# Development of a Multifunctional Unmanned Boat Platform for Aquaculture Automation
## 1 Introduction  
Aquaculture is a crucial pillar of China's food security. According to the FAO's 2020 report, The State of World Fisheries and Aquaculture, global fish production reached approximately 1.79 \(\times\) 10\({}^{8}\) tons in 2018, with aquaculture production accounting for about 8.21 \(\times\) 10\({}^{7}\) tons. China remains the world's largest fish producer and exporter, where small- and medium-sized aquaculture enterprises constitute over 90% of the sector. Aquaculture has significantly contributed to rural economic development and sustainable utilization of fishery resources in China. Efficient aquaculture practices, particularly automated aquaculture, are essential for industry advancement. Feeding management represents a critical component in aquaculture operations, constituting a major portion of production costs [1]. Current feeding methods include manual feeding, fixed feeders [2, 3], and mobile feeding machines. Aquaculture robots play apivotal role in monitoring fish behavior [4], enabling automated feeding, and reducing human intervention. By comparison, mobile feeding machines, represented by unmanned boats [5,6], have garnered attention due to their flexibility and ease of use, and are gradually being applied in fishery feeding. By integrating advanced sensors, control systems, communication technologies, and artificial intelligence, unmanned boats are capable of performing efficient and precise operations in complex and variable aquatic environments. In the field of aquaculture, unmanned boats have been preliminarily applied in water quality monitoring, fishery feeding, and marine life observation. In terms of water quality monitoring [7,8], unmanned boats address issues such as inflexible monitoring locations and limited monitoring range. In terms of feeding [9,10], compared to traditional feeding methods, unmanned boat feeding technology resolves problems such as long feeding times, high labor requirements, and uneven feed distribution. In terms of marine life observation, artificial intelligence technologies, like deep learning, are applied to the identification of aquaculture subjects [11,12,13], providing crucial technical support for the refined management of fishery farming. However, there are also some common challenges in the widespread adoption of unmanned boats, such as high costs and difficulties in multifunctional integration. By connecting to internet-enabled underwater sensors, the water quality in aquaculture areas can be monitored in real time, and the status of the cultured organisms can be tracked, enabling precise feeding and management. Water quality significantly influences the behavior of fish [14], and maintaining the aquatic environment within a range that supports optimal metabolic efficiency can maximize growth rates and feed conversion ratios. This approach can reduce feed consumption, minimize waste, and lower production costs. Additionally, the identification and real-time monitoring of aquatic animals are prerequisites for feeding management by aquaculture robots and for assisting farmers in decision-making. Adopting different feeding methods based on the growth stages of the cultured organisms can increase feed utilization, reduce feed waste, and minimize water pollution. Currently, manual feeding still dominates in aquaculture. Some existing unmanned feeding machines can only achieve automatic feeding functions, but struggle to monitor the water quality in aquaculture areas, overlooking the impact and changes in the aquatic environment. Moreover, relatively fixed feeding patterns may fail to account for changes in the growth stages of the cultured organisms, potentially leading to feed waste, water pollution, and other issues. Therefore, this paper develops an integrated unmanned boat platform for aquaculture using EVA foam board hulls and embedded hardware components, including STM32, NodeMCU, and NeZha development boards. Leveraging the platform's mobility, it collects real-time water quality data (temperature, pH, TDS) and underwater imagery.