A method for reducing drift risk in plant protection drone spraying operations

Abstract

The present invention discloses a method for reducing drift risk during plant protection drone spraying operations. The method includes first obtaining the drone's flight path while spraying pesticides in an operation area; obtaining the drone's real-time location and calculating a boundary distance x; obtaining the drone's height h relative to the target crop canopy in the operation area, the lateral wind speed v, and the sprayed droplet size α; and obtaining the spray drift distance d based on the relationship between the droplet size α, the ambient wind speed v, the height h relative to the target crop canopy, and the spray drift distance d. The droplet drift distance d is then compared with the boundary distance x. If the droplet drift distance d is less than the boundary distance x, the sprayed particle size is maintained unchanged; if it is greater than or equal to the boundary distance x, the sprayed particle size is increased so that the adjusted spray drift distance d is less than the boundary distance x. This method can solve the droplet drift problem of existing drone spraying, improving droplet utilization while reducing harm to adjacent crops, livestock, and humans.

Description

Technical Field

[0001] The present invention relates to the field of drone operations, and in particular to a method for reducing the drift risk of plant protection drone spraying operations. Background Art

[0002] Plant protection drones are a new type of equipment that utilizes ultra-low-volume spraying technology for pest control. They offer advantages such as high application efficiency and the conservation of pesticides and water resources. However, due to their high operating altitudes, plant protection drones are significantly affected by wind speeds, resulting in a significant problem of mid-air drift of pesticide droplets. This not only reduces pesticide utilization but also poses a risk to sensitive crops, non-target organisms, and human health.

[0003] Currently, there are two main approaches to addressing the problem of droplet drift: one is to control flight altitude and speed to reduce drift, and the other is to add adjuvants to the pesticide to improve the droplet's anti-drift and anti-evaporation properties. The first approach does not fundamentally improve droplet drift and sacrifices the speed and efficiency of plant protection drone operations. The second approach has achieved some anti-drift effectiveness with certain specific pesticides. For example, in a trial using drones to spray cotton defoliants, the anti-drift adjuvants Beibeijia and Feishoubao were added to the cotton defoliant. The results showed that the addition of adjuvants increased the droplet deposition density, thereby reducing droplet drift. Improving drift by adding adjuvants or changing the pesticide type essentially increases the spray droplet size. Larger droplets are less susceptible to the natural environment and can deposit on the target crop.

[0004] However, existing formulations in the domestic market are insufficient to meet the actual demand for aerial spraying. Currently, there are no registered pesticide formulations or adjuvants specifically designed for drone application in China, and this remains a blank slate. Drones for agricultural protection require specialized pesticides with good settling properties and high safety, but these ultra-low-volume formulations are still scarce and lack selectivity, creating a bottleneck for improving operational efficiency. The domestic aerial spraying adjuvant market urgently needs systematization and standardization. Currently, my country has not formally established clear regulations and requirements for the management of pesticide adjuvants, and the performance of products on the market varies significantly. The use of adjuvants to reduce droplet drift remains immature and unstable, lacking relevant standards, making it difficult to achieve the desired results in practical application.

[0005] Therefore, there is an urgent need for a drone spraying method that can not only solve the problem of droplet drift in existing drone spraying, but also ensure the spraying range of the drone as much as possible, avoid the drone's frequent return to replenish the liquid medicine, and thus improve the drone's spraying efficiency. Summary of the Invention

[0006] In order to overcome the shortcomings of the existing technology, the present invention provides a method for reducing the drift risk of plant protection drone spraying operations. The method can solve the problem of droplet drift in existing drone spraying, while improving the utilization rate of droplets and reducing the harm to adjacent crops, livestock and people; at the same time, the method can also ensure the spraying range of the drone, avoid the drone frequently returning to replenish the liquid medicine, thereby ensuring the spraying range and spraying efficiency of the drone.

[0007] The technical solution of the present invention to solve the above technical problems is:

[0008] A method for reducing the drift risk of a plant protection drone spraying operation comprises the following steps:

[0009] S1: Obtain the flight path of the drone when spraying pesticides in the operation area;

[0010] S2: Get the real-time position of the drone and calculate the boundary distance x between it and the boundary;

[0011] S3: Obtain the real-time ambient wind speed v and the height h relative to the target crop canopy of the UAV in the operation area;

[0012] S4: Obtain the spray droplet size α;

[0013] S5: Based on the droplet size α and the real-time ambient wind speed v, the relationship between the height h relative to the target crop canopy and the spray drift distance d, the spray drift distance d is obtained;

[0014] S6. Compare the droplet drift distance d and the boundary distance x, and execute different variable particle size spraying strategies according to the comparison results: wherein the variable particle size spraying strategy is:

[0015] If the droplet drift distance d is less than the boundary distance x, the spraying particle size remains unchanged;

[0016] If the droplet drift distance d is greater than or equal to the boundary distance x, increase the spraying particle size so that the adjusted spray drift distance d is less than the boundary distance x.

[0017] Preferably, in step S2, the boundary distance d between the UAV and the boundary of the working area is obtained by the positioning module carried by the UAV; the height h of the UAV relative to the target crop canopy is obtained by the height determination module carried by the UAV.

[0018] Preferably, in step S3, the real-time environmental wind speed v of the UAV during operation is obtained by a lateral wind speed acquisition module carried by the UAV.

[0019] Preferably, in step S4, the drug particle size data sent by the processing module is received by the spraying module carried by the drone, and drug droplets with variable particle sizes are sprayed.

[0020] Preferably, the wind speed acquisition module adopts a wind speed sensor; the processing module adopts a microprocessor, and the spraying module adopts a variable particle size atomization device, and the variable particle size drug droplet spraying is achieved by controlling the speed of the centrifugal motor in the variable particle size atomization device.

[0021] Preferably, in step S5, it is necessary to first obtain the relationship between the droplet size α, the ambient wind speed v, and the spray drift distance d, specifically:

[0022] Calculate the drift distance of the same drug with different particle sizes under different wind speed environments and different altitudes;

[0023] Based on the above calculations, the functional relationship between the drug droplet drift distance d and the droplet particle size α, the height h of the drone relative to the target crop canopy and the ambient wind speed v is obtained: d = f(α, v, h).

[0024] Preferably, in step S6, when the ambient wind speed v is greater than a preset threshold, the operation is stopped.

[0025] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0026] 1. The method for reducing the drift risk of plant protection drone spraying operations of the present invention can automatically change the spray droplet size based on the functional relationship between the drift distance of the drug droplets and the droplet particle size, the height of the drone relative to the target crop canopy and the ambient wind speed, by calculating the real-time boundary distance, the height of the target crop canopy and the ambient wind speed, so that the droplet drift amount of the drone when spraying drugs is within an allowable range.

[0027] 2. The method of the present invention for reducing the drift risk of plant protection drone spraying operations can automatically change the spray droplet size according to real-time location information, altitude information and wind speed information, so that when the drone sprays the medicine, the droplets drift as little as possible outside the target crop, thereby improving the droplet utilization rate and reducing the harm to neighboring crops, livestock and people.

[0028] 3. The method for reducing the drift risk of plant protection drone spraying operations of the present invention can automatically change the size of the spray droplets according to real-time position information, altitude information and wind speed information. While ensuring that the droplets drift as little as possible outside the target crops when the drone is spraying the medicine, it also reduces the loss of the medicine liquid as much as possible, avoids the drone from frequently returning to replenish the medicine liquid, and thus ensures the spraying range and spraying efficiency of the drone. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1The present invention is a flow chart of a method for reducing the drift risk of spraying operations by plant protection drones.

[0030] Figure 2 The present invention is a schematic diagram of the method for reducing the drift risk of plant protection UAV spraying operations. DETAILED DESCRIPTION

[0031] The present invention will be described in further detail below with reference to the embodiments and drawings, but the embodiments of the present invention are not limited thereto.

[0032] See also Figure 1 and Figure 2 The method for reducing the drift risk of a plant protection UAV spraying operation of the present invention comprises the following steps:

[0033] S1: Obtain the flight path of the drone when spraying pesticides in the operation area;

[0034] S2: Obtain the real-time position of the UAV and its height h relative to the target crop canopy, and calculate the boundary distance x between it and the boundary;

[0035] S3: Obtain the real-time lateral wind speed v of the UAV in the operation area (i.e., the real-time ambient wind speed);

[0036] S4: Obtain the spray droplet size α;

[0037] S5: Based on the relationship between the droplet size α, the ambient wind speed v, the height h relative to the target crop canopy and the spray drift distance d, the spray drift distance d is obtained;

[0038] S6. Compare the droplet drift distance d and the boundary distance x, and execute different variable particle size spraying strategies according to the comparison results: wherein the variable particle size spraying strategy is:

[0039] If the droplet drift distance d is less than the boundary distance x, the spraying particle size remains unchanged;

[0040] Because larger droplet size increases droplet mass and downward kinetic energy, it's easier for droplets to overcome air resistance and crosswinds and settle within the target area. Conversely, smaller droplet size decreases droplet mass and downward kinetic energy, making them more susceptible to air resistance and crosswinds, drifting to non-target areas (i.e., the boundary area). Therefore, if the droplet drift distance d is greater than or equal to the boundary distance x, increase the spray particle size so that the adjusted spray drift distance d is less than the boundary distance x.

[0041] See also Figure 1 and Figure 2 In step S2, the distance d between the UAV and the boundary of the operation area is obtained through the positioning module carried by the UAV.

[0042] See also Figure 1 and Figure 2 In step S3, the real-time lateral wind speed v of the UAV during operation is obtained through the lateral wind speed acquisition module carried by the UAV.

[0043] See also Figure 1 and Figure 2 In step S4, the spraying module carried by the drone receives the drug particle size data sent by the processing module and sprays drug droplets with variable particle sizes.

[0044] See also Figure 1 and Figure 2 The wind speed acquisition module adopts a wind speed sensor; the processing module adopts a microprocessor, and the spraying module adopts a variable particle size atomization device. By controlling the speed of the centrifugal motor in the variable particle size atomization device, the variable particle size drug droplet spraying is achieved.

[0045] See also Figure 1 and Figure 2 In step S5, it is necessary to first obtain the relationship between the droplet size α, the ambient wind speed v, and the spray drift distance d, specifically:

[0046] Calculate the drift distance of the same drug with different particle sizes under different wind speed environments and different altitudes;

[0047] Based on the above calculations, the functional relationship between the drug droplet drift distance d and the droplet particle size α, the height h relative to the target crop canopy and the ambient wind speed v is obtained: d = f(α, v, h); the droplet drift distance d is calculated under the influence of three variables: the lateral wind speed v, the height h relative to the target crop canopy and the particle size α of the sprayed droplets.

[0048] The following are specific implementation cases:

[0049] See also Figure 1 and Figure 2 The method for reducing the drift risk of a plant protection UAV spraying operation of the present invention comprises the following steps:

[0050] Step 1: Obtain the flight path of the UAV in the operation area and divide the operation area;

[0051] Specifically, an algorithm is performed based on the functional relationship between the drift distance of drug droplets and the droplet size α, the height h relative to the target crop canopy and the ambient wind speed v to make spraying strategy decisions. The time interval between each decision is δt, thereby controlling the particle size of the sprayed drug in real time.

[0052] Step 2: The drone takes off and enters time t1. At this time, the lateral wind speed is v1, and the distance to the boundary of the operation area is x1. The algorithm calculates the drug drift distance d1. Assuming that d1 is less than x1 at this time, the sprayed drug will not drift outside the boundary, that is, it can be sprayed according to the conventional method, and the sprayed droplet size is α1.

[0053] Specifically, the conventional method refers to selecting the optimal spraying particle size for spraying to ensure the coverage and uniformity of the drug.

[0054] Step 3, enter the moment t2 (t2=t1+δt), at this time the lateral wind speed increases to v2, the distance from the boundary of the working area is x2, and the algorithm calculates the drug drift distance to be d2. Assuming that d2 is equal to x2 at this time, there is a risk of the sprayed drug drifting outside the boundary, and the particle size of the drug droplets needs to be increased to α2 to reduce the drift distance.

[0055] Step 4: At time t3, the drone approaches the boundary of the work area and the distance to the boundary of the work area is shortened to x3. At this time, the lateral wind speed is v3. The algorithm calculates the drug drift distance d3. Assuming that d3 is greater than x3 at this time, the sprayed drug will drift outside the boundary. It is necessary to increase the droplet size to α3, or even not spray the drug. The drone hovers in the air and waits for the wind speed to decrease before operating to prevent the droplets from drifting into the buffer zone or even other crop areas or residential areas farther away.

[0056] In particular, when the wind speed is greater than a certain threshold, the operation is stopped, as in step 4.

[0057] In particular, during drone spraying operations, the drone makes spraying decisions by comparing the distance x from the boundary of the operating area with the drug drift distance d in real time. Depending on the actual environment and local regulations, the boundary distance x can have a flexible threshold range, specifically:

[0058] (1) When the sprayed drug has little impact on the environment, in order to improve the coverage rate of the drug, the limit of the boundary distance x can be appropriately relaxed and corrected to x + σ, that is, some drugs are allowed to float out of the boundary and into the buffer zone, where σ is the width of the buffer zone in meters;

[0059] (2) When the sprayed pesticide is highly hazardous or local laws and regulations have strict restrictions on the drift of agricultural drones, in order to reduce the impact of the pesticide on the surrounding environment, the boundary distance x can be appropriately tightened and modified to x-σ, that is, the maximum distance the pesticide can drift is far away from the boundary, where σ is the safety distance in meters. This setting can achieve better spraying effects in different application scenarios.

[0060] Specifically, the drone spraying system uses a centrifugal nozzle, which controls the speed of the centrifugal motor in the centrifugal nozzle to achieve variable particle size drug droplet spraying; when the drone is closer to the boundary or the wind speed is greater, the speed of the centrifugal motor is lower, the sprayed droplet size is larger, and the drift distance of the droplets is shorter, thereby reducing the possibility of droplets drifting outside the work area.

[0061] In this embodiment, the initial spray particle size range is determined based on the target crop and pest type, based on existing research and experience. For example, for weeds, herbicide droplets of 100μm to 300μm are suitable. This prevents small droplets from drifting onto non-target crops and large droplets from wasting liquid, ensuring that droplets are deposited and absorbed on the weed surface, achieving both weed suppression and killing effects. To reduce drift, the spray particle size can be increased to above 300μm. The specific increase will be determined based on the crosswind speed and the distance between the drone and the boundary.

[0062] Step 5: The drone completes the operation and lands.

[0063] The above is a preferred embodiment of the present invention, but the embodiment of the present invention is not limited to the above content. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be equivalent replacement methods and are included in the scope of protection of the present invention.

Claims

1. A method for reducing the risk of drift during spraying operations using a plant protection drone, characterized in that: The following steps are involved: S1: Obtain the flight path of the drone when spraying pesticides in the operation area; S2: Get the real-time position of the drone and calculate the boundary distance x between it and the boundary; S3: Obtain the real-time ambient wind speed v and the height h relative to the target crop canopy of the UAV in the operation area; S4: Obtain the spray droplet size α; S5: Based on the droplet size α and the real-time ambient wind speed v, the relationship between the height h relative to the target crop canopy and the spray drift distance d, the spray drift distance d is obtained; S6. Compare the droplet drift distance d and the boundary distance x, and execute different variable particle size spraying strategies according to the comparison results: wherein the variable particle size spraying strategy is: If the droplet drift distance d is less than the boundary distance x, the spraying particle size remains unchanged; If the droplet drift distance d is greater than or equal to the boundary distance x, increase the spraying particle size so that the adjusted spray drift distance d is less than the boundary distance x.

2. The method for reducing the drift risk of spraying operations by a plant protection drone according to claim 1, characterized in that: In step S2, the boundary distance d between the UAV and the boundary of the working area and the height h relative to the target crop canopy are obtained by using the positioning module and the height determination module carried by the UAV.

3. The method for reducing the drift risk of spraying operations by a plant protection drone according to claim 2, characterized in that: In step S3, the real-time environmental wind speed v of the UAV during operation is obtained through the lateral wind speed acquisition module carried by the UAV.

4. The method for reducing drift risk in spraying operations of plant protection drones according to claim 3, characterized in that: In step S4, the spraying module carried by the drone receives the drug particle size data sent by the processing module and sprays drug droplets with variable particle sizes.

5. The method for reducing drift risk in spraying operations of plant protection drones according to claim 4, characterized in that: The wind speed acquisition module adopts a wind speed sensor; the processing module adopts a microprocessor; the spraying module adopts a variable particle size atomization device, and the variable particle size drug droplet spraying is achieved by controlling the speed of the centrifugal motor in the variable particle size atomization device.

6. The method for reducing drift risk in spraying operations of plant protection drones according to claim 4, characterized in that: In step S5, it is necessary to first obtain the relationship between the droplet size α, the ambient wind speed v, and the spray drift distance d, specifically: Calculate the drift distance of the same drug with different particle sizes under different wind speed environments and different altitudes; Based on the above calculations, the functional relationship between the drug droplet drift distance d and the droplet particle size α, the height h of the drone relative to the target crop canopy and the ambient wind speed v is obtained: d = f(α, v, h).

7. The method for reducing drift risk in spraying operations of plant protection drones according to claim 4, characterized in that: In step S6, when the ambient wind speed v is greater than a preset threshold, the operation is stopped.

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