CN109754127A - Rice grain amylose content estimating and measuring method based on unmanned plane imaging EO-1 hyperion - Google Patents
Rice grain amylose content estimating and measuring method based on unmanned plane imaging EO-1 hyperion Download PDFInfo
- Publication number
- CN109754127A CN109754127A CN201910098197.XA CN201910098197A CN109754127A CN 109754127 A CN109754127 A CN 109754127A CN 201910098197 A CN201910098197 A CN 201910098197A CN 109754127 A CN109754127 A CN 109754127A
- Authority
- CN
- China
- Prior art keywords
- rice grain
- amylose content
- imaging
- rice
- hyperion
- 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.)
- Pending
Links
Landscapes
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
A kind of rice grain amylose content estimating and measuring method based on unmanned plane imaging EO-1 hyperion, the described method comprises the following steps: 1) Imaging Hyperspectral Data converts；2) the imaging EO-1 hyperion spectral variables for rice quality estimation determine；3) the rice grain amylose content estimation models building based on more breeding times；4) rice grain amylose content estimates precision test.The process employs unmanned aerial vehicle platform imaging datas, overcome ground and satellite data source there are the problem of, consider comprehensive function of more breeding time information to prediction rice grain amylose content, introduce boot stage, maturity period, pustulation period, maturity period data improve single developmental model, for the monitoring and improvement of rice grain amylose content provide the new idea and method of one kind.
Description
Technical field
The present invention is constructed a kind of based on more using the more breeding time reflectivity informations of rice that unmanned aerial vehicle remote sensing technology obtains
The rice grain amylose estimating and measuring method of breeding time unmanned aerial vehicle remote sensing data is realized to the extensive of amylose in rice quality
Prediction.
Background technique
Rice is one of world's Three major grain crops, and there is 60% or more population in China using it as staple food, thus, rice is made
The production and breeding of object have particularly important meaning to China's grain security.Within the quite a long time, to solve temperature
High-Yielding is paid attention in full problem, the Rice Production work and research in China unilaterally always, and with the development of social economy and
The raising of living standards of the people, people also have higher requirement the quality of rice.The quality of rice directly affects it
Commodity value and plantation are promoted, in the big epoch of market economy, to peasant household's production imcome, people's food health, National agricultural
There is particularly important meaning in the fields such as policy making.
Rice grain amylose content is one of the important indicator for evaluating rice quality.Usually, amylose contains
Amount is high, and the grain of rice is more elongated, and toughness mouthfeel is lower, and elasticity is also low；Conversely, rice viscosity, toughness mouthfeel and bullet after it is cooked
Property all can be relatively high, good in taste.
Traditional quality determination is to send sample to laboratory again after the acquisition of field to carry out chemical analysis, and the method is not only
Sample can be damaged, and be taken time and effort, sample is from collecting in continuous mode and to have by many programs, result
Hysteresis quality can not react crop quality situation quickly, in real time.After there is portable bloom spectrometer, numerous studies are by spectrum
Instrument is collected processing to crop canopies reflectivity information, with the qualities such as this inverting crop water content, protein, content of starch ginseng
Number proposes a variety of methods and model, but the information collection of portable bloom spectrometer still relies on manpower, information collection time-consuming consumption
Power, and its result by Samples selecting, the sampling time, light conditions and other people be affected for operation factors, can not yet
The spectral information of large area field is obtained simultaneously, and there are certain defects.Meanwhile also there is research to turn one's attention to satellite image number
According to, done certain research as data source, but since Rice Cropping region concentrates on In Middle And Lower Reaches of Changjiang River, this area exist
Cloudy rainy day gas in the Rice Cropping time is difficult to obtain complete effective time of infertility data, and due to satellite revisiting period
Limitation, the data information of acquisition is mostly the average result in a certain period, directly can not definitely react the instant growing state of rice.
On the other hand, existing research mostly uses single breeding time data to carry out quality prediction, with maturity period canopy spectra number
According in the majority with seed indoor spectral, but according to existing research, the accumulation of rice fecula particle is one and overflows long and complex mistake
Journey, individual maturity period data can not completely state this comprehensive function.It researchs and proposes based on rice ear sprouting period canopy
The protein prediction model of establishment of spectrum is better than its maturity period model, it is seen that the breeding time of rice quality prediction data source selects to need
It more to explore.
Summary of the invention
In order to overcome existing ground high spectrometer and satellite image data source to estimate the defect and Dan Sheng of amylose in rice
Issue is educated according to bring loss of learning, and the invention proposes a kind of rice grain amyloses based on unmanned plane imaging EO-1 hyperion
Content estimating and measuring method obtains the image data of large area field with emerging unmanned aerial vehicle platform data source fast and flexible, without
It is influenced by manpower and cloud layer situation；Single breeding time information is overcome in the model prediction method that more breeding time spectral variables are established
Limitation, improve the precision of model and the accuracy of prediction technique.
The technical solution adopted by the present invention to solve the technical problems is:
It is a kind of based on unmanned plane imaging EO-1 hyperion rice grain amylose content estimating and measuring method, the method includes with
Lower step:
1) Imaging Hyperspectral Data converts
It, will using the method for exhaustion for the Imaging Hyperspectral Data that Rise's boot period, heading stage, pustulation period, maturity period obtain
All wave bands carry out combination of two and calculate separately NDVI, RVI, DVI and EVI2 vegetation index, and the calculation formula of each vegetation index is such as
Under:
DVIi,j=Ri-Rj (2)
EVI2I, j=2.5 (NIRi-REDj)/(NIRi+2.4REDj+1) (4)
In formula, NIRiSpectral reflectivity corresponding to the wave band for being i for a certain wavelength of near infrared band range, REDjIt is red
The a certain wavelength of light wave segment limit is spectral reflectivity corresponding to the wave band of j, Ri、RjIt is right for wave band that wavelength is respectively i and j
The spectral reflectivity answered；
2) the imaging EO-1 hyperion variable for rice quality estimation determines
By Imaging Hyperspectral Data convert obtained all band combinations vegetation index and actual measurement rice grain it is straight
Chain content of starch carries out correlation analysis by formula (5)；
In formula, ρXYFor the related coefficient of variable X and Y；Cov (X, Y) is X, and the covariance of Y, D (X), D (Y) are respectively X, Y
Variance；
According to the related coefficient that the above correlation analysis obtains, the highest spectral variables value of each breeding time related coefficient is selected
Spare spectral variables as the estimation of rice grain amylose content；
3) the rice grain amylose content estimation models building based on more breeding times
Using obtained imaging of more breeding times EO-1 hyperion variable selected above as independent variable, rice grain amylose contains
Amount is used as dependent variable, and it is as follows to establish quality estimation models:
Y=f (SVBoot stage, SVHeading stage, SVPustulation period, SVMaturity period) (6)
In formula, Y is rice grain amylose content, SVBoot stage, SVHeading stage, SVPustulation period, SVMaturity periodTo screen obtained rice
The imaging EO-1 hyperion variable that boot stage, heading stage, pustulation period, maturity period correlation are put up the best performance, f () are indicated for modeling letter
Number；
4) rice grain amylose content estimates precision test
By the SV of target fieldBoot stage, SVHeading stage, SVPustulation period, SVMaturity periodIt is brought into model (7) and obtains estimation rice grain YEstimation,
The yield by estimation precision test is carried out using formula (7) (8),
In formula, RMSE is root-mean-square error, and RE is relative error, YEstimationThe rice seed estimated for target field by model
Grain amylose content, YActual measurementFor the rice rice grain amylose content of target field actual measurement, N is field quantity, i
For field serial number.
Further, in the step 3), the statistical model for being fitted modeling is multivariate linear model.
Beneficial effects of the present invention are mainly manifested in: with the flexible convenient and fast advantage quick obtaining some scale of unmanned aerial vehicle platform
Imaging Hyperspectral Data, overcome the acquisition of ground canopy spectra and the shortcomings that satellite data, obtained it is more accurate completely
Time of infertility data, and comprehensive function of the distant more breeding time information to prediction rice grain amylose content is considered, it introduces
Boot stage, maturity period, pustulation period, maturity period data improve single developmental model, and accuracy is higher.
Detailed description of the invention
Fig. 1 is actual measurement amylose content and prediction amylose content comparison diagram.
Fig. 2 is that actual measurement amylose content and leaving-one method predict amylose content comparison diagram.
Fig. 3 is the flow chart of the rice grain amylose content estimating and measuring method based on unmanned plane imaging EO-1 hyperion.
Specific embodiment
The invention will be further described below in conjunction with the accompanying drawings.
Referring to Fig.1~Fig. 3, a kind of rice grain amylose content estimating and measuring method based on unmanned plane imaging EO-1 hyperion,
The following steps are included:
1) Imaging Hyperspectral Data converts
It, will using the method for exhaustion for the Imaging Hyperspectral Data that Rise's boot period, heading stage, pustulation period, maturity period obtain
All wave bands carry out combination of two and calculate separately NDVI, RVI, DVI and EVI2 vegetation index, and the calculation formula of each vegetation index is such as
Under:
DVIi,j=Ri-Rj (2)
EVI2i,j=2.5 (NIRi-REDj)/(NIRi+2.4REDj+1) (4)
In formula, NIRiSpectral reflectivity corresponding to the wave band for being i for a certain wavelength of near infrared band range, REDjIt is red
The a certain wavelength of light wave segment limit is spectral reflectivity corresponding to the wave band of j, Ri、RjIt is right for wave band that wavelength is respectively i and j
The spectral reflectivity answered；
Imaging Hyperspectral Data is tested with Deqing rice Growing state survey in 2017, it is pregnant by formula (1)~(5) calculate rice
Ear period, heading stage, the pustulation period, maturity period all band combinations NDVI, DVI, RVI, EVI2；
2) the imaging EO-1 hyperion spectral variables for rice quality estimation determine
By Imaging Hyperspectral Data convert obtained all band combinations vegetation index and actual measurement rice grain it is straight
Chain content of starch carries out correlation analysis by formula (5)；
In formula, ρXYFor the related coefficient of variable X and Y；Cov (X, Y) is X, and the covariance of Y, D (X), D (Y) are respectively X, Y
Variance；
According to the related coefficient that the above correlation analysis obtains, the highest spectral variables value of each breeding time related coefficient is selected
Spare spectral variables as the estimation of rice grain amylose content；
The Grain Amylose content that the spectral variables that transformation obtains in step 1) are measured with laboratory is done into correlation point
Analysis, screens each highest spectral variables of breeding time correlation, obtains each breeding time optimal spectrum variable and its best band group
It is combined into the RVI in boot stage[632,528], heading stage DVI[800,856], the pustulation period DVI[864,832]With maturity period RVI[588,685]；
3) the rice grain amylose content estimation models building based on more breeding times
Using obtained imaging of more breeding times EO-1 hyperion variable selected above as independent variable, rice grain amylose contains
Amount is used as dependent variable, and it is as follows to establish quality estimation models:
Y=f (SVBoot stage, SVHeading stage, SVPustulation period, SVMaturity period) (6)
In formula, Y is rice grain amylose content, SVBoot stage, SVHeading stage, SVPustulation period, SVMaturity periodTo screen obtained rice
The imaging EO-1 hyperion variable that boot stage, heading stage, pustulation period, maturity period correlation are put up the best performance, f () are indicated for modeling letter
Number；
By taking Deqing experimental data in 2017 as an example, the test block totally 20 pieces of experimental fields are loaded using unmanned plane as needed
Hyperspectral imager obtain different times remotely-sensed data and carry out the rice reflectivity that different growing is calculated.It chooses
The reflectivity data in boot stage, heading stage, four periods of pustulation period and maturity period.Then using calculating needed for each experimental field
Spectral variables, specific data are as shown in table 1.
Table 1
It is as follows with this calculated Production Forecast Models:
Y=-40.49+25.241 × RVI[632,528](Filling)+91.888×DVI[800,856](Booting)+524.051×
DVI[864,832](Heading)+34.086×RVI[588,685](Filling)
In formula, Y is rice prediction Grain starch content, unit %, the modeling R of the model2Reach 0.7576；
4) rice grain amylose content estimates precision test
By the SV of target fieldBoot stage, SVHeading stage, SVPustulation period, SVMaturity periodIt is brought into model (7) and obtains estimation rice grain YEstimation,
The yield by estimation precision test is carried out using formula (7) (8),
In formula, RMSE is root-mean-square error, and RE is relative error, YEstimationThe rice seed estimated for target field by model
Grain amylose content, YActual measurementFor the rice rice grain amylose content of target field actual measurement, N is field quantity, i
For field serial number.
By taking Deqing experimental data in 2017 as an example, amylose content measured value is with predicted value average relative error
3.81%, RMSE are 0.628 (%).Concrete outcome is as shown in Table 2 and Fig. 1.
Table 2
In addition, being verified with leaving-one method, i.e., retain a field every time, establishes an interim mould with remaining all fields
Type, then the spectral variables value of retained field is substituted into the temporary pattern corresponding to it and obtains predicted value, amylose contains
It is 0.902 (%) that amount measured value and leaving-one method predicted value average relative error, which are 4.80%, RMSE,.As a result as shown in Table 3 and Fig. 2
Table 3.
Claims (2)
1. a kind of rice grain amylose content estimating and measuring method based on unmanned plane imaging EO-1 hyperion, which is characterized in that described
Method the following steps are included:
1) Imaging Hyperspectral Data converts
For the Imaging Hyperspectral Data that Rise's boot period, heading stage, pustulation period, maturity period obtain, will be owned using the method for exhaustion
Wave band carries out combination of two and calculates separately NDVI, RVI, DVI and EVI2 vegetation index, and the calculation formula of each vegetation index is as follows:
DVIi,j=Ri-Rj (2)
EVI2i,j=2.5 (NIRi-REDj)/(NIRi+2.4REDj+1) (4)
In formula, NIRiSpectral reflectivity corresponding to the wave band for being i for a certain wavelength of near infrared band range, REDjFor feux rouges wave
The a certain wavelength of segment limit is spectral reflectivity corresponding to the wave band of j, Ri、RjIt is respectively corresponding to the wave band of i and j for wavelength
Spectral reflectivity；
2) the imaging EO-1 hyperion variable for rice quality estimation determines
Imaging Hyperspectral Data is converted to the vegetation index of obtained all band combinations and the rice grain straight chain shallow lake of actual measurement
Powder content carries out correlation analysis by formula (5)；
In formula, ρXYFor the related coefficient of variable X and Y；Cov (X, Y) is X, and the covariance of Y, D (X), D (Y) are respectively the side of X, Y
Difference；
According to the related coefficient that the above correlation analysis obtains, select the highest spectral variables value of each breeding time related coefficient as
The spare spectral variables of rice grain amylose content estimation；
3) the rice grain amylose content estimation models building based on more breeding times
Using obtained imaging EO-1 hyperion variable of more breeding times selected above as independent variable, rice grain amylose content is made
For dependent variable, it is as follows to establish quality estimation models:
Y=f (SVBoot stage, SVHeading stage, SVPustulation period, SVMaturity period) (6)
In formula, Y is rice grain amylose content, SVBoot stage, SVHeading stage, SVPustulation period, SVMaturity periodTo screen obtained rice booting
The imaging EO-1 hyperion variable that phase, heading stage, pustulation period, maturity period correlation are put up the best performance, f () indicate to be used for modeling functions；
4) rice grain amylose content estimates precision test
By the SV of target fieldBoot stage, SVHeading stage, SVPustulation period, SVMaturity periodIt is brought into model (7) and obtains estimation rice grain YEstimation, utilize
Formula (7) (8) carries out the yield by estimation precision test,
In formula, RMSE is root-mean-square error, and RE is relative error, YEstimationThe rice grain estimated for target field by model is straight
Chain content of starch, YActual measurementFor the rice grain amylose content of target field actual measurement, N is field quantity, and i is field sequence
Number.
2. the rice grain amylose content estimating and measuring method as described in claim 1 based on unmanned plane imaging EO-1 hyperion,
It is characterized in that, in the step 3), the statistical model for being fitted modeling is multivariate linear model.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910098197.XA CN109754127A (en) | 2019-01-31 | 2019-01-31 | Rice grain amylose content estimating and measuring method based on unmanned plane imaging EO-1 hyperion |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910098197.XA CN109754127A (en) | 2019-01-31 | 2019-01-31 | Rice grain amylose content estimating and measuring method based on unmanned plane imaging EO-1 hyperion |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109754127A true CN109754127A (en) | 2019-05-14 |
Family
ID=66407107
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910098197.XA Pending CN109754127A (en) | 2019-01-31 | 2019-01-31 | Rice grain amylose content estimating and measuring method based on unmanned plane imaging EO-1 hyperion |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109754127A (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111829965A (en) * | 2020-08-04 | 2020-10-27 | 淮阴师范学院 | Remote sensing inversion model and method for starch accumulation amount of rice overground part |
| CN111855589A (en) * | 2020-08-04 | 2020-10-30 | 淮阴师范学院 | Remote sensing inversion model and method for rice leaf nitrogen accumulation |
| CN111855591A (en) * | 2020-08-04 | 2020-10-30 | 淮阴师范学院 | Rice overground part carbon-nitrogen ratio remote sensing inversion model and method |
| CN111855592A (en) * | 2020-08-04 | 2020-10-30 | 淮阴师范学院 | Remote sensing inversion model and method for upper dry matter weight in unit area of rice |
| CN111855590A (en) * | 2020-08-04 | 2020-10-30 | 淮阴师范学院 | Remote sensing inversion model and method for rice leaf starch accumulation |
| CN111855593A (en) * | 2020-08-04 | 2020-10-30 | 淮阴师范学院 | Remote sensing inversion model and method for starch content of rice leaf |
| CN112595675A (en) * | 2020-09-29 | 2021-04-02 | 华中农业大学 | Hyperspectrum-based rice grain quality functional gene analysis method |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101858971A (en) * | 2010-06-02 | 2010-10-13 | 浙江大学 | Rice yield remote sensing estimation method based on MODIS data |
| CN107024439A (en) * | 2017-03-23 | 2017-08-08 | 西北农林科技大学 | A kind of paddy rice different growing chlorophyll content EO-1 hyperion estimating and measuring method |
| CN108663330A (en) * | 2018-04-19 | 2018-10-16 | 中国国土资源航空物探遥感中心 | A kind of vegetation-covered area soil copper inversion method based on blade measured spectra |
| CN109212505A (en) * | 2018-09-11 | 2019-01-15 | 南京林业大学 | A kind of forest stand characteristics inversion method based on the multispectral high degree of overlapping image of unmanned plane |
-
2019
- 2019-01-31 CN CN201910098197.XA patent/CN109754127A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101858971A (en) * | 2010-06-02 | 2010-10-13 | 浙江大学 | Rice yield remote sensing estimation method based on MODIS data |
| CN107024439A (en) * | 2017-03-23 | 2017-08-08 | 西北农林科技大学 | A kind of paddy rice different growing chlorophyll content EO-1 hyperion estimating and measuring method |
| CN108663330A (en) * | 2018-04-19 | 2018-10-16 | 中国国土资源航空物探遥感中心 | A kind of vegetation-covered area soil copper inversion method based on blade measured spectra |
| CN109212505A (en) * | 2018-09-11 | 2019-01-15 | 南京林业大学 | A kind of forest stand characteristics inversion method based on the multispectral high degree of overlapping image of unmanned plane |
Non-Patent Citations (2)
| Title |
|---|
| 唐延林: "水稻高光谱特征及其生物理化参数模拟与估测模型研究", 《中国博士学位论文全文数据库 农业科技辑》 * |
| 潘海珠 等: "无人机高光谱遥感数据在冬小麦叶面积指数反演中的应用", 《中国农业资源与区划》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111829965A (en) * | 2020-08-04 | 2020-10-27 | 淮阴师范学院 | Remote sensing inversion model and method for starch accumulation amount of rice overground part |
| CN111855589A (en) * | 2020-08-04 | 2020-10-30 | 淮阴师范学院 | Remote sensing inversion model and method for rice leaf nitrogen accumulation |
| CN111855591A (en) * | 2020-08-04 | 2020-10-30 | 淮阴师范学院 | Rice overground part carbon-nitrogen ratio remote sensing inversion model and method |
| CN111855592A (en) * | 2020-08-04 | 2020-10-30 | 淮阴师范学院 | Remote sensing inversion model and method for upper dry matter weight in unit area of rice |
| CN111855590A (en) * | 2020-08-04 | 2020-10-30 | 淮阴师范学院 | Remote sensing inversion model and method for rice leaf starch accumulation |
| CN111855593A (en) * | 2020-08-04 | 2020-10-30 | 淮阴师范学院 | Remote sensing inversion model and method for starch content of rice leaf |
| CN112595675A (en) * | 2020-09-29 | 2021-04-02 | 华中农业大学 | Hyperspectrum-based rice grain quality functional gene analysis method |
| CN112595675B (en) * | 2020-09-29 | 2022-03-08 | 华中农业大学 | Hyperspectrum-based rice grain quality functional gene analysis method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109754127A (en) | Rice grain amylose content estimating and measuring method based on unmanned plane imaging EO-1 hyperion | |
| CN110376167A (en) | Rice leaf nitrogen content monitoring method based on unmanned plane EO-1 hyperion | |
| CN102788752B (en) | Non-destructive detection device and method of internal information of crops based on spectrum technology | |
| CN108875913B (en) | Tricholoma matsutake rapid nondestructive testing system and method based on convolutional neural network | |
| CN107271382A (en) | A kind of different growing rape leaf SPAD value remote sensing estimation methods | |
| CN108876917A (en) | A kind of forest ground biomass remote sensing estimation universal model construction method | |
| CN104483271B (en) | Forest biomass amount retrieval method based on collaboration of optical reflection model and microwave scattering model | |
| CN106841052B (en) | A kind of method and system of quick detection meat freshness | |
| CN108520127A (en) | A kind of EO-1 hyperion inversion method of seeds leaf area index | |
| CN110567892B (en) | Summer corn nitrogen hyperspectral prediction method based on critical nitrogen concentration | |
| CN109030378A (en) | Japonica rice canopy chlorophyll content inverse model approach based on PSO-ELM | |
| CN104199027B (en) | Method based on the polarimetric radar large area near real-time Monitoring Rice phenological period of compacting | |
| CN111044516B (en) | Remote sensing estimation method for chlorophyll content of rice | |
| CN113252583B (en) | Method for calculating alpine hay coverage based on hay vegetation index | |
| CN109827957A (en) | A kind of rice leaf SPAD value estimating and measuring method based on computer vision and system | |
| Huang et al. | Assimilating remotely sensed information with the wheatgrow model based on the ensemble square root filter for improving regional wheat yield forecasts | |
| CN111751376A (en) | Rice nitrogen nutrition estimation method based on canopy image feature derivation | |
| CN115443889A (en) | Accurate irrigation method and device for crops | |
| CN109870419B (en) | Method for predicting nitrogen, phosphorus and potassium content of black soil by adopting aviation hyperspectral data | |
| CN109060676A (en) | Based on the determination method of the Summer Corn Canopy SPAD value appraising model of EO-1 hyperion | |
| CN114460015A (en) | Method and model for predicting chlorophyll content of canopy of rice in cold region | |
| CN106203673A (en) | Consider the crops Biomass remote sensing estimation method of water stress | |
| CN108535250A (en) | ' Fuji ' ripe apples degree lossless detection method based on Streif indexes | |
| Qian et al. | Development and application of crop monitoring system for detecting chlorophyll content of tomato seedlings | |
| CN107169161A (en) | Segmentation directly correction and slope and the soil nutrient Model transfer method of intercept amendment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190514 |
|
| RJ01 | Rejection of invention patent application after publication |