## 2 Background and Related Literature
Notably, the authors highlight edge computing as an alternative to cloud computing for local data analysis and inference using low-power/energy-efficient computational devices. While the paper dates from early 2023, the rapid evolution of this technological field means that some of the presented solutions may already have been surpassed by current technologies. The review concludes with a brief section dedicated to the use of AI algorithms for data classification and regression in agriculture. Given the breadth and structure of this topic, dedicated reviews like [8] are recommended for further exploration. In [9], several technological pillars of smart agriculture are identified, aligning with a common vision shared by domain experts: the Internet of Things (IoT), artificial intelligence (AI), mobile robotics, and big data. In applications like farming, the first two are so intertwined that a dedicated acronym, AIoT, has been introduced. Among mobile platforms, the review focuses on an area we believe will see significant development in the near future: the collaboration between UGVs and drones. As with previous, still recent, works, the list of commercial technological solutions needs careful consideration, as newer and more advanced versions of similar devices are continually emerging. Control methods are one of the key topics in [10], where several crucial issues are identified: wireless communication, soil effects on UGV traction, terramechanics (interaction between robots and soil), and environment recognition. The review is extensive and comprehensive, addressing many aspects of the dynamics of the use of autonomous rovers for agriculture. Particular mention is given to (i) vehicle motion models and (ii) logic and control systems. Additionally, attention is paid to computer vision algorithms that are rarely used in this field, such as the use of Hough transform for plant row detection, although their effectiveness in partially structured environments like crops needs verification for specific cases. Data communication is another valuable topic in this review, which compares different standards along with their strengths and weaknesses. In [11], a general analysis of the benefits and challenges of automation in agriculture is presented. Of particular interest is the discussion of the use of UGVs for data collection and analysis, highlighting the innovative concepts of digital twins and \"new ecologies of automation\". A crucial part of the analysis regards the feasibility of automation, including the inevitable economic shifts it will bring, as well as the advantages and disadvantages of adopting these innovative technologies. In our view, these are the most noteworthy recent reviews of the use of UGVs in agriculture. They provide a foundation for introducing basic concepts and paradigms, facilitating the formulation and development of new ideas. We will not repeat common topics that have already been extensively covered in the literature, such as the classification of UGVs based on their steering geometry or the fundamentals of robot navigation; for these, we refer to the aforementioned surveys. Our purpose is twofold: (i) to address topics that are less frequently considered, either because they require a multidisciplinary approach across different areas of ICT or involve cutting-edge technology that was introduced very recently, and (ii) to provide a useful tool for supporting the decisions of potential UGV buyers or builders, helping them make well-informed choices. Our paper is organized as follows. In Section 3, we describe the methodology used to create the two-level bibliographic dataset. In Section 4, we present the structure of a typical robotic platform for crop monitoring and describe the conceptual data flow between components, including optical and positioning sensors, computational modules, AI classifiers, and possible actuators. Section 5 focuses on sensors, analyzing recent trends in technology, with attention to different types of cameras, LiDARs, GNSSs, and IMUs.