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.

Autonomous sprayer UAVs are one of the most used aerial machines in modern agriculture. During flight missions, some common narrow obstacles appear in the flying zone. These are non-detectable from satellite images and one of the biggest challenges for autonomous sprayer UAVs in farmland. This work introduces an obstacle avoidance architecture specifically for sprayer UAVs. This architecture has generality in the spraying UAV problem, and it reduces the reliance on the global mapping of farmland. This approach computes the avoiding path based on the onboard sensor fusion system in real-time. Moreover, it autonomously determines the transition of several maneuver states using the current spraying liquid data and the UAV dynamics data obtained by offline system identification. This approach accurately tracks the avoidance path for the nonlinear time-variant spraying UAV systems. To verify the performance of the approach, we performed multiple simulations with different spraying missions, and the method demonstrated a high spraying coverage of more than 98% while successfully avoiding all vertical obstacles. We also demonstrated the adaptability of our control architecture; the safe distance between the UAV and obstacles can be changed by specifying the value of a high-level parameter on the controller. The proposed method adds value to precision agriculture, reduces mission time, and maximizes the spraying area coverage.

Research shows that the accurate acquisition of flight parameters of the plant protection UAV and accurate evaluation of flight parameter quality have great significance for improving the effect and precision of spraying. In order to further improve the accuracy of the flight parameter quality evaluation of the plant protection UAV, this study conducted an evaluation and experiment of the flight parameter quality of the plant protection UAV using a laser tracker. The experimental results showed that the current plant protection UAV used the average altitude and speed of the onboard sensors to determine whether it reached the preset flight operation parameters, but this interpretation method could not accurately reflect the actual flight situation. Laser trackers could obtain more accurate flight parameters, especially instantaneous flight parameters. Compared with the laser tracker, the flight trajectory, altitude, and speed of the UAV reflected by onboard sensors were erroneous and tended to be smooth and stable. This method can obtain more accurate flight parameters, improve the accuracy of the flight parameter quality evaluation of the plant protection UAV, and provide data support and a reference for the precision spraying and performance improvement of the plant protection UAV.

Droplet retention during pesticide application is a serious problem because run-off droplets flow out of the target area and pose a hazard to human health and the environment. The present study was conducted with the aim to measure the droplet retention of sprayed droplets on crop leaves in vivo using a constructed test system. In the measurement, three crop species with different surface properties (tomato, chili pepper, and winter wheat) were selected for droplet retention determination, and the variations in the time intervals of maximum retention and stable retention were determined. Contact angle and surface roughness ($R_a$), which are the most important surface properties of crop leaves, were used as independent variables. The $R_a$ values of tomato, pepper, and winter wheat were 24.73 $\mathsf{\mu }\mathrm{m}$, 5.28 $\mathsf{\mu }\mathrm{m}$, and 17.59 $\mathsf{\mu }\mathrm{m},$ respectively, while the contact angles of tomato, pepper, and winter wheat were 97.67°, 70.07° and 131.98°, respectively. The results showed that the curves of droplet retention on sprayed tomato and wheat leaves had similar patterns over time and could be divided into four periods (rapidly increasing period, slowly increasing period, collapsing period, and stable period). The maximum droplet retention on tomato leaf surface was $R_{max}$ = 0.169 $\mathrm{g}\cdot {\mathrm{cm}}^{-2}$, and the stable retention was $R_{st}$ = 0.134 $\mathrm{g}\cdot {\mathrm{cm}}^{-2}$. The maximum droplet retention on the surface of winter wheat leaf was $R_{max}$ = 0.244 $\mathrm{g}\cdot {\mathrm{cm}}^{-2}$, and the stable retention was $R_{st}$ = 0.093 $\mathrm{g}\cdot {\mathrm{cm}}^{-2}$. However, droplet retention on pepper leaves was different from that on tomato and wheat leaves. The curve pattern of droplet retention on pepper leaves over time showed two peaks and two valleys. Moreover, the maximum retention, $R_{max}$, was in the range of 0.149~0.151 $\mathrm{g}\cdot {\mathrm{cm}}^{-2}$, and the stable retention was $R_{st}$ = 0.077 $\mathrm{g}\cdot {\mathrm{cm}}^{-2}$. It is expected that the obtained results can be used to characterize the properties of crop leaves and that this study can contribute to the improvement of droplet retention for effective chemical application and the reduction in the environmental pollution caused by agricultural pesticides.

To enhance obstacle avoidance abilities of the plant protection UAV in unstructured farmland, this article improved the traditional A* algorithms through dynamic heuristic functions, search point optimization, and inflection point optimization based on millimeter wave radar and monocular camera data fusion. Obstacle information extraction experiments were carried out. The performance between the improved algorithm and traditional algorithm was compared. Additionally, obstacle avoidance experiments were also carried out. The results show that the maximum error in distance measurement of data fusion method was 8.2%. Additionally, the maximum error in obstacle width and height measurement were 27.3% and 18.5%, respectively. The improved algorithm is more useful in path planning, significantly reduces data processing time, search grid, and turning points. The algorithm at most increases path length by 2.0%, at least reduces data processing time by 68.4%, search grid by 74.9%, and turning points by 20.7%. The maximum trajectory offset error was proportional to the flight speed, with a maximum trajectory offset of 1.4 m. The distance between the UAV and obstacle was inversely proportional to flight speed, with a minimum distance of 1.6 m. This method can provide a new idea for obstacle avoidance of the plant protection UAV.

The performance of sprayer UAVs largely depends on accurate trajectory control while spraying. A large amount of a liquid payload may create a sloshing effect inside the liquid tank, which may occur largely during hazardous phenomena, such as wind gusts and obstacle avoidance. This all-way sloshing force inside the tank may disturb the UAV’s trajectory by, for example, a displacement from the planned path or collision with an obstacle. A large number of existing sprayer UAVs already carry various-shaped tanks. A UAV’s liquid-sloshing problem must be reduced for existing and future plant protection. Applying suitable methods can achieve these goals and provide better performance. Moreover, various tank models have different structures and capabilities, which must be fixed using a flexible solution. This article proposes a simple baffle solution for all forms of pesticide tanks and compares baffle systems’ impacts using primary shaped tanks. Indoor lab experiments showed the extreme impacts inside the tanks. Outdoor UAV mission experiments provided the practical effectiveness of the tank structures, and primary shaped tank comparison results provided guidance for future UAV pesticide-tank manufacturing. A new baffle ball design is presented for a universal solution. A one-axis linear slider was used for optical observations, an open-source flight controller was used for on-field compliance, and plenty of tests were done to prove the concept and show the efficiency. The flat hexagonal tank and baffle ball system showed better results in both indoor and outdoor experiments.