Advancements of Spraying Technology in Agriculture

Plant protection activities are most important practices during crop production. Application of maximum pesticide products with the sprayer. The application of fungicides, herbicides, and insecticides is one of the most recurrent and significant tasks in agriculture. Conventional agricultural spraying techniques have made the inconsistency between economic growth and environmental protection in agricultural production. Spraying techniques continuously developed in recent decades. For pesticide application, it is not the only sprayer that is essential, but all the parameters like the type and area of the plant canopy, area of a plant leaf, height of the crop, and volume of plants related to plant protection product applications are very important for obtaining better results. From this point of view, the advancement in agriculture sprayer has been started in last few decades. Robotics and automatic spraying technologies like variable rate sprayers, UAV sprayers, and electrostatic sprayers are growing to Increase the utilization rate of pesticides, reduce pesticide residues, real-time, cost-saving, high compatibility of plant protection products application. These technologies are under the “umbrella” of precision agriculture. The mechanized spraying system, usually implemented by highly precise equipment or mobile robots, which, makes possible the selective targeting of pesticide application on desire time and place. These advanced spraying technologies not only reduces the labour cost but also effective in environmental protection. Researchers are conducting experimental studies on the design, development and testing of precision spraying technologies for crops and orchards

Design and Performance of Inductive Electrostatic Sprayer

Abstract: In order to improve spraying results, an inductive electrostatic sprayer was designed. The performance of the sprayer was then tested. The test result shows that the charge-to-mass ratio can reach 0.951 mc/Kg when electrostatic voltage is 20 KV and working pressure is 0.25 to 0.4 MPa. The particle size distribution of charged droplets are more concentrated than that of uncharged droplets, the axial velocity of charged droplets is faster than that of uncharged droplets, and the velocity distribution uniformity is also improved. The average deposition rate under charging conditions is 14% higher than that in uncharged conditions. Moreover, the deposit rate of the back of the leaf is evident

Contact Electrification of Liquid Droplets Impacting Living Plant Leaves

Contact electrification has attracted interest as a mechanism for generating electrical charges on surfaces. To explore the factors contributing to electrification by droplets impacting the leaf surface, high-speed image capture and current measurements were used to quantitatively characterize the electrical response under different droplet parameters and leaf surface conditions. Upon impact and rebound from the leaf surface, neutral droplets acquire a positive charge. While this electrification phenomenon has been observed previously, there has been limited understanding of the parameters influencing the extent of droplet charging. In this study, we examine the effects of four parameters (droplet size, impact velocity, droplet ion concentration, and various leaf surfaces) on the electrical response signal. The results indicate that this electrification phenomenon is contingent upon the droplet–leaf contact area and droplet ion concentration. We propose a theoretical model based on the electric double layer to elucidate the electrification process

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

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

Effects of Leaf Surface Roughness and Contact Angle on In Vivo Measurement of Droplet Retention

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 (Ra), which are the most important surface properties of crop leaves, were used as independent variables. The Ra values of tomato, pepper, and winter wheat were 24.73 μm, 5.28 μm, and 17.59 μm, 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 Rmax = 0.169 g⋅cm−2, and the stable retention was Rst = 0.134 g⋅cm−2. The maximum droplet retention on the surface of winter wheat leaf was Rmax = 0.244 g⋅cm−2, and the stable retention was Rst = 0.093 g⋅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, Rmax, was in the range of 0.149~0.151 g⋅cm−2, and the stable retention was Rst = 0.077 g⋅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

A Data-Driven Dynamic Obstacle Avoidance Method for Liquid-Carrying Plant Protection UAVs

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

Effects of Crop Protection Unmanned Aerial System Flight Speed, Height on Effective Spraying Width, Droplet Deposition and Penetration Rate, and Control Effect Analysis on Wheat Aphids, Powdery Mildew, and Head Blight

As a new type of crop protection machinery, the Crop Protection Unmanned Aerial System (CPUAS) has developed rapidly and been widely used in China; currently, how to use the CPUAS scientifically has become a top priority. However, the relationships between the operating parameters of the CPUAS and the effective spraying width (ESW), droplet distribution characteristics, and control effects of insect pests and diseases are not clear yet. Therefore, three levels of flight speed (FS) as 3, 4, and 5 m/s, three levels of flight height (FH) as 1.5, 2.0, and 2.5 m, and spraying volume 2.0 L/min experiments were carried out to investigate the effects of FS and FH on the ESW, droplet deposition uniformity (DDU), and droplet penetration rate (DPR) by using an electric single-rotor CPUAS CE20. Based on the obtained results, combined with the insect pests and diseases occurrence agronomic laws, the optimal operation parameters of the CPUAS were selected to control the wheat aphids, powdery mildew, and head blight. The results showed that the ESW of CE20 was not consistent, the maximum value was 5.78 m, and the minimum one was 2.51 m. The FS had a highly significant impact on ESW (p = 0.0033 < 0.01), while the FH and the interaction between FS and FH had no significant impact on ESW. The coefficients of variation (CV) of the droplet deposition were between 23.3% and 34.4%, which meant good deposition uniformity. The FH (p = 0.0019) and the interaction between FS and FH (p = 0.02) had significant impacts on the DDU. The control effects on aphids were 78.71% (1 day), 84.88% (3 days), and 90.42% (7 days), the control effects on powdery mildew were 77.17% (7 days) and 82.83% (14 days), and the control effect on head blight was 88.32% (20 days). This study proved that by the optimization of parameters and the combination of agronomy, good control effects for insect pests and diseases could be achieved by the CPUAS. The research results would provide some technical supports for CPUAS application

Comparison of Water Sensitive Paper and Glass Strip Sampling Approaches to Access Spray Deposit by UAV Sprayers

Target and off-target spray depositions determine the spray’s effectiveness and impact on the environment. A decisive stage in the measurement of spray deposition and drift is selecting an appropriate sampling approach under field conditions. There are various approaches available for sampling spray deposition and drift, during the evaluation of ground sprayers used for the UAV sprayer assessment, under field conditions. In this study, two sampling approaches (water-sensitive paper, and glass strip collectors) were compared to analyze spray deposition in target and off-target zones. The results showed a variation in the estimation of the spray deposits among the two applied sampling methods. The results showed that the water-sensitive paper recorded the droplet deposition in the target zone with a range from 0.049 to 4.866 µLcm−2, whereas the glass strip recorded from 0.11 to 0.793 µLcm−2. The results also showed the water sensitive paper recorded an 80.3% higher deposition than that of the glass strip at zero position during the driving flight height 2 m and flight speed 2 ms−1 (T1 treatment). It can be concluded that variation in recorded depositing is due to the sampling material. It is recommended that the confident deposition results, measurement methods and sampling approaches must be standardized for UAV sprayers according to the field conditions and controlled within artificial assessments

Comparison of Water Sensitive Paper and Glass Strip Sampling Approaches to Access Spray Deposit by UAV Sprayers

Target and off-target spray depositions determine the spray’s effectiveness and impact on the environment. A decisive stage in the measurement of spray deposition and drift is selecting an appropriate sampling approach under field conditions. There are various approaches available for sampling spray deposition and drift, during the evaluation of ground sprayers used for the UAV sprayer assessment, under field conditions. In this study, two sampling approaches (water-sensitive paper, and glass strip collectors) were compared to analyze spray deposition in target and off-target zones. The results showed a variation in the estimation of the spray deposits among the two applied sampling methods. The results showed that the water-sensitive paper recorded the droplet deposition in the target zone with a range from 0.049 to 4.866 µLcm−2, whereas the glass strip recorded from 0.11 to 0.793 µLcm−2. The results also showed the water sensitive paper recorded an 80.3% higher deposition than that of the glass strip at zero position during the driving flight height 2 m and flight speed 2 ms−1 (T1 treatment). It can be concluded that variation in recorded depositing is due to the sampling material. It is recommended that the confident deposition results, measurement methods and sampling approaches must be standardized for UAV sprayers according to the field conditions and controlled within artificial assessments

The Improved A* Obstacle Avoidance Algorithm for the Plant Protection UAV with Millimeter Wave Radar and Monocular Camera Data Fusion

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