## 7 On-Board Hardware
It is provided by (i) the above-mentioned GPUs equipped with CUDA; (ii) NPUs, i.e., ASICs (application-specific integrated circuits) exclusively designed for deep learning inference, hosted either on systems on a chip (SoCs) or systems on a module (SoMs); and (iii) hardware accelerators for AI inference, which are similar to NPUs but attachable to other devices via fast USB connections, such as the Coral TPU and Intel Neural Compute Sticks [77]. A statistical analysis of the prevalence of computational devices in UGVs for monitoring (excluding CPUs) based on the past decade's publications is illustrated in Figure 4 (right). These data were obtained using Google Scholar with the query template 'UGV AND agriculture AND [device type]', where the device type was alternately GPU, FPGA, ASIC, or VPU. While theuse of ASICs and VPUs as standalone tools appears negligible, it should be noted that these devices can be integrated into several optical cameras (some listed in Table 1) to perform inference for simplified image classifiers. A comparison of the AI accelerators is provided in Table 4. Interestingly, new generations of consumer \(\times\)64 CPUs have been announced and will be released by AMD and Intel by the end of 2024. These systems-on-a-module come equipped with CPU, GPU, and NPU on a single device. While their AI performance may not match that of the most powerful Nvidia devices, they offer an intriguing compromise among the various components (with CPUs being significantly more powerful than Arm) within a framework of limited power consumption at an appealing price. The Nvidia GPUs [50; 78; 79] come in either desktop or edge versions. The former are mainly represented by the current GeForce RTX 40-series powered by the Ada Lovelace architecture, while the latter are the Jetson series [55; 79; 80; 81; 75; 75; 81]. More precisely, the Jetson series consists of systems on a chip (SoCs) with their own CPU, GPU, and, in the most powerful models, a hardware accelerator for AI inference called NVDLA. The Google Coral TPU [76; 78] brings accelerated machine learning inference to existing systems via a USB port, offering an impressive energy consumption to performance ratio (0.5 W/TOPS, where TOPS means ter operations per second). Despite its release in November 2019, the Coral TPU can still perform image classification at resolutions of up to 513 \(\times\) 513 pixels in 52 ms [82], enabling real-time analysis at approximately 19 frames per second (fps). This performance is more than sufficient, as a UGV moving at 3 m/s with a crop coverage depth of 30 cm would only require 10 fps to capture all images necessary for monitoring the plants. However, these data need in-field verification, especially when tested on more recent deep learning classifiers. The Nvidia Jetson series varies significantly in performance, with AI capabilities differing by more than 500-fold between models. The most powerful, the Orin AGX, delivers a TOPS comparable to entry-level GeForce cards but with a much lower TDP. With 0.22 W/TOPS, the Jetson Orin is the undisputed leader in terms of its energy consumption to performance ratio. Its AI inference capabilities, combined with its large memory capacity, provide it with surprising applications, such as running large language models (LLMs) like Llama 2 with 70 billion parameters in real-time [83], theoretically allowing the UGV to engage in human conversation beyond standard image classification tasks. Currently, the Jetson series is likely the best candidate for UGV implementation in agriculture, although the need for rugged versions can double the cost. When faced with strict budget or energy constraints, a low-end model like the Nvidia Jetson Orin Nano, listed in the table, can be selected. Unlike the Nvidia GeForce and the Coral TPU, the Jetson SoCs include a CPU that, even in the high-end Orin model, is a modest Arm Cortex-A78 AE, launched years ago for the automotive industry with limited computational power.