## 5 Conclusions  
This article reviewed the current advances in obstacle detection and collision avoidance scenarios concerning sprayer UAVs. In doing so, the most relevant obstacle detection and collision avoidance techniques were reviewed and discussed, including their application with agricultural sprayer UAVs. In addition, the recent obstacle detection algorithms that are used in agricultural UAVs and the structural challenges of sprayer UAVs were described. After analyzing the main issues of autonomous sprayer UAVs on farmlands, a thorough survey of the recent articles on obstacle detection and collision avoidance techniques was presented. Besides, various constraints of agriculture sprayer UAVs were detailed together with the operational pattern. The detection sensors and control architectures for collision avoidance are thoroughly highlighted to pave the way for future researchers to design their own agriculture sprayer UAV systems. Specific to the physical structure of the sprayer UAV, the liquid load and sloshing, and the most widely used detection sensors were described. For autonomous navigation, the obstacle detection method is an important part. A major obstacle detection challenge arises from severe weather conditions and environments with changing illumination conditions. As agricultural sprayer UAVs require data-intensive algorithms for the processing of the images acquired, expertise in the field of autonomous flight is usually needed. The present study provides a comprehensive review analysis of the obstacle detection methods under UAV spraying conditions and concluded that UAV sprayers are still facing obstacle detection challenges due to their dynamic operating and loading conditions. Moreover, and most importantly, the relevant obstacle detection and collision avoidance algorithms were also presented, wherein, comparative analysis of the obstacles on farmland, including obstacle detection technologies, were tabulated. The Bug2 algorithm is suitable for rotorcraft UAVs. It can ensure the coverage of spraying because of following the exact border of the obstacle. Modifying the algorithm by fixing the heading direction may reduce the time duration. The other algorithms also have their merits under various working conditions. Furthermore, the most relevant open challenges concerning agricultural sprayer UAVs were highlighted. Among these challenges is \"spray deposition and coverage\", and these parameters are directly related to the drone weight and payload. Another open challenge that has to do with the high cost of the intelligent sensors and that of the UAV system was highlighted. The short UAV flight times and the high cost of operation, which are major concerns to farmers, were also disclosed. Finally, the gap between real and virtually simulated environments, which limits the applicability of offline simulation policies to the real world, was stated. In all, this review work defines a clear roadmap for future research. Conceptualization, S.A. and B.Q. ; methodology, S.A.; B.Q. and F.A. ; validation, S.A. and B.Q. ; formal analysis, S.A. F.A. ; investigation, S.A., C.-W.K. and H.X. Resources, S.A. and B.Q. ; data curation, S.A.; C.-W.K., H.X. ; writing--original draft preparation, S.A. F.A. C.-W.K. and B.Q. ; writing--review and editing, S.A. and B.Q. ; supervision, B.Q. ; project administra  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Avoidance Technique** & & **Features** & **Sprayer UAV’s Insights** \\ \hline \multirow{4}{*}{Vector Field Histogram} &  
tion, B.Q. funding acquisition, B.Q. All authors have read and agreed to the published version of the manuscript. The authors are grateful for the support of the National Key Research and Development Plan (No2017 YFD0701005). Data are available in the manuscript. The authors acknowledge the support of the School of Agricultural Engineering, Jiangsu University, China.