## 7 On-Board Hardware  
Crop monitoring using UGVs requires advanced techniques for perception, localization, and movement planning. These processes often utilize deep learning algorithms that demand significant computational power, typically reserved for high-powered computers, which are not always suitable for mobile robotic platforms. Therefore, for effective implementation of these algorithms, UGVs must be equipped with state-of-the-art integrated computers. These devices should not only provide the required computational power but also be lightweight, energy-efficient, and compatible with the space and weight constraints typical of agricultural robotic platforms. UGVs used for agriculture operate outdoors in challenging environments, such as uneven terrain with soft and slippery soil, dust, vibrations, and often harsh weather conditions. Rugged hardware is essential to protect sensitive components and ensure operational continuity. The CPU is the all-purpose computational device of the UGV, hosting the ROS node, managing navigation tasks, handling I/O workflow from sensors and RTK localization within the crop, and coordinating the general activities of the machine. The traditional CPU family is the \(\times\)64 architecture, based on a 64-bit instruction set introduced by AMD and later adopted by Intel. Until recently, this was the de facto standard for consumer and professional CPUs, providing high computational power and a complete system of governing and mathematical software, including ROS, TensorFlow, PyTorch, optimized C/C++ compilers, Python implementations, OpenCV, and all other tools necessary for advanced autonomous robots [58; 74; 75]. Typically, these CPUs are paired with consumer Nvidia GPUs equipped with CUDA to handle computationally intensive tasks related to deep learning or general linear algebra algorithms (GPCPU, general-purpose computing on GPUs). Recent Intel 14 th gen and AMD Ryzen 7000 Series consumer CPUs are undoubtedly powerful and suitable for the tasks mentioned above. However, they have a significant drawback: their energy consumption. With a sustained power dissipation (thermal design power, TDPs) of up to 250 W, their prolonged field use may be seriously limited. Even in other application fields, this energy consumption is a well-known drawback of \(\times\)64 devices. As a result, an alternative architecture from the smartphone world has recently made an assertive appearance in the PC market: Arm devices, based on the RISC instruction set (while \(\times\)64 is based on CISC). Although Arm processors have been also used for high-performance computing (the Japanese Fugaku installation was the world's fastest computer on the top 500 list from 2020 to 2022), typical Arm implementations are often preferred for mobile applications, where energy autonomy and lightness are priorities, given that even the most energy-consuming devices in this family rarely exceed 50 W. This makes them good candidates for UGVs in agriculture, as they support monitoring and basic navigation tasks well [76; 77; 78]. While Arm CPUs tick many boxes related to farming UGVs, a significant question remains: are they powerful enough to manage the new generation of autonomous rovers for agriculture, with improved features and the need to face more demanding challenges? The use of artificial intelligence to analyze the data collected by UGVs in real-time enables the early diagnosis of issues such as infestations or nutritional deficiencies but raises the need for hardware accelerators for AI inference, a task that cannot be performed by the CPU within the expected time. Hardware acceleration facilitates on-site AI inference, reducing latency and improving system responsiveness.