## 10 Discussion and Conclusions  
Continuous monitoring in agriculture, while impractical for humans due to labor constraints, has the potential to significantly advance everyday farming practices. This approach offers clear benefits for farm economies, environmental protection, and biosphere health, including human health. Thanks to cutting-edge developments in scientific hardware and AI-based data analysis, the continuous monitoring of crops by unmanned, fully autonomous robots is now attainable. However, this does not imply that finished, affordable, robust, and effective UGVs for autonomous monitoring are easily realizable. Instead, the current ICT technology, in principle, allows for the design and implementation of prototype systems with a reasonable cost, capable of sophisticated operations such as in-field navigation, the real-time detection of crop emergencies, precise georeferencing, alert report preparation, and high-quality data collection to maintain a digital twin of the crop over time. Remarkably, researchers with minimal or no experience in mechatronics can now plan R&D activities in unmanned ground vehicles for agricultural monitoring. This is largely thanks to robotic technology providers who design and build custom robots capable of accommodating various hardware devices and sensors, which are ready to host AI models and digital twins of plants and crops. While these tasks are not trivial, they can largely be managed within an ICT context. This contrasts sharply with more critical applications, such as robots designed for field treatments like spraying, weeding, seeding, pruning, and picking fruits. These UGVs are significantly more complex in terms of mechatronics and their design necessitates detailed, crop-specific agronomic knowledge. On the other hand, recent advancements in ICT have enabled the development of affordable UGVs for monitoring, as discussed in Sections 1, 5, and 7, which address the costs of complete UGV systems, sensors, and on-board hardware. Additionally, operational autonomy has significantly improved, driven by better batteries and new low-power hardware components (see Table 4). For such rovers, the hardware market for AI inference is becoming quite saturated, with powerful edge devices already available and intriguing new proposals expected in the coming months. Sensors such as RGB-D cameras and 3D LiDARs have advanced significantly, offering high-performance products at affordable prices. This naturally suggest a shift from a pure cloud paradigm to an approach mostly based on edge computing. The use of deep learning technology for image classification has also seen substantial progress, resulting in models with an improved performance that are primarily constrained by the quality of the training datasets rather than the models themselves. Data remain a critical challenge, as is common in other deep learning applications. Additionally, the seasonality of plants and vegetables is a problem, as data collection is restricted to narrow time windows throughout the year, differently from industrial applications. Furthermore, the variability in plant phenotypes and their interaction with the environment and climate can create significant year-to-year differences, which must be included in datasets to train high-quality, generalizable models. There is ample opportunity for research and development in this area. The concept of the digital twin is particularly promising, as it allows for the continuous remote monitoring of a crop--or even individual plants in low- to mid-density fields--over time. However, the most challenging aspects are likely to involve autonomous navigation in unstructured environments, including irregular terrain and potentially harsh weather conditions. For both mapping and routing, as well as for creating the digital twin, the interaction between UGVs and UAVs appears very promising and definitely merits field experiments.