Method and system for measuring porosity in canopy based on laser radar, storage medium and computer equipment

Abstract

The invention discloses a method and system for measuring porosity in a canopy based on a laser radar, a storage medium and computer equipment, and the method comprises the steps: employing an unmanned plane, employing dual-view point cloud data, and purifying the point cloud data through filtering outliers and removing ground points; secondly, registering the point cloud data to ensure the consistency of the data; and finally, an AlphaShape-Graham algorithm is applied to carry out analysis and calculation on the layered porosity of the cotton canopy. Compared with the existing manual measurement technology and vehicle-mounted acquisition equipment, the non-contact data acquisition mode not only reduces invasion to crops, but also provides more comprehensive cotton phenotype information, and has important application value for scientific management such as cotton growth monitoring, yield estimation, precise pesticide spraying and the like.

Description

Technical Field

[0001] The invention relates to agricultural monitoring technology, and in particular to a method, system, storage medium and computer equipment for measuring porosity in a canopy based on laser radar. Background Art

[0002] Cotton is one of the main cash crops, and it is also one of the most labor-intensive and time-consuming crops. With the rapid development and promotion of mechanized cotton production technology, mechanized cotton harvesting has become an important way to reduce labor intensity and improve the benefits of cotton planting. Compared with the damage to cotton fields and the shedding of cotton bolls that may be caused by ground machinery, agricultural drone spraying has the advantages of low cost, water saving, high efficiency and no damage to cotton, and has been increasingly widely used in China. However, a large number of studies and practices have shown that there are still some problems in agricultural drone spraying operations. An important reason for this problem is that when agricultural drones are used for aerial spraying, they cannot perform precise aerial spraying according to different cotton varieties, growth conditions and canopy parameters. Therefore, it is of great significance to develop high-precision cotton growth status monitoring tools.

[0003] The main difficulties in detecting parameters of field cotton canopy are: (1) the cotton planting density is high, and the cotton growth structure is complex with many branches and leaves, which is not convenient for large-scale ground measurement; (2) there is mutual occlusion between branches and leaves in the cotton canopy, and it is difficult to directly reconstruct the occlusion structure of the canopy from the outside. Summary of the invention

[0004] The first purpose of the present invention is to overcome the shortcomings of the above prior art and provide a method for measuring porosity in the canopy based on laser radar. This method for measuring porosity in the canopy based on laser radar improves scanning efficiency, enriches data collection results, reduces invasion of crops and saves investment, and realizes rapid and accurate measurement of cotton canopy structure.

[0005] The second object of the present invention is to provide a canopy porosity measurement system based on laser radar.

[0006] A third object of the present invention is to provide a storage medium.

[0007] A fourth object of the present invention is to provide a computer device.

[0008] The first object of the present invention is achieved by the following technical solution: The method for measuring porosity in a canopy based on a laser radar comprises the following steps:

[0009] S1. Build a UAV-mounted laser radar system and use it to collect dual-view point cloud data of cotton fields;

[0010] S2, purifying the dual-view point cloud data by filtering out cluster points and removing ground points;

[0011] S3, use Intrinsic Shape Signatures feature points and Fast Point Feature Histograms descriptors to perform rough registration of the dual-view point cloud data, and then apply the ICP algorithm for fine registration;

[0012] S4. Use the AlphaShape algorithm to determine the concave hull area of ​​the point cloud, use the Graham algorithm to determine the convex hull area of ​​the point cloud, and then analyze and calculate the stratified porosity of the cotton canopy.

[0013] Preferably, the UAV-mounted laser radar system includes a UAV, a laser radar, a radar loading board, a gimbal, an onboard computer, a power supply and a DC regulator. The radar loading board is installed on the arm of the UAV, the laser radar is installed on the radar loading board through the gimbal, the onboard computer is connected to the laser radar through a converter, the power supply is connected to the converter through a DC regulator, and the power supply is connected to the onboard computer at the same time, and the onboard computer is connected to a mobile hotspot.

[0014] Preferably, the specific process of using a drone equipped with a laser radar system to collect dual-view point cloud data of a cotton field in step S1 is as follows:

[0015] The flight route of the UAV is set, and the UAV is made to fly at a certain flight altitude and speed. During the flight, the laser radar performs dual-angle point cloud scanning on the cotton field from left to right and from right to left to form dual-angle point cloud data.

[0016] Preferably, step S2 includes the following specific steps:

[0017] S21, obtain IMU and radar pose information through SLAM mapping algorithm Fast-lio2, correct the dual-view point cloud data, and obtain the global point cloud map of the cotton field;

[0018] S22. Use the ROI function in CloudCompare software to select the region of interest in the point cloud and remove irrelevant background point clouds;

[0019] S23, performing k neighborhood statistical analysis on each point in the point cloud global map, calculating the average point distance from the point to the k neighboring points, assuming that the obtained distance conforms to the Gaussian normal distribution, then defining the average point distance outside the set first threshold m as an outlier point, and removing it from the data set;

[0020] S24, select three ground points of the point cloud to fit into a quadratic surface z0, calculate the difference between the elevation fitting value and the actual value of the adjacent points, and set the second threshold T i Distinguish ground points and crop points, and then remove ground points.

[0021] Preferably, the first threshold m=5, the second threshold T i =5cm.

[0022] Preferably, the Intrinsic Shape Signatures feature point selection in step S3 is based on the local point cloud curvature change, and the curvature threshold is set to 0.01m -1 , the number of iterations of the ICP algorithm is set to 3.

[0023] Preferably, the porosity in step S4 is calculated using the following formula:

[0024]

[0025] Among them, A t (r) is the concave hull area of ​​the cotton canopy excluding pores, and the convex hull area of ​​the cotton canopy including pores.

[0026] The second object of the present invention is achieved by the following technical solution: a canopy porosity measurement system based on laser radar includes a data acquisition module and an execution module.

[0027] The data acquisition module is used to collect point cloud data of the cotton field;

[0028] The execution module is used to execute the lidar-based canopy porosity measurement method described in the first purpose.

[0029] The third object of the present invention is achieved through the following technical solution: a storage medium stores a program, and when the program is executed by a processor, the method for measuring porosity within the canopy based on laser radar described in any one of claims 1 to 7 is implemented.

[0030] The fourth purpose of the present invention is achieved through the following technical solution: a computer device includes a processor and a memory for storing programs executable by the processor. When the processor executes the program stored in the memory, the lidar-based canopy porosity measurement method described in the first purpose is implemented.

[0031] The present invention has the following advantages over the prior art:

[0032] 1. The non-contact data collection method reduces the invasion of crops and avoids interference with cotton growth.

[0033] 2. It provides more comprehensive cotton phenotypic information, which helps to more accurately monitor cotton growth status and estimate yield.

[0034] 3. Highly efficient data collection and processing procedures have greatly improved the efficiency of data collection and reduced the investment in manpower and material resources.

[0035] 4. It provides important data support for scientific management such as precise spraying of pesticides, which helps to improve the efficiency and benefits of agricultural production.

[0036] 5. By implementing the present invention, the accuracy and efficiency of cotton canopy structure analysis can be effectively improved, providing strong technical support for precision agriculture. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 The present invention provides a flowchart of the cotton field data collection method using the UAV laser radar.

[0038] Figure 2 This is a structural diagram of the airborne laser radar system described in the present invention.

[0039] Figure 3 It is a three-dimensional assembly diagram of the radar loading board of the present invention.

[0040] Figure 4 Schematic diagram of different viewing angles of the drone of the present invention scanning a cotton field.

[0041] Figure 5 This is a schematic diagram of ROI area selection according to the present invention.

[0042] Figure 6 This is a comparison diagram of the cotton field point cloud before and after the SOR filtering described in the present invention.

[0043] Figure 7 This is the point cloud fitting surface effect diagram described in the present invention.

[0044] Figure 8 This is the effect diagram of the dual-view point cloud registration described in the present invention.

[0045] Fig. 9 It is a schematic diagram of calculating the concave hull and convex hull areas of each layer of the point cloud projection of the cotton plant according to the present invention.

[0046] Among them, 1 is a drone, 2 is a laser radar, 3 is a radar loading board, 4 is a gimbal, 5 is an onboard computer, 6 is a power supply, 7 is a DC voltage regulator, 8 is a converter, 9 is a mobile hotspot, 10 is a support column, 11 is an upper support plate, 12 is a lower support plate, 13 is a connecting ring, 14 is a base, and 15 is a convex body. DETAILED DESCRIPTION

[0047] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0048] like Figure 1 As shown, the method for measuring porosity in the canopy based on LiDAR includes the following steps:

[0049] S1. Build a UAV-mounted laser radar system and use it to collect dual-view point cloud data of cotton fields;

[0050] Among them, Figure 2 As shown, the UAV-mounted laser radar system includes a UAV, a laser radar, a radar loading board, a gimbal, an onboard computer, a power supply and a DC regulator. The radar loading board is installed on the arm of the UAV, the laser radar is installed on the radar loading board through the gimbal, the onboard computer is connected to the laser radar through a converter, the power supply is connected to the converter through a DC regulator, and the power supply is connected to the onboard computer, and the onboard computer is connected to a mobile hotspot. The onboard computer of this embodiment is a microprocessor, and the mobile hotspot uses mobile wifi. Figure 3 As shown in the figure, the radar loading plate is mainly composed of two support columns, a lower support plate, an upper support plate and a connecting ring. The upper ends of the two support columns are fixed with connecting rings, so as to be connected to the arm of the UAV through the connecting rings. The lower ends of the two support columns are fixed to the two ends of the lower support plate through the corresponding bases, and the upper support plate is installed on the upper support plate through double-headed locking nuts, and a pair of fan-shaped convex bodies are provided on the upper support plate to facilitate the installation and fixation of the gimbal. In order to provide good support for the laser radar, the lower support plate is made of carbon fiber material, and the upper support plate is made of polysensitive resin. The thickness of the upper support plate and the lower support plate is 5-10mm. When the laser radar is fixed to the gimbal, the scanning direction of the laser radar is at an angle of 80° with the horizontal line, so that the drone carrying the laser radar can collect dual-view point cloud data of the cotton field from two perspectives parallel to the cotton rows.

[0051] The specific process of using a drone equipped with a laser radar system to collect dual-view point cloud data of a cotton field in step S1 is as follows:

[0052] The flight route of the UAV is set, and the UAV is made to fly at a certain flight altitude and speed. During the flight, the laser radar performs dual-angle point cloud scanning on the cotton field from left to right and from right to left to form dual-angle point cloud data.

[0053] In this embodiment, the flight path of the drone is as follows: Figure 1 The trajectory of the data acquisition point in the figure is 10m high and 1.5m / s fast. The drone is set to automatic flight mode. Figure 4As shown in the figure, the cotton field was scanned from left to right in perspective 1 and from right to left in perspective 2. The cotton point cloud data was collected using LiDAR to obtain a relatively complete cotton canopy point cloud and reduce the measurement error caused by the mutual occlusion of cotton branches and leaves. During the flight of the drone, the ROS robot control system in the onboard computer controls the LiDAR in real time to ensure the completion of the collection of cotton point cloud data.

[0054] S2, purifying the dual-view point cloud data by filtering out cluster points and removing ground points, so as to complete the preprocessing of the dual-view point cloud data;

[0055] Step S2 includes the following specific steps:

[0056] S21, obtain IMU (inertial measurement unit) and radar pose information through SLAM mapping algorithm Fast-lio2, correct the dual-view point cloud data, and obtain the global point cloud map of the cotton field;

[0057] S22. Use the ROI function in CloudCompare software to select the region of interest in the point cloud and remove irrelevant background point clouds to improve the effectiveness of the point cloud, such as Figure 5 As shown;

[0058] S23, perform k neighborhood statistical analysis on each point in the point cloud global map, calculate the average point distance from the point to the k neighboring points, and assume that the distance obtained conforms to the Gaussian normal distribution, then define the average point distance outside the first threshold m as an outlier point and remove it from the data set, such as Figure 6 As shown; in this embodiment, the first threshold m=5.

[0059] S24, select three ground points of the point cloud to fit into a quadratic surface z0, calculate the difference between the elevation fitting value and the actual value of the adjacent points, and set the second threshold T i Identify ground points and crop points, and then remove ground points to complete the preprocessing of dual-view point cloud data, such as Figure 7 In this embodiment, the second threshold T i =5cm.

[0060] S3, first use the voxel method to collect the pre-processed dual-view point cloud data, use the IntrinsicShape Signatures feature points and Fast Point Feature Histograms descriptors to perform rough registration of the dual-view point cloud data, and then apply the ICP algorithm for fine registration, such as Figure 8 As shown; in step S3, the Intrinsic ShapeSignatures feature point selection is based on the local point cloud curvature change, and the curvature threshold is set to 0.01m -1, the number of iterations of the ICP algorithm is set to 3.

[0061] S4. Use the AlphaShape algorithm to determine the concave hull area of ​​the point cloud, use the Graham algorithm to determine the convex hull area of ​​the point cloud, and then analyze and calculate the stratified porosity of the cotton canopy. Fig. 9 As shown, the AlphaShape algorithm is used to determine the concave area A of the cotton canopy without pores. t (r) Graham algorithm is used to determine the convex hull area A of the cotton canopy containing pores p (r), and then calculate using the following formula:

[0062]

[0063] Among them, A t (r) is the concave hull area of ​​the cotton canopy excluding pores, and the convex hull area of ​​the cotton canopy including pores.

[0064] After adopting the above scheme, the present invention adopts a non-contact data collection method to reduce the invasion of crops and avoid interference with cotton growth; the present invention provides more comprehensive cotton phenotypic information, which is helpful to more accurately monitor the growth status of cotton and estimate the yield; the efficient data collection and processing flow of the present invention greatly improves the efficiency of data collection and reduces the investment of manpower and material resources; the present invention provides important data support for scientific management such as precise spraying of pesticides, which helps to improve the efficiency and benefits of agricultural production; by implementing the present invention, the accuracy and efficiency of cotton canopy structure analysis can be effectively improved, providing strong technical support for precision agriculture.

[0065] The canopy porosity measurement system based on LiDAR includes a data acquisition module and an execution module.

[0066] The data acquisition module is used to collect point cloud data of the cotton field;

[0067] The execution module is used to execute the lidar-based canopy porosity measurement method described in the first purpose.

[0068] A storage medium stores a program, and when the program is executed by a processor, the method for measuring porosity in a canopy based on a laser radar as described in any one of claims 1 to 7 is implemented.

[0069] A computer device includes a processor and a memory for storing programs executable by the processor. When the processor executes the program stored in the memory, the method for measuring porosity within the canopy based on laser radar as described in the first purpose is achieved.

[0070] The above specific implementation modes are preferred embodiments of the present invention and cannot be used to limit the present invention. Any other changes or other equivalent replacement methods that do not deviate from the technical solution of the present invention are included in the protection scope of the present invention.

Claims

1. A method for measuring porosity in a canopy based on laser radar, characterized in that: The following steps are involved: S1. Build a UAV-mounted laser radar system and use it to collect dual-view point cloud data of cotton fields; S2, purifying the dual-view point cloud data by filtering out cluster points and removing ground points; S3, use Intrinsic Shape Signatures and Fast Point Feature Histograms descriptors to perform rough registration of the dual-view point cloud data, and then apply the ICP algorithm for fine registration; S4. Use the AlphaShape algorithm to determine the concave hull area of ​​the point cloud, use the Graham algorithm to determine the convex hull area of ​​the point cloud, and then analyze and calculate the stratified porosity of the cotton canopy.

2. The method for measuring porosity in a canopy based on laser radar according to claim 1, characterized in that: The laser radar system carried by the drone includes a drone, a laser radar, a radar loading board, a gimbal, an onboard computer, a power supply and a DC regulator. The radar loading board is installed on the arm of the drone, the laser radar is installed on the radar loading board through the gimbal, the onboard computer is connected to the laser radar through a converter, the power supply is connected to the converter through a DC regulator, and the power supply is connected to the onboard computer at the same time, and the onboard computer is connected to a mobile hotspot.

3. The method for measuring porosity in a canopy based on laser radar according to claim 2, characterized in that: The specific process of using a drone equipped with a laser radar system to collect dual-view point cloud data of a cotton field in step S1 is as follows: The flight route of the UAV is set, and the UAV is made to fly at a certain flight altitude and speed. During the flight, the laser radar performs dual-angle point cloud scanning on the cotton field from left to right and from right to left to form dual-angle point cloud data.

4. The method for measuring porosity in a canopy based on laser radar according to claim 1, characterized in that: Step S2 includes the following specific steps: S21, obtain IMU and radar pose information through SLAM mapping algorithm Fast-lio2, correct the dual-view point cloud data, and obtain the global point cloud map of the cotton field; S22. Use the ROI function in CloudCompare software to select the region of interest in the point cloud and remove irrelevant background point clouds; S23, performing k neighborhood statistical analysis on each point in the point cloud global map, calculating the average point distance from the point to the k neighboring points, assuming that the obtained distance conforms to the Gaussian normal distribution, then defining the average point distance outside the set first threshold m as an outlier point, and removing it from the data set; S24, select three ground points of the point cloud to fit into a quadratic surface z0, calculate the difference between the elevation fitting value and the actual value of the adjacent points, and set the second threshold T i Distinguish ground points and crop points, and then remove ground points.

5. The method for measuring porosity in a canopy based on laser radar according to claim 4, characterized in that: The first threshold m=5, the second threshold T i =5cm.

6. The method for measuring porosity in a canopy based on laser radar according to claim 1, characterized in that: In step S3, the Intrinsic Shape Signatures feature point selection is based on the local point cloud curvature change, and the curvature threshold is set to 0.01m -1 , the number of iterations of the ICP algorithm is set to 3.

7. The method for measuring porosity in a canopy based on laser radar according to claim 1, characterized in that: The porosity in step S4 is calculated using the following formula: Among them, A t (r) is the concave hull area of ​​the cotton canopy excluding pores, and the convex hull area of ​​the cotton canopy including pores.

8. A canopy porosity measurement system based on LiDAR, characterized in that: Including data acquisition module and execution module, The data acquisition module is used to collect point cloud data of the cotton field; The execution module is used to execute the method for measuring porosity within a canopy based on laser radar as described in any one of claims 1 to 7.

9. A storage medium storing a program, characterized in that: When the program is executed by a processor, the method for measuring porosity in a canopy based on laser radar as described in any one of claims 1 to 7 is implemented.

10. A computer device comprising a processor and a memory for storing a program executable by the processor, characterized in that When the processor executes the program stored in the memory, the method for measuring porosity within a canopy based on laser radar as described in any one of claims 1 to 7 is implemented.

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