Abstract
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.
Highlights
The integration of Unmanned Aerial Vehicles (UAVs) with IoT (Internet of Things) devices, such as embedded sensors and communication elements, for agricultural operations is growing at a significantly faster pace than expected [1,2]. These IoT devices greatly enhance the capabilities of UAVs and enable UAVs to be used in a wide range of agricultural crop management operations, including field mapping [3,4], plant-stress detection [5,6], biomass estimation [7,8], weed management [9,10], inventory counting [11], etc
A major obstacle detection challenge arises from severe weather conditions and environments with changing illumination conditions
This article reviewed the current advances in obstacle detection and collision avoidance scenarios concerning sprayer UAVs
Summary
The integration of Unmanned Aerial Vehicles (UAVs) with IoT (Internet of Things) devices, such as embedded sensors and communication elements, for agricultural operations is growing at a significantly faster pace than expected [1,2]. Since the judgement of a manned pilot is often prone to a larger error margin, the fundamental objectives of the operation (which is precise spraying) may not be achieved, and the procedure may at the same time be hazardous To mitigate this limitation, autonomous sprayer UAVs have been widely employed.
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