## 4 Challenges, Technical Limitations, and Analytical Comparison for Sprayer UAVs  
### Obstacle Detection and Collision Avoidance Challenges  
If positive and precise spraying is to be achieved, autonomous sprayer UAVs must be capable of taking a coherent decision as to which scenarios involve hazard, in order to avoid obstacles that lead to actions that are not only wrong but deleterious to other farming tools and farm workers. This section highlights the major open challenges of obstacle detection and collision avoidance algorithms.  
* A major obstacle detection challenge arises from severe weather conditions and environments with changing illumination conditions. Windy environments and fog can obscure the detection sensors or cameras, which render the processed data inadequate for obstacle avoidance. Even though this challenge can be resolved with the use of radar, which has no problems detecting in fog, rain, or heavy snow, a major drawback of the radar is its limited lateral vision. In other words, the radar only covers a relatively small angular section of about 15 degrees. To enhance lateral vision, multiple sensors have to be employed, which complicates the system. Thus, in order to measure an accurate reconstruction of the environment in such unfavorable conditions, robust algorithms have to be developed to effectively detect and classify the obstacle.
* Obstacle avoidance techniques, such as Neural Network and Fuzzy Logic, require the extraction of hierarchical abstractions from preprocessed data used throughout the training or learning stages, and the ability of generalization relies on the availability of a large dataset. As a drawback, the large computational cost is a major challenge and has to be highlighted.
* Realistically, most of the obstacle detection algorithms have to be trained offline through simulation analysis before they are integrated into the sprayer UAV system. The huge gap between real and virtually simulated environments limits the applicability of offline simulation policies to the real world. The development of a realistic virtual dataset is still an open challenge.
* Accuracy of temporal and spatial alignments among different sensors used in the sprayer UAV system also impacts the quality of the collected data.  
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