly in broodfish gender identification and sorting. The findings indicate that, when the YOLO model is properly trained and optimized, it can achieve a recognition accuracy exceeding 95%, even under challenging conditions such as varying lighting and environments mixed with bubbles and excrement. This high level of accuracy demonstrates that the YOLO model is highly suitable for real-time applications in aquaculture. After selecting YOLO as the AI model, we began training with different samples, conducted in two phases. In the first phase, the model was trained using a single batch of images, with various parameter settings applied to test its effectiveness. In the second phase, additional sample training data were introduced to enhance generalization. Based on the validation results, the following conclusions were drawn, and parameters were adjusted accordingly:  
1. Model accuracy varies significantly based on the recording device and environment: continuous training and learning are required for the model. 2. Annotation has a significant impact on model accuracy: the best results were obtained by annotating the fish fins, randomly scaling sizes, and avoiding rotation in the modules. 3. There are at least tens of thousands of variations in the shape of male and female fish genitalia. 4. Female fish are easier to identify than male fish. 5. The model's generalization is insufficient: 1. The accuracy of AI identification varies greatly with different mobile devices. 2. Most of the failed identifications were due to genital shapes that were not present in the training data. 3. The sample size was insufficient. 6. Inconsistent photo formats: many photos did not capture the fish fins. 7. Some photos contained bubbles or excrement, leading to identification failures. Figure 7: Male fish recognition rate distribution charts: (**a**) male fish high-recognition-rate distribution; (**b**) male fish low-recognition-rate distribution. * The identification accuracy of videos shot on the same day was low, mainly due to poor mobile shooting quality, which caused generalization issues. * We should improve model generalization and control for the impact of different shooting environments. This is particularly important for large-scale fish farms, where traditional manual sorting is not only labor-intensive but also prone to human error. By automating the sorting process, this system significantly reduces reliance on skilled labor, addressing a major bottleneck in the industry. Additionally, the reduction in labor costs and the increase in efficiency brought by this system are expected to substantially improve the economic benefits for aquaculture enterprises. This study also identified several challenges that require further improvement. One challenge is the model's sensitivity to variations in image quality, particularly under different photographic equipment and environmental conditions. Our results suggest that enhancing the diversity of the training dataset, especially by including images captured under various conditions and with different devices, can improve the model's generalization ability.