Rice cultivation species paddy field surface landform and plowing depth integrated measurement method and rice cultivation species bed preparation apparatus

Abstract

Rice cultivation bed field topography and plowing depth integrated measurement method and rice cultivation bed preparation equipment, with rotary tillage tools as the measurement platform, double antenna GNSS and IMU are installed on the machine frame and the drag plate to measure the tool heading angle, pitch angle and roll angle, and the rotation angle of the drag plate relative to the machine frame; a vehicle body coordinate system is established; the spatial position vectors of the deepest soil cutting point of the rotary blade and the profiling point of the drag plate in the vehicle body coordinate system are established; during rotary tillage operation, the three-dimensional coordinates of the deepest soil cutting point and the profiling point in the northeast celestial coordinate system are measured synchronously using the homogeneous coordinate transformation method, and the deepest soil cutting point set and the profiling point set are constructed; the two point sets are merged, the planar coordinates of the farmland boundary are extracted, and planar grid points are arranged; the field topography height of each planar grid point is estimated based on the profiling point set; the plow bottom layer topography height of each planar grid point is estimated based on the deepest soil cutting point set, and the plowing depth of each grid point is calculated. The present application is more efficient and accurate, and belongs to the technical field of mechanized agricultural tool manufacturing.

Description

Integrated Measurement Method of Rice Cultivation Seed Bed Topography and Tillage Depth and Equipment for Rice Cultivation Seed Bed Preparation Technical Field

[0001] This invention relates to the manufacturing technology of mechanized agricultural machinery, specifically to intelligent agricultural machinery used in rice cultivation, and also to an integrated measurement method for field topography and tillage depth implemented using intelligent agricultural machinery. Background Technology

[0002] Rice seedbed preparation is a crucial step in rice production, directly impacting subsequent planting and growth. Tillage depth significantly affects rice growth and development, and also provides guidance for precise paddy field leveling; the field topography forms the basis for determining reference heights for leveling, allocating excavation and filling volumes, and planning pathways. Currently, paddy field leveling methods mainly include "dry leveling" and "horizontal leveling," with dry leveling seeing a continuously increasing demand due to its high operational efficiency.

[0003] Methods for measuring tillage depth are divided into two categories: contact and non-contact. Contact methods rely on the terrain-following movement of the machine's wheels or ground wheels, combined with the rotation angle and geometric relationships of the lifting arm or rocker arm to calculate the tillage depth. Non-contact methods use non-contact sensors, such as ultrasonic or optical sensors, to monitor the height of the implement frame relative to the ground to calculate the tillage depth. However, factors such as soil moisture content, operating temperature, and surface stubble can affect the accuracy of non-contact measurements. More importantly, existing research shows that the contour-following wheel bottom points in contact methods and the field surface monitoring points in non-contact methods are not vertically distributed with the deepest tillage point, leading to fundamental errors in tillage depth measurement accuracy. Furthermore, the lack of planar coordinate information regarding the distribution of tillage depth in farmland makes it difficult to effectively integrate with subsequent operational stages.

[0004] Field topographic surveying methods are mainly divided into two categories: remote sensing and vehicle-mounted methods. Remote sensing methods refer to using drones as platforms equipped with non-contact sensors to measure farmland topography. However, remote sensing measurements require significant post-processing time and frequent data transmission, increasing time costs. Vehicle-mounted methods refer to using all-terrain vehicles, tractors, and rice transplanters as platforms, equipped with surveying equipment to acquire three-dimensional farmland topography. Although both vehicle-mounted and remote sensing methods are applicable to all stages of leveling operations, the inability to transport earthwork before leveling increases labor and energy costs. Therefore, there is an urgent need to research efficient and cost-effective topographic surveying methods before leveling operations.

[0005] Tillage depth and field topography are important references for fine leveling of farmland, especially in rice production. However, independently measuring tillage depth and field topography increases costs, extends operation time, and raises the risk of farmland compaction. Currently, there are no research reports on integrated measurement methods for tillage depth and field topography. Summary of the Invention

[0006] In view of the technical problems existing in the prior art, the purpose of this invention is to provide an integrated measurement method for rice cultivation seedbed topography and tillage depth, which can achieve measurement more efficiently and accurately.

[0007] Another objective of this invention is to provide a rice cultivation seedbed preparation device.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] A method for integrating the measurement of rice cultivation seedbed topography and tillage depth uses a rotary tiller as the measurement platform. The rotary tiller includes a frame, a rotating cutter shaft, rotating blades, a cover, and a drag plate. During operation, the rotating cutter shaft drives the rotating blades to cut the soil and throw it backward. The soil impacts the cover and drag plate, is broken up, and falls to the surface. The end of the drag plate contacts the field surface and moves in a contour-following manner. The method includes the following steps.

[0010] S1. Install dual-antenna GNSS and IMU on the frame and the trailer respectively to measure the heading angle, pitch angle and roll angle of the equipment, as well as the rotation angle of the trailer relative to the frame;

[0011] S2. Establish the vehicle coordinate system With the GNSS main antenna positioning point as the origin O, the direction of the equipment's movement as the X-axis, the left side of the direction of movement as the Y-axis, and the Z-axis determined according to the right-hand orthogonal criterion;

[0012] S3. Establish the spatial position vectors of the deepest soil cutting point of the rotating blade and the contour point of the trailing plate in the vehicle coordinate system;

[0013] S4. During rotary tillage, the homogeneous coordinate transformation method is used to simultaneously measure the three-dimensional coordinates of the deepest incision point and the contour point under the northeast celestial coordinate system, and to construct the set of the deepest incision point and the set of contour points.

[0014] S5. Merge the deepest cut soil point set and the contour point set, extract the planar coordinates of the farmland boundary, and arrange the planar grid points;

[0015] S6. Using the contour point set as data sample, estimate the field surface elevation of each planar grid point; using the deepest cut soil point set as data sample, estimate the topsoil elevation of each planar grid point, and calculate the tillage depth of each grid point.

[0016] As a preferred embodiment, in step S1, the baseline direction of the dual-antenna GNSS is from the main antenna to the secondary antenna, and the baseline direction is perpendicular to the swath direction of the frame, used to measure the heading angle of the equipment. With pitch angle The X-axis or Y-axis of the IMU's internal coordinate system is parallel to the width direction of the frame and is used to measure the roll angle of the equipment. The rotation angle of the slide relative to the frame .

[0017] As a preferred option, in step S3, the spatial position vector of the deepest soil cutting point varies with the pitch angle of the rotary tiller. Change; Set the position vector of the center point of the rotary tool axis as The radius of rotation of the rotary tiller blade is Establish the spatial location vector of the deepest soil incision point. Its expression is as follows:

[0018] .

[0019] As a preferred embodiment, in step S3, the slide rotates in real time during operation, and the spatial position vector of the contour point changes accordingly, affected by the pitch angle of the rotary tiller. Rotation angle of the slide relative to the frame Together; the rotation center position vector of the slide is set as The distance between the contour point and the center of rotation is Establish the spatial position vector of the contouring point Its expression is as follows:

[0020] .

[0021] As a preferred option, in step S4, the coordinates of the origin of the vehicle coordinate system in the northeast-central coordinate system are set as follows: The rotation matrix is The homogeneous coordinate transformation method was used to calculate the northeast-sky coordinates of the deepest incised soil point and the contour point. and for:

[0022] .

[0023] As a preferred option, in step S5, the long and short sides of the farmland are determined based on the deepest cut point set or the contour point set, so that the grid points are distributed along the long and short sides of the farmland; the distribution of the grid points meets the subsequent agricultural machinery operation heading requirements, and the spacing between adjacent grid points in the short side direction of the farmland is determined by the operation width.

[0024] As a preferred option, in step S5, principal component analysis is used to analyze the correlation between variables X and Y and determine the direction of maximum correlation. The direction of maximum correlation is the direction of the largest eigenvalue of the covariance matrix, which represents the direction of the long side of the farmland. The singular value decomposition algorithm is used to calculate the eigenvalues ​​and eigenvectors of the covariance matrix.

[0025] As a preferred option, in step S6, the height of the field surface and the height of the topsoil are estimated for the same grid point to ensure the accuracy of the tillage depth calculation and avoid the error caused by the non-vertical distribution of the height points of the field surface and the topsoil in traditional tillage depth measurement.

[0026] As a preferred option, in step S6, based on the characteristics of rotary tillage operations, a sample approximation Gaussian process regression algorithm is used to estimate the field surface terrain height and the topsoil terrain height at any grid point.

[0027] A rice cultivation seedbed preparation device uses a rotary tiller to implement an integrated measurement method for rice cultivation seedbed field topography and tillage depth.

[0028] The present invention has the following advantages:

[0029] 1. This invention employs an intelligent agricultural machine that accurately measures tillage depth based on the field surface elevation and tillage depth information at the same grid point, effectively reducing the errors of traditional non-vertical tillage depth measurement methods.

[0030] 2. The integrated measurement method of field topography and tillage depth reduces the production cost of independently measuring field topography and tillage depth, reduces the number of times agricultural machinery enters the field, and effectively reduces the risk of agricultural machinery compacting the farmland.

[0031] 3. In farmland preparation, the rotary tillage operation stage can directly obtain information on the field topography and tillage depth. Through information processing, it can provide precise guidance for farmland leveling operations, significantly reducing the time and energy costs generated by topographic surveying before traditional leveling operations. Attached Figure Description

[0032] Figure 1 is a flowchart of a method for integrating the measurement of rice cultivation seedbed topography and tillage depth.

[0033] Figure 2 is a schematic diagram of the rotary tiller structure and the installation positions of the dual-antenna GNSS and IMU.

[0034] Figure 3 is a schematic diagram of the vehicle coordinate system and the northeast-sky coordinate system.

[0035] Figure 4 is a schematic diagram of the spatial location vector of the deepest cut soil point.

[0036] Figure 5 is a schematic diagram of the spatial position vector of the field surface contour points.

[0037] Figure 6 is a grid point distribution map.

[0038] Figure 7 shows the results of the field surface elevation estimation.

[0039] Figure 8 shows the results of the tillage depth estimation.

[0040] Among them, 1 is the rack, 2 is the enclosure, 3 is the dual-antenna GNSS, 4 is the IMU, and 5 is the slide. Detailed Implementation

[0041] The present invention will now be described in further detail with reference to specific embodiments.

[0042] Example 1

[0043] This embodiment uses a rotary tiller as the measurement platform. The rotary tiller includes a frame, a rotating cutter shaft, rotating blades, a cover, and a trailing plate. The rotating cutter shaft is located below the frame and drives the rotating blades to rotate. The cover is fixedly connected to the frame and located above the rotating cutter shaft. The upper end of the trailing plate is oscillatingly connected to the frame and located behind the rotating cutter shaft. The rotary tiller is shown in Figure 2. During rotary tillage, the rotating blades cut the soil and throw it backward. The soil impacts the cover and trailing plate, is broken up, and falls to the ground surface. The end of the trailing plate contacts the field surface and moves in a contour-following manner with the terrain.

[0044] As shown in Figure 1, the specific method for integrating the measurement of rice cultivation seedbed topography and tillage depth in this embodiment is as follows:

[0045] S1. Install dual-antenna GNSS and IMU on the frame and the trailer respectively to measure the heading angle, pitch angle, roll angle of the equipment, and the rotation angle of the trailer relative to the frame.

[0046] In this embodiment, the baseline direction of the dual-antenna GNSS (i.e., the direction from the main antenna to the secondary antenna) is perpendicular to the swath direction of the frame, and is used to measure the heading angle of the equipment. With pitch angle The X-axis or Y-axis of the IMU's internal coordinate system is parallel to the width direction of the frame and is used to measure the roll angle of the equipment. The rotation angle of the slide relative to the frame As shown in Figure 3.

[0047] S2. Establish the vehicle coordinate system With the GNSS main antenna positioning point as the origin O, the direction of the equipment's movement as the X-axis, the left side of the direction of movement as the Y-axis, and the Z-axis determined according to the right-hand orthogonal criterion, as shown in Figure 3.

[0048] S3. Establish the spatial position vectors of the deepest soil cutting point of the rotating blade and the contour point of the trailer in the vehicle coordinate system.

[0049] In this embodiment, the spatial position vector of the deepest soil cutting point varies with the pitch angle of the rotary tiller. The changes are shown in Figure 4. The position vector of the center point T of the rotary tool axis is set as... The radius of rotation of the rotary tiller blade is Establish the spatial location vector of the deepest soil point B. Its expression is as follows:

[0050] .

[0051] The slide rotates in real time during operation, and the spatial position vector of the contour point changes accordingly, affected by the pitch angle of the rotary tiller. Rotation angle of the slide relative to the frame The two forces work together, as shown in Figure 5. The rotation center R of the slide is set as... The distance between the contour point and the center of rotation is Establish the spatial position vector of the contour point C. Its expression is as follows:

[0052] .

[0053] S4. During rotary tillage, the homogeneous coordinate transformation method is used to simultaneously measure the three-dimensional coordinates of the deepest incision point and the contour point under the northeast celestial coordinate system, and to construct the set of the deepest incision point and the set of contour points.

[0054] In this embodiment, the coordinates of the origin of the vehicle coordinate system in the northeast-northeast coordinate system are set as follows: The rotation matrix is The homogeneous coordinate transformation method was used to calculate the northeast-sky coordinates of the deepest incised soil point and the contour point. and for:

[0055] .

[0056] S5. Extract the planar coordinates of the farmland boundary using the deepest cut point set or the contour point set, and arrange the planar grid points.

[0057] In this embodiment, taking the measured contour point set as an example, the specific steps are as follows:

[0058] 1) Determine the direction of the long side of the farmland.

[0059] The number of contour points to be measured is set to m, and the set of plane coordinates of each contour point is as follows:

[0060] .

[0061] in, Let X be the X coordinate of the i-th contour point. Let be the Y-coordinate of the i-th contour point.

[0062] Principal component analysis (PCA) was used to analyze the correlation between variables X and Y and to determine the direction of maximum correlation. The direction of maximum correlation is the direction of the largest eigenvalue of the covariance matrix, representing the direction of the longer side of the field. The formula for calculating the covariance matrix is ​​as follows:

[0063] ,

[0064] .

[0065] The eigenvalues ​​and eigenvectors of the covariance matrix are calculated using the singular value decomposition algorithm:

[0066] ,

[0067] in, These are all the eigenvectors of the covariance matrix, and the eigenvectors are sorted by the size of their eigenvalues. It is a diagonal matrix, and its diagonal elements are the square roots of the eigenvalues ​​of the covariance matrix. It is an orthogonal matrix.

[0068] 2) Rotation point set:

[0069] Representing a two-dimensional point set in matrix form :

[0070] ,

[0071] Rotating 2D point set Align the long and short sides of the farmland with the EN coordinate axes:

[0072] .

[0073] 3) Arrange grid points:

[0074] Compute point set The extreme values ​​in the X and Y axes ( ), ( , ), and establish farmland boundaries. Set the spacing between adjacent grid points in the X and Y axes as follows: and The grid point set is arranged as follows:

[0075] ,

[0076] Arrange grid points along the local boundaries of farmland:

[0077] ,

[0078] Merge grid points:

[0079] ,

[0080] Rotate the grid point set to restore the long and short sides of the farmland to their original orientation:

[0081] ,

[0082] The grid points are distributed along the long and short sides of the farmland, and the distribution of the grid points must meet the needs of subsequent operations. Specifically, the working direction of the agricultural machinery is usually consistent with the long side of the farmland, and the spacing between adjacent grid points along the short side of the farmland is determined by the working width, as shown in Figure 6.

[0083] S6. Using the contour point set as data sample, estimate the field surface elevation of each planar grid point; using the deepest cut soil point set as data sample, estimate the topsoil elevation of each planar grid point, and calculate the tillage depth of each grid point.

[0084] In this embodiment, based on the characteristics of rotary tillage operations, a Sample Approximation Gaussian Process Regression (SA-GPR) algorithm is proposed to estimate the terrain height and tillage depth of arbitrary grid points. Specifically, regions of interest (ROIs) with high correlation to the terrain height of grid points are defined, and the ROI point set is approximated to the grid points without loss of accuracy. Based on this, the optimized ROI point set is used as the observation sample, and the terrain height and tillage depth of the grid points are estimated using Gaussian process regression. (Field topographic point cloud is used as an example.) For example, the implementation process is as follows:

[0085] With the width of rotary tillage operation Divide the grid points into regions with a radius that affects each grid point. Region of Interest (ROI) for terrain height estimation. Extracting the terrain point set of the ROI. :

[0086] ,

[0087] in, For point set Any point.

[0088] Based on the characteristics of rotary tillage operations, multiple contour points measured simultaneously exhibit spatial collinearity. Therefore, a time variable is added. Supplement the definition of contour points And through time variables Consistency assessment of collinear points. If there are no collinear independent points within the ROI, then that point is removed. Equations for plane lines are established based on the collinear points. And draw a perpendicular line to the straight line through the grid points. The two lines intersect at the nearest point. Determine the closest point to the line. The positional relationship is as follows:

[0089] .

[0090] If the nearest point satisfies the above conditions, calculate the terrain height of that point based on spatial linearity. Simultaneously, add this point within the ROI and remove corresponding collinear points. Conversely, only retain collinear points closest to the grid point. Rotation-optimized ROI point set planar coordinates:

[0091] .

[0092] Assuming terrain height It is a Gaussian process Combined with Gaussian noise terms:

[0093] ,

[0094] in, It is a Gaussian process model, representing the process at the input point. The actual terrain elevation at the location; It is independent and identically distributed Gaussian noise. It is the noise variance.

[0095] Output value Follows a multivariate Gaussian distribution:

[0096] ,

[0097] in, It is the covariance matrix of the training samples, whose elements are determined by the kernel function. Calculated; It is the covariance matrix of the observation noise.

[0098] Radial basis functions (RBFs) are suitable for terrains that are smooth and continuously varying, consistent with the topographic characteristics of the cultivated subsurface and field surface. The kernel function is defined as:

[0099] ,

[0100] in, It is the signal variance; It is a length dimension parameter.

[0101] This embodiment optimizes model parameters using maximum likelihood estimation. , , .

[0102] For each grid point function value Terrain height relative to training samples Follows a joint Gaussian distribution:

[0103] ,

[0104] in, and It is the covariance matrix between the training data and the grid points; It is the covariance scalar of the grid points themselves.

[0105] Based on the properties of the joint Gaussian distribution, the conditional distribution of grid points is as follows:

[0106] .

[0107] Similarly, using the deepest soil point of the optimized ROI as the observation sample, the SA-GPR algorithm is used to estimate the topographic height of the cultivated subsurface at each grid point. Set the estimated height of the field terrain at the grid points to be... Then the tillage depth is:

[0108] .

[0109] As shown in Figure 7, the true coordinates of 20 known measurement points were selected as input, and their terrain height was estimated. By comparing the estimated terrain height with the true values, the trends of the estimated and true terrain height curves were significantly consistent, and the errors were all less than 30 mm. Statistical analysis results show that the average error of terrain height estimation was 17.95 mm, and the root mean square error was 17.13 mm, verifying the accuracy of this method in terrain height estimation.

[0110] As shown in Figure 8, the trend of the estimated tillage depth curve is consistent with that of the actual curve, and most of the errors are less than 20 mm. Statistical analysis shows that the average error of the tillage depth estimation is 14.53 mm, and the root mean square error is 16.50 mm, indicating that this method can accurately estimate the tillage depth.

[0111] The estimation accuracy of both tillage depth and topographic height is constrained by the accuracy of GNSS measurements, the accuracy of simultaneous measurements of the tillage layer and field surface topography, and the estimation accuracy. The results show that the error in tillage depth estimation is slightly lower than the error in topographic height estimation. This is because tillage depth is the relative vertical height between the tillage layer and the field surface, reducing the impact of GNSS measurement errors on its estimation.

[0112] Therefore, the method of the present invention realizes the integrated measurement of field topography and rotary tillage depth, and performs excellently in terms of measurement accuracy.

[0113] Example 2

[0114] A rice cultivation seedbed preparation device, using a rotary tiller, implements an integrated measurement method for rice cultivation seedbed surface topography and tillage depth according to Example 1.

[0115] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A method for integrating the measurement of rice cultivation seedbed topography and tillage depth, using a rotary tiller as the measurement platform. The rotary tiller includes a frame, a rotating cutter shaft, rotating blades, a cover, and a drag plate. During operation, the rotating cutter shaft drives the rotating blades to cut the soil and throw it backward. The soil impacts the cover and drag plate, is broken up, and falls to the surface. The end of the drag plate contacts the field surface and moves in a contour-following manner with the terrain. The method is characterized by... The steps include the following: S1. Install dual-antenna GNSS and IMU on the frame and the trailer respectively to measure the heading angle, pitch angle and roll angle of the equipment, as well as the rotation angle of the trailer relative to the frame; S2. Establish the vehicle coordinate system With the GNSS main antenna positioning point as the origin O, the direction of the equipment's movement as the X-axis, the left side of the direction of movement as the Y-axis, and the Z-axis determined according to the right-hand orthogonal criterion; S3. Establish the spatial position vectors of the deepest soil cutting point of the rotating blade and the contour point of the trailing plate in the vehicle coordinate system; S4. During rotary tillage, the homogeneous coordinate transformation method is used to simultaneously measure the three-dimensional coordinates of the deepest incised point and the contour point under the northeast celestial coordinate system, and construct the deepest incised point set and the contour point set; S5. The deepest incised point set and the contour point set are merged, the planar coordinates of the farmland boundary are extracted, and planar grid points are arranged; S6. Using the contour point set as data samples, the field surface topography height of each planar grid point is estimated; Using the deepest incised soil point set as data samples, the topographic height of the cultivated layer at each planar grid point is estimated, and the tillage depth at each grid point is calculated. In step S1, the baseline direction of the dual-antenna GNSS is from the main antenna to the secondary antenna, and the baseline direction is perpendicular to the swath direction of the frame, used to measure the heading angle of the implement. With pitch angle The X-axis or Y-axis of the IMU's internal coordinate system is parallel to the width direction of the frame and is used to measure the roll angle of the equipment. The rotation angle of the slide relative to the frame In step S3, the spatial position vector of the deepest soil cutting point varies with the pitch angle of the rotary tiller. Change; Set the position vector of the center point of the rotary tool axis as The radius of rotation of the rotary tiller blade is Establish the spatial location vector of the deepest soil incision point. Its expression is as follows: In step S3, the slide rotates in real time during operation, and the spatial position vector of the contour point changes accordingly, affected by the pitch angle of the rotary tiller. Rotation angle of the slide relative to the frame Together; the rotation center position vector of the slide is set as The distance between the contour point and the center of rotation is Establish the spatial position vector of the contouring point Its expression is as follows: 。 2. The integrated measurement method for rice cultivation seedbed topography and tillage depth according to claim 1, characterized in that: In step S4, the coordinates of the origin of the vehicle coordinate system in the northeast-central coordinate system are set as follows: The rotation matrix is The homogeneous coordinate transformation method was used to calculate the northeast-sky coordinates of the deepest incised soil point and the contour point. and for: 。 3. The integrated measurement method for rice cultivation seedbed topography and tillage depth according to claim 1, characterized in that: In step S5, the long and short sides of the farmland are determined based on the deepest cut point set or the contour point set, so that the grid points are distributed along the long and short sides of the farmland. The distribution of the grid points meets the navigation requirements of subsequent agricultural machinery operations, and the spacing between adjacent grid points in the short side direction of the farmland is determined by the working width.

4. The integrated measurement method for rice cultivation seedbed topography and tillage depth according to claim 3, characterized in that: In step S5, principal component analysis is used to analyze the correlation between variables X and Y and determine the direction of maximum correlation. The direction of maximum correlation is the direction of the largest eigenvalue of the covariance matrix, which represents the direction of the long side of the farmland. The singular value decomposition algorithm is used to calculate the eigenvalues ​​and eigenvectors of the covariance matrix.

5. The integrated measurement method for rice cultivation seedbed topography and tillage depth according to claim 1, characterized in that: In step S6, for the same grid point, the height of the field surface topography and the height of the topography of the cultivated bottom are estimated to ensure the accuracy of the tillage depth calculation and avoid the error caused by the non-vertical distribution of the height points of the field surface topography and the topography of the cultivated bottom in traditional tillage depth measurement.

6. The integrated measurement method for rice cultivation seedbed topography and tillage depth according to claim 5, characterized in that: In step S6, based on the characteristics of rotary tillage, a sample approximation Gaussian process regression algorithm is used to estimate the field surface topography height and the topography height of the tillage layer at any grid point.

7. A rice cultivation seedbed preparation device, characterized in that: A rotary tillage machine is used to implement the integrated measurement method of rice cultivation seedbed topography and tillage depth as described in any one of claims 1-6.

Images