## 2 Background and Related Literature  
While debates and discussions about the potential realization of autonomous rovers date back a long time, we are interested in recent works addressing the real applications and practical implementations of UGVs. [PERSON] et al. [1] analyze Autonomous Mobile Robots (AMRs) across various sectors, including agriculture, providing a useful introduction to common issues these machines face: locomotion, perception, and navigation. They also examine future challenges related to path planning, localization, and obstacle avoidance, as well as sensor technologies, with a special focus on fusion algorithms. Interestingly, they introduce a relevant question for agriculture: the use of artificial landmarks. Their work serves as an introductory resource to the world of recent UGVs for agriculture, clarifying common concepts and ideas. In [2], the authors focus on an often underrated but crucial aspect of the implementation of UGVs in real-world field activities: stability. They examine various soil types commonly encountered in everyday farming practices and the mechanical damping systems UGVs require to navigate them. The authors introduce stability criteria for both static and dynamic cases and provide a comprehensive list of agricultural UGVs, analyzing their suitability for different soil conditions. In [3], the focus is on both the automation of conventional vehicles--an area not covered in this review--and specifically designed UGVs. The authors examine wheeled robots, including wheel-legged variants, and focus on common steering and driving systems, discussing their advantages and drawbacks. Many real robot examples are considered. They provide a highly illustrative table of key features to consider when evaluating the suitability of an agricultural UGV for a specific real-world context: dimensions, weight, speed, position accuracy, clearance and track width, and autonomy. A review of collision avoidance methods for mobile robots is presented in [4]. While the focus is on wheeled machines, many of the outlined results can be extended to generic UGVs in agriculture. Obstacle detection is analyzed in detail, with a comprehensive list being provided of the most common sensors used for this purpose. The subsequent step, collision avoidance, is considered in its various implementations, including potential fields, population-based search algorithms, neural networks, and fuzzy logic. Most reviews of agricultural UGVs share a focus on crop monitoring and plant treatments. [PERSON] et al. [5] study the application of robots to field activities such as planting, spraying, and harvesting, and specifically, crop inspection--the focus of our review. They introduce computer vision and machine learning as key technologies for the early detection of diseases in both greenhouses and open fields. The authors also provide a valuable list of vision schemes used in target detection, detailing their functions, advantages, and disadvantages. In the real farming world, the end-users are often small-scale producers with limited economic resources for digitalizing their work compared to large industrial-scale farms. Nevertheless, ICT transition is still possible, even for this form of agriculture, as illustrated in [6]. While the general landscape of ICT technology is considered, without the study being limited to robotics, this work is notable for the vision the authors provide, as well as its detailed review of both the technical and socio-economic points of view. A highly recommended review is [7], which is primarily focused on the use of UGVs for agricultural mapping. In addition to hardware (robots and sensors), localization and mapping, path planning and exploration, and machine learning and computer vision, the review also addresses significant pre- and post-processing challenges, such as handling outliers and noise from the high-resolution sensors used for 3D point cloud and plant model generation.