CN107037001A - A kind of corn monoploid seed discrimination method based on near-infrared spectrum technique - Google Patents
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Abstract
The invention discloses a kind of corn monoploid seed mirror method for distinguishing based on near-infrared spectrum technique, including：Original spectrum file is read in, spectrum intensity data is obtained；Data set is divided；The feature normalization of spectroscopic data；Dimension is reduced using partial least-square regression method；Neural network classifier parameter regulation；Neural network classifier is finely tuned and performance evaluation；Preserve neural network parameter.This method can identify corn monoploid seed from heterozygote seed, while ensuring higher correct resolution and model stability.
Description
Technical field
The invention belongs to computer and optical technology application field, more particularly to a kind of jade based on near-infrared spectrum technique
Rice monoploid seed discrimination method.
Background technology
The discriminating of corn monoploid seed is a new technical field of computer and spectrum of use, due to corn monoploid
Seed generation rate during cultivating seeds is extremely low, and usual 10% less than artificial screening corn monoploid seed is than relatively time-consuming consumption
Power.Therefore, it is Land use models mirror method for distinguishing using near-infrared spectrum technique and computer technology purpose, to realize computer
Monoploid seed screening function is aided in, so as to realize automation seed selection, the effect of optimization agricultural is reached.
Corn monoploid authentication technique based on near-infrared spectrum technique, such as obtains seed spectrum using diffusing transmission method,
The seed inner material information of non-uniform Distribution can be obtained.But, because spectral component is complicated, diffusing transmission mechanism is not also complete
Kind theoretical explanation, differentiates seed seed by spectrum in actual applications, in addition it is also necessary to reference to CRT technology algorithm
To improve the correct resolution of monoploid seed as far as possible.
CRT technology algorithm is designed, and is typically pre-processed from feature normalization, Feature Dimension Reduction denoising, Yi Jite
Three aspects of classification are levied to account for.Center normalization or Standard normalization are conventional at present
Feature normalization processing method, but still there is strong correlation between feature after this method characteristic processing.
In summary there is provided rational pattern discrimination method, the discriminating performance of monoploid seed spectrum can be improved, so that
The need for meeting in real work, effectively to realize that the screening of automation corn monoploid seed provides computational methods.
The content of the invention
(1) technical problem to be solved
, can be by jade the invention provides a kind of corn monoploid seed mirror method for distinguishing based on near-infrared spectrum technique
Rice monoploid seed is identified from heterozygote seed, while ensuring higher correct resolution and model stability.
(2) technical scheme
The present invention is achieved by the following technical solutions：
A kind of corn monoploid seed discrimination method based on near-infrared spectrum technique, comprises the following steps：
Spectroscopic data is obtained, and spectroscopic data is divided；
Spectroscopic data after division is subjected to feature normalization；
Spectroscopic data after normalization is reduced into dimension；
Spectroscopic data after dimensionality reduction is classified.
Preferably, the acquisition spectroscopic data, and the step of spectroscopic data is divided in, using diffusing reflection or unrestrained saturating
The mode of penetrating gathers the spectroscopic data of corn monoploid and heterozygote seed, and obtains corresponding corn monoploid and heterozygote seed
The label data of grain.
Preferably, the acquisition spectroscopic data, and the step of spectroscopic data is divided in, spectroscopic data is divided into three
Individual data set：Training set, checking collection and test set；Training set is used for training pattern, and checking collection is used to adjust model parameter, tested
Collect for test model performance.
Preferably, in the step of spectroscopic data by after division carries out feature normalization, using Zero-phase
Components analysis methods carry out spectroscopic data intensities normalised, the spectroscopic data after being normalized.
Preferably, the calculation formula of spectroscopic data progress feature normalization is：
Wherein, X represents eigenmatrix,Represent that eigenmatrix X subtracts the zero-mean matrix after mean vector, U represents empty
Between base, S represents diagonal matrix, and eps is a small constant,Represent the eigenmatrix after normalization.
Preferably, in the step of spectroscopic data by after normalization reduces dimension, using PLS side
Method reduces the dimension of spectroscopic data, obtains score matrix XSAnd its transformation matrix ST.
Preferably, the dimension of the use partial least-square regression method reduction spectroscopic data includes：
Wherein,For the eigenmatrix after normalization, y is label data, and p is characterized matrixProjecting direction, q for mark
Data y projecting direction is signed, transformation matrix ST is calculated according to the p obtained every time and obtained, score matrix XsForAfter being projected through ST
Matrix.
Preferably, the step of spectroscopic data to after dimensionality reduction carries out tagsort includes：
Spectroscopic data after dimensionality reduction is inputted into neutral net, neural network parameter is adjusted；
Neural network parameter is finely tuned and performance evaluation；
Preserve neural network parameter.
Preferably, in the step of spectroscopic data by after dimensionality reduction inputs neutral net, regulation neural network parameter, adopt
The spectroscopic data after dimensionality reduction is classified with L2 norm regularizations reverse transmittance nerve network, the form of its cost function is：
Wherein, m represents sample size, xiRepresent i-th of sample, yiRepresent the true value of the label data of i-th of sample, hθ
() represents neural network model hypothesis, and λ represents punishment parameter, and θ represents model parameter to be learned.
Preferably, reverse neural network model learning parameter θ is initialized using random fashion, and its more new formula is：
Wherein, ε is learning rate parameter, JbFor cost function.
(3) beneficial effect
Compared with prior art, the invention has the advantages that：
(1) the corn monoploid seed discrimination method proposed by the present invention based on near-infrared spectrum technique, realizes single times
The discriminating of body seed, goes to design discrimination method, is effectively improved the identity of model from the angle of the vivid geometry of higher dimensional space
Energy.
(2) Zero-phase components analysis feature normalization methods of the present invention, Neng Gouyou
Effect ground removes the correlation between spectral signature, so that effectively enhanced spectrum feature.
(3) PLS of the present invention realizes the reduction of data characteristics dimension, can preferably retain
The weak characteristic component of data, and removal noise and redundancy are realized by dimension reduction, so that efficiency of algorithm is improved.
(4) neural network model grader of the present invention, can be realized in the embedded hardware systems such as FPGA
Parallel computation, so as to improve training and predetermined speed of model, collateral security differentiates operational efficiency.
Brief description of the drawings
Fig. 1 is the corn monoploid seed mirror method for distinguishing stream provided in an embodiment of the present invention based on near-infrared spectrum technique
Cheng Tu；
Fig. 2 is the corn monoploid seed mirror method for distinguishing stream provided in an embodiment of the present invention based on near-infrared spectrum technique
Journey total figure；
Fig. 3 is punishment parameter regulation comparison diagram provided in an embodiment of the present invention；
Fig. 4 is average correct recognition rata provided in an embodiment of the present invention and training set size relationship figure.
Embodiment
For the object, technical solutions and advantages of the present invention are more clearly understood, below in conjunction with specific embodiment, and reference
Accompanying drawing, the present invention is described in more detail.It should be appreciated that described herein is only a part of embodiment of the invention, ability
The every other embodiment that domain those of ordinary skill is obtained on the premise of creative work is not made, belongs to the present invention
The scope of protection.
Fig. 1 and Fig. 2 are the corn monoploid seed discriminating sides provided in an embodiment of the present invention based on near-infrared spectrum technique
The method flow diagram of method, for convenience of description, illustrate only the part related to the embodiment of the present invention, the discrimination method it is main by
The normalization of spectral signature, the reduction of spectral signature dimension and spectral signature, which are classified, to be constituted.As depicted in figs. 1 and 2, this method includes
Following steps：
Step S1：Spectroscopic data is obtained, and spectroscopic data is divided.
Step S1 includes：
Sub-step S11, reads in original spectrum file, obtains spectroscopic data, and spectroscopic data includes spectroscopic data collection and spectrum
Label data collection.
Specifically, reading in original spectrum file, the spectroscopic data of acquisition includes original spectrum intensity data, the label of spectrum
The dimension information of information and spectrum, the dimension information of spectrum includes dimension and bar number, is designated as n and mT。
The present embodiment gathers the near infrared spectrum data collection X of corn monoploid and heterozygote seed by diffusing transmission modeT
And the spectrum label data set y of corresponding corn monoploid and heterozygote.Spectroscopic data collection XTIncluding original spectrum intensity number
According to, the information such as spectral dimension information, spectral Dimensions n is 125 dimensions, and spectral wavelength ranges are floating number between 900nm-1700nm
Value is represented.In spectrum label data set y, label information is monoploid spectral marker 1, heterozygote spectral marker 0.
Sub-step S12, spectroscopic data collection is divided.
Spectroscopic data collection is divided, including using random without spectrum is extracted by the way of putting back to, m is arrived in generation 1TRandom manifold R, will
Spectroscopic data random manifold corresponding with label data, the m for proportionally concentrating spectroscopic dataTBar spectroscopic data is divided into three numbers
According to collection：Training set, checking collection and test set.Training set is that checking collection is that, for regulation parameter, test set is for training pattern
For test model performance.
It is preferred that, training set, checking collects the ratio with test set, may be configured as 2: 1: 2 or 3: 1: 1.
Step S2：Spectroscopic data after division is subjected to feature normalization.
Spectral signature is normalized, and is that the spectroscopic data for detecting near infrared spectrometer carries out intensities normalised, the present embodiment
The detecting instrument used is MicroNIR-1700 near infrared spectrometer.Utilize Zero-phase component analysis
(ZCA) correlation between feature can be removed very well.
Specifically, the present embodiment carries out Zero-phase components using the training set of gained in sub-step S12
Analysis (ZCA), obtains its normalization matrix, computational methods：
In formula, eigenmatrix X represents the original spectral data of training set, and size is n × m, and n represents wall scroll spectral signature
Dimension, m is training samples number.Mean (X) represents the average value of training set spectrum,Subtracted for training set eigenmatrix X
It is worth the spectroscopic data after vector mean (X), is zero-mean matrix.Svd () is singular value decomposition, by singular value decomposition
(singular value decomposition), U is by the space base vector that is obtained after decomposition, and S is is obtained to angular moment
Battle array.Eps represents a small constant, 1e-6 preferably.For the new spy of training set data gained after ZCA feature normalizations
Levy data.
Spectroscopic data in training set utilized above, according to Zero-phase components analysis (ZCA) sides
The calculating of method has obtained normalization matrix Z, ZThen checking collection and test set normalizing are calculated according to Z
Spectroscopic data after change.I.e. according to the average value for verifying collection and test set, and above-mentioned U and S collect and survey to calculate checking
The normalized data of examination collection, training set, checking collects the spectroscopic data after being normalized with test set, Tr, Va and Te is designated as respectively.Protect
Deposit the data of the gained after normalization.
Step S3：Spectroscopic data after normalization is reduced into dimension.
Spectral signature dimension is reduced, and is to extract effective spectral signature information.Partial least-square regression method dimensionality reduction denoising phase
Than the feature that PCA can retain some weak components, the present embodiment is using partial least-square regression method to spectrum number
According to progress dimensionality reduction.
Specifically, the present embodiment utilizes the training set data and corresponding number of tags after the normalization obtained in step S2
According to concurrently setting the dimension k of data, it is preferred that k is set { 3,10,20 } intermediate value, with reference to partial least-square regression method, meter
Calculation form has：
In formula,Represent that spectroscopic data Tr, p after training set normalization are training set dataProjecting direction, q is
The corresponding spectrum label data set y projecting directions of training set.Meanwhile, p in each iteration, q meets the limitation of mould a length of 1.So as to
Obtain training set score matrix Xs and its transformation matrix ST, score matrix Xs beMatrix after being projected through ST, ST foundations
The p obtained every time, which is calculated, to be obtained, and is recycled identical linear transformation to be calculated respectively by Va and Te and is verified collection and test set
Score matrix.Accordingly, as the low-dimensional characteristic after dimensionality reduction.
Step S4：Tagsort is carried out to the spectroscopic data after dimensionality reduction.
Step S4 includes：
Sub-step S41, neural network classifier parameter regulation.
Spectral signature is classified, and is to distinguish monoploid seed spectrum and heterozygote seed spectral matching factor, neural network classification
Device can extract the hiding feature in feature, it is adaptable to the complicated situation of some features.The present embodiment uses L2 norm regularizations
Reverse transmittance nerve network (back propagation neural network) method, to the score square obtained in step S3
Battle array Xs carries out two and classified, and reverse neural network has three layers, input layer, hidden layer and output layer, the god of input layer and hidden layer
All it is above-mentioned dimension k through first number, output layer neuron number is 1 or 2.
Specifically, by training set data feature (score matrix Xs obtained by step S3), accessing the neutral net of k-k-1 structures
In.The cost function of neutral net is：
In formula, m represents training set sample size, xiRepresent i-th of training sample of wall scroll, yiRepresent i-th of sample correspondence
Spectrum label data, hθ() is the model hypothesis of neutral net, and λ is punishment parameter, and θ represents reverse neural network learning
Parameter set, θ is initialized using random fashion, and its more new formula is：
In formula, ε is learning rate parameter, JbFor cost function.
It is preferred that, setting frequency of training N=800 times, λ can be collection { 1,10-1, 10-2, 10-3, 10-4, 10-5, 10-6Intermediate value,
ε is 0.05, by verifying that 50 Average Accuracies of collection are according to the optimum value for drawing λ, while according to fixed intrinsic dimensionality
Determine k probable ranges.It is illustrated in figure 3 λ in influences of the λ of drafting to checking ensemble average recognition accuracy, this specific embodiment
For 10-4, while determining k scope between [3,10].
Sub-step S42, neural network classifier fine setting and performance evaluation.
Punishment parameter is fixed, training set and test set is obtained, obtained neural network model, profit are trained using training set
Final mask Performance Evaluation is carried out with test set, optimal hidden layer neuron number k values are determined, exhaustion determines feature in scope
Dimension, repeats the flow n times, finally optimal characteristics dimension is determined using the distribution situation of test set accuracy rate, obtains simultaneously
Optimal models Performance Evaluation, exhaustion 3 to 10 in the range of k value, it is preferred that repeat 50 times acquisition test sets Average Accuracy and
Its standard deviation is used as model performance judging basis.It is training set Average Accuracy as shown in table 1 in this specific embodiment
The Average Accuracy (Average test accuracy) and standard deviation of (Average training accuracy), test set
Value (SD of test accuracy) is with the variation relation of k values, comprehensive Average Accuracy and standard deviation, institute in the present embodiment
It is 6 to obtain optimal k.Meanwhile, according to parameters obtained, we can further draw out shadow of the training set sample size to model performance
Ring, be illustrated in figure 4 the relation curve of training set size and test set Average Accuracy.
Table 1
Sub-step S43, preserves neural network parameter.
Specifically, neural network parameter θ, punishment parameter λ and optimal lower dimensional space dimension k obtained by fixing step S42,
Training set and test set are reacquired, using training set training pattern, model training is carried out according to above-mentioned identified parameter, protected
The training set average of gained, feature normalization matrix, offset minimum binary transformation matrix and neutral net network parameter are deposited, simultaneously
Generation model Parameter File.
The embodiment of the present invention by using unified calculating body structure, obtain near infrared spectrum different wave length point data and its
Dimension information, by the normalization to spectral signature, dimensionality reduction, a kind of corn monoploid seed mirror in conjunction with neural fusion
Method for distinguishing, is gone to design recognizer using the angle from higher-dimension bio-information geometry, is effectively improved Model Identification rate
And stability.
Through the above description of the embodiments, those skilled in the art can be understood that the present invention can be by
Software adds the mode of required general hardware platform to realize, naturally it is also possible to which by hardware, but in many cases, the former is most
Good embodiment.Understood based on such, what technical scheme substantially contributed to prior art in other words
Part can be embodied in the form of software product, be stored in computer software product in a storage medium, if including
Dry instruction is to cause a station terminal equipment (can be personal computer, server, or embedded device etc.) to perform this hair
Method described in each bright embodiment.The present embodiment test platform：CPU：Pentium(R)Dual-Core CPU E5800@
3.20GHz；Internal memory：4.00GB；The bit manipulation systems of system Windows 732；Software：Matlab R2013a.
Particular embodiments described above, has been carried out further in detail to the purpose of the present invention, technical scheme and beneficial effect
Describe in detail it is bright, should be understood that the foregoing is only the present invention specific embodiment, be not intended to limit the invention, it is all
Within the spirit and principles in the present invention, any modification, equivalent substitution and improvements done etc., should be included in the guarantor of the present invention
Within the scope of shield.
Claims (10)
1. a kind of corn monoploid seed discrimination method based on near-infrared spectrum technique, comprises the following steps：
Spectroscopic data is obtained, and spectroscopic data is divided；
Spectroscopic data after division is subjected to feature normalization；
Spectroscopic data after normalization is reduced into dimension；
Spectroscopic data after dimensionality reduction is classified.
2. corn monoploid seed discrimination method as claimed in claim 1, wherein, the acquisition spectroscopic data, and by spectrum
In the step of data are divided, the spectrum number of corn monoploid and heterozygote seed is gathered using diffusing reflection or diffusing transmission mode
According to, and obtain the label data of corresponding corn monoploid and heterozygote seed.
3. corn monoploid seed discrimination method as claimed in claim 1 or 2, wherein, the acquisition spectroscopic data, and by light
In the step of modal data is divided, spectroscopic data is divided into three data sets：Training set, checking collection and test set；Training set
For training pattern, checking collection is used to adjust model parameter, and test set is used for test model performance.
4. corn monoploid seed discrimination method as claimed in claim 1 or 2, wherein, the spectroscopic data by after division
In the step of carrying out feature normalization, spectroscopic data is carried out using Zero-phase components analysis methods strong
Degree standardization, the spectroscopic data after being normalized.
5. corn monoploid seed discrimination method as claimed in claim 4, wherein, spectroscopic data carries out the meter of feature normalization
Calculating formula is：
Wherein, X represents eigenmatrix,Represent that eigenmatrix X subtracts the zero-mean matrix after mean vector, U representation spaces
Base, S represents diagonal matrix, and eps is a small constant,Represent the eigenmatrix after normalization.
6. corn monoploid seed discrimination method as claimed in claim 1, wherein, the spectroscopic data by after normalization drops
In the step of low-dimensional number, the dimension of spectroscopic data is reduced using partial least-square regression method, score matrix X is obtainedSAnd its become
Change matrix ST.
7. corn monoploid seed discrimination method as claimed in claim 6, wherein, the use partial least-square regression method
The dimension of reduction spectroscopic data includes：
Wherein,For the eigenmatrix after normalization, y is label data, and p is characterized matrixProjecting direction, q is number of tags
According to y projecting direction, transformation matrix ST is calculated according to the p obtained every time and obtained, score matrix XsForSquare after being projected through ST
Battle array.
8. corn monoploid seed discrimination method as claimed in claim 1, wherein, the spectroscopic data to after dimensionality reduction is carried out
The step of tagsort, includes：
Spectroscopic data after dimensionality reduction is inputted into neutral net, neural network parameter is adjusted；
Neural network parameter is finely tuned and performance evaluation；
Preserve neural network parameter.
9. corn monoploid seed discrimination method as claimed in claim 8, wherein, the spectroscopic data by after dimensionality reduction is inputted
Neutral net, regulation neural network parameter the step of in, using L2 norm regularization reverse transmittance nerve networks to dimensionality reduction after
Spectroscopic data is classified, and the form of its cost function is：
Wherein, m represents sample size, xiRepresent i-th of sample, yiRepresent the true value of the label data of i-th of sample, hθ(·)
Neural network model hypothesis is represented, λ represents punishment parameter, and θ represents model parameter to be learned.
10. corn monoploid seed discrimination method as claimed in claim 9, wherein, reverse neural network model learning parameter θ
Initialized using random fashion, its more new formula is：
Wherein, ε is learning rate parameter, JbFor cost function.
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