CN110108644A - A kind of maize variety identification method based on depth cascade forest and high spectrum image - Google Patents
A kind of maize variety identification method based on depth cascade forest and high spectrum image Download PDFInfo
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Abstract
The present invention provides a kind of maize variety identification methods based on depth cascade forest and high spectrum image, comprising: pre-processes to the corn image data, obtains all band image data；Choose the corn image data of effective wave band；And using the corn image data of effective wave band as feature set, category of model is carried out using depth cascade forest model.Maize variety identification method of the invention makes full use of hyperspectral technique and depth to cascade forest model and carries out corn classification, and compared to common machine learning algorithm, it is higher to identify precision.
Description
Technical field
The present invention relates to the discrimination methods of corn variety, more particularly to a kind of depth that is based on to cascade forest and high spectrum image
Maize variety identification method.
Background technique
Corn is important one of the cereal crops in China, accounts for 30% of total output of grain or more, production of hybrid seeds amount is every year more than 10
Hundred million tons.With the corn diversification of demand, China's corn variety also increases year by year.In breeding process, there is illegal retailer to fill with secondary
It is good, pretend to be high-grade maize kind using corn variety inferior, corn yield is caused to decline, to national food production and agricultural safety
Bring huge hidden danger, therefore how precise and high efficiency nondestructively identifies the kind of corn seed is of great significance.
The method for identifying corn seed at present mainly has artificial detection, Physico-chemical tests, Computer Vision Detection and EO-1 hyperion
Technology detection.The features such as size, form and color of the artificial detection mainly by naked eyes based on corn judge, this mode by
The subjective factor influence of people is very big, and not only detection accuracy is low, speed is slow, but also large labor intensity, it is difficult to form unified standard.
Physico-chemical tests are mainly based upon the biochemical characteristic of different cultivars corn to realize and identify, and such methods accuracy rate is high, still
It is merely able to be detected by sampling, damages and process is complicated, it is cumbersome, it is difficult to meet the market demand.Computer vision inspection
The morphological feature information of the near infrared light spectrum information and visible light that are based primarily upon seed is surveyed, application image mode identification technology is built
Vertical kind discrimination model, has the advantages that instant, efficient, lossless and accurate；But the characteristic information that such method is extracted is less, and
And be the evaluation based on surface mostly, the feature for characterizing its internal component can not be obtained, testing result reliability is by shadow
It rings；In addition, the crossover phenomenon of seed characteristics is serious as seed variety data increase, the separability of model is caused to be deteriorated.
Hyperspectral technique is to integrate the technology of spectrum and image, compared to machine vision technique and near infrared spectrum point
For analysis technology, hyperspectral technique can not only obtain the image information of object, and can obtain spectral information, can effective earth's surface
Levy the inner structural features and Chemical Composition Characteristics of observation object；In addition, the technological merit that hyperspectral technique is quick with its, lossless
Solve the problems, such as that detection of agricultural products field is cumbersome, the application in terms of nondestructive measuring method of the farm product is increasingly extensive.
Summary of the invention
For the defect of above method, the invention proposes a kind of corns based on depth cascade forest and high spectrum image
Variety discriminating method provides theoretical foundation in corn variety automatic identification field for high spectrum image.
A kind of maize variety identification method based on depth cascade forest and high spectrum image of the invention, comprising: step
1: corn image data being pre-processed, all band image data is obtained；Step 2: choosing the corn image number of effective wave band
According to；And step 3: using the corn image data of effective wave band as feature set, model is carried out using depth cascade forest model
Classification.
In above-mentioned maize variety identification method, step 1 includes: to acquire corn image by high spectrum image acquisition system
Data；Corn image data is corrected；Extract area-of-interest (ROI) image data of corn；And the jade to extraction
The area-of-interest image data of rice carries out spectrum correction, obtains all band image data.
In above-mentioned maize variety identification method, corn image data is acquired by high spectrum image acquisition system, and
Only it is retained in the corn image data of 146 wave bands equidistantly selected in wavelength band 533-893.4nm；
Corn image data is corrected using updating formula, updating formula are as follows:
Area-of-interest (ROI) image of each corn is extracted using the label fractional spins based on range conversion
Data；
Using smooth (S-G the is smooth) algorithm of Savitzky-Golay to corn area-of-interest (ROI) image number of extraction
According to progress spectrum correction.
In above-mentioned maize variety identification method, all band image is screened using Boruta algorithm, is selected wherein
Effective wave band image.
In above-mentioned maize variety identification method, the process of Boruta algorithm is as follows:
It is mixed into training set for each feature construction shadow feature, and removes being associated with for these features and classification；
The training random forest grader on the training set for being mixed into shadow feature；
Average loss and standard deviation calculation Z-score using feature；
Delete feature of those Z-score than shadow feature difference；And
When all features are identified or algorithm reaches the number of iterations of setting, algorithm stops.
In above-mentioned maize variety identification method, it includes two stages that depth, which cascades forest model: more granularity scannings and grade
Join the forest stage.
In above-mentioned maize variety identification method, each layer of structure of forest is cascaded by being made of 500 decision trees
Four random forest compositions, wherein four random forests include two completely random forests and two common random forests, wherein
The first two represents completely random forest, and remaining two represent common random forest, then divides screening split vertexes by gini.
In the cascade forest stage, every layer of four random forests all use k and roll over cross validation method, one layer in cascade forest
After completing training, one inspection set is predicted with this model, if the accuracy rate of current layer is than the accuracy rate of preceding layer
Height then continues next layer of construction cascade forest, until current layer to the accuracy rate of inspection set compared with preceding layer accuracy rate no longer
It is promoted, training will terminate, therefore model determines.
In above-mentioned maize variety identification method, more granularity scannings are using various sizes of sliding window on corn image
It slides, obtains primitive character information.
In above-mentioned maize variety identification method, slided on corn image using the window of 7,9 and 11 3 kind of scale,
Classification is three classes.
In above-mentioned maize variety identification method, window 7 will generate 19 7 × 7 training sub-images, these data are defeated
Enter a completely random forest and a common random forest training, each sub-image generate the classification possibility of one 3 dimension to
Amount generates the feature vector of one 114 dimension after cascade, window 9 will obtain the feature vector of 108 dimensions, and window 11 will obtain
To the feature vector of 102 dimensions, the cascade forest of the feature vector training first order of 114 dimensions, the feature vector training second of 108 dimensions
The cascade forest of grade, the cascade forest of the feature vector training third level of 102 dimensions, repeats this process, until essence on inspection set
Degree convergence, a possibility that taking the last layer of cascade forest vector classification average value, be maximized later as final pre-
Survey result.
The present invention provides a kind of maize variety identification methods based on depth cascade forest and high spectrum image, sufficiently benefit
Corn classification is carried out with hyperspectral technique and depth cascade forest model, compared to common machine learning algorithm, identifies precision
It is higher.
Detailed description of the invention
It, below will be to specific in order to illustrate more clearly of the specific embodiment of the invention or technical solution in the prior art
Embodiment or attached drawing needed to be used in the description of the prior art are briefly described.
Fig. 1 is a kind of maize variety identification method based on depth cascade forest and high spectrum image according to the present invention
Techniqueflow chart.
Fig. 2 a is the cascade forest in depth cascade forest model corn disaggregated model；Fig. 2 b is depth cascade forest model
More granularities scanning cascade forest in corn disaggregated model.
Fig. 3 is corn variety sample.
Fig. 4 is corn area-of-interest (ROI) extraction process.
Fig. 5 is corn classification results.
Fig. 6 is depth cascade forest model classification results.
Fig. 7 is classification results of the depth cascade forest under different parameters.
Specific embodiment
The present invention provides a kind of maize variety identification methods based on depth cascade forest and high spectrum image, comprising:
Corn image data is pre-processed, all band image data is obtained；Choose the corn image data of effective wave band；And it will
The corn image data of effective wave band carries out category of model as feature set, using depth cascade forest model.
A kind of technology of maize variety identification method cascading forest and high spectrum image based on depth provided by the invention
Process is as shown in Figure 1, mainly include three parts: data prediction；Effective waveband selection；Category of model.
1.1 data prediction
1.1.1 corn image collection
Using high spectrum image acquisition system acquire corn image data, wherein high spectrum image acquisition system by light source,
Mobile platform and acquisition unit composition.Image acquisition units include image light spectrometer, charge-coupled device (CCD) detector
(1392 × 1040 area array CCD cameras) and camera lens (F/2.4, MCT, C-mount).The slit of high spectrum image spectroscopic system is wide
Degree is 30 μm, and spectral region is 400~1000nm, spectral resolution 2.8nm.Since there are signals to make an uproar in data acquisition
Sound only retains the corn image number of 146 wave bands of wavelength band 533.1-893.4nm to guarantee corn image quality
According to, wherein 146 wave bands are equidistantly selected in wavelength band 533.1-893.4nm.
1.1.2 corn adjustment of image
Due to light source light uneven, influence of dark current in addition according to intensity distribution, image can have many noises, therefore need
It is corrected to weaken or eliminate the interference of noise, updating formula are as follows:
Wherein, ISFor original Hyperspectral imaging, IDFor blackboard calibration image, IWFor blank calibration image, R is after correcting
Hyperspectral imaging.
1.1.3 corn area-of-interest (ROI) Extraction of Image
Before the spectral information and texture information for extracting each corn, need to automatically extract corn from original image
ROI image.The ROI image of each corn, the corn of extraction are extracted using the label fractional spins based on range conversion
ROI image is placed in 44 × 44 × 146 blank array, background value 0.
1.1.4 Pretreated spectra
Since spectral information has many noises, it is therefore desirable to carry out pretreatment and eliminate noise jamming raising model prediction ability
And stability, spectrum school is carried out to the corn ROI image of extraction using smooth (S-G the is smooth) algorithm of Savitzky-Golay herein
Just.Algorithmic procedure is as follows: by odd number (this paper setup parameter is 5), spectrum o'clock is as a window, using multinomial algorithm pair
Data in window do least square fitting, the first derivative and smoothed data value of calculation window central point, and moving window repeats
The above process does smoothing processing to whole picture image, obtains all band image data.
1.2 effective waveband selections
In order to reduce the higher-dimension of high-spectral data, need to choose feature set of effective wave band as category of model.Herein
146 wave bands are screened using Boruta algorithm, select effective wave band image data therein.Boruta algorithm is a kind of
Effective feature selecting algorithm mainly using the Z-score of feature as the measurement of feature importance, and then filters out important
Feature, algorithmic procedure are as follows:
(1) training set is mixed into for each feature construction shadow feature, and remove being associated with for these features and classification；
(2) the training random forest grader on the training set for being mixed into shadow feature；
(3) average loss of feature and standard deviation calculation Z-score are utilized；
(4) feature of those Z-score than shadow feature difference is deleted；
(5) when all features are identified or algorithm reaches the number of iterations of setting, algorithm stops.
1.3 category of model
The depth cascade forest model used herein includes two stages: more granularity scannings and cascade forest stage, such as being schemed
Shown in 2a and Fig. 2 b.
1.3.1 forest is cascaded
Cascade forest each layer (such as in attached drawing 2a and 2b, respectively with level 1, level 2 ... level m or
level 1A、level 1B、level 1C……level MA、level MB、level MCIndicate) be all made of decision tree
Forest composition, each layer are made of two different forests (being expressed as forest A and forest B), and the number of every kind of forest is 2,
Such result makes cascade forest more have diversity.In each layer of structure of cascade forest, the first two of Fig. 2 a is gloomy
Lin represents completely random forest, and remaining two forests represent common random forest, and completely random forest and common random forest are all
It is made of 500 decision trees, screening split vertexes is then divided by gini.Four of every layer are random gloomy in the cascade forest stage
Woods all uses k folding cross validation method, wherein k can be customized, such as k can be 3 or 5.One layer in cascade forest
After completing training, one inspection set is predicted with this model, if the accuracy rate of current layer is than the accuracy rate of preceding layer
Height then continues next layer of construction cascade forest, until current layer to the accuracy rate of inspection set compared with preceding layer accuracy rate no longer
It is promoted, training will terminate, therefore model determines.
1.3.2 more granularity scannings
Window slide has commonly used in the time series datas such as text, voice and image data, is inspired by this, more granularities are swept
It retouches and is slided on image data using various sizes of sliding window, obtain primitive character information, as shown in Figure 2 b.Assuming that adopting
It is slided on corn image with the window of 7,9 and 11 3 kind of scale, classification is 3 classes.Window 7 will generate 19 7 × 7 instructions
Practice sub-image, these data input a completely random forest and a common random forest training, and each sub-image generates one
The classification possibility vector of a 3 dimension, generates the feature vector of one 114 dimension after cascade, window 9 will obtain the spy of 108 dimensions
Vector is levied, window 11 will obtain the feature vector of 102 dimensions.The cascade forest of the 114 dimensional feature vectors training first order, 108 dimensions
The cascade forest of the feature vector training second level, the cascade forest of the 102 dimensional feature vectors training third level.This process is repeated,
Until precision convergence on inspection set.The classification average value of a possibility that taking cascade forest the last layer vector, is maximized later
As final prediction result.
Table 1: variable declaration
The realization of method
This research chooses the corn of agriculture China 213, silver jade 274 and beautiful 439 3 kinds of silver as research object, and dominant hue is equal
For yellow, each kind chooses 200, sample, and total sample number is 600, as shown in Figure 3.Every class corn according to 7:3 ratio
Random division is training set (420) and test set (180), and training set is used for precision evaluation for establishing model, test set.
Classification and Identification model is established using depth cascade forest algorithm under all band (FS) and effective wave band (ES), and is compared traditional
Progressive tree (GBDT) model of gradient, random forest (RF) model and support vector machines (SVM) model, utilize 5 folding cross validation sides
Method determines model parameter, is predicted on test set and calculates classification accuracy (indicating with %).
(1) corn ROI extracts (as shown in Figure 4)
Step 1: the smoothed out image of SG (that is, image after correction) setting threshold value is extracted into two-value image；
Step 2: the preposition region of corn seed is extracted using morphological transformation method, obtains morphological transformation image；
Step 3: Image Segmentation seed is extracted according to the result after morphological transformation；
Step 4: seed boundary is divided according to Image Segmentation seed, to obtain segmentation result；
(2) corn classification results
Step 1: respectively extract corn ROI all band and effective wave band spectral signature, using gradient it is progressive tree, with
Machine forest and support vector machines establish model to classify；
Step 2: corn ROI is inputted respectively in the spectrum image of all band and effective wave band, is built using depth cascade forest
Formwork erection type is classified；
Step 3: establishing model using 5 folding cross validation methods on training set, determine parameter, predict on test set,
View result；
We compare more granularity scanning (MGSCF) models, progressive tree (GBDT) model of gradient, random forest (RF) model
With classification results of support vector machines (SVM) model under all band (FS) and effective wave band (ES).As shown in figure 5, part 1
It is the classifying quality of the lower 4 kinds of models of all band, part 2 is the classifying quality of the lower 4 kinds of models of effective wave band.
(3) depth cascade forest classified parameter list compares
It can be further improved the performance of depth cascade forest model, such as more sliding window rulers by adjusting parameter
It is very little, simply final forest model (RF) is replaced with the progressive tree-model of gradient (GBDT).The depth cascade forest model point of acquisition
Class result is as shown in Figure 6.The present invention compare depth cascade forest model under different grain size and forest model effect (including
Effect under all band (FS) and effective wave band (ES)), as shown in table 2 and Fig. 7.
Window size under 2 different grain size of table
The superiority of this method:
Maize variety identification method provided by the invention based on depth cascade forest and high spectrum image, comprising: to jade
Rice image data is pre-processed, and all band image data is obtained；Choose the corn image data of effective wave band；And it will be effective
The corn image data of wave band carries out category of model as feature set, using depth cascade forest model.This method makes full use of
Hyperspectral technique and depth cascade forest model carry out corn classification, compared to common machine learning algorithm, identify precision more
It is high.
Above embodiments, only a specific embodiment of the invention, to illustrate technical solution of the present invention, rather than to it
Limitation, scope of protection of the present invention is not limited thereto, although the present invention is described in detail referring to the foregoing embodiments,
Those skilled in the art should understand that: anyone skilled in the art the invention discloses technology model
In enclosing, still it can modify to technical solution documented by previous embodiment or variation can be readily occurred in, or to it
Middle some technical characteristics are equivalently replaced；And these modifications, variation or replacement, do not make the essence of corresponding technical solution de-
Spirit and scope from technical solution of the embodiment of the present invention, should be covered by the protection scope of the present invention.Therefore, of the invention
Protection scope should be subject to the protection scope in claims.
Claims (10)
1. a kind of maize variety identification method based on depth cascade forest and high spectrum image, which is characterized in that including following
Step:
Step 1: corn image data being pre-processed, all band image data is obtained；
Step 2: choosing the corn image data of effective wave band；And
Step 3: using the corn image data of effective wave band as feature set, model being carried out using depth cascade forest model
Classification.
2. maize variety identification method according to claim 1, which is characterized in that wherein, the step 1 includes: to pass through
High spectrum image acquisition system acquires corn image data；The corn image data is corrected；The sense for extracting corn is emerging
Interesting region (ROI) image data；And spectrum correction is carried out to the area-of-interest image data of the corn of extraction, it obtains
The all band image data.
3. maize variety identification method according to claim 2, which is characterized in that wherein,
Corn image data is acquired by high spectrum image acquisition system, and is only retained in wavelength band 533-893.4nm
The corn image data of 146 wave bands equidistantly selected；
The corn image data is corrected using updating formula, the updating formula are as follows:
Area-of-interest (ROI) image number of each corn is extracted using the label fractional spins based on range conversion
According to；
Using smooth (S-G the is smooth) algorithm of Savitzky-Golay to corn area-of-interest (ROI) the image number of extraction
According to progress spectrum correction.
4. maize variety identification method according to claim 1, which is characterized in that using Boruta algorithm to all band shadow
As data are screened, effective wave band image data therein is selected.
5. maize variety identification method according to claim 4, which is characterized in that the process of the Boruta algorithm is such as
Under:
It is mixed into training set for each feature construction shadow feature, and removes being associated with for these features and classification；
The training random forest grader on the training set for being mixed into shadow feature；
Average loss and standard deviation calculation Z-score using feature；
Delete feature of the Z-score than shadow feature difference；And
When all features are identified or algorithm reaches the number of iterations of setting, algorithm stops.
6. maize variety identification method according to claim 1, which is characterized in that the depth cascades forest model and includes
Two stages: more granularity scannings and cascade forest stage.
7. maize variety identification method according to claim 6, which is characterized in that each layer of structure of the cascade forest
It is made of four random forests being made of 500 decision trees, wherein four random forests include two completely randoms
Forest and two common random forests, wherein the first two represents the completely random forest, remaining two represent it is described commonly with
Then machine forest divides screening split vertexes by gini；In the cascade forest stage, every layer of four random forests are all
Cross validation method is rolled over using k, after cascading one layer of completion training in forest, one inspection set is carried out with this model
Prediction continues next layer of construction cascade forest if the accuracy rate of current layer is higher than the accuracy rate of preceding layer, until current
Layer no longer promotes the accuracy rate of inspection set compared with the accuracy rate of preceding layer, and training will terminate, therefore model determines.
8. maize variety identification method according to claim 6, which is characterized in that more granularity scannings use different rulers
Very little sliding window slides on corn image, obtains primitive character information.
9. maize variety identification method according to claim 8, which is characterized in that using 7,9 and the window of 11 3 kind of scale
Mouth slides on corn image, and classification is three classes.
10. maize variety identification method according to claim 9, which is characterized in that window 7 will generate 19 7 × 7
Training sub-image, these data input a completely random forest and a common random forest training, and each sub-image generates
The classification possibility vector of one 3 dimension, generates the feature vector of one 114 dimension after cascade, window 9 will obtain 108 dimensions
Feature vector, window 11 will obtain the feature vector of 102 dimensions；The cascade of the feature vector training first order of 114 dimension is gloomy
Woods, the cascade forest of the feature vector training second level of 108 dimension, the grade of the feature vector training third level of 102 dimension
Join forest, repeat this process, until inspection set on precision convergence, take cascade forest the last layer a possibility that vector class
Other average value, is maximized later as final prediction result.
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