CN102288606B - Pollen viability measuring method based on machine vision - Google Patents
Pollen viability measuring method based on machine vision Download PDFInfo
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- CN102288606B CN102288606B CN 201110115873 CN201110115873A CN102288606B CN 102288606 B CN102288606 B CN 102288606B CN 201110115873 CN201110115873 CN 201110115873 CN 201110115873 A CN201110115873 A CN 201110115873A CN 102288606 B CN102288606 B CN 102288606B
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Abstract
The invention relates to a pollen viability measuring method based on a machine vision. The method comprises the following steps of: 1, acquiring a pollen image and determining a threshold value segmentation point of a grayscale image, and an average-value area and average-value radius size of single-grain pollen; 2, carrying out a single threshold value segmentation on a pretreated image to obtain completely-separated single-grain pollen; 3, carrying out a color parameter extraction on each grain of pollen by using an extremum wavelet edge detection method, calculating average values of characteristics of three sets of color systems of RGB (Red, Green, Blue), HIS (Hue, Saturation, Intensity) and Lab within the edge contour of the single-grain pollen, and obtaining viability color information of each grain of pollen; and 4, obtaining measuring parameters of the pollen viability through carrying out a data analysis on extracted pollen color characteristic information, and carrying out a pollen viability evaluation. According to the method provided by the invention, the speeds and precisions of image acquisition, image processing and color characteristic extraction are increased, and the pollen color characteristic and viability measuring can be better realized.
Description
Technical field
The present invention relates to a kind of pollen viability measuring method based on machine vision, belong to the genetic breeding field.
Background technology
Pollen Activity refers to the under normal operation ability of pollen germination, is one of foundation of assessment pollen cell activity.Pollen Activity is measured and relatively can be offered reference for genetic breeding work, and the accuracy of Pollen Activity measurement result is the important channel that guarantees the pollen quality.The vigor of plant pollen is because of the different to some extent differences of plant species, and the Pollen Activity of most plants is except being subjected to gene affects, environmental factor as before and after drawing materials and the factors such as the temperature of the process of preservation, humidity also have a great impact.It is frequent testing index in the plant reproduction biology research, in the breeding work, in the sterile line evaluation that Pollen Activity is measured.Be divided into by its measuring principle: Direct Test method, dyeing identification method, germination test method (medium culture method), Morphological Identification method.Decoration method is simple, rapid, in decoration method, I2-KI decoration method, TTC decoration method, FOR decoration method FCR decoration method (fluorescent dye method), Differentiation, Baker ' s decoration method, p-PDA method etc. is arranged.At present, with the most normal application of TTC method, but when observing, be affected by human factors larger.
Along with the application of computer technology in the quality of agricultural product checkout procedure is increasingly extensive, inquire into and use machine vision and image processing techniques, identify rapidly and accurately the pollen color character, realize robotization and intellectuality that Pollen Activity is measured, reduce beyond doubt the new way of human factor impact, raising determination efficiency.Therefore, to be applied to based on the pollen viability measuring method of machine vision in the actual production process, significant to realizing the technical merit that Pollen Activity detects, be conducive to improve accuracy that Pollen Activity measures and ageing, promote the development of China's genetic breeding work.
Summary of the invention
To be applied to based on the pollen viability measuring method of machine vision in the actual production process, can improve accuracy that Pollen Activity measures and ageing, promote the development of China's genetic breeding work.In order to address the above problem, the invention provides a kind of pollen viability measuring method based on machine vision.
A kind of pollen viability measuring method based on machine vision, concrete steps are as follows:
S1: the method that adopts the high-amplification-factor microscope to combine with the industrial CCD camera is carried out the pollen image acquisition, and 24 RGB images that collect are read in computing machine, obtains original pollen image;
S2: on the basis that gathers original pollen image, adopt grey level histogram method and contour area detection method that image is carried out pre-service, to obtain accurately image segmentation threshold and simple grain pollen average area and average radius; Use the asymmetric erosion operation of morphology to determine the pollen center, obtain simple grain pollen image in conjunction with the average radius;
S3: adopt the single threshold method that the pollen image is carried out initial partitioning, obtain many subimages of every image, adopt the area Comparison Method to detect to subimage, determine the pollen number that subimage comprises;
S4: for subimage pollen number greater than 1 situation, the pollen adhesion phenomenon namely appears, adopt the asymmetric configuration disposal route, subimage is repeatedly corroded, obtain the image distribution center of pollen, take this center as round dot, delimit the pollen effective coverage take simple grain pollen average radius as radius, carry out adhesion pollen and cut apart, obtain each pollen image;
S5: adopt gray level image extreme value Wavelet Edge Detection method to obtain the accurate distributed areas of each pollen, extract the rgb color parameter size in this zone, obtain HIS color parameter and Lab color parameter as the basis take the rgb color parameter;
S6: set up the characteristic parameter that Pollen Activity is measured according to rgb color parameter, HIS color parameter and Lab color parameter, Pollen Activity is measured.
Among the described step S5, by following steps the pollen image is carried out color parameter and extracts:
S5.1: first image is carried out gray processing and process, make image lose color information, be conducive to that it is carried out gray level image and strengthen;
S5.2: considered that salt-pepper noise exists, adopted median filtering technology to make its reduction;
S5.3: carry out the individual element access in the edge sensing range, extract the rgb value size of each pixel, determine HIS and the Lab color information size of each pixel according to the RGB size;
S5.4: RGB, HIS and Lab to each pixel get the average computing, as the color information size of each pollen.
Among the described step S6, by setting up a kind of pollen viability measuring method based on machine vision, realized the automatic assay of Pollen Activity, its concrete steps are:
S6.1: by experiment, set up the relational model of Pollen Activity and rgb color parameter, HIS color parameter, Lab color parameter;
S6.2: according to the relational model that S6.1 sets up, determine the characteristic parameter that Pollen Activity is measured, set up the proper vector that Pollen Activity is measured, draw the weight control method of each component;
S6.3: according to the proper vector that Pollen Activity is measured, Pollen Activity is measured;
S6.4: adopt dye-binding assay and germination test method that the accuracy of measurement based on the pollen viability measuring method of machine vision is evaluated.
Pollen viability measuring method provided by the invention at first carries out the pollen image acquisition, image is carried out grey level histogram to be processed, obtain the intensity profile situation of pollen image, determine average area and the average radius size of Threshold segmentation point and the simple grain pollen of gray level image; Secondly pretreatment image being carried out single threshold cuts apart, judge whether cut apart the subimage that obtains exists adhesion phenomenon, adopt the asymmetric configuration erosion operation to carry out dividing processing, the simple grain pollen that is separated fully for area greater than the adhesion zone of simple grain pollen average area; Again adopt small echo extreme value edge detection method that each pollen is carried out color parameter and extract, RGB, HIS and Lab three in the statistics simple grain pollen edge contour overlap color architectural feature averages, obtain the vigor color information of every pollen; By the pollen color character information that extracts is carried out data analysis, obtain the location parameter of Pollen Activity at last, carry out the evaluation of Pollen Activity.
By the method, can improve speed and the precision of image acquisition, image processing, color character extraction, can realize better pollen color character mensuration and vitality test.By adjusting the parameter of modules, go for the Pollen Activity evaluation under the different condition, reduce owing to extraneous factor changes the evaluated error that causes.
Description of drawings
Fig. 1 is based on the pollen viability measuring method schematic block diagram of machine vision.
Embodiment
Following embodiment is used for explanation the present invention, but is not used for limiting the scope of the invention.
As shown in Figure 1, the present invention comprises 5 aspects altogether: 1. pollen image acquisition; 2. pollen image pre-service; 3. the Target Segmentation of pollen pretreatment image and extraction; 4. the pollen particles color character gathers; 5. the color character of Pollen Activity is measured.
1, pollen image acquisition
With TTC pollen is done dyeing and process, and the pollen slide of making is placed on the high-amplification-factor microscopically uses the industrial CCD camera to carry out image acquisition, 24 RGB images that collect are read in computing machine according to time sequencing and preserve.
2, pollen image pre-service
Mainly be to use software programming to process to the pollen image based on the pollen viability measuring method collection of machine vision, adopt grey level histogram method and contour area detection method that image is carried out pre-service the pollen image after gathering, the number that has the pixel of every kind of gray level in the grey level histogram presentation video, every kind of frequency that gray scale occurs in the reflection image.The grey level histogram operation can be effective to the figure image intensifying, provides the image statistics data of usefulness, is easy to calculate in software.Process by grey level histogram, obtain the intensity profile situation of pollen image, determine average area and the average radius size of Threshold segmentation point and the simple grain pollen of gray level image.
3, the Target Segmentation of pollen pretreatment image and extraction
Pretreatment image is carried out single threshold to be cut apart, obtain cutting apart subimage, use programming to carry out Area Ratio pair to the subimage element after cutting apart, determine Average pollen number and the adhesion situation of every subimage: greater than 1 situation, namely occurred the pollen adhesion phenomenon for subimage pollen number.Adopt the asymmetric configuration disposal route, subimage is repeatedly corroded, use the asymmetric erosion operation of morphology to determine the pollen center, obtain simple grain pollen image in conjunction with the average radius.Thereby use the asymmetric erosion operation of morphology to determine pollen center and the definite pollen scope of average radius, can obtain more accurately simple grain pollen image.Through repeatedly corroding the image distribution center that obtains pollen, take this center as round dot, delimit the pollen effective coverage take simple grain pollen average radius as radius, carry out adhesion pollen and cut apart, obtain each pollen image, thereby realize the Accurate Segmentation of pollen image.
4, the pollen particles color character gathers
Adopt gray level image extreme value Wavelet Edge Detection method first image to be carried out gray processing and process, so that image loses color information, be conducive to that it is carried out gray level image and strengthen.Considered that salt-pepper noise exists, adopted median filtering technology to make its reduction.Individual element conducts interviews in the edge sensing range, extracts the rgb value size of each pixel, determines HIS and the Lab color information size of each pixel according to the RGB size.RGB, HIS and Lab to each pixel get the average computing, as the color information size of each pollen.Extract the rgb color parameter size in this zone, obtain HIS color parameter and Lab color parameter as the basis take the rgb color parameter, RGB, the HIS in the statistics simple grain pollen edge contour and Lab three cover color architectural feature averages.
5, the Pollen Activity color character is measured
Set up the relational model of Pollen Activity and rgb color parameter, HIS color parameter and Lab color parameter, determine the characteristic parameter that Pollen Activity is measured, set up the proper vector that Pollen Activity is measured, provide the weight control method of each component, proper vector according to Pollen Activity mensuration, Pollen Activity is evaluated, adopted at last dye-binding assay and germination test method that the accuracy of measurement based on the pollen viability measuring method of machine vision is evaluated.
The various variations of making in the situation that does not break away from the spirit and scope of the present invention and modification, the technical scheme that all are equal to also belong to category of the present invention.
Claims (1)
1. pollen viability measuring method based on machine vision is characterized in that may further comprise the steps:
S1: the method that adopts the high-amplification-factor microscope to combine with the industrial CCD camera is carried out the pollen image acquisition, and 24 RGB images that collect are read in computing machine, obtains original pollen image;
S2: on the basis that gathers original pollen image, adopt grey level histogram method and contour area detection method that image is carried out pre-service, to obtain accurately image segmentation threshold and simple grain pollen average area and average radius; Use the asymmetric erosion operation of morphology to determine the pollen center, obtain simple grain pollen image in conjunction with the average radius;
S3: adopt the single threshold method that the pollen image is carried out initial partitioning, obtain many subimages of every image, adopt the area Comparison Method to detect to subimage, determine the pollen number that subimage comprises;
S4: for subimage pollen number greater than 1 situation, the pollen adhesion phenomenon namely appears, adopt the asymmetric configuration disposal route, subimage is repeatedly corroded, obtain the image distribution center of pollen, take this center as round dot, delimit the pollen effective coverage take simple grain pollen average radius as radius, carry out adhesion pollen and cut apart, obtain each pollen image;
S5: adopt gray level image extreme value Wavelet Edge Detection method to obtain the accurate distributed areas of each pollen, extract R, G, B color parameter size in this zone, obtain H, I, S color parameter and L, a, b color parameter as the basis take R, G, B color parameter; Specifically by following steps the pollen image being carried out color parameter extracts:
S5.1: first image is carried out gray processing and process, make image lose color information, strengthen gradation of image;
S5.2: adopt median filtering technology to make the salt-pepper noise reduction;
S5.3: carry out the individual element access in the edge sensing range, extract R, G, the B value size of each pixel, determine H, I, S and L, a, the b color information size of each pixel according to R, G, B size;
S5.4: R, G, B, H, I, S and L, a, b to each pixel get the average computing, as the color information size of each pollen;
S6: set up the characteristic parameter that Pollen Activity is measured according to R, G, B, H, I, S and L, a, b color parameter, Pollen Activity is measured; Its concrete determination step is:
S6.1: the relational model of setting up Pollen Activity and R, G, B, H, I, S and L, a, b color parameter;
S6.2: according to the relational model that S6.1 sets up, determine the characteristic parameter that Pollen Activity is measured, set up the proper vector that Pollen Activity is measured, draw the weight control method of each component;
S6.3: according to the proper vector that Pollen Activity is measured, Pollen Activity is measured;
S6.4: adopt dye-binding assay and germination test method that the accuracy of measurement based on the pollen viability measuring method of machine vision is evaluated.
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| CN104197866B (en) * | 2014-07-28 | 2017-01-11 | 山东农业大学 | Method for quantitative determination of cutin to starch percentage of corn kernel cross section |
| CN105403989B (en) * | 2015-10-28 | 2018-03-27 | 清华大学 | Nematode identifying system and nematode recognition methods |
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| WO2018048689A1 (en) * | 2016-09-08 | 2018-03-15 | Wal-Mart Stores, Inc. | Systems and methods for pollinating crops via unmanned vehicles |
| CN108734731B (en) * | 2018-05-29 | 2021-09-28 | 中科院广州电子技术有限公司 | Machine vision-based quasi-circular particle target counting method |
| CN108872096A (en) * | 2018-07-11 | 2018-11-23 | 中国农业科学院蜜蜂研究所 | A kind of multifarious measurement method of honeybee herborization pollen |
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| CN111128310B (en) * | 2019-12-24 | 2022-05-24 | 国家林业和草原局桉树研究开发中心 | Method for identifying eucalyptus tree species and genetic relationship between species |
| CN114345719A (en) * | 2022-01-17 | 2022-04-15 | 青岛齐林智信自控技术有限公司 | Automatic grading device for casing sleeve and shrinkage pipe |
| CN116806485B (en) * | 2023-08-25 | 2023-10-31 | 云南省农业科学院质量标准与检测技术研究所 | Quantitative detection and analysis method for rice seed viability based on CIELAB color space |
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