CN101271522A - Automatic recognition method for yellow-colored rice in rice - Google Patents
Automatic recognition method for yellow-colored rice in rice Download PDFInfo
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- CN101271522A CN101271522A CNA2008101120842A CN200810112084A CN101271522A CN 101271522 A CN101271522 A CN 101271522A CN A2008101120842 A CNA2008101120842 A CN A2008101120842A CN 200810112084 A CN200810112084 A CN 200810112084A CN 101271522 A CN101271522 A CN 101271522A
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Abstract
The invention discloses an automatic recognition method for yellow-colored rice, which belongs to the technical field of computer image processing. The method includes: rice samples are arranged in an image collector to collect original image information; the original image information is read, and the background is separated from the rice samples; the original chrominance information R, G and B values of every grain of rice, and the R, G and B chrominance values are transformed into an even color model chromaticity value L<*>a<*>b<*> through ideal chrominance models X, Y, Z; the characteristic chrominance value b<*> is selected and determined, and if the characteristic chrominance value b<*> of every grain of rice in all pixels exceeds a chrominance threshold value and the occupying rate is larger than an area threshold value, the rice is determined to be the yellow-colored rice. The chrominance threshold value and the area threshold value are set by an operator according to the variety and the producing area of the rice. The method of the invention can be widely applied to the detection process of rice quality in rice field acquisition and market transaction, thus resulting in rapid, objective and accurate detection.
Description
Technical field
The invention belongs to the computer image processing technology field, the automatic identifying method of yellow rice kernel in particularly a kind of rice.
Background technology
Yellow rice kernel is when brown rice is worn into precision and is the first-class rice of national standard, and it is yellow that endosperm is, and with the visibly different particle of normal grain of rice color and luster, the percent that yellow rice kernel weight accounts for sample weight is called the yellow rice kernel rate.The detection of standard GB/T 17891-1999 " high quality paddy " regulation yellow rice kernel detects by standard GB 1350-1999 " paddy " prescriptive procedure, and the not elaboration of yellow rice kernel detection method among the standard GB 1350-1999 " paddy ", point out that yellow rice kernel detects by standard GB 5496-1985 " grain, oil plant check yellow rice kernel and cracked kernel method of inspection " and undertaken.
Standard GB 5496-1985 " grain, oil plant check yellow rice kernel and cracked kernel method of inspection " regulation yellow rice kernel detection method is: paddy is after the check brown rice yield, its brown rice sample is milled to the precision of the second-class rice of approximate test with miniature rice mill, remove the chaff powder, W weighs, as sample weight, sort out yellow rice kernel more in accordance with regulations, W weighs
1Paddy yellow rice kernel content by formula calculates: yellow rice kernel (%)=(W
1/ W) * 100%.Wherein the recognition method of yellow rice kernel is: divide and get the about 50g of rice sample or when check is cracked rice, sort out yellow rice kernel in accordance with regulations, its foundation that picks relies on human eye to carry out fully.This method is because detection time is long, subjectivity is strong, accuracy is low, operability, poor repeatability, and defective such as easily affected by environment is difficult to satisfy in on-the-spot purchase of paddy and marketing the requirement to Quality Detection is quick, objective, accuracy is high.
The present invention proposes based on L at the problems referred to above
*a
*b
*The yellow rice kernel detection method of color model is introduced paddy quality evaluation with computer image processing technology, makes that detection technique is accurate, objective, workable, automaticity is high.
Summary of the invention
The invention provides a kind of based on L
*a
*b
*The yellow rice kernel of color model is the method for identification automatically, it is characterized in that comprising the following steps:
Obtain grain of rice image information;
Analyze grain of rice chrominance information;
The identification yellow rice kernel.
The described grain of rice image information of obtaining specifically comprises the following steps:
The rice sample of some is placed image acquisition device, gather original image information;
Read original image information, cut apart the background and the grain of rice, and background colour is set to and other color of grain of rice color phase region.
Described rice sample quantity is the 10-1000 grain.
The described method of cutting apart the background and the grain of rice is a process of iteration.
Described process of iteration specifically comprises the following steps:
Obtain the minimum and maximum gray-scale value Z in the image
1And Z
k, make threshold value initial value T
k=(Z
1+ Z
k)/2;
According to threshold value T
kImage segmentation is become target and background two parts, obtain two-part average gray value Z
0, Z
B
Obtain new threshold value T
K+1=(Z
0+ Z
B)/2;
If T
k=T
K+1, then gained is threshold value, otherwise according to the T that calculates
kValue continues calculated threshold, iterative computation.
Described when cutting apart the background and the grain of rice, it is RGB (0,0,0) that background color is selected ater.
The concrete steps of described analysis grain of rice chrominance information comprise:
Original RGB chrominance information is changed into L
*a
*b
*Chrominance information, wherein L
*Be lightness index, span is 0～100, a
*Be the chromaticity index, to shiny red, span is-120～120 to the expression color, b by bottle green
*Be the chromaticity index, to coke yellow, span is-120～120 to the expression color by sapphirine;
Analyze the L of each pixel in every rice
*a
*b
*Chrominance information is selected L
*a
*b
*B in the chrominance information
*As the feature chromatic value of analyzing yellow rice kernel.
The described L that utilizes
*a
*b
*Feature chromatic value b in the color model
*Judge, pass through desired color model XYZ to L by the RGB color model
*a
*b
*The concrete conversion formula of color model is:
X＝0.4124f(R/255)+0.3576f(G/255)+0.1805f(B/255)
Y＝0.2126f(R/255)+0.7152f(G/255)+0.0722f(B/255)
Z＝0.0193f(R/255)+0.1192f(G/255)+0.9505f(B/255)
Wherein: R, G, B are the original chrominance information value of the grain of rice that is read,
Function
By desirable XYZ color model to L
*a
*b
*The color model conversion formula is:
L
*＝116f(Y/Y
0)-16
a
*＝500[f(X/X
0)-f(Y/Y
0)]
b
*＝200[f(Y/Y
0)-f(Z/Z
0)]
The concrete steps of described identification yellow rice kernel comprise:
Utilize yellow rice kernel to detect colourity threshold value and area threshold that software set is judged yellow rice kernel;
With L
*a
*b
*B in the color model
*Be the basis,, judge whether every rice is yellow rice kernel, is drawn in its border, statistics yellow rice kernel rate in conjunction with colourity threshold value and area threshold.
Describedly judge that when whether rice was yellow rice kernel, the colourity threshold value was provided with according to different rice varieties and grown place by the operator voluntarily with area threshold, its range of choice is a colourity threshold value 15～30; Area threshold 5%～40%.
Beneficial effect of the present invention is: utilize computer image processing technology to replace testing staff's human eye diagnostic method of GB/T 17891-1999 " high quality paddy " regulation, can discern the yellow rice kernel in the polished rice quick, objective, exactly, overcome that detection time is long in the prior art scheme, subjectivity is strong, accuracy is low, defective such as operability and poor repeatability.Satisfied in on-the-spot purchase of paddy and marketing requirement to Quality Detection is quick, objective, accuracy is high.And, computer picture recognition system according to the method for the invention establishment, the function that also has the detection of many indexs such as can finishing chalkiness degree, the white grain of chalk rate, head rice rate, grain type and fluent meterial simultaneously, make the detection of carrying out the many index that is mutually independent in the national standard, can finish together by a cover system, can detect 1000 rice at most at every turn, have the automaticity height, operate quick, easy characteristics.
Description of drawings
Fig. 1 is Image Acquisition of the present invention and treating apparatus connection diagram.
Number in the figure:
The 1-scanner; The 2-computing machine; The 3-printer.
Embodiment
The invention will be further described below in conjunction with accompanying drawing:
Fig. 1 is Image Acquisition of the present invention and treating apparatus connection diagram,
1, utilize the tally sampling plate of holding concurrently to get about 10～1000 rice samples from rice lot sample to be measured, put sampler on scanner 1, images acquired is stored as 24 bmp formatted files.Wherein the brightness of scanner 1, contrast are made as between-15～25.
2, utilize yellow rice kernel in the yellow rice kernel detection system recognition image on computing machine 2, concrete is:
A) read original image information, store the chrominance information of each pixel in every grain of rice, its original chrominance information is the RGB colouring information;
B) utilize process of iteration to cut apart the background and the grain of rice, it is RGB (0,0,0) that background is made as ater,
The concrete steps of process of iteration are:
(1) obtains minimum and maximum gray-scale value Z in the image
1And Z
k, make threshold value initial value T
k=(Z
1+ Z
k)/2;
(2) according to threshold value T
kImage segmentation is become target and background two parts, obtain two-part average gray value Z
0, Z
B
(3) obtain new threshold value T
K+1=(Z
0+ Z
B)/2;
(4) if T
k=T
K+1, then gained is threshold value, otherwise changes (2), iterative computation;
C) the RGB colouring information with the grain of rice changes into L
*a
*b
*Colouring information, wherein L
*Be lightness index, span is 0～100, a
*Be the chromaticity index, to shiny red, span is-120～120 to the expression color, b by bottle green
*Be the chromaticity index, to coke yellow, span is-120～120 to the expression color by sapphirine;
Pass through desired color model XYZ to L by the RGB color model
*a
*b
*The concrete conversion formula of color model is:
X＝0.4124f(R/255)+0.3576f(G/255)+0.1805f(B/255)
Y＝0.2126f(R/255)+0.7152f(G/255)+0.0722f(B/255)
Z＝0.0193f(R/255)+0.1192f(G/255)+0.9505f(B/255)
Wherein: R, G, B are the original chrominance information value of the grain of rice that is read,
Function
By desirable XYZ color model to L
*a
*b
*The color model conversion formula is:
L
*＝116f(Y/Y
0)-16
a
*＝500[f(X/X
0)-f(Y/Y
0)]
b
*＝200[f(Y/Y
0)-f(Z/Z
0)]
D) L of every grain of rice of analysis
*a
*b
*Chrominance information, and select chromatic value b for use
*As the feature chromatic value of analyzing yellow rice kernel;
E) utilize yellow rice kernel to detect software setting colourity threshold value and area threshold, the setting of threshold value is set according to the place of production and the kind of tested rice sample by the operator, and its range of choice is a colourity threshold value 15～30; Area threshold 5%～40%;
F) with chromatic value b
*Be the basis,, judge whether every rice is that the concrete resolution principle of yellow rice kernel is: chromatic value b in every rice in conjunction with colourity threshold value and area threshold
*Pixel greater than the colourity threshold value surpasses the value that area threshold is stipulated, then this rice is a yellow rice kernel.And, add up yellow rice kernel grain rate simultaneously with the draw profile of this grain of rice of yellow line;
G) testing result is utilized human-computer interaction interface output, if need to understand the average chrominance information of every rice, or each pixel chrominance information in every rice, can input among the EXCEL by the menu item that detects the interface and check;
H) on computing machine 2, the gained result is outputed to printer 3, print.
Above-described embodiment is a more preferably embodiment of the present invention, and those skilled in the art can make various modifications within the scope of the appended claims.
Claims (10)
1. the automatic identifying method of yellow rice kernel in the rice is characterized in that comprising the following steps:
Obtain grain of rice image information;
Analyze grain of rice chrominance information;
The identification yellow rice kernel.
2. according to the automatic identifying method of yellow rice kernel in the described rice of claim 1, it is characterized in that the described grain of rice image information of obtaining specifically comprises the following steps:
The rice sample is placed image acquisition device, gather original image information;
Read original image information, and background colour is set to and other color of grain of rice color phase region, cuts apart the background and the grain of rice.
3. according to the automatic identifying method of yellow rice kernel in the described rice of claim 2, it is characterized in that described rice sample quantity is the 10-1000 grain.
4. according to the automatic identifying method of yellow rice kernel in the described rice of claim 2, it is characterized in that the described method of cutting apart the background and the grain of rice is a process of iteration.
5. according to the automatic identifying method of yellow rice kernel in the described rice of claim 4, it is characterized in that described process of iteration specifically comprises the following steps:
Obtain the minimum and maximum gray-scale value Z in the image
1And Z
k, make threshold value initial value T
k=(Z
1+ Z
k)/2;
According to threshold value T
kImage segmentation is become target and background two parts, obtain two-part average gray value Z
0, Z
B
Obtain new threshold value T
K+1=(Z
0+ Z
B)/2;
If T
k=T
K+1, then gained is threshold value, otherwise according to the T that calculates
kValue continues calculated threshold, iterative computation.
6. yellow rice kernel according to claim 2 is the method for identification automatically, it is characterized in that, described when cutting apart background and the grain of rice, background color selection ater is RGB (0,0,0).
7. according to the automatic identifying method of yellow rice kernel in the described rice of claim 1, it is characterized in that the concrete steps of described analysis grain of rice chrominance information comprise:
Original RGB chrominance information is changed into L
*a
*b
*Chrominance information, wherein L
*Be lightness index, span is 0～100, a
*Be the chromaticity index, to shiny red, span is-120～120 to the expression color, b by bottle green
*Be the chromaticity index, to coke yellow, span is-120～120 to the expression color by sapphirine;
Analyze the L of each pixel in every rice
*a
*b
*Chrominance information is selected L
*a
*b
*B in the chrominance information
*As the feature chromatic value of analyzing yellow rice kernel.
8. according to the automatic identifying method of yellow rice kernel in the described rice of claim 7, it is characterized in that the described L that utilizes
*a
*b
*Feature chromatic value b in the color model
*Judge, pass through desired color model XYZ to L by the RGB color model
*a
*b
*The concrete conversion formula of color model is:
X＝0.4124f(R/255)+0.3576f(G/255)+0.1805f(B/255)
Y＝0.2126f(R/255)+0.7152f(G/255)+0.0722f(B/255)
Z＝0.0193f(R/255)+0.1192f(G/255)+0.9505f(B/255)
Wherein: R, G, B are the original chrominance information value of the grain of rice that is read,
Function
By desirable XYZ color model to L
*a
*b
*The color model conversion formula is:
L
*＝116f(Y/Y
0)-16
a
*＝500[f(X/X
0)-f(Y/Y
0)]
b
*＝200[f(Y/Y
0)-f(Z/Z
0)]
9. according to the automatic identifying method of yellow rice kernel in the described rice of claim 1, it is characterized in that the concrete steps of described identification yellow rice kernel comprise:
Utilize yellow rice kernel to detect colourity threshold value and area threshold that software set is judged yellow rice kernel;
With L
*a
*b
*B in the color model
*Be the basis,, judge whether every rice is yellow rice kernel, is drawn in its border, statistics yellow rice kernel rate in conjunction with colourity threshold value and area threshold.
10. according to the automatic identifying method of yellow rice kernel in the described rice of claim 9, it is characterized in that describedly judging when whether rice is yellow rice kernel, the colourity threshold value is provided with according to different rice varieties and grown place by the operator voluntarily with area threshold, and its range of choice is a colourity threshold value 15～30; Area threshold 5%～40%.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CNA2008101120842A CN101271522A (en) | 2008-05-21 | 2008-05-21 | Automatic recognition method for yellow-colored rice in rice |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CNA2008101120842A CN101271522A (en) | 2008-05-21 | 2008-05-21 | Automatic recognition method for yellow-colored rice in rice |
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| Publication Number | Publication Date |
|---|---|
| CN101271522A true CN101271522A (en) | 2008-09-24 |
Family
ID=40005484
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|---|---|---|---|
| CNA2008101120842A Pending CN101271522A (en) | 2008-05-21 | 2008-05-21 | Automatic recognition method for yellow-colored rice in rice |
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|---|---|---|---|---|
| CN102564993A (en) * | 2011-12-31 | 2012-07-11 | 江南大学 | Method for identifying rice varieties by using Fourier transform infrared spectrum and application of method |
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| CN103649993A (en) * | 2011-06-29 | 2014-03-19 | 宝洁公司 | System and method for inspecting components of hygienic articles |
| CN106881171A (en) * | 2017-01-16 | 2017-06-23 | 国粮武汉科学研究设计院有限公司 | A kind of method of the coproduction processing of rice with remained germ and many grade rice |
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| CN110610183A (en) * | 2018-06-15 | 2019-12-24 | 佛山市顺德区美的电热电器制造有限公司 | Grain evaluation method, grain evaluation device, and storage medium |
| CN111435428A (en) * | 2019-01-14 | 2020-07-21 | 珠海格力电器股份有限公司 | Rice identification method and device based on chromaticity detection |
| CN111507148A (en) * | 2019-12-31 | 2020-08-07 | 浙江苏泊尔家电制造有限公司 | Control system and control method of rice storage device |
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| CN103649993A (en) * | 2011-06-29 | 2014-03-19 | 宝洁公司 | System and method for inspecting components of hygienic articles |
| CN103649993B (en) * | 2011-06-29 | 2016-05-11 | 宝洁公司 | For the system and method for the article assembly that inspects the sanitary conditions |
| CN102564993A (en) * | 2011-12-31 | 2012-07-11 | 江南大学 | Method for identifying rice varieties by using Fourier transform infrared spectrum and application of method |
| CN102564993B (en) * | 2011-12-31 | 2015-07-15 | 江南大学 | Method for identifying rice varieties by using Fourier transform infrared spectrum and application of method |
| CN108885178A (en) * | 2016-02-22 | 2018-11-23 | 株式会社佐竹 | Shot-like particle appearance condition discriminating gear |
| CN108885178B (en) * | 2016-02-22 | 2021-12-10 | 株式会社佐竹 | Granular object appearance phase discriminating device |
| CN106881171A (en) * | 2017-01-16 | 2017-06-23 | 国粮武汉科学研究设计院有限公司 | A kind of method of the coproduction processing of rice with remained germ and many grade rice |
| CN108742663A (en) * | 2018-04-03 | 2018-11-06 | 深圳蓝韵医学影像有限公司 | Exposure dose evaluation method, device and computer readable storage medium |
| CN110610183A (en) * | 2018-06-15 | 2019-12-24 | 佛山市顺德区美的电热电器制造有限公司 | Grain evaluation method, grain evaluation device, and storage medium |
| CN111435428A (en) * | 2019-01-14 | 2020-07-21 | 珠海格力电器股份有限公司 | Rice identification method and device based on chromaticity detection |
| CN111435428B (en) * | 2019-01-14 | 2023-10-31 | 珠海格力电器股份有限公司 | Rice identification method and device based on chromaticity detection |
| CN110009609B (en) * | 2019-03-26 | 2021-03-30 | 江南大学 | Method for rapidly detecting yellow rice |
| CN110009609A (en) * | 2019-03-26 | 2019-07-12 | 江南大学 | A kind of method of quick detection yellow rice kernel |
| CN111767772A (en) * | 2019-11-12 | 2020-10-13 | 方勤 | Customized sheep body purchasing platform, method and storage medium |
| CN111507148A (en) * | 2019-12-31 | 2020-08-07 | 浙江苏泊尔家电制造有限公司 | Control system and control method of rice storage device |
| CN111507148B (en) * | 2019-12-31 | 2023-10-24 | 浙江苏泊尔家电制造有限公司 | Control system and control method of rice storage device |
| CN111814808A (en) * | 2020-07-17 | 2020-10-23 | 安徽萤瞳科技有限公司 | Method for identifying yellowing materials for rice color sorting |
| CN112070741A (en) * | 2020-09-07 | 2020-12-11 | 浙江师范大学 | Rice whiteness degree detection system based on image saliency region extraction method |
| CN112070741B (en) * | 2020-09-07 | 2024-02-23 | 浙江师范大学 | Rice chalkiness degree detecting system based on image salient region extracting method |
| CN113458013A (en) * | 2021-05-13 | 2021-10-01 | 深圳市蓝际工业技术有限公司 | Construction waste sorting system and method based on CCD detection and belt feeding |
| CN116503402A (en) * | 2023-06-26 | 2023-07-28 | 中储粮成都储藏研究院有限公司 | Method and device for detecting impurity content of grain shoulder |
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