CN107194913A - A kind of most suitable Research scale detection method and device of crop groups - Google Patents

Abstract

The invention provides a method and device for detecting the optimal research scale of a crop group. The method includes: obtaining three-dimensional point cloud data C of a target crop group; uniformly resampling the obtained three-dimensional point cloud data C to obtain sampled three-dimensional points cloud data Statistically sampled 3D point cloud data The number of data points in each preset voxel, and according to the sampled 3D point cloud data The number of data points in each preset voxel determines the optimum number of rows and the optimum number of plants per row, and the optimum research scale range of the target crop group is determined by the optimum number of rows and the optimum number of plants per row. The three-dimensional point cloud of the crop group with occlusion relationship obtained by the three-dimensional data acquisition device in the present invention can obtain the optimum research scale range of the crop group, and further improve the use efficiency of the crop group test plot and improve the crop population under the premise of ensuring the calculation accuracy. Computational efficiency of crowd light distribution simulation etc. plays an important role.

Description

A method and device for detecting the optimal research scale of crop groups

technical field

The invention relates to the field of agricultural technology, in particular to a method and device for detecting the optimum research scale of crop groups.

Background technique

As an organizational system that fulfills the functions of photosynthesis and material production, crop populations have an important impact on light interception capacity, canopy photosynthetic efficiency and crop yield. At the same time, the population structure also reflects the genetic characteristics of crop varieties and their adaptability to the environment. Under the influence of genetic and environmental factors, the morphological structure of crop populations has temporal and spatial variability. So far, the morphological characteristics of crop populations have been recognized by humans , The most basic way to analyze and evaluate crops.

In the study of crop cultivation and breeding, how large a range of crop populations can reflect the population characteristics of crops is an important issue, that is, to ensure that the central area of crops has typical population characteristics and avoid marginal effects. For example, in the study of the light interception ability of a new variety of corn populations with different densities, it is planned to measure the distribution of photosynthetically active radiation at different heights in the central area of the population to characterize the light interception ability of the variety. If the planting range of the population is too small, the light exposure of the surrounding plants will be reduced. The occlusion makes the measurement results unrepresentative; too large a group planting range will significantly increase the investment and experimental workload. This situation also exists in the light interception experiments of virtual crop groups. The construction range of virtual crop groups is too small to represent the occlusion of the surrounding groups to the light of the middle plants. The construction range of virtual crop groups is too large, which will greatly increase the number of geometric model elements and reduce the Efficiency of canopy light distribution calculations.

There are two solutions in actual research. One is to ensure the population characteristics of the crop plants in the center by expanding the planting range as much as possible; Experimental workload. The practical problem of these two schemes is that it is impossible to find the most suitable scale of crop population planting density, and ensure that the plants in the center of the planting area have typical population characteristics under the premise of ensuring the smallest planting area.

Contents of the invention

Aiming at the defects in the prior art, the present invention provides a method and device for detecting the optimum research scale of crop populations, which can obtain the optimum research scale range of crop populations.

Specifically, the present invention provides the following technical solutions:

In the first aspect, the present invention provides a method for detecting the optimum research scale of crop groups, comprising:

Acquire the three-dimensional point cloud data C of the target crop group at a preset designated position in the target crop group; the center of the three-dimensional coordinate system where the three-dimensional point cloud data C is located is the preset designated position, and the three-dimensional point cloud data The Z axis of the three-dimensional coordinate system where C is located represents the crop height direction, the X axis represents the crop row direction, and the Y axis represents the plant direction perpendicular to the crop row direction;

Perform uniform resampling on the acquired 3D point cloud data C to obtain the sampled 3D point cloud data

Statistically sampled 3D point cloud data The number of data points in each preset voxel, and according to the sampled 3D point cloud data The number of data points in each preset voxel determines the optimum number of rows and the optimum number of plants in each row, and the optimum research scale range of the target crop group is determined by the optimum number of rows and the optimum number of plants in each row;

Wherein, each preset voxel is the three-dimensional point cloud data after sampling Multiple independent cuboid spaces obtained after space division of the three-dimensional coordinate system.

Further, the obtained 3D point cloud data C is uniformly resampled to obtain sampled 3D point cloud data Specifically include:

Set the resampling distance parameter L and the threshold number parameter Q;

Divide the point cloud space into a cube whose length, width and height are both L. If the number of points where the 3D point cloud data C falls into a certain cube is greater than or equal to Q, then the center point of the cube will be used as the weight in the cube space. Sampling point, after such uniform resampling of the 3D point cloud data C, the sampled 3D point cloud data is obtained

Further, according to the sampled 3D point cloud data The number of data points within each preset voxel determines the optimum number of rows and the optimum number of plants per row, including:

Carry out space division on the XOY plane of the three-dimensional coordinate system, divide the X-axis into M equal-length pixel segments in turn, and divide the Y-axis into N equal-length pixel segments in turn;

Statistical 3D point cloud data The number of data points in the preset voxel V _mn , wherein the preset voxel V _mn represents the mth pixel segment in the X-axis direction, the n-th pixel segment in the Y-axis direction, and all pixels in the Z-axis direction The voxel determined by the point, 1≤m≤M, 1≤n≤N;

If the number of data points in V _mn ≤ the preset critical pixel number s ₀ , then determine the pixel point farthest from the preset specified position in the pixel segment m in the X-axis direction corresponding to the preset voxel V _mn And the pixel point farthest from the preset designated position in the pixel segment n in the Y-axis direction is the position coordinate that has no influence on the preset designated position, according to the determined position coordinates that have no influence on the preset designated position, combined with the crop group The optimal row number and the optimal number of plants per row were obtained.

Further, the acquisition of the three-dimensional point cloud data C of the target crop group specifically includes:

Obtaining the three-dimensional point cloud data C of the target crop group by using a crop group scale measurement device set at a preset designated position;

Wherein, the crop population scale measurement device includes: a three-dimensional point cloud acquisition device, a height adjustment device and a tripod support device; the lower end of the three-dimensional point cloud acquisition device is connected to the height adjustment device, and the lower end of the height adjustment device is connected to the height adjustment device. The tripod support device is connected;

Wherein, the three-dimensional point cloud acquisition device is a three-dimensional scanner or a total station in the form of a laser, and the measurement radius of the three-dimensional point cloud acquisition device is greater than or equal to 40m; the height adjustment device includes a telescopic rod, on which engraved with a scale, the telescopic rod is used to adjust the height of the three-dimensional point cloud acquisition device, and realizes the precise control of the height of the three-dimensional point cloud acquisition device; A telescopic support rod, the top structure is provided with horizontal adjustment air bubbles; the hollow structure of the tripod is provided with a telescopic rod for measuring the vertical distance between the tripod and the ground surface;

Correspondingly, the acquisition of the three-dimensional point cloud data C of the target crop group by using the crop group scale measurement device set at a preset designated position specifically includes:

The tripod support device is placed on a preset designated position in the target crop group, and the tripod support device is adjusted to the level by using the level adjustment bubble;

Sequentially adjust the height of the height adjustment device, so that the three-dimensional point cloud acquisition device acquires the three-dimensional point cloud data of the target crop group at different heights; wherein, the acquisition range of the three-dimensional point cloud acquisition device when acquiring the three-dimensional point cloud data The horizontal direction is 360 degrees, and the vertical direction is greater than 135 degrees; when adjusting the height of the height adjustment device, the height of the height adjustment device is increased in an equal gradient, and the highest point does not exceed the height H of the target crop group.

Further, the method also includes:

According to the optimal research scale range of the target crop group, the calculation and analysis of the light distribution of the target crop canopy is carried out.

In the second aspect, the present invention also provides a detection device for the optimal research scale of crop groups, including:

The acquisition module is used to obtain the three-dimensional point cloud data C of the target crop group at a preset designated position in the target crop group; the center of the three-dimensional coordinate system where the three-dimensional point cloud data C is located is the preset designated position, so The Z axis of the three-dimensional coordinate system where the three-dimensional point cloud data C is located represents the crop height direction, the X axis represents the crop row direction, and the Y axis represents the plant direction perpendicular to the crop row direction;

The sampling module is used to uniformly resample the acquired 3D point cloud data C to obtain sampled 3D point cloud data

Determination module, used for statistical sampling of 3D point cloud data The number of data points in each preset voxel, and according to the sampled 3D point cloud data The number of data points in each preset voxel determines the optimum number of rows and the optimum number of plants in each row, and the optimum research scale range of the target crop group is determined by the optimum number of rows and the optimum number of plants in each row;

Wherein, each preset voxel is the three-dimensional point cloud data after sampling Multiple independent cuboid spaces obtained after space division of the three-dimensional coordinate system.

Further, the sampling module includes a setting unit and a sampling unit; wherein:

The setting unit is used to set the resampling distance parameter L and the threshold number parameter N;

The sampling unit is used to divide the point cloud space into a cube whose length, width and height are all L. If the number of points where the three-dimensional point cloud data C falls into a certain cube is greater than or equal to N, then the center point of the cube is used as The resampling points in the cube space, after such uniform resampling of the 3D point cloud data C, obtain the sampled 3D point cloud data

Further, the determination module is based on the sampled three-dimensional point cloud data When the number of data points in each preset voxel determines the optimum number of rows and the optimum number of plants per row, it is specifically used for:

Carry out space division on the XOY plane of the three-dimensional coordinate system, divide the X-axis into M equal-length pixel segments in turn, and divide the Y-axis into N equal-length pixel segments in turn;

Statistical 3D point cloud data The number of data points in the preset voxel V _mn , wherein the preset voxel V _mn represents the mth pixel segment in the X-axis direction, the n-th pixel segment in the Y-axis direction, and all pixels in the Z-axis direction The voxel determined by the point, 1≤m≤M, 1≤n≤N;

If the number of data points in V _mn ≤ the preset critical pixel number s ₀ , then determine the pixel point farthest from the preset specified position in the pixel segment m in the X-axis direction corresponding to the preset voxel V _mn And the pixel point farthest from the preset designated position in the pixel segment n in the Y-axis direction is the position coordinate that has no influence on the preset designated position, according to the determined position coordinates that have no influence on the preset designated position, combined with the crop group The optimal row number and the optimal number of plants per row were obtained.

Further, the acquisition module is specifically used to: acquire the three-dimensional point cloud data C of the target crop group by using a crop group scale measurement device set at a preset designated position;

Wherein, the crop population scale measurement device includes: a three-dimensional point cloud acquisition device, a height adjustment device and a tripod support device; the lower end of the three-dimensional point cloud acquisition device is connected to the height adjustment device, and the lower end of the height adjustment device is connected to the height adjustment device. The tripod support device is connected;

Wherein, the three-dimensional point cloud acquisition device is a three-dimensional scanner or a total station in the form of a laser, and the measurement radius of the three-dimensional point cloud acquisition device is greater than or equal to 40m; the height adjustment device includes a telescopic rod, on which engraved with a scale, the telescopic rod is used to adjust the height of the three-dimensional point cloud acquisition device, and realizes the precise control of the height of the three-dimensional point cloud acquisition device; A telescopic support rod, the top structure is provided with horizontal adjustment air bubbles; the hollow structure of the tripod is provided with a telescopic rod for measuring the vertical distance between the tripod and the ground surface;

Correspondingly, the acquisition module is specifically used for:

The tripod support device is placed on a preset designated position in the target crop group, and the tripod support device is adjusted to the level by using the level adjustment bubble;

Sequentially adjust the height of the height adjustment device, so that the three-dimensional point cloud acquisition device acquires the three-dimensional point cloud data of the target crop group at different heights; wherein, the acquisition range of the three-dimensional point cloud acquisition device when acquiring the three-dimensional point cloud data The horizontal direction is 360 degrees, and the vertical direction is greater than 135 degrees; when adjusting the height of the height adjustment device, the height of the height adjustment device is increased in an equal gradient, and the highest point does not exceed the height H of the target crop group.

Further, the device further includes: a light distribution analysis and calculation module;

The light distribution analysis and calculation module is used to calculate and analyze the light distribution of the target crop canopy according to the optimal research scale range of the target crop group.

It can be seen from the above technical solution that the method for detecting the most suitable research scale of crop groups provided by the present invention, combined with modern 3D point cloud acquisition and processing technology, can detect the most suitable crop group by acquiring and analyzing the occlusion relationship of each plant and each organ in the crop group. The experimental range or the virtual population construction range. The present invention can be used to guide the optimal experimental range of different crops and crop populations of different densities; it can be used to guide the optimal construction range of crop virtual populations. The computational efficiency of crop population light distribution simulation plays an important role.

Description of drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are For some embodiments of the present invention, those skilled in the art can also obtain other drawings based on these drawings without creative work.

Fig. 1 is a kind of flow chart of the method for detecting the optimal research scale of crop groups provided by an embodiment of the present invention;

Fig. 2 is a schematic diagram of the acquisition principle of the three-dimensional point cloud data of the target crop group obtained by the crop group scale measurement device provided by an embodiment of the present invention;

Fig. 3 and Fig. 4 are the principle schematic diagrams of determining the optimal research scale range of the target crop group provided by an embodiment of the present invention;

Fig. 5 is another flow chart of the crop group optimal research scale detection method provided by an embodiment of the present invention;

Fig. 6 is a schematic structural diagram of a crop group optimum research scale detection device provided by another embodiment of the present invention;

Fig. 7 is another structural schematic diagram of a detection device for the optimum research scale of a crop group provided by another embodiment of the present invention.

detailed description

In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

Fig. 1 shows a flow chart of a method for detecting an optimal research scale of a crop group provided by an embodiment of the present invention. Referring to Fig. 1, the method for detecting the optimal research scale of crop groups provided in this embodiment includes the following steps:

Step 101: Obtain the 3D point cloud data C of the target crop group at a preset designated position in the target crop group; the center of the 3D coordinate system where the 3D point cloud data C is located is the preset designated position, and the 3D point cloud data C is The Z axis of the three-dimensional coordinate system where the point cloud data C is located represents the crop height direction, the X axis represents the crop row direction, and the Y axis represents the plant direction perpendicular to the crop row direction.

In this step, the three-dimensional point cloud data C of the target crop population can be obtained by using a crop population scale measurement device set at a preset designated position.

Here, referring to Fig. 2, the crop group scale measurement device includes: a three-dimensional point cloud acquisition device, a height adjustment device and a tripod support device; the lower end of the three-dimensional point cloud acquisition device is connected with the height adjustment device, and the height adjustment device The lower end of the device is connected with the tripod support device;

Wherein, the three-dimensional point cloud acquisition device is a three-dimensional scanner or a total station in the form of a laser, and the measurement radius of the three-dimensional point cloud acquisition device is greater than or equal to 40m; the height adjustment device includes a telescopic rod, on which engraved with a scale, the telescopic rod is used to adjust the height of the three-dimensional point cloud acquisition device, and realizes the precise control of the height of the three-dimensional point cloud acquisition device; A telescopic support rod, the top structure is provided with horizontal adjustment air bubbles; the hollow structure of the tripod is provided with a telescopic rod for measuring the vertical distance between the tripod and the ground surface;

Correspondingly, the acquisition of the three-dimensional point cloud data C of the target crop group by using the crop group scale measurement device set at a preset designated position specifically includes:

The tripod support device is placed on a preset designated position in the target crop group, and the tripod support device is adjusted to the level by using the level adjustment bubble;

Sequentially adjust the height of the height adjustment device, so that the three-dimensional point cloud acquisition device acquires the three-dimensional point cloud data of the target crop group at different heights; wherein, the acquisition range of the three-dimensional point cloud acquisition device when acquiring the three-dimensional point cloud data The horizontal direction is 360 degrees, and the vertical direction is greater than 135 degrees; when adjusting the height of the height adjustment device, the height of the height adjustment device is increased in an equal gradient, and the highest point does not exceed the height H of the target crop group. For example, denote the set of point clouds obtained at each height as C _hi , where h represents the height value obtained by the current point cloud. When adjusting the height adjustment device, make h increase in an equal gradient, and the highest point does not exceed the height H of the target crop population.

For example, in this step, assuming that the height of the lowest point is h ₁ , the number of acquisitions is n, and the highest point is Hh ₁ , the gradient of height increase is

It can be understood that, the preset designated position is generally a position where the growth of the crop population is normal and located in the center of the crop population. In addition, when acquiring the three-dimensional point cloud data C, it is preferable to carry out in calm weather.

In addition, when acquiring 3D point cloud data C, due to mutual occlusion problems among plants, it is necessary to set target balls at several positions for registration of later point cloud data. For example, after obtaining the point cloud set C _hi at each height position, the target ball position is set in advance, and the 3D point cloud data registration method is used for point cloud registration to obtain a relatively complete and accurate 3D point cloud data C .

Step 102: Uniformly resampling the acquired 3D point cloud data C to obtain sampled 3D point cloud data

In this step, the resampling distance parameter L and the threshold number parameter Q are first set, and then the point cloud space is divided into cubes whose length, width and height are both L. If the 3D point cloud data C falls into a cube If the number is greater than or equal to Q, then the center point of the cube is used as the resampling point in the cube space. After such uniform resampling of the 3D point cloud data C, the sampled 3D point cloud data is obtained

Step 103: Statistically sampled 3D point cloud data The number of data points in each preset voxel, and according to the sampled 3D point cloud data The number of data points in each preset voxel determines the optimum number of rows and the optimum number of plants per row, and the optimum research scale range of the target crop group is determined by the optimum number of rows and the optimum number of plants per row.

In this step, each preset voxel is the sampled three-dimensional point cloud data Multiple independent cuboid spaces obtained after space division of the three-dimensional coordinate system. It can be understood that if the three-dimensional point cloud acquisition device located at the central position (preset specified position) obtains fewer point cloud data points for the crop group located in a certain voxel (cube space) (for example, just less than or equal to the preset Critical value but greater than a minimum preset value, if the number of data points acquired in a certain voxel is 6, just less than or equal to the preset critical value 6 but greater than a minimum preset value 4), it means that the voxel is The corresponding farthest position point will hardly have any light impact on the central position point, so the optimum number of rows and the optimum number of plants per row can be determined accordingly, and then the optimum research scale range can be determined.

Therefore, this step can count the sampled 3D point cloud data The number of data points in each preset voxel, and according to the sampled 3D point cloud data The number of data points in each preset voxel determines the optimum number of rows and the optimum number of plants in each row, and the optimum research scale range of the target crop group is determined by the optimum number of rows and the optimum number of plants in each row; Wherein, each preset voxel is the three-dimensional point cloud data after sampling Multiple independent cuboid spaces obtained after space division of the three-dimensional coordinate system.

In this step, according to the sampled three-dimensional point cloud data The number of data points within each preset voxel determines the optimum number of rows and the optimum number of plants per row, including:

Carry out space division on the XOY plane of the three-dimensional coordinate system, divide the X-axis into M equal-length pixel segments in turn, and divide the Y-axis into N equal-length pixel segments in turn;

Statistical 3D point cloud data The number of data points in the preset voxel V _mn , wherein the preset voxel V _mn represents the mth pixel segment in the X-axis direction, the n-th pixel segment in the Y-axis direction, and all pixels in the Z-axis direction The voxel determined by the point, 1≤m≤M, 1≤n≤N;

If the number of data points in V _mn ≤ the preset critical pixel number s ₀ , then determine the pixel point farthest from the preset specified position in the pixel segment m in the X-axis direction corresponding to the preset voxel V _mn And the pixel point farthest from the preset designated position in the pixel segment n in the Y-axis direction is the position coordinate that has no influence on the preset designated position, according to the determined position coordinates that have no influence on the preset designated position, combined with the crop group The optimal row number and the optimal number of plants per row were obtained.

Referring to the principle schematic diagrams shown in FIG. 3 and FIG. 4 , wherein FIG. 3 is a schematic diagram of a light shielding effect, and FIG. 4 is a statistical effect diagram of data points. The measurement position in FIG. 3 is the preset designated position, and the evaluation position in FIG. 3 is the position where it needs to be investigated whether light will affect the preset designated position (that is, the measurement position). The effective positions in FIG. 4 are the positions that will cause light shading to the preset specified position (ie, the measurement position).

Among them, after obtaining the optimum number of rows and the optimum number of plants per row, the optimum research scale range of the target crop population can be determined by the optimum number of rows and the optimum number of plants per row, and the populations outside this range Note that it has no effect on the plants at the current position (that is, the preset designated position).

It can be seen that the embodiment of the present invention uses a three-dimensional data acquisition device to obtain the three-dimensional structure and shape information of the crop population, and further combines the processing methods such as registration and resampling of the three-dimensional point cloud to obtain whether the crop populations in different ranges have any influence on the plants at the current position in the population. impact conclusions.

In an optional implementation manner, referring to FIG. 3, the method further includes:

Step 104: Calculate and analyze the light distribution of the target crop canopy according to the optimal research scale range of the target crop group.

In this embodiment, based on the conclusions obtained in the above steps (the optimum number of rows, the optimum number of plants per row), a virtual population of crops can be constructed for calculation and analysis of the light distribution of the crop canopy.

It can be seen from the scheme described above that the method for detecting the optimal research scale of crop populations provided by the embodiment of the present invention, combined with modern 3D point cloud acquisition and processing technology, detects the crop population by acquiring and analyzing the occlusion relationship of each plant and each organ in the crop population. The most suitable experimental range or virtual population construction range for the population. The embodiment of the present invention can be used to guide the optimal experimental range of different crops and crop populations of different densities; it can be used to guide the optimal construction range of the crop virtual population. It plays an important role in improving the calculation efficiency of crop population light distribution simulation under the premise.

Another embodiment of the present invention provides a detection device for the optimal research scale of crop groups, referring to Fig. 4, the device includes: an acquisition module 21, a sampling module 22 and a determination module 23; wherein:

The acquisition module 21 is used to obtain the three-dimensional point cloud data C of the target crop group at a preset designated position in the target crop group; the center of the three-dimensional coordinate system where the three-dimensional point cloud data C is located is the preset designated position, The Z axis of the three-dimensional coordinate system where the three-dimensional point cloud data C is located represents the crop height direction, the X axis represents the crop row direction, and the Y axis represents the plant direction perpendicular to the crop row direction;

The sampling module 22 is used to uniformly resample the acquired 3D point cloud data C to obtain sampled 3D point cloud data

Determining module 23, used for statistically sampling the three-dimensional point cloud data The number of data points in each preset voxel, and according to the sampled 3D point cloud data The number of data points in each preset voxel determines the optimum number of rows and the optimum number of plants in each row, and the optimum research scale range of the target crop group is determined by the optimum number of rows and the optimum number of plants in each row;

Wherein, each preset voxel is the three-dimensional point cloud data after sampling Multiple independent cuboid spaces obtained after space division of the three-dimensional coordinate system.

In an optional implementation manner, the sampling module 22 includes a setting unit 221 and a sampling unit 222; wherein:

The setting unit 221 is used to set the resampling distance parameter L and the threshold number parameter Q;

The sampling unit 222 is used to divide the point cloud space into a cube whose length, width and height are all L. If the number of points where the three-dimensional point cloud data C falls into a certain cube is greater than or equal to Q, then the center point of the cube As the resampling point in the cube space, after such uniform resampling of the 3D point cloud data C, the sampled 3D point cloud data is obtained

In an optional implementation manner, the determination module 23 is based on the sampled three-dimensional point cloud data When the number of data points in each preset voxel determines the optimum number of rows and the optimum number of plants per row, it is specifically used for:

Carry out space division on the XOY plane of the three-dimensional coordinate system, divide the X-axis into M equal-length pixel segments in turn, and divide the Y-axis into N equal-length pixel segments in turn;

Statistical 3D point cloud data The number of data points in the preset voxel V _mn , wherein the preset voxel V _mn represents the mth pixel segment in the X-axis direction, the n-th pixel segment in the Y-axis direction, and all pixels in the Z-axis direction The voxel determined by the point, 1≤m≤M, 1≤n≤N;

If the number of data points in V _mn ≤ the preset critical pixel number s ₀ , then determine the pixel point farthest from the preset specified position in the pixel segment m in the X-axis direction corresponding to the preset voxel V _mn And the pixel point farthest from the preset designated position in the pixel segment n in the Y-axis direction is the position coordinate that has no influence on the preset designated position, according to the determined position coordinates that have no influence on the preset designated position, combined with the crop group The optimal row number and the optimal number of plants per row were obtained.

In an optional embodiment, the acquisition module 23 is specifically configured to: acquire the three-dimensional point cloud data C of the target crop population by using a crop population scale measurement device set at a preset designated position;

Wherein, the crop population scale measurement device includes: a three-dimensional point cloud acquisition device, a height adjustment device and a tripod support device; the lower end of the three-dimensional point cloud acquisition device is connected to the height adjustment device, and the lower end of the height adjustment device is connected to the height adjustment device. The tripod support device is connected;

Wherein, the three-dimensional point cloud acquisition device is a three-dimensional scanner or a total station in the form of a laser, and the measurement radius of the three-dimensional point cloud acquisition device is greater than or equal to 40m; the height adjustment device includes a telescopic rod, on which engraved with a scale, the telescopic rod is used to adjust the height of the three-dimensional point cloud acquisition device, and realizes the precise control of the height of the three-dimensional point cloud acquisition device; A telescopic support rod, the top structure is provided with horizontal adjustment air bubbles; the hollow structure of the tripod is provided with a telescopic rod for measuring the vertical distance between the tripod and the ground surface;

Correspondingly, the acquiring module 23 is specifically used for:

The tripod support device is placed on a preset designated position in the target crop group, and the tripod support device is adjusted to the level by using the level adjustment bubble;

Sequentially adjust the height of the height adjustment device, so that the three-dimensional point cloud acquisition device acquires the three-dimensional point cloud data of the target crop group at different heights; wherein, the acquisition range of the three-dimensional point cloud acquisition device when acquiring the three-dimensional point cloud data The horizontal direction is 360 degrees, and the vertical direction is greater than 135 degrees; when adjusting the height of the height adjustment device, the height of the height adjustment device is increased in an equal gradient, and the highest point does not exceed the height H of the target crop group.

In an optional implementation manner, referring to FIG. 5 , the device further includes: a light distribution analysis and calculation module 24;

The light distribution analysis and calculation module 24 is used to calculate and analyze the light distribution of the target crop canopy according to the optimal research scale range of the target crop group.

The apparatus for detecting the optimal research scale of crop populations provided in this embodiment can be used to implement the method for detecting the optimal research scale of crop populations described in the above-mentioned embodiments. The principles and beneficial effects are similar and will not be described in detail here.

In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "upper", "lower", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description. It is not intended to indicate or imply that the referred device or element must have a particular orientation, be constructed in a particular orientation, and operate in a particular orientation, and thus should not be construed as limiting the invention. Unless otherwise clearly specified and limited, the terms "installation", "connection" and "connection" should be interpreted in a broad sense, for example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection, It can also be an electrical connection; it can be a direct connection, or an indirect connection through an intermediary, or an internal communication between two components. Those of ordinary skill in the art can understand the specific meanings of the above terms in the present invention according to specific situations.

It should also be noted that in this article, relational terms such as first and second etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that these entities or operations Any such actual relationship or order exists between. Furthermore, the term "comprises", "comprises" or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article, or apparatus comprising a set of elements includes not only those elements, but also includes elements not expressly listed. other elements of or also include elements inherent in such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article or apparatus comprising said element.

The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: it can still be described in the foregoing embodiments Modifications are made to the recorded technical solutions, or equivalent replacements are made to some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims (10)

1. A method for detecting the optimal research scale of a crop population, characterized in that it comprises: Acquire the three-dimensional point cloud data C of the target crop group at a preset designated position in the target crop group; the center of the three-dimensional coordinate system where the three-dimensional point cloud data C is located is the preset designated position, and the three-dimensional point cloud data The Z axis of the three-dimensional coordinate system where C is located represents the crop height direction, the X axis represents the crop row direction, and the Y axis represents the plant direction perpendicular to the crop row direction; Perform uniform resampling on the acquired 3D point cloud data C to obtain the sampled 3D point cloud data Statistically sampled 3D point cloud data The number of data points in each preset voxel, and according to the sampled 3D point cloud data The number of data points in each preset voxel determines the optimum number of rows and the optimum number of plants in each row, and the optimum research scale range of the target crop group is determined by the optimum number of rows and the optimum number of plants in each row; Wherein, each preset voxel is the three-dimensional point cloud data after sampling Multiple independent cuboid spaces obtained after space division of the three-dimensional coordinate system. 2. The method according to claim 1, wherein the described three-dimensional point cloud data C obtained is uniformly resampled to obtain the three-dimensional point cloud data after sampling Specifically include: Set the resampling distance parameter L and the threshold number parameter N; Divide the point cloud space into a cube whose length, width and height are both L. If the number of points where the 3D point cloud data C falls into a certain cube is greater than or equal to N, then the center point of the cube is used as the weight in the cube space. Sampling point, after such uniform resampling of the 3D point cloud data C, the sampled 3D point cloud data is obtained 3. method according to claim 1, is characterized in that, said according to the three-dimensional point cloud data after sampling The number of data points within each preset voxel determines the optimum number of rows and the optimum number of plants per row, including: Carry out space division on the XOY plane of the three-dimensional coordinate system, divide the X-axis into M equal-length pixel segments in turn, and divide the Y-axis into N equal-length pixel segments in turn; Statistical 3D point cloud data The number of data points in the preset voxel V _mn , wherein the preset voxel V _mn represents the mth pixel segment in the X-axis direction, the n-th pixel segment in the Y-axis direction, and all pixels in the Z-axis direction The voxel determined by the point, 1≤m≤M, 1≤n≤N; If the number of data points in V _mn ≤ the preset critical pixel number s ₀ , then determine the pixel point farthest from the preset specified position in the pixel segment m in the X-axis direction corresponding to the preset voxel V _mn And the pixel point farthest from the preset designated position in the pixel segment n in the Y-axis direction is the position coordinate that has no influence on the preset designated position, according to the determined position coordinates that have no influence on the preset designated position, combined with the crop group The optimal row number and the optimal number of plants per row were obtained. 4. method according to claim 1, is characterized in that, the three-dimensional point cloud data C of described acquisition target crop group, specifically comprises: Obtaining the three-dimensional point cloud data C of the target crop group by using a crop group scale measurement device set at a preset designated position; Wherein, the crop population scale measurement device includes: a three-dimensional point cloud acquisition device, a height adjustment device and a tripod support device; the lower end of the three-dimensional point cloud acquisition device is connected to the height adjustment device, and the lower end of the height adjustment device is connected to the height adjustment device. The tripod support device is connected; Wherein, the three-dimensional point cloud acquisition device is a three-dimensional scanner or a total station in the form of a laser, and the measurement radius of the three-dimensional point cloud acquisition device is greater than or equal to 40m; the height adjustment device includes a telescopic rod, on which engraved with a scale, the telescopic rod is used to adjust the height of the three-dimensional point cloud acquisition device, and realizes the precise control of the height of the three-dimensional point cloud acquisition device; A telescopic support rod, the top structure is provided with horizontal adjustment air bubbles; the hollow structure of the tripod is provided with a telescopic rod for measuring the vertical distance between the tripod and the ground surface; Correspondingly, the acquisition of the three-dimensional point cloud data C of the target crop group by using the crop group scale measurement device set at a preset designated position specifically includes: The tripod support device is placed on a preset designated position in the target crop group, and the tripod support device is adjusted to the level by using the level adjustment bubble; Sequentially adjust the height of the height adjustment device, so that the three-dimensional point cloud acquisition device acquires the three-dimensional point cloud data of the target crop group at different heights; wherein, the acquisition range of the three-dimensional point cloud acquisition device when acquiring the three-dimensional point cloud data The horizontal direction is 360 degrees, and the vertical direction is greater than 135 degrees; when adjusting the height of the height adjustment device, the height of the height adjustment device is increased in an equal gradient, and the highest point does not exceed the height H of the target crop group. 5. The method according to any one of claims 1 to 4, characterized in that the method further comprises: According to the optimal research scale range of the target crop group, the calculation and analysis of the light distribution of the target crop canopy is carried out. 6. A detection device for the optimal research scale of crop groups, characterized in that it comprises: The acquisition module is used to obtain the three-dimensional point cloud data C of the target crop group at a preset designated position in the target crop group; the center of the three-dimensional coordinate system where the three-dimensional point cloud data C is located is the preset designated position, so The Z axis of the three-dimensional coordinate system where the three-dimensional point cloud data C is located represents the crop height direction, the X axis represents the crop row direction, and the Y axis represents the plant direction perpendicular to the crop row direction; The sampling module is used to uniformly resample the acquired 3D point cloud data C to obtain sampled 3D point cloud data Determination module, used for statistical sampling of 3D point cloud data The number of data points in each preset voxel, and according to the sampled 3D point cloud data The number of data points in each preset voxel determines the optimum number of rows and the optimum number of plants in each row, and the optimum research scale range of the target crop group is determined by the optimum number of rows and the optimum number of plants in each row; Wherein, each preset voxel is the three-dimensional point cloud data after sampling Multiple independent cuboid spaces obtained after space division of the three-dimensional coordinate system. 7. The device according to claim 6, wherein the sampling module comprises a setting unit and a sampling unit; wherein: The setting unit is used to set the resampling distance parameter L and the threshold number parameter Q; The sampling unit is used to divide the point cloud space into a cube whose length, width and height are all L. If the number of points where the three-dimensional point cloud data C falls into a certain cube is greater than or equal to Q, then the center point of the cube is used as The resampling points in the cube space, after such uniform resampling of the 3D point cloud data C, obtain the sampled 3D point cloud data 8. The device according to claim 6, wherein the determination module is based on the sampled three-dimensional point cloud data When the number of data points in each preset voxel determines the optimum number of rows and the optimum number of plants per row, it is specifically used for: Carry out space division on the XOY plane of the three-dimensional coordinate system, divide the X-axis into M equal-length pixel segments in turn, and divide the Y-axis into N equal-length pixel segments in turn; Statistical 3D point cloud data The number of data points in the preset voxel V _mn , wherein the preset voxel V _mn represents the mth pixel segment in the X-axis direction, the n-th pixel segment in the Y-axis direction, and all pixels in the Z-axis direction The voxel determined by the point, 1≤m≤M, 1≤n≤N; If the number of data points in V _mn ≤ the preset critical pixel number s ₀ , then determine the pixel point farthest from the preset specified position in the pixel segment m in the X-axis direction corresponding to the preset voxel V _mn And the pixel point farthest from the preset designated position in the pixel segment n in the Y-axis direction is the position coordinate that has no influence on the preset designated position, according to the determined position coordinates that have no influence on the preset designated position, combined with the crop group The optimal row number and the optimal number of plants per row were obtained. 9. The device according to claim 6, wherein the acquiring module is specifically used for: Obtaining the three-dimensional point cloud data C of the target crop group by using a crop group scale measurement device set at a preset designated position; Wherein, the crop population scale measurement device includes: a three-dimensional point cloud acquisition device, a height adjustment device and a tripod support device; the lower end of the three-dimensional point cloud acquisition device is connected to the height adjustment device, and the lower end of the height adjustment device is connected to the height adjustment device. The tripod support device is connected; Wherein, the three-dimensional point cloud acquisition device is a three-dimensional scanner or a total station in the form of a laser, and the measurement radius of the three-dimensional point cloud acquisition device is greater than or equal to 40m; the height adjustment device includes a telescopic rod, on which engraved with a scale, the telescopic rod is used to adjust the height of the three-dimensional point cloud acquisition device, and realizes the precise control of the height of the three-dimensional point cloud acquisition device; A telescopic support rod, the top structure is provided with horizontal adjustment air bubbles; the hollow structure of the tripod is provided with a telescopic rod for measuring the vertical distance between the tripod and the ground surface; Correspondingly, the acquisition module is specifically used for: The tripod support device is placed on a preset designated position in the target crop group, and the tripod support device is adjusted to the level by using the level adjustment bubble; Sequentially adjust the height of the height adjustment device, so that the three-dimensional point cloud acquisition device acquires the three-dimensional point cloud data of the target crop group at different heights; wherein, the acquisition range of the three-dimensional point cloud acquisition device when acquiring the three-dimensional point cloud data The horizontal direction is 360 degrees, and the vertical direction is greater than 135 degrees; when adjusting the height of the height adjustment device, the height of the height adjustment device is increased in an equal gradient, and the highest point does not exceed the height H of the target crop population. 10. The device according to any one of claims 6-9, characterized in that the device further comprises: a light distribution analysis and calculation module; The light distribution analysis and calculation module is used to calculate and analyze the light distribution of the target crop canopy according to the optimal research scale range of the target crop group.