# Emerging Technologies for Automation in Environmental Sensing: Review
## 1 Introduction
These robots have special tools and sensors to do their job and are helpful in areas like marine science, oceanography, and checking the environment underwater [29]. Scientists developed a system [30] using a special robot (ROV) to check water quality near hydropower sites. The robot has a dissolved oxygen sensor and moves with a solar-powered system. It can be controlled through a website showing real-time data on Google Earth. Tested at McNary Dam, it effectively checks water quality at various depths and locations, offering a flexible way to monitor challenging aquatic environments. The historical context of ground robotics traces back to the development of the first mobile robots in the 1960-70 decade, such as [PERSON]'s Shakey and the Laboratory of Systems Analysis and Architecture's Hilare robot [31]. Since then, the field has witnessed significant growth, with rovers and minibots playing important roles in modern robotics. Rovers, exemplified by notable systems like AIR-K and ROSI, showcase advancements in autonomous ground inspection and monitoring. These robots employ tracked wheels, sensors like cameras and gas sensors, and varying maturity levels to execute operations and observations in emergencies [32]. Additionally, minibots, with their tracked wheelsand laser scanner sensors, offer versatility in ground exploration [33]. The recent surge has led to applications in agriculture, industry, military, medical, and logistics, underlining the need for robust security solutions in robotics [34]. Ground robotic systems are also gaining prominence in disaster search-and-rescue missions, where autonomous mobile robotics research plays an important role [35; 36]. The integration of advanced technologies, including artificial intelligence, computer vision, and sensor fusion, is explored to enhance the autonomy and efficiency of these robotic systems [37]. Therefore, this paper discusses the significant impact of automation on environmental sensing, focusing on the integration of advanced technologies such as Unmanned Aerial Systems (UASs) and environmental sensor networks (ESNs). It explores how these technologies overcome traditional limitations in data collection and analysis. This paper examines challenges in pollution monitoring, smart agriculture, and cybersecurity in environmental sensing. It also addresses financial barriers to adopting smart agricultural technology and stresses the importance of ongoing research and innovation in this area. Additionally, the integration of robotics, especially underwater vehicles, is highlighted for its crucial role in enhancing environmental monitoring applications. Overall, this article emphasizes the importance of automation and robotics in tackling complexities and advancing efficient, accurate, and scalable environmental monitoring solutions. Figure 1 shows the integration and synergies between environmental sensing automation, robotics, and edge computing technologies. This paper is structured as follows: Section 2 outlines the methodologies employed in formulating this review article. Section 3 introduces emerging technologies such as deep learning, transfer learning, and edge computing, crucial for achieving automation in environmental sensing frameworks. Section 4 addresses the challenges associated with implementing these automated frameworks. Section 5 explores the future of automation in environmental remote sensing. Finally, Section 6 provides the conclusion of the review article.