CN102612892A - Identification method for sprouting conditions of wheat ears - Google Patents
Identification method for sprouting conditions of wheat ears Download PDFInfo
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- CN102612892A CN102612892A CN2012100537008A CN201210053700A CN102612892A CN 102612892 A CN102612892 A CN 102612892A CN 2012100537008 A CN2012100537008 A CN 2012100537008A CN 201210053700 A CN201210053700 A CN 201210053700A CN 102612892 A CN102612892 A CN 102612892A
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Abstract
The invention discloses an identification method for sprouting conditions of wheat ears, which comprises the following steps: S1 collecting hyperspectral images of the wheat ears; S2 performing pretreatment to the hyperspectral images of the wheat ears; S3 synthesizing the hyperspectral images of the wheat ears after the pretreatment to obtain red green blue (RGB) images; S4 analyzing areas of the RGB images which operators are interested in, and obtaining average spectra of the areas which the operators are interested in; S5 judging whether the wheat ears sprout under a characteristic wave band; S6 calculating the proportion of sprouting areas in the whole wheat ear area; S7 extracting spectral reflectivity of the sprouting areas, and judging the sprouting degree of the wheat ears; and S8 calculating the sprouting grade according to the proportion of the sprouting areas in the whole wheat ear area and the spectral reflectivity. The identification method adopts the hyperspectral image technology to perform wheat ear sprouting detection on the wheat ears normally harvested in the fields, and rapid screening of wheat ear sprouting can be achieved.
Description
Technical field
The present invention relates to crop breeding and image recognition technology field, relate in particular to a grow wheat wheat head germination recognition methods.
Background technology
Wheat ear germinating is meant that seed is in the phenomenon of head sprouting when running into rainy weather before the results, and fringe germinates and not only influences output, and has a strong impact on quality (especially processing quality) and plant with value.The general underproduction about 10% of wheatland that visible fringe germinates takes place, and can have no harvest when serious.Wheat ear germinating not only makes the remarkable underproduction of wheat, and its processing, nutritional quality and kind all are affected with being worth, and causes serious economy loss, and therefore, the identification of wheat ear germinating is identified significant for wheat breeding.At some Mai Qu, often run into continuous rainy weather before the harvesting wheat, cause fringe to germinate and take place.In recent years, along with the adjustment of agricultural planting structure, the cultivated area of white skin wheat is enlarging gradually, but since the ear germinating resistance of white skin wheat generally a little less than, so the wheat ear germinating problem is serious day by day.
The authentication method of wheat ear germinating and index have multiple, and existing test differentiates that the technology of wheat ear germinating has: artificial visually examine's method mainly relies on people's experience to judge the state of wheat ear germinating; And method such as alpha-amylase activity mensuration.
Wherein, the artificial visually examine discerns the wheat ear germinating method, receives subjective influence factor bigger in early days, and the recognition result standardization is relatively poor; Adopt that biochemical method is destructive to be detected, but these methods too complicacy or identification of indicator reliability are relatively poor, alpha-amylase activity is measured the process more complicated, efficient is low; The culture dish seed sprouting needs bigger lab space, and will carry out water management and monitor with continuous the observation, lacks technological means harmless, the ability continuous monitoring.The needed authentication method of wheat breeding man must have the characteristics of simple and effective, stresses its applicability.
Summary of the invention
The technical problem that (one) will solve
The technical problem that the present invention will solve is: the recognition methods of a kind of wheat head of wheat fast and effectively germination is provided.
(2) technical scheme
For addressing the above problem, the invention provides a grow wheat wheat head germination recognition methods, may further comprise the steps:
S1: gather wheat head high spectrum image;
S2: said wheat head high spectrum image is carried out preliminary treatment;
S3: with the synthetic RGB image that obtains of pretreated said wheat head high spectrum image;
S4: in said RGB image, select interesting areas to analyze, obtain the averaged spectrum of said area-of-interest;
S5: whether germinate at the characteristic wave bands wheat head that judges;
S6: calculate the ratio that the zone of germinateing accounts for whole wheat head area;
S7: extract the spectral reflectivity in the zone of germinateing, judge the germination degree of the wheat head;
S8:, calculate the germination grade according to said regional ratio and the spectral reflectivity that accounts for whole wheat head area that germinate.
Preferably, the preliminary treatment of said step S2 comprises that the wheat head high spectrum image to gathering splices, and forms the step of the image of BSQ form.
Preferably, the preliminary treatment of said step S2 comprise to the wheat head high spectrum image of gathering proofread and correct, the step of filtering and enhancement process.
Preferably, step S3 specifically comprises: pretreated said wheat head high spectrum image is extracted spectrum dimension characteristic of correspondence wavelength be respectively the spectrum picture of 680nm, 550nm and 450nm and carry out Integral Transformation, syntheticly obtain said RGB image.
Preferably, among the step S4 interesting areas analyzed specifically and comprise: said area-of-interest is amplified, extract the spectrum of each pixel in the said area-of-interest, then calculating mean value.
Preferably, the wave-length coverage of the high spectrum image among the said step S1 is 400-1000nm.
Preferably, the characteristic wave bands described in the step S5 is in 450～900nm spectral wavelength scope.
Preferably, the characteristic wave bands described in the step S5 is the 675nm spectral wavelength.
Preferably; Step S6 specifically comprises: respectively said area-of-interest is carried out Threshold Segmentation; Extract the pixel value in wheatear portion germination zone and the pixel value of the whole wheat head; Calculate respectively the area that calculates the germinate zone and the whole wheat head according to the number of pixel value, obtain the regional gross area that germinates again and account for the percentage of whole wheat head image area, thereby the area that obtains germinateing accounts for the percentage of the whole wheat head.
(3) beneficial effect
Carry out analysis-by-synthesis after the withdrawing spectral information of the high light spectrum image-forming of the present invention through having combined spectral technique and image technique to the morphological feature of wheat ear germinating and wheat ear germinating area-of-interest, thereby realize the fringe germination quick identification of the whole wheat head of wheat.Traditional relatively non-imaging spectral analysis, the present invention can intuitively find out the position of germination; With respect to the machine vision imaging technique, the present invention can judge the germination grade through the reflectivity of spectrum, can judge the wheat ear germinating situation in early days more accurately.
Description of drawings
Fig. 1 is the steps flow chart sketch map according to embodiment of the invention recognition methods.
Fig. 2 is according to the germination of embodiment of the invention recognition methods extraction and the average light spectrogram at the position of not germinateing.
Embodiment
Below in conjunction with accompanying drawing and embodiment the present invention is elaborated as follows.
As shown in Figure 1, present embodiment has been put down in writing a grow wheat wheat head germination recognition methods.
In the present embodiment, laboratory sample is divided into four groups, is respectively: water every day and water three water, every days the dry wheat head that primary water, whole day soaked, do not water in 24 hours, its high spectrum image information is gathered in each 30 strain, observation wheat ear germinating situation.Before doing the high spectrum image experiment, all being dried to through the wheat head sample that waters and the original consistent effect of wheatear, prevent that wheat further germinates in the experimentation, and then gather its high spectrum image, reduce the influence of moisture difference simultaneously to spectrum.
The recognition methods of present embodiment may further comprise the steps:
S1: gather wheat head high spectrum image;
In the present embodiment, adopt the push-broom type hyperspectral imager of 400-1000nm wavelength band, the wheat wheat head is scanned.The wheat head is kept flat, gather wheatear portion positive and negative two sides collection of illustrative plates respectively once, guarantee omnibearing observation the wheat head.
Wherein, the hyperspectral imager that present embodiment adopts is the PIS112 two generations hyperspectral imager of Chinese University of Science and Technology's development, and spectrometer has adopted the CCD of 1400 (space dimension) * 1024 (spectrum dimension) to carry out the array push-scanning image, spectral range: 400～1000nm; Spectral resolution: 2nm, sampling interval 0.7nm; Sample frequency: the 8-30 width of cloth/second; The angle of visual field: 16 °.
S2: said wheat head high spectrum image is carried out preliminary treatment;
In the present embodiment, said preliminary treatment comprise to the wheat head high spectrum image of gathering proofread and correct, the step of filtering and enhancement process, to eliminate the noise that reasons such as owing to non-linear sensitivity, the spectral response of CCD inhomogeneous and quantum efficiency imbalance cause.Said treatment for correcting comprises blank and dark current correction.
The preliminary treatment of said step S2 comprises that the wheat head high spectrum image to gathering splices, and forms the step of the image of BSQ form (wave band order format).Because the initial data of imaging spectrometer collection is the picture of BMP form, at first need be spliced into the image of BSQ form.Processing procedure concrete in the present embodiment is: the view picture image that at first is spliced into the picture of BMP form with Matlab software programming program the BIL form; Extract with IDL programming carrying out image reflectivity then, comprise that the reflectivity based on the experience linear approach extracts, five go on foot the progressively smoothing processing of the method for average, save as the image of BSQ form at last.
S3: with the synthetic RGB image that obtains of pretreated said wheat head high spectrum image;
High spectrum image is one with the traditional images dimension with spectrum dimension information fusion; When obtaining wheatear portion spatial image; Obtain the continuous spectrum information of each fringe portion pixel; The spectrum picture that promptly forms images is an image cube, and it is made up of three parts: spatial image dimension, spectrum dimension, characteristic spectrum dimension.Present embodiment step S3 extracts spectrum dimension characteristic of correspondence wavelength to pretreated said wheat head high spectrum image and is respectively the spectrum picture of 680nm, 550nm and 450nm (R component, G component and the B component with color of image is corresponding respectively for three wavelength) and carries out Integral Transformation, synthesizes to obtain said RGB image.
S4: in said RGB image, select interesting areas to analyze, obtain the averaged spectrum of said area-of-interest;
In the present embodiment; Step 4 is specially: according to synthetic RGB image; Select interesting areas to amplify analysis, extract the spectrum of each pixel in the said area-of-interest, then calculating mean value; Obtain the averaged spectrum of area-of-interest, for follow-up spectral absorption characteristics parameter spectral informations such as (absorbing wavelength position, the absorption degree of depth, absorption width) is excavated lays the foundation.
S5: whether germinate at the characteristic wave bands wheat head that judges;
Described characteristic wave bands is in 450～900nm spectral wavelength scope; Preferably, described characteristic wave bands is the 675nm spectral wavelength.
To the wheat head different parts averaged spectrum of extracting; Draw following characteristics: in 450～900nm wave-length coverage; The spectral reflectivity of germinated wheat fringe is a reflection-absorption paddy at the 675nm place, and the 714nm place is the reflection-absorption peak, and this is similar basically with typical vegetation curve of spectrum variation characteristic; And not the germinated wheat fringe no spectrum absorbs paddy and occurs at the 675nm place, so present embodiment with the characteristic absorption of 675nm as judging wheat ear germinating whether foundation.As shown in Figure 2, present embodiment can tentatively judge at the single image at 675nm place whether identification wheatear portion germinates through extracting wheatear portion spectrum picture.
S6: calculate the ratio that the zone of germinateing accounts for whole wheat head area;
Step S6 specifically comprises: respectively said area-of-interest is carried out Threshold Segmentation, the algorithm of use mainly contains: inverse, maximum variance between clusters binaryzation, medium filtering; Extract the pixel value in wheatear portion germination zone and the pixel value of the whole wheat head; Calculate the area that calculates the germinate zone and the whole wheat head respectively according to the number of pixel value; The gross area of obtaining the zone of germinateing again accounts for the percentage of whole wheat head image area, thereby the area that obtains germinateing accounts for the percentage of the whole wheat head.
S7: extract the spectral reflectivity in the zone of germinateing, judge the germination degree of the wheat head;
Part contains the chlorophyll composition information owing to germinate; The curve map trend that its spectral curve appears is similar basically with green crop chlorophyll spectral reflectivity curve map; Have following characteristic: the chlorophyll characteristic wave bands of wheat ear germinating part is in the 675nm position; Have tangible absorption trough, judge the light and heavy degree of identification wheat ear germinating according to the big I of this wave band place spectral reflectivity.It is serious to germinate, and chlorophyll content is high, and the corresponding spectral reflectivity that therefore extracts can be higher.
S8:, judge the light and heavy degree grade of identification wheat ear germinating, with the situation of this multifactorial evaluation wheat wheat head germination according to said regional ratio and the spectral reflectivity that accounts for whole wheat head area that germinate.The grade that fringe germinates comprises nothing germination, slight germination, severe germination Three Estate.
The present invention extracts spectral information, handles through image and can carry out the monitoring of wheat ear germinating through gathering unknown wheat ear germinating sample high spectrum image.The present invention can adopt the high light spectrum image-forming technology to carry out the fringe germination and detect to the wheat head of field normal harvest, can realize the rapid screening that fringe germinates.
Above embodiment only is used to explain the present invention; And be not limitation of the present invention; The those of ordinary skill in relevant technologies field under the situation that does not break away from the spirit and scope of the present invention, can also be made various variations and modification; Therefore all technical schemes that are equal to also belong to category of the present invention, and scope of patent protection of the present invention should be defined by the claims.
Claims (9)
1. a grow wheat wheat head germination recognition methods is characterized in that, may further comprise the steps:
S1: gather wheat head high spectrum image;
S2: said wheat head high spectrum image is carried out preliminary treatment;
S3: with the synthetic RGB image that obtains of pretreated said wheat head high spectrum image;
S4: in said RGB image, select interesting areas to analyze, obtain the averaged spectrum of said area-of-interest;
S5: whether germinate at the characteristic wave bands wheat head that judges;
S6: calculate the ratio that the zone of germinateing accounts for whole wheat head area;
S7: extract the spectral reflectivity in the zone of germinateing, judge the germination degree of the wheat head;
S8:, calculate the germination grade according to said regional ratio and the spectral reflectivity that accounts for whole wheat head area that germinate.
2. wheat wheat head germination as claimed in claim 1 recognition methods is characterized in that, the preliminary treatment of said step S2 comprises that the wheat head high spectrum image to gathering splices, and forms the step of the image of BSQ form.
3. wheat wheat head germination as claimed in claim 1 recognition methods is characterized in that, the preliminary treatment of said step S2 comprise to the wheat head high spectrum image of gathering proofread and correct, the step of filtering and enhancement process.
4. wheat wheat head germination as claimed in claim 1 recognition methods; It is characterized in that; Step S3 specifically comprises: pretreated said wheat head high spectrum image is extracted spectrum dimension characteristic of correspondence wavelength be respectively the spectrum picture of 680nm, 550nm and 450nm and carry out Integral Transformation, syntheticly obtain said RGB image.
5. wheat wheat head germination as claimed in claim 1 recognition methods; It is characterized in that; Among the step S4 interesting areas analyzed specifically and comprise: said area-of-interest is amplified, extract the spectrum of each pixel in the said area-of-interest, then calculating mean value.
6. wheat wheat head germination as claimed in claim 1 recognition methods is characterized in that the wave-length coverage of the high spectrum image among the said step S1 is 400-1000nm.
7. wheat wheat head germination as claimed in claim 1 recognition methods is characterized in that the characteristic wave bands described in the step S5 is in 450～900nm spectral wavelength scope.
8. wheat wheat head germination as claimed in claim 7 recognition methods is characterized in that the characteristic wave bands described in the step S5 is the 675nm spectral wavelength.
9. wheat wheat head germination as claimed in claim 1 recognition methods; It is characterized in that; Step S6 specifically comprises: respectively said area-of-interest is carried out Threshold Segmentation, extract the pixel value in wheatear portion germination zone and the pixel value of the whole wheat head, calculate the area that calculates the germinate zone and the whole wheat head respectively according to the number of pixel value; The gross area of obtaining the zone of germinateing again accounts for the percentage of whole wheat head image area, thereby the area that obtains germinateing accounts for the percentage of the whole wheat head.
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102855485A (en) * | 2012-08-07 | 2013-01-02 | 华中科技大学 | Automatic wheat earing detection method |
| CN102948282A (en) * | 2012-10-31 | 2013-03-06 | 北京农业信息技术研究中心 | Wheatear germination degree detection method |
| CN104849287A (en) * | 2015-06-10 | 2015-08-19 | 国家电网公司 | Composite insulator contamination degree non-contact detection method |
| CN105300895A (en) * | 2015-11-05 | 2016-02-03 | 浙江大学 | Method for performing early warning against potato germination defects by utilizing feature point tangent included angles |
| CN105424623A (en) * | 2015-11-05 | 2016-03-23 | 浙江大学 | Method for utilizing feature triangle height for early warning germination of potatoes |
| CN105424622A (en) * | 2015-11-05 | 2016-03-23 | 浙江大学 | Method for utilizing feature triangle area for early warning germination of potatoes |
| CN109164014A (en) * | 2018-06-28 | 2019-01-08 | 浙江理工大学 | A kind of multicolour cloth wetting zones recognition methods based on Hyperspectral imagery processing |
| CN109843034A (en) * | 2016-10-19 | 2019-06-04 | 巴斯夫农化商标有限公司 | Production forecast for wheatland |
| CN110132862A (en) * | 2019-05-30 | 2019-08-16 | 安徽大学 | Wheat scab detects exclusive disease index construction method and its application |
| CN111344103A (en) * | 2018-10-24 | 2020-06-26 | 合刃科技（深圳）有限公司 | Coating area positioning method and device based on hyperspectral optical sensor and glue removing system |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1900695A (en) * | 2005-07-21 | 2007-01-24 | 李少昆 | Field quick monitoring method for wheat nitrogen content and seed protein quality based on high light spectrum |
-
2012
- 2012-03-02 CN CN201210053700.8A patent/CN102612892B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1900695A (en) * | 2005-07-21 | 2007-01-24 | 李少昆 | Field quick monitoring method for wheat nitrogen content and seed protein quality based on high light spectrum |
Non-Patent Citations (2)
| Title |
|---|
| 张新华等: "植物的超微弱发光", 《山东农业大学学报（自然科学版）》, vol. 34, no. 4, 31 December 2003 (2003-12-31), pages 605 - 608 * |
| 王纪华等: "光谱反射率在小麦品质监测上的应用研究", 《成像光谱技术与应用研讨会论文集》, 1 July 2002 (2002-07-01), pages 72 - 76 * |
Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102855485A (en) * | 2012-08-07 | 2013-01-02 | 华中科技大学 | Automatic wheat earing detection method |
| CN102855485B (en) * | 2012-08-07 | 2015-10-28 | 华中科技大学 | The automatic testing method of one grow wheat heading |
| CN102948282A (en) * | 2012-10-31 | 2013-03-06 | 北京农业信息技术研究中心 | Wheatear germination degree detection method |
| CN104849287A (en) * | 2015-06-10 | 2015-08-19 | 国家电网公司 | Composite insulator contamination degree non-contact detection method |
| CN105300895B (en) * | 2015-11-05 | 2017-12-26 | 浙江大学 | A kind of method using characteristic point tangent line angle early warning potato sprouting defect |
| CN105424623A (en) * | 2015-11-05 | 2016-03-23 | 浙江大学 | Method for utilizing feature triangle height for early warning germination of potatoes |
| CN105424622A (en) * | 2015-11-05 | 2016-03-23 | 浙江大学 | Method for utilizing feature triangle area for early warning germination of potatoes |
| CN105424623B (en) * | 2015-11-05 | 2017-12-12 | 浙江大学 | A kind of method using feature triangle height early warning potato sprouting |
| CN105300895A (en) * | 2015-11-05 | 2016-02-03 | 浙江大学 | Method for performing early warning against potato germination defects by utilizing feature point tangent included angles |
| CN105424622B (en) * | 2015-11-05 | 2018-01-30 | 浙江大学 | A kind of method using feature triangle area early warning potato sprouting |
| CN109843034A (en) * | 2016-10-19 | 2019-06-04 | 巴斯夫农化商标有限公司 | Production forecast for wheatland |
| CN109843034B (en) * | 2016-10-19 | 2022-08-23 | 巴斯夫农化商标有限公司 | Yield prediction for grain fields |
| CN109164014A (en) * | 2018-06-28 | 2019-01-08 | 浙江理工大学 | A kind of multicolour cloth wetting zones recognition methods based on Hyperspectral imagery processing |
| CN109164014B (en) * | 2018-06-28 | 2020-11-06 | 浙江理工大学 | Hyperspectral image processing-based method for identifying wetting area of multicolor fabric |
| CN111344103A (en) * | 2018-10-24 | 2020-06-26 | 合刃科技（深圳）有限公司 | Coating area positioning method and device based on hyperspectral optical sensor and glue removing system |
| CN111344103B (en) * | 2018-10-24 | 2022-03-25 | 合刃科技（深圳）有限公司 | Coating area positioning method and device based on hyperspectral optical sensor and glue removing system |
| CN110132862A (en) * | 2019-05-30 | 2019-08-16 | 安徽大学 | Wheat scab detects exclusive disease index construction method and its application |
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