A method and system for fixed-point spraying of fruit trees, a terminal and a storage medium

Abstract

The application provides a fruit tree fixed-point spraying method and system, a terminal and a storage medium, relates to the technical field of fruit tree fixed-point spraying, and comprises the following steps: obtaining a starting signal of a UAV; controlling a preset UAV spraying device to work according to preset survey operation parameters and a preset flight route; obtaining a UAV working image and a current environmental wind speed; analyzing the UAV working image to determine a leaf density; analyzing the leaf density, the UAV working image, the survey operation parameters and the current environmental wind speed to determine actual operation parameters; and adjusting the working state of the UAV spraying device according to the actual operation parameters. The application has the effect of guaranteeing the improvement of the utilization rate of fruit tree fixed-point spraying liquid.

Description

Technical Field

[0001] This application relates to the technical field of spot spraying of fruit trees, and in particular to a method, system, terminal and storage medium for spot spraying of fruit trees. Background Technology

[0002] The fruit tree spot spraying method refers to a plant protection method that uses drone spraying devices to apply pesticides to fruit trees, with the aim of improving the utilization rate of the sprayed pesticide solution.

[0003] In related technologies, the fixed-point spraying method for fruit trees usually involves surveying the height of the fruit trees and the terrain, setting the flight path of the drone spraying device, and then the operator starts the drone spraying device to spray the liquid directly onto the target area. After spraying is completed, the drone spraying device moves to the next target area to repeat the operation, thereby completing the liquid spraying process for the fruit trees.

[0004] Regarding the aforementioned technologies, the pesticide solution is sprayed directly onto the target area. After spraying, the drone spraying device moves to the next target area to repeat the operation. However, if the canopy of the fruit tree has internal obstruction or the leaves have a high degree of overlap, the pesticide solution often only adheres to the outer layer of the canopy and is difficult to penetrate the dense branches and leaves to reach the inside of the canopy, resulting in a decrease in the utilization rate of the sprayed pesticide solution. There is still room for improvement. Summary of the Invention

[0005] To ensure the effectiveness of improving the utilization rate of pesticide solution for targeted spraying of fruit trees, this application provides a method, system, terminal, and storage medium for targeted spraying of fruit trees.

[0006] Firstly, this application provides a method for targeted spraying of fruit trees, employing the following technical solution: A method for targeted spraying of fruit trees includes: Obtain the drone start signal; The system controls the pre-set drone spraying device to operate according to the pre-set survey operation parameters and the pre-set flight path. Acquire images of the drone operation and the current ambient wind speed; Analyze drone operation images to determine leaf density; Analyze the leaf density, UAV operation images, survey operation parameters, and current ambient wind speed to determine the actual operation parameters; Adjust the operating status of the drone spraying device according to the actual operating parameters.

[0007] Optionally, the step of analyzing drone operation images to determine foliage density includes: Iterate through the pixels in the drone operation image to extract the green channel value; Determine whether the green channel value is greater than the preset green intensity threshold; If the value is greater than the preset value, the preset counter is controlled to perform a preset increment operation to determine the area of ​​the leaf pixel. If it is not greater than, then control the preset counter to perform a preset equal counting operation to determine the leaf pixel area; Calculate the quotient of the leaf pixel area and the total area of ​​the preset target region to generate leaf density.

[0008] Optionally, the steps to analyze blade density, UAV operation images, survey operation parameters, and current ambient wind speed to determine actual operation parameters include: Determine whether the leaf density is less than the preset pulse airflow start-up density; If it is less than, then the survey operation parameters are defined as the actual operation parameters; If the values ​​are not less than the specified values, the survey operation parameters, UAV operation images, and current ambient wind speed will be analyzed to determine the actual operation parameters.

[0009] Optionally, the steps of analyzing survey operation parameters, UAV operation images, and current ambient wind speed to determine actual operation parameters include: The drone operation images are input into a preset image processing algorithm for analysis to determine the average blade size; The current ambient wind speed and average blade size are analyzed to determine the final airflow intensity; The average blade size was analyzed to determine the airflow pulse frequency; Obtain the drone's mass and the number of rotor motors; The mass of the drone, the number of rotor motors, and the final airflow intensity were analyzed to determine the motor duty cycle; Associate survey operation parameters, final airflow intensity, airflow pulse frequency, and motor duty cycle to generate actual operation parameters.

[0010] Optionally, the steps of analyzing the current ambient wind speed and average blade size to determine the final airflow intensity include: Calculate the product of the current ambient wind speed and the preset wind speed compensation factor to generate the first adjustment factor; Calculate the product of the average blade size and the preset blade airflow influence factor to generate a second adjustment factor; The sum of the first adjustment factor, the second adjustment factor, and the preset base adjustment factor is calculated to generate the airflow intensity adjustment factor; Calculate the product of the airflow intensity adjustment factor and the preset reference airflow intensity to generate the final airflow intensity.

[0011] Optionally, the step of analyzing the average blade size to determine the airflow pulse frequency includes: Calculate the product of the average blade size and the preset frequency influence coefficient to generate the frequency adjustment factor; Calculate the sum of the frequency adjustment factor and the preset frequency base influence factor to generate the pulse frequency adjustment factor; Calculate the quotient of the preset reference pulse frequency and the pulse frequency adjustment factor to generate the airflow pulse frequency.

[0012] Optionally, the steps of analyzing the drone's mass, the number of rotor motors, and the final airflow intensity to determine the motor duty cycle include: Calculate the product of the drone's mass and the preset gravitational acceleration to generate the gravitational thrust value; Calculate the quotient of gravity thrust and the number of rotor motors to generate single rotor thrust; The airflow reverse thrust is determined based on the relationship between the final airflow intensity and the preset reverse thrust. Calculate the quotient of airflow reverse thrust and the number of rotor motors to generate single-rotor airflow thrust; Calculate the sum of the single rotor thrust and the single rotor airflow thrust to generate the total reverse thrust of the single rotor; The total reverse thrust of the single rotor is input into a preset thrust duty cycle relationship function for solution to determine the motor duty cycle.

[0013] Secondly, this application provides a fruit tree spot spraying system, which adopts the following technical solution: A fruit tree spot spraying system, comprising: The acquisition module is used to acquire the drone's start-up signal, drone operation images, and current ambient wind speed; A memory for storing a program for a fruit tree spot spraying method as described in any of the above items; The processor and the program in the memory can be loaded and executed by the processor to implement a fruit tree spot spraying method as described in any of the above.

[0014] Thirdly, this application provides a smart terminal, which adopts the following technical solution: A smart terminal includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed as described in any of the preceding claims, a method for targeted spraying of fruit trees.

[0015] Fourthly, this application provides a computer storage medium capable of storing corresponding programs, which facilitates the implementation of measures to improve the utilization rate of pesticide solution for targeted spraying of fruit trees, and adopts the following technical solution: A computer-readable storage medium storing a computer program that can be loaded by a processor and executed any of the above-described fruit tree spot spraying methods.

[0016] In summary, this application includes at least one of the following beneficial technical effects: 1. By controlling the drone spraying device to operate according to the survey operation parameters and flight path, the leaf density is determined after analyzing the drone operation images. The actual operation parameters are determined after analyzing the leaf density, drone operation images, survey operation parameters and current environmental wind speed. The operation status of the drone spraying device is then adjusted according to the actual operation parameters. Pulsed airflow is used to assist according to the leaf density, so that the leaves in the target area vibrate to create gaps. This effectively avoids the situation where the liquid only adheres to the outer layer of the canopy due to high leaf overlap, thus ensuring the effect of improving the utilization rate of the liquid for targeted spraying of fruit trees. 2. After traversing the pixels in the UAV operation image, the green channel value is extracted. When the green channel value is greater than the green intensity threshold, the counter is incremented to determine the leaf pixel area. If it is not greater, the counter is incremented to determine the leaf pixel area. The leaf density is then calculated by dividing the leaf pixel area by the total area of ​​the target area. Based on the green channel value in the UAV operation image, the leaf density is determined. Then, based on the leaf density, it is determined whether to activate pulse airflow assistance to ensure the effectiveness of improving the utilization rate of the spray solution for the fruit trees. 3. By inputting the drone operation images into the image processing algorithm for analysis, the average blade size is determined. Then, based on the analysis of the current environmental wind speed and the average blade size, the final airflow intensity is determined. The airflow pulse frequency is determined based on the average blade size analysis. Finally, the motor duty cycle is determined based on the analysis of the drone mass, the number of rotor motors, and the final airflow intensity. By correlating the survey operation parameters, the final airflow intensity, the airflow pulse frequency, and the motor duty cycle, actual operation parameters are generated. Based on these actual operation parameters, the operation status of the drone spraying device is adjusted. By using pulsed airflow to assist in causing the blades in the target area to flutter and create gaps, the situation where the pesticide only adheres to the outer layer of the tree canopy due to high blade overlap is effectively avoided. Simultaneously, the rotor thrust is adjusted to effectively counteract the reaction force generated by the pulsed airflow, thereby ensuring improved utilization of the pesticide solution for targeted spraying of fruit trees. Attached Figure Description

[0017] Figure 1 This is a flowchart of a method for spot spraying fruit trees according to an embodiment of this application.

[0018] Figure 2 This is a flowchart of the steps in this application embodiment to analyze drone operation images to determine leaf density.

[0019] Figure 3This is a flowchart illustrating the steps in this application embodiment to analyze leaf density, UAV operation images, survey operation parameters, and current ambient wind speed to determine actual operation parameters.

[0020] Figure 4 This is a flowchart illustrating the steps in this application embodiment to analyze survey operation parameters, UAV operation images, and current environmental wind speed to determine actual operation parameters.

[0021] Figure 5 This is a flowchart of the steps in this application embodiment to analyze the current ambient wind speed and average blade size to determine the final airflow intensity.

[0022] Figure 6 This is a flowchart of the steps in this application embodiment to analyze the average blade size to determine the airflow pulse frequency.

[0023] Figure 7 This is a flowchart of the steps in this application embodiment to analyze the mass of the UAV, the number of rotor motors, and the final airflow intensity to determine the motor duty cycle. Detailed Implementation

[0024] To make the purpose, technical solution, and advantages of this application clearer, the following description is provided in conjunction with the appendix. Figures 1 to 7 The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.

[0025] This application discloses a method for targeted spraying of fruit trees, specifically a drone spraying device and a processing terminal. The processing terminal is communicatively connected to the drone spraying device to achieve data interaction and control. After receiving the drone start signal, the processing terminal controls the drone spraying device to operate according to the survey operation parameters and flight path. The leaf density is determined by analyzing the drone operation image. The actual operation parameters are determined by analyzing the leaf density, drone operation image, survey operation parameters, and current environmental wind speed. The operation status of the drone spraying device is adjusted according to the actual operation parameters. Pulsed airflow is used to assist based on the leaf density, causing the leaves in the target area to vibrate and create gaps. This effectively avoids the situation where the pesticide only adheres to the outer layer of the canopy due to high leaf overlap, thus ensuring the utilization rate of the pesticide solution for targeted spraying of fruit trees is improved.

[0026] Reference Figure 1 This application discloses a method for targeted spraying of fruit trees, comprising the following steps: Step S100: Obtain the drone start signal.

[0027] Among them, the drone start signal refers to the signal that starts the drone spraying device. It is triggered by the operator to activate the drone spraying device, thereby sending the level signal representing the drone start signal to the processing terminal, which provides the start conditions for the subsequent control of the drone spraying device to carry out operations according to the survey operation parameters and flight path.

[0028] Step S101: Control the preset drone spraying device to operate according to the preset survey operation parameters and preset flight path.

[0029] After receiving the drone start signal, the processing terminal responds to the drone start signal and controls the drone spraying device to operate according to the survey operation parameters and flight path, so as to obtain drone operation images and current environmental wind speed in the future.

[0030] A drone spraying device is a spraying system used for targeted spraying of fruit trees. It mainly consists of a fuselage, rotor motors, propellers, a camera, a pulse airflow generator, a spraying module, and a real-time dynamic differential module. The fuselage provides the framework for the entire drone spraying device, while the rotor motors and propellers provide the core power for flight. A camera for capturing images of the drone's operation and an anemometer for measuring the current ambient wind speed are mounted under the fuselage. The pulse airflow generator also operates under the fuselage, emitting pulsed airflow towards the target tree canopy. This causes overlapping or obstructed leaves to vibrate or flip, creating gaps. This, combined with the spraying module, allows the pesticide to be sprayed through the gaps in the pulsed airflow, thereby improving the utilization rate of the pesticide during targeted spraying of fruit trees. The spraying module consists of a pesticide tank, a pump, and nozzles, which are connected in series to complete the storage and spraying of the pesticide. The real-time dynamic differential module receives signals from the Global Navigation Satellite System and combines them with differential data from ground reference stations to accurately locate the drone spraying device, thereby effectively preventing the drone spraying device from drifting.

[0031] Survey and operation parameters refer to the operational parameters of the drone spraying device determined after surveying the fruit tree growing environment before the drone spraying device begins operation. These parameters include the drone's flight speed, altitude, spray flow rate, spray distance, and spray area, and are obtained by the operator after conducting a preliminary survey of the fruit tree growing environment. The operator conducts an on-site survey of the fruit tree growing environment to obtain the tree's growth density, growing area, and average height. Based on the growing area and preset operation time, the operator determines the drone's flight speed and spray area; based on the average height, the drone's flight altitude; and based on the tree's growth density, the spray flow rate and spray distance. The operator then controls the drone spraying device to operate according to these survey and operation parameters, facilitating the subsequent acquisition of drone operation images and current ambient wind speed.

[0032] A flight path refers to the route taken by a drone spraying device during operation. The flight path includes multiple waypoints. The drone spraying device completes the operation along the entire flight path by moving in a straight line between these waypoints. This path is determined by operators after surveying the fruit tree's growing environment in advance. Operators conduct on-site surveys of the fruit tree's growing environment to determine the tree's growing area and spraying distance. Based on this, they determine several waypoints. With the drone's spraying distance, these waypoints can completely cover the fruit tree's growing area, allowing the drone spraying device to move in a straight line between these waypoints to complete the operation along the entire flight path.

[0033] Step S102: Acquire images of the drone operation and the current ambient wind speed.

[0034] Among them, drone operation images refer to images of the fruit tree canopy area captured by cameras mounted on the drone spraying device during operation. By acquiring drone operation images, the green channel values ​​in the pixels of the drone operation images can be determined, and then the leaf pixel area can be determined based on the green channel values, providing data support for subsequent determination of leaf density.

[0035] The current ambient wind speed refers to the wind speed at which the drone spraying device operates, detected by the anemometer onboard the drone spraying equipment. By determining the current ambient wind speed, we can determine its impact on the pulse airflow intensity, thereby providing data support for subsequently determining the actual operational parameters.

[0036] Step S103: Analyze the drone operation images to determine the leaf density.

[0037] Among them, leaf density refers to the value that measures the density of tree canopy foliage during drone spraying operations. It is obtained by processing terminal analyzing drone operation images. The specific method is described in [reference needed]. Figure 2 The steps are as follows. The greater the leaf density, the more abundant the canopy leaves, and the greater the probability that the canopy leaves will be blocked by the upper leaves, thus providing data support for determining the actual operation parameters.

[0038] Step S104: Analyze the blade density, UAV operation images, survey operation parameters, and current ambient wind speed to determine the actual operation parameters.

[0039] The actual operating parameters refer to the parameters that the UAV spraying device adjusts in real time during operation, including operating parameters under normal conditions and pulsed airflow assisted conditions. These parameters include surveying parameters, final airflow intensity, airflow pulse frequency, and motor duty cycle. These parameters are obtained by the processing terminal through analysis of blade density, UAV operating images, surveying parameters, and current ambient wind speed. Specific methods are detailed in [reference needed]. Figure 3The steps are as follows: By determining the actual operating parameters, the operating status of the drone spraying device is adjusted accordingly. By using pulsed airflow to assist in causing the leaves in the target area to vibrate and create gaps, the situation where the pesticide only adheres to the outer layer of the canopy due to high leaf overlap is effectively avoided, thereby ensuring the improved utilization rate of the pesticide solution for targeted spraying of fruit trees.

[0040] Step S105: Adjust the operating status of the drone spraying device according to the actual operating parameters.

[0041] After determining the actual operating parameters, the processing terminal adjusts the operating status of the drone spraying device according to the actual operating parameters. By using pulsed airflow to assist the operation, the liquid is sprayed during the intervals between pulsed airflows. At the same time, the leaves in the target area vibrate to create gaps, which effectively avoids the situation where the liquid only adheres to the outer layer of the canopy due to high leaf overlap, thereby ensuring the effect of improving the utilization rate of the liquid for targeted spraying of fruit trees.

[0042] In this step, the pesticide is sprayed during the intervals between pulsed airflows. The total duration of a single spraying cycle is the reciprocal of the airflow pulse frequency. A complete cycle of an airflow pulse consists of jetting, delaying, and spraying. Since blade fluttering and gap formation require a certain amount of time, spraying immediately after jetting would affect the atomization effect and spraying direction. Therefore, a certain delay time is needed. Sufficient time is also required for pesticide spraying to ensure the required dosage and coverage. Thus, the three steps of jetting, delaying, and spraying all require a certain amount of time to complete. For example, the proportions of jetting, delaying, and spraying relative to the total duration of a single spraying cycle are 33%, 33%, and 34%, respectively. This allows the treatment terminal to control the drone spraying device to operate according to the sequence and timing of jetting, delaying, and spraying.

[0043] Reference Figure 2 The steps for analyzing drone operation images to determine foliage density include: Step S200: Traverse the pixels in the drone operation image to extract the green channel value.

[0044] The green channel value refers to the brightness intensity of the green light component of each pixel in the UAV operation image, which is obtained by the processing terminal after traversing all pixels in the UAV operation image. By determining the green channel value, the brightness intensity of the green light of each pixel in the UAV operation image is determined, thereby determining whether the pixel is a blade region, and thus providing data support for subsequent determination of the blade pixel area.

[0045] Step S201: Determine whether the green channel value is greater than the preset green intensity threshold.

[0046] The green intensity threshold is a critical value used to measure the green brightness intensity of a pixel in a leaf region versus a non-leaf region; it is preset by the operator. By determining whether the green channel value is greater than the green intensity threshold, it is determined whether a pixel is a leaf region. A higher green channel value indicates that the pixel contains more green elements, which helps in determining the leaf pixel area.

[0047] In step S2011, if the value is greater than the preset value, the preset counter is controlled to perform a preset increment operation to determine the area of ​​the leaf pixel.

[0048] If the green channel value is greater than the green intensity threshold, it means that the brightness intensity of the green light of the current pixel exceeds the green intensity threshold of the leaf. At this time, the processing terminal controls the counter to perform an increment operation to generate the leaf pixel area, thereby determining the area of ​​the leaf region in the UAV operation image. The leaf pixel area is strongly correlated with the leaf density. When the leaf pixel area is larger, it means that there are more leaves in the UAV operation image, and the leaf density is higher, thus providing data support for the subsequent determination of leaf density.

[0049] A counter is a program used to count events where the green channel value exceeds a green intensity threshold. It starts at 0 and is stored by the operator in the processing terminal. By determining the counter, the number of times the green channel value exceeds the green intensity threshold is determined, and thus the leaf pixel area is determined.

[0050] The increment operation refers to adding one to the existing count result, which is then stored by the operator in the processing terminal. By determining the increment operation, one is added to the existing count result when the green channel value is greater than the green intensity threshold, in order to determine the leaf pixel area.

[0051] The blade pixel area refers to the area covered by blades in a UAV operation image, obtained by incrementing a counter controlled by the processing terminal. The blade pixel area is strongly correlated with blade density; a larger blade pixel area indicates more blades in the UAV operation image, resulting in higher blade density and providing data support for subsequent determination of blade density.

[0052] In step S2012, if the value is not greater than the preset value, the preset counter is controlled to perform a preset equal counting operation to determine the area of ​​the blade pixel.

[0053] If the green channel value is not greater than the green intensity threshold, it means that the green channel value of the current pixel does not reach the green brightness intensity of the leaf. At this time, the pixel is a non-leaf area. Therefore, the processing terminal controls the counter to perform an equal counting operation to generate the leaf pixel area. Thus, when the green channel value is not greater than the green intensity threshold, no increment operation is performed. In this way, the pixels are filtered to obtain the area of ​​the leaf area, so as to determine the leaf density in the future.

[0054] The counter used in this step is the same as the counter used in step S2011.

[0055] The equal-counting operation refers to adding 0 to the existing count result, which is then stored by the operator in the processing terminal. By determining the equal-counting operation, no increment is performed when the green channel value is not greater than the green intensity threshold. This allows for the filtering of pixels to obtain the area of ​​the leaf region, facilitating the subsequent determination of leaf density.

[0056] The leaf pixel area in this step is the same as the leaf pixel area in step S2011. The difference is that the leaf pixel area in this step is obtained by the processing terminal controlling the counter to perform equal counting operations.

[0057] Step S202: Calculate the quotient of the leaf pixel area and the preset total area of ​​the target region to generate leaf density.

[0058] After determining the leaf pixel area, the processing terminal calculates the quotient of the leaf pixel area and the total area of ​​the target region to generate the leaf density. With the total area of ​​the target region remaining constant, a larger leaf pixel area indicates more leaves in the UAV operation image, resulting in higher leaf density, which facilitates the subsequent determination of actual operation parameters.

[0059] The total area of ​​the target region refers to the area of ​​the target tree canopy in the image that the camera can capture, which is preset by the operator. By determining the total area of ​​the target region, the proportion of the leaf area in the total area of ​​the target region can be determined based on the leaf pixel area, thus providing data support for determining the leaf density.

[0060] Reference Figure 3 The steps to determine the actual operational parameters by analyzing leaf density, UAV operation images, survey operation parameters, and current ambient wind speed include: Step S300: Determine whether the blade density is less than the preset pulse airflow start-up density.

[0061] The pulse airflow activation density refers to the critical value used to determine whether the canopy requires airflow-assisted penetration. This value is preset by the operator. For fruit trees with thick, layered leaves, the pulse airflow activation density is set lower, for example, 0.4, meaning that pulse airflow assistance is activated when the canopy has a certain degree of density. For fruit trees with sparse leaves, the pulse airflow activation density is set higher, for example, 0.7, meaning that pulse airflow assistance is activated only when the canopy has a high degree of density. By determining whether the leaf density is lower than the pulse airflow activation density, it is determined whether pulse airflow assistance is needed, thereby ensuring the effectiveness of improving the utilization rate of the targeted spraying solution on the fruit trees.

[0062] Step S3001: If it is less than, then define the survey operation parameters as the actual operation parameters.

[0063] If the leaf density is less than the pulse airflow activation density, it means that the leaf density in the current work area has not reached the condition for activating pulse airflow assistance. At this time, the canopy leaves are relatively sparse. The sprayed liquid can effectively penetrate the upper leaves and reach the lower layer under the conventional spraying mode. Therefore, the treatment terminal directly defines the survey operation parameters as the actual operation parameters, thereby ensuring the effect of improving the utilization rate of the fixed-point spraying liquid on fruit trees.

[0064] The surveying parameters in this step are the same as those in step S101.

[0065] Step S3002: If it is not less than, then analyze the survey operation parameters, UAV operation images and current environmental wind speed to determine the actual operation parameters.

[0066] If the leaf density is not less than the pulse airflow activation density, it indicates that the leaf density in the current work area has reached the condition for activating pulse airflow assistance. At this time, the overlap of the tree canopy leaves is relatively high. If pulse airflow assistance is not used, the sprayed pesticide will only adhere to the outer leaves of the canopy, thus reducing the utilization rate of the sprayed pesticide. Therefore, the processing terminal determines the actual operation parameters after analyzing the survey operation parameters, UAV operation images, and current environmental wind speed. The specific method is as follows: Figure 4 The steps involve determining the actual operating parameters and adjusting the operating status of the drone spraying device accordingly. By using pulsed airflow to assist in creating gaps by causing leaf vibration in the target area, the utilization rate of the sprayed pesticide on the fruit trees is improved.

[0067] The surveying parameters in this step are the same as those in step S101.

[0068] The actual operation parameters in this step are the same as those in step S3001. The difference is that the actual operation parameters in this step are obtained by the processing terminal after analyzing the survey operation parameters, UAV operation images and the current environmental wind speed.

[0069] Reference Figure 4 The steps for analyzing survey operation parameters, UAV operation images, and current environmental wind speed to determine actual operation parameters include: Step S400: Input the UAV operation image into a preset image processing algorithm for analysis to determine the average blade size.

[0070] The average blade size refers to the average size of the blades in the drone's operational image, obtained by the processing terminal after inputting the drone's operational image into the image processing algorithm for analysis. The average blade size is strongly correlated with the final airflow intensity and airflow pulse frequency. A larger average blade size indicates that the size of the leaves in the tree canopy in the drone's spraying area is larger. In this case, a stronger airflow and a longer reaction time are required to make the blades flutter and create gaps, thus providing data support for subsequent determination of the final airflow intensity and airflow pulse frequency.

[0071] Image processing algorithms, specifically those used to calculate the average leaf size, are stored by the operator on the processing terminal. The algorithm first converts the RGB image of the UAV operation to the HSV color space. Then, within the HSV color space, it uses color thresholding to separate the green leaves from the background, generating a binary image. For tree canopy images under typical natural lighting conditions, a preferred threshold range for green leaves is hue between 35 and 80, saturation between 40 and 255, and brightness between 40 and 255. Morphological operations such as opening and closing operations are used to clean up noise and fill holes. Then, connected component analysis and contour detection are used to identify and extract individual leaf objects. The minimum bounding rectangle geometric features of each leaf object are calculated, and combined with pixel and physical dimensions, a transformation is performed to calculate the average value of all leaf feature dimensions, thus obtaining the average leaf size.

[0072] This step, which combines pixel and physical size conversion, first obtains the vertical distance from the camera to the target tree canopy using the real-time dynamic differential module mounted on the drone spraying device. Obtain the physical side length of a single pixel in the image sensor of the camera. Obtain the camera lens focal length The minimum bounding rectangle of the leaf object has a pixel length of 1. Based on the geometric similarity of pinhole imaging, the physical actual size / object distance = image size on the sensor / focal length, where the vertical distance... This refers to the object distance, which is the physical side length of a single pixel. That is, the pixel size, and the pixel length. This is the length of the blade at the pixel level, therefore This represents the imaging size on the sensor. Therefore, by deriving the formula based on the geometric similarity principle of pinhole imaging, the actual physical size corresponding to each pixel in the UAV operation image is: This allows the physical length on the sensor to be converted into the number of pixels using the known pixel size, thereby establishing a linear mapping between the pixel size and the actual physical size.

[0073] Step S401: Analyze the current ambient wind speed and average blade size to determine the final airflow intensity.

[0074] The final airflow intensity refers to the actual air pressure output by the pulse airflow generator mounted on the drone spraying device. This pressure is obtained by the processing terminal after analyzing the current ambient wind speed and average blade size. For specific methods, please refer to [reference needed]. Figure 5 The steps are as follows. The average size of the blades and the current wind speed will affect the effect of the pulse airflow on the blade fluttering. When the current wind speed is higher, the lateral drift of the droplets during the spraying process is more severe, and a stronger airflow is required to maintain the penetration trajectory of the droplets. When the average size of the blades is larger, the blade inertia is greater, and the power required to make the blades flutter is greater, so as to facilitate the subsequent determination of actual operating parameters.

[0075] Step S402: Analyze the average blade size to determine the airflow pulse frequency.

[0076] The airflow pulse frequency refers to the number of airflow pulses emitted by the pulse airflow generator per unit time, which is obtained by analyzing the average blade size at the processing terminal. The specific method is described in [reference needed]. Figure 6 The steps are as follows: By determining the airflow pulse frequency, when the average blade size is large, the airflow pulse frequency of the pulse airflow generator is reduced to allow sufficient flutter time for the blades. When the average blade size is small, the blade response speed is faster, so the processing terminal increases the airflow pulse frequency of the pulse airflow generator to facilitate the subsequent determination of actual operating parameters.

[0077] Step S403: Obtain the mass of the drone and the number of rotor motors.

[0078] The drone mass refers to the mass of the drone spraying device, which is determined by the operator from the product specifications provided by the drone spraying device manufacturer. The drone mass is strongly correlated with the gravitational thrust value; the greater the drone mass, the greater the gravitational influence on the spraying device, resulting in a larger gravitational thrust value, thus providing data support for subsequent determination of the gravitational thrust value.

[0079] The number of rotor motors refers to the number of rotor motors in the drone spraying device, which is determined by the operator from the product specifications provided by the drone spraying device manufacturer. The number of rotor motors is strongly related to the force exerted on each individual rotor motor. The more rotor motors there are, the smaller the gravitational force and the reaction force of the pulsed airflow on each individual rotor, thus providing data support for determining the total reverse thrust of a single rotor.

[0080] Step S404: Analyze the mass of the UAV, the number of rotor motors, and the final airflow intensity to determine the motor duty cycle.

[0081] The motor duty cycle refers to the percentage of the pulse width of the PWM signal controlling the rotational speed of the drone's spraying device's rotor motor. It is obtained by the processing terminal after analyzing the drone's mass, the number of rotor motors, and the final airflow intensity. For specific methods, please refer to [reference needed]. Figure 7 The steps are as follows. When the drone spraying device emits a downward pulse of airflow, the airflow will generate an upward reaction force on the drone spraying device. If the reaction force is not interfered with, it will cause the drone spraying device to become unstable. Therefore, by determining the motor duty cycle, the thrust of the rotor is adjusted synchronously at the moment of pulse airflow emission, thereby actively counteracting the reaction force and maintaining the flight attitude of the drone spraying device.

[0082] In this step, adjusting the rotor thrust is equivalent to reducing the rotor lift. When the pulse airflow is launched downwards and generates an upward reaction force, in order to maintain the original flight attitude of the drone spraying device, the drone spraying device needs to reduce the downward thrust generated by the rotor. At this time, the upward lift generated by the rotor will decrease, thereby actively counteracting the reaction force.

[0083] Step S405: Associate the survey operation parameters, final airflow intensity, airflow pulse frequency, and motor duty cycle to generate actual operation parameters.

[0084] After determining the motor duty cycle, the system processes the terminal-related survey operation parameters, final airflow intensity, airflow pulse frequency, and motor duty cycle to generate actual operation parameters. Based on these parameters, the operating status of the drone spraying device is adjusted. By using pulsed airflow to assist in creating gaps by causing leaf flutter in the target area, the system effectively avoids the situation where the pesticide only adheres to the outer layer of the canopy due to high leaf overlap. At the same time, the rotor thrust is adjusted to effectively counteract the reaction force generated by the pulsed airflow, thereby ensuring the improved utilization rate of the pesticide solution for targeted spraying of fruit trees.

[0085] Reference Figure 5 The steps to determine the final airflow intensity by analyzing the current ambient wind speed and average blade size include: Step S500: Calculate the product of the current ambient wind speed and the preset wind speed compensation factor to generate the first adjustment factor.

[0086] The first adjustment factor measures the impact of the current ambient wind speed on the intensity of the pulse airflow. It is obtained by calculating the product of the current ambient wind speed and the wind speed compensation factor from the processing terminal. By determining the first adjustment factor, the influence of the current ambient wind speed on the airflow intensity is quantified. The higher the current ambient wind speed, the more severe the lateral drift of the droplets sprayed by the UAV spraying device during the descent. Therefore, a stronger pulse airflow is needed to maintain the penetration trajectory of the droplets, thus providing data support for the subsequent determination of the airflow intensity adjustment factor.

[0087] The wind speed compensation factor is an adjustment coefficient for the intensity of the pulsed airflow to the wind speed of the working environment, obtained by the operator through prior calibration experiments. First, a medium-sized blade is selected as the test sample and fixed to a simulated tree branch. Then, the output airflow intensity of the pulsed airflow generator is set to the baseline airflow intensity. Different environmental wind speeds are simulated using a fan. At each set environmental wind speed, the output intensity of the pulsed airflow generator is gradually increased until the blades achieve the desired vibration effect. The wind speed compensation factor is obtained by curve fitting using the least squares method on the actual output airflow intensity at different environmental wind speeds.

[0088] The desired shaking effect in this step refers to the shaking effect that causes the overlapping blades to shake, creating gaps between the blades, so that the droplets sprayed by the drone spraying device can be sprayed onto the lower layer of blades.

[0089] In this step, the reference airflow intensity refers to the baseline value of the airflow intensity that the pulse airflow generator can stably output without the influence of wind speed and blade size. This value is set in advance by the operator. By determining the reference airflow intensity, the initial output of the pulse airflow generator is set during the calibration experiment, which facilitates the subsequent determination of the airflow intensity adjustment factor.

[0090] Step S501: Calculate the product of the average blade size and the preset blade airflow influence factor to generate the second adjustment factor.

[0091] The second adjustment factor measures the influence of average leaf size on pulsed airflow intensity. It is obtained by calculating the product of average leaf size and leaf airflow influence factor at the processing terminal. By determining the second adjustment factor, the influence of average leaf size on airflow intensity is quantified. A larger average leaf size indicates larger leaf size in the tree canopy, resulting in greater leaf inertia and thus greater power required for leaf flutter. This provides data support for subsequently determining the airflow intensity adjustment factor.

[0092] The blade airflow influence factor refers to the adjustment coefficient of pulsed airflow intensity on the flutter effect of different blade sizes, which is obtained by the operator through prior calibration experiments. First, real leaf samples of different sizes are prepared and fixed onto simulated tree branches. Then, the output airflow intensity of the pulse generator is set to the baseline airflow intensity, and the smallest blade is tested. The airflow intensity at which the blade just reaches the desired flutter effect is recorded. Then, larger blades are tested one by one. By using the least squares method to perform curve fitting on different blade sizes and the required airflow intensity for each blade size, the blade airflow influence factor is obtained.

[0093] The desired jitter effect in this step is the same as the desired jitter effect in step S500.

[0094] The reference airflow intensity in this step is the same as the reference airflow intensity in step S500.

[0095] Step S502: Calculate the sum of the first adjustment factor, the second adjustment factor, and the preset basic adjustment factor to generate the airflow intensity adjustment factor.

[0096] The airflow intensity adjustment factor measures the overall impact of leaf size and current ambient wind speed on the pulse airflow intensity. It is calculated by the processing terminal as the sum of the first adjustment factor, the second adjustment factor, and the base adjustment factor. By determining the airflow intensity adjustment factor, the pulse airflow intensity can be adjusted according to leaf size and ambient wind speed, effectively preventing the pesticide from adhering only to the outer layer of the tree canopy due to high leaf overlap, thus ensuring improved utilization of the pesticide solution during targeted spraying of fruit trees.

[0097] The baseline adjustment factor is a value that ensures the final airflow intensity is equal to the baseline airflow intensity without the influence of blade size and ambient wind speed. In this step, it is set to 1 and is pre-set by the operator. By determining the baseline adjustment factor, a value that ensures the final airflow intensity is equal to the baseline airflow intensity without the influence of blade size and ambient wind speed is guaranteed, facilitating the subsequent determination of the final airflow intensity.

[0098] Step S503: Calculate the product of the airflow intensity adjustment factor and the preset reference airflow intensity to generate the final airflow intensity.

[0099] In this process, after determining the airflow intensity adjustment factor, the processing terminal calculates the product of the airflow intensity adjustment factor and the reference airflow intensity to generate the final airflow intensity. This allows the reference airflow intensity to be adjusted according to the leaf size and the working environment, thereby enabling the leaves to vibrate effectively under the final airflow intensity. This effectively avoids the situation where the pesticide only adheres to the outer layer of the tree canopy due to high leaf overlap, thus ensuring the utilization rate of the pesticide solution for targeted spraying of fruit trees is improved.

[0100] The reference airflow intensity in this step is the same as the reference airflow intensity in step S500.

[0101] Reference Figure 6 The steps for analyzing the average blade size to determine the airflow pulse frequency include: Step S600: Calculate the product of the average blade size and the preset frequency influence coefficient to generate the frequency adjustment factor.

[0102] The frequency adjustment factor is a value that measures the influence of blade size on the pulse airflow frequency. It is obtained by calculating the product of the average blade size and the frequency influence coefficient at the processing terminal. When the blade size is larger, the blade inertia is greater, and the motion response speed under airflow impact is slower, thus requiring a longer airflow time and resulting in a smaller airflow pulse frequency, which facilitates the subsequent determination of the pulse frequency adjustment factor.

[0103] The frequency influence coefficient is a value used to quantify the sensitivity of blade size to pulse frequency, obtained by operators through prior calibration experiments. First, real leaf samples of different sizes are prepared and fixed onto simulated tree branches. Then, the output airflow intensity of the pulse airflow generator is set to the baseline airflow intensity, and the pulse frequency is set to the baseline pulse frequency. The airflow pulse frequency is gradually adjusted, and the blade response is observed from a fixed angle, recording the pulse frequency at which the blade can be effectively fluttered to form an effective gap. The experiment is then repeated multiple times with different blade sizes, recording the pulse frequency at which the blade can be effectively fluttered to form an effective gap. Finally, the least squares method is used to perform curve fitting on different blade sizes and corresponding pulse frequencies to obtain the frequency influence coefficient.

[0104] In this step, the reference pulse frequency refers to the fundamental operating frequency of the pulse airflow generator without considering the influence of blade size, which is set in advance by the operator. By determining the reference pulse frequency, the initial output frequency of the pulse airflow generator can be set in the calibration experiment, which facilitates the subsequent determination of the pulse frequency adjustment factor.

[0105] Step S601: Calculate the sum of the frequency adjustment factor and the preset frequency basis influence factor to generate the pulse frequency adjustment factor.

[0106] The pulse frequency adjustment factor is a value that measures the magnitude of pulse frequency adjustment considering blade size. It is obtained by the processing terminal calculating the sum of the frequency adjustment factor and the frequency basis influence factor. By determining the pulse frequency adjustment factor, when the blade size is large, the pulse frequency adjustment factor is increased, thereby reducing the airflow pulse frequency, increasing the airflow impact time, and thus giving larger blades sufficient response time.

[0107] The frequency baseline influence factor refers to the benchmark adjustment coefficient when the frequency adjustment factor is zero. In this step, it is set to 1 and is predetermined by the operator. By determining the frequency baseline influence factor, the airflow pulse frequency is made equal to the benchmark pulse frequency when the frequency adjustment factor is zero, which facilitates the subsequent determination of the airflow pulse frequency.

[0108] Step S602: Calculate the quotient of the preset reference pulse frequency and the pulse frequency adjustment factor to generate the airflow pulse frequency.

[0109] The pulse frequency adjustment factor is determined by the processing terminal after calculating the quotient of the reference pulse frequency and the pulse frequency adjustment factor. Under the condition that the reference pulse frequency remains unchanged, the larger the blade size, the greater the blade inertia, the larger the pulse frequency adjustment factor, and thus the smaller the airflow pulse frequency. This, in turn, prolongs the airflow impact time when the blade size is large, to ensure that the blade has sufficient response time.

[0110] The reference pulse frequency in this step is the same as the reference pulse frequency in step S600.

[0111] Reference Figure 7 The steps for determining the motor duty cycle by analyzing the drone's mass, number of rotor motors, and final airflow intensity include: Step S700: Calculate the product of the drone's mass and the preset gravitational acceleration to generate the gravitational thrust value.

[0112] The gravity thrust value refers to the magnitude of the gravity that the drone spraying device overcomes to maintain flight. It is obtained by the processing terminal calculating the product of the drone's mass and gravitational acceleration. Under the condition of constant gravitational acceleration, the greater the mass of the drone, the greater the gravity that the drone spraying device overcomes, and therefore the greater the gravity thrust value, which is helpful for subsequently determining the single-rotor thrust.

[0113] Gravitational acceleration refers to the acceleration of an object when it falls freely under the influence of Earth's gravity. In this step, the standard value of gravitational acceleration is used. The data is stored by the operator on the processing terminal. By determining the acceleration due to gravity, the magnitude of the gravitational force that the UAV must overcome to maintain flight is determined, thus providing data support for determining the gravitational thrust value.

[0114] Step S701: Calculate the quotient of the gravity thrust value and the number of rotor motors to generate single rotor thrust.

[0115] The single-rotor thrust refers to the force of gravity overcome by a single rotor motor of the drone's spraying device. It is calculated by the operator by dividing the gravitational thrust value by the number of rotor motors. With a constant number of rotor motors, a larger gravitational thrust value indicates a greater force of gravity overcome by the drone's spraying device. Therefore, a single rotor motor overcomes a greater force of gravity, resulting in a larger single-rotor thrust, which in turn provides data support for determining the total reverse thrust of the single rotor.

[0116] Step S702: Determine the airflow reverse thrust based on the relationship between the final airflow intensity and the preset reverse thrust.

[0117] The reverse thrust correspondence refers to the relationship between the final airflow intensity and the reverse thrust. Operators create a mapping table by mapping the final airflow intensity to the reverse thrust. This correspondence is obtained through pre-calibration by the operators. The drone spraying device is fixed to a test bench equipped with force sensors. The pulse airflow generator on the drone spraying device emits pulse airflows of varying intensities. For each different pulse airflow intensity, the force sensor measures and records the upward thrust generated by the pulse airflow in real time. The correspondence between airflow intensity and reverse thrust is obtained by fitting these airflow intensities and their corresponding thrusts.

[0118] Airflow reverse thrust refers to the upward thrust generated by the pulse generator's airflow intensity on the UAV spraying device. It is obtained by the processing terminal based on a mapping table showing the correspondence between the final airflow intensity and reverse thrust. By determining the airflow reverse thrust, the reaction force on the UAV spraying device at the instant of the pulse airflow impact can be determined, which facilitates the subsequent determination of the single-rotor airflow thrust.

[0119] Step S703: Calculate the quotient of airflow reverse thrust and the number of rotor motors to generate single-rotor airflow thrust.

[0120] The single-rotor airflow thrust refers to the reverse thrust experienced by a single rotor motor at the instant of the pulsed airflow impact. It is obtained by calculating the airflow reverse thrust and the number of rotor motors from the processing terminal. Under the condition that the number of rotor motors remains unchanged, the greater the airflow reverse thrust, the greater the reaction force on the entire UAV spraying device. Therefore, the greater the reaction force experienced by a single rotor motor, thus providing data support for subsequently determining the total reverse thrust of a single rotor.

[0121] Step S704: Calculate the sum of the single rotor thrust and the single rotor airflow thrust to generate the total single rotor reverse thrust.

[0122] The total reverse thrust of a single rotor refers to the sum of the gravity and the reaction force of the pulsed airflow borne by a single rotor motor. It is obtained by the processing terminal after calculating the sum of the single rotor thrust and the single rotor airflow thrust. By determining the total reverse thrust of a single rotor, a downward force is actively generated based on the reverse thrust experienced by the rotor motor to effectively counteract the upward reaction force of the airflow, thereby ensuring the attitude stability of the UAV spraying device at the moment of pulsed airflow launch.

[0123] Step S705: Input the total reverse thrust of the single rotor into the preset thrust duty cycle relationship function for solution to determine the motor duty cycle.

[0124] In this process, after determining the total reverse thrust of a single rotor, the processing terminal inputs the total reverse thrust of the single rotor into the thrust duty cycle relationship function and solves it to obtain the result. In this way, a downward force is actively generated based on the reverse thrust of the rotor motor to effectively counteract the upward reaction force generated by the pulse airflow, thereby ensuring the attitude stability of the UAV spraying device at the moment of pulse airflow launch.

[0125] The thrust duty cycle function describes the relationship between the reaction force acting on the rotor motor and the duty cycle. It is obtained through controlled experiments conducted by the operator beforehand. By fixing the drone spraying device to a test bench equipped with a force sensor, and then controlling the rotor motor of the spraying device with PWM signals of different duty cycles via a PWM signal generator, the force sensor measures and records the downward thrust generated by the rotor motor in real time for each input PWM value. This experiment is repeated multiple times, collecting multiple sets of data including thrust and duty cycle. Then, the least squares method is used to perform curve fitting on these data to obtain the thrust duty cycle function. The resulting functional relationship can be expressed as: ,in This represents the total reverse thrust of a single rotor. Indicates duty cycle, , and The coefficients are obtained through curve fitting during calibration experiments. The specific values ​​are obtained by operators through controlled experiments on the drone spraying device. Since maintaining the drone spraying device's attitude stability is crucial in this step, the downward thrust generated by the rotor motor must equal the total reverse thrust of a single rotor. This effectively counteracts the reaction force generated instantaneously by the pulsed airflow. Therefore, in this step, the motor duty cycle is obtained by substituting the total reverse thrust of the single rotor into the thrust duty cycle function.

[0126] Based on the same inventive concept, embodiments of this application provide a fruit tree spot spraying system, including: The acquisition module is used to acquire the drone's start-up signal, drone operation images, current ambient wind speed, drone mass, and number of rotor motors. A memory used to store a program for a method of spot spraying fruit trees; The processor can load and execute programs in memory to implement a method for targeted spraying of fruit trees.

[0127] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0128] This application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed as a method for spot spraying of fruit trees.

[0129] Computer storage media include, for example, USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media that can store program code.

[0130] Based on the same inventive concept, this application provides a smart terminal, including a memory and a processor, wherein the memory stores a computer program that can be loaded and executed by the processor to perform a method for spot spraying of fruit trees.

[0131] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0132] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is only one example of a series of equivalent or similar features.

Claims

1. A method for targeted spraying of fruit trees, characterized in that, include: Obtain the drone start signal; The system controls the pre-set drone spraying device to operate according to the pre-set survey operation parameters and the pre-set flight path. Acquire images of the drone operation and the current ambient wind speed; Analyze drone operation images to determine leaf density; Analyze the leaf density, UAV operation images, survey operation parameters, and current ambient wind speed to determine the actual operation parameters; Adjust the operating status of the drone spraying device according to the actual operating parameters; The steps for analyzing drone operation images to determine foliage density include: Iterate through the pixels in the drone operation image to extract the green channel value; Determine whether the green channel value is greater than the preset green intensity threshold; If the value is greater than the preset value, the preset counter is controlled to perform a preset increment operation to determine the area of ​​the leaf pixel. If it is not greater than, then control the preset counter to perform a preset equal counting operation to determine the leaf pixel area; Calculate the quotient of the leaf pixel area and the total area of ​​the preset target region to generate leaf density; The steps to determine the actual operational parameters by analyzing leaf density, UAV operation images, survey operation parameters, and current ambient wind speed include: Determine whether the leaf density is less than the preset pulse airflow start-up density; If it is less than, then the survey operation parameters are defined as the actual operation parameters; If the actual parameters are not less than the specified values, the survey operation parameters, UAV operation images, and current environmental wind speed will be analyzed to determine the actual operation parameters. The steps for analyzing survey operation parameters, UAV operation images, and current ambient wind speed to determine actual operation parameters include: The drone operation images are input into a preset image processing algorithm for analysis to determine the average blade size; The current ambient wind speed and average blade size are analyzed to determine the final airflow intensity; The average blade size was analyzed to determine the airflow pulse frequency; Obtain the drone's mass and the number of rotor motors; The mass of the drone, the number of rotor motors, and the final airflow intensity were analyzed to determine the motor duty cycle; Associate survey operation parameters, final airflow intensity, airflow pulse frequency, and motor duty cycle to generate actual operation parameters; The steps to determine the final airflow intensity by analyzing the current ambient wind speed and average blade size include: Calculate the product of the current ambient wind speed and the preset wind speed compensation factor to generate the first adjustment factor; Calculate the product of the average blade size and the preset blade airflow influence factor to generate a second adjustment factor; The sum of the first adjustment factor, the second adjustment factor, and the preset base adjustment factor is calculated to generate the airflow intensity adjustment factor; Calculate the product of the airflow intensity adjustment factor and the preset reference airflow intensity to generate the final airflow intensity.

2. The method for targeted spraying of fruit trees according to claim 1, characterized in that, The steps for analyzing the average blade size to determine the airflow pulse frequency include: Calculate the product of the average blade size and the preset frequency influence coefficient to generate the frequency adjustment factor; Calculate the sum of the frequency adjustment factor and the preset frequency base influence factor to generate the pulse frequency adjustment factor; Calculate the quotient of the preset reference pulse frequency and the pulse frequency adjustment factor to generate the airflow pulse frequency.

3. The method for targeted spraying of fruit trees according to claim 1, characterized in that, The steps to determine the motor duty cycle by analyzing the drone's mass, number of rotor motors, and final airflow intensity include: Calculate the product of the drone's mass and the preset gravitational acceleration to generate the gravitational thrust value; Calculate the quotient of gravity thrust and the number of rotor motors to generate single rotor thrust; The airflow reverse thrust is determined based on the relationship between the final airflow intensity and the preset reverse thrust. Calculate the quotient of airflow reverse thrust and the number of rotor motors to generate single-rotor airflow thrust; Calculate the sum of the single rotor thrust and the single rotor airflow thrust to generate the total reverse thrust of the single rotor; The total reverse thrust of the single rotor is input into a preset thrust duty cycle relationship function for solution to determine the motor duty cycle.

4. A fruit tree spot spraying system, characterized in that, include: The acquisition module is used to acquire the drone's start-up signal, drone operation images, and current ambient wind speed; A memory for storing a program for a method of spot spraying fruit trees as described in any one of claims 1 to 3; The processor and the program in the memory can be loaded and executed by the processor to implement the fruit tree spot spraying method as described in any one of claims 1 to 3.

5. A smart terminal, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed as described in any one of claims 1 to 3, a method for spot spraying of fruit trees.

6. A computer-readable storage medium, characterized in that, The computer program is stored and can be loaded by a processor and executed as described in any one of claims 1 to 3, which is a method for spot spraying of fruit trees.

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