Image analysis-based testing method and system for rice seedling throwing effect of rice seedling throwing drone

Abstract

The present invention discloses a method and system for testing the transplanting effect of a transplanting drone based on image analysis, and belongs to the field of transplanting drones. The present invention performs transplanting quality analysis based on obtaining the distribution uniformity of transplanted seedlings in farmland, the degree of stand-up of transplanted seedlings and the position of seedlings, and comprehensively evaluates the transplanting quality according to the distribution and stand-up status of seedlings in farmland after transplanting, thereby improving the accuracy of the transplanting quality analysis process. At the same time, in the transplanting effect analysis process, the environmental impact analysis of the transplanting process is performed based on the environmental data during the transplanting process, thereby removing the impact caused by the environment when evaluating the drone performance, and further improving the accuracy and efficiency of the transplanting effect analysis.

Description

Technical Field

[0001] The present invention belongs to the field of rice seedling throwing drones, and in particular to a rice seedling throwing effect testing method and system of rice seedling throwing drones based on image analysis. Background Art

[0002] A rice seedling throwing drone is an advanced equipment used in agricultural production. It is mainly used for rice seedling throwing during rice planting. The cultivated rice seedlings are loaded into the drone's throwing device. The seedlings are usually arranged at a certain density to ensure uniform throwing. The drone is fully inspected, including battery power, motor status, whether the throwing device is normal, etc., to ensure that the equipment is in good working condition. According to the actual situation of the farmland, the drone's flight route and throwing density are planned to ensure that the seedlings can be evenly distributed. The operator starts the drone and makes it take off smoothly to the predetermined operating altitude. The drone is accurately positioned through GPS or other positioning systems and flies according to the pre-planned route. During the flight, the drone's throwing device evenly throws the seedlings into the farmland according to the set time and density. The throwing device usually uses rotation or vibration to ensure that the seedlings are evenly dispersed. During the throwing process, the throwing effect needs to be tested and analyzed to understand whether the drone can perform the throwing task. At this time, a rice seedling throwing effect testing method and system of a rice seedling throwing drone is needed.

[0003] In the process of testing the effect of seedling throwing by a seedling throwing drone, the existing technology usually simply analyzes the effect of seedling throwing by the distribution of seedlings in the farmland after throwing, ignoring the impact of the standing state of the seedlings on the quality of throwing. At the same time, abnormal environments in different environments will cause inaccurate impact on the seedling throwing effect test, which the existing technology has not considered. In order to solve the problems raised by this background technology, this application designs a seedling throwing effect testing method and system for a seedling throwing drone based on image analysis. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the present invention proposes a method and system for testing the seedling throwing effect of a seedling throwing drone based on image analysis.

[0005] To achieve the above objectives, the present invention provides the following technical solutions: In the first aspect, a method for testing the effect of seedling throwing by a seedling throwing drone based on image analysis comprises the following specific steps:

[0006] Fly the drone to the designated location to obtain environmental data during the rice seedling throwing process, as well as the distribution and angle of the rice seedlings in the field;

[0007] The quality of seedling throwing is analyzed based on the uniformity of distribution of seedlings in the farmland, the degree of standing of seedlings and the position of seedlings.

[0008] Based on the environmental data during the rice seedling throwing process, the environmental impact analysis of the rice seedling throwing process is carried out to obtain the environmental impact analysis results;

[0009] Analyze the effect of transplanting rice seedlings based on the results of transplanting rice seedling quality analysis and environmental impact analysis;

[0010] Based on the analysis results of the rice seedling throwing effect, it is judged whether the rice seedling throwing drone meets the requirements.

[0011] It should be noted that as a preferred technical solution for the image analysis-based method for testing the effect of seedling throwing by a drone, the specific steps of flying the drone to a designated location, obtaining environmental data during the drone seedling throwing process, and obtaining the distribution and angle of the thrown seedlings in the farmland are as follows:

[0012] S11, placing the rice seedlings to be scattered at the corresponding position of the rice seedling-scattering drone, flying the drone to the designated position, and scattering the rice seedlings. During the scattering process, the environmental wind speed and environmental wind speed change are obtained, and the obtained data are stored in the first storage component;

[0013] S12. After the seedlings land, the distribution and spacing of the seedlings in the planting area are obtained, and at the same time, the angle data of each seedling after landing are obtained, and the obtained data are stored in the second storage component.

[0014] It should be noted that as a preferred technical solution for the rice seedling throwing effect testing method of a rice seedling throwing drone based on image analysis, the rice seedling throwing quality analysis based on obtaining the distribution uniformity of the thrown rice seedlings in the farmland, the degree of standing of the thrown rice seedlings, and the position of the rice seedlings includes the following specific steps:

[0015] S21. Observe the distribution of rice seedlings at various angles on the spreading surface, taking the vertical projection of the drone's spreading position relative to the ground as the center. Also, obtain distance data between each seedling after spreading and its nearest adjacent seedling. Evaluate the uniformity of rice seedling distribution based on deviations in the distribution of rice seedlings at various angles on the spreading surface and deviations in the distance data between each seedling after spreading and its nearest adjacent seedling.

[0016] The specific contents of the seedling distribution uniformity assessment are as follows:

[0017] S211. The spreading surface is evenly divided into a plurality of angle ranges, and the distribution of the seedlings at each angle on the spreading surface is obtained. The average value of the number of seedlings distributed at each angle is obtained, and the distribution of the seedlings corresponding to each angle and the average value of the number of seedlings distributed at each angle are introduced into the angle distribution discreteness calculation formula to calculate the angle distribution discreteness. The angle distribution discreteness calculation formula can be any calculation formula that reflects the distribution discreteness, such as variance or standard deviation. For example, the angle distribution discreteness calculation formula can be: , where n is the number of angle ranges, xi is the number of seedlings in the i-th angle range, and xm is the average number of seedlings distributed at each angle. It should be noted that the number of angle ranges here is determined in advance, because the more angle ranges there are, the more accurate the uniformity calculation will be.

[0018] S212. Obtaining distance data from each seedling to its nearest neighboring seedling after sowing, adding the distance data and dividing by the number of seedlings to obtain an average distance value, substituting the obtained distance data from each seedling to its nearest neighboring seedling after sowing and the average distance value into a distance distribution uniformity calculation formula to calculate the distance distribution dispersion. The distance distribution dispersion calculation formula may also be any calculation formula reflecting distribution dispersion, such as variance or standard deviation;

[0019] S213, obtaining the calculated angle distribution discreteness and distribution discreteness, performing weighted summation, normalizing the obtained seedling distribution discreteness, and then calculating the inverse thereof to obtain the seedling distribution uniformity;

[0020] S22, analyzing the difficulty of maintaining the angle based on the position of the seedlings, and analyzing the abnormality of the spreading angle based on the difficulty of maintaining the angle of the seedlings at the corresponding position and the angle deviation of the seedlings at the corresponding position;

[0021] S23. Obtain the obtained seedling distribution uniformity analysis result and the throwing angle abnormality analysis result, and perform weighted summation on the seedling distribution uniformity analysis result and the inverse of the throwing angle abnormality analysis result to obtain the seedling throwing quality.

[0022] It should be noted that as a preferred technical solution for the rice seedling throwing effect testing method of a rice seedling throwing drone based on image analysis, the environmental impact analysis of the rice seedling throwing process based on environmental data during the rice seedling throwing process includes the following specific steps:

[0023] S31, obtaining the wind force and wind direction changes during the rice seedling throwing process;

[0024] S32. Based on the magnitude of wind force and the frequency of wind direction changes during the seedling throwing process, a comprehensive analysis of the impact of the environment on the seedling throwing process is conducted. During the seedling throwing process, the magnitude of wind force and the frequency of wind direction changes directly affect the uniformity of the seedling throwing process.

[0025] It should be noted that as a preferred technical solution for the rice seedling throwing effect testing method of a rice seedling throwing drone based on image analysis, the rice seedling throwing effect analysis based on the rice seedling throwing quality analysis results and the environmental impact analysis results also includes the following specific contents:

[0026] Obtain the seedling throwing quality analysis results and the impact of the environment on the seedling throwing process, and obtain the drone seedling throwing effect by dividing the seedling throwing quality analysis results by the impact of the environment on the seedling throwing process. Compare the obtained drone seedling throwing effect with the set standard threshold. If the obtained drone seedling throwing effect is greater than or equal to the set standard threshold, it means that the seedling throwing drone meets the seedling throwing requirements. If the obtained drone seedling throwing effect is less than the set standard threshold, it means that the seedling throwing drone does not meet the seedling throwing requirements and the drone needs to be debugged.

[0027] The second aspect is a rice seedling throwing effect testing system of a rice seedling throwing drone based on image analysis, which is implemented based on the above-mentioned rice seedling throwing effect testing method of a rice seedling throwing drone based on image analysis. It specifically includes a data acquisition module, a rice seedling throwing quality analysis module, an environmental impact analysis module, a rice seedling throwing effect analysis module and a judgment module, wherein the data acquisition module is used to fly the drone to a specified location, obtain environmental data during the rice seedling throwing process of the drone, and simultaneously obtain the distribution and angle of the rice seedlings thrown in the farmland;

[0028] The seedling throwing quality analysis module performs seedling throwing quality analysis based on the distribution uniformity, standing degree and position of the seedlings in the farmland;

[0029] The environmental impact analysis module performs an environmental impact analysis of the rice seedling throwing process based on the environmental data during the rice seedling throwing process to obtain an environmental impact analysis result;

[0030] The rice seedling throwing effect analysis module performs rice seedling throwing effect analysis based on the rice seedling throwing quality analysis results and the environmental impact analysis results;

[0031] The judgment module judges whether the rice seedling throwing drone meets the requirements based on the rice seedling throwing effect analysis result.

[0032] According to a third aspect, an electronic device includes: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0033] The processor executes the above-mentioned image analysis-based rice seedling throwing effect testing method of the rice seedling throwing drone by calling the computer program stored in the memory.

[0034] In a fourth aspect, a computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the above-mentioned image analysis-based method for testing the effect of rice seedlings being transplanted by a rice seedling transplanting drone.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] The present invention analyzes the quality of seedling throwing based on the uniformity of distribution of the seedlings in the farmland, the degree of standing of the seedlings, and the position of the seedlings. The quality of seedling throwing is comprehensively evaluated according to the distribution and standing state of the seedlings in the farmland after throwing, thereby improving the accuracy of the seedling throwing quality analysis process.

[0037] During the seedling throwing effect analysis process, the present invention performs an environmental impact analysis of the seedling throwing process based on the environmental data during the seedling throwing process, and then removes the impact caused by the environment when evaluating the performance of the drone, further improving the accuracy and efficiency of the seedling throwing effect analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a schematic diagram of the overall process of the rice seedling throwing effect testing method of a rice seedling throwing drone based on image analysis of the present invention;

[0039] Figure 2 This is a schematic flow chart of the steps for evaluating the uniformity of rice seedling distribution in the rice seedling throwing effect testing method of a rice seedling throwing drone based on image analysis of the present invention;

[0040] Figure 3 This is a schematic diagram of the overall framework of the rice seedling throwing effect testing system of the rice seedling throwing drone based on image analysis of the present invention;

[0041] Figure 4 It is a schematic diagram of the rice seedling throwing scene of the present invention. DETAILED DESCRIPTION

[0042] The following will be combined with the accompanying drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the embodiments described are only some embodiments of the present application, rather than all embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is in no way intended to limit the present application, its application, or use.

[0043] Example 1

[0044] The implementation scenario of this embodiment is as follows Figure 4 As shown, specifically: the operator starts the drone and makes it take off smoothly to the predetermined operating height. The drone is accurately positioned using GPS or other positioning systems and flies according to the pre-planned route. During the flight, the seedling throwing device evenly spreads the seedlings into the farmland according to the set time and density. The image acquisition terminal on the drone collects the distribution and angle images of the seedlings in the farmland in real time. At the same time, the environmental acquisition terminal collects environmental data during the drone's seedling throwing process.

[0045] The present invention provides a preferred embodiment: Figure 1-Figure 2 As shown in FIG, a method for testing the effect of seedling throwing by a seedling throwing drone based on image analysis includes the following specific steps:

[0046] Fly the drone to the designated location to obtain environmental data during the rice seedling throwing process, as well as the distribution and angle of the rice seedlings in the field;

[0047] In a specific embodiment, the specific steps of flying a drone to a designated location, obtaining environmental data during the drone seedling throwing process, and obtaining the distribution and angle of the thrown seedlings in the farmland are as follows:

[0048] S11. The rice seedlings to be scattered are placed at the corresponding position of the rice seedling-scattering drone. The drone flies to the designated position and scatters the rice seedlings. During the scattering process, the magnitude of the environmental wind force and the change of the environmental wind force are obtained, and the obtained data are stored in the first storage component. The image acquisition terminal on the drone can collect the distribution and angle images of the rice seedlings in the farmland in real time to provide data support for subsequent analysis and optimization. During the flight of the drone, the camera continuously captures the distribution and angle images of the rice seedlings in the farmland. The collected images are pre-processed, such as denoising and enhancement, to improve the image quality. The distribution and angle features of the rice seedlings are extracted through image processing algorithms, for example, the position, density, tilt angle, etc. of the rice seedlings are identified.

[0049] S12, obtaining the distribution and spacing of the seedlings in the planting area after the seedlings have landed, and simultaneously obtaining angle data of each seedling after landing, and storing the obtained data in a second storage component;

[0050] The quality of seedling throwing is analyzed based on the uniformity of distribution of seedlings in the farmland, the degree of standing of seedlings and the position of seedlings.

[0051] In a specific embodiment, as a preferred technical solution for a method for testing the seedling throwing effect of a seedling throwing drone based on image analysis, the seedling throwing quality analysis based on obtaining the distribution uniformity of the thrown seedlings in the farmland, the degree of standing of the thrown seedlings, and the position of the seedlings includes the following specific steps:

[0052] S21. Observe the distribution of rice seedlings at various angles on the spreading surface, taking the vertical projection of the drone's spreading position relative to the ground as the center. Also, obtain distance data between each seedling after spreading and its nearest adjacent seedling. Evaluate the uniformity of rice seedling distribution based on deviations in the distribution of rice seedlings at various angles on the spreading surface and deviations in the distance data between each seedling after spreading and its nearest adjacent seedling.

[0053] In a specific embodiment, the specific content of the seedling distribution uniformity assessment is:

[0054] S211. The spreading surface is evenly divided into a plurality of angle ranges, and the distribution of the seedlings at each angle on the spreading surface is obtained. The average value of the number of seedlings distributed at each angle is obtained, and the distribution of the seedlings corresponding to each angle and the average value of the number of seedlings distributed at each angle are introduced into the angle distribution discreteness calculation formula to calculate the angle distribution discreteness. The angle distribution discreteness calculation formula can be any calculation formula that reflects the distribution discreteness, such as variance or standard deviation. For example, the angle distribution discreteness calculation formula can be: , where n is the number of angle ranges, xi is the number of seedlings in the i-th angle range, and xm is the average number of seedlings distributed at each angle. It should be noted that the number of angle ranges here is determined in advance, because the more angle ranges there are, the more accurate the uniformity calculation will be.

[0055] S212. Obtain the distance data between each seedling after sowing and its nearest adjacent seedling, add them up and divide by the number of seedlings to obtain an average distance value, substitute the obtained distance data between each seedling after sowing and its nearest adjacent seedling and the average distance value into a distance distribution uniformity calculation formula to calculate the distance distribution discreteness. The distance distribution discreteness calculation formula may also be any calculation formula reflecting the distribution discreteness, such as variance or standard deviation. For example, the distance distribution discreteness calculation formula is: , where Kj is the distance between the jth seedling and the nearest adjacent seedling, Kc is the average distance, and m is the number of seedlings;

[0056] S213, obtaining the calculated angle distribution discreteness and distribution discreteness, performing weighted summation, normalizing the obtained seedling distribution discreteness, and then calculating the inverse thereof to obtain the seedling distribution uniformity;

[0057] S22. Analyze the difficulty of maintaining the angle based on the position of the seedlings. During spreading, the farther the distance from the vertical projection of the drone relative to the ground is, the larger the tangent angle becomes due to the influence of the parabola, and the more difficult it is to ensure that the angle is vertical in the paddy field. Therefore, the difficulty of maintaining the angle of the seedlings at different positions is different. Therefore, when performing the quality analysis of the seedling throwing angle, it is necessary to analyze the difficulty of maintaining the angle in advance. The difficulty of maintaining the angle is proportional to the cosine of the angle between the line connecting the drone and the seedling throwing position and the paddy field plane. Therefore, the calculation formula for the difficulty of maintaining the angle of the jth seedling can be: , where aj is the angle between the line from the drone to the jth seedling throwing position and the paddy field plane, and cos is the cosine of the angle. is a conversion constant used to reflect the proportional relationship between the holding difficulty and the cosine of the angle between the line connecting the drone and the seedling throwing position and the paddy field plane. The throwing angle anomaly analysis is performed based on the angle holding difficulty and the angle deviation of the seedlings at the corresponding position. The calculation formula for the throwing angle anomaly analysis is: , where m is the number of seedlings;

[0058] S23, obtaining the obtained rice seedling distribution uniformity analysis result and the obtained throwing angle abnormality analysis result, and performing a weighted summation on the inverse of the rice seedling distribution uniformity analysis result and the throwing angle abnormality analysis result to obtain the rice seedling throwing quality;

[0059] Based on the environmental data during the rice seedling throwing process, the environmental impact analysis of the rice seedling throwing process is carried out to obtain the environmental impact analysis results;

[0060] In a specific embodiment, performing an environmental impact analysis of the rice seedling throwing process based on environmental data during the rice seedling throwing process includes the following specific steps:

[0061] S31, obtaining the wind force and wind direction changes during the rice seedling throwing process;

[0062] S32. Comprehensively analyze the impact of the environment on the seedling throwing process based on the magnitude of the wind force and the frequency of wind direction changes during the seedling throwing process. During the seedling throwing process, the magnitude of the wind force and the frequency of wind direction changes directly affect the uniformity of the seedling throwing process. The formula for analyzing the impact of the environment on the seedling throwing process can be: , where T is the duration of the seedling throwing process, Yt is the wind force at time t during the seedling throwing process, Ym is the standard wind force value, sinat is the sine of the absolute value of the wind direction change angle between time t and time t-1, and dt is the time integral constant. is the wind impact weight, The weight of wind direction change;

[0063] Based on the results of the seedling throwing quality analysis and the environmental impact analysis, the seedling throwing effect analysis is carried out, and based on the results of the seedling throwing effect analysis, it is judged whether the seedling throwing drone meets the requirements;

[0064] In a specific embodiment, the seedling throwing quality analysis results and the impact of the environment on the seedling throwing process are obtained, and the drone seedling throwing effect is obtained by dividing the seedling throwing quality analysis results by the impact of the environment on the seedling throwing process. The obtained drone seedling throwing effect is compared with the set standard threshold. If the obtained drone seedling throwing effect is greater than or equal to the set standard threshold, it means that the seedling throwing drone meets the seedling throwing requirements. If the obtained drone seedling throwing effect is less than the set standard threshold, it means that the seedling throwing drone does not meet the seedling throwing requirements and the drone needs to be debugged.

[0065] It should be noted that the values ​​of the various setting parameters in this embodiment are determined by obtaining environmental data from a representative historical drone seedling throwing process, as well as the distribution and angle of the seedlings in the farmland during the historical throwing process, hiring experts to manually determine whether the seedling throwing drone meets the requirements, and then substituting the obtained historical data into the calculation results and judgment results of each step in this embodiment into the fitting software to output the values ​​of the various setting parameters that meet the highest judgment accuracy.

[0066] The advantages of this embodiment over the prior art are: the seedling throwing quality analysis is performed based on the uniformity of distribution of the seedlings to be thrown in the farmland, the degree of standing of the seedlings to be thrown and the position of the seedlings; the quality of the seedling throwing is comprehensively evaluated according to the distribution and standing status of the seedlings in the farmland after throwing, thereby improving the accuracy of the seedling throwing quality analysis process; in the seedling throwing effect analysis process, the environmental impact analysis of the seedling throwing process is performed based on the environmental data during the seedling throwing process, and then the impact of the environment is removed during the drone performance evaluation, thereby further improving the accuracy and efficiency of the seedling throwing effect analysis.

[0067] Example 2

[0068] like Figure 3 As shown, the seedling throwing effect testing system of the seedling throwing drone based on image analysis is implemented based on the above-mentioned seedling throwing effect testing method of the seedling throwing drone based on image analysis, which specifically includes a data acquisition module, a seedling throwing quality analysis module, an environmental impact analysis module, a seedling throwing effect analysis module and a judgment module, wherein the data acquisition module is used to fly the drone to a specified location, obtain environmental data during the drone seedling throwing process, and simultaneously obtain the distribution and angle of the thrown seedlings in the farmland;

[0069] The seedling throwing quality analysis module analyzes the quality of the seedlings by obtaining the uniformity of distribution of the seedlings in the farmland, the degree of standing of the seedlings, and the position of the seedlings;

[0070] Environmental impact analysis module, which performs environmental impact analysis of the rice seedling throwing process based on the environmental data during the rice seedling throwing process and obtains the environmental impact analysis results;

[0071] The seedling throwing effect analysis module analyzes the seedling throwing effect based on the seedling throwing quality analysis results and the environmental impact analysis results; the judgment module judges whether the seedling throwing drone meets the requirements based on the seedling throwing effect analysis results; at the same time, Figure 3 The arrows in the figure represent the data transmission connection relationship between modules.

[0072] Example 3

[0073] This embodiment provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0074] The processor executes the above-mentioned image analysis-based rice seedling throwing effect testing method of the rice seedling throwing drone by calling the computer program stored in the memory.

[0075] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors and one or more memories, wherein the memories store at least one computer program, which is loaded and executed by the processor to implement the image analysis-based rice seedling throwing effect testing method for a rice seedling throwing drone provided in the above-mentioned method embodiment. The electronic device may also include other components for implementing the functions of the device. For example, the electronic device may also have components such as a wired or wireless network interface and an input / output interface for data input and output. This embodiment will not be described in detail here.

[0076] Example 4

[0077] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0078] When the computer program is run on a computer device, the computer device is caused to execute the above-mentioned image analysis-based method for testing the effect of rice seedlings being thrown by a rice seedling throwing drone.

[0079] For example, the computer readable storage medium can be a read-only memory, a random access memory, a read-only CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0080] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions according to the embodiments of the present invention are fully or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, computer instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via a wired network or / and a wireless network. A computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

Claims

1. A method for testing the effect of seedling throwing by a seedling throwing drone based on image analysis, characterized in that: It includes the following specific steps: Fly the drone to the designated location to obtain environmental data during the rice seedling throwing process, as well as the distribution and angle of the rice seedlings in the field; The quality of seedling throwing is analyzed based on the uniformity of distribution of seedlings in the farmland, the degree of standing of seedlings and the position of seedlings. The specific steps include: The vertical projection of the drone's spreading position relative to the ground is used as the center to obtain the distribution of seedlings at various angles on the spreading surface. The distance data between each seedling after spreading and its nearest adjacent seedling are also obtained. The uniformity of seedling distribution is evaluated by the deviation of the seedling distribution at various angles on the spreading surface and the deviation of the distance data between each seedling after spreading and its nearest adjacent seedling. Analyze the difficulty of maintaining the angle based on the position of the seedlings, and analyze the abnormality of the spreading angle based on the difficulty of maintaining the angle of the seedlings at the corresponding position and the angle deviation of the seedlings at the corresponding position; Obtaining the obtained seedling distribution uniformity analysis results and the throwing angle abnormality analysis results, and performing a weighted summation of the seedling distribution uniformity analysis results and the reciprocal of the throwing angle abnormality analysis results to obtain the seedling throwing quality; Based on the environmental data during the rice seedling throwing process, the environmental impact analysis of the rice seedling throwing process is carried out to obtain the environmental impact analysis results; Analyze the effect of transplanting rice seedlings based on the results of transplanting rice seedling quality analysis and environmental impact analysis; Based on the analysis results of the rice seedling throwing effect, it is judged whether the rice seedling throwing drone meets the requirements.

2. The method for testing the effect of seedling throwing by a seedling throwing drone based on image analysis according to claim 1, wherein: The environmental impact analysis of the rice seedling throwing process based on the environmental data during the rice seedling throwing process comprises the following specific steps: Obtain the changes in wind force and direction during seedling transplanting; Based on the magnitude of wind force and the frequency of wind direction changes during the seedling throwing process, a comprehensive analysis of the impact of the environment on the seedling throwing process was conducted.

3. The method for testing the effect of seedling throwing by a seedling throwing drone based on image analysis as claimed in claim 2, wherein: As a preferred technical solution for the rice seedling throwing effect testing method of a rice seedling throwing drone based on image analysis, the rice seedling throwing effect analysis based on the rice seedling throwing quality analysis results and the environmental impact analysis results includes the following specific contents: Obtain the seedling throwing quality analysis results and the impact of the environment on the seedling throwing process, and obtain the drone seedling throwing effect by dividing the seedling throwing quality analysis results by the impact of the environment on the seedling throwing process. Compare the obtained drone seedling throwing effect with the set standard threshold. If the obtained drone seedling throwing effect is greater than or equal to the set standard threshold, it means that the seedling throwing drone meets the seedling throwing requirements. If the obtained drone seedling throwing effect is less than the set standard threshold, it means that the seedling throwing drone does not meet the seedling throwing requirements and the drone needs to be debugged.

4. The method for testing the effect of seedling throwing by a seedling throwing drone based on image analysis as claimed in claim 3, wherein: The specific contents of the seedling distribution uniformity assessment are: The spreading surface is evenly divided into a number of angle ranges, the distribution of seedlings at each angle on the spreading surface is obtained, the average value of the number of seedlings distributed at each angle is obtained, and the distribution of seedlings corresponding to each angle and the average value of the number of seedlings distributed at each angle are introduced into the angle distribution discreteness calculation formula to calculate the angle distribution discreteness; Obtain the distance data between each seedling after sowing and its nearest adjacent seedling, add them together and divide by the number of seedlings to obtain the average distance. Substitute the distance data between each seedling after sowing and its nearest adjacent seedling and the average distance into the distance distribution uniformity calculation formula to calculate the distance distribution discreteness. After obtaining the calculated angle distribution discreteness and distribution discreteness and performing weighted summation, the obtained seedling distribution discreteness is normalized and the inverse is calculated to obtain the seedling distribution uniformity.

5. The method for testing the effect of seedling throwing by a seedling throwing drone based on image analysis as claimed in claim 4, characterized in that: The specific steps of flying the drone to a designated location, obtaining environmental data during the drone seedling throwing process, and obtaining the distribution and angle of the thrown seedlings in the farmland are as follows: The seedlings to be scattered are placed at the corresponding position of the seedling-scattering drone. The drone flies to the designated position and scatters the seedlings. During the scattering process, the environmental wind speed and the environmental wind speed change are obtained, and the obtained data are stored in the first storage component. After the seedlings land, the distribution and spacing of the seedlings in the planting area are obtained, and the angle data of each seedling after landing is obtained, and the obtained data is stored in the second storage component.

6. The method for testing the effect of seedling throwing by a seedling throwing drone based on image analysis according to claim 5, wherein: The angle distribution discreteness calculation formula is any one of the calculation formulas that reflect the distribution discreteness of the variance or the standard deviation, and the distance distribution discreteness calculation formula is any one of the calculation formulas that reflect the distribution discreteness of the variance or the standard deviation.

7. A rice seedling throwing effect testing system of a rice seedling throwing drone based on image analysis, which is implemented based on the rice seedling throwing effect testing method of any one of claims 1 to 6, and is characterized in that: It specifically includes a data acquisition module, a seedling throwing quality analysis module, an environmental impact analysis module, a seedling throwing effect analysis module and a judgment module. The data acquisition module is used to fly the drone to a designated location, obtain environmental data during the drone's seedling throwing process, and simultaneously obtain the distribution and angle of the thrown seedlings in the farmland; The seedling throwing quality analysis module performs seedling throwing quality analysis based on the distribution uniformity, standing degree and position of the seedlings in the farmland; The environmental impact analysis module performs an environmental impact analysis of the rice seedling throwing process based on the environmental data during the rice seedling throwing process to obtain an environmental impact analysis result; The rice seedling throwing effect analysis module performs rice seedling throwing effect analysis based on the rice seedling throwing quality analysis results and the environmental impact analysis results; The judgment module judges whether the rice seedling throwing drone meets the requirements based on the rice seedling throwing effect analysis result.

8. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; It is characterized in that the processor executes the rice seedling throwing effect testing method of a rice seedling throwing drone based on image analysis as described in any one of claims 1 to 6 by calling the computer program stored in the memory.

9. A computer-readable storage medium, characterized in that Instructions are stored, and when the instructions are run on a computer, the computer is caused to execute the rice seedling throwing effect testing method of a rice seedling throwing drone based on image analysis as described in any one of claims 1 to 6.

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