Method, system, electronic equipment and medium for determining optimal flight altitude of unmanned aerial vehicle

Abstract

The present invention discloses a method, system, electronic device and medium for determining the optimal flight altitude of a drone, and relates to the field of drone low-altitude remote sensing technology, mainly comprising: obtaining a multi-band crop spectral image of a test area in a set scenario and performing data processing to obtain a plurality of multi-spectral orthophotographs of sample plots; calculating the ground resolution of the multi-spectral orthophotograph of each sample plot, and simulating and determining the multi-band crop spectral image of each sample plot at different flight altitudes according to the nearest neighbor interpolation method and the ground resolution of the multi-spectral orthophotograph of each sample plot; for the multi-band crop spectral image of each sample plot at different flight altitudes, a hypothesis test method is used to determine the optimal flight altitude corresponding to each sample plot. The present invention determines the optimal flight altitude through the relationship between the drone flight altitude and the ground resolution, and lays a foundation for obtaining crop spectral images that meet the requirements for phenotypic parameter extraction accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) low-altitude remote sensing, and in particular to a method for determining an optimal flight altitude of an unmanned aerial vehicle, a determination system, an electronic device and a computer-readable storage medium. Background Art

[0002] Low-altitude remote sensing by unmanned aerial vehicles is an important means of monitoring the growth and physiological status of farmland crops. However, when the sensors and shooting parameters carried by unmanned aerial vehicles are fixed, the ground resolution has a great impact on phenotypic analysis such as canopy temperature, ground cover, vegetation index and plant diseases. In this process, the calculation of ground resolution is closely related to the flight altitude. The higher the flight altitude of the aircraft, the lower the ground resolution of the acquired image. In order to obtain high-ground resolution images of field plots, it is necessary to lower the flight altitude. For plots of the same size, lowering the flight altitude will lead to longer flight time, thereby increasing the number of flights of the unmanned aerial vehicle platform and the cost of the experiment. Conversely, a higher flight altitude will improve flight efficiency, but will reduce the ground resolution and aggravate the mixed pixel effect of the image, which will affect the accuracy of the subsequent data phenotypic analysis. Therefore, how to balance the relationship between the flight altitude of the unmanned aerial vehicle and the ground resolution to obtain images that meet the requirements of phenotypic parameter extraction accuracy is a problem that needs to be explored and solved. Summary of the invention

[0003] In view of this, the purpose of the present invention is to provide a method, system, electronic device and medium for determining the optimal flight altitude of an unmanned aerial vehicle. The optimal flight altitude is determined by the relationship between the flight altitude of the unmanned aerial vehicle and the ground resolution, laying the foundation for obtaining crop spectral images that meet the requirements of phenotypic parameter extraction accuracy.

[0004] To achieve the above object, the present invention provides the following solutions:

[0005] In a first aspect, the present invention provides a method for determining an optimal flight altitude of a drone, comprising:

[0006] Acquire a multi-band crop spectral image of the test area in a set scenario; the set scenario is a scenario in which the flight parameters of the drone and the setting parameters of the spectral camera remain unchanged during the flight of the drone equipped with the spectral camera;

[0007] Processing the multi-band crop spectral image to obtain a plurality of sample plot multi-spectral orthophotographs; wherein the test area includes a plurality of sample plots;

[0008] Calculating the ground resolution of the multispectral orthophoto of each sample plot, and simulating and determining the multi-band crop spectral image of each sample plot at different flight altitudes based on the nearest neighbor interpolation method and the ground resolution of the multispectral orthophoto of each sample plot;

[0009] For the multi-band crop spectral image of each sample plot at different flight altitudes, a hypothesis testing method is used to determine the optimal flight altitude corresponding to each sample plot.

[0010] Optionally, the first aspect further includes:

[0011] Determine the optimal flight trajectory corresponding to the test area according to the optimal flight altitude corresponding to each sample cell;

[0012] The UAV is controlled to fly according to the optimal flight trajectory to obtain multi-band crop spectral images that meet the accuracy requirements for phenotypic parameter extraction.

[0013] Optionally, the data processing of the multi-band crop spectral image to obtain a plurality of sample plot multi-spectral orthomosaic images specifically includes:

[0014] Using Agisoft Photoscan software, image stitching is performed on the multi-band crop spectral images of the test area collected by the spectral camera to obtain a multi-spectral orthographic image;

[0015] Using MATLAB software, performing spectral correction and reflectance correction on the multispectral orthophoto to obtain a corrected multispectral orthophoto;

[0016] The corrected multispectral orthophoto is subjected to image segmentation to obtain a plurality of sample cell multispectral orthophotos.

[0017] Optionally, the plurality of multi-band crop spectral images corresponding to any sample plot are divided into different flight gradients according to the flight altitude; and the multi-band crop spectral images of each sample plot at different flight altitudes, using a hypothesis testing method to determine the optimal flight altitude corresponding to each sample plot, specifically includes:

[0018] For any sample cell, perform at least one hypothesis test operation in the order of flight gradients from low to high until the preset condition is met; wherein two hypothesis test operations correspond to different flight gradients; the i-th hypothesis test operation corresponds to the i-th flight gradient; i is greater than or equal to 1 and less than or equal to N; and N is the total number of flight gradients;

[0019] The i-th hypothesis testing operation includes:

[0020] Performing a hypothesis test on the multi-band crop spectral image of the marked sample plot under the i-th flight gradient, and determining the hypothesis test result corresponding to the marked sample plot at the i-th flight gradient; the marked sample plot is any sample plot;

[0021] When the hypothesis test result is less than or equal to the set threshold, the flight height corresponding to the i-1th flight gradient is determined as the optimal flight height of the marked sample cell;

[0022] The preset condition includes: the hypothesis test result is less than or equal to the set threshold.

[0023] Optionally, determining the hypothesis test result corresponding to the marked sample cell at the i-th flight gradient specifically includes:

[0024] Determine a first target average reflectance spectrum; the first target average reflectance spectrum is an average reflectance spectrum of a multi-band crop spectral image marking a sample plot at the i-th flight gradient;

[0025] Determine a second target average reflectance spectrum; the second target average reflectance spectrum is an average reflectance spectrum of a multi-band crop spectral image of a marked sample plot when the UAV is flying;

[0026] A hypothesis test is performed on the first target average reflectance spectrum and the second target average reflectance spectrum to determine a hypothesis test result corresponding to the marked sample cell at the i-th flight gradient.

[0027] In a second aspect, the present invention provides a system for determining an optimal flight altitude of a drone, comprising:

[0028] The test area multi-band crop spectral image acquisition module is used to acquire the test area multi-band crop spectral image in a set scenario; the set scenario is a scenario in which the flight parameters of the UAV and the setting parameters of the spectral camera remain unchanged during the flight of the UAV equipped with the spectral camera;

[0029] A sample plot multispectral orthophoto determination module is used to perform data processing on the multi-band crop spectral image to obtain a plurality of sample plot multispectral orthophotos; wherein the test area includes a plurality of sample plots;

[0030] The module for determining multispectral images at different flight altitudes of the sample plot is used to calculate the ground resolution of the multispectral orthophoto of each sample plot, and simulate and determine the multi-band crop spectral image of each sample plot at different flight altitudes based on the nearest neighbor interpolation method and the ground resolution of the multispectral orthophoto of each sample plot;

[0031] The optimal flight height determination module is used to determine the optimal flight height corresponding to each sample plot by using a hypothesis testing method for the multi-band crop spectral image of each sample plot at different flight heights.

[0032] Optionally, the second aspect further includes:

[0033] An optimal flight trajectory determination module, used to determine the optimal flight trajectory corresponding to the test area according to the optimal flight altitude corresponding to each of the sample cells;

[0034] The multi-band crop spectral image determination module is used to control the UAV to fly according to the optimal flight trajectory to obtain a multi-band crop spectral image that meets the phenotypic parameter extraction accuracy requirements.

[0035] Optionally, a plurality of multi-band crop spectral images corresponding to any sample plot are divided into different flight gradients according to the flight altitude; the optimal flight altitude determination module specifically includes:

[0036] For any sample cell, perform at least one hypothesis test operation in the order of flight gradients from low to high until the preset condition is met; wherein two hypothesis test operations correspond to different flight gradients; the i-th hypothesis test operation corresponds to the i-th flight gradient; i is greater than or equal to 1 and less than or equal to N; and N is the total number of flight gradients;

[0037] The i-th hypothesis testing operation includes:

[0038] Performing a hypothesis test on the multi-band crop spectral image of the marked sample plot under the i-th flight gradient, and determining the hypothesis test result corresponding to the marked sample plot at the i-th flight gradient; the marked sample plot is any sample plot;

[0039] When the hypothesis test result is less than or equal to the set threshold, the flight height corresponding to the i-1th flight gradient is determined as the optimal flight height of the marked sample cell;

[0040] The preset condition includes: the hypothesis test result is less than or equal to the set threshold.

[0041] In a third aspect, the present invention provides an electronic device, comprising a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the method for determining the optimal flight altitude of a drone according to the first aspect.

[0042] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method for determining the optimal flight altitude of a drone as described in the first aspect.

[0043] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0044] In view of the problems in the above-mentioned prior art, the present invention mainly explores the influence of different flight altitudes on the quality of multi-band crop spectral images collected by the spectral camera carried by the UAV. It mainly includes firstly obtaining the multi-band crop spectral images of the test area in the set scenario; wherein the set scenario is a scenario in which the flight parameters of the UAV and the setting parameters of the spectral camera remain unchanged during the flight of the UAV equipped with the spectral camera; secondly, data processing is performed on the multi-band crop spectral images to obtain multi-spectral orthophotographs of multiple sample plots; then the ground resolution of the multi-spectral orthophotograph of each sample plot is calculated, and the multi-band crop spectral images of each sample plot at different flight altitudes are simulated and determined according to the nearest neighbor interpolation method and the ground resolution of the multi-spectral orthophotograph of each sample plot, so as to reduce the factors affected by the external environment; finally, for the multi-band crop spectral images of each sample plot at different flight altitudes, a hypothesis test is used to verify whether there are significant differences in the multi-band crop spectral images of different flight altitudes for phenotypic analysis, so as to obtain the relatively optimal flight altitude when the UAV is equipped with fixed equipment and fixed parameter settings, thereby improving flight efficiency and reducing time cost. Therefore, the optimal flight altitude is determined through the relationship between the UAV flight altitude and the ground resolution, laying the foundation for obtaining crop spectral images that meet the accuracy requirements for phenotypic parameter extraction. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0046] Figure 1 A schematic diagram of a flow chart of a method for determining an optimal flight altitude of a drone provided by an embodiment of the present invention;

[0047] Figure 2 A position relationship diagram of mapping the interpolation points provided in an embodiment of the present invention back to the original image;

[0048] Figure 3 A schematic diagram of the p-value and significance level α of a two-sided test provided in an embodiment of the present invention;

[0049] Figure 4 A schematic structural diagram of a system for determining an optimal flight altitude of a UAV provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0052] Embodiment 1

[0053] like Figure 1 As shown, a method for determining an optimal flight altitude of a drone provided by an embodiment of the present invention includes:

[0054] Step 100: Acquire a multi-band crop spectral image of the test area in a set scenario; the set scenario is a scenario in which the flight parameters of the drone and the setting parameters of the spectral camera remain unchanged during the flight of the drone equipped with the spectral camera.

[0055] An example: In cloudless and windless weather, with a fixed flight speed (e.g. 2.5 m / s), a fixed flight altitude (e.g. 10 m), a fixed overlap (e.g. 75% / 60% heading / sideways), and fixed spectral camera parameters (exposure time, aperture, ISO, focal length, etc.), a spectral camera mounted on a drone is used to collect multi-band crop spectral images of the test area.

[0056] Step 200: Process the multi-band crop spectral image to obtain a plurality of sample plot multi-spectral orthophographs; wherein the test area includes a plurality of sample plots.

[0057] This step 200 specifically includes:

[0058] Step A: Using Agisoft Photoscan software, image stitching is performed on the multi-band crop spectral images of the test area collected by the spectral camera to obtain a multi-spectral orthographic image.

[0059] In step A, the specific steps are divided into image alignment, grid establishment, and image stitching.

[0060] Step B: Using MATLAB software, the multispectral orthophoto is subjected to spectral correction and reflectance correction to obtain a corrected multispectral orthophoto.

[0061] Step C: performing image segmentation on the corrected multispectral orthophoto to obtain a plurality of sample cell multispectral orthophotos.

[0062] An example: After the above operations, multispectral orthophotos of 300 sample plots are obtained.

[0063] Step 300: Calculate the ground resolution of the multispectral orthophograph of each sample plot, and simulate and determine the multi-band crop spectral image of each sample plot at different flight altitudes based on the nearest neighbor interpolation method and the ground resolution of the multispectral orthophograph of each sample plot.

[0064] The ground resolution is defined as the actual target length described by a unit pixel, and its calculation formula is as follows:

[0065] GSD = S × H / f (1);

[0066] In formula (1), GSD represents the ground resolution, S represents the actual size of a single pixel of the spectral camera, H represents the flight altitude, and f represents the focal length of the spectral camera lens. When the flight altitude increases, the actual target length described by a unit pixel becomes longer, that is, the ground resolution decreases, exacerbating the mixed pixel effect of the image.

[0067] The nearest neighbor interpolation method is a method for scaling an image, also known as the zero-order interpolation method. The value of the pixel to be interpolated is the value of the nearest pixel among the four neighboring pixels around it, as shown in formula (2). First, the point to be interpolated is mapped back to the original image, as shown in Figure 2 As shown, its coordinates are (x+i, y+j), and then the distance between the point to be interpolated and the surrounding four adjacent pixel points (A, B, C, D) is calculated, and the pixel value of the point closest to the point to be interpolated is taken as the pixel value of the point to be interpolated, thus completing the nearest neighbor interpolation process.

[0068]

[0069] The nearest neighbor interpolation method is used to change the ground resolution of the multispectral orthomosaic of each sample plot, thereby reducing the ground resolution of the multispectral orthomosaic of the sample plot. Then, the process of obtaining the multi-band crop spectral images of the sample plot by UAV at different flight altitudes is simulated according to different ground resolutions, and finally the multi-band crop spectral images of the sample plot at different flight altitudes are obtained.

[0070] As an example, after the above operations, multi-band crop spectral images of 50 sample plots at different flight altitudes are obtained.

[0071] The nearest neighbor interpolation method is used to change the ground resolution of the multispectral orthomosaic image of the sample plot, simulating images taken by drones at different altitudes, thereby reducing the ground resolution corresponding to the image. Compared with traditional outdoor experimental methods, this method reduces the influence of the external environment and makes subsequent analysis more accurate.

[0072] Step 400: for the multi-band crop spectral image of each sample plot at different flight altitudes, a hypothesis testing method is used to determine the optimal flight altitude corresponding to each sample plot.

[0073] The average reflectance spectrum of the sample cell is calculated by using the ground reflectance correction plate and formula (3) to calculate the average reflectance spectrum of the sample cell. The spectral reflectance calculation formula is:

[0074]

[0075] In formula (3), DN is the image grayscale value (Digital Number), DN0 is the average image grayscale value of the known ground reflectance correction plate, f0 is the reflectance of the known ground reflectance correction plate, and the reflectance of the known ground reflectance correction plate is uniform, DN n is the image grayscale value of each pixel of the single band spectral image of the nth sample cell, f n is the reflectance of each pixel of the single band spectral image of the nth sample cell. n The average reflectance value of the sample cell is obtained by averaging, and the above operation is performed on all bands to obtain the average reflectance spectrum of the sample cell.

[0076] The hypothesis test method is to test the average reflectance spectra of 300 sample cells at 50 different flight altitudes with the average reflectance spectra obtained from the flight test, such as Figure 3 As shown. Assuming that the two samples are independent random samples and follow a normal distribution, the confidence interval (1-α) is given as 0.95, the significance level (α) is 0.05, and the p value is calculated. When p≤0.001, there is an extremely significant statistical difference, 0.001≤p≤0.01 means there is a significant statistical difference, 0.01≤p≤0.05 means there is a statistical difference, and 0.05≤p means there is no statistical difference. In this case, the multi-band spectral image of the drone obtained by the flight test is assumed to be true, so during the flight test, the flight altitude of the drone should be as low as possible.

[0077] Based on this, step 400 specifically includes:

[0078] The multiple multi-band crop spectral images corresponding to any sample plot are divided into different flight gradients according to the flight altitude.

[0079] For any sample cell, at least one hypothesis test operation is performed in the order of flight gradients from low to high until the preset conditions are met; wherein two hypothesis test operations correspond to different flight gradients; the i-th hypothesis test operation corresponds to the i-th flight gradient; i is greater than or equal to 1 and less than or equal to N; and N is the total number of flight gradients.

[0080] The i-th hypothesis testing operation includes:

[0081] Performing a hypothesis test on the multi-band crop spectral image of the marked sample plot under the i-th flight gradient, and determining the hypothesis test result corresponding to the marked sample plot at the i-th flight gradient; the marked sample plot is any sample plot;

[0082] When the hypothesis test result is less than or equal to the set threshold, the flight height corresponding to the i-1th flight gradient is determined as the optimal flight height of the marked sample cell;

[0083] When the hypothesis test result is greater than the set threshold, the i-th flight gradient is updated to the i+1-th flight gradient, and the process returns to the step of performing a hypothesis test on the multi-band crop spectral image of the marked sample plot under the i-th flight gradient to determine the hypothesis test result corresponding to the marked sample plot at the i-th flight gradient.

[0084] The preset condition includes: the hypothesis test result is less than or equal to the set threshold.

[0085] Among them, determining the hypothesis test result corresponding to the marked sample cell at the i-th flight gradient specifically includes:

[0086] First, a first target average reflectance spectrum is determined; the first target average reflectance spectrum is the average reflectance spectrum of the multi-band crop spectral image marking the sample plot at the i-th flight gradient.

[0087] Secondly, determine the second target average reflectance spectrum; the second target average reflectance spectrum is the average reflectance spectrum of the multi-band crop spectral image of the marked sample plot when the UAV is flying.

[0088] Finally, a hypothesis test is performed on the first target average reflectance spectrum and the second target average reflectance spectrum to determine the hypothesis test result corresponding to the marked sample cell at the i-th flight gradient.

[0089] An example is: in order of increasing flight altitude (H m , m=1,2,…,50), the average reflectance spectrum of the multi-band crop spectral image of the sample plot is tested against the average reflectance spectrum obtained from the actual flight test. m-1 The high hypothesis test result p>0.05, H m When the hypothesis test result of height is p≤0.05, it means that the height (H m) and above is statistically different from the actual average reflectance spectrum, which will cause errors in the average reflectance spectrum of multi-band crop spectral images. Therefore, when the UAV is equipped with this spectral camera for image acquisition, the altitude should be lower than H m flight.

[0090] In order to improve flight efficiency, reduce flight costs and obtain correct spectral images, the optimal flight altitude is H m-1 .

[0091] A hypothesis test method is used to verify whether there is a significant difference in the average spectra of different flight altitudes used for phenotypic analysis, so as to obtain the relatively optimal flight altitude when the drone is fixed with equipment and other parameters. Compared with the traditional practice of setting parameters based on experience for drone experiments, which is inefficient and costly, the optimal flight altitude setting provided by the embodiment of the present invention without damaging the accuracy of data can improve flight efficiency and reduce operating costs.

[0092] Furthermore, the method described in the embodiment of the present invention further includes:

[0093] The optimal flight trajectory corresponding to the test area is determined according to the optimal flight altitude corresponding to each sample cell.

[0094] The UAV is controlled to fly according to the optimal flight trajectory to obtain multi-band crop spectral images that meet the accuracy requirements for phenotypic parameter extraction.

[0095] Embodiment 2

[0096] In order to execute the method corresponding to the above-mentioned embodiment 1 to achieve the corresponding functions and technical effects, a method for determining the optimal flight altitude of a drone is provided below.

[0097] like Figure 4 As shown, an embodiment of the present invention provides a system for determining an optimal flight altitude of a drone, comprising:

[0098] The test area multi-band crop spectral image acquisition module 1 is used to acquire the test area multi-band crop spectral image in a set scenario; the set scenario is a scenario in which the flight parameters of the drone and the setting parameters of the spectral camera remain unchanged during the flight of the drone equipped with the spectral camera;

[0099] The sample plot multispectral orthophoto determination module 2 is used to process the multi-band crop spectral image to obtain a plurality of sample plot multispectral orthophotos; wherein the test area includes a plurality of sample plots;

[0100] The multispectral image determination module 3 for different flight altitudes of the sample plot is used to calculate the ground resolution of the multispectral orthophotograph of each sample plot, and simulate and determine the multi-band crop spectral image of each sample plot at different flight altitudes according to the nearest neighbor interpolation method and the ground resolution of the multispectral orthophotograph of each sample plot;

[0101] The optimal flight height determination module 4 is used to determine the optimal flight height corresponding to each sample plot by using a hypothesis testing method for the multi-band crop spectral image of each sample plot at different flight heights.

[0102] Furthermore, the system provided by the embodiment of the present invention also includes:

[0103] The optimal flight trajectory determination module is used to determine the optimal flight trajectory corresponding to the test area according to the optimal flight altitude corresponding to each of the sample cells.

[0104] The multi-band crop spectral image determination module is used to control the UAV to fly according to the optimal flight trajectory to obtain a multi-band crop spectral image that meets the phenotypic parameter extraction accuracy requirements.

[0105] Furthermore, a plurality of multi-band crop spectral images corresponding to any sample plot are divided into different flight gradients according to the flight altitude.

[0106] The optimal flight altitude determination module 4 specifically includes:

[0107] For any sample cell, at least one hypothesis test operation is performed in the order of flight gradients from low to high until the preset conditions are met; wherein two hypothesis test operations correspond to different flight gradients; the i-th hypothesis test operation corresponds to the i-th flight gradient; i is greater than or equal to 1 and less than or equal to N; and N is the total number of flight gradients.

[0108] The i-th hypothesis testing operation includes:

[0109] Performing a hypothesis test on the multi-band crop spectral image of the marked sample plot under the i-th flight gradient, and determining the hypothesis test result corresponding to the marked sample plot at the i-th flight gradient; the marked sample plot is any sample plot;

[0110] When the hypothesis test result is less than or equal to the set threshold, the flight height corresponding to the (i-1)th flight gradient is determined as the optimal flight height of the marked sample cell.

[0111] The preset condition includes: the hypothesis test result is less than or equal to the set threshold.

[0112] Embodiment 3

[0113] An embodiment of the present invention provides an electronic device including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the method for determining the optimal flight altitude of a drone of embodiment 1.

[0114] Optionally, the above-mentioned electronic device may be a server.

[0115] In addition, an embodiment of the present invention further provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method for determining the optimal flight altitude of a drone in embodiment 1.

[0116] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0117] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for determining the optimal flight altitude of an unmanned aerial vehicle, characterized in that: include: Acquire a multi-band crop spectral image of the test area in a set scenario; the set scenario is a scenario in which the flight parameters of the drone and the setting parameters of the spectral camera remain unchanged during the flight of the drone equipped with the spectral camera; Processing the multi-band crop spectral image to obtain a plurality of sample plot multi-spectral orthophotographs; wherein the test area includes a plurality of sample plots; Calculating the ground resolution of the multispectral orthophotograph of each sample plot, and simulating and determining the multi-band crop spectral image of each sample plot at different flight altitudes based on the nearest neighbor interpolation method and the ground resolution of the multispectral orthophotograph of each sample plot; For the multi-band crop spectral images of each sample plot at different flight altitudes, a hypothesis testing method is used to determine the optimal flight altitude corresponding to each sample plot; The multiple multi-band crop spectral images corresponding to any sample plot are divided into different flight gradients according to the flight altitude; The method of using a hypothesis test method to determine the optimal flight height corresponding to each sample plot for the multi-band crop spectral image of each sample plot at different flight heights specifically includes: For any sample cell, perform at least one hypothesis test operation in the order of flight gradients from low to high until the preset condition is met; wherein two hypothesis test operations correspond to different flight gradients; the i-th hypothesis test operation corresponds to the i-th flight gradient; i is greater than or equal to 1 and less than or equal to N; and N is the total number of flight gradients; The i-th hypothesis testing operation includes: Performing a hypothesis test on the multi-band crop spectral image of the marked sample plot under the i-th flight gradient, and determining the hypothesis test result corresponding to the marked sample plot at the i-th flight gradient; the marked sample plot is any sample plot; When the hypothesis test result is less than or equal to the set threshold, the flight height corresponding to the i-1th flight gradient is determined as the optimal flight height of the marked sample cell; The preset conditions include: the hypothesis test result is less than or equal to the set threshold; The step of determining the hypothesis test result corresponding to the marked sample cell at the i-th flight gradient specifically includes: Determine a first target average reflectance spectrum; the first target average reflectance spectrum is an average reflectance spectrum of a multi-band crop spectral image marking a sample plot at an i-th flight gradient; Determine a second target average reflectance spectrum; the second target average reflectance spectrum is an average reflectance spectrum of a multi-band crop spectral image of a marked sample plot when the UAV is flying; A hypothesis test is performed on the first target average reflectance spectrum and the second target average reflectance spectrum to determine a hypothesis test result corresponding to the marked sample cell at the i-th flight gradient.

2. The method for determining the optimal flight altitude of a drone according to claim 1, characterized in that: Also includes: Determine the optimal flight trajectory corresponding to the test area according to the optimal flight altitude corresponding to each sample cell; The UAV is controlled to fly according to the optimal flight trajectory to obtain multi-band crop spectral images that meet the accuracy requirements for phenotypic parameter extraction.

3. The method for determining the optimal flight altitude of a UAV according to claim 1, characterized in that: The step of performing data processing on the multi-band crop spectral image to obtain a plurality of sample plot multi-spectral orthographic images specifically includes: Using Agisoft Photoscan software, image stitching is performed on the multi-band crop spectral images of the test area collected by the spectral camera to obtain a multi-spectral orthographic image; Using MATLAB software, performing spectral correction and reflectance correction on the multispectral orthophoto to obtain a corrected multispectral orthophoto; The corrected multispectral orthophoto is subjected to image segmentation to obtain a plurality of sample cell multispectral orthophotos.

4. A system for determining the optimal flight altitude of an unmanned aerial vehicle, characterized in that: include: The test area multi-band crop spectral image acquisition module is used to acquire the test area multi-band crop spectral image in a set scenario; the set scenario is a scenario in which the flight parameters of the UAV and the setting parameters of the spectral camera remain unchanged during the flight of the UAV equipped with the spectral camera; A sample plot multispectral orthophoto determination module is used to perform data processing on the multi-band crop spectral image to obtain a plurality of sample plot multispectral orthophotos; wherein the test area includes a plurality of sample plots; The module for determining multispectral images at different flight altitudes of the sample plot is used to calculate the ground resolution of the multispectral orthophoto of each sample plot, and simulate and determine the multi-band crop spectral image of each sample plot at different flight altitudes based on the nearest neighbor interpolation method and the ground resolution of the multispectral orthophoto of each sample plot; An optimal flight height determination module is used to determine the optimal flight height corresponding to each sample plot by using a hypothesis testing method for the multi-band crop spectral image of each sample plot at different flight heights; The multiple multi-band crop spectral images corresponding to any sample plot are divided into different flight gradients according to the flight altitude; The optimal flight altitude determination module specifically includes: For any sample cell, perform at least one hypothesis test operation in the order of flight gradients from low to high until the preset condition is met; wherein two hypothesis test operations correspond to different flight gradients; the i-th hypothesis test operation corresponds to the i-th flight gradient; i is greater than or equal to 1 and less than or equal to N; and N is the total number of flight gradients; The i-th hypothesis testing operation includes: Performing a hypothesis test on the multi-band crop spectral image of the marked sample plot under the i-th flight gradient, and determining the hypothesis test result corresponding to the marked sample plot at the i-th flight gradient; the marked sample plot is any sample plot; When the hypothesis test result is less than or equal to the set threshold, the flight height corresponding to the i-1th flight gradient is determined as the optimal flight height of the marked sample cell; The preset conditions include: the hypothesis test result is less than or equal to the set threshold; The step of determining the hypothesis test result corresponding to the marked sample cell at the i-th flight gradient specifically includes: Determine a first target average reflectance spectrum; the first target average reflectance spectrum is an average reflectance spectrum of a multi-band crop spectral image marking a sample plot at an i-th flight gradient; Determine a second target average reflectance spectrum; the second target average reflectance spectrum is an average reflectance spectrum of a multi-band crop spectral image of a marked sample plot when the UAV is flying; A hypothesis test is performed on the first target average reflectance spectrum and the second target average reflectance spectrum to determine a hypothesis test result corresponding to the marked sample cell at the i-th flight gradient.

5. The optimal flight altitude determination system for a UAV according to claim 4, characterized in that: Also includes: An optimal flight trajectory determination module, used to determine the optimal flight trajectory corresponding to the test area according to the optimal flight altitude corresponding to each of the sample cells; The multi-band crop spectral image determination module is used to control the UAV to fly according to the optimal flight trajectory to obtain a multi-band crop spectral image that meets the phenotypic parameter extraction accuracy requirements.

6. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the method for determining the optimal flight altitude of a drone according to any one of claims 1 to 3.

7. A computer-readable storage medium, characterized in that: It stores a computer program, which, when executed by a processor, implements the method for determining the optimal flight altitude of a drone as described in any one of claims 1 to 3.

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