# Development of a Multifunctional Unmanned Boat Platform for Aquaculture Automation
## 4 Discussion  
According to the demand for aquaculture automation and mechanization, an unmanned boat containing cruise path generation, water quality detection, and aquaculture monitoring was designed. Sailing experiments demonstrated that the unmanned boat designed in this paper based on the Monte Carlo simulation method has a faster cruise path generation speed and can find the approximate optimal path in a shorter time. Additionally, the maximum deviation of the boat's sailing does not exceed 1.5 m.  
The cruising path of the boat is stable, and the positioning accuracy of the monitoring points is greatly affected by the water current and wind. This result demonstrates that the unmanned boat can cruise more accurately along the predetermined path in practical applications. However, under complex environmental conditions, such as waters with strong winds and waves, the positioning accuracy may decrease, necessitating further optimization for wind and wave resistance.  
This paper assumes that the wind-resistant performance can be strengthened by improving the hull structure. By adding a V-shaped bottom to the existing chamfered design, the wave impact decomposition angle can be increased from 30\({}^{\circ}\) to 45\({}^{\circ}\), which is expected to reduce the transverse rocking angle by 20-30%. At the same time, removable EVA fins can be added to both sides of the hull to reduce the risk of hull pitching.  
Water quality monitoring system tests show that the unmanned boat can work stably during 5 to 10 s of normal navigation. The parameter accuracy of the industrial control board, the NodeMCU development board, and the sensor module meets the water quality monitoring requirements. Specifically, the measurement error of the water temperature sensor is within \(\pm\)0.5 \({}^{\circ}\)C, the measurement error of the pH sensor is within \(\pm\)0.1 pH, and the measurement error of the TDS sensor is within \(\pm\)1%. The developed cloud terminal real-time monitoring system works stably and is able to return real-time online data from sensors, such as pH, temperature, and dissolved oxygen of the water body, realizing real-time data visualization. This allows farmers to keep abreast of the water quality conditions in the aquaculture area and take timely measures to avoid the impact of water quality problems on aquaculture animals.  
The underwater vision system is based on the fusion of SKNet and YOLOv5 aquaculture detection methods and, in actual operation, the recognition accuracies for sea crabs, sea cucumbers, and scallops are 94.3%, 87.8%, and 89.6%, respectively. Although this recognition accuracy meets the practical application requirements to a certain extent, there is still room for improvement. The recognition accuracy can be further improved by adding more training data and optimizing the model structure, especially to enhance the robustness of the model under complex background and lighting conditions.  
At the same time, this study also encountered many challenges during its execution. For example, abnormal data may be generated on the hardware side of the unmanned boat (such as water quality values exceeding reasonable ranges or invalid coordinates), requiring the development of anomaly detection and handling mechanisms on the software side to prevent erroneous data from affecting system operations. When storing heteroge  
\begin{table}
\begin{tabular}{c c c} \hline
**Specification** & **UBP** & **Manual** \\ \hline Boat Price & 1431 & 1500 \\ Labor Cost, RMB/day & 100 & 200 \\ Feed Cost, RMB/day & 19 & 25 \\ Maintenance Costs, RMB/day & 1.2 & - \\ Total Cost per day, RMB & 120.2 & 225.0 \\ \hline \end{tabular}
\end{table}
Table 8: Comparison of total cost: UBP vs. manual method.  
neous data from multi-source sensors, it is necessary to match and call databases storing different data.