UGVs offer capabilities beyond mere observation, like the capacity to systematically assess the health and developmental status of all plants within a crop, thus enabling the generation of prescription maps for subsequent precision treatments, where distinct actions are tailored to specific areas of the crop. Furthermore, robots are capable of generating digital twins of crops with low-density patterns, such as orchards, olive groves, and vineyards. For instance, the 3D representation of a tree canopy as a surface grid composed of triangles that potentially evolve over time allows for the straightforward calculation of physical quantities such as plant dimensions, canopy volume, and leaf area. These parameters provide valuable information for planning interventions, optimizing agricultural practices, and evaluating the amount of forthcoming production. Such functionality can also be extended to crops with medium-density patterns, as observed in certain horticultural scenarios. UGVs could also be an effective way to create systematic agrometeorological datasets, thanks to onboard sensors measuring temperature, humidity, and other relevant parameters: the latter can feed suitable risk models, which are useful to anticipate the probability and severity of future weather-related adversities in agricultural contexts. Remarkably, thanks to UGVs, these operations can be conducted continuously, and in some cases, even during nighttime, irrespective of weather temperature, labor availability, or vacation days. While rovers designed for field treatment necessitate higher power capabilities to accommodate tanks filled with active ingredients or operate pumps and other tools, UGVs dedicated solely to monitoring have a limited size and weight; hence, they are well-suited for all-electric implementations, offering advantages in terms of carbon mitigation, particularly when integrated with renewable energy sources. UGVs and drones (UAVs, unmanned aerial vehicles) should not be perceived as mutually exclusive alternatives, but rather as complementary technologies. UAVs possess distinct benefits, notably the capability to monitor crops, including those inaccessible to ground rovers, at close range without causing damage to plants. Additionally, they exhibit much greater speed compared to UGVs, although this characteristic may be constrained by the processing capabilities of onboard sensors and computational devices. Moreover, their navigation is not impeded by plant rows or ground structures within the field. On the other hand, there are also significant advantages to using UGVs. They are easier to equip with autonomous navigation systems, possess extended autonomy lasting several hours compared to the tens of minutes typically seen with drones, offer closer-proximity views of plants ranging from soil to human eye level, can accommodate heavier and more advanced sensors, computational devices, and AI models, and are generally subject to less restrictive regulations. When selecting the appropriate solution for monitoring and data collection in agriculture, the primary consideration is the plant density of the crop. On one end of the spectrum are tree crops characterized by typical planting layouts featuring rows and plants spaced meters apart. These conditions are highly favorable for UGVs, which can efficiently navigate the entire field. Conversely, crops such as wheat and rice exhibit densities often exceeding 250 plants per square meter. Traditionally, this scenario is managed using specialized machinery, such as tractors equipped with wide-spraying booms spanning tens of meters, aimed at minimizing damage while covering expansive field areas. UGVs are not suited for this context, which is better addressed by UAVs or \"sensorized\" machinery, i.e., standard tractors and other equipment outfitted with sensors, computational devices, and deterministic or AI algorithms. Such sensorized gears are beyond the scope of this review.