## 3 Methodology  
A bibliographic database was collected using the following search engines: Scopus by Elsevier [12], Google Scholar [13], arXiv [14], IEEE Xplore [15], PubMed [16], Science.gov [17], ScienceDirect [18], Semantic Scholar [19], and WorldWideScience [20]. The research queries used were: \"UGV\" AND \"agriculture\"; \"algorithm\" AND \"agriculture\" AND \"UGV\"; \"deep learning\" AND \"agriculture\" AND \"UGV\"; \"artificial intelligence\" AND \"agriculture\" AND \"UGV\"; \"digital twin\" AND \"agriculture\" AND \"UGV\"; \"software\" AND \"agriculture\" AND \"UGV\"; \"ROS\" AND \"agriculture\" AND \"UGV\"; \"operating systems\" AND \"agriculture\" AND \"UGV\"; \"simulation\" AND \"environment\" AND \"agriculture\" AND \"UGV\"; \"embedded\" AND \"system\" AND \"agriculture\" AND \"UGV\". After removing duplicates and outliers, further selection was conducted based on the following criteria: (i) only articles/reviews from scientific journals, book chapters, conference proceedings, electronic preprints available in open-access repositories such as arXiv, and technical web pages (including those from sensors/hardware manufacturers); (ii) English language only; (iii) open access only, with very few unavoidable exceptions; (iv) only resources from recent years (approximately the last decade; see Figure 1 right), with very few unavoidable exceptions. This resulted in a gross list of more than 1400 resources: the BibTeX version of the list is available upon request. Figure 1 displays some features of the dataset. Figure 1: On the left, the bibliographic dataset we collected, categorized by topic (DT stands for ‘digital twin’). The sum of works in each category exceeds the total entries, as resources may belong to multiple groups. On the right, the distribution of publications over time. The largest category, 'algorithms', includes methods for (i) rover navigation in unstructured environments like crops, (ii) AI-based image and video classifiers, and (iii) task-specific algorithms for data acquisition. The 'UGV' group gathers all resources concerning the use of robots in agriculture, while 'navigation' includes works on distinct functions like path planning, obstacle avoidance, Simultaneous Localization and Mapping, and plant row recognition. As the name suggests, the 'UAV' group collects all works focused on drones, which may seem unrelated to the object of this review. However, drones share several important technologies with UGVs, such as the use of LiDARs for acquiring 3D point clouds to define digital twins of crops, AI-based models for in-field information collection, and localization and mapping algorithms. As we will discuss later, we believe UAV-UGV collaboration is one of the most promising R&D lines in digital agriculture. The aforementioned categories, along with'reviews'--a group devoted to surveys on farming robotics--account for the majority of our bibliographic database, which aims to be a high-fidelity portrait of recent scientific production on the topic. Most of the bibliographic dataset concentrates on one or more of these subjects. Moving to a more agronomic focus, significant effort is being made to find robotic solutions tailored to the management of specific crops, typically fruit trees, vineyards, or vegetables. These include both monitoring and field operations (group 'field'). Often, the less populated categories provide the most challenging and stimulating perspectives for future farming, despite the limited literature available. One such category is the rover-drone collaboration ('UGV-UAV'), where the goal is to complement near-soil information with data acquired from above. UAVs can also support the rapid mapping of unexplored crops to facilitate route planning for UGV navigation. Advanced sensors like high-resolution 3D LiDARs and high-performing RGB-D and hyperspectral cameras are now available at affordable costs, opening up unprecedented possibilities for acquiring large, high-quality datasets ('sensors').