In between these extremes lie horticultural crops, characterized by varying plant layouts and accessibility conditions that evolve significantly over time due to plant growth. Each case within this category deserves individual consideration; often, strategies such as navigating between rows during the early stages of plant development and exploiting dedicated lanes for later inspections are employed. UAVs can complement rovers by providing the aerial perspectives necessary for comprehensive 3D plant representations, even for orchards, vineyards, and olive groves. Additionally, they can generate coarse maps of vegetation indices or other pertinent information to facilitate targeted inspections by UGVs. Beyond drones, remote sensing technology may provide valuable information through satellite imagery. However, for precision farming applications, such images are constrained by a low spatial resolution, typically around 10 m for publicly available datasets like those from Copernicus. While commercial satellites offer higher resolutions of up to 0.30 m, their service policies often pose challenges for agricultural applications: high-resolution images typically come at a cost ranging from EUR 50 to 70 per square kilometer (1 km\({}^{2}\) = 100 hectares), and for such applications, there is the impractical requirement of purchasing images covering areas of 25 km\({}^{2}\) or larger. Based on the considerations above, one could argue that the \"best\" UGV is one with a minimal size, to optimize the accessibility of plants within the crop. Furthermore, small rovers are more \"gentle\" with the crop soil, as they exert less pressure (if L is the UGV's typical linear size, weight increases as L\({}^{3}\) while the wheels/track size increases as L\({}^{2}\)). These points need to be well balanced with other practical constraints. First, a monitoring-focused rover requires stability to prevent the capture of blurred images and noisy measurements, particularly when sensors are positioned at a distance from the ground. Given that soil surfaces are often rugged, heavy vehicles equipped with suitable suspension systems demonstrate superior performance in this regard. Secondly, adequately sized batteries are essential to power the engine, high-performance sensors, computing devices (particularly for AI inference), and communication equipment. These batteries, despite being of medium size, contribute significantly to the weight of the rover. Last, appropriately dimensioned rovers exhibit improved traction and achieve higher maximum speeds on soft soils, especially those that are wet, sloping, and tilled. Two primary classes of rovers exist: those adopting a general-purpose approach capable of handling diverse crops and situations, and those specifically engineered for particular scenarios, such as a single crop with a predetermined planting layout typically found in flat soils with smooth surfaces. Operating within such controlled environments enables performance optimization but may sacrifice generality. In both cases, all-electric engines emerge as the preferable choice due to their ability to reduce the environmental impacts and achieve more compact mechanical designs, and the lesser importance of high speeds. While mechanized farming treatments can occur at speeds of 15 km/h or higher, detailed monitoring involving real-time data acquisition and AI inference is typically conducted at lower speeds. This is also due to practical constraints, such as the risk of image quality degradation, particularly on uneven terrain. What is the surveillance capability of a UGV like the one described above? To illustrate, let us examine two distinct scenarios: a vineyard employing a typical guyot system and an artichoke crop with planting layouts of 2 m \(\times\) 1 m and 1 m \(\times\) 1 m, respectively (measures refer to the distance between rows and the distance between plants on the same row, respectively). These correspond to linear developments of 5 km/ha and 10 km/ha (1 ha = 10,000 m\({}^{2}\)).