CN109708578A - Device, method and system for measuring plant phenotype parameters - Google Patents

Device, method and system for measuring plant phenotype parameters Download PDF

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CN109708578A
CN109708578A CN201910135784.1A CN201910135784A CN109708578A CN 109708578 A CN109708578 A CN 109708578A CN 201910135784 A CN201910135784 A CN 201910135784A CN 109708578 A CN109708578 A CN 109708578A
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plant
point cloud
tested
image
camera
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CN109708578B (en
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张永恩
许世卫
王盛威
王强
邸佳颖
程海
庄家煜
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Agricultural Information Institute of CAAS
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Abstract

The present invention discloses a kind of plant phenotype parameter measuring apparatus, method and system.Measuring device includes: scanning head, test chassis, test platform and computer;Scanning head is fixed in test chassis, and test chassis can move up and down;Test platform is for bearing plant to be measured;Scanning head is with the plant image of the moving sweep of test chassis plant different height to be measured, and by plant image transmitting to computer, computer is used to determine the phenotypic parameter of plant to be measured according to plant image;Scanning head includes multiple video cameras and two linear laser transmitters, and multiple positions for video camera are in first level face, and multiple video camera equidistantly distributeds are on virtual circular arc;Two linear laser transmitters are located at the second horizontal plane below first level face.There is blind area when can be avoided data splicing in the present invention, improves the precision of plant phenotype parameter measurement.

Description

A kind of plant phenotype parameter measuring apparatus, method and system
Technical field
The present invention relates to plant growth parameter measurement fields, more particularly to a kind of plant phenotype parameter measuring apparatus, side Method and system.
Background technique
The core ideas of precision agriculture is to obtain inside plants and external information, guidance by advanced measurement means It irrigates, the process of fertilising, physiology and high crop yield, efficient, degeneration-resistant crop production theoretical system.Inside plants letter Breath is also known as plant physiology information, the wherein plant phenotype parameter in plant physiology information, be not all accurately measured always with A specifications of surveys is formed, thus the phenotypic parameter of accurate quick obtaining plant is of great significance.
Traditional measurement plant phenotype parameter mainly obtains data by manual type, but this method damages plant life Long and inefficiency.Currently, the method for research plant phenotype parameter mainly has based on image processing method and three-dimensional measurement side Method, wherein optical three-dimensional measuring method becomes the method for three-dimensional measurement of mainstream.For example, passive vision measurement method and actively view Feel that measurement method has been largely used to three-dimensional reconstruction and the phenotypic parameter measurement of plant.Passive vision measurement method has equipment Simply, the features such as environmental restrictions are small, and human-computer interaction is few, but there are the problems such as computationally intensive, precision is low.Active vision measurement side Method includes Depth Imaging method, laser ranging method and structural light measurement method.Depth Imaging method and laser ranging method can be obtained accurately Take plant three-dimensional information, but there are equipment it is expensive, data volume is big, environmental disturbances the problems such as.Currently based on the three-dimensional of line-structured light Measurement method has the characteristics that scanning real-time, high-precision, high stability is applied to plant three-dimensional measurement field.Feng Qingchun It needs to be acquired after combining necessary rotating device to rotate plant to different angle Deng based on line-structured light measuring system, Finally by the point cloud matching of each angle acquisition and splicing, the method has collection period long and acquisition blind zone problem, in addition by Plant is stationary state in actual production, is only suitable for potted plant in this approach.Liu Jie etc. passes through twin camera and knot Structure light integrated system is available to arrive the higher three-dimensional point cloud of precision, but can only collect the data of object visible face, and plant The branches and leaves infall of object has more noise spot.
Summary of the invention
The object of the present invention is to provide a kind of plant phenotype parameter measuring apparatus, method and system, to improve phenotypic parameter Measurement accuracy, reduce the complexity of measuring device.
To achieve the above object, the present invention provides following schemes:
A kind of plant phenotype parameter measuring apparatus, comprising: scanning head, test chassis, test platform and computer;Institute It states scanning head to be fixed in the test chassis, the test chassis can move up and down;The test platform for bear to Measure plant;The scanning head is for plant different height to be measured described in the moving sweep with the test chassis Plant image, and by the plant image transmitting to the computer, the computer is used to be scanned according to the scanning head Plant image determine the phenotypic parameter of the plant to be measured;
The scanning head includes multiple video cameras and two linear laser transmitters, and multiple positions for video camera are in the first water Plane, multiple video camera equidistantly distributeds are on virtual circular arc;Two linear laser transmitters are located at described first The second horizontal plane below horizontal plane, two linear laser transmitters tops respectively correspond two, virtual circular arc ends The video camera of point;The third that the laser that two linear laser transmitters issue is located parallel to the test platform is horizontal On face, the third horizontal plane and the first level face and second horizontal plane are parallel;The shooting angle of multiple video cameras Degree is diagonally downward.
Optionally, further includes: motion control card and stepper motor, the test chassis and the stepper motor are and institute Motion control card connection is stated, the motion state of the test chassis is adjusted by the stepper motor.
Optionally, further includes: multiple narrow band filters, the central wavelength of the narrow band filter are 532nm, Mei Gesuo It states and is correspondingly arranged the narrow band filter on the camera lens of video camera.
A kind of plant phenotype measurement method of parameters, the plant phenotype measurement method of parameters are applied to plant table above-mentioned Shape parameter measuring device, the plant phenotype measurement method of parameters include:
Obtain the plant image to be measured of each video camera shooting;
Laser stripe center line is extracted using the grey scale centre of gravity method of striation skeleton;
According to the calibrating parameters of each video camera, the three-dimensional point cloud under each camera angles is obtained;
The unification of all three-dimensional point clouds is spliced to global coordinate system, obtains initial plant three-dimensional point cloud to be measured;
The initial plant three-dimensional point cloud to be measured is projected to the OXY plane of the global coordinate system, plant to be measured is obtained Strain two-dimensional projection point image;
By the stem edge in plant to be measured two-dimensional projection point image described in manual interaction, stem contour area is obtained;
According to the corresponding relationship of subpoint in three-dimensional point cloud and the global coordinate system, initial plant to be measured three is obtained The stem point cloud sector domain of plant to be measured in dimension point cloud;
According to the Euclidean distance between the initial plant three-dimensional point cloud to be measured, the initial plant to be measured is determined Multiple blade point cloud sectors domain of plant to be measured in three-dimensional point cloud;
The stem point cloud sector domain of the plant to be measured and blade point cloud sector domain are carried out using K mean cluster algorithm to be measured The organ point cloud segmentation of plant obtains corresponding cloud sector of each organ of plant to be measured domain;
According to corresponding cloud sector of each organ of plant to be measured domain, the ginseng of each organ of plant to be measured is calculated Number, obtains the phenotypic parameter of the plant to be measured.
Optionally, the plant image to be measured for obtaining each video camera shooting, before further include:
Internal reference and outer ginseng to each video camera are demarcated, and the calibrating parameters of each video camera are obtained;
Optic plane equations where the laser of linear laser transmitter transmitting are demarcated;
It, will be under the local coordinate system unification to global coordinate system of each video camera using global calibration method.
Optionally, the grey scale centre of gravity method using striation skeleton extracts laser stripe center line, specifically includes:
Using threshold filter method and gaussian filtering method is removed dryness to the plant image progress image to be measured and image binaryzation Processing, obtains binary image;
Skeletal extraction is carried out to the binary image using morphology, determines the normal direction of every, skeleton;
Grey scale centre of gravity is calculated in each normal direction;
Grey scale centre of gravity corresponding in each normal direction is connected, the center line of laser stripe is obtained.
Optionally, described to splice the unification of all three-dimensional point clouds to global coordinate system, obtain initial plant to be measured Three-dimensional point cloud, later further include:
The denoising of internal high frequency point cloud is carried out to the initial plant three-dimensional point cloud to be measured, the plant to be measured after being denoised Strain three-dimensional point cloud.
A kind of plant phenotype parameter measurement system, the plant phenotype parameter measurement system are applied to plant table above-mentioned Shape parameter measuring device, the plant phenotype parameter measurement system include:
Plant image collection module to be measured, for obtaining the plant image to be measured of each video camera shooting;
Laser stripe central line pick-up module, for extracting laser stripe center using the grey scale centre of gravity method of striation skeleton Line;
Three-dimensional point cloud obtains module and obtains under each camera angles for the calibrating parameters according to each video camera Three-dimensional point cloud;
Splicing module obtains initial plant to be measured for splicing the unification of all three-dimensional point clouds to global coordinate system Strain three-dimensional point cloud;
Projection module is put down for projecting the initial plant three-dimensional point cloud to be measured to the OXY of the global coordinate system Face obtains plant two-dimensional projection to be measured point image;
Manual interaction module, for passing through the stem edge in plant two-dimensional projection to be measured point image described in manual interaction, Obtain stem contour area;
The stem point cloud sector domain of plant to be measured obtains module, for throwing according in three-dimensional point cloud and the global coordinate system The corresponding relationship of shadow point obtains the stem point cloud sector domain of plant to be measured in initial plant three-dimensional point cloud to be measured;
The blade point cloud sector domain of plant to be measured obtains module, for according to the initial plant three-dimensional point cloud to be measured it Between Euclidean distance, determine multiple blade point cloud sectors domain of plant to be measured in the initial plant three-dimensional point cloud to be measured;
Organ point cloud segmentation module, for using K mean cluster algorithm to the stem point cloud sector domain of the plant to be measured and Blade point cloud sector domain carries out the organ point cloud segmentation of plant to be measured, obtains described corresponding cloud sector of each organ of plant to be measured Domain;
Phenotypic parameter computing module, for according to corresponding cloud sector of each organ of plant to be measured domain, described in calculating The parameter of each organ of plant to be measured, obtains the phenotypic parameter of the plant to be measured.
Optionally, further includes:
Camera calibration module, for each video camera internal reference and outer ginseng demarcate, obtain each video camera Calibrating parameters;
Optic plane equations demarcating module, optic plane equations where laser for emitting linear laser transmitter into Rower is fixed;
Coordinate system unified modules are unified to complete by the local coordinate system of each video camera for utilizing global calibration method Under office's coordinate system.
Optionally, the laser stripe central line pick-up module, specifically includes:
Image processing unit, for carrying out figure to the plant image to be measured using threshold filter method and gaussian filtering method It is handled as removing dryness with image binaryzation, obtains binary image;
Skeletal extraction unit determines the every point of skeleton for carrying out skeletal extraction to the binary image using morphology Normal direction;
Grey scale centre of gravity computing unit, for calculating grey scale centre of gravity in each normal direction;
The center line acquiring unit of laser stripe is obtained for connecting grey scale centre of gravity corresponding in each normal direction The center line of laser stripe.
The specific embodiment provided according to the present invention, the invention discloses following technical effects:
By the cooperation of multiple cameras and laser emitter, comprehensive scanning is carried out to plant from multiple angles, is avoided There is blind area when splicing in data, which carries out that 3-D scanning is more efficient, strong applicability, at low cost to plant, and Point cloud data is more complete, and it is also more preferable to rebuild effect.
Since green blade has absorption peak in feux rouges and blue wave band, so laser scanning is carried out using green (light) laser, And in order to enhance the contrast of laser stripe, to obtain higher precision, central wavelength added on the camera lens of each camera For the narrow band filter of 532nm, cable architecture Light stripes center extraction essence is extracted and improved which simplify line-structured light strip area Degree.
In order to complete plant three dimensional structure separation, three-dimensional point is projected and generates two-dimensional projection's point and is clicked through based on two dimension The network forming of row triangle and lighting process generate two-dimensional projection image, are partitioned into stem region based on two-dimensional projection image, then be based on two The corresponding relationship of subpoint and three-dimensional point is tieed up, it is automatic to obtain stem point cloud sector domain and blade point cloud sector domain in three-dimensional point cloud, due to Blade three-dimensional point cloud is segmented into different blade point cloud sector domains according to Euclidean distance, is then based on each separated region and adopts Each of final cloud sector domain is obtained with K-means cluster segmentation.Three-dimensional computations are converted to two dimension and are calculated by whole process, drop It is low direct to divide the complexity for carrying out organ separation using three-dimensional point cloud.
Three-dimensional point cloud segmentation is on the basis of obtaining based on stem three-dimensional point cloud region, to carry out different leaves point cloud sector domain minute It cuts, then K-means cluster segmentation is carried out based on each point cloud sector domain divided.Entire three-dimensional point cloud segmentation is compared to without first The point cloud segmentation of knowledge is tested, segmentation is more efficient, divides more acurrate.
Detailed description of the invention
It in order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, below will be in embodiment Required attached drawing is briefly described, it should be apparent that, the accompanying drawings in the following description is only some realities of the invention Example is applied, it for those of ordinary skill in the art, without any creative labor, can also be according to these Attached drawing obtains other attached drawings.
Fig. 1 is the structural schematic diagram of plant phenotype parameter measuring apparatus of the present invention;
Fig. 2 is the flow diagram of plant phenotype measurement method of parameters of the present invention;
Fig. 3 is the structural schematic diagram of plant phenotype parameter measurement system of the present invention;
Fig. 4 is angles of corn plant leaves piece length and width measurement figure in present invention specific implementation case.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts it is all its His embodiment, shall fall within the protection scope of the present invention.
In order to make the foregoing objectives, features and advantages of the present invention clearer and more comprehensible, with reference to the accompanying drawing and specific real Applying mode, the present invention is described in further detail.
Fig. 1 is the structural schematic diagram of plant phenotype parameter measuring apparatus of the present invention.As shown in Figure 1, the plant phenotype ginseng Number measuring device includes with flowering structure: scanning head, test chassis 3, test platform 4 and computer (not identifying in figure).It is described Scanning head is fixed in the test chassis 3, and the test chassis 3 can move up and down;The test platform 4 is for bearing Plant to be measured;The scanning head is for plant different height to be measured described in the moving sweep with the test chassis 3 Plant image, and by the plant image transmitting to the computer, the computer according to the scanning head for sweeping The plant image retouched determines the phenotypic parameter of the plant to be measured.
The scanning head includes multiple video cameras and two linear lasers transmitter 2-1 and 2-2, multiple positions for video camera In first level face, multiple video camera equidistantly distributeds are on virtual circular arc.In figure by taking 3 video cameras as an example, including take the photograph Camera 1-1, video camera 1-2 and video camera 1-3.
Two linear laser transmitters are located at the second horizontal plane below the first level face, and two described linear sharp The video camera of two endpoints of virtual circular arc is respectively corresponded above optical transmitting set, it is right above linear laser emitter 2-1 in figure Answer video camera 1-1, corresponding video camera 1-3 above linear laser transmitter 2-2.The laser that two linear laser transmitters issue It is located parallel on the third horizontal plane of the test platform, the third horizontal plane and the first level face and described the Two horizontal planes are parallel.The shooting angle of multiple video cameras diagonally downward so that when what two linear laser transmitters issued swashs When light is located at the top of plant to be measured, each video camera can take the image on plant top to be measured.Since green blade exists Feux rouges and blue wave band have absorption peak, and linear laser transmitter can carry out laser scanning using green (light) laser.
The plant phenotype parameter measuring apparatus of the present embodiment further include: motion control card and stepper motor, the test machine Frame 3 and the stepper motor are connect with the motion control card, adjust the test chassis 3 by the stepper motor Motion state.The plant phenotype parameter measuring apparatus of the present embodiment can also include: multiple narrow band filters, the narrowband filter The central wavelength of mating plate is 532nm, is correspondingly arranged the narrow band filter on the camera lens of each video camera.
Fig. 2 is the flow diagram of plant phenotype measurement method of parameters of the present invention.As shown in Fig. 2, the plant phenotype ginseng Number measurement methods the following steps are included:
Step 100: obtaining the plant image to be measured of each video camera shooting.
Step 200: extracting laser stripe center line using the grey scale centre of gravity method of striation skeleton.
Step 300: according to the calibrating parameters of each video camera, obtaining the three-dimensional point cloud under each camera angles.
Step 400: the unification of all three-dimensional point clouds being spliced to global coordinate system, obtains initial plant to be measured three Dimension point cloud.
Step 500: the initial plant three-dimensional point cloud to be measured being projected to the OXY plane of the global coordinate system, is obtained To plant two-dimensional projection to be measured point image.
Step 600: by the stem edge in plant to be measured two-dimensional projection point image described in manual interaction, obtaining stem Contour area.
Step 700: according to the corresponding relationship of subpoint in three-dimensional point cloud and the global coordinate system, obtain it is initial to Survey the stem point cloud sector domain of plant to be measured in plant three-dimensional point cloud.
Step 800: according to the Euclidean distance between the initial plant three-dimensional point cloud to be measured, determining described initial Multiple blade point cloud sectors domain of plant to be measured in plant three-dimensional point cloud to be measured.
Step 900: using K mean cluster algorithm to the stem point cloud sector domain and blade point cloud sector domain of the plant to be measured The organ point cloud segmentation for carrying out plant to be measured obtains corresponding cloud sector of each organ of plant to be measured domain.
Step 1000: according to corresponding cloud sector of each organ of plant to be measured domain, it is each to calculate the plant to be measured The parameter of organ obtains the phenotypic parameter of the plant to be measured.
It needs to demarcate video camera and optic plane equations parameter before step 100, detailed process is as follows:
Internal reference and outer ginseng to each video camera are demarcated, and the calibrating parameters of each video camera are obtained;
Optic plane equations where the laser of linear laser transmitter transmitting are demarcated;
It, will be under the local coordinate system unification to global coordinate system of each video camera using global calibration method.
Step 200 specifically includes:
Using threshold filter method and gaussian filtering method is removed dryness to the plant image progress image to be measured and image binaryzation Processing, obtains binary image;
Skeletal extraction is carried out to the binary image using morphology, determines the normal direction of every, skeleton;
Grey scale centre of gravity is calculated in each normal direction;
Grey scale centre of gravity corresponding in each normal direction is connected, the center line of laser stripe is obtained.
After step 400 further include:
The denoising of internal high frequency point cloud is carried out to the initial plant three-dimensional point cloud to be measured, the plant to be measured after being denoised Strain three-dimensional point cloud.
Fig. 3 is the structural schematic diagram of plant phenotype parameter measurement system of the present invention.As shown in figure 3, the plant phenotype ginseng Number measuring system includes with flowering structure:
Plant image collection module 301 to be measured, for obtaining the plant image to be measured of each video camera shooting;
Laser stripe central line pick-up module 302, for being extracted in laser stripe using the grey scale centre of gravity method of striation skeleton Heart line;
Three-dimensional point cloud obtains module 303 and obtains each camera angles for the calibrating parameters according to each video camera Under three-dimensional point cloud;
Splicing module 304, for splicing to global coordinate system by all three-dimensional point clouds are unified, obtain it is initial to Survey plant three-dimensional point cloud;
Projection module 305, for projecting the initial plant three-dimensional point cloud to be measured to the global coordinate system OXY plane obtains plant two-dimensional projection to be measured point image;
Manual interaction module 306, for passing through the stem side in plant two-dimensional projection to be measured point image described in manual interaction Edge obtains stem contour area;
The stem point cloud sector domain of plant to be measured obtains module 307, for according in three-dimensional point cloud and the global coordinate system The corresponding relationship of subpoint obtains the stem point cloud sector domain of plant to be measured in initial plant three-dimensional point cloud to be measured;
The blade point cloud sector domain of plant to be measured obtains module 308, for according to the initial plant three-dimensional point cloud to be measured Between Euclidean distance, determine multiple blade point cloud sectors domain of plant to be measured in the initial plant three-dimensional point cloud to be measured;
Organ point cloud segmentation module 309, for the stem point cloud sector using K mean cluster algorithm to the plant to be measured Domain and blade point cloud sector domain carry out the organ point cloud segmentation of plant to be measured, obtain the corresponding point of each organ of plant to be measured Cloud sector domain;
Phenotypic parameter computing module 3010, for calculating according to corresponding cloud sector of each organ of plant to be measured domain The parameter of each organ of plant to be measured, obtains the phenotypic parameter of the plant to be measured.
Wherein, the laser stripe central line pick-up module 302 specifically includes:
Image processing unit, for carrying out figure to the plant image to be measured using threshold filter method and gaussian filtering method It is handled as removing dryness with image binaryzation, obtains binary image;
Skeletal extraction unit determines the every point of skeleton for carrying out skeletal extraction to the binary image using morphology Normal direction;
Grey scale centre of gravity computing unit, for calculating grey scale centre of gravity in each normal direction;
The center line acquiring unit of laser stripe is obtained for connecting grey scale centre of gravity corresponding in each normal direction The center line of laser stripe.
Above system further include:
Camera calibration module, for each video camera internal reference and outer ginseng demarcate, obtain each video camera Calibrating parameters;
Optic plane equations demarcating module, optic plane equations where laser for emitting linear laser transmitter into Rower is fixed;
Coordinate system unified modules are unified to complete by the local coordinate system of each video camera for utilizing global calibration method Under office's coordinate system.
The solution of the present invention is further illustrated below with reference to a specific implementation case.
This specific implementation case is used to measure the phenotypic parameter of plant.Measuring device is by 3 CCD industrial cameras, 3 A optical filter, 2 linear laser transmitters, test chassis, test platform, a computer composition.Using green (light) laser into Row laser scanning, and in order to enhance the contrast of laser stripe, to obtain higher precision, add on the camera lens of each camera The narrow band filter that central wavelength is 532nm is gone up.
Linear laser and video camera are fixed as scanning head, relative position remains constant, equidistantly solid respectively Dingan County is mounted in 3 positions of rack.The linear laser that 2 lasers issue is maintained at same plane and is parallel to test platform, phase Machine, which is tilted down, is kept fixed angle with laser.Rack is linked with motion control card, and stepper motor and motion control card connect It connects, realizes that rack moves up and down by labview programming Control.
It before device starting, needs first to demarcate scanning head, the three-dimensional point under corresponding camera perspective is obtained with this Coordinate information, while the overall situation is carried out to all three-dimensional point clouds by the positional relationship between camera and is calibrated under uniform coordinate, it completes The splicing of entire three-dimensional point cloud.Specific calibration process is as follows:
1, camera (video camera) is demarcated
Camera internal reference calibration and usage plane gridiron pattern target carries out corresponding operation.The detailed process of camera calibration is as follows:
(1) scaling board placed into different location, different angle, in different positions shot, shoot 10 calibration in total Image.
(2) to each calibration picture, sub-pix angle point information is extracted;(angle point is extracted, approximating method is then based on and asks Sub-pixel location is taken, this is conventional existing method, is achieved that using computer vision kit calling)
(3) after getting chessboard calibration figure angle point image coordinate, according to camera calibration principle, calculate camera internal reference and Outer parameter;
(4) the camera inside and outside parameter being calculated is advanced optimized, obtains optimal camera inside and outside parameter.Optimized Journey is the prior art: since the camera inside and outside parameter that (3) are found out is an approximate solution, being carried out by Maximum-likelihood estimation non- Linear optimization.Meanwhile putting plane target drone in corn growth plane, the image of plane target drone is obtained by camera.By aforementioned mark Determine principle, that is, can determine and singly answer relational matrix between camera image plane and corn growth plane, it is thick for calculating plant height strain Etc. growth planes information.
2, optic plane equations parameter calibration
(1) internal reference of above method calibration for cameras is utilized;
(2) calibration target is put into different location in space, so that projection light of the structure light in target plane Item can be in camera image plane blur-free imaging;
(3) in the sub-pixel laser stripe for obtaining laser stripe image using the grey scale centre of gravity method based on striation skeleton The heart
(4) according to structured light vision sensor measurement model and camera calibration matrix, determine optical plane in camera coordinates system Under normal vector, and finally determine expressed intact equation of the optical plane under camera coordinates system;
(5) optical plane each in laser sensor is repeated the above process, that is, can determine that all light are flat in laser sensor The expression equation of face in the camera.So far, being changed to optic plane equations parameter calibration terminates.
3, global calibration
Due between single camera coordinate system independently of one another, it is therefore desirable to using global calibration method by each camera Under local coordinate system unification to global coordinate system, that is, each camera coordinates system is determined to the spin matrix of global coordinate system and is put down Move matrix.
It is mounted with 3 groups of scanning heads in the implementation case, utilizes camera calibration method and line-structured light laser stripe image Rebuild the plant three-dimensional point cloud under each camera visual angle.In order to which the three-dimensional point cloud that will acquire organic can be stitched together, this reality Case is applied using No. 1 camera coordinates system as the frame of reference i.e. global coordinate system, then calibrates other camera coordinates system overall situations To the frame of reference.Overall situation calibration is exactly the rotational translation matrix for calculating other camera coordinates systems to the frame of reference.
The transformational relation of each camera coordinates system are as follows:
Wherein: RnFor 3 × 3 spin matrixs between two cameras;TnFor 3 × 1 translation vectors between two cameras.
Using equation (1) by calculating the rotational translation matrix between camera two-by-two, determine that all camera coordinates systems arrive The three dimensional point cloud obtained under each camera can then be carried out unification by the rotational translation matrix between global coordinate system.
After device starting, when step motor control rack and scanning head are at the uniform velocity scanned from top to bottom, linear laser Device projects laser stripe to plant surface to be measured, and 3 cameras realize picture synchronization collection, acquisition gained by signal generator Image data is sent into computer by network interface card and carries out real-time three-dimensional reconstruction, completes when probe to be scanned is from upper scanning to bottom The three-dimensional reconstruction of entire plant is separated with organ point cloud.Detailed process is as follows:
Step 1: cable architecture Light stripes center extraction
Plant to be reconstructed is placed into test platform center, scanning head starts to scan from top to bottom, linear laser Device projects laser striation to tested plant surface, and camera acquires the laser stripe image Jing Guo plant surface modulation.Due to each Camera lens added the narrow band filter that central wavelength is 532nm respectively, so most of background quilt of camera acquisition image It filters out, main target region is laser stripe.The implementation case line-structured light fringe center extracting method is mainly using based on light The grey scale centre of gravity method of skeleton extracts optical losses, and main flow is as follows: carrying out figure first with threshold filter, gaussian filtering As denoising and image binaryzation processing, the normal side of every, skeleton secondly is determined to bianry image skeletal extraction using morphology To finally finding out grey scale centre of gravity in the normal direction, and the grey scale centre of gravity in normal direction everywhere is connected, just obtain The center line of striation.
Step 2: plant three-dimensional reconstruction
1, plant three-dimensional point cloud obtains
Laser stripe central point is extracted using above-mentioned laser stripe extracting method, and utilizes each camera calibration parameter, then The three-dimensional point cloud under each camera perspective can be obtained, then utilizes camera coordinates system global calibration method by all three-dimensional point clouds It adjusts to unified coordinate system, completes the splicing of three-dimensional point cloud, obtain initial plant three-dimensional point cloud.
2, point cloud denoising
Image Acquisition is carried out based on polyphaser line-structured light integrated approach, completes point cloud using global calibration method, Will cause splicing part to a certain extent has a cloud data redundancy problem.For this purpose, the application utilizes existing preferable solution party Method carries out the removal of internal high frequency point cloud to the three-dimensional point cloud for having spliced completion.On the one hand it avoids going out during subsequent triangle network forming Existing mistake, improves the precision of three-dimensional reconstruction;On the other hand the data volume for reducing three-dimensional point cloud, improves operation time.
Step 3: plant phenotypic parameter extracts
The plant three-dimensional point cloud that will acquire is projected to the OXY plane of global coordinate system, forms plant two-dimensional projection point Image, the stem region of manual interaction two-dimensional projection point image obtain stem point using the projection relation of three-dimensional point and subpoint Cloud approximate region;Finally, realizing each organ point cloud segmentation of plant using K mean cluster algorithm.Manual interaction is exactly to utilize Left mouse button is clicked along stem zone boundary outline line, obtains stem contour area.
Three-dimensional point using two-dimensional projection's point is generated on camera matrix projection to two dimensional image, establish projection two-dimensional points with The corresponding relationship of three-dimensional point, two-dimensional projection's point triangle network forming and lighting process generate two-dimension projection, pass through manual interaction two dimension Stem edge in perspective view obtains stem contour area, and the two dimension being enclosed in profile is calculated according to stem contour area and is thrown Shadow point has corresponding relationship according to two-dimensional projection's point and three-dimensional point, has been known that in three-dimensional point cloud which point cloud genera is in stem area Domain;
According to obtained three-dimensional stem point cloud sector domain, three-dimensional point cloud can be divided into stem point cloud sector domain and blade point Cloud sector domain, since blade three-dimensional point cloud is segmented into different leaves part (based on neighbor point space topological knot according to Euclidean distance The dividing method of structure), each separated region, which is then based on, using K-means cluster segmentation obtains final each organ pair The point cloud sector domain answered.
Step 4: calculating phenotypic parameter
1, plant height measures
It finds whole plant stem point cloud minimum point and Euclidean distance is sought in highest point.Choose a point P (x at the top of planti,yi, zi) and one point Q (x of bottomj,yj,zj), then plant plant height are as follows:
2, stem thickness measures
Stem region point cloud is obtained after each organ point cloud segmentation of plant, is taken x, the y-coordinate of point cloud data, will be put cloud Data projection obtains fitting result, elliptic equation to OXZ plane, using ellipse fitting method are as follows:
Ax2+Bxz+Cz2+ Dx+Ez+F=0 (3)
Stem thickness parameter extraction: being fitted the ellipse of acquisition, and long axis and short axle parameter can be equal to the stem thickness of corn, benefit Elliptical center X can be acquired with equation group (4)c、Yc, equation group (5) can acquire elliptical major semiaxis L and semi-minor axis S.
3, blade area
Using triangulation to blade point cloud data network forming, the area of tri patch is calculated using Heron's formula, adds up three The area of edged surface piece obtains spoon of blade area.Blade area calculation formula is as follows:
Formula (7) is Heron's formula, and wherein a, b, c are respectively Atria side length, and p is triangle semi-perimeter, SiIt is one The area of a triangle, S is cumulative for all triangle areas in formula (8).
4, blade length and width measure
Blade point cloud is projected on the face XOY, carry out triangle network forming using triangulation methodology and adds lighting process.Benefit Blade profile edge is extracted with image outline extraction algorithm and seeks rectangle minimum circumscribed rectangle, the length and width of minimum circumscribed rectangle The as length and width of blade, as shown in figure 4, Fig. 4 is angles of corn plant leaves piece length and width measurement figure in present invention specific implementation case, Wherein the part a is plant blade point cloud chart, and the part b is plant blade perspective view.In figure, W and H are respectively blade Length and width.
The implementation case can be realized it is following the utility model has the advantages that
1, a kind of plant phenotypic parameter measuring system integrated based on multi-line structured light by more cameras and is swashed The cooperation of optical transmitting set carries out comprehensive scanning to plant from three angles, blind area occurs when avoiding data splicing, the system Carry out that 3-D scanning is more efficient, strong applicability, at low cost to plant, and point cloud data is more complete, rebuilds effect also more It is good.
Since green blade has absorption peak in feux rouges and blue wave band, so we select green (light) laser to carry out laser Scanning, and in order to enhance the contrast of laser stripe, to obtain higher precision, we add on the camera lens of each camera Central wavelength is the narrow band filter of 532nm, extracts and improve cable architecture striation which simplify line-structured light strip area Center extraction precision;
3, in order to complete plant three dimensional structure separation, three-dimensional point is projected and generates two-dimensional projection's point and based on two-dimensional points It carries out triangle network forming and lighting process generates two-dimensional projection image, stem region is partitioned into based on two-dimensional projection image, then be based on The corresponding relationship of two-dimensional projection point and three-dimensional point, it is automatic to obtain stem point cloud sector domain and blade point cloud sector domain in three-dimensional point cloud, by Different leaves point cloud sector domain is segmented into according to Euclidean distance in blade three-dimensional point cloud, each separated region is then based on and adopts Each of final cloud sector domain is obtained with K-means cluster segmentation.Three-dimensional computations are converted to two dimension and are calculated by whole process, drop It is low direct to divide the complexity for carrying out organ separation using three-dimensional point cloud.
4, three-dimensional point cloud segmentation is on the basis of obtaining based on stem three-dimensional point cloud region, to carry out different leaves point cloud sector domain Segmentation, then K-means cluster segmentation is carried out based on each point cloud sector domain divided.Entire three-dimensional point cloud segmentation, which is compared, not to be had The point cloud segmentation of priori knowledge, segmentation is more efficient, divides more acurrate.
Each embodiment in this specification is described in a progressive manner, the highlights of each of the examples are with its The difference of his embodiment, the same or similar parts in each embodiment may refer to each other.For being disclosed in embodiment For system, since it is corresponded to the methods disclosed in the examples, so being described relatively simple, related place is referring to method portion It defends oneself bright.
Used herein a specific example illustrates the principle and implementation of the invention, above embodiments Illustrate to be merely used to help understand method and its core concept of the invention;At the same time, for those skilled in the art, According to the thought of the present invention, there will be changes in the specific implementation manner and application range.In conclusion this specification Content should not be construed as limiting the invention.

Claims (10)

1.一种植株表型参数测量装置,其特征在于,包括:扫描探头、测试机架、测试平台和计算机;所述扫描探头固定于所述测试机架上,所述测试机架可上下运动;所述测试平台用于承放待测量植株;所述扫描探头用于随着所述测试机架的运动扫描所述待测量植株不同高度的植株图像,并将所述植株图像传输至所述计算机,所述计算机用于根据所述扫描探头扫描的植株图像确定所述待测量植株的表型参数;1. a plant phenotypic parameter measuring device, is characterized in that, comprising: scanning probe, test frame, test platform and computer; Described scanning probe is fixed on described test frame, and described test frame can move up and down The test platform is used to hold the plant to be measured; the scanning probe is used to scan the plant images of different heights of the plant to be measured along with the movement of the test rack, and transmit the plant image to the a computer, configured to determine the phenotypic parameters of the plant to be measured according to the plant image scanned by the scanning probe; 所述扫描探头包括多个摄像机和两个线性激光发射器,多个摄像机位于第一水平面,多个所述摄像机等间距分布在虚拟圆弧上;两个所述线性激光发射器位于所述第一水平面下方的第二水平面,两个所述线性激光发射器上方分别对应所述虚拟圆弧两个端点的摄像机;两个所述线性激光发射器发出的激光位于平行于所述测试平台的第三水平面上,所述第三水平面与所述第一水平面和所述第二水平面均平行;多个摄像机的拍摄角度倾斜向下。The scanning probe includes a plurality of cameras and two linear laser transmitters, the plurality of cameras are located on the first horizontal plane, and the plurality of the cameras are equally spaced on a virtual arc; the two linear laser transmitters are located on the first horizontal plane. On the second horizontal plane below a horizontal plane, the cameras above the two linear laser emitters correspond to the two end points of the virtual arc respectively; On three horizontal planes, the third horizontal plane is parallel to the first horizontal plane and the second horizontal plane; the shooting angles of the plurality of cameras are inclined downward. 2.根据权利要求1所述的植株表型参数测量装置,其特征在于,还包括:运动控制卡和步进电机,所述测试机架和所述步进电机均与所述运动控制卡连接,通过所述步进电机调节所述测试机架的运动状态。2. The plant phenotype parameter measuring device according to claim 1, further comprising: a motion control card and a stepper motor, wherein the test rack and the stepper motor are both connected to the motion control card , and adjust the motion state of the test rack through the stepper motor. 3.根据权利要求1所述的植株表型参数测量装置,其特征在于,还包括:多个窄带滤光片,所述窄带滤光片的中心波长为532nm,每个所述摄像机的镜头上对应设置一个所述窄带滤光片。3. The device for measuring plant phenotype parameters according to claim 1, further comprising: a plurality of narrow-band filters, wherein the center wavelength of the narrow-band filters is 532 nm, and a lens of each of the cameras is provided with a central wavelength of 532 nm. One of the narrow-band filters is correspondingly arranged. 4.一种植株表型参数测量方法,其特征在于,所述植株表型参数测量方法应用于权利要求1-3任一项所述的植株表型参数测量装置,所述植株表型参数测量方法包括:4. A method for measuring a plant phenotype parameter, wherein the method for measuring a plant phenotype parameter is applied to the plant phenotype parameter measuring device according to any one of claims 1-3, and the plant phenotype parameter measures Methods include: 获取每个摄像机拍摄的待测植株图像;Obtain the image of the plant to be tested captured by each camera; 利用光条骨架的灰度重心法提取激光条纹中心线;The center line of laser stripe is extracted by gray-scale centroid method of light stripe skeleton; 根据每个摄像机的标定参数,获取每个摄像机视角下的三维点云;According to the calibration parameters of each camera, obtain the 3D point cloud under the perspective of each camera; 将所有三维点云统一至全局坐标系进行拼接,得到初始的待测植株三维点云;Unify all 3D point clouds to the global coordinate system for splicing to obtain the initial 3D point cloud of the plant to be tested; 将所述初始的待测植株三维点云投影至所述全局坐标系的OXY平面,得到待测植株二维投影点图像;Projecting the initial three-dimensional point cloud of the plant to be tested onto the OXY plane of the global coordinate system to obtain a two-dimensional projected point image of the plant to be tested; 通过手工交互所述待测植株二维投影点图像中的茎干边缘,得到茎干轮廓区域;By manually interacting the stem edge in the two-dimensional projection point image of the plant to be tested, the stem contour area is obtained; 根据三维点云与所述全局坐标系中投影点的对应关系,获得初始的待测植株三维点云中待测植株的茎干点云区域;According to the correspondence between the three-dimensional point cloud and the projection point in the global coordinate system, obtain the stem point cloud area of the plant to be tested in the initial three-dimensional point cloud of the plant to be tested; 根据所述初始的待测植株三维点云之间的欧式距离,确定所述初始的待测植株三维点云中待测植株的多个叶片点云区域;According to the Euclidean distance between the initial three-dimensional point clouds of the plants to be tested, determine multiple leaf point cloud regions of the plants to be tested in the initial three-dimensional point clouds of the plants to be tested; 利用K均值聚类算法对所述待测植株的茎干点云区域和叶片点云区域进行待测植株的器官点云分割,得到所述待测植株每个器官对应的点云区域;Use the K-means clustering algorithm to segment the point cloud of the plant to be tested on the stem point cloud area and the leaf point cloud area of the plant to be tested, to obtain the point cloud area corresponding to each organ of the plant to be tested; 根据所述待测植株每个器官对应的点云区域,计算所述待测植株每个器官的参数,得到所述待测植株的表型参数。According to the point cloud area corresponding to each organ of the plant to be tested, the parameters of each organ of the plant to be tested are calculated to obtain the phenotype parameters of the plant to be tested. 5.根据权利要求4所述的植株表型参数测量方法,其特征在于,所述获取每个摄像机拍摄的待测植株图像,之前还包括:5. The method for measuring plant phenotype parameters according to claim 4, wherein the acquisition of the plant image to be tested captured by each camera further comprises: 对每个摄像机的内参和外参进行标定,得到每个摄像机的标定参数;The internal and external parameters of each camera are calibrated to obtain the calibration parameters of each camera; 对线性激光发射器发射的激光所在的光平面方程进行标定;Calibrate the light plane equation where the laser emitted by the linear laser transmitter is located; 利用全局校准方法,将每个摄像机的局部坐标系统一至全局坐标系下。Using the global calibration method, the local coordinate system of each camera is aligned to the global coordinate system. 6.根据权利要求4所述的植株表型参数测量方法,其特征在于,所述利用光条骨架的灰度重心法提取激光条纹中心线,具体包括:6. The method for measuring plant phenotype parameters according to claim 4, wherein the extraction of the laser stripe centerline by the gray-scale barycentric method of the light stripe skeleton specifically comprises: 利用阈值滤波法和高斯滤波法对所述待测植株图像进行图像去燥和图像二值化处理,得到二值化图像;Image de-drying and image binarization are performed on the plant image to be tested by using the threshold filtering method and the Gaussian filtering method to obtain a binarized image; 利用形态学对所述二值化图像进行骨架提取,确定骨架每点的法线方向;The skeleton is extracted from the binarized image by morphology, and the normal direction of each point of the skeleton is determined; 在每个所述法线方向上计算灰度重心;calculating the gray-scale centroid in each of the normal directions; 将各个法线方向上对应的灰度重心连接,得到激光条纹的中心线。Connect the corresponding gray centers of gravity in each normal direction to obtain the center line of the laser stripe. 7.根据权利要求4所述的植株表型参数测量方法,其特征在于,所述将所有三维点云统一至全局坐标系进行拼接,得到初始的待测植株三维点云,之后还包括:7. The method for measuring plant phenotype parameters according to claim 4, characterized in that, said unifying all three-dimensional point clouds to a global coordinate system for splicing, obtaining an initial three-dimensional point cloud of plants to be measured, and then comprising: 对所述初始的待测植株三维点云进行内部高频点云去噪,得到去噪后的待测植株三维点云。The internal high-frequency point cloud denoising is performed on the initial three-dimensional point cloud of the plant to be tested, to obtain a three-dimensional point cloud of the plant to be tested after denoising. 8.一种植株表型参数测量系统,其特征在于,所述植株表型参数测量系统应用于权利要求1-3任一项所述的植株表型参数测量装置,所述植株表型参数测量系统包括:8. A plant phenotype parameter measurement system, wherein the plant phenotype parameter measurement system is applied to the plant phenotype parameter measurement device according to any one of claims 1-3, and the plant phenotype parameter measurement The system includes: 待测植株图像获取模块,用于获取每个摄像机拍摄的待测植株图像;The image acquisition module of the plant to be tested is used to acquire the image of the plant to be tested captured by each camera; 激光条纹中心线提取模块,用于利用光条骨架的灰度重心法提取激光条纹中心线;The laser stripe centerline extraction module is used to extract the laser stripe centerline using the gray-scale centroid method of the light stripe skeleton; 三维点云获取模块,用于根据每个摄像机的标定参数,获取每个摄像机视角下的三维点云;The 3D point cloud acquisition module is used to acquire the 3D point cloud under the viewing angle of each camera according to the calibration parameters of each camera; 拼接模块,用于将所有三维点云统一至全局坐标系进行拼接,得到初始的待测植株三维点云;The splicing module is used to unify all the 3D point clouds into the global coordinate system for splicing to obtain the initial 3D point cloud of the plant to be tested; 投影模块,用于将所述初始的待测植株三维点云投影至所述全局坐标系的OXY平面,得到待测植株二维投影点图像;a projection module for projecting the initial three-dimensional point cloud of the plant to be tested to the OXY plane of the global coordinate system to obtain a two-dimensional projected point image of the plant to be tested; 手工交互模块,用于通过手工交互所述待测植株二维投影点图像中的茎干边缘,得到茎干轮廓区域;The manual interaction module is used for manually interacting the stem edge in the two-dimensional projection point image of the plant to be tested to obtain the stem outline area; 待测植株的茎干点云区域获取模块,用于根据三维点云与所述全局坐标系中投影点的对应关系,获得初始的待测植株三维点云中待测植株的茎干点云区域;The stem point cloud area acquisition module of the plant to be tested is used to obtain the stem point cloud area of the plant to be tested in the initial three-dimensional point cloud of the plant to be tested according to the corresponding relationship between the three-dimensional point cloud and the projection point in the global coordinate system ; 待测植株的叶片点云区域获取模块,用于根据所述初始的待测植株三维点云之间的欧式距离,确定所述初始的待测植株三维点云中待测植株的多个叶片点云区域;The leaf point cloud area acquisition module of the plant to be tested is used to determine a plurality of leaf points of the plant to be tested in the initial three-dimensional point cloud of the plant to be tested according to the Euclidean distance between the initial three-dimensional point clouds of the plant to be tested cloud area; 器官点云分割模块,用于利用K均值聚类算法对所述待测植株的茎干点云区域和叶片点云区域进行待测植株的器官点云分割,得到所述待测植株每个器官对应的点云区域;The organ point cloud segmentation module is used to segment the organ point cloud of the plant to be tested on the stem point cloud area and the leaf point cloud area of the plant to be tested by using the K-means clustering algorithm to obtain each organ of the plant to be tested The corresponding point cloud area; 表型参数计算模块,用于根据所述待测植株每个器官对应的点云区域,计算所述待测植株每个器官的参数,得到所述待测植株的表型参数。The phenotype parameter calculation module is configured to calculate the parameters of each organ of the plant to be tested according to the point cloud area corresponding to each organ of the plant to be tested, and obtain the phenotypic parameters of the plant to be tested. 9.根据权利要求8所述的植株表型参数测量系统,其特征在于,还包括:9. plant phenotype parameter measurement system according to claim 8, is characterized in that, also comprises: 摄像机标定模块,用于对每个摄像机的内参和外参进行标定,得到每个摄像机的标定参数;The camera calibration module is used to calibrate the internal and external parameters of each camera to obtain the calibration parameters of each camera; 光平面方程标定模块,用于对线性激光发射器发射的激光所在的光平面方程进行标定;The light plane equation calibration module is used to calibrate the light plane equation where the laser emitted by the linear laser transmitter is located; 坐标系统一模块,用于利用全局校准方法,将每个摄像机的局部坐标系统一至全局坐标系下。A coordinate system module is used to convert the local coordinate system of each camera to the global coordinate system by using the global calibration method. 10.根据权利要求8所述的植株表型参数测量系统,其特征在于,所述激光条纹中心线提取模块,具体包括:10. The plant phenotype parameter measurement system according to claim 8, wherein the laser stripe centerline extraction module specifically comprises: 图像处理单元,用于利用阈值滤波法和高斯滤波法对所述待测植株图像进行图像去燥和图像二值化处理,得到二值化图像;an image processing unit, configured to perform image desiccation and image binarization processing on the image of the plant to be tested by using a threshold filtering method and a Gaussian filtering method to obtain a binarized image; 骨架提取单元,用于利用形态学对所述二值化图像进行骨架提取,确定骨架每点的法线方向;a skeleton extraction unit, used to extract the skeleton from the binarized image using morphology, and determine the normal direction of each point of the skeleton; 灰度重心计算单元,用于在每个所述法线方向上计算灰度重心;a gray-scale centroid calculation unit, used for calculating the gray-scale centroid in each of the normal directions; 激光条纹的中心线获取单元,用于将各个法线方向上对应的灰度重心连接,得到激光条纹的中心线。The centerline acquisition unit of the laser stripe is used to connect the corresponding grayscale centers of gravity in each normal direction to obtain the centerline of the laser stripe.
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