Orchard Sprayer Research Articles

Operator exposure and cabin protection in plant protection product application

AbstractTightly sealed driver's cabs can effectively protect the user from exposure when applying crop protection products. Therefore, under certain conditions, users in such cabins can avoid wearing personal protective equipment for skin and eyes. The protective effect depends on the technical design and handling of the different cabin types and can be divided into categories with different levels of protection. Since the level of protection of the different types of cabins and the associated possibility of not wearing personal protective equipment has not been sufficiently scientifically verified in the past, the present study was carried out to close this gap. Field and laboratory test methods were developed and used to assess exposure levels. External contamination of the cabin was measured using various dosimeters. Exposure inside the cabins was measured on coveralls and gloves to measure dermal exposure and with aerosol sampling pumps to determine inhalation exposure. Tests were conducted on tractors with different cab categories using an air-assisted orchard sprayer as a worst-case scenario for exposure to spray drift. Additionally, an alternative laboratory test method was developed and conducted to evaluate filter efficiency under controlled conditions and compare it with the field tests. The results showed that exposure inside closed cabins was generally low and was not notably correlated with the cabin category. All types of cabins tested were found to provide significant protection from dermal and inhalation exposure.

Research on Deposition Characteristics of a New Air-Assisted Electrostatic Sprayer

This study evaluated the properties of a new double air-assisted electrostatic orchard sprayer, focusing particularly on the impact of its electrostatic system on droplet deposition. The sprayer featured 10 nozzles, a centrifugal fan, and an axial fan, with an overall flow rate of 3.5 L/min and nozzle air velocities of 25 m/s (interior) and 12.5 m/s (exterior). Droplet sampling was conducted using paper cards and ponceau solution as a tracer. Field measurements in a Y-tree trained pear orchard assessed the effects of tractor speeds (0.34, 0.52, 0.84, and 1.05 m/s) and sampling positions (exposed and hidden faces). Tractor speed did not significantly affect overall droplet density, but the normalized droplet density on the exposed face increased with speed, while it decreased on the hidden face. The average normalized droplet density at all speeds was approximately 114.6 dot/cm2. The electrostatic system improved droplet deposition on the hidden face of leaves, particularly on the side of the tree near the sprayer. The coefficient of variation (CV) of the exposed face increased with tractor speed, while tractor speed had no impact on the CV of the hidden face. Overall, the electrostatic system reduced the CV. This research offers valuable insights into the use of electrostatic sprayers.

On-Target Deposition from Two Engine-Powered Sprayers in Medium-Foliage-Density Citrus Canopies

Spray penetration into citrus canopies is critical for adequate coverage and deposition to ensure effective pest control. However, mismatch of air assistance to target canopy characteristics can lead to unintended spraying losses through overpenetration. To evaluate the effect of air assistance on on-target deposition, two sprayers (surrogates for airflow rates) were used to apply a fluorescent tracer dye solution @ a target concentration of 300 ppm to 16 medium-foliage-density tree blocks in a commercial mandarin orchard. The complete factorial experiment in three replications, also designed for validating a model-based spray decision support tool, comprised two forward travel speeds (1.6 and 4.8 km/h), two disc-core nozzles (TeeJet® D3-25 and D6-45), and either one or two nozzle rows to obtain a wide range of application rates (496 to 9719 L/ha). Dye deposition significantly decreased with canopy depth (p =< 0.001) by nearly seven times across the 3.4 m wide canopies but was not significant over 1.2 to 2.2 m sampling height (p = 0.867). Deposition obtained with the low-airflow-rate sprayer was significantly greater (p =< 0.001) than that obtained with the high-airflow-rate sprayer over the dose range likely due to too much air pushing out spray droplets. This study underscores the importance of matching the air assistance of orchard sprayers to the target canopy.

Effects of different spray parameters of plant protection UAV on the deposition characteristics of droplets in apple trees

Plant protection UAVs have developed rapidly as a new type of orchard pesticide application equipment. This study was conducted to explore the influence of plant protection UAVs' operation parameters on the properties of droplet deposition. The DJI T30 plant protection UAV was used to affect the influences of spray volume (60 L/ha and 90 L/ha), spray height (2m and 2.5m), with or without an adjuvant and spray patterns (Regular pattern and Orchard pattern) on characteristics of droplet deposition. The test results showed that the interaction of multiple factors has no significant difference in most droplet deposition on various layers of fruit trees. The deposition characteristics of droplets were improved at a spray height of 2 m and a spray volume of 90 L/ha in the presence of adjuvants. In the orchard spray pattern, the penetration of droplet deposition is improved, but the impact on droplet density and coverage is different. Overall, the orchard spraying pattern can achieve better coverage, while the droplet density is the opposite. The spray performance is closely related to the spray volume, with or without an adjuvant, spray pattern, and spray height. The research results provide a theoretical basis for selecting appropriate operating parameters and spray patterns for plant protection UAVs in apple orchards.

Deep learning improved YOLOv8 algorithm: Real-time precise instance segmentation of crown region orchard canopies in natural environment

Autonomous detection and segmentation of tree canopies, trunks, and nontarget areas is crucial for optimizing pesticide use and minimizing off-target spray deposition in orchards for precision spraying. To improve the precision of pesticide application in orchards, a real-time orchard tree segmentation method using an improved YOLOv8 deep learning algorithm was proposed. First, to enhance precision, a dilated convolution approach was introduced into the backbone of the YOLO algorithm, and then, to better converge and regularize the model GELU activation function was added into the dilated convolution network. Finally, a total of 600 images of manually labeled orchard canopies were used, and the adaptive gradient algorithm (AdaGrad) was employed to adjust the weights and minimize the loss function. Additionally, the ReduceLROnPlateau scheduler was utilized to fine-tune the model during training. The performance of the proposed method was assessed through a comparative analysis. The results of the improved algorithm showed a mean average precision (mAP) of 93.3 % with an improvement of 1.3 %, precision (P) of 93.6 % with an improvement of 3.8 %, and inference time of 28 ms. To verify the effectiveness of the proposed method, the results were compared with those of YOLO family algorithms and Mask RCNN. An outdoor experiment was conducted to assess the algorithm’s performance to evaluate the deposition outcomes of the spray for variable-rate spraying, which proved to be reduced by 40 % compared to uncontrolled spraying. The deposit coverage was more than 20 % on trees of different sizes, which were positioned 1.7 m away from each side of the spraying robot. The obtained results confirm the satisfactory performance of the improved YOLOv8 algorithm and aim to offer valuable technical assistance for improving the efficiency and precision of an orchard spraying machine.

Field evaluation of spray quality and environmental risks of unmanned aerial spray systems in mango orchards

Unmanned Aerial Spraying Systems (UASS) have garnered substantial market attention as efficient tools for applying Plant Protection Products (PPPs). However, their application has primarily focused on low-growing crops such as rice and wheat. This study emphasizes the need for additional UASS field trial data, specifically addressing spray quality and environmental risks associated with 3D fruit trees. The research aims to quantitatively assess the impact of canopy structure characteristics of mango trees and droplet size on spray quality. Simultaneously, it explores the exposure risks of UASS spraying in orchards to applicators, bystanders, and machine residue, comparing these risks with traditional ground machinery. Research findings reveal substantial differences in the distribution of UASS among mango tree canopies, with variations linked to the size of the canopy. Contrary to expectations, as tree volume increases, droplet deposition does not exhibit exponential growth. More droplets are deposited in the upper part of the canopy rather than the bottom. Altering droplet size significantly influences the deposition and distribution of droplets within the mango tree canopy. Notably, the coverage effect of fine droplets at the bottom of the canopy is 2.5 times that of coarse droplets. The ground loss from ground spray guns was lower than that of UASS, but the exposure risk was higher. Specific body parts with elevated operator exposure risks are identified as the legs and chest. UASS operators are advised to remain vigilant regarding the risk of pesticide adhesion on the equipment rack.

Digital twins for mitigation of orchard spray drift

Digital twins for mitigation of orchard spray drift

GPU computing for simulation of explicit droplet movement in orchard spray practices

GPU computing for simulation of explicit droplet movement in orchard spray practices

Innovating an electrostatic charging unit with an insulated induction electrode for air-assisted orchard sprayers

Orchard pesticide applications using electrostatic spraying technology enhance droplet deposition and reduce environmental pollution. However, the incorporation of charging systems into conventional air-assisted sprayers remains limited. This is primarily due to challenges such as the necessity for high air velocity to prevent droplet adherence to electrodes, electrode-nozzle design flaws, and safety concerns related to the high voltage used for charging. To address these issues, this study developed five charging units that employ insulated induction electrodes, eliminating exposure to external elements. These units were incorporated into an air-assisted sprayer to assess their effectiveness in charging spray droplets and improving deposition. The units successfully limited electrical leakage, even under high-voltage conditions (up to 18 kV) and in the absence of airflow. At an electrode voltage of 14 kV, and an air velocity of 27 m s−1, the electrostatic units achieved an average charge-to-mass ratio of up to 0.67 mC kg−1. Activation of the charging units significantly enhanced droplet deposition with an increase of 90% on the front and 3250% on the back surfaces of the artificial targets (aluminum plates). On real plant leaves, the increase was 54% on the front and 112% on the back surfaces compared with uncharged standard spraying.

Navigation system for orchard spraying robot based on 3D LiDAR SLAM with NDT_ICP point cloud registration

Autonomous navigation of orchard spraying robots can effectively improve operational efficiency and reduce workload. The conventional robot navigation solutions based on the Global Navigation Satellite System (GNSS) are susceptible to signal loss in orchards due to interference from the canopy of fruit trees. In this paper, an autonomous navigation scheme for orchard spraying robots based on multi-sensor devices and the three-dimensional (3D) LiDAR Simultaneous Localization and Mapping (SLAM) technology. The initial step of the scheme involved the construction of a 3D point cloud map of the orchard using the 3D LiDAR SLAM algorithm. Subsequently, a designed point cloud map transformation algorithm was used to convert the 3D point cloud map of the orchard into a two-dimensional (2D) grid map containing key feature information of the orchard. To enhance the mapping accuracy of the orchard point cloud, a point cloud registration algorithm combining Iterative Closet Point (ICP) and Normal Distributions Transform (NDT) was proposed. This algorithm initially utilized the NDT algorithm for coarse point cloud registration to obtain transformation parameters. Then, the ICP algorithm was applied in conjunction with the obtained parameters for fine registration. Regarding obstacle avoidance, a cooperative obstacle avoidance algorithm based on the Robot Operating System (ROS) multithreaded communication mechanism was introduced. Experimental trials conducted in a standardized spindle-shaped peach orchard validated the successful completion of the SLAM mapping trajectory, which was achieved through the utilization of the proposed NDT_ICP point cloud registration algorithm. The average absolute pose error between the mapped trajectory and the reference trajectory was 1.173 m, with a standard deviation of 0.498 m. The robot's positioning and navigation errors increased with higher speeds, with the largest positioning deviation observed at 1.2 m/s speed, with a maximum lateral positioning error of 7.04 cm. The robot's average lateral navigation error did not exceed 16 cm, and the average heading deviation was less than 8° at different speeds of 0.4, 0.8 and 1.2 m/s. Both the positioning and navigation accuracies of the robot were sufficient to meet the autonomous operational requirements of orchard spraying robots.

Design of an Automatic Navigation and Operation System for a Crawler-Based Orchard Sprayer Using GNSS Positioning

In order to enhance the efficiency of agricultural machinery in orchard rows and minimize harm to personnel caused by pesticide spraying, this study developed a GNSS-based (Global Navigation Satellite System) automatic navigation driving system for tracked orchard sprayers. The tracked sprayer was used as a platform for this research. We constructed both a crawler hydraulic platform and spraying working parts based on orchard operation requirements. Additionally, we designed the hydraulic and electrical sub-control process of the crawler platform. By utilizing the motion model of the tracked mobile platform, we designed a linear path tracking control method using position deviation and heading deviation as state quantities. This allows the research platform to automatically initiate and terminate, travel in a straight line between rows, and complete spraying operations. Experimental verification confirmed that the tracked sprayer designed in this study successfully achieves automatic driving. The best automatic driving performance is achieved at a speed of 1.0 m/s. When the sprayer’s speed is 1.2 m/s, the maximum value of the straight-line path tracking accuracy of the platform’s automatic driving is better than 5.6 cm, with a standard deviation of 2.8 cm. This system effectively meets the requirements of automatic operation for an automatic spraying machine, thereby establishing a foundation for the implementation of automatic spraying operations in orchards.

Reducing environmental exposure to PPPs in super-high density olive orchards using UAV sprayers.

The increasing demand for optimizing the use of agricultural resources will require the adoption of cutting-edge technologies and precision farming management. Unmanned Aerial Vehicle (UAV) sprayers seem promising due to their potential to perform precision or spot spraying, particularly in woody crop environments where total surface spraying is unnecessary. However, incorporating this technology is limited by the lack of scientific knowledge about the environmental risks associated with UAV sprayers and the strict legal framework. Nonetheless, these spraying systems' characteristic downwash airflow and the limited swath width can potentially mitigate drift in hedgerow crops. During our study we performed comparative studies aimed to compare the airborne drift, soil, and crop depositions between a conventional orchard sprayer and a UAV sprayer in a commercial superhigh-density orchard in the South Iberian Peninsula in 2022. Our findings reveal that, in superhigh-density olive orchards, the UAV sprayer presents a substantial reduction in airborne drift, while soil depositions showed no significant differences compared to those of a conventional terrestrial orchard sprayer. Crop depositions were significantly lower when utilizing the UAV sprayer. These results suggest that introducing UAV spraying technology in Mediterranean agricultural systems, under specific scenarios, can effectively reduce the environmental impact of crop spraying and encourage the responsible use of plant protection products (PPPs).

Development of an Electric Variable Air Assist System for Apple Orchard Sprayers

Highlights Electric air assist system was developed with electric fans, pulse width modulation (PWM) controllers, a custom-designed air channel, and a battery. Adjusting PWM duty cycles from 0% to 100% achieved average exit air velocities from 0 to 11.3 m s-1. Spray applications with the system at 50% and 70% power achieved canopy pass through spray coverages from 4.1% to 19.9% and 14.2% to 24.2%, respectively. A multivariable regression model was developed to adjust air assist outputs based on tree canopy density and off-target loss. Abstract. Air assist for apple orchard spray applications is a necessity to deliver pesticides to target crops. However, conventional orchard sprayers use axial fans to provide air assists which are generally designed for tall crops. Thus, these systems have very limited capabilities to control airflows to match canopy densities. An electric air assist system (EAAS) was developed with an electric fans, pulse width modulation (PWM) controllers, a custom-design air channel, and a 400-Ah LiFePO4 battery to address such limitations. The system could ramp up its airflow to nearly 80% of its maximum in 2.5 s, while it took approximately 4 s to reach 100% airflow from no air assist. The EAAS provided airflow of 11.3 and 5.3 m s-1 at the fan outlet and 1 m away, respectively, with 100% duty cycles (DC) of PWM. It was capable of modulating its average air velocities from 0 to 11.3 ms-1 by changing DCs. Spray coverage samples of constant rate applications with different air assist levels were collected from the behind apple tree canopies throughout the 2023 growing season to characterize the potential for off-target spray drift. A multivariable regression model for controlling DCs was developed with the spray coverage data and leaf area index (LAI) to minimize the off-target drift. The EAAS was a promising approach to developing more advanced air assist spraying systems to enable the adjustment of both air and liquid flows based on canopy characteristics to maximize spray depositions on intended targets while minimizing spray drift. Keywords: Air assisted sprayer, Automation, Crop protection, Fan, Penetration, Pesticide, Pulse width modulation, Variable air assistance.

Potential Improvement of an Air-Conveyor Sprayer Performance Using a Spray Controller in Pear Orchard

Highlights An orchard sprayer was equipped with a spray controller to monitor and manage the working conditions. Foliar deposits and losses of the optimized and conventional sprayer were tested on two different sized pear trees. No significant differences in leaf deposits were found between canopy positions with the optimized sprayer. Ground losses with the optimized sprayer in the target rows were 45% lower compared to the conventional sprayer. Abstract. Currently, mistblowers are the most common sprayer type used for pesticide application in orchards. However, maximizing canopy deposition with high application efficiency remained a challenge due to the intricate canopy structure. This study compares the performance of two air-conveyor sprayers on pear trees: a conventional sprayer and an optimized sprayer. The optimized sprayer was equipped with a spray controller that displays its operational conditions (forward speed, spraying pressure, etc.) during application through a user interface of the device. In addition, the controller was capable of recommending spray volume for an orchard with the operator’s inputs based on the Tree Row Volume (TRV) method. Two experimental blocks in two pear orchards, with different tree sizes referred to as 'small trees' and 'large trees', were designated to evaluate the foliage deposition and off-target losses of the two sprayers. The conventional sprayer applied a spray volume of 1160 L ha-1 on both orchards following the farmers' conventional procedure. In contrast, the spray volumes of the optimized sprayer were determined by the controller based on the TRV model, resulting in a reduction of 29% and 55% for the large and small pear trees, respectively. The foliar deposition of each block was sampled from five randomly selected trees in a central row, with leaves collected from nine different canopy positions for small trees and twelve positions for large trees. Petri dishes were placed on the ground to assess the ground losses. Poles with nylon threads were mounted at various positions to evaluate airborne drift. The results showed that the mean normalized foliar depositions of the optimized sprayer were 1.57 and 1.81 µg cm-2 / kg-1 tracer ha-1 for large and small trees, respectively, whereas with the conventional sprayer, the depositions were 0.85 and 2.11 µg cm-2 / kg-1 tracer ha-1 for large and small trees, respectively. The optimized sprayer achieved a more uniform deposition distribution throughout the canopy compared to the conventional sprayer, as no significant differences in foliage deposition were detected between positions with the optimized sprayer. The optimized sprayer was capable of providing higher normalized deposits than the conventional sprayer for the large trees with less application volume. In addition, the optimized sprayer reduced spray losses in the air by 10% and 64% compared to the conventional sprayer for small and large trees, respectively, whereas ground losses were diminished by 45% and 50%, respectively. The results of this study suggest that controlling sprayer parameters, along with the spray volume adjustment based on canopy characteristics, may increase foliar deposition and could reduce pesticide use, although its influences on off-target loss reduction were insignificant. Keywords: Air conveyor, Constant-rate application, Digital control, Off-target losses, Pear fruit, Pesticide spraying, Tree row volume.

Design and test of Kinect-based variable spraying control system for orchards.

Target detection technology and variable-rate spraying technology are key technologies for achieving precise and efficient pesticide application. To address the issues of low efficiency and high working environment requirements in detecting tree information during variable spraying in orchards, this study has designed a variable spraying control system. The system employed a Kinect sensor to real-time detect the canopy volume of citrus trees and adjusted the duty cycle of solenoid valves by pulse width modulation to control the pesticide application. A canopy volume calculation method was proposed, and precision tests for volume detection were conducted, with a maximum relative error of 10.54% compared to manual measurements. A nozzle flow model was designed to determine the spray decision coefficient. When the duty cycle ranged from 30% to 90%, the correlation coefficient of the flow model exceeded 0.95, and the actual flow rate of the system was similar to the theoretical flow rate. Field experiments were conducted to evaluate the spraying effectiveness of the variable spraying control system based on the Kinect sensor. The experimental results indicated that the variable spraying control system demonstrated good consistency between the theoretical spray volume and the actual spray volume. In deposition tests, compared to constant-rate spraying, the droplets under the variable-rate mode based on canopy volume exhibited higher deposition density. Although the amount of droplet deposit and coverage slightly decreased, they still met the requirements for spraying operation quality. Additionally, the variable-rate spray mode achieved the goal of reducing pesticide use, with a maximum pesticide saving rate of 57.14%. This study demonstrates the feasibility of the Kinect sensor in guiding spraying operations and provides a reference for their application in plant protection operations.

A Simulation and Experiment on the Optimization Design of an Air Outlet Structure for an Air-Assisted Sprayer

In response to the issues of low-velocity zones and non-uniform jet velocity distribution in the airflow field of traditional air-assisted orchard sprayers, an arc-shaped air outlet suitable for axial-flow air-assisted systems is designed. This article employs the method of CFD numerical simulation and experimental verification to compare and analyze the internal flow field of the air-assisted system and validates the reliability of the numerical simulation results through calculation error and chi-square test. The wind speed of the cross-section is measured at different distances from the outlet, and the distribution characteristics of the outflow field wind speed before and after the structural optimization of the air-assisted system are compared. The horizontal distribution of fog droplets is collected using a fog collection chamber. The experimental results show that the design of the arc-shaped outlet increases the average wind speed of the annular outlet from 14.95 m/s to 18.20 m/s and reduces the proportion of low-speed area from 20.83% to 0.71%. When the rounded corner radius of the air outlet is 50 mm, optimal parameters are attained. The maximum error between the simulated and experimental values is 9.52%. At a significance level of 0.05, the χ2 value is 0.252, indicating that the simulated values follow the distribution of the actual measurement values. On the cross-sections located at distances of 0.5, 0.75, 1, 1.25, and 1.5 m from the air outlet, the wind speed distribution with no arc-shaped air outlets exhibits a “low left and high right” type, tending to shift towards the right as a whole. Fog droplets also display a drift tendency towards the right side. The wind speed distribution with arc-shaped air outlets shows a symmetric “high in the middle and low on the sides” type. Fog droplets concentrate in the central position. The optimized air-assisted system can reduce the air field’s low-flow area, increase the airflow distribution uniformity, improve the average wind speed at the outlet, and decrease fog droplet drift. This provides a reference for the structural design of air-assisted systems in current orchard sprayers of the same type.

Atomization Characteristics of a Hollow Cone Nozzle for Air-Assisted Variable-Rate Spraying

During variable-rate spraying in orchards, the atomization characteristics and distribution of droplets in and out of the target area can be affected by the sprayer pressure. In this study, a variable-rate spraying control system test bench was designed, and a hollow cone nozzle QY82.317.22 was selected. The droplet atomization characteristics, including volume median diameter (Dv0.5), the relative span of the droplet spectrum, and droplet velocity at different spray pressures, were studied at distances ranging from 0.4 to 2.4 m from the nozzle orifice with an air velocity of 10 m/s at the nozzle orifice position. The effects of longitudinal distance, transverse distance, and spray pressure on Dv0.5, relative span, and droplet velocity were analysed by multiple linear regression analysis, and the regression model was established. The experimental results show that at a longitudinal distance of 1.8 m, Dv0.5 ranges from 120 to 150 μm, meeting the requirements for optimal droplet size for controlling crawling pests and plant diseases on crop leaves; and the relative span is 1.2, indicating a wide droplet spectrum. At different pressure conditions, Dv0.5 decreases as pressure increases. Through multiple linear regression analysis, the longitudinal distance, the transverse distance, and the spray pressure have high significance for Dv0.5 and the droplet velocity. The longitudinal distance and the transverse distance have a highly significant effect on the relative span. In this study, the mathematical relational model of droplet characteristics at different spatial positions and different pressures was established, providing an agricultural reference for predicting the droplet characteristics at different spatial positions to achieve the best application effect. This model is conducive to the effective use of pesticides and reduces environmental pollution.

Research on decoupled air speed and air volume adjustment methods for air-assisted spraying in orchards.

Different fruit tree canopies have different requirements for air speed and air volume. Due to the strong relationship between air speed and air volume, the decoupled control of air speed and air volume cannot be achieved using the existing sprayers. In this study, an innovative air-assisted sprayer that supports the independent adjustment of fan speed (0-2940 r/min) and air outlet area (1022.05-2248.51 cm2) is developed, and the maximum air speed and air volume of the sprayer outlet are 45.98 m/s and 37239.94 m3/h, respectively. An independent adjustment test of the fan speed and air outlet area was carried out. The results indicated that the fan speed and air outlet area have opposing adjustment effects on air speed and air volume; decoupled control of the outlet air speed and air volume can thus be achieved through combined control of the fan speed and air outlet area. A test was carried out on combined fan speed and air outlet area control. Two decoupled air speed and air volume adjustment models were established, one with a constant air speed and variable air volume and the other with a constant air volume and variable air speed. The test results show that the air volume adjustment model with constant air speed had a maximum mean error of 1.13%, and the air speed adjustment model with constant air volume had a maximum mean error of 1.67%. The results will provide theoretical and methodological support for the development of airflow adjustment systems for orchard air-assisted sprayer.

Combine Effect of Blade Type of Blower and Speed of Operation on Droplet Size in Air Assisted Orchard Sprayer

Droplet size analysis of orchard sprayer having axial flow blower was carried out to determine the changes in droplet size at different blade types and speed of operation. Droplet size analysis was carried out at two different positions (Outer and Inner) of crop canopy and at three different heights (Top, Middle, Bottom) of each crop canopy. On outer canopy, at top position, at speed of operation 2.5 km/h, 3.0 km/h and 3.5 km/h the droplet size decreased by 6.5 %, 7.6 % and 10.3 % respectively when the blade was changes from blade type 1 to blade type 2. When the blade was changes from blade type 1 to blade type 2 in combination of speed of operation 2.5 km/h, 3 km/h and 3.5 km/h the droplet size decreased by 6.2 %, 7.3 % and 8.6 % and 5.4 %, 6.8 % and 9.0 % for middle and bottom position. On inner canopy, at top position, when the blade type was changes from blade type 1 to blade type 2 at speed of operation 2.5 km/h, 3.0 km/h, 3.5 km/h, then the droplet size decreased by 7.3 %, 9.2 % and 10.2 % respectively. Whereas at middle position the droplet size decreased by 7.1 %, 7.6 % and 9.0 % respectively. Similar trend was observed in bottom position and decrease in values of droplet size was 5.6 %, 7.1 % and 8.9 % observed respectively. Keywords: Droplet Size, orchard sprayer, axial flow blower, crop canopy.

Citrus Tree Canopy Segmentation of Orchard Spraying Robot Based on RGB-D Image and the Improved DeepLabv3+

The accurate and rapid acquisition of fruit tree canopy parameters is fundamental for achieving precision operations in orchard robotics, including accurate spraying and precise fertilization. In response to the issue of inaccurate citrus tree canopy segmentation in complex orchard backgrounds, this paper proposes an improved DeepLabv3+ model for fruit tree canopy segmentation, facilitating canopy parameter calculation. The model takes the RGB-D (Red, Green, Blue, Depth) image segmented canopy foreground as input, introducing Dilated Spatial Convolution in Atrous Spatial Pyramid Pooling to reduce computational load and integrating Convolutional Block Attention Module and Coordinate Attention for enhanced edge feature extraction. MobileNetV3-Small is utilized as the backbone network, making the model suitable for embedded platforms. A citrus tree canopy image dataset was collected from two orchards in distinct regions. Data from Orchard A was divided into training, validation, and test set A, while data from Orchard B was designated as test set B, collectively employed for model training and testing. The model achieves a detection speed of 32.69 FPS on Jetson Xavier NX, which is six times faster than the traditional DeepLabv3+. On test set A, the mIoU is 95.62%, and on test set B, the mIoU is 92.29%, showing a 1.12% improvement over the traditional DeepLabv3+. These results demonstrate the outstanding performance of the improved DeepLabv3+ model in segmenting fruit tree canopies under different conditions, thus enabling precise spraying by orchard spraying robots.