Farmland crop harvest area automatic measurement method and system based on computer analysis

Abstract

The invention discloses a computer analysis-based farmland crop harvesting area automatic measurement method and system, and relates to the technical field of crop harvesting area calculation, and the method comprises the steps: respectively marking field ridge points and farmland points in a farmland region according to the gradient of each point in the farmland region; obtaining a field ridge proportion coefficient of each strip according to the field ridge points and the number of the farmland points contained in each strip, and marking inextensible strips and extensible strips in each strip according to the field ridge proportion coefficients; single harvest areas corresponding to the inextensible strips and the extensible strips are accumulated to obtain the total harvest area, the inextensible strips and the extensible strips are screened according to the field ridge proportion, three-dimensional area reduction is conducted on the extensible strips which fluctuate obviously, area calculation is conducted on flat areas of the inextensible strips, and the total harvest area is obtained. And obtaining a harvesting area matched with the real surface shape.

Description

Technical Field

[0001] This invention belongs to the field of crop harvest area measurement technology, specifically a method and system for automatic measurement of farmland crop harvest area based on computer analysis. Background Technology

[0002] With the development of remote sensing and geographic information systems, methods for vectorizing land parcels on a two-dimensional plane using satellite or aerial imagery and then calculating their area have become quite common. However, existing methods for measuring farmland area, whether traditional GPS tracking or the more prevalent two-dimensional vectorization estimation based on satellite / aerial imagery, generally suffer from a significant drawback: the calculated result only represents the horizontal projected area of ​​the land parcel. This method assumes the land surface is approximately flat. In hilly areas, terraced fields, or farmland with numerous ridges and slopes, the surface exhibits significant slopes and undulations, resulting in a substantial discrepancy between the planar projected area and the actual cultivated three-dimensional surface area. This leads to a severe underestimation of the harvested area, making it difficult to accurately reflect the true area of ​​the land surface. Therefore, this paper proposes an automatic method and system for measuring the harvested area of ​​farmland crops based on computer analysis. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for automatically measuring the harvested area of ​​farmland crops based on computer analysis, so as to solve the problems mentioned in the background art.

[0004] A computer-based method for automatically measuring the harvested area of ​​farmland crops includes the following steps: Step 1: Extract the spatial coordinates Pi (Xi, Yi, Zi) of each point in the point cloud data of the farmland area, where i represents a different point in the point cloud; Step 2: Calculate the gradient of each point in the X and Y directions based on its spatial coordinates; Step 3: Calculate the slope of each point in the farmland area based on the gradient in the X and Y directions; Step 4: Based on the slope of each point within the farmland area, mark the field ridge points and farmland points within the farmland area according to the set threshold. Step 5: Divide the farmland area into multiple long strips, analyze the number of field ridges and farmland points contained in each strip, obtain the field ridge ratio coefficient of each strip, and mark the non-extendable strips and extendable strips in each strip according to the field ridge ratio coefficient. Step 6: Calculate the harvest area of ​​each individual strip corresponding to the non-extendable strip and the extendable strip, and finally accumulate the total harvest area.

[0005] As a further aspect of the present invention: the specific method for calculating the gradient of each point in the X and Y directions is as follows: Obtain the spatial coordinates P0=(X0,Y0,Z0) and P1=(X1,Y1,Z1) of a single point and its neighboring points, through... and The gradients GX1 and GY1 of this single point in the X and Y directions are calculated. The same method is used to analyze the remaining points to obtain the gradients GXi and GYi of each point in the X and Y directions.

[0006] As a further aspect of the present invention, the specific method for obtaining the neighboring points of a single point is as follows: For a single point, obtain the distance between it and all its surrounding points, and select the point with the smallest distance as its neighbor. If there are multiple points with the smallest distance, randomly select one as its neighbor.

[0007] As a further aspect of the present invention, the specific method for obtaining the slope of each point within the farmland area is as follows: pass The slope Di at each point is calculated.

[0008] As a further aspect of the present invention, the specific method for marking field ridge points and field points within the farmland area based on a set threshold is as follows: Points with a slope greater than a set threshold are marked as field ridge points, and points with a slope less than or equal to the set threshold are marked as farmland points. The set threshold ranges from 8 degrees to 15 degrees.

[0009] As a further aspect of the present invention: the specific method for marking the non-extendable and extendable strips in each strip according to the field ridge ratio coefficient is as follows: The number of points marked as field ridges and the number of points marked as farmland in each strip are counted. The ratio between the number of field ridges and the total number of field ridges and farmland points in each strip is used as the field ridge ratio coefficient of each strip. If the field ridge ratio coefficient is less than the set threshold Y1, it is marked as an immobile strip. If the field ridge ratio coefficient is greater than or equal to the set threshold Y1, it is marked as an mobile strip. The set threshold Y1 = 0.2.

[0010] As a further aspect of the present invention, the specific method for calculating the single harvest area corresponding to the inextensible strip and the extensible strip is as follows: For strips marked as non-extendable strips, the projected grid area △S of the farmland area point cloud is obtained, and the grid occupancy of farmland points within the non-extendable strip is also obtained. The product of the grid occupancy of the same non-extendable strip and the projected grid area △S of the farmland area is taken as the single harvest area corresponding to the non-extendable strip. For strips marked as extendable strips, the slope value Dk of each farmland point in the non-extendable strip is converted into radians θk by Dk× (π / 180). The actual surface area of ​​each farm point is summed up to obtain the single harvest area of ​​the non-extendable strip, where M is the total number of farm points in the strip, k is the different farm points in the extendable strip, and θk is the radian of the slope value of different farm points in the extendable strip.

[0011] As a further aspect of the present invention, the specific method for obtaining the projected grid area △S of the point cloud of farmland area is as follows: After projecting the point cloud onto the X-Y plane, the farmland area is divided into multiple grids of equal size. The side length A of each grid cell is obtained, and the area of ​​each grid cell is A. 2 This is then used as the projected grid area △S of the farmland area.

[0012] As a further aspect of the present invention, the specific method for obtaining the grid occupancy number of farmland points within an inextensible strip is as follows: Obtain the positions of all farm points within the non-extendable strip in all grids of the farm area, mark the grids with farm points as occupied grids, and when a farm point is on the dividing line between two grids, mark both grids corresponding to the dividing line as occupied grids, obtain the total number of occupied grids and use it as the grid occupancy count.

[0013] An automatic measurement system for harvested crop area based on computer analysis includes: The spatial coordinate extraction module extracts the spatial coordinates Pi (Xi, Yi, Zi) of each point in the point cloud data of the farmland area, where i represents a different point in the point cloud. The directional gradient acquisition module calculates the gradient of each point in the X and Y directions based on the spatial coordinates of each point; The slope acquisition module calculates the slope of each point within the farmland area based on the gradient of each point in the X and Y directions. The field ridge and farmland point marking module marks the field ridge and farmland points in the farmland area according to the slope of each point and based on the set threshold. The non-extendable and extendable strip marking module divides the farmland area into multiple long strips, analyzes the number of field ridges and farmland points contained in each strip, obtains the field ridge ratio coefficient of each strip, and marks the non-extendable and extendable strips in each strip according to the field ridge ratio coefficient. The total harvest area acquisition module calculates the harvest area of ​​each individual strip corresponding to both non-extendable and extendable strips, and finally accumulates the total harvest area.

[0014] Compared with the prior art, the beneficial effects of the present invention are: (1) The present invention solves the problem that traditional measurements cannot effectively distinguish between field ridges and cultivated areas by marking points with a slope greater than a threshold as field ridge points and points with a slope less than or equal to the threshold as farmland points. (2) In this invention, multiple narrow strips are divided along the normal direction of the skeleton line. The number of field ridge points and farmland points in each strip are counted to calculate the field ridge ratio coefficient. Based on this coefficient, it is determined whether the strip is an extendable strip or an inextensible strip. The strips are automatically classified, so that flat areas do not require complex three-dimensional calculations and can be directly used by projected area, which greatly improves the overall calculation efficiency. (3) In this invention, for non-extendable strips and extendable strips, the former is directly calculated by multiplying the number of farmland points by the area of ​​the uniform projection grid; for the latter, for each farmland point, its projected area is divided by the cosine of the slope angle radian and accumulated to realize the three-dimensional surface area restoration. Finally, the total harvest area of ​​crops is obtained by summing the areas of all strips, so that the final harvest area matches the real surface morphology, providing a more accurate area basis for yield estimation, operation cost accounting and policy subsidies. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the method framework structure of the present invention; Figure 2 This is a schematic diagram of the system framework structure of the present invention. Detailed Implementation

[0016] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Example 1: Please refer to Figure 1 This application provides a computer-analyzed method for automatically measuring the harvested area of ​​farmland crops, including the following steps: Step 1: Use drones or lidar technology to acquire 3D point cloud data of farmland areas. Unify the coordinates of the acquired point cloud data to obtain a set of points. Ensure that the point cloud data are under the same reference coordinate system. Each point cloud data includes its position in the X, Y and Z coordinates. Extract the spatial coordinates Pi (Xi, Yi, Zi) of each point from the acquired point cloud data, where i represents different points in the point cloud. Using photogrammetric equipment or lidar systems mounted on drones, high-density data collection is conducted above farmland to acquire 3D point cloud data containing X, Y, and Z coordinates. Subsequently, georegistration and coordinate transformation techniques are used to ensure all point cloud data are unified to the same reference coordinate system. This provides a high-precision 3D geometric foundation for subsequent accurate calculations, overcoming the limitation of 2D image data which only provides planar information. It also provides reliable source data with elevation information for subsequent slope analysis and 3D area reconstruction. Ensuring all measurement data are spatially aligned eliminates coordinate errors caused by differences in measurement equipment or time.

[0018] Step 2: Based on the spatial coordinates Pi (Xi, Yi, Zi) of each point, calculate the gradient of each point in the X and Y directions using a point cloud difference algorithm, i.e., the local slope of each point in the X and Y directions. The specific method is as follows: For a single point P0 = (X0, Y0, Z0), obtain its distances to all surrounding points. Select the point with the smallest distance as its neighbor. If multiple points have the smallest distance, randomly select one as its neighbor. The spatial coordinates of this single point P0 = (X0, Y0, Z0) and its corresponding neighbor P1 = (X1, Y1, Z1) are obtained through... and ; Calculate the gradients GX1 and GY1 of this single point in the X and Y directions, and use the same method to analyze the remaining points to obtain the gradients GXi and GYi of each point in the X and Y directions, which reflect the rate of change between adjacent points in these three directions, where i refers to different points. For example, suppose that in the obtained point cloud data, the X, Y, Z coordinates of a certain point are P0=(2,3,5) and its neighboring point is P1=(3,4,6). Then the gradient in the X direction is GX1=6−5 / 3-2=1 and the gradient in the Y direction is GY1=6−5 / 4-3=1. A point cloud difference algorithm is employed to analyze the rate of change of a target point P0 and its nearest neighbor P1 in the horizontal and vertical directions. Specifically, the local slope in the X and Y directions is calculated, i.e., the ratio of elevation change ΔZ to horizontal displacement ΔX or ΔY. These gradients reflect the degree of inclination of the land surface in different directions, achieving mathematical quantification of the minute undulations of farmland surfaces and providing basic data for distinguishing between flat cultivated areas and steep field ridges. High computational efficiency: Compared to complex algorithms that fit the entire plane, the difference calculation based on neighborhood points is simple in principle and fast in computation.

[0019] Step 3: Based on the gradients GXi and GYi of each point in the X and Y directions, calculate the slope of each point, thereby obtaining the slope of each point within the farmland area. The specific method is as follows: First, using the Pythagorean theorem, the X-direction gradient and Y-direction gradients GXi and GYi of each point are vectorized to obtain the maximum rate of change tan(θi) of each point. Then, it is converted into an angle value to obtain the slope of each point in the farmland area. That is, through The slope Di at each point is calculated; where, Represents the total gradient modulus after synthesis, (180 / π) is used to convert radians to degrees; For example, the gradient of a point in the X direction is GX1=1, the gradient in the Y direction is GY1=1, and the slope of this point is arctan(θ / θ). × (180 / π) = arctan(1.414) × 57.3 ≈ 54.7 degrees; The Pythagorean theorem is used to vector synthesize the X-direction and Y-direction gradients obtained in step two to obtain the modulus of the combined gradient. Subsequently, the arctan function is used to convert this modulus into a slope angle Di in degrees, obtaining the slope angle value of the surface in the direction of maximum inclination. This is a key input parameter for 3D surface area reconstruction, ensuring that the area calculation is based on the actual terrain slope angle. Standardized terrain features: Complex gradient data is transformed into angle values ​​that are easy to understand and have thresholds set.

[0020] Step 4: Based on the slope of each point within the farmland area, mark the field ridge points and farmland points within the farmland area according to the set threshold. Points with a slope greater than a set threshold are marked as field ridge points, and points with a slope less than or equal to the set threshold are marked as farmland points. The specific value of the set threshold is determined according to actual needs, and the value range is 8 degrees to 15 degrees. For example, if the threshold is set to 15 degrees, points with a slope of 45 degrees will be marked as field ridge points, and points with a slope of 8 degrees will be marked as farmland points. Based on an empirically set slope threshold, the slope of each point obtained in step three is assessed. Points with a slope greater than the threshold are marked as field ridge points, and points with a slope less than or equal to the threshold are marked as farmland points. This solves the problem of traditional measurements failing to effectively distinguish between field ridges and cultivated areas, and excludes non-harvested areas from the total area, greatly improving the effectiveness and accuracy of the harvested area calculation. Automated processing avoids the tediousness and subjectivity of manually identifying and delineating field ridge boundaries.

[0021] Step 5: Obtain the central skeleton line of the farmland area. Extend the skeleton line to both sides to cut the farmland area into multiple narrow strips. Analyze the number of field ridges and farmland points contained in each strip to obtain the field ridge ratio coefficient of each strip. Mark the non-extendable strips and extendable strips in each strip according to the field ridge ratio coefficient. The central skeleton line of the farmland area projected onto the XY plane is extracted using a morphological thinning algorithm. A perpendicular line of preset width L is drawn along the normal direction of the skeleton line to divide the farmland area into N long strips. The specific value of the preset width L is determined by relevant personnel according to actual needs. Here, the preset width L = 0.5 meters. The specific method for extracting the central skeleton line of farmland area projected onto the XY plane using morphological thinning algorithms is as follows: First, the 3D point cloud data of the farmland area is projected onto the XY plane. Then, the farmland area boundary is rasterized and identified to form a 2D polygon or a rasterized binary image. The inside of the farmland is 1 and the outside is 0. Morphological thinning operation is applied to gradually delete boundary points until no more points can be deleted. What remains is the central skeleton line of the farmland area. The skeleton line is the set of points that are equidistant from each boundary of the farmland. Then, strips are divided along the normal direction of the skeleton line. The ratio between the number of field ridge points in a single strip and the total number of field ridge points and farmland points is obtained. This ratio is used as the field ridge ratio coefficient of the corresponding strip. Strips with a field ridge ratio coefficient less than a set threshold Y1 are marked as non-extendable strips, and vice versa. Here, the threshold Y1 is set to 0.2. The number of points marked as field ridges and the number of points marked as farmland in each strip are counted. The field ridge ratio coefficient of each strip is calculated by dividing the number of field ridges by the number of field ridges and the number of farmland. If the field ridge ratio coefficient is less than the set threshold Y1, it means that the terrain of the strip is relatively flat or there are very few field ridges, and it is marked as an inextensible strip, which means that the calculation error based on the plane projection is very small; if the field ridge ratio coefficient is greater than or equal to the set threshold Y1, it means that the strip contains many field ridges or the terrain is undulating, and it is marked as an extensible strip, which means that a three-dimensional surface area reconstruction calculation is required. For example, suppose there are 25 field ridge points and 75 farmland points in a long strip. The field ridge ratio coefficient of this strip is 25 / (25+75)=0.25. Set the threshold Y1=0.2. Since 0.25 is greater than 0.2, this long strip is marked as an extendable strip. A morphological refinement algorithm is used to extract the central skeleton line of the farmland area. Then, multiple narrow strips are divided along the normal direction of the skeleton line. Next, the number of field ridge points and farmland points within each strip is counted to calculate the field ridge ratio coefficient. Finally, this coefficient is used to determine whether a strip is extendable or inextensible, incorporating the field ridge ratio as a criterion to achieve adaptive calculation. For flat strips, simple projected area calculation is used directly, significantly improving overall computational efficiency. The skeleton line division method ensures that the strips follow the natural orientation of the farmland plots to the greatest extent possible, automatically classifying the strips so that flat areas do not require complex 3D calculations and can directly use projected area, greatly improving overall computational efficiency.

[0022] Step 6: Calculate the harvest area of ​​each individual strip corresponding to the non-extendable strip and the extendable strip, and finally accumulate them to obtain the total harvest area; The specific method for calculating the harvest area of ​​a single non-extendable strip is as follows: For strips marked as non-extendable strips, assuming that their terrain is flat, the projected area is directly used for calculation. First, the projected grid area △S of the farmland area point cloud is obtained, and the number of grid occupancy of farmland points within the non-extendable strip is obtained. The product between the number of grid occupancy within the same non-extendable strip and the projected grid area △S of the farmland area is taken as the single harvest area corresponding to the non-extendable strip. The specific method for obtaining the projected grid area △S of the farmland area is as follows: After projecting the point cloud onto the X-Y plane, the farmland area needs to be regularly rasterized into two dimensions, dividing the farmland area into multiple rasteres of the same size. The side length A of each raster is then obtained, and the area of ​​each raster is A. 2 , and then use it as the projected grid area △S of the farmland area; The method for obtaining the grid occupancy of farmland points within non-extendable strips is as follows: Obtain the position of all farm points within the non-extendable strip in all grids of the farm area, mark the grids with farm points as occupied grids, when a farm point is on the dividing line between two grids, mark both grids corresponding to the dividing line as occupied grids, obtain the total number of occupied grids and use it as the grid occupancy count. For example: If a strip is marked as an inextensible strip and contains 100 farmland points, then the harvest area of ​​the inextensible strip = 100 × 0.1 = 10 square meters; The specific method for calculating the area of ​​a single stretchable strip is as follows: For strips marked as stretchable strips, the actual area of ​​each farmland point in the stretchable strip needs to be reconstructed based on the slope and projected grid area ΔS calculated in step three. Using the principles of calculus, firstly, the slope value Dk of each farmland point obtained in step three is converted into radians θk. Then, the projected area of ​​each farmland point is divided by the cosine of its radians to obtain the actual surface area of ​​each farmland point. The specific method for restoring the actual area of ​​each farmland point in the stretchable strip is as follows: By using Dk × (π / 180), the slope value Dk of each farmland point in the inextensible strip is converted into radians θk: That is, it passes; The actual surface area of ​​each farm point is summed up to obtain the single harvest area of ​​the non-extendable strip, where M is the total number of farm points in the strip, k is the different farm points in the extendable strip, and θk is the radian of the slope value of different farm points in the extendable strip. For example: When a strip is marked as an extendable strip and contains one farmland point, the slope calculated in step three is 60 degrees. Using 60 × (π / 180) = π / 3 (radians), the actual harvested area of ​​the farmland point = 0.1 / cos(π / 3) = 0.1 / 0.5 = 0.2 square meters. Traverse all inextensible and extensible strips, sum up the individual areas of all inextensible and extensible strips, and then obtain the final total harvested area of ​​farmland crops. For non-extendable strips: A simplified projected area calculation is used, i.e., number of farmland points × projected grid area ΔS. For extendable strips: the projected area ΔS of each farmland point is divided by the cosine of its slope angle θk, cos(θk), to obtain the actual surface area Sk. Then, all Sks are summed, overcoming the errors of traditional two-dimensional measurement, especially in complex terrain, ensuring the authenticity and reliability of harvested area data. Providing final decision data: The areas of all strips are accumulated, outputting the final total harvested area of ​​farmland crops, directly serving operational planning and subsidy calculation.

[0023] This method utilizes drones or lidar to acquire 3D point cloud data of farmland, extracting the spatial coordinates of each point within a unified coordinate system. Then, it calculates the local gradients of each point in the X and Y directions using point cloud differencing and synthesizes the slope angle, achieving a quantitative characterization of the farmland's micro-topographical undulations. Based on a slope threshold, the point cloud is automatically divided into field ridge points and farmland points. The point cloud is then projected onto the XY plane, and the farmland's central skeleton line is extracted using rasterization and morphological thinning algorithms. The farmland is then divided into multiple narrow strips along the normal direction of the skeleton line. The proportion of field ridge points in each strip is statistically analyzed, distinguishing between non-extendable and extendable strips: the former is calculated directly by multiplying the number of farmland points by the area of ​​the unified projection grid; the latter, for each farmland point, has its projected area divided by the cosine of the slope angle in radians and summed to reconstruct the 3D surface area. Finally, the total harvested area of ​​the crops is obtained by summing the areas of all strips. By fully utilizing the spatial information and point-level slope characteristics of 3D point clouds, this method overcomes the problems of traditional manual measurement, GPS trajectory, and 2D remote sensing methods, which can only obtain horizontal projected area and systematically underestimate the actual surface area in areas with dense ridges and field ridges. This makes the estimated harvested area closer to the actual cultivated area, providing a fairer and more accurate basis for yield assessment and subsidy calculation. By dividing the strips using skeleton lines and then filtering non-extendable and extendable strips based on the proportion of field ridges, only the undulating extendable strips are subjected to 3D area reconstruction, while the flat areas of the non-extendable strips are calculated. While ensuring a significant improvement in overall accuracy, the final harvested area matches the actual surface morphology, providing a fairer and more accurate area basis for yield estimation, operating cost accounting, and policy subsidies.

[0024] Example 2: Please refer to Figure 2 As shown, this embodiment also provides an automatic measurement system for the harvested area of ​​farmland crops based on computer analysis. This system implements the aforementioned automatic measurement method for the harvested area of ​​farmland crops based on computer analysis, specifically including: The spatial coordinate extraction module extracts the spatial coordinates Pi (Xi, Yi, Zi) of each point in the point cloud data of the farmland area, where i represents a different point in the point cloud. Point cloud data acquired through photogrammetry equipment or lidar systems mounted on drones provides geometric information in three-dimensional space. By using drones or lidar technology to acquire point cloud data of farmland areas, the coordinate system of the point cloud data is unified to ensure that all point cloud data are unified under the same reference coordinate system, and the spatial coordinates of each point are extracted for subsequent analysis.

[0025] The directional gradient acquisition module calculates the gradient of each point in the X and Y directions based on its spatial coordinates, reflecting the slope change of each point in the horizontal direction. It calculates the rate of elevation change between adjacent points using a difference algorithm and outputs the gradient value of each point, providing data support for subsequent slope calculations.

[0026] The slope acquisition module calculates the slope of each point in the farmland area based on the gradient of each point in the X and Y directions, calculates the slope of each point, and converts it into an angle value for use in terrain feature differentiation. The Pythagorean theorem is used to synthesize the gradients in the X and Y directions of each point into a total gradient. The arctan function is used to calculate the slope, and the slope value of each point is output for subsequent marking of field ridges and farmland points.

[0027] The field ridge and farmland point marking module marks the field ridge and farmland points in the farmland area according to the slope of each point and based on the set threshold. Set a slope threshold range and classify each point according to its slope. Points with a slope greater than the set threshold are marked as field ridge points; points with a slope less than or equal to the set threshold are marked as farmland points.

[0028] The non-extendable and extendable strip marking module divides the farmland area into multiple long strips, analyzes the number of field ridges and farmland points contained in each strip, obtains the field ridge ratio coefficient of each strip, and marks the non-extendable and extendable strips in each strip according to the field ridge ratio coefficient. A morphological thinning algorithm was used to extract the central skeleton line of the farmland area, and multiple narrow strips were divided along its normal direction. The proportion coefficient of field ridge points within each strip was calculated. If the proportion coefficient of field ridge points was less than a set threshold, it was marked as an inextensible strip; if the proportion coefficient was greater than or equal to the set threshold, it was marked as an extensible strip.

[0029] The total harvest area acquisition module calculates the harvest area for each individual strip, both inextensible and extensible, and then sums them up to obtain the total harvest area. For inextensible strips, the projected area is used directly for calculation. For extensible strips, the actual surface area is reconstructed using the slope. The actual area is obtained by dividing the projected area of ​​each farmland point by the cosine of its slope angle. The total harvest area is then output by summing the areas of all strips.

[0030] Example 3: As Example 3 of the present invention, in specific implementation, compared with Example 1 and Example 2, the technical solution of this example is to combine the solutions of Example 1 and Example 2.

[0031] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0032] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An automatic method for measuring the harvested area of ​​farmland crops based on computer analysis, characterized in that, include: Step 1: Extract the spatial coordinates Pi (Xi, Yi, Zi) of each point in the point cloud data of the farmland area, where i represents a different point in the point cloud; Step 2: Calculate the gradient of each point in the X and Y directions based on its spatial coordinates; Step 3: Calculate the slope of each point in the farmland area based on the gradient in the X and Y directions; Step 4: Based on the slope of each point within the farmland area, mark the field ridge points and farmland points within the farmland area according to the set threshold. Step 5: Divide the farmland area into multiple long strips, analyze the number of field ridges and farmland points contained in each strip, obtain the field ridge ratio coefficient of each strip, and mark the non-extendable strips and extendable strips in each strip according to the field ridge ratio coefficient. Step 6: Calculate the harvest area of ​​each individual strip corresponding to the non-extendable strip and the extendable strip, and finally accumulate the total harvest area.

2. The automatic measurement method for harvested area of ​​farmland crops based on computer analysis according to claim 1, characterized in that, The specific method for calculating the gradient of each point in the X and Y directions is as follows: Obtain the spatial coordinates of a single point and its neighboring points P0=(X0,Y0,Z0) and P1=(X1,Y1,Z1), through and ; The gradients GX1 and GY1 of this single point in the X and Y directions are calculated. The same method is used to analyze the remaining points to obtain the gradients GXi and GYi of each point in the X and Y directions.

3. The automatic measurement method for harvested area of ​​farmland crops based on computer analysis according to claim 2, characterized in that, The specific method for obtaining the neighboring points of a single point is as follows: For a single point, obtain the distance between it and all its surrounding points, and select the point with the smallest distance as its neighbor. If there are multiple points with the smallest distance, randomly select one as its neighbor.

4. The automatic measurement method for harvested crop area based on computer analysis according to claim 2, characterized in that, The specific method for obtaining the slope of each point within the farmland area is as follows: pass The slope Di at each point is calculated.

5. The automatic measurement method for harvested crop area based on computer analysis according to claim 4, characterized in that, The specific method for marking field ridges and field points within the farmland area based on set thresholds is as follows: Points with a slope greater than a set threshold are marked as field ridge points, and points with a slope less than or equal to the set threshold are marked as farmland points. The set threshold ranges from 8 degrees to 15 degrees.

6. The automatic measurement method for harvested area of ​​farmland crops based on computer analysis according to claim 5, characterized in that, The specific method for marking the non-extendable and extendable strips in each strip according to the field ridge ratio coefficient is as follows: The number of points marked as field ridges and the number of points marked as farmland in each strip are counted. The ratio between the number of field ridges and the total number of field ridges and farmland points in each strip is used as the field ridge ratio coefficient of each strip. If the field ridge ratio coefficient is less than the set threshold Y1, it is marked as an immobile strip. If the field ridge ratio coefficient is greater than or equal to the set threshold Y1, it is marked as an mobile strip. The set threshold Y1 = 0.

2.

7. The automatic measurement method for harvested area of ​​farmland crops based on computer analysis according to claim 6, characterized in that, The specific method for calculating the single harvest area corresponding to inextensible and extensible strips is as follows: For strips marked as non-extendable strips, the projected grid area △S of the farmland area point cloud is obtained, and the grid occupancy of farmland points within the non-extendable strip is also obtained. The product of the grid occupancy of the same non-extendable strip and the projected grid area △S of the farmland area is taken as the single harvest area corresponding to the non-extendable strip. For strips marked as extendable strips, the slope value Dk of each farmland point in the non-extendable strip is converted into radians θk by Dk× (π / 180). The actual surface area of ​​each farm point is summed up to obtain the single harvest area of ​​the non-extendable strip, where M is the total number of farm points in the strip, k is the different farm points in the extendable strip, and θk is the radian of the slope value of different farm points in the extendable strip.

8. The automatic measurement method for harvested area of ​​farmland crops based on computer analysis according to claim 7, characterized in that, The specific method for obtaining the projected grid area △S of the point cloud of farmland area is as follows: After projecting the point cloud onto the X-Y plane, the farmland area is divided into multiple grids of equal size. The side length A of each grid cell is obtained, and the area of ​​each grid cell is A. 2 This is then used as the projected grid area △S of the farmland area.

9. The automatic measurement method for harvested area of ​​farmland crops based on computer analysis according to claim 7, characterized in that, The specific method for obtaining the grid occupancy of farmland points within the non-extendable strip is as follows: Obtain the positions of all farm points within the non-extendable strip in all grids of the farm area, mark the grids with farm points as occupied grids, and when a farm point is on the dividing line between two grids, mark both grids corresponding to the dividing line as occupied grids, obtain the total number of occupied grids and use it as the grid occupancy count.

10. An automatic measurement system for harvested crop area based on computer analysis, wherein the system implements the automatic measurement method for harvested crop area based on computer analysis as described in any one of claims 1-9, characterized in that: The spatial coordinate extraction module extracts the spatial coordinates Pi (Xi, Yi, Zi) of each point in the point cloud data of the farmland area, where i represents a different point in the point cloud. The directional gradient acquisition module calculates the gradient of each point in the X and Y directions based on the spatial coordinates of each point; The slope acquisition module calculates the slope of each point within the farmland area based on the gradient of each point in the X and Y directions. The field ridge and farmland point marking module marks the field ridge and farmland points in the farmland area according to the slope of each point and based on the set threshold. The non-extendable and extendable strip marking module divides the farmland area into multiple long strips, analyzes the number of field ridges and farmland points contained in each strip, obtains the field ridge ratio coefficient of each strip, and marks the non-extendable and extendable strips in each strip according to the field ridge ratio coefficient. The total harvest area acquisition module calculates the harvest area of ​​each individual strip corresponding to both non-extendable and extendable strips, and finally accumulates the total harvest area.

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