# Gender Identification of Chinese Mitten Crab Juveniles Based on Improved Faster R-CNN
## 5 Conclusions  
In this study, we have merged deep learning technology with the unique needs of Chinese mitten crab juveniles to intelligently differentiate between male and female specimens. This advancement fosters the transition of modern fishery sorting equipment from semi-mechanized systems to intelligent ones. We introduce an improved Faster R-CNN algorithm. By adjusting image characteristics like brightness, adding noise, rotating, cropping, flipping, and employing other data augmentation techniques, we effectively simulated a variety of real-world conditions relevant to practical applications. The integration of the Swin Transformer and the path aggregation feature pyramid network has greatly enhanced the extraction of complex features pertinent to this task. Implementing soft non-maximum suppression and focal loss markedly improved overall detection accuracy. Additionally, integrating a region-of-interest pooling network with a self-attention mechanism enhanced the model's ability to understand both intra- and inter-regional correlations, thereby improving feature representation. This multifaceted enhancement not only improved accuracy but also expedited processing speed. The refined model attained a mAP of 98.45%. The application of this advanced algorithm holds the potential to elevate the level of automation in aquaculture, offering substantial labor cost reductions and significantly boosting the efficiency of large-scale Chinese mitten crab farming. This study serves as a valuable reference for propelling forward smart aquaculture practices.  
Looking ahead, the potential applications of this research extend beyond the realm of aquaculture. The methodologies and technologies developed here can be adapted to other species and industries, paving the way for broader impacts in automated classification and environmental sustainability. However, challenges such as data availability, model generalizability, and real-world application constraints remain. Future research should  
Figure 12: Illustration of the results of the improved algorithm in this article.  
focus on these areas, aiming to enhance the robustness and applicability of our model across diverse scenarios. Moreover, this research contributes to the technological advancements in machine learning and computer vision. By successfully applying the Swin Transformer to a novel domain, we demonstrate its versatility and potential for further innovative applications. This study also underscores the importance of interdisciplinary approaches, combining expertise in biology, computing, and engineering, to address complex real-world problems.  
In summary, our study not only achieves high accuracy in the gender identification of Chinese mitten crab juveniles but also opens avenues for future research and application in various fields. It stands as a testament to the power of combining cutting-edge technology with practical applications, driving forward the progress in smart aquaculture and beyond.  
Conceptualization, M.C.; methodology, H.G.; software, H.G.; validation, D.G.; resources, M.C.; data curation, D.G.; writing--original draft, H.G.; writing--review and editing, M.C.; visualization, D.G.; funding acquisition, M.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Research and Development Planning in Key Areas of Guangdong Province, NO. 2021B0202070001.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to Significant investment of time and money in data acquisition.  
The authors declare no conflict of interest.