Land inspection robots, endowed with autonomous navigation, obstacle avoidance, and mobility capabilities, are increasingly showcasing their unique advantages and practical utility across various sectors, particularly in fisheries and aquaculture. This paper delves into the critical technologies underpinning land inspection robots and explores their potential application in modernizing and adding intelligence to the aquaculture industry. The paper comprehensively analyzes the trifecta of land inspection robots: sensor technology, mechanical design, and control systems. Sensors play a pivotal role in capturing an array of environmental parameters, including temperature, humidity, light, pressure, sound, and imagery, providing a comprehensive dataset for decision-making. The mechanical structure serves as the backbone, enabling the robots to traverse challenging terrains, negotiate obstacles, and perform a diverse range of inspection tasks. The control technology, meanwhile, governs autonomous movement, task execution, and decision-making, encompassing motion control, path planning, and task scheduling. In the context of aquaculture, land inspection robots hold immense promise. They facilitate real-time monitoring of critical water quality parameters such as dissolved oxygen, pH, and ammonia, ensuring optimal conditions for fish health and enhancing production efficiency and product quality. The robots' ability to detect fish abnormalities early on enables proactive management, reducing risks and improving disease control. Furthermore, their remote observation and control capabilities streamline intelligent management of fish breeding facilities, lowering labor costs and enhancing operational efficiency. However, the application of land inspection robots in aquaculture faces unique challenges stemming from the complex and variable nature of the environment. The robots must exhibit a high degree of adaptability, interference resistance, and self-learning capabilities to accommodate diverse fishery scenarios and species. Additionally, the stringent requirements for information accuracy and real-time data necessitate robust stability and reliability in data gathering and transmission processes. Technical bottlenecks, such as limitations in environmental perception, image quality degradation due to water reflections and turbidity, and energy management in wet and corrosive environments, hinder widespread adoption. To overcome these challenges and unlock the full potential of land inspection robots in aquaculture, several development suggestions are proposed. Firstly, enhancing environmental adaptability designs, including waterproofing and corrosion resistance, is crucial for stable operation in harsh conditions. Secondly, advancing image processing technologies can improve image quality and fish recognition accuracy. Thirdly, establishing a comprehensive data processing platform, integrating cloud computing and big data analytics, will streamline data collection, storage, and analysis, enabling smarter decision-making. Lastly, optimizing energy management systems, including high-capacity battery technologies and autonomous charging capabilities, will prolong operational durations and reduce downtime. In conclusion, while the application of land inspection robots in aquaculture faces technical hurdles, their potential to revolutionize the industry remains substantial. With continued technological advancements and strategic development efforts, these robots are poised to play a pivotal role in driving the modernization and intelligent transformation of fisheries and aquaculture.