CN102630429A - Bud injury preventing system for sugarcane cutting - Google Patents

Abstract

The invention discloses a bud injury preventing system for sugarcane cutting, comprising a cutter and a clamping conveying mechanism driven by a motor controller, wherein the distance between the clamping conveying mechanism and the cutter is fixed; a camera is arranged above the clamping conveying mechanism; the distance from the center position of the camera to the cutting position of sugarcane is just the length of the sugarcane after being cut off; an output end of the camera is connected with a computer equipped with a MATLAB (MATrix LABoratory) software; and an output end of the computer is connected with the motor controller of the clamping conveying mechanism. According to the invention, accurate shutter triggering time interval is matched according to the rotating speed of the cutter and the conveying speed of the sugarcane to ensure that image collecting positions are consistent with actual cutting positions; and filtering, noise reduction and binarization processing are carried out on the collected images to judge whether sugarcane joints are cut by the cutter or not. The bud injury preventing system disclosed by the invention has the advantages that the rapid and accurate judgment on the sugarcane positions cut by the cutter is realized; the bud injury rate of the sugarcane seed stem joints and the production cost of the sugarcane are reduced; the sugarcane seed is saved; and the labour productivity is improved.

Description

The anti-bud system of hindering of sugarcane cutting

Technical field

The present invention relates to a kind of agricultural technology field, specifically is a kind of anti-bud system of hindering of precision that utilizes computer vision technique to judge sugarcane cutter device cutting sugarcane position.

Background technology

China is world sugarcane production big country, and simultaneously as the sugared big country of the big product in third place in the world, the development of cane planting industry directly has influence on the development of the tens million of sugarcane growers' of China livelihood and sugar industry.The world has realized the mechanization of cane planting in each sugarcane place of production mostly to a certain extent, but all exists deficiency.Though external planter has possessed superperformance, function is tending towards perfect, be not equipped with the anti-of specialty as yet and hinder the bud shearing device, and mechanism is too complicated, price is too expensive, so sugarcane producing region at home is to be difficult to promote.The then more difficult purpose of preventing hindering bud in the sugarcane kind cutting-off process automatically that is implemented in of domestic planter.Research in this field all also is in the starting stage of fumbling both at home and abroad at present.Close research has sugarcane stipes feature extraction and the identification of Lu Shang equality based on machine vision, and is external, and Iranian Moshashai K utilizes the method for gray level image threshold segmentation that the sugarcane stipes is discerned and done Primary Study.As the damage of sugarcane bud appears, and then influence crop yield, and therefore, sugarcane cutter device prevents that hindering the bud system has very important significance to being installed.But up to now, Shang Weijian has pair sugarcane cutter device to be equipped with the anti-relevant report of hindering bud shearing device or method of specialty.

Summary of the invention

Technical problem to be solved by this invention provides the anti-bud system of hindering of a kind of sugarcane cutting; It effectively handles the analysis with characteristic parameter extraction through the utilization computer image processing technology to sugarcane kind image; Judge cutting position in advance and conveying plant is adjusted before will destroying, thereby avoid the bud damage at the sugarcane bud.

The present invention solves the problems of the technologies described above with following technical scheme:

The anti-bud system of hindering of sugarcane cutting of the present invention; Comprise cutter and the clamping conveying mechanism that drives by electric machine controller; Fixed distance between clamping conveying mechanism and the cutter; Above the clamping conveying mechanism, camera is installed, the center of camera just is the length after sugarcane is cut off to the cutting position of sugarcane; The output of camera is connected with the computer that MATLAB software is housed, and the output of computer is connected with the electric machine controller of clamping conveying mechanism;

The cutting of said sugarcane is anti-, and to hinder the operating procedure of bud system following:

(1) starts cutter and clamping conveying mechanism, the synchronization of the speed of clamping conveying mechanism and cutter hobboing cutter; A pair of hobboing cutter whenever turns around and cuts twice in the cutter, according to the rotating speed of hobboing cutter, confirms the camera shutter triggered time, and making the target image that collects just is cutting part next time; The frame period computing formula of native system is following: L=(150-1000Vt)/1000Vt

Wherein: the L-frame period;

V-sugarcane transporting velocity (m/s);

T=0.009s is for gathering the single-frame images required time;

(2) frame period that draws according to aforementioned calculation is selected the speed of camera collection image, starts camera then and begins to gather image;

(3) then to the tool box of the image that collects utilization MATLAB software judge, processing and identification, judge whether it is the sugarcane joint;

(4) judged result feeds back to electric machine controller 6 execution that control clamping conveying mechanism moves, if switch to joint, then triggers electric machine controller and drags sugarcane displacement backward; If cut less than joint; Then do not trigger electric machine controller, thereby avoided cutter cuts, to reduce bud injury rate to the sugarcane joint.

Sugarcane cutting of the present invention is anti-hinders the bud system, and the concrete operations of said operating procedure (3) are following:

1) adopt gray scale adjustment, filtering noise reduction, morphology processing method commonly used that original image is handled, expression formula is:

$g(x,y)=\frac{1}{M}\underset{(m,n)\∈S}{\mathrm{\Σ}}f(x-m,y-n)$

Wherein: M is the pixel sum that is comprised in the neighborhood S; S is pre-determined neighborhood;

2) get the neighborhood averaging of threshold value, get the mathematical notation of the neighborhood averaging of threshold value:

$g(x,y)=\left\{\begin{array}{c}\frac{1}{M}\underset{(m,n)\∈S}{\mathrm{\Σ}}f(x-m,y-n)\\ f(x,y)\end{array}\right.$

$|f(x,y)-\frac{1}{M}\underset{m,n\∈S}{\mathrm{\Σ}}f(x-m,y-n)|>T$

$|f(x,y)-\frac{1}{M}\underset{m,n\∈S}{\mathrm{\Σ}}f(x-m,y-n)|\≤T$

T is the threshold value of predesignating;

3) to binarization processing of images, the image that is about to 256 brightness degrees is chosen the binary image that obtains still can reflect integral image and local feature through suitable threshold, and the two-dimensional matrix of bianry image only constitutes by 0,1; Be about to bianry image and regard a special gray level image that only comprises B&W as; Suppose that I is the original fingerprint image, size is M * N, and 0≤i (i; J)≤(L-1), get threshold values so

$T=(\underset{K=1}{\overset{L-1}{\mathrm{\Σ}}}{N}_K\×N)/(M\×N)$

N wherein _kBe that gray scale is the number of pixels of K value, obtain binary image thus, and

$B(i,j)=\left\{\begin{array}{cc}1& I(i,j)>T\\ 0& I(i,j)\≤T\end{array}\right.$

4) select for use a kind of method of iteration to come to select appropriate threshold automatically again to each pictures;

5), the matrix summation of gray level image is on average obtained again the initial threshold of iteration according to pretreated result

K _IjThe image that is M * N is at any pixel (i, gray value j);

Suppose that gray level image has the i.e. 0～L-1 of L level gray value, utilizes threshold value T to be divided into R1 and two zones of R2 to image, with the gray average μ of computes region R 1 and R2 ₁And μ ₂:

N in the formula _iIt is the probability that gray level i level occurs;

Calculate μ ₁And μ ₂After, the threshold value T that makes new advances with computes _I+1:

$T_{i+1}=\frac{1}{2}(u_1+u_2)$

Bring the result into circulation, up to | T _I+1-T _i|≤0.1 o'clock, T _I+1Threshold value as binaryzation;

Choose the iteration result and bring into, obtain binary image as the threshold value of binaryzation.

Sugarcane cutting of the present invention is anti-hinders the bud system, and the concrete operations of said operating procedure (4) are following:

Image is negated, and with the gray value upset of binary image, to the pairing value summation of all pixels, formula is following then:

$A=\underset{i=0}{\overset{n-1}{\mathrm{\Σ}}}\underset{j=0}{\overset{m-1}{\mathrm{\Σ}}}B[i,j]$

Wherein, B [i, j] is the mark value of this point; M, n are respectively gauge point pixel i, the sum of j;

The corresponding numerical value of binary image is accurately discerned sugarcane joint and cane stalk through the relatively calculating function of numerical value, with this electric machine controller is sent instruction.

4. hinder the bud system according to the said sugarcane cutting of claim 3 is anti-, it is characterized in that, adopt all the pixel corresponding gray scale value summations of binary image after following program calculates every processing in the said operating procedure (4):

The present invention utilizes machine vision to follow the tracks of the position of cutter cuts sugarcane, has tentatively built the sugarcane kind stem cutting test platform based on computer vision.Adopt neighborhood averaging to make the target in the image outstanding, stipes is distinguished obviously, and variation of image grayscale more tends towards stability, and helps next step processing.Adopt a kind of way of iteration to obtain the threshold value of binaryzation program, come each picture of marking on a map of opening one's eyes wide of self adaptation, eliminated artificial factor.This recognition system is tested, and 100% recognition accuracy, the algorithm execution time of single image are 0.634s.The utilization computer image processing technology is realized quick, the accurately judgement at cutter cuts sugarcane position.Reduce bud injury rate, the sugarcane production cost of sugarcane kind stipes, practiced thrift the sugarcane kind, improved labour productive forces.

Description of drawings

Fig. 1 is the anti-hardware configuration sketch map of hindering the bud system of sugarcane cutting of the present invention;

Fig. 2 sugarcane joint preliminary treatment figure;

Fig. 3 cane stalk preliminary treatment figure;

Figure after the final processing of Fig. 4 sugarcane joint;

Figure after the final processing of Fig. 5 cane stalk.

Embodiment

Below in conjunction with accompanying drawing the present invention is done further description:

As shown in Figure 1; System of the present invention comprises computer 4, camera 2, cutter 1, the clamping conveying mechanism 3 that MATLAB software is housed; Camera 2 is installed in the top of clamping conveying mechanism 3 and is connected with computer 4, and the distance of itself and sugarcane 5 remains constantly, makes that the picture that photographs is clear to get final product.Clamping conveying mechanism 3 is certain with cutter 1 distance, and the center that makes camera just is the length after sugarcane is cut off to the cutting position of sugarcane 5, to guarantee the accurate of the disconnected sugarcane cutting part of anticipation.The delivery port of clamping conveying mechanism 3 is over against the material inlet of cutter 1.The output of computer 4 is connected with the electric machine controller 6 of clamping conveying mechanism 3.

Below be to use system implementation example of the present invention, specific operation process is following:

1, starts cutter and clamping conveying mechanism, the synchronization of the speed of clamping conveying mechanism and cutter hobboing cutter.A pair of hobboing cutter whenever turns around and cuts twice in the cutter, according to the rotating speed of hobboing cutter, confirms the camera shutter triggered time, and making the target image that collects just is cutting part next time.Get sugarcane kind stem section and meet agriculture requirement for 150mm.In order to make the sugarcane image that collects identical with the position of sugarcane cutting, for the IMAQ under the friction speed, different acquisition times need be set at interval, promptly frame period has so just been realized the correct location of cutting part, and can have been avoided redundant image information.The frame period computing formula of native system is following: L=(150-1000Vt)/1000Vt

Wherein: the L-frame period;

V-sugarcane transporting velocity (m/s);

T=0.009s is for gathering the single-frame images required time;

2, the frame period that draws according to aforementioned calculation is selected the speed of camera collection image, starts camera then and begins to gather image;

3, then the image that collects is used the tool box of MATLAB software

Adopt gray scale adjustment, filtering noise reduction, morphology processing method commonly used at present that original image is handled.The gray scale adjustment just uses the method for enhancing contrast ratio to strengthen the contrast of image each several part.The filtering noise reduction adopts the neighborhood averaging noise reduction, can effectively eliminate Gaussian noise.Expression formula is:

$g(x,y)=\frac{1}{M}\underset{(m,n)\∈S}{\mathrm{\Σ}}f(x-m,y-n)$

Wherein: M is the pixel sum that is comprised in the neighborhood S; S is that (this neighborhood does not comprise (x, y) point) to pre-determined neighborhood.

Get the neighborhood averaging of threshold value again.A window (3 * 3) moves (line by line) along image, obtains the mean value of the whole pixel gray values except that pending pixel in the window earlier.If the absolute value of the difference of pending pixel gray value and this mean value has surpassed a certain predetermined threshold value, then the gray scale of this pixel uses mean value to replace; Otherwise, keep the gray scale of this pixel constant.Get the mathematical notation of the neighborhood averaging of threshold value:

$g(x,y)=\left\{\begin{array}{c}\frac{1}{M}\underset{(m,n)\∈S}{\mathrm{\Σ}}f(x-m,y-n)\\ f(x,y)\end{array}\right.$

$|f(x,y)-\frac{1}{M}\underset{m,n\∈S}{\mathrm{\Σ}}f(x-m,y-n)|>T$

$|f(x,y)-\frac{1}{M}\underset{m,n\∈S}{\mathrm{\Σ}}f(x-m,y-n)|\≤T$

T is the threshold value of predesignating.The design sketch that after preliminary treatment, obtains as shown in Figures 2 and 3.

To binarization processing of images, exactly the some gray scale on the image is changed to 0 or 1 again, just makes entire image show tangible black and white effect.The image that is about to 256 brightness degrees is chosen the binary image that obtains still can reflect integral image and local feature through suitable threshold.The two-dimensional matrix of bianry image only constitutes by 0,1, and bianry image can be regarded a special gray level image that only comprises B&W as.Suppose that I is the original fingerprint image, size is M * N, and 0≤i (i j)≤(L-1), gets threshold values so

$T=(\underset{K=1}{\overset{L-1}{\mathrm{\Σ}}}{N}_K\×N)/(M\×N)$

N wherein _kBe that gray scale is the number of pixels of K value.Obtain binary image thus, and

$B(i,j)=\left\{\begin{array}{cc}1& I(i,j)>T\\ 0& I(i,j)\≤T\end{array}\right..$

Select for use a kind of method of iteration to come to select appropriate threshold automatically again to each pictures.

According to pretreated result, the matrix summation of gray level image is on average obtained again the initial threshold of iteration

K _IjThe image that is M * N is at any pixel (i, gray value j).

Suppose gray level image have L level gray value (0～L-1), utilize threshold value T to be divided into two zones---R1 and R2 to image, with computes region R 1 and R2

Gray average μ ₁And μ ₂:

N in the formula _iIt is the probability that gray level i level occurs.

Calculate μ ₁And μ ₂After, the threshold value T that makes new advances with computes _I+1:

$T_{i+1}=\frac{1}{2}({\mathrm{<figure-callout\; id="1"\; label="u"\; filenames="120424113636.png"\; state="\{\{state\}\}">u</figure-callout>}}_1+u_2)$

Bring the result into circulation, up to | T _I+1-T _i|≤0.1 o'clock, T _I+1Threshold value as binaryzation.

Choosing the iteration result brings into as the threshold value of binaryzation.The design sketch that obtains such as Fig. 4 and shown in Figure 5.

4) data and result judge:

Image is negated, and with the gray value upset of binary image, briefly is exactly to make black bleaching, and makes leucismus black, and adopts all the pixel corresponding gray scale value summations of binary image after following program calculates every processing:

Program is following:

To the pairing value summation of all pixels, formula is following then:

$A=\underset{i=0}{\overset{n-1}{\mathrm{\&Sigma;}}}\underset{j=0}{\overset{m-1}{\mathrm{\&Sigma;}}}B[i,j]$

Wherein, B [i, j] is the mark value of this point.M, n are respectively gauge point pixel i, the sum of j.

The corresponding numerical value of binary image, the A value that draws the sugarcane joint through test is far smaller than the A value of cane stalk.Accurately discern sugarcane joint and cane stalk through the relatively calculating function of numerical value, electric machine controller is sent instruction with this.

Get 100 samples and make an experiment, wherein the cane stalk picture is 84,16 in sugarcane joint picture.Image is handled the every pictures summation in back and is obtained the A value,

A＝[6284，6053，6174，6135，6265，6224，2810，6125，5984，6243，6122，6177，3488，6234，6275，6216，2893，6035，6109，6280，6274，6111，6266，2877，5997，6092，6240，3401，5982，6023，6044，6042，6173，3285，6066，6043，5983，6219，2779，6122，6279，6129，2674，6113，6251，6148，6044，6195，3416，6248，5985，6198，6101，2832，6246，6140，6207，6117，6077，2916，6040，6041，6198，6076，3221，6153，6027，6203，6100，6255，6253，2904，6170，6138，6268，6243，6186，6242，6191，6089，3059，6072，6088，6150，6213，6078，6248，6161，6098，6205，6154，2719，6122，6202，6179，6234，6287，3496，6147，6262]

The value that can find out cane stalk A generally between 5900 to 6300, the value of sugarcane joint A generally between 3500 to 2600, concrete statistical data such as table 1.During test, choose 3500 and be the identification critical value, caused once erroneous judgement.After the back is revised critical value and is 4700, be joint or stem all has very big buffering, the recognition accuracy of all samples is 100%.

Table 1

Claims (4)

1. the sugarcane cutting prevents hindering the bud system; It is characterized in that; Comprise cutter and the clamping conveying mechanism that drives by electric machine controller; Fixed distance between clamping conveying mechanism and the cutter is equipped with camera above the clamping conveying mechanism, the center of camera just is the length after sugarcane is cut off to the cutting position of sugarcane; The output of camera is connected with the computer that MATLAB software is housed, and the output of computer is connected with the electric machine controller of clamping conveying mechanism;

The cutting of said sugarcane is anti-, and to hinder the operating procedure of bud system following:

(1) starts cutter and clamping conveying mechanism, the synchronization of the speed of clamping conveying mechanism and cutter hobboing cutter; A pair of hobboing cutter whenever turns around and cuts twice in the cutter, according to the rotating speed of hobboing cutter, confirms the camera shutter triggered time, and making the target image that collects just is cutting part next time; The frame period computing formula of native system is following: L=(150-1000Vt)/1000Vt

Wherein: the L-frame period;

V-sugarcane transporting velocity (m/s);

T=0.009s is for gathering the single-frame images required time;

(2) frame period that draws according to aforementioned calculation is selected the speed of camera collection image, starts camera then and begins to gather image;

(3) then to the tool box of the image that collects utilization MATLAB software judge, processing and identification, judge whether it is the sugarcane joint;

(4) judged result feeds back to electric machine controller 6 execution that control clamping conveying mechanism moves, if switch to joint, then triggers electric machine controller and drags sugarcane displacement backward; If cut less than joint; Then do not trigger electric machine controller, thereby avoided cutter cuts, to reduce bud injury rate to the sugarcane joint.

2. hinder the bud system according to the said sugarcane cutting of claim 1 is anti-, it is characterized in that the concrete operations of said operating procedure (3) are following:

1) adopt gray scale adjustment, filtering noise reduction, morphology processing method commonly used that original image is handled, expression formula is:

Wherein: M is the pixel sum that is comprised in the neighborhood S; S is pre-determined neighborhood;

2) get the neighborhood averaging of threshold value, get the mathematical notation of the neighborhood averaging of threshold value:

T is the threshold value of predesignating;

3) to binarization processing of images, the image that is about to 256 brightness degrees is chosen the binary image that obtains still can reflect integral image and local feature through suitable threshold, and the two-dimensional matrix of bianry image only constitutes by 0,1; Be about to bianry image and regard a special gray level image that only comprises B&W as; Suppose that I is the original fingerprint image, size is M * N, and 0≤i (i; J)≤(L-1), get threshold values so

N wherein _kBe that gray scale is the number of pixels of K value, obtain binary image thus, and

4) select for use a kind of method of iteration to come to select appropriate threshold automatically again to each pictures;

5), the matrix summation of gray level image is on average obtained again the initial threshold of iteration according to pretreated result

K _IjThe image that is M * N is at any pixel (i, gray value j);

Suppose that gray level image has the i.e. 0～L-1 of L level gray value, utilizes threshold value T to be divided into R1 and two zones of R2 to image, with the gray average μ of computes region R 1 and R2 ₁And μ ₂:

N in the formula _iIt is the probability that gray level i level occurs;

Calculate μ ₁And μ ₂After, the threshold value T that makes new advances with computes _I+1:

Bring the result into circulation, up to | T _I+1-T _i|≤0.1 o'clock, T _I+1Threshold value as binaryzation;

Choose the iteration result and bring into, obtain binary image as the threshold value of binaryzation.

3. hinder the bud system according to the said sugarcane cutting of claim 1 is anti-, it is characterized in that the concrete operations of said operating procedure (4) are following:

Image is negated, and with the gray value upset of binary image, to the pairing value summation of all pixels, formula is following then:

Wherein, B [i, j] is the mark value of this point; M, n are respectively gauge point pixel i, the sum of j;

The corresponding numerical value of binary image is accurately discerned sugarcane joint and cane stalk through the relatively calculating function of numerical value, with this electric machine controller is sent instruction.

4. hinder the bud system according to the said sugarcane cutting of claim 3 is anti-, it is characterized in that, adopt all the pixel corresponding gray scale value summations of binary image after following program calculates every processing in the said operating procedure (4):

。

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