CN102288606B - Pollen viability measuring method based on machine vision - Google Patents
Pollen viability measuring method based on machine vision Download PDFInfo
- Publication number
- 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
- Authority
- CN
- China
- Prior art keywords
- pollen
- image
- color
- viability
- grain
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
- 238000000034 method Methods 0.000 title claims abstract description 52
- 230000035899 viability Effects 0.000 title claims abstract description 19
- 238000005259 measurement Methods 0.000 claims abstract description 7
- 238000003708 edge detection Methods 0.000 claims abstract description 6
- 239000000284 extract Substances 0.000 claims description 9
- 238000005516 engineering process Methods 0.000 claims description 5
- 238000003556 assay Methods 0.000 claims description 4
- 230000035784 germination Effects 0.000 claims description 4
- 239000006002 Pepper Substances 0.000 claims description 3
- 238000001514 detection method Methods 0.000 claims description 3
- 230000024100 pollen adhesion Effects 0.000 claims description 3
- 238000003709 image segmentation Methods 0.000 claims description 2
- 230000000877 morphologic effect Effects 0.000 claims description 2
- 238000003026 viability measurement method Methods 0.000 claims 4
- 235000002566 Capsicum Nutrition 0.000 claims 1
- 235000016761 Piper aduncum Nutrition 0.000 claims 1
- 235000017804 Piper guineense Nutrition 0.000 claims 1
- 244000203593 Piper nigrum Species 0.000 claims 1
- 235000008184 Piper nigrum Nutrition 0.000 claims 1
- 238000005260 corrosion Methods 0.000 claims 1
- 230000007797 corrosion Effects 0.000 claims 1
- 238000003672 processing method Methods 0.000 claims 1
- 150000003839 salts Chemical class 0.000 claims 1
- 238000010186 staining Methods 0.000 claims 1
- 231100000747 viability assay Toxicity 0.000 claims 1
- 230000011218 segmentation Effects 0.000 abstract description 7
- 238000012545 processing Methods 0.000 abstract description 6
- 238000000605 extraction Methods 0.000 abstract description 4
- 238000007405 data analysis Methods 0.000 abstract description 2
- 230000000694 effects Effects 0.000 description 28
- 238000005034 decoration Methods 0.000 description 7
- 230000008569 process Effects 0.000 description 6
- 241000196324 Embryophyta Species 0.000 description 4
- 230000003628 erosive effect Effects 0.000 description 4
- 238000012214 genetic breeding Methods 0.000 description 4
- 238000010998 test method Methods 0.000 description 4
- 238000011156 evaluation Methods 0.000 description 3
- 230000032683 aging Effects 0.000 description 2
- 238000005520 cutting process Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 239000000975 dye Substances 0.000 description 2
- 238000004043 dyeing Methods 0.000 description 2
- 238000001914 filtration Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 239000002245 particle Substances 0.000 description 2
- 238000000159 protein binding assay Methods 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 238000004260 weight control Methods 0.000 description 2
- 238000009395 breeding Methods 0.000 description 1
- 230000001488 breeding effect Effects 0.000 description 1
- 238000012136 culture method Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000004069 differentiation Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 239000007850 fluorescent dye Substances 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000007198 pollen germination Effects 0.000 description 1
- 238000004321 preservation Methods 0.000 description 1
- 108090000623 proteins and genes Proteins 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
- 238000000638 solvent extraction Methods 0.000 description 1
Images
Landscapes
- Image Analysis (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
- Investigating Or Analysing Biological Materials (AREA)
Abstract
本发明涉及一种基于机器视觉的花粉活力测定方法，首先进行花粉图像采集，确定出灰度图像的阈值分割点和单粒花粉的均值面积及均值半径大小；其次对预处理图像进行单阈值分割，得到完全分离的单粒花粉；再采用小波极值边缘检测法对每一粒花粉进行色彩参数提取，统计单粒花粉边缘轮廓内的RGB、HIS和Lab三套色彩体系特征均值，得到每粒花粉的活力色彩信息；最后通过对提取出的花粉色彩特征信息进行数据分析，得到花粉活力的测定参数，进行花粉活力的评定。通过该方法，能够提高图像采集、图像处理、色彩特征提取的速度和精度，可以更好地实现花粉色彩特征测定和活力测定。
The invention relates to a method for measuring pollen vigor based on machine vision. Firstly, the pollen image is collected to determine the threshold segmentation point of the grayscale image and the average area and average radius of a single grain of pollen; secondly, single threshold segmentation is performed on the preprocessed image. , to obtain completely separated single grain pollen; then use the wavelet extreme value edge detection method to extract the color parameters of each grain of pollen, and count the mean values of RGB, HIS and Lab color system features in the edge contour of single grain pollen, and obtain the color parameters of each grain of pollen. The pollen vigor and color information; finally, through the data analysis of the extracted pollen color feature information, the measurement parameters of the pollen viability are obtained, and the pollen viability is evaluated. Through this method, the speed and accuracy of image acquisition, image processing, and color feature extraction can be improved, and the pollen color feature measurement and vitality measurement 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)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 201110115873 CN102288606B (en) | 2011-05-06 | 2011-05-06 | Pollen viability measuring method based on machine vision |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 201110115873 CN102288606B (en) | 2011-05-06 | 2011-05-06 | Pollen viability measuring method based on machine vision |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN102288606A CN102288606A (en) | 2011-12-21 |
| CN102288606B true CN102288606B (en) | 2013-04-03 |
Family
ID=45335202
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN 201110115873 Expired - Fee Related CN102288606B (en) | 2011-05-06 | 2011-05-06 | Pollen viability measuring method based on machine vision |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102288606B (en) |
Families Citing this family (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103091322A (en) * | 2011-11-03 | 2013-05-08 | 长江大学 | Method for determining pollen number in rice anther |
| CN103868842A (en) * | 2014-02-11 | 2014-06-18 | 中国农业科学院蜜蜂研究所 | Method for detecting quantity of stigma pollens after pollination of flowering plants |
| 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 |
| WO2018048689A1 (en) * | 2016-09-08 | 2018-03-15 | Wal-Mart Stores, Inc. | Systems and methods for pollinating crops via unmanned vehicles |
| MX2019002644A (en) | 2016-09-08 | 2019-06-17 | Walmart Apollo Llc | Systems and methods for dispensing an insecticide via unmanned vehicles to defend a crop-containing area against pests. |
| 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 |
| CN110742047B (en) * | 2019-10-12 | 2021-12-17 | 西南大学 | Automatic paper changing system based on computer vision effect and insect pest monitoring system |
| 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 |
| CN111833342A (en) * | 2020-07-23 | 2020-10-27 | 江西省养蜂研究所(江西省蜂业技术推广站) | A method for measuring honeybee colony structure and health status based on machine vision |
| CN113688939B (en) * | 2021-09-08 | 2025-06-24 | 华中农业大学 | Pollen activity identification model training method, system and identification method, system |
| CN114345719B (en) * | 2022-01-17 | 2024-06-11 | 青岛齐林智信自控技术有限公司 | Automatic grading device for casing sleeve shrinkage tube |
| 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 |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101126700A (en) * | 2007-09-27 | 2008-02-20 | 中国气象局北京城市气象研究所 | A pollen image data analysis method and system |
| CN101614663A (en) * | 2009-07-30 | 2009-12-30 | 浙江大学 | Identification method of bee pollen species by visible and near-infrared spectroscopy |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6137893A (en) * | 1996-10-07 | 2000-10-24 | Cognex Corporation | Machine vision calibration targets and methods of determining their location and orientation in an image |
| JPH10283472A (en) * | 1997-04-03 | 1998-10-23 | Tec Corp | Face image characteristic point extracting device and personal identification system using the device |
-
2011
- 2011-05-06 CN CN 201110115873 patent/CN102288606B/en not_active Expired - Fee Related
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101126700A (en) * | 2007-09-27 | 2008-02-20 | 中国气象局北京城市气象研究所 | A pollen image data analysis method and system |
| CN101614663A (en) * | 2009-07-30 | 2009-12-30 | 浙江大学 | Identification method of bee pollen species by visible and near-infrared spectroscopy |
Non-Patent Citations (9)
| Title |
|---|
| JP特开平10-283472A 1998.10.23 |
| 基于形状与纹理特征的显微图像识别;张树恒等;《计算机工程与设计》;20110430;第32卷(第4期);1379-1382 * |
| 张树恒等.基于形状与纹理特征的显微图像识别.《计算机工程与设计》.2011,第32卷(第4期),1379-1382. |
| 张馨等.高分辨率棉花异性纤维彩色图像的快速分割方法.《农业机械学报》.2011,第42卷(第1期),159-192. |
| 植物花粉图像识别系统研究——Ⅰ.图象处理及特征提取;赵先贵等;《西北林学院学报》;19990430;第14卷(第4期);31-34 * |
| 水稻花粉颗粒显微图像采集与分析系统研究;辛莉等;《应用光学》;20040131;第25卷(第1期);43-45 * |
| 赵先贵等.植物花粉图像识别系统研究——Ⅰ.图象处理及特征提取.《西北林学院学报》.1999,第14卷(第4期),31-34. |
| 辛莉等.水稻花粉颗粒显微图像采集与分析系统研究.《应用光学》.2004,第25卷(第1期),43-45. |
| 高分辨率棉花异性纤维彩色图像的快速分割方法;张馨等;《农业机械学报》;20110131;第42卷(第1期);159-192 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN102288606A (en) | 2011-12-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102288606B (en) | Pollen viability measuring method based on machine vision | |
| EP2927311B1 (en) | Cell observation device, cell observation method and program thereof | |
| US7796815B2 (en) | Image analysis of biological objects | |
| KR102643990B1 (en) | Method and system for automated microbial colony counts from streaked samples on plated media | |
| CN107771212B (en) | Colony contrast collection | |
| CN103745478B (en) | Machine vision determination method for wheat germination rate | |
| CN104651462A (en) | Method for detecting magnaporthe grisea spore based on microscopic image analysis | |
| Chen et al. | An automated bacterial colony counting and classification system | |
| Capus et al. | Application of high-content image analysis for quantitatively estimating lipid accumulation in oleaginous yeasts with potential for use in biodiesel production | |
| CN108564085A (en) | A kind of method of automatic reading pointer type instrument reading | |
| CN104198325A (en) | Method for measuring ratio of cut stem to cut tobacco based on computer vision | |
| Ates et al. | An image-processing based automated bacteria colony counter | |
| Wei et al. | An in situ probe for on‐line monitoring of cell density and viability on the basis of dark field microscopy in conjunction with image processing and supervised machine learning | |
| Zhang et al. | An automated bacterial colony counting system | |
| JP2025081295A (en) | System and method for monitoring bacterial growth of bacterial colonies and predicting colony biomass | |
| EP0575450A1 (en) | Improvements in, or relating to, biomass and differentiation measurements on microbial organisms | |
| CN105761259A (en) | Wheat leaf stoma density measurement method based on microscopic image | |
| CN109633113B (en) | Water quality monitoring and early warning method and system based on medaka step-by-step behavior model | |
| Oh et al. | Morphological recognition of fungal spore germination by a computer-aided image analysis and its application to antifungal activity evaluation | |
| Belini et al. | Morphometric quantification of a pseudohyphae forming Saccharomyces cerevisiae strain using in situ microscopy and image analysis | |
| CN110675363A (en) | Automatic calculation method of DNA index for cervical cells | |
| EP2909330B1 (en) | Automated yeast budding measurement | |
| Baicu et al. | Viability diagnosis in biotechnological cultures through image processing | |
| Sieracki¹ | Enumeration and sizing of micro-organisms using digital image analysis | |
| CN116030460B (en) | Image feature-based bacterial microscopic image identification method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20130403 Termination date: 20200506 |