CN109387484A - A kind of ramee variety recognition methods of combination EO-1 hyperion and support vector cassification - Google Patents
A kind of ramee variety recognition methods of combination EO-1 hyperion and support vector cassification Download PDFInfo
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Abstract
The present invention proposes the ramee variety recognition methods of a kind of combination EO-1 hyperion and support vector cassification, comprising the following steps: sample collection and hyperspectral measurement, sample sets division, PCA feature extraction, SVC identification model is established and determining best model；The present invention determines the method for extracting the SVC ramie EO-1 hyperion variety ecotype model that feature establishes Linear, Polynomial, RBF and Sigmoid kernel function respectively by using grid data service, model recognition correct rate can achieve 95% or more, have the advantages that reliable and effective and quick, easy, the theoretical foundation and key technology for improving the ramee variety identification based on EO-1 hyperion, assistant breeding, the high yield and high quality to realize ramie and numb field Precision management support, ramee variety recognition cycle can be shortened, reduce manpower and material resources consumption.
Description
Technical field
The present invention relates to the technical field of ramee variety identification more particularly to a kind of combination EO-1 hyperion and support vector machines point
The ramee variety recognition methods of class.
Background technique
The perennial root type herbaceous plant of Section of Genus Boehmeria Urticaceae Section of Genus Boehmeria, is the specialty in China, is known as Chinese grass.I
State is one of main Chan Ma state in the world, possesses the most abundant ramee variety resource, ramie planting area and raw material output account for generation
95% or more of boundary, in national economy, ramie has always higher economic status, currently, the type of China's ramee variety,
Mainly there are the ecotype divided by planting area, the morphotype divided by botanic conformation, by yield and quality division
Economic ecology type and the ripe phase type etc. divided by breeding time.
What the identification of traditional ramee variety was mainly divided according to the ecotype of planting area division, by botanic conformation
Morphotype, the economic ecology type divided by yield and quality and the standards such as ripe phase type divided by breeding time, then rely on
Artificial experience is identified that time-consuming for these recognition methods, at high cost, subjectivity is strong, and accuracy rate is low, is unsuitable for a large amount of
Ramee variety screening identification, although some research achievements for carrying out crop identification using EO-1 hyperion are existing many, for not
The bloom spectral property of homogenic type ramie establishes the model of ramee variety identification still with (selection) effective spectral signature is extracted
So more lack, although the research that some methods using support vector cassification SVC carry out qualitative classification identification is also very much,
The selection of support vector cassification Kernel Function is extremely crucial, kernel function and and its criterion that does not uniquely determine of parameter setting, need
It wants experience and the determination that makes repeated attempts, takes time and effort larger.Therefore, the present invention proposes a kind of combination EO-1 hyperion and support vector machines point
The ramee variety recognition methods of class, to solve shortcoming in the prior art.
Summary of the invention
In view of the above-mentioned problems, the present invention propose by using grid data service determine extract feature establish respectively Linear,
The method of the SVC ramie EO-1 hyperion variety ecotype model of Polynomial, RBF and Sigmoid kernel function, model recognition correct rate
It can achieve 95% or more, have the advantages that reliable and effective and quick, easy, improve ramee variety based on EO-1 hyperion and know
Not, assistant breeding, the theoretical foundation of the high yield and high quality to realize ramie and numb field Precision management and key technology support, can contract
Short ramee variety recognition cycle reduces manpower and material resources consumption.
The present invention proposes the ramee variety recognition methods of a kind of combination EO-1 hyperion and support vector cassification, including following step
It is rapid:
Step 1: sample collection and hyperspectral measurement
The ramie for collecting different cultivars, as ramie sample, then by ramie sample using portable field spectroradiometer and portable
The matched hand-held leaf clip leaf spectra detector of formula field spectroradiometer selected in ramie leaf samples 4 sampled points measure into
Row high-spectral data collection, sample point data do the blade EO-1 hyperion number being averaged after breakpoint correction again as the ramie sample
According to；
Step 2: sample set divides
In the above step 1 after the blade high-spectral data collection of the ramie sample of different cultivars, by the ramie sample of different cultivars
This blade high-spectral data is randomly assigned according to the ratio of 2:1, successively labeled as at modeling collection and forecast set；
Step 3: PCA feature extraction
The leaf characteristic that ramie sample is extracted using PCA selects ingredient of all characteristic values greater than 1 as PCA main gene or side
Poor contribution rate of accumulative total reaches the preceding n main gene of 85%-95% as PCA main gene；
Step 4: SVC identification model is established
Utilize SVC algorithm by the PCA main cause subcharacter after extracting in above-mentioned steps three in stoichiometry software Unscrambler
Variable since variance accumulate n-th of main gene of contribution rate >=85%, successively increase PCA main gene number, then respectively with difference
SVC kernel function and the optimal parameter of kernel function be combined, the moulds of multiple identification ramee varieties are established after then being analyzed
Type obtains best features variable number, SVC kernel function and parameter combination；
Step 5: best model is determined
The prediction data of forecast set in step 2 is substituted into the various combined models that step 4 is established, evaluates and identify pre-
It surveys as a result, obtaining the model of optimal parameter combination.
Further improvement lies in that: sample collection is using local varieties, Cultivars, different root and stem of certain plants type, no in the step 1
With the sample in maturity period and different output, sample collection selects 9 kinds of ramee varieties, and in the prosperous long-term acquisition ramie of ramie sample
The blade high-spectral data of sample, each kind acquire 162 high light datas of blade, and 9 kind symbiosis are at 1458 blade samples
This high-spectral data.
Further improvement lies in that: 9 kinds of ramee varieties are respectively Jinsha Chinese holly skin fiber crops, Bijie circle is numb, Xiang Tan chicken bone is white, river flowing from Guizhou Province through Hunan into Dongting Lake
River Huang shell morning, Pingtang machete fiber crops, middle ramie 1, Shaoyang 4, bimodal great Ye fiber crops and Suining piemarker.
Further improvement lies in that: in the step 1 when high-spectral data collection, the main lobe arteries and veins of ramie leaf samples is avoided,
The blade clamp holder clamping of hand-held leaf clip leaf spectra detector is first tested to the leaf of ramie sample when high-spectral data collection
The surveyed position of piece, then the blade EO-1 hyperion of the probe measurement ramie sample with hand-held leaf clip leaf spectra detector, ramie sample
For this blade Samples selecting on blade main lobe arteries and veins both sides, blade main lobe arteries and veins both sides respectively select 2 sampled points, select 4 altogether
Sampled point, sample point data do the blade high-spectral data being averaged after breakpoint correction again as the ramie sample.
Further improvement lies in that: in order to which the blade high-spectral data for eliminating ramie sample is first in acquisition in the step 1
The noise that end is generated with end, the spectrum number when blade high-spectral data collection of ramie sample between selection 420nm-2450nm
According to being analyzed, and portable field spectroradiometer per half an hour does an OPT optimization and blank reference.
Further improvement lies in that: the blade high-spectral data modeling collection of the ramie sample of different cultivars is used in the step 2
In establishing variety ecotype model, the blade high-spectral data forecast set of the ramie sample of different cultivars is not involved in modeling, is only used for
Evaluate and test the accuracy rate of model.
Further improvement lies in that: use PCA to accumulate when extracting the leaf characteristic of ramie sample with variance in the step 3
It is appropriate to increase main gene number based on contribution rate judging result, eventually by the accuracy of choosing comprehensively model prediction collection, drop
Dynamics and variance accumulation contribution rate of dimension etc. is because usually determining main gene number.
Further improvement lies in that: the kernel function and parameter setting in the step 4 in order to avoid SVC algorithm are to model
Precision has an impact with complexity, while in order to find best kernel function and parameter, needing using different SVC kernel function and core
Function establish ramee variety identification mould, specially four kinds different SVC kernel functions, including Linear, Polynomial, RBF and
Sigmoid kernel function.
Further improvement lies in that: Linear, Polynomial, RBF and Sigmoid kernel function establishes ramee variety knowledge
When other model, use grid data service and cross validation accuracy for selection criteria come determine optimal Linear,
The penalty factor and kernel functional parameter γ value of Polynomial, RBF and Sigmoid kernel function.
Further improvement lies in that: first forecast set data in step 2 are substituted into above-mentioned steps four in the step 5 and are established
Various combined models in predicted, SVC kernel function and its parameter, most are then determined as standard using forecast set accuracy
The model of good main gene number, it is optimal selection that accuracy highest kernel function is selected in the case where identical main gene number,
Forecast set accuracy of the best kernel function model in different main gene numbers is analyzed again, then adds main gene number,
Select forecast set accuracy be no longer significantly increased or reach expected requirement when main gene number be used as best main cause
Subnumber.
The invention has the benefit that the present invention passes through the blade to multiple and different kinds, different genotype ramie sample
The leaf characteristic of a variety of ramie samples is studied and compared to bloom spectral property, and principal component analysis PCA effect can be improved just
True rate, and the hair method of contribution rate is accumulated to determine best main gene by tradeoff model accuracy, the dynamics of dimensionality reduction and variance
Number, and determine that extract feature establishes Linear, Polynomial, RBF and Sigmoid core respectively by using grid data service
The method of the SVC ramie EO-1 hyperion variety ecotype model of function, then using forecast set accuracy as standard, available optimal ramie
Numb variety ecotype model, in the reasonable situation of selection parameter, model recognition correct rate can achieve 95% or more, the method for the present invention
Have the advantages that reliable and effective and quick, easy applied to ramee variety identification, improves the ramee variety based on EO-1 hyperion
Identification, assistant breeding, the high yield and high quality to realize ramie and numb field Precision management theoretical foundation and key technology support, can be with
Shorten ramee variety recognition cycle, reduces manpower and material resources consumption, while cost can be shortened, be suitable for large batch of ramee variety
Identification.
Detailed description of the invention
Fig. 1 is that the present invention is based on EO-1 hyperions and SVC ramee variety to identify modeling procedure figure.
Fig. 2 is that the present invention is different principal component numbers, different kernel function SVC Model checking result schematic diagrams.
Fig. 3 is the accumulation contribution rate schematic diagram of 20 principal components before the present invention.
Specific embodiment
In order to deepen the understanding of the present invention, the present invention is further described below in conjunction with embodiment, the present embodiment
For explaining only the invention, it is not intended to limit the scope of the present invention..
According to Fig. 1,2 and 3, the present embodiment proposes the ramie product of a kind of combination EO-1 hyperion and support vector cassification
Kind recognition methods, comprising the following steps:
Step 1: sample collection and hyperspectral measurement
Sample collection using local varieties, Cultivars, different root and stem of certain plants type, different ripening stages and different output sample, sample receives
Collection selection Jinsha Chinese holly skin fiber crops, Bijie circle fiber crops, Xiang Tan chicken bone is white, the yellow shell of Yuanjiang is early, Pingtang machete fiber crops, middle ramie 1, Shaoyang 4, double
Peak great Ye fiber crops and Suining piemarker totally 9 kinds of ramee varieties, as ramie sample, and in the prosperous long-term acquisition ramie sample of ramie sample
Blade high-spectral data, each kind acquires 162 high light datas of blade, and 9 kind symbiosis are high at 1458 blade samples
Then ramie sample is used portable field spectroradiometer and the matched hand-held leaf clip of portable field spectroradiometer by spectroscopic data
Leaf spectra detector carries out high-spectral data collection in ramie leaf samples, when high-spectral data collection, avoids ramie sample
The main lobe arteries and veins of this blade, first by the blade clamp holder clamping quilt of hand-held leaf clip leaf spectra detector when high-spectral data collection
Survey the surveyed position of blade of ramie sample, then the blade of the probe measurement ramie sample with hand-held leaf clip leaf spectra detector
EO-1 hyperion, for the blade Samples selecting of ramie sample on blade main lobe arteries and veins both sides, blade main lobe arteries and veins both sides respectively select 2 samplings
Point, selects altogether 4 sampled points, and sample point data does the blade bloom being averaged after breakpoint correction again as the ramie sample
Modal data；
Step 2: sample set divides
In the above step 1 after the blade high-spectral data collection of the ramie sample of different cultivars, by the ramie sample of different cultivars
This blade high-spectral data is randomly assigned according to the ratio of 2:1, is successively labeled as into modeling collection and forecast set, different cultivars
For the blade high-spectral data modeling collection of ramie sample for establishing variety ecotype model, the blade of the ramie sample of different cultivars is high
Spectroscopic data forecast set is not involved in modeling, is only used for the accuracy rate of evaluation and test model；
Step 3: PCA feature extraction
The leaf characteristic that ramie sample is extracted using PCA selects ingredient of all characteristic values greater than 1 as PCA main gene or side
Poor contribution rate of accumulative total reaches 90% preceding 20 main genes as PCA main gene, and the leaf characteristic of ramie sample is extracted using PCA
When contribution rate judging result is accumulated by variance based on, it is appropriate to increase main gene number, eventually by choosing comprehensively model prediction
The accuracy of collection, the dynamics of dimensionality reduction and variance accumulation contribution rate etc. are because usually determining main gene number；
Step 4: SVC identification model is established
Utilize SVC algorithm by the PCA main cause subcharacter after extracting in above-mentioned steps three in stoichiometry software Unscrambler
Variable successively increases PCA main gene number since variance accumulates contribution rate >=85% 20th main gene, then respectively and not
The optimal parameter of same SVC kernel function and kernel function is combined, and multiple identification ramee varieties are established after then being analyzed
Model obtains best features variable number, SVC kernel function and parameter combination, in order to avoid the kernel function and parameter of SVC algorithm are set
It sets and the precision of model is had an impact with complexity, while in order to find best kernel function and parameter, needing using different SVC
Kernel function and kernel function establish ramee variety identification mould, specially four kinds different SVC kernel functions, including Linear,
Polynomial, RBF and Sigmoid kernel function, Linear, Polynomial, RBF and Sigmoid kernel function establish ramie product
When kind of identification model, use grid data service and cross validation accuracy for selection criteria come determine optimal Linear,
The penalty factor and kernel functional parameter γ value of Polynomial, RBF and Sigmoid kernel function；
Step 5: best model is determined
First forecast set data in step 2 are substituted into the various combined models established in above-mentioned steps four and predicted, then
The model for determining SVC kernel function and its parameter, best main gene number as standard using forecast set accuracy, in identical main gene
It is optimal selection that the highest kernel function of accuracy is selected in the case where number, then analyzes best kernel function model in different main genes
Forecast set accuracy in the case of number, then add main gene number, select forecast set accuracy be no longer significantly increased or
Person reach expected requirement when main gene number be used as best number of main factor.
Forecast set data are substituted into above-mentioned steps four after being predicted in the various combined models established, it is different it is main at
Number, different kernel function SVC models is divided to differentiate that accuracy is as shown in table 1 below in detail in modeling collection and forecast set:
Different principal component numbers, different kernel function SVC Model checking accuracy (%)
Table 1
It follows that the SVC ramee variety of Linear, Polynomial, RBF and Sigmoid kernel function is high according to upper table 1
In spectrum discrimination model, RBF kernel function model effect is best, overall to be higher than other three kinds.
Selecting the forecast set effect of 20 principal components is best, modeling collection and pre- in similar kernel function SVC model
Survey collects overall and each kind and determines that result is as shown in table 2 below:
20 principal component parameter SVC differentiate accuracy (%)
Table 2
It can be concluded that, choosing comprehensively accuracy and calculation amount, 20 principal components and RBF method are best according to upper table 2
Selection, forecast set accuracy are 96.91%.
PCA is carried out to modeling collection sample to analyze, and takes preceding 20 principal components PC, the detailed contributions rate such as following table of each ingredient
Shown in 3:
The accumulation contribution rate of preceding 20 principal components
Table 3
It can be concluded that, it is that contribution rate is maximum in all PC that the 1st PC contribution rate, which is 75.78%, as shown in upper table 3；Preceding 2 PC
It accumulates contribution rate to increase rapidly, the 2nd PC accumulation contribution rate is 86.68%, and each PC accumulation contribution rate is slowly increased later；Preceding 20
A principal component accumulation contribution rate is to 99.98%, and only surplus 0.02% spectral information fails to express.
9 different genotype ramee variety sample sets are divided, dividing condition is as shown in table 4 below:
9 different genotype ramee variety sample set dividing conditions
Table 4
The present invention is studied and is compared by the blade bloom spectral property to multiple and different kinds, different genotype ramie sample
The accuracy of principal component analysis PCA effect can be improved in the leaf characteristic of a variety of ramie samples, and correct by tradeoff model
Rate, the dynamics of dimensionality reduction and variance accumulate the hair method of contribution rate to determine best main gene number, and by using grid search
Method, which determines, extracts the SVC ramie EO-1 hyperion kind that feature establishes Linear, Polynomial, RBF and Sigmoid kernel function respectively
The method of identification model, then using forecast set accuracy as standard, available optimal ramee variety identification model, selection parameter
In reasonable situation, model recognition correct rate can achieve 95% or more, and the method for the present invention has applied to ramee variety identification can
By effective and quick, easy advantage, improves the ramee variety identification based on EO-1 hyperion, assistant breeding, is to realize ramie
High yield and high quality and numb field Precision management theoretical foundation and key technology support, ramee variety recognition cycle can be shortened, subtracted
Few manpower and material resources consumption, while cost can be shortened, it is suitable for large batch of ramee variety and identifies.
The basic principles, main features and advantages of the invention have been shown and described above.The technical staff of the industry should
Understand, the present invention is not limited to the above embodiments, and the above embodiments and description only describe originals of the invention
Reason, without departing from the spirit and scope of the present invention, various changes and improvements may be made to the invention, these changes and improvements
It all fall within the protetion scope of the claimed invention.The claimed scope of the invention is by appended claims and its equivalent circle
It is fixed.
Claims (10)
1. the ramee variety recognition methods of a kind of combination EO-1 hyperion and support vector cassification, it is characterised in that: including following step
It is rapid:
Step 1: sample collection and hyperspectral measurement
The ramie for collecting different cultivars, as ramie sample, then by ramie sample using portable field spectroradiometer and portable
The matched hand-held leaf clip leaf spectra detector of formula field spectroradiometer selected in ramie leaf samples 4 sampled points measure into
Row high-spectral data collection, sample point data do the blade EO-1 hyperion number being averaged after breakpoint correction again as the ramie sample
According to；
Step 2: sample set divides
In the above step 1 after the blade high-spectral data collection of the ramie sample of different cultivars, by the ramie sample of different cultivars
This blade high-spectral data is randomly assigned according to the ratio of 2:1, successively labeled as at modeling collection and forecast set；
Step 3: PCA feature extraction
The leaf characteristic that ramie sample is extracted using PCA selects ingredient of all characteristic values greater than 1 as PCA main gene or side
Poor contribution rate of accumulative total reaches the preceding n main gene of 85%-95% as PCA main gene；
Step 4: SVC identification model is established
Utilize SVC algorithm by the PCA main cause subcharacter after extracting in above-mentioned steps three in stoichiometry software Unscrambler
Variable since variance accumulate n-th of main gene of contribution rate >=85%, successively increase PCA main gene number, then respectively with difference
SVC kernel function and the optimal parameter of kernel function be combined, the moulds of multiple identification ramee varieties are established after then being analyzed
Type obtains best features variable number, SVC kernel function and parameter combination；
Step 5: best model is determined
The prediction data of forecast set in step 2 is substituted into the various combined models that step 4 is established, evaluates and identify pre-
It surveys as a result, obtaining the model of optimal parameter combination.
2. the ramee variety recognition methods of a kind of combination EO-1 hyperion and support vector cassification according to claim 1,
Be characterized in that: sample collection is using local varieties, Cultivars, different root and stem of certain plants types, different ripening stages and difference in the step 1
The sample of yield, sample collection select 9 kinds of ramee varieties, and in the blade bloom of the prosperous long-term acquisition ramie sample of ramie sample
Modal data, each kind acquire 162 high light datas of blade, and 9 kind symbiosis are at 1458 blade sample high-spectral datas.
3. the ramee variety recognition methods of a kind of combination EO-1 hyperion and support vector cassification according to claim 2,
Be characterized in that: 9 kinds of ramee varieties are respectively Jinsha Chinese holly skin fiber crops, Bijie circle fiber crops, Xiang Tan chicken bone is white, the yellow shell of Yuanjiang is early, Pingtang
Machete fiber crops, middle ramie 1, Shaoyang 4, bimodal great Ye fiber crops and Suining piemarker.
4. the ramee variety recognition methods of a kind of combination EO-1 hyperion and support vector cassification according to claim 1,
It is characterized in that: in the step 1 when high-spectral data collection, avoiding the main lobe arteries and veins of ramie leaf samples, high-spectral data collection
When the blade clamp holder clamping of hand-held leaf clip leaf spectra detector is first tested to the surveyed position of blade of ramie sample, then use
The blade EO-1 hyperion of the probe measurement ramie sample of hand-held leaf clip leaf spectra detector, the blade sampling of ramie sample click
It selects on blade main lobe arteries and veins both sides, blade main lobe arteries and veins both sides respectively select 2 sampled points, select 4 sampled points, sample point data altogether
Do the blade high-spectral data being averaged after breakpoint correction again as the ramie sample.
5. the ramee variety recognition methods of a kind of combination EO-1 hyperion and support vector cassification according to claim 1,
It is characterized in that: eliminating blade high-spectral data head end and end generation in acquisition of ramie sample in the step 1
Noise, the spectroscopic data when blade high-spectral data collection of ramie sample between selection 420nm-2450nm are analyzed, and
And portable field spectroradiometer per half an hour does an OPT optimization and blank reference.
6. the ramee variety recognition methods of a kind of combination EO-1 hyperion and support vector cassification according to claim 1,
Be characterized in that: the blade high-spectral data modeling collection of the ramie sample of different cultivars is for establishing variety ecotype in the step 2
The blade high-spectral data forecast set of model, the ramie sample of different cultivars is not involved in modeling, is only used for the accurate of evaluation and test model
Rate.
7. the ramee variety recognition methods of a kind of combination EO-1 hyperion and support vector cassification according to claim 1,
It is characterized in that: PCA being used to accumulate contribution rate judging result when extracting the leaf characteristic of ramie sample with variance in the step 3
Based on, it is appropriate to increase main gene number, eventually by the accuracy of choosing comprehensively model prediction collection, the dynamics of dimensionality reduction and variance
Contribution rate etc. is accumulated because usually determining main gene number.
8. the ramee variety recognition methods of a kind of combination EO-1 hyperion and support vector cassification according to claim 1,
It is characterized in that: in order to avoid the kernel function of SVC algorithm and parameter setting produce the precision and complexity of model in the step 4
It is raw to influence, while in order to find best kernel function and parameter, it needs to establish ramie product using different SVC kernel function and kernel function
Kind identification mould, specially four kinds different SVC kernel functions, including Linear, Polynomial, RBF and Sigmoid kernel function.
9. the ramee variety recognition methods of a kind of combination EO-1 hyperion and support vector cassification according to claim 8,
It is characterized in that: when Linear, Polynomial, RBF and Sigmoid kernel function establishes ramee variety identification model, using
Grid data service and cross validation accuracy be selection criteria determine optimal Linear, Polynomial, RBF and
The penalty factor and kernel functional parameter γ value of Sigmoid kernel function.
10. the ramee variety recognition methods of a kind of combination EO-1 hyperion and support vector cassification according to claim 1,
It is characterized in that: forecast set data in step 2 first being substituted into the various combined moulds established in above-mentioned steps four in the step 5
It is predicted in type, SVC kernel function and its parameter, best main gene number is then determined as standard using forecast set accuracy
Model, it is optimal selection that accuracy highest kernel function is selected in the case where identical main gene number, then analyzes best core letter
Then forecast set accuracy of the exponential model in different main gene numbers adds main gene number, select forecast set correct
Rate be no longer significantly increased or reach expected requirement when main gene number be used as best number of main factor.
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| CN111476320A (en) * | 2020-05-09 | 2020-07-31 | 河南科技大学 | Visible near infrared spectrum-based method for establishing early incubation fertilized egg discrimination model |
| CN114994150A (en) * | 2022-05-31 | 2022-09-02 | 中国标准化研究院 | Electronic tongue rapid classification method for spicy degree of zanthoxylum schinifolium |
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| CN111272668A (en) * | 2020-01-22 | 2020-06-12 | 中国农业科学院农产品加工研究所 | Construction method of wheat variety identification model |
| CN111476320A (en) * | 2020-05-09 | 2020-07-31 | 河南科技大学 | Visible near infrared spectrum-based method for establishing early incubation fertilized egg discrimination model |
| CN111476320B (en) * | 2020-05-09 | 2023-03-24 | 河南科技大学 | Visible near infrared spectrum-based method for establishing early incubation fertilized egg discrimination model |
| CN114994150A (en) * | 2022-05-31 | 2022-09-02 | 中国标准化研究院 | Electronic tongue rapid classification method for spicy degree of zanthoxylum schinifolium |
| CN114994150B (en) * | 2022-05-31 | 2023-10-27 | 中国标准化研究院 | Electronic tongue rapid classification method for redpepper tingling degree |
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