# Development of a Multifunctional Unmanned Boat Platform for Aquaculture Automation
## 5 Conclusions  
The integrated unmanned boat platform designed in this paper for aquaculture adopts a modular design, with better compatibility between sensors and development boards, supporting multiple sensor models and achieving lower implementation costs. The hull material is highly malleable, cost-effective, and easy to disassemble and improve.  
Compared to existing unmanned systems in the aquaculture industry, which often suffer from complex structures and high deployment costs, the UBP proposed in this study achieves a lightweight design, with the entire unit weighing less than 10 kg, making it more convenient for small-scale farmers to use. Additionally, it is equipped with a crowdsourced sensor array, integrating multi-source sensing modules, such as water quality monitoring and BeiDou positioning, covering daily aquaculture functions. The cost of this unmanned boat system is approximately 1431 RMB, which is only 20-40% of the current market price for unmanned boats. Compared to manual feeding, using the unmanned boat for feeding can save 24.33% of the feed. In summary, the use of the unmanned boat reduces the daily breeding cost from 225 RMB to 120.2 RMB, achieving an overall reduction of 46.7%, effectively helping farmers lower labor costs in aquaculture. This UBP provides a cost-effective automated solution for small and medium-sized farmers, assisting them in reducing labor costs, improving traditional aquaculture methods, and enhancing aquaculture efficiency. It offers a feasible solution for increasing the level of mechanization and automation in aquaculture, demonstrating significant application value.  
Although this study has made some progress, there are still issues that need further resolution. The experimental environment in this study was relatively favorable, with minimal impact from factors such as wind speed on navigation accuracy. Therefore, future research will focus on enhancing the unmanned boat's resistance to wind and waves in complex environments. Algorithms for wind and wave resistance will be designed to improve navigation accuracy under challenging conditions. Additionally, hull design and structure will be further refined, testing different materials and hull shapes to enhance the stability of the unmanned boat in complex water environments. Secondly, the robustness of underwater recognition algorithms under complex backgrounds and lighting conditions needs further improvement. This will be achieved by increasing the diversity of training datasets and expanding the adaptability of algorithms to enhance their robustness under varying lighting and background conditions.  
Conceptualization, X.X., J.H. and J.D.; methodology, X.X., J.H. and J.D.; software, X.X. and J.H.; validation, X.X., J.H. and J.D.; formal analysis, X.X. and J.H.; investigation, X.X., J.H. and J.D.; resources, Y.L., Y.H., L.M. and D.J.; data curation, X.X., J.H. and J.D.; writing--original draft preparation, X.X. and Y.H.; writing--review and editing, Y.H. and X.X.; visualization, X.X. and J.H.; supervision, Y.L., Y.H., L.M. and D.J.; project administration, Y.L., Y.H., L.M. and D.J.; funding acquisition, [PERSON], [PERSON], [PERSON] and D.J. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the National Natural Science Foundation of China, grant number 42401570; the Open Foundation of the Key Laboratory of Coupling Process and Effect of Natural Resources Elements, grant number 2024 KFKT020; NUPTSF, grant number NY220165.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** The authors express their gratitude towards the journal editors and the reviewers, whose thoughtful suggestions played a significant role in improving the quality of this paper.  
**Conflicts of Interest:** The authors declare no conflicts of interest.