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Review

A State-of-the-Art Analysis of Obstacle Avoidance Methods from the Perspective of an Agricultural Sprayer UAV’s Operation Scenario

by 1,2, 1,2,*, 1,2,3, 4 and 1,2
1
School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
2
Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education, Jiangsu University, Zhenjiang 212013, China
3
Department of Agricultural Engineering, Bahauddin Zakariya University, Multan, Punjab 60800, Pakistan
4
School of Aerospace Engineering, University of Michigan, 1320 Beal Avenue, Ann Arbor, MI 48109, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Craig Morley
Agronomy 2021, 11(6), 1069; https://doi.org/10.3390/agronomy11061069
Received: 14 April 2021 / Revised: 11 May 2021 / Accepted: 17 May 2021 / Published: 26 May 2021
Over the last decade, Unmanned Aerial Vehicles (UAVs), also known as drones, have been broadly utilized in various agricultural fields, such as crop management, crop monitoring, seed sowing, and pesticide spraying. Nonetheless, autonomy is still a crucial limitation faced by the Internet of Things (IoT) UAV systems, especially when used as sprayer UAVs, where data needs to be captured and preprocessed for robust real-time obstacle detection and collision avoidance. Moreover, because of the objective and operational difference between general UAVs and sprayer UAVs, not every obstacle detection and collision avoidance method will be sufficient for sprayer UAVs. In this regard, this article seeks to review the most relevant developments on all correlated branches of the obstacle avoidance scenarios for agricultural sprayer UAVs, including a UAV sprayer’s structural details. Furthermore, the most relevant open challenges for current UAV sprayer solutions are enumerated, thus paving the way for future researchers to define a roadmap for devising new-generation, affordable autonomous sprayer UAV solutions. Agricultural UAV sprayers require data-intensive algorithms for the processing of the images acquired, and expertise in the field of autonomous flight is usually needed. The present study concludes that UAV sprayers are still facing obstacle detection challenges due to their dynamic operating and loading conditions. View Full-Text
Keywords: agricultural sprayer UAVs; Internet of Things; obstacles on farmland; operation pattern; obstacle detection; collision avoidance; path planning; spray coverage agricultural sprayer UAVs; Internet of Things; obstacles on farmland; operation pattern; obstacle detection; collision avoidance; path planning; spray coverage
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MDPI and ACS Style

Ahmed, S.; Qiu, B.; Ahmad, F.; Kong, C.-W.; Xin, H. A State-of-the-Art Analysis of Obstacle Avoidance Methods from the Perspective of an Agricultural Sprayer UAV’s Operation Scenario. Agronomy 2021, 11, 1069. https://doi.org/10.3390/agronomy11061069

AMA Style

Ahmed S, Qiu B, Ahmad F, Kong C-W, Xin H. A State-of-the-Art Analysis of Obstacle Avoidance Methods from the Perspective of an Agricultural Sprayer UAV’s Operation Scenario. Agronomy. 2021; 11(6):1069. https://doi.org/10.3390/agronomy11061069

Chicago/Turabian Style

Ahmed, Shibbir, Baijing Qiu, Fiaz Ahmad, Chun-Wei Kong, and Huang Xin. 2021. "A State-of-the-Art Analysis of Obstacle Avoidance Methods from the Perspective of an Agricultural Sprayer UAV’s Operation Scenario" Agronomy 11, no. 6: 1069. https://doi.org/10.3390/agronomy11061069

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