Area measurement method and system for crop planting

Abstract

The invention is suitable for the technical field of area measurement, and particularly relates to an area measurement method and system for crop planting, and the method comprises the steps: carrying out the aerial photographing of a to-be-measured region, obtaining an aerial image, and calling a planting region map; performing picture analysis on the aerial image, detecting a crop planting area, and generating a depth information measurement path based on the crop planting area; depth information collection is carried out, and edge depth information coordinates are obtained; and performing line sampling on the crop planting area, constructing a three-dimensional curved surface of the planting area, and calculating to obtain the crop planting area. The crop planting area is identified according to the color information, three-dimensional modeling is performed on the planting area in a mode of constructing the space coordinate system, and the planting area is calculated based on the curved surface obtained through modeling, so that automatic detection of the crop planting area is realized, the detection precision is improved, and area deviation caused by different heights of the planting area is avoided.

Description

Technical Field

[0001] The invention belongs to the technical field of area measurement, and in particular relates to a method and system for measuring the area of ​​crop planting. Background Art

[0002] The measurement of crop planting area is an important part of agricultural management, which involves the use of various technical means to accurately assess the planting range of specific crops. Traditional methods include field measurements using tools such as tape measures or measuring ropes, but this method is time-consuming and labor-intensive and has limited accuracy. With the development of science and technology, modern measurement technologies such as the Global Positioning System (GPS), remote sensing technology and geographic information system (GIS) have been widely used, which not only improves the efficiency and accuracy of measurement, but also enables rapid monitoring of large areas of farmland. Data obtained through satellite images or drone aerial photography, combined with professional software analysis, can accurately calculate the planting area of ​​crops, providing a scientific basis for agricultural production planning, resource allocation, yield forecasting and pest and disease control.

[0003] For crops in flat planting areas, the planting area can be directly calculated through remote sensing technology or drone aerial photography. However, for crops planted on mountains, such as chestnuts and oranges, the area of ​​the planting area cannot be accurately calculated through aerial photography or remote sensing technology because the mountains are not regular planes, and there are large errors. Summary of the invention

[0004] The purpose of the present invention is to provide a method for measuring the area of ​​crop planting, aiming to solve the problem that for crops planted on mountains, the area of ​​the planting area cannot be accurately calculated by aerial photography or remote sensing technology, resulting in large errors.

[0005] The present invention is achieved by providing a method for measuring the area of ​​crop planting, the method comprising:

[0006] Take aerial photos of the area to be measured, obtain aerial images, and retrieve the planting area map;

[0007] Analyze aerial images, detect crop planting areas, and generate depth information measurement paths based on crop planting areas;

[0008] Depth information is collected from the area to be measured along the depth information measurement path to obtain edge depth information coordinates;

[0009] Line sampling is performed on the crop planting area, and a three-dimensional surface of the planting area is constructed according to the edge depth information coordinates to calculate the crop planting area.

[0010] Preferably, the step of performing image analysis on the aerial image, detecting the crop planting area, and generating a depth information measurement path based on the crop planting area specifically includes:

[0011] Acquire crop image data, perform pixel statistics based on the crop image data, and obtain pixel statistical information;

[0012] Classify the pixels in the picture according to the pixel statistical information, divide the pixels into multiple pixel groups, identify the pixel groups according to the crop image data, and determine the crop planting area, each pixel group contains all pixels in a preset color value range;

[0013] Identify the boundaries of the crop planting area, query the planting area map based on the boundaries, determine the coordinate values ​​of each point on the boundary, and generate a depth information measurement path.

[0014] Preferably, the step of collecting depth information of the area to be measured along the depth information measurement path to obtain edge depth information coordinates specifically includes:

[0015] Control the drone to move along the depth information measurement path according to the preset moving step length, and set a detection point each time it moves;

[0016] Control the drone to measure the depth information at the detection point, and use the laser ranging module to perform spiral sampling measurement to obtain the detection point ranging data set;

[0017] The depth information of the detection point ranging data set is analyzed to determine the measurement depth of the detection point, and the edge depth information coordinates are generated based on the positioning data of the UAV.

[0018] Preferably, the step of performing line sampling on the crop planting area, constructing a three-dimensional surface of the planting area according to the edge depth information coordinates, and calculating the crop planting area specifically includes:

[0019] Construct a three-dimensional coordinate system, mark the edge depth information coordinates in the three-dimensional coordinate system, and generate an enclosure curve based on the positional relationship of the detection points;

[0020] The aerial image is gridded to generate multiple groups of grid detection lines, and line sampling is performed on the area inside the enclosed curve along the grid detection lines to obtain line sampling depth coordinates;

[0021] The line sampling depth coordinates are marked and combined with the enclosing curve to generate a three-dimensional surface of the planting area, and the crop planting area is calculated.

[0022] Preferably, in the step of controlling the UAV to measure the depth information at the detection point, a spiral line is generated with the detection point as the center according to a preset detection radius, and the UAV is controlled to fly along the spiral line. During the flight, depth measurement is performed at preset time intervals.

[0023] Another object of the present invention is to provide an area measurement system for crop planting, the system comprising:

[0024] Aerial image acquisition module, used to take aerial photos of the area to be measured, obtain aerial images, and retrieve planting area maps;

[0025] A measurement path generation module is used to analyze the aerial images, detect the crop planting area, and generate a depth information measurement path based on the crop planting area;

[0026] A depth coordinate generation module is used to collect depth information of the area to be measured along the depth information measurement path and obtain edge depth information coordinates;

[0027] The planting area calculation module is used to perform line sampling on the crop planting area, construct a three-dimensional surface of the planting area according to the edge depth information coordinates, and calculate the crop planting area.

[0028] Preferably, the measurement path generation module includes:

[0029] A pixel information statistics unit is used to obtain crop image data, perform pixel statistics based on the crop image data, and obtain pixel statistical information;

[0030] A planting area identification unit is used to classify pixels in the picture according to pixel statistical information, divide the pixels into multiple pixel groups, identify the pixel groups according to crop image data, and determine the crop planting area, each pixel group includes all pixels in a preset color value range;

[0031] The flight path generation unit is used to identify the boundary of the crop planting area, query the planting area map based on the boundary, determine the coordinate values ​​of each point on the boundary, and generate a depth information measurement path.

[0032] Preferably, the depth coordinate generation module includes:

[0033] A detection point generation unit is used to control the UAV to move along the depth information measurement path according to a preset moving step length, and to set a detection point each time it moves;

[0034] The single-point detection unit is used to control the UAV to measure the depth information at the detection point, and perform spiral sampling measurement through the laser ranging module to obtain the detection point ranging data set;

[0035] The edge coordinate generation unit is used to perform depth information analysis on the detection point ranging data set, determine the measurement depth of the detection point, and generate edge depth information coordinates based on the positioning data of the drone.

[0036] Preferably, the planting area calculation module includes:

[0037] An edge curve generating unit is used to construct a three-dimensional coordinate system, mark the edge depth information coordinates in the three-dimensional coordinate system, and generate an enclosure curve according to the positional relationship of the detection points;

[0038] A depth coordinate sampling unit is used to grid the aerial image, generate multiple groups of grid detection lines, perform line sampling on the area inside the enclosed curve along the grid detection lines, and obtain line sampling depth coordinates;

[0039] The area calculation unit is used to mark the line sampling depth coordinates, combine the enclosing curve, generate the three-dimensional surface of the planting area, and calculate the crop planting area.

[0040] Preferably, in the step of controlling the UAV to measure the depth information at the detection point, a spiral line is generated with the detection point as the center according to a preset detection radius, and the UAV is controlled to fly along the spiral line. During the flight, depth measurement is performed at preset time intervals.

[0041] The present invention provides a method for measuring the area of ​​crop planting. The method automatically identifies crops in the picture through aerial images, and then identifies the planting area of ​​the crops according to color information. The planting area is three-dimensionally modeled by constructing a spatial coordinate system, and the planting area is calculated based on the surface obtained by the modeling. This realizes automated detection of the crop planting area, improves detection accuracy, and avoids area deviation caused by different heights of the planting area. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A flow chart of a method for measuring the area of ​​crop planting provided by an embodiment of the present invention;

[0043] Figure 2 A flowchart of the steps of analyzing an aerial image, detecting a crop planting area, and generating a depth information measurement path based on the crop planting area provided in an embodiment of the present invention;

[0044] Figure 3 A flowchart of the steps of collecting depth information of a region to be measured along a depth information measurement path and obtaining edge depth information coordinates provided by an embodiment of the present invention;

[0045] Figure 4A flowchart of the steps of performing line sampling on a crop planting area, constructing a three-dimensional surface of the planting area according to edge depth information coordinates, and calculating the crop planting area provided by an embodiment of the present invention;

[0046] Figure 5 An architectural diagram of an area measurement system for crop planting provided by an embodiment of the present invention;

[0047] Figure 6 An architecture diagram of a measurement path generation module provided by an embodiment of the present invention;

[0048] Figure 7 An architecture diagram of a depth coordinate generation module provided by an embodiment of the present invention;

[0049] Figure 8 An architectural diagram of a planting area calculation module provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0051] like Figure 1 FIG. 1 is a flow chart of a method for measuring the area of ​​crop planting provided by an embodiment of the present invention, the method comprising:

[0052] S100, aerial photography is performed on the area to be measured to obtain an aerial image, and a planting area map is retrieved.

[0053] In this step, an aerial photo is taken of the area to be measured, and image acquisition coordinates are sent to a drone. The drone flies to the execution area according to the image acquisition coordinates, thereby taking a downward photo to obtain an aerial image. The aerial image includes the entire planting area. If the planting area is large, multiple aerial photos can be taken by the drone, and the results obtained from each aerial photo are merged to obtain an aerial image including the entire planting area, and the planting area map is retrieved. The planting area map is a remote sensing map that records the horizontal coordinate information of each location.

[0054] S200, analyzing the aerial image, detecting the crop planting area, and generating a depth information measurement path based on the crop planting area.

[0055] In this step, the aerial images are analyzed. Different crops have different image features, which are reflected in color and shape. The area where the crops are located is identified based on the color and / or shape in the image to confirm the crop planting area, identify the edge of the crop planting area, and mark the edge of the crop planting area on the planting area map. The corresponding spatial coordinates are generated based on the horizontal coordinate information of each point. The Z coordinate of the spatial coordinate is a preset value, which is higher than the altitude of the corresponding area. A depth information measurement path is generated based on the spatial coordinates.

[0056] S300 , collecting depth information of a region to be measured along a depth information measurement path, and obtaining edge depth information coordinates.

[0057] In this step, depth information is collected for the measured area along the depth information measurement path. The drone flies along the depth information measurement path, multiple detection points are set on the entire depth information measurement path, and depth information is collected at each detection point. Since the crops planted below will block the ground, in order to determine the height of each detection point, multi-position detection of the detection point can be performed, and depth information analysis is performed based on the multi-position detection results, so as to determine the depth information of each position on the depth information measurement path and obtain the edge depth information coordinates.

[0058] S400, performing line sampling on the crop planting area, constructing a three-dimensional surface of the planting area according to edge depth information coordinates, and calculating the crop planting area.

[0059] In this step, line sampling is performed on the crop planting area. By performing line sampling, the depth changes in the middle part of the crop planting area can be identified, thereby filling in the depth situation in the middle of the crop planting area in the three-dimensional coordinate system. The enclosing curve constructed based on the edge depth information coordinates and the depth situation in the middle of the crop planting area can generate a three-dimensional surface. The three-dimensional surface is the ground model of the crop planting area. The crop planting area can be calculated using the area statistics tool in the three-dimensional software.

[0060] like Figure 2 As shown, as a preferred embodiment of the present invention, the step of analyzing the aerial image, detecting the crop planting area, and generating a depth information measurement path based on the crop planting area specifically includes:

[0061] S201, acquiring crop image data, performing pixel statistics based on the crop image data, and obtaining pixel statistical information.

[0062] In this step, the crop image data is obtained, and a crop database is constructed by collecting images of various crops in advance. The crop database stores image data of various crops (all from a bird's-eye view). When it is necessary to count the planting area of ​​a certain crop, the corresponding image data is directly retrieved from the crop database, and then pixel statistics are performed on the crop image data. When performing pixel statistics, at least the color information of the pixels is recorded, including RGB three-channel color information. The color information of the crops in the picture is different at different times.

[0063] S202, classifying the pixels in the image according to the pixel statistical information, dividing the pixels into a plurality of pixel groups, identifying the pixel groups according to the crop image data, and determining the crop planting area, wherein each pixel group includes all pixels in a preset color value range.

[0064] In this step, the pixels in the picture are classified according to the pixel statistical information, the color of each pixel in the picture is counted, and the pixels are classified according to the color. The pixels in the picture can be divided into green, dark green, light green and other pixels according to the color value. The specific color value range is set according to the needs to obtain different pixel groups. For example, pixels with R channel color values ​​lower than 50, B channel color values ​​lower than 50, and G channel color values ​​higher than 150 are classified as green pixels, and multiple pixel groups are obtained according to the above rules.

[0065] S203, identifying the boundary of the crop planting area, querying the planting area map according to the boundary, determining the coordinate value of each point on the boundary, and generating a depth information measurement path.

[0066] In this step, the boundaries of the crop planting area are identified. Based on the crop image data, the area in the picture that may contain crops can be determined. Then, the various pixel groups are combined into a whole through an expansion algorithm, and the shape formed by the whole is subjected to crop verification. It is compared with the crop image data to determine the crop planting area, and the boundaries of the crop planting area are further determined. The planting area map is queried to determine the coordinate values ​​of each point on the boundary and generate a depth information measurement path.

[0067] like Figure 3 As shown, as a preferred embodiment of the present invention, the step of collecting depth information of the area to be measured along the depth information measurement path and obtaining edge depth information coordinates specifically includes:

[0068] S301, controlling the UAV to move along the depth information measurement path according to a preset moving step length, and setting a detection point each time it moves.

[0069] In this step, the UAV is controlled to move according to a preset moving step length on the depth information measurement path. Specifically, the moving step length is determined according to the flight altitude of the UAV. The higher the flight altitude of the UAV, the smaller the moving step length. Conversely, the smaller the flight altitude of the UAV, the larger the moving step length. During the flight, the UAV moves a distance of a moving step length each time. After the movement, the location of the UAV is the detection point. The UAV has an image acquisition function and is equipped with a laser rangefinder.

[0070] S302, controlling the UAV to measure the depth information at the detection point, performing spiral sampling measurement through the laser ranging module, and obtaining a detection point ranging data set.

[0071] In this step, the UAV is controlled to measure the depth information at the detection point. When measuring, in the step of controlling the UAV to measure the depth information at the detection point, a spiral line is generated according to a preset detection radius with the detection point as the center. The detection radius is larger than the radius of a single crop. The UAV is controlled to fly along the spiral line. During the flight, depth measurement is performed at preset time intervals. Therefore, at each detection point, multiple vertical distances are measured in a spiral manner. Due to the obstruction of crops, only part of the vertical distance is the distance from the UAV to the ground, and the other distance is the distance between the UAV and the crops. During the spiral flight of the UAV, multiple vertical distances will be measured, and the collection of the above vertical distances is the detection point ranging data set.

[0072] S303, performing depth information analysis on the detection point ranging data set, determining the measurement depth of the detection point, and generating edge depth information coordinates based on the positioning data of the drone.

[0073] In this step, the depth information of the detection point ranging data set is analyzed, and the crop growth parameters are retrieved. The height of the trunk of the crop is different in different growth periods, that is, the distance between the main branches and leaves of the crop and the ground will be greater than the preset value, such as 1000mm. The difference between any set of vertical distances and the adjacent vertical distances is calculated. If the difference is greater than the preset value, the larger value of the two sets of vertical distances is retained and used as the alternative depth value. If the difference between the alternative depth values ​​is greater than the preset value, such as 500mm, the smaller value is discarded, and the average value of the remaining alternative depth values ​​is calculated to obtain the detection point measurement depth, obtain the positioning data of the drone, and generate the edge depth information coordinates according to the positioning data of the drone and the detection point measurement depth. For example, if the coordinates of the drone are (x1, y1, z1), and the detection point measurement depth is z0, the edge depth information coordinates are (x1, y1, z1-z0).

[0074] like Figure 4As shown, as a preferred embodiment of the present invention, the steps of performing line sampling on the crop planting area, constructing a three-dimensional surface of the planting area according to the edge depth information coordinates, and calculating the crop planting area specifically include:

[0075] S401, constructing a three-dimensional coordinate system, marking the edge depth information coordinates in the three-dimensional coordinate system, and generating an enclosure curve according to the positional relationship of the detection points.

[0076] In this step, a three-dimensional coordinate system is constructed, with the position of the drone at the first detection point as the origin, all edge depth information is recorded in the three-dimensional coordinate system, and the three-dimensional coordinate points corresponding to each edge depth information coordinate are connected with a smooth curve to obtain an enclosure curve.

[0077] S402, gridding the aerial image to generate multiple groups of grid detection lines, performing line sampling on the area inside the enclosed curve along the grid detection lines to obtain line sampling depth coordinates.

[0078] In this step, the aerial image is gridded into multiple vertical strip grids, the grid width can be 50m, and the grid lines are set according to the grid size. The entire aerial image is divided into multiple strip areas by the grid lines. The drone is controlled to fly along the grid lines and continuous measurements are performed to obtain the vertical distances of multiple points on each set of grid lines. Similarly, the difference between any set of vertical distances and adjacent vertical distances on each grid line is calculated. If the difference is greater than a preset value, the larger value of the two sets of vertical distances is retained and used as an alternative depth value. If the difference between the alternative depth values ​​is greater than a preset value, such as 500mm, the smaller value is discarded, and the average value of the remaining alternative depth values ​​is calculated to obtain a grid line vertical distance set. The line sampling depth coordinates of the corresponding points on the grid line are generated according to the coordinates of the drone and the grid line vertical distance set.

[0079] S403, marking the line sampling depth coordinates, combining with the enclosing curve, generating a three-dimensional surface of the planting area, and calculating the crop planting area.

[0080] In this step, the line sampling depth coordinates are marked in a three-dimensional coordinate system, and a fitting curve is generated according to all the line sampling depth coordinates on each group of grid lines. Multiple groups of fitting curves are generated inside the enclosed curve, and the multiple groups of fitting curves are used as guide lines to generate corresponding three-dimensional surfaces inside the enclosed curve. Specifically, multiple horizontal grid lines can be set in a direction perpendicular to the vertical grid lines, and the coordinates of the intersections of the horizontal grid lines, the fitting curves and the enclosed curves are determined. Fitting is performed based on the above coordinates to obtain multiple groups of horizontal curves, and adjacent horizontal curves are connected with smooth surfaces to generate a three-dimensional surface. The area of ​​the three-dimensional surface is counted using three-dimensional software to obtain the crop planting area.

[0081] like Figure 5 As shown, a system for measuring the area of ​​crop planting provided by an embodiment of the present invention includes:

[0082] The aerial image acquisition module 100 is used to take aerial photos of the area to be measured, obtain aerial images, and retrieve a planting area map.

[0083] In this system, the aerial image acquisition module 100 takes aerial photos of the area to be measured and sends image acquisition coordinates to the drone. The drone flies to the execution area according to the image acquisition coordinates, thereby taking a downward shot to obtain an aerial image. The aerial image includes the entire planting area. If the planting area is large, multiple aerial photos can be taken by the drone, and the results obtained from each aerial photo are merged to obtain an aerial image including the entire planting area, and the planting area map is retrieved. The planting area map is a remote sensing map that records the horizontal coordinate information of each location.

[0084] The measurement path generation module 200 is used to perform picture analysis on the aerial image, detect the crop planting area, and generate a depth information measurement path based on the crop planting area.

[0085] In this system, the measurement path generation module 200 performs image analysis on the aerial images. Different crops have different image features, which are reflected in color and shape. The area where the crops are located is identified according to the color and / or shape in the image to confirm the crop planting area, identify the edge of the crop planting area, and mark the edge of the crop planting area on the planting area map. The corresponding spatial coordinates are generated according to the horizontal coordinate information of each point. The Z coordinate of the spatial coordinate is a preset value, which is higher than the altitude of the corresponding area. A depth information measurement path is generated according to the spatial coordinates.

[0086] The depth coordinate generating module 300 is used to collect depth information of the area to be measured along the depth information measurement path, and obtain edge depth information coordinates.

[0087] In this system, the depth coordinate generation module 300 collects depth information of the measured area along the depth information measurement path. The drone flies along the depth information measurement path, and multiple detection points are set on the entire depth information measurement path. Depth information is collected at each detection point. Since the crops planted below will block the ground, in order to determine the height of each detection point, multi-position detection of the detection point can be performed, and depth information analysis is performed based on the multi-position detection results, so as to determine the depth information of each position on the depth information measurement path and obtain the edge depth information coordinates.

[0088] The planting area calculation module 400 is used to perform line sampling on the crop planting area, construct a three-dimensional surface of the planting area according to the edge depth information coordinates, and calculate the crop planting area.

[0089] In this system, the planting area calculation module 400 performs line sampling on the crop planting area. By performing line sampling, the depth change in the middle part of the crop planting area can be identified, thereby filling in the depth situation in the middle of the crop planting area in the three-dimensional coordinate system. The enclosing curve constructed based on the edge depth information coordinates and the depth situation in the middle of the crop planting area can generate a three-dimensional surface. The three-dimensional surface is the ground model of the crop planting area. The crop planting area can be calculated using the area statistics tool in the three-dimensional software.

[0090] like Figure 6 As shown, as a preferred embodiment of the present invention, the measurement path generation module 200 includes:

[0091] The pixel information statistics unit 201 is used to obtain crop image data, perform pixel statistics based on the crop image data, and obtain pixel statistics information.

[0092] In this module, the pixel information statistics unit 201 obtains crop image data, and constructs a crop database by collecting images of various crops in advance. The crop database stores image data of various crops (all from a bird's-eye view). When it is necessary to count the planting area of ​​a certain crop, the corresponding image data is directly retrieved from the crop database, and then pixel statistics are performed on the crop image data. When performing pixel statistics, at least the color information of the pixel is recorded, including RGB three-channel color information. The color information of the crops in the picture is different at different times.

[0093] The planting area identification unit 202 is used to classify the pixels in the picture according to the pixel statistical information, divide the pixels into multiple pixel groups, identify the pixel groups according to the crop image data, and determine the crop planting area. Each pixel group contains all pixels in a preset color value range.

[0094] In this module, the planting area identification unit 202 classifies the pixels in the picture according to the pixel statistical information, counts the color of each pixel in the picture, classifies the pixels according to the color, and divides the pixels in the picture into green, dark green, light green and other pixels according to the color value. The specific color value range is set according to the needs to obtain different pixel groups. For example, pixels with R channel color values ​​lower than 50, B channel color values ​​lower than 50, and G channel color values ​​higher than 150 are classified as green pixels, and multiple pixel groups are obtained according to the above rules.

[0095] The flight path generation unit 203 is used to identify the boundary of the crop planting area, query the planting area map according to the boundary, determine the coordinate value of each point on the boundary, and generate a depth information measurement path.

[0096] In this module, the flight path generation unit 203 identifies the boundary of the crop planting area, and based on the crop image data, it can determine the area in the picture that may contain crops, and then use the expansion algorithm to combine each pixel group into a whole, and perform crop verification on the shape formed by the whole, and compare it with the crop image data to determine the crop planting area, further determine the boundary of the crop planting area, query the planting area map, determine the coordinate values ​​of each point on the boundary, and generate a depth information measurement path.

[0097] like Figure 7 As shown, as a preferred embodiment of the present invention, the depth coordinate generation module 300 includes:

[0098] The detection point generating unit 301 is used to control the UAV to move along the depth information measurement path according to a preset moving step length, and to set a detection point each time it moves.

[0099] In this module, the detection point generation unit 301 controls the UAV to move according to a preset moving step length in the depth information measurement path. Specifically, the moving step length is determined according to the flight altitude of the UAV. The higher the flight altitude of the UAV, the smaller the moving step length. Conversely, the smaller the flight altitude of the UAV, the larger the moving step length. During the flight, the UAV moves a distance of a moving step length each time. After the movement, the location of the UAV is the detection point. The UAV has an image acquisition function and is equipped with a laser rangefinder.

[0100] The single point detection unit 302 is used to control the UAV to measure the depth information at the detection point, and perform spiral sampling measurement through the laser ranging module to obtain a detection point ranging data set.

[0101] In this module, the single-point detection unit 302 controls the UAV to measure the depth information at the detection point. When measuring, in the step of controlling the UAV to measure the depth information at the detection point, a spiral line is generated according to a preset detection radius with the detection point as the center. The detection radius is larger than the radius of a single crop. The UAV is controlled to fly along the spiral line. During the flight, depth measurement is performed at preset time intervals. Therefore, at each detection point, multiple vertical distances are measured in a spiral manner. Due to the obstruction of crops, only part of the vertical distance is the distance from the UAV to the ground, and the other distance is the distance between the UAV and the crops. During the spiral flight of the UAV, multiple vertical distances will be measured, and the collection of the above vertical distances is the detection point ranging data set.

[0102] The edge coordinate generating unit 303 is used to perform depth information analysis on the detection point ranging data set, determine the detection point measurement depth, and generate edge depth information coordinates based on the positioning data of the drone.

[0103] In this module, the edge coordinate generation unit 303 performs depth information analysis on the detection point ranging data set, retrieves crop growth parameters, and the heights of the trunks of crops are different in different growth periods, that is, the distance between the main branches and leaves of the crops and the ground will be greater than a preset value, such as 1000 mm. The difference between any set of vertical distances and adjacent vertical distances is calculated. If the difference is greater than the preset value, the larger value of the two sets of vertical distances is retained and used as an alternative depth value. If the difference between the alternative depth values ​​is greater than the preset value, such as 500 mm, the smaller value is discarded, and the average value of the remaining alternative depth values ​​is calculated to obtain the detection point measurement depth, obtain the positioning data of the drone, and generate edge depth information coordinates based on the positioning data of the drone and the detection point measurement depth. For example, if the coordinates of the drone are (x1, y1, z1) and the detection point measurement depth is z0, the edge depth information coordinates are (x1, y1, z1-z0).

[0104] like Figure 8 As shown, as a preferred embodiment of the present invention, the planting area calculation module 400 includes:

[0105] The edge curve generating unit 401 is used to construct a three-dimensional coordinate system, mark the edge depth information coordinates in the three-dimensional coordinate system, and generate an enclosure curve according to the positional relationship of the detection points.

[0106] In this module, the edge curve generation unit 401 constructs a three-dimensional coordinate system, takes the position of the drone at the first detection point as the origin, records all edge depth information in the three-dimensional coordinate system, and connects the three-dimensional coordinate points corresponding to each edge depth information coordinate with a smooth curve to obtain an enclosure curve.

[0107] The depth coordinate sampling unit 402 is used to perform grid division on the aerial image to generate multiple groups of grid detection lines and perform line sampling on the area inside the enclosed curve along the grid detection lines to obtain line sampling depth coordinates.

[0108] In this module, the depth coordinate sampling unit 402 grids the aerial image into a plurality of vertical strip grids, the grid width can be 50m, and the grid lines are set according to the grid size. The entire aerial image is divided into a plurality of strip areas by the grid lines, and the drone is controlled to fly along the grid lines and continuously measured to obtain the vertical distances of a plurality of points on each set of grid lines. Similarly, the difference between any set of vertical distances and the adjacent vertical distances on each grid line is calculated. If the difference is greater than a preset value, the larger value of the two sets of vertical distances is retained and used as an alternative depth value. If the difference between the alternative depth values ​​is greater than a preset value, such as 500mm, the smaller value is discarded, and the average value of the remaining alternative depth values ​​is calculated to obtain a set of grid line vertical distances, and the line sampling depth coordinates of the corresponding points on the grid lines are generated according to the coordinates of the drone and the set of grid line vertical distances.

[0109] The area calculation unit 403 is used to mark the line sampling depth coordinates, combine the enclosing curve, generate a three-dimensional surface of the planting area, and calculate the crop planting area.

[0110] In this module, the area calculation unit 403 marks the line sampling depth coordinates and marks them in the three-dimensional coordinate system. A fitting curve is generated according to all the line sampling depth coordinates on each group of grid lines. Multiple groups of fitting curves are generated inside the enclosed curve. The multiple groups of fitting curves are used as guide lines to generate corresponding three-dimensional surfaces inside the enclosed curve. Specifically, multiple horizontal grid lines can be set in a direction perpendicular to the vertical grid lines, and the coordinates of the intersections of the horizontal grid lines, the fitting curves and the enclosed curves are determined. Fitting is performed based on the above coordinates to obtain multiple groups of horizontal curves. Adjacent horizontal curves are connected with smooth surfaces to generate a three-dimensional surface. The area of ​​the three-dimensional surface is counted using three-dimensional software to obtain the crop planting area.

[0111] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for measuring the area of ​​crop planting, characterized in that: The method comprises: Take aerial photos of the area to be measured, obtain aerial images, and retrieve the planting area map; Analyze aerial images, detect crop planting areas, and generate depth information measurement paths based on crop planting areas; Depth information is collected from the area to be measured along the depth information measurement path to obtain edge depth information coordinates; Line sampling is performed on the crop planting area, and a three-dimensional surface of the planting area is constructed according to the edge depth information coordinates to calculate the crop planting area.

2. The method for measuring the area of ​​crop planting according to claim 1, characterized in that: The step of analyzing the aerial image, detecting the crop planting area, and generating a depth information measurement path based on the crop planting area specifically includes: Acquire crop image data, perform pixel statistics based on the crop image data, and obtain pixel statistical information; Classify the pixels in the picture according to the pixel statistical information, divide the pixels into multiple pixel groups, identify the pixel groups according to the crop image data, and determine the crop planting area, each pixel group contains all pixels in a preset color value range; Identify the boundaries of the crop planting area, query the planting area map based on the boundaries, determine the coordinate values ​​of each point on the boundary, and generate a depth information measurement path.

3. The method for measuring the area of ​​crop planting according to claim 1, characterized in that: The step of collecting depth information of the area to be measured along the depth information measurement path and obtaining edge depth information coordinates specifically includes: Control the drone to move along the depth information measurement path according to the preset moving step length, and set a detection point each time it moves; Control the drone to measure the depth information at the detection point, and use the laser ranging module to perform spiral sampling measurement to obtain the detection point ranging data set; The depth information of the detection point ranging data set is analyzed to determine the measurement depth of the detection point, and the edge depth information coordinates are generated based on the positioning data of the UAV.

4. The method for measuring the area of ​​crop planting according to claim 1, characterized in that: The step of performing line sampling on the crop planting area, constructing a three-dimensional surface of the planting area according to the edge depth information coordinates, and calculating the crop planting area specifically includes: Construct a three-dimensional coordinate system, mark the edge depth information coordinates in the three-dimensional coordinate system, and generate an enclosure curve based on the positional relationship of the detection points; The aerial image is gridded to generate multiple groups of grid detection lines, and line sampling is performed on the area inside the enclosed curve along the grid detection lines to obtain line sampling depth coordinates; The line sampling depth coordinates are marked and combined with the enclosing curve to generate a three-dimensional surface of the planting area, and the crop planting area is calculated.

5. The method for measuring the area of ​​crop planting according to claim 3, characterized in that: In the step of controlling the drone to measure the depth information at the detection point, a spiral line is generated with the detection point as the center according to a preset detection radius, and the drone is controlled to fly along the spiral line. During the flight, depth measurement is performed at preset time intervals.

6. A system for measuring the area of ​​crop planting, characterized in that: The system comprises: Aerial image acquisition module, used to take aerial photos of the area to be measured, obtain aerial images, and retrieve planting area maps; A measurement path generation module is used to analyze the aerial images, detect the crop planting area, and generate a depth information measurement path based on the crop planting area; A depth coordinate generation module is used to collect depth information of the area to be measured along the depth information measurement path and obtain edge depth information coordinates; The planting area calculation module is used to perform line sampling on the crop planting area, construct a three-dimensional surface of the planting area according to the edge depth information coordinates, and calculate the crop planting area.

7. The area measurement system for crop planting according to claim 6, characterized in that: The measurement path generation module comprises: A pixel information statistics unit is used to obtain crop image data, perform pixel statistics based on the crop image data, and obtain pixel statistical information; A planting area identification unit is used to classify pixels in the picture according to pixel statistical information, divide the pixels into multiple pixel groups, identify the pixel groups according to crop image data, and determine the crop planting area, each pixel group includes all pixels in a preset color value range; The flight path generation unit is used to identify the boundary of the crop planting area, query the planting area map based on the boundary, determine the coordinate values ​​of each point on the boundary, and generate a depth information measurement path.

8. The area measurement system for crop planting according to claim 6, characterized in that: The depth coordinate generation module comprises: A detection point generation unit is used to control the UAV to move along the depth information measurement path according to a preset moving step length, and to set a detection point each time it moves; The single-point detection unit is used to control the UAV to measure the depth information at the detection point, and perform spiral sampling measurement through the laser ranging module to obtain the detection point ranging data set; The edge coordinate generation unit is used to perform depth information analysis on the detection point ranging data set, determine the measurement depth of the detection point, and generate edge depth information coordinates based on the positioning data of the drone.

9. The area measurement system for crop planting according to claim 6, characterized in that: The planting area calculation module includes: An edge curve generating unit is used to construct a three-dimensional coordinate system, mark the edge depth information coordinates in the three-dimensional coordinate system, and generate an enclosure curve according to the positional relationship of the detection points; A depth coordinate sampling unit is used to grid the aerial image, generate multiple groups of grid detection lines, perform line sampling on the area inside the enclosed curve along the grid detection lines, and obtain line sampling depth coordinates; The area calculation unit is used to mark the line sampling depth coordinates, combine the enclosing curve, generate the three-dimensional surface of the planting area, and calculate the crop planting area.

10. The area measurement system for crop planting according to claim 8, characterized in that: In the step of controlling the drone to measure the depth information at the detection point, a spiral line is generated with the detection point as the center according to a preset detection radius, and the drone is controlled to fly along the spiral line. During the flight, depth measurement is performed at preset time intervals.

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