## 6 Conclusions  
The smart aquaculture solution developed in this study, centered around the YOLO-based AI model, has demonstrated exceptional performance in fish fingerprinting separation and identification, particularly in addressing the gender-sorting challenges encountered during aquaculture breeding processes. Compared to traditional manual recognition techniques, this system offers significant advantages in several areas. Traditional recognition technologies are typically applied to the identification of uniform and simple features in objects like standard industrial products. In contrast, the AI system developed in this study can analyze thousands of different reproductive feature patterns, making it more adaptable to handling complex and diverse biological characteristics.  
In terms of environmental parameters, the AI model in this study can operate under various outdoor lighting conditions, such as sunlight, shadow, and low light, with an adjustable recognition distance, which enhances its adaptability. Traditional industrial recognition technologies usually operate under fixed indoor lighting and with a set recognition distance; thus, their performance is more limited when dealing with environmental changes. Additionally, the AI system can effectively handle irregular biological samples mixed with impurities and bubbles, whereas traditional technologies require clean surfaces and distinct features for accurate recognition. This capability allows the system developed in this study to maintain high recognition efficiency even under suboptimal environmental and sample conditions, whereas traditional techniques may experience significant performance degradation. In terms of recognition difficulty, the system developed in this study must manage a wide variety of reproductive features and unstable environmental factors, making the recognition process relatively challenging. However, the system's ability to perform instant recognition at a rate of 12 fish per minute, with an accuracy rate exceeding 95%, reduces the workload of laborers in the aquaculture industry and improves sorting efficiency.  
Introducing this system into the aquaculture management of smart cities not only enhances production efficiency and reduces labor costs but also addresses the issue of labor shortages. It has the potential to attract younger generations to the aquaculture industry, promoting sustainable development within the sector. Within the framework of smart cities, the application of such technologies contributes to achieving intelligent agricultural management, enhancing overall urban operational efficiency, and providing critical support in meeting future global food demands.  
Conceptualization, C.-Y.K. and I.-C.C.; methodology, C.-Y.K.; validation, C.-Y.K. and I.-C.C.; formal analysis, C.-Y.K. and I.-C.C.; investigation, C.-Y.K.; data curation, C.-Y.K. and I.-C.C.; writing--original draft preparation, C.-Y.K.; writing--review and editing, C.-Y.K. and I.-C.C.; visualization, C.-Y.K.; supervision, C.-Y.K. and I.-C.C. All authors have read and agreed to the published version of the manuscript.  
Appreciation is extended to the Industrial Development Bureau, Ministry of Economic Affairs, Republic of China, for the support provided through the CITD project \"Innovation and R&D for Traditional Industries Affected by the COVID-19 Pandemic\". This research outcome was jointly published with Vossic Technology.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
Author [PERSON] was employed by the company Vossic Technology, New Taipei 235030, Taiwan. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interes.