At a reference speed of 3.6 km/h (1 m/s), introducing a slow-down factor of 2 for the time spent on stops for detailed inspections, u-turns between inter-row aisles, etc., assuming a \"working day\" of 8 h for the UGV, this yields a monitoring capacity of 2.8 ha/day for the vineyard and 1.4 ha/day for the artichoke crop. Now, taking into account a revisit time, that is, the interval between two successive observations of the same point in the crop, of 4 days, it is roughly estimated that a single rover can autonomously surveil 12 ha of a vineyard or 6 ha of an artichoke crop. Needless to say, the profitability of these outcomes significantly depends on thorough considerations of the costs and benefits for each specific situation, especially concerning common crop emergencies, the resources required to address them, and the potential savings that early diagnosis could yield. While several commercial UGVs exist for field operations like seeding, weeding, and pruning--each with its own limitations--UGVs designed exclusively for monitoring are far less common. Although the former can also be used for data acquisition and emergency detection, UGVs explicitly designed for crop surveillance are preferable for several reasons: they are generally smaller, lighter, less power-demanding, and more affordable, reducing navigation difficulties and minimizing unnecessary energy and cost; they can be more easily implemented as fully autonomous vehicles; they pose a lower risk of damaging crops or causing harm to people; their focus on ICT-oriented solutions enables advanced functions, such as edge-based image classification for the real-time diagnosis of pests and diseases, compared to traditional UGVs that are more focused on field operations; their deployment in fields is more practical. In addition, UGVs for monitoring have the potential advantage of addressing a wider range of situations compared to UGVs designed for crop treatments, which are tailored to specific tasks or types of cultivation. In fact, while the specialization of the latter is primarily achieved by installing specific mechanical tools (such as nozzles for spraying or scissors for cutting), the former can address different applications (e.g., monitoring various crops) through software. This is achieved by selecting or calibrating different AI models and algorithms to suit the situation at hand. During our research, we did not find any 'turnkey' commercial UGV that meets all the criteria outlined above. For these reasons, and given the clear advantages of open solutions over proprietary market models, we favored robotic prototype architectures--specifically those where expert manufacturers handle the design and construction of the mechatronic platform, while sensors and computational devices are selected and installed through a decision process that is shared with the customer. This approach allows research groups focused primarily on AI and computer science, rather than robotics, to enter this field of study. These prototypes, typically costing between EUR 20 and 40 k (depending on the required sensors and hardware specifications), offer full customization and the flexibility to test various ICT setups, helping to identify the most suitable configurations. Therefore, in the following sections, the emphasis will be on cutting-edge ICT solutions that can be integrated to design a state-of-the-art UGV for crop monitoring, rather than on the existing all-in-one proprietary models currently available on the market.