Non-contact testing method and testing system for a variable spray control system

Abstract

This invention discloses a non-contact testing method and system for a variable spray control system. The method includes the following steps: projecting a near-infrared structured light surface through a structured light array; simultaneously acquiring a droplet scattering image sequence S(n,t) with a binocular camera and a grayscale area array camera simultaneously acquiring a grayscale image sequence G(m,t); obtaining the local three-dimensional point cloud P(n,t), droplet size distribution histogram H(m,t), and droplet number density ρ(m,t) for each segment based on S(n,t) and G(m,t), respectively; and stitching the local three-dimensional point clouds P(n,t) of all segments into a full-frame point cloud P0(n,t). The droplet distribution field F(t) is obtained by mapping the liquid H(m,t) and ρ(m,t) to the global coordinate system of the full-width point cloud. A virtual sampling surface is defined at a preset height H below the nozzle and divided into cells C(i,j) according to the grid. The application rate Q(i,j) of each cell is calculated based on F(t). Combined with the vehicle speed v(t) and the spray boom width L, Q(i,j) is converted into the lateral application rate distribution Q1(i,t) and the average application rate per acre Q2(t) of the full width. Based on Q(i,j), Q1(i,t) and Q2(t), the index used to evaluate the spraying status is calculated.

Description

Technical Field

[0001] This invention relates to the field of variable spray testing technology, and in particular to a non-contact testing method and system for variable spray control systems. Background Technology

[0002] Variable spray control systems adjust the spray volume of each nozzle in real time based on prescription maps or sensor feedback, making them a key component for precise pesticide application. The group standard T / CAAMM 274—2023 specifies two core indicators: "application volume control accuracy" and "application volume adjustment time." However, the following deficiencies still exist in actual testing methods:

[0003] First, existing methods all involve connecting flow sensors in series in the pesticide pipeline to obtain the dosage, which is an invasive measurement method. After the sensor is installed, additional flow resistance is introduced, changing the pressure-flow relationship in the pipeline and causing the tested system to deviate from the actual operating state under test conditions.

[0004] Second, existing methods can only obtain the total flow rate or single-pipe flow rate at the pipe cross-section, which is one-dimensional numerical information and cannot reflect the spatial distribution characteristics of the spray.

[0005] Third, existing methods simplify the evaluation of pesticide application rate to a numerical comparison over a time series, lacking a comprehensive representation of the pesticide application rate in both spatial and temporal dimensions. Summary of the Invention

[0006] Purpose of the invention: In order to overcome the shortcomings of the existing technology, the present invention provides a non-contact testing method and system for variable spray control systems, which can achieve multi-dimensional index testing of the dosage of the variable spray control system without intruding into the liquid circuit of the system under test or changing its working state.

[0007] Technical Solution: To achieve the above objectives, the present invention provides a non-contact testing method for a variable spray control system, which involves deploying a structured light and camera array below the spray boom of the variable spray system under test. The structured light array includes N sets of structured light projectors arranged along the spray boom direction, and the camera array includes N sets of binocular near-infrared cameras paired with the projectors and M grayscale area array cameras distributed along the spray boom direction. The N sets of structured light projectors correspond to N segments.

[0008] The method includes the following steps:

[0009] Step S1: Project a near-infrared structured light surface with a known coded pattern onto the area below the spray bar using the structured light array; and trigger all cameras to expose synchronously using a hardware trigger signal, so that N groups of binocular cameras synchronously acquire droplet scattering image sequence S(n,t) at frame rate f1, and M grayscale area array cameras synchronously acquire grayscale image sequence G(m,t) at frame rate f2, while recording the timestamp T(t) corresponding to each frame; where n and m are the labels of the binocular camera and the grayscale area array camera, respectively, and t is the time;

[0010] Step S2: Based on S(n,t) and G(m,t), obtain the local three-dimensional point cloud P(n,t), droplet size distribution histogram H(m,t), and droplet number density ρ(m,t) corresponding to each of the N segments, respectively; wherein, the local three-dimensional point cloud P(n,t) is obtained based on S(n,t), and the latter two are obtained based on G(m,t);

[0011] Step S3: The local three-dimensional point clouds P(n,t) of all N segments are stitched together into a full-width point cloud P0(t) through a pre-calibrated spatial transformation matrix. The droplet size distribution histogram H(m,t) and droplet number density ρ(m,t) are mapped to the global coordinate system of the full-width point cloud to obtain the full-width droplet distribution field F(t) carrying the particle size distribution and number density information.

[0012] Step S4: Define a virtual sampling surface at a preset height H below the nozzle and divide it into cells C(i,j) according to the grid; calculate the dosage Q(i,j) of each cell based on the full-width droplet distribution field F(t); where i and j are the column number and row number of the cell, respectively;

[0013] Step S5: Combining vehicle speed v(t) and spray boom width L, convert Q(i,j) into lateral application rate distribution Q1(i,t) and average application rate per acre Q2(t) across the entire width;

[0014] Step S6: Based on Q(i,j), Q1(i,t) and Q2(t), calculate the indicators used to evaluate the spraying status. The indicators include one or more of the following four indicators: spraying amount control accuracy E1, adjustment time E2, lateral uniformity coefficient E3, and response synchronization degree E4.

[0015] Furthermore, the local three-dimensional point cloud P(n,t) in step S2 is obtained by performing structured light phase decoding and binocular triangulation on S(n,t).

[0016] Furthermore, the droplet size distribution histogram H(m,t) and droplet number density ρ(m,t) in step S2 are obtained by image segmentation and statistics of G(m,t);

[0017] The specific method for obtaining the droplet size distribution histogram H(m,t) includes: removing the background and performing adaptive threshold segmentation on the grayscale image G(m,t), extracting the droplet foreground region, calculating the projected area of ​​each connected component and converting it into an equivalent particle size according to the calibrated spatial resolution, and counting the number of droplets in each interval according to the preset particle size interval to form a histogram.

[0018] The droplet number density ρ(m,t) is the total number of droplets detected in a single frame divided by the known spatial volume corresponding to the camera's field of view. Given that the actual spatial size corresponding to the camera's field of view is w0×h0 and the depth of field is Δz, the field of view volume V1 = w0×h0×Δz, and the number density ρ = N1 / V1, where N1 is the total number of droplets detected in a single frame.

[0019] Further, the step S4, which involves calculating the dosage Q(i,j) of each cell based on the full-range droplet distribution field F(t), includes:

[0020] Calculate the droplet number density ρ(i,j) and particle size distribution H(i,j) above each cell, and then calculate the dosage Q(i,j) of each cell according to the physical model.

[0021] Furthermore, the structured light projector uses a near-infrared laser in conjunction with a digital micromirror array to generate a Gray code and phase-shift composite coding pattern, with the center wavelength of the laser being 850nm or 940nm; the binocular near-infrared camera is equipped with a narrowband filter that matches the structured light band, with a passband width not exceeding 20nm.

[0022] Furthermore, the spatial transformation matrix is ​​obtained in the following manner:

[0023] Before testing, calibration targets are placed sequentially in the overlapping areas of adjacent sections, and all cameras simultaneously photograph the calibration targets. The rigid body transformation matrix between sections is calculated by the correspondence between the target feature points in the coordinate systems of each camera. The coordinate mapping relationship between the grayscale area array camera and the binocular camera is also determined by synchronously photographing the same calibration target.

[0024] Furthermore, the formula for calculating the dosage Q(i,j) in step S4 is as follows:

[0025] ;

[0026] Where: ρ(i,j) is the average droplet number density within the corresponding spatial cylinder; V0 is the volume of the spatial cylinder; W is the length of the statistical time window; n k d represents the number of droplets in the k-th interval of the particle size distribution histogram; k Let be the representative particle size of the k-th interval; N0 is the total number of droplets; and Q(i,j) is in μL / cm.2 . The average volume of a single droplet is a representative value obtained by weighting the droplet volume according to the particle size distribution.

[0027] Furthermore, the dosage control accuracy E1 is calculated based on the following formula:

[0028] ;in: It is the average application rate per acre across the entire area measured within the k-th time window of the steady-state interval, where k=1,2… ; Target dosage; This represents the total number of time windows within the steady-state interval.

[0029] The adjustment time E2 is calculated as follows: E2 = t1 - t0; where t0 is the control command from Q. t1 Change to Q t2 The moment; t1 is the first time Q2(t) enters Q after t0. t2 The time interval between ±δ and lasts for more than 2 seconds;

[0030] The lateral uniformity coefficient E3 is calculated as follows: δ[Q1(i,t)] / μ[Q1(i,t)] × 100%; where δ[Q1(i,t)] represents the standard deviation of all lateral application rate distributions; and μ[Q1(i,t)] represents the average value of all lateral application rate distributions.

[0031] The response synchronization degree E4 is calculated as follows: E4 = max(t s (i)) - min(t s (i)); where t s (i) represents the moment when the dosage of the drug in the i-th column reaches a new steady state after the instruction is changed.

[0032] A non-contact testing system for a variable spray control system includes:

[0033] The structured light projection array includes N groups of near-infrared structured light projectors arranged along the direction of the spray bar;

[0034] The camera array includes N sets of binocular near-infrared cameras paired with the projector and M grayscale area array cameras. All cameras are connected to the controller via hardware trigger lines.

[0035] The positioning module is installed on the vehicle under test and outputs vehicle speed and position data;

[0036] The controller generates a unified hardware trigger signal to drive all cameras and projectors to work synchronously, enabling the implementation of the non-contact testing method for the aforementioned variable spray control system.

[0037] Beneficial Effects: The non-contact testing method and system for the variable spray control system of the present invention have the following beneficial effects:

[0038] (1) The method of the present invention acquires droplet scattering images by deploying structured light and camera array outside the liquid path of the tested system, and obtains the spatial distribution of the dosage through three-dimensional distribution field reconstruction and virtual sampling surface inverse calculation. It does not contact the pipeline of the tested system at all, fundamentally solving the problem that the invasive sensor introduces additional flow resistance and changes the working state of the tested system. At the same time, it breaks through the limitation of the traditional method that can only obtain one-dimensional flow rate, and realizes performance evaluation at multiple levels.

[0039] (2) Using an area array camera to perform overall statistics on the droplet swarm within the field of view instead of tracking them one by one reduces the complexity and computational load of the algorithm, enabling the system to complete real-time processing at a higher frame rate; the particle size conversion method based on the calibration spatial resolution is simple and reliable, and the histogram statistical method naturally has robustness against single-point noise interference. Attached Figure Description

[0040] Figure 1 A schematic diagram of the structure of a non-contact testing system for a variable spray control system;

[0041] Figure 2 A schematic diagram illustrating the principle of reverse calculation of pesticide dosage;

[0042] Figure 3 Flowchart for 3D point cloud reconstruction;

[0043] Figure 4 This is a flowchart for droplet size statistics and number density calculation.

[0044] In the diagram: 1-vehicle body; 2-spray boom; 3-nozzle; 4-structured light projector; 5-binocular near-infrared camera; 6-grayscale area array camera. Detailed Implementation

[0045] The invention will now be further described with reference to the accompanying drawings.

[0046] like Figure 1 The non-contact testing system for the variable spray control system shown includes:

[0047] The vehicle body 1 and the spray bar 2 installed at the front end of the vehicle body 1, with nozzles 3 arranged at equal intervals on the spray bar 2;

[0048] The structured light projection array includes N groups of near-infrared structured light projectors 4 arranged along the direction of the spray bar; the N groups of structured light projectors 4 correspond to N segments;

[0049] The camera array includes N sets of binocular near-infrared cameras 5 paired with the projector and M grayscale area array cameras 6. All cameras are connected to the controller via hardware trigger lines.

[0050] The positioning module is installed on the vehicle under test and outputs vehicle speed and position data;

[0051] The controller generates a unified hardware trigger signal to drive all cameras and projectors to work synchronously, and is used to implement the non-contact testing method of the variable spray control system of the present invention.

[0052] A non-contact testing method for variable spray control systems includes the following steps:

[0053] Step S1: Project a near-infrared structured light surface with a known coded pattern onto the area below the spray bar using the structured light array; and trigger all cameras to expose synchronously using a hardware trigger signal, so that N groups of binocular cameras synchronously acquire droplet scattering image sequence S(n,t) at frame rate f1, and M grayscale area array cameras synchronously acquire grayscale image sequence G(m,t) at frame rate f2, while recording the timestamp T(t) corresponding to each frame; where n and m are the labels of the binocular camera and the grayscale area array camera, respectively, and t is the time;

[0054] Step S2: Based on S(n,t) and G(m,t), obtain the local three-dimensional point cloud P(n,t), droplet size distribution histogram H(m,t), and droplet number density ρ(m,t) corresponding to each of the N segments, respectively; wherein, the local three-dimensional point cloud P(n,t) is obtained based on S(n,t), and the latter two are obtained based on G(m,t);

[0055] Step S3, as follows Figure 3 As shown, the local three-dimensional point clouds P(n,t) of all N segments are stitched together into a full-width point cloud P0t through a pre-calibrated spatial transformation matrix, and the droplet size distribution histogram H(m,t) and droplet number density ρ(m,t) are mapped to the global coordinate system of the full-width point cloud to obtain the full-width droplet distribution field Ft carrying the particle size distribution and number density information.

[0056] Step S4: Define a virtual sampling surface at a preset height H below the nozzle and divide it into cells C(i,j) according to the grid, such as... Figure 2 As shown, the length and width of cell C(i,j) are a and b, respectively; the dosage Q(i,j) of each cell is calculated based on the full-width droplet distribution field Ft; where i and j are the column number and row number of the cell, respectively;

[0057] Step S5: Combining vehicle speed vt and spray boom width L, convert Q(i,j) into lateral application rate distribution Q1(i,t) and average application rate per acre Q2(t) across the entire width;

[0058] Step S6: Based on Q(i,j), Q1(i,t) and Q2(t), calculate the indicators used to evaluate the spraying status. The indicators include one or more of the following four indicators: spraying amount control accuracy E1, adjustment time E2, lateral uniformity coefficient E3, and response synchronization degree E4.

[0059] The method of this invention acquires droplet scattering images by deploying structured light and camera arrays outside the liquid path of the tested system. The spatial distribution of the dosage is obtained through three-dimensional distribution field reconstruction and inverse calculation of virtual sampling surface. It does not contact the pipeline of the tested system at all, fundamentally solving the problem that invasive sensors introduce additional flow resistance and change the working state of the tested system. At the same time, it breaks through the limitation of traditional methods that can only obtain one-dimensional flow rate, and realizes multi-level performance evaluation.

[0060] Preferably, the local three-dimensional point cloud P(n,t) in step S2 is obtained by performing structured light phase decoding and binocular triangulation on S(n,t).

[0061] Preferably, the droplet size distribution histogram H(m,t) and droplet number density ρ(m,t) in step S2 are obtained by image segmentation and statistics of G(m,t);

[0062] like Figure 4 As shown, the specific method for obtaining the droplet size distribution histogram H(m,t) includes: removing the background and performing adaptive threshold segmentation on the grayscale image G(m,t), extracting the droplet foreground region, calculating the projected area of ​​each connected component and converting it into an equivalent particle size according to the calibrated spatial resolution, and counting the number of droplets in each interval according to the preset particle size interval to form a histogram.

[0063] The droplet number density ρ(m,t) is the total number of droplets detected in a single frame divided by the known spatial volume corresponding to the camera's field of view. Given that the actual spatial size corresponding to the camera's field of view is w0×h0 and the depth of field is Δz, the field of view volume V1 = w0×h0×Δz, and the number density ρ = N1 / V1, where N1 is the total number of droplets detected in a single frame.

[0064] Using an area array camera to perform overall statistics on the droplet swarm within the field of view instead of tracking each droplet individually reduces algorithm complexity and computational load, enabling the system to complete real-time processing at a high frame rate. The particle size conversion method based on the calibration spatial resolution is simple and reliable, and the histogram statistical method naturally has robustness against single-point noise interference.

[0065] Preferably, the step S4 of calculating the dosage Q(i,j) of each cell based on the full-width droplet distribution field Ft includes:

[0066] Calculate the droplet number density ρ(i,j) and particle size distribution H(i,j) above each cell, and then calculate the dosage Q(i,j) of each cell according to the physical model.

[0067] Preferably, the structured light projector uses a near-infrared laser in conjunction with a digital micromirror array to generate a Gray code and phase-shift composite coding pattern, with the center wavelength of the laser being 850nm or 940nm; the binocular near-infrared camera is equipped with a narrowband filter that matches the structured light band, with a passband width not exceeding 20nm.

[0068] Near-infrared structured light combined with narrowband filters effectively suppresses background interference from sunlight during field operations, improving the signal-to-noise ratio of droplet scattering images; Gray code and phase-shift composite coding balance decoding robustness and spatial resolution, enabling the system to obtain reliable phase information even under dense droplet occlusion conditions.

[0069] Preferably, the spatial transformation matrix is ​​obtained in the following manner:

[0070] Before testing, calibration targets were placed sequentially in the overlapping areas of adjacent segments. All cameras simultaneously captured images of the calibration targets, and the rigid transformation matrix between segments was calculated using the correspondence between target feature points in each camera's coordinate system. The coordinate mapping relationship between the grayscale area array camera and the stereo camera was also determined through synchronous shooting with the same calibration target. Based on this one-time calibration method using a common calibration target, spatial mapping relationships were simultaneously established between stereo cameras and between stereo cameras and grayscale cameras, ensuring spatial consistency of data from different sensors in the global coordinate system and providing a precise geometric basis for the complete stitching of the full-frame droplet distribution field.

[0071] Preferably, the formula for calculating the dosage Q(i,j) in step S4 is:

[0072] ;

[0073] Where: ρ(i,j) is the average droplet number density within the corresponding spatial cylinder; V0 is the volume of the spatial cylinder; W is the length of the statistical time window; n k d represents the number of droplets in the k-th interval of the particle size distribution histogram; k Let be the representative particle size of the k-th interval; N0 is the total number of droplets; and Q(i,j) is in μL / cm. 2 . The average volume representing a single droplet is obtained by weighting the droplet volume according to the particle size distribution to obtain a representative value. This calculation formula combines the droplet number density with the particle size distribution histogram, and inversely calculates the dosage per unit area by weighted accumulation of droplet volumes in each particle size interval. The calculation process is simple and efficient, and the use of statistical averaging reduces the impact of single-frame detection errors on the final result.

[0074] Preferably, the application rate control accuracy E1 is calculated based on the following formula:

[0075] ;in: It is the average application rate per acre across the entire area measured within the k-th time window of the steady-state interval, where k=1,2… ; Target dosage; This represents the total number of time windows within the steady-state interval; the criteria for determining the steady-state interval can be: the coefficient of variation of Q2 is <3% within 10 consecutive windows.

[0076] The adjustment time E2 is calculated as follows: E2 = t1 - t0; where t0 is the control command from Q. t1 Change to Q t2 The moment; t1 is the first time Q2(t) enters Q after t0. t2 For moments within the ±δ range that last for more than 2 seconds, the default value is δ=5%.

[0077] The lateral uniformity coefficient E3 is calculated as follows: E3 = δ[Q1(i,t)] / μ[Q1(i,t)] × 100%; where δ[Q1(i,t)] represents the standard deviation of all lateral application rate distributions; and μ[Q1(i,t)] represents the average value of all lateral application rate distributions.

[0078] The response synchronization degree E4 is calculated as follows: E4 = maxt s (i) - mint s (i); where t s (i) represents the moment when the cell in column i reaches a new steady state after the instruction is changed.

[0079] A non-contact testing system for a variable spray control system includes:

[0080] The structured light projection array includes N groups of near-infrared structured light projectors arranged along the direction of the spray bar;

[0081] The camera array includes N sets of binocular near-infrared cameras paired with the projector and M grayscale area array cameras. All cameras are connected to the controller via hardware trigger lines.

[0082] The positioning module is installed on the vehicle under test and outputs vehicle speed and position data;

[0083] The controller generates a unified hardware trigger signal to drive all cameras and projectors to work synchronously, enabling the implementation of the non-contact testing method for the aforementioned variable spray control system.

[0084] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A non-contact testing method for a variable spray control system, characterized in that, A structured light and camera array is deployed below the spray boom of the variable spray system under test; the structured light array includes N sets of structured light projectors arranged along the spray boom direction, and the camera array includes N sets of binocular near-infrared cameras paired with the projectors and M grayscale area array cameras distributed along the spray boom direction; the N sets of structured light projectors correspond to N segments; The method includes the following steps: Step S1: Project a near-infrared structured light surface onto the area below the spray bar using the structured light array; and simultaneously expose all cameras, so that N sets of binocular cameras simultaneously acquire droplet scattering image sequence S(n,t), and M grayscale area array cameras simultaneously acquire grayscale image sequence G(m,t), while recording the timestamp T(t) corresponding to each frame; where n and m are the labels of the binocular camera and the grayscale area array camera, respectively, and t is the time; Step S2: Based on S(n,t) and G(m,t), the local three-dimensional point cloud P(n,t), droplet size distribution histogram H(m,t), and droplet number density ρ(m,t) corresponding to each of the N segments are obtained respectively. Step S3: The local three-dimensional point clouds P(n,t) of all N segments are stitched together into a full-width point cloud P0(t) through a pre-calibrated spatial transformation matrix, and the droplet size distribution histogram H(m,t) and droplet number density ρ(m,t) are mapped to the global coordinate system of the full-width point cloud to obtain the full-width droplet distribution field F(t). Step S4: Define a virtual sampling surface at a preset height H below the nozzle and divide it into cells C(i,j) according to the grid; calculate the dosage Q(i,j) of each cell based on the full-width droplet distribution field F(t); where i and j are the column number and row number of the cell, respectively; Step S5: Combining vehicle speed v(t) and spray boom width L, convert Q(i,j) into lateral application rate distribution Q1(i,t) and average application rate per acre Q2(t) across the entire width; Step S6: Based on Q(i,j), Q1(i,t) and Q2(t), calculate the indicators used to evaluate the spraying status. The indicators include one or more of the following four indicators: spraying amount control accuracy E1, adjustment time E2, lateral uniformity coefficient E3, and response synchronization degree E4.

2. The non-contact testing method for the variable spray control system according to claim 1, characterized in that, The local three-dimensional point cloud P(n,t) in step S2 is obtained by performing structured light phase decoding and binocular triangulation on S(n,t).

3. The non-contact testing method for the variable spray control system according to claim 1, characterized in that, The droplet size distribution histogram H(m,t) and droplet number density ρ(m,t) in step S2 are obtained by image segmentation and statistics on G(m,t); The specific method for obtaining the droplet size distribution histogram H(m,t) includes: removing the background and performing adaptive threshold segmentation on the grayscale image G(m,t), extracting the droplet foreground region, calculating the projected area of ​​each connected component and converting it into an equivalent particle size according to the calibrated spatial resolution, and counting the number of droplets in each interval according to the preset particle size interval to form a histogram. The droplet number density ρ(m,t) is the total number of droplets detected in a single frame image divided by the known spatial volume corresponding to the camera's field of view.

4. The non-contact testing method for the variable spray control system according to claim 1, characterized in that, The step S4, which involves calculating the dosage Q(i,j) of each cell based on the full-range droplet distribution field F(t), includes: Calculate the droplet number density ρ(i,j) and particle size distribution H(i,j) above each cell, and then calculate the dosage Q(i,j) of each cell according to the physical model.

5. The non-contact testing method for the variable spray control system according to claim 1, characterized in that, The structured light projector uses a near-infrared laser in conjunction with a digital micromirror array to generate a Gray code and phase-shift composite coding pattern, with the center wavelength of the laser being 850nm or 940nm; the binocular near-infrared camera is equipped with a narrowband filter that matches the structured light band, with a passband width not exceeding 20nm.

6. The non-contact testing method for the variable spray control system according to claim 1, characterized in that, The spatial transformation matrix is ​​obtained in the following way: Before testing, calibration targets are placed sequentially in the overlapping areas of adjacent sections, and all cameras simultaneously photograph the calibration targets. The rigid body transformation matrix between sections is calculated by the correspondence between the target feature points in the coordinate systems of each camera. The coordinate mapping relationship between the grayscale area array camera and the binocular camera is also determined by synchronously photographing the same calibration target.

7. The non-contact testing method for the variable spray control system according to claim 1, characterized in that, The formula for calculating the dosage Q(i,j) in step S4 is as follows: ； Where: ρ(i,j) is the average droplet number density within the corresponding spatial cylinder; V0 is the volume of the spatial cylinder; W is the length of the statistical time window; n k d represents the number of droplets in the k-th interval of the particle size distribution histogram; k is the representative particle size of the k-th interval; N0 is the total number of droplets.

8. The non-contact testing method for the variable spray control system according to claim 1, characterized in that, The dosage control accuracy E1 is calculated based on the following formula: ;in: It is the average application rate per acre across the entire area measured within the k-th time window of the steady-state interval, where k=1,2… ; Target dosage; This represents the total number of time windows within the steady-state interval. The adjustment time E2 is calculated as follows: E2 = t1 - t0; where t0 is the control command from Q. t1 Change to Q t2 The moment; t1 is the first time Q2(t) enters Q after t0. t2 The time interval between ±δ and lasts for more than 2 seconds; The lateral uniformity coefficient E3 is calculated as follows: E3 = δ[Q1(i,t)] / μ[Q1(i,t)] × 100%; where δ[Q1(i,t)] represents the standard deviation of all lateral application rate distributions; and μ[Q1(i,t)] represents the average value of all lateral application rate distributions. The response synchronization degree E4 is calculated as follows: E4 = max(t s (i)) - min(t s (i)); where t s (i) represents the moment when the dosage of the drug in the i-th column reaches a new steady state after the instruction is changed.

9. A non-contact testing system for a variable spray control system, characterized in that, include: The structured light projection array includes N groups of near-infrared structured light projectors arranged along the direction of the spray bar; The camera array includes N sets of binocular near-infrared cameras paired with the projector and M grayscale area array cameras. All cameras are connected to the controller via hardware trigger lines. The positioning module, installed on the vehicle under test, outputs vehicle speed and position data; The controller generates a unified hardware trigger signal to drive all cameras and projectors to work synchronously, for implementing the non-contact testing method of the variable spray control system as described in any one of claims 1-8.

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