### General Background and Related Work  
According to [PERSON] et al. [37], obstacle avoidance and navigation problems can be separated into two main parts: global path planning and local collision avoidance. Global path planning creates a set of waypoints, from an initial starting point to a goal point, and maneuver around obstacles in a working space, whales local collision avoidance creates waypoint tasks, and uses local sensing as a local goal for obstacle avoidance [38]. These obstacle avoidance techniques employ sophisticated algorithms, including the graph technique [14], heuristic search technique [39], and potential fields technique [40]. For this review, autonomy-level techniques specific to agricultural UAVs are analyzed, in which the sprayer UAV should be able to perform both path planning and obstacle detection in order to avoid collisions during navigation. Early works on collision avoidance focused on \"static obstacles\", in which the autonomous system senses its environment to avoid stationary obstacles [41; 42] and difficult situations using path planning techniques [43] and various other algorithms, such as decision trees [44]. In view of this, static obstacles can further be categorized as either \"single static obstacle\" or \"multiple static obstacles\". For the \"single static obstacle\", the collision cone technique by [PERSON] and [PERSON] [45], velocity obstacle approach by [PERSON] and [PERSON] [46], radar-assisted collision guidance strategy (RACAGS) by [PERSON] and [PERSON] [47], and cross-entropy by [PERSON] et al. [48] are examples of the collision avoidance methods. On the other hand, the Mixed-Integer Linear Program (MILP) approach by [PERSON] and [PERSON] [49], visibility graph method by [PERSON] and [PERSON] [42], modified Grossberg Neural Network (GNN) by [PERSON], et al. [50], and ellipsoidal bounding box method by [PERSON] and [PERSON] [51] are some examples of \"multiple static obstacles\" avoidance techniques. In the case of obstacle avoidance methods for UAVs, numerous reviews and surveys have been conducted either for general UAVs or for specified fixed-wing or multirotor UAVs. [PERSON] et al. [52] presented a systematic review of sensors and vision-based methods for vehicle detection and tracking for collision avoidance systems in the context of on-road driving. [PERSON] [53] also presented an interesting survey, where the focus on space robotics and the major challenges of collision-free trajectory planning for manipulators mounted on large orbital structures, small satellites, and space robots that navigate in proximity to large orbital structures were presented. [PERSON] et al. [54] presented a review of the vision-based methods for positioning and mapping, including obstacle avoidance and path planning. Regarding the use of deep learning (DL) approaches for robotic solutions, [PERSON] and [PERSON] [55] presented a survey on the use of DL techniques for robotic perception, including robotic control and exploration, as well as robotic navigation and autonomous driving. However, there are only a few articles on the usage of agricultural sprayer UAVs. For instance, [PERSON] et al. [56] analyzed the effect of the number of communication messages between UAVs and the WSN (wireless sensor network). They concluded that the wastage of pesticides and fertilizers could be significantly minimized, if feedback information from the sensors is utilized to adjust the routes. [PERSON] and [PERSON] [57] also analyzed the effectiveness a roll-balanced helicopter, and how the balancing behavior of the designed helicopter can result in left-right balanced spray patterns. [PERSON] and [PERSON] [58] have also evaluated the economic and technical feasibility of the deployment of the RMAX helicopter (Yamaha Motor Co. Ltd, Iwata, Shizuoka, Japan) in the spraying of the commercial vineyards.