CN109765194B - Wolfberry producing area identification method based on hyperspectral imaging technology - Google Patents
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Abstract
The invention discloses a wolfberry producing area identification method based on a hyperspectral imaging technology. It comprises the following steps: performing spectrum scanning on medlar seed samples of the same variety in different producing areas, and collecting hyperspectral data of 1000-2400 nm; RAD correction and black-and-white correction are carried out, the data are processed into relative reflectivity data, then threshold segmentation is carried out on the data, and small-area operation is deleted; extracting the region of interest of the data to obtain an average spectral value of the region of interest; dividing the test set into three parts, and recording the three parts as a training set, a verification set and test set spectral data; processing the three parts of data by using ZCA whitening; modeling main spectral information obtained by the training set spectrum and producing area information by using partial least square regression to obtain a wolfberry producing area prediction model; using the spectral data verification set and the test spectral data debugging model; and identifying the producing area of the medlar by the finally established producing area identification model. The invention can identify the producing area, reduce the manual identification cost and improve the efficiency, the accuracy and the scientificity of identification.
Description
Technical Field
The invention relates to a wolfberry producing area identification method based on a hyperspectral imaging technology, and belongs to the field of traditional Chinese medicine material identification.
Background
The medlar resource is widely distributed in China, according to the description of Chinese plant journal, the northern part of China, such as the northern Hebei part, inner Mongolia, the northern part of Shanxi, the northern part of Shaanxi, Gansu, Ningxia, Qinghai and Xinjiang, have wild fruits, and the fruits are cultivated as the medicines, so that the cultivation is introduced in the middle and south parts of China except the provinces, and particularly, the cultivation is more and the yield is high in the Ningxia and Tianjin areas. According to the examination of many aspects, Ningxia is now used to locate the genuine producing area of the medicinal wolfberry fruit. However, due to the fact that the number of producing areas is large, quality control is difficult, and the sources of commodities circulating in the market cannot be guaranteed, so that the market of the wolfberry fruits is disordered and the phenomena that the wolfberry fruits are good again and reach the producing areas of the producing areas by other producing areas are frequent. In the market transaction process, the quality of the medlar is mostly identified by an empirical identification method, the method has larger error and stronger subjectivity, and the variety of the medlar is more, so that the credibility of identification is lower only according to experience. Chemical detection and molecular detection cannot be popularized due to the complex operation method, time and labor consumption.
In recent years, the hyperspectral imaging technology has been developed rapidly, and is only applied to the field of aerospace for the earliest time. And performing geological exploration and ore identification. And then, the method steps into the agricultural field to identify the quality of the crops and distinguish the types of the crops. Therefore, the hyperspectral imaging technology has been deeply involved in the aspects of life, and is only involved in the field of traditional Chinese medicine.
Disclosure of Invention
The invention aims to provide a wolfberry producing area identification method based on a hyperspectral imaging technology, and the technical operation flow is beneficial to market circulation monitoring of genuine medicinal materials; the cost of manual identification is reduced, and the efficiency, accuracy and scientificity of identification are improved.
The invention provides a wolfberry producing area identification method based on a hyperspectral imaging technology, which comprises the following steps: 1) performing spectrum scanning on the same variety of the barbary wolfberry fruits in different producing areas, and collecting 1000-2400nm hyperspectral data each time;
2) RAD correction is carried out on the original hyperspectral data of the sample;
3) performing black and white correction on the data subjected to RAD correction in the step 2) to process the data into relative reflectivity data;
4) carrying out threshold segmentation on the relative reflectivity data, and deleting small-area operation;
5) extracting the region of interest from the data processed in the step 4), and then calculating to obtain an average spectral value of the region of interest;
6) dividing the average spectral value of the region of interest obtained after the data of the spectral scanning is processed in the step 5) into three parts, and recording the three parts as the spectral data of a training set, a verification set and a test set;
7) processing the spectral data of the training set, the verification set and the test set obtained by processing in the step 6) by using ZCA whitening;
8) modeling main spectral information and producing area information obtained by the training spectral data processed in the step 7) by using partial least squares regression to obtain a wolfberry producing area identification model, and debugging the model by using the spectral data verification set and the test spectral data; and identifying the producing area of the medlar by the finally established producing area identification model.
In the above method, the number of samples is greater than or equal to 100;
performing the spectral scanning with a hyperspectral imager;
the conditions for the spectral scan are as follows: the distance between a lens of the hyperspectral imager and the wolfberry fruit can be 20-30 cm; the moving speed of the platform can be 1.5 mm/s; when the collected spectrum range is 1000-2400nm, the integration time can be 4500 mus, and the frame time can be 46928;
the number of spectral scans was 3.
In the above method, the RAD calibration in step 2) is Radiometric calibration, which is a self-contained calibration software of the apparatus.
In the above method, the black and white correction formula in step 3) is as follows:
wherein R represents the relative reflectivity of the corrected image, IRRepresenting the energy value, I, of the original imageWEnergy value, I, representing a whiteboard imageBRepresenting the energy value of the blackboard image.
In the method, MATLAB software is adopted in the step 4) to carry out threshold segmentation and delete small-area operation.
In the above method, step 5) adopts MATLAB software to perform the region of interest extraction and average spectrum calculation.
In step 5), the extraction criteria of the region of interest extraction are according to common knowledge in the art, and the invention specifically extracts spectral data of the medlar subpart.
In the above method, the operation of dividing the average spectrum value of the region of interest in step 6) into three parts is as follows: by using randderm function in matlab software, the data of the spectrum scanning is divided into three parts on average, wherein the two parts are calculated according to the following formula of 2: 1, training and verifying collection spectral data and randomly grouping, and using the rest collection data as a test set. In a specific embodiment, two of the 3 spectral scans were collected at 2: 1, training and verifying collection spectrum data and randomly grouping, and collecting data as a test set at the other time.
In the invention, ZCA whitening is adopted during spectrum pretreatment in step 7);
the ZCA whitening processing method is defined as multiplying a feature vector matrix on the basis of PCA whitening, and rotating the feature vector matrix back to an original data space to obtain a new feature close to the original data. The algorithm for ZCA whitening implements the following equation:
in the formula, X is input data, the dimensionality is mxn, m represents the number of samples, and n represents the input characteristic dimensionality; by calculating the covariance matrix of the input data X
Then, SVD decomposition is carried out to obtain a left eigenvector matrix U and an eigenvector matrix S, and finally, a new eigenvector matrix X is calculatednew。
In the method, MATLAB software is adopted in the step 8) to establish the partial least squares regression model. The partial least squares regression is used as the set and evolution of multiple linear regression, typical correlation analysis and principal component analysis, and the thought is as follows: extracting component t from autovariate set Xh(h-1, 2, …), each component being independent of the other. Subsequently establishing an extracted component thAnd the dependent variable Y.
In the above method, the production area of the same kind of fructus Lycii of different production areas can be at least one of Sinkiang, Nemourning, Gansu, Qinghai and Ningxia;
the variety of the wolfberry fruit of the same variety in different producing areas is Ningqi No. 7.
The invention has the following advantages:
the hyperspectral imaging technology is adopted, and the hyperspectral imaging technology is applied to the field of identification of the production areas of the traditional Chinese medicinal materials, so that the market circulation monitoring of genuine medicinal materials is facilitated; the cost of manual identification is reduced, and the accuracy and the scientificity of identification are improved. The invention applies hyperspectrum to the identification of Chinese medicinal material varieties, and the key point is to find out the relation between a hyperspectral curve and the production area environment, the properties and characteristic components of the medicinal materials.
Drawings
FIG. 1 is a flow chart of the present invention for identifying wolfberry fruit of different producing areas based on hyperspectral imaging spectrometer.
FIG. 2 is an overall apparatus for use with the present invention.
Fig. 3 shows an original placement diagram of the medlar.
Fig. 4 is a threshold segmentation image.
FIG. 5 is a spectrum curve of different producing areas, wherein XJ, NX, QH, NM, GS respectively show the spectrum curves of Ningqi No. 7 produced in Xinjiang, Ningqi No. 7 produced in Ningxia, Ningxia No. 7 produced in Qinghai, Ningxia No. 7 produced in inner Mongolia and Ningqi No. 7 produced in Gansu.
FIG. 6 is a graph of validation set accuracy as a function of PLS principal component number.
Detailed Description
The experimental procedures used in the following examples are all conventional procedures unless otherwise specified.
Materials, reagents and the like used in the following examples are commercially available unless otherwise specified.
Examples
According to the flow chart shown in figure 1 and by using the device shown in figure 2, the same variety of wolfberry fruits in different producing areas are identified based on a hyperspectral imaging spectrometer, and the specific steps are as follows:
1. and performing spectrum scanning on the same wolfberry variety in different producing areas by using a hyperspectral imager, wherein during scanning, a 1000-plus 2400nm lens works, and hyperspectral data are collected.
Scanning the same variety of medlar seed samples in different producing areas, wherein the range of the lens is not exceeded as much as possible during each scanning. When putting the medlar, the characteristics of each particle are highlighted, the dense pendulum which is not overlapped is avoided as much as possible, and a white board for black and white correction is put at the position 5cm behind the sample. And waiting for instrument connection and self-checking. Setting the scanning parameters of the hyperspectral imager, the lens distance of 30cm, the platform moving speed of 1.5mm/s, the integration time of the 1000-2400nm lens of 4500 mus and the frame time of 46928. The integration time is the number of photons entering the lens in unit time, and the longer the integration time is, the higher the image quality is without generating an overexposure point. The frame time reflects the aspect ratio of the image, and the larger the value is, the larger the proportion of the scanned object in the horizontal direction is, so that repeated debugging is required to find the optimal proportion for data recording.
2. The scanned result is corrected by RAD correction software carried by a spectrometer, and the correction can eliminate bands and noise caused by unstable external environment during scanning, so that the image quality is better.
3. And (3) importing hyperspectral data by matlab software, and processing the image original data into relative reflectivity data by using a black and white correction formula.
4. The spectral image subjected to black and white correction is subjected to threshold segmentation, and a small area is deleted, so that a region-of-interest mask of the image is obtained, as shown in fig. 4.
5. And extracting the region of interest of the determined mask image by utilizing matlab software, and calculating an average spectral value in the ROI region.
6. The data is divided into three parts: training set, verifying set and testing set; the division of the spectral data set, which comprises extracting the spectrum in a random non-playback manner, first generates 1 to nRThe random number set A corresponds the label information to the random data set, and the spectrum data corresponds to each label. According to the set proportion, the spectrum information is divided into the following parts by extracting different label information: training set, validation set and test set. The training set is used for training the model, the verification set is used for adjusting parameters, and the test set is used for testing the performance of the model.
225 samples of Lycium barbarum were collected in 3 replicates, and the data collected from two of the 3 spectral scans was recorded at 2: 1, training and verifying collection spectrum data and randomly grouping, and collecting data as a test set at the other time. The samples are divided into a training set, a verification set and a test set, and the specific classification table is shown in the following table 1.
TABLE 1 data distribution
7. Performing ZCA vector normalization on the data to screen out main spectral information, wherein the specific principle is as follows:
the ZCA whitening processing method is defined as multiplying a feature vector matrix on the basis of PCA whitening, and rotating the feature vector matrix back to an original data space to obtain a new feature close to the original data. The algorithm for ZCA whitening implements the following equation:
in the formula, X is input data, and the dimensionality is mxn; m represents the number of samples, and n represents the dimension of the input feature; by calculating the covariance matrix of the input data X
Then, SVD decomposition is carried out to obtain a left eigenvector matrix U and an eigenvector matrix S, and finally, a new eigenvector matrix X is calculatednew。
According to the flow chart shown in figure 1 and by using the device shown in figure 2, the method of the invention is adopted to identify Xinjiang Ningqi No. 7, Ningxia Ningqi No. 7, Qinghai Ningxia No. 7, Nengmeng Ningxia No. 7 and Gansu Ningqi No. 7, and the specific steps are as follows:
1. 90 wolfberry samples of Ningqi No. 7 produced by Xinjiang, Ningxia, Gansu, Qinghai and inner Mongolia are taken and put on a mobile platform to the extent that the samples do not exceed the range of a lens as much as possible. 225 medlar samples of the same variety in different producing areas are taken, each 75 medlar samples are scanned 3 times, and the lens range is not exceeded as much as possible during each scanning, as shown in figure 3. When putting the medlar, the characteristics of each particle are highlighted, the dense pendulum which is not overlapped is avoided as much as possible, and a white board for black and white correction is put at the position 5cm behind the sample. And waiting for instrument connection and self-checking. And setting scanning parameters of the hyperspectral imager, wherein the lens distance is 30cm, and the platform moving speed is 1.5 mm/s. The integration time of the 400-plus 1000nm shot is set to 4350 mus, and the frame time 18000. The integration time for the 1000- 2400nm shot is 4500 mus, frame time 46928.
2. The scanned result is corrected by RAD correction software carried by a spectrometer, and the correction can eliminate bands and noise caused by unstable external environment during scanning, so that the image quality is better.
3. And (3) importing hyperspectral data by matlab software, and processing the image original data into relative reflectivity data by using a black and white correction formula.
4. And (4) performing threshold segmentation on the relative reflectivity data, and deleting small areas to obtain an interested area mask of the image.
The threshold segmentation method mentioned in this step is mainly to set the image as f (x, y) image whose gray acquisition range is [0, L ], select a proper gray value T between 0 and L, then the image can be segmented with the background according to the gray value T, the specific formula is as follows:
the g (x, y) image obtained at this time is a binary image, and the region of interest is extracted from the original spectral image using the obtained binary image, as shown in fig. 4.
5. The determined mask image is used for extracting a region of interest (spectrum data of the medlar subpart) by utilizing matlab software, and an average spectrum value in the ROI region is calculated, as shown in FIG. 5.
6. Two of the 3 spectral scans were collected at 2: 1 ratio training set and validation
Spectral data were randomly grouped and data were collected another time as a test set.
7. And performing ZCA whitening processing on the data to screen out main spectral information.
8. Establishing a partial least squares regression discriminant (PLS-DA) model by using a training set sample; testing the set inspection result by using the verification set; the results are shown in FIG. 6.
After the training set is preprocessed, PLS-DA modeling is adopted, the average accuracy of the training set is 100%, the average accuracy of the verification set is 99.29%, the accuracy of the test set is 91.04%, and the standard deviation of the accuracy of the test set is 0.0105.
Claims (4)
1. A wolfberry producing area identification method based on a hyperspectral imaging technology comprises the following steps: 1) performing spectrum scanning on the same variety of the barbary wolfberry fruits in different producing areas, and collecting 1000-2400nm hyperspectral data each time;
in the step of spectrum scanning, when the wolfberry is placed, the characteristics of each particle are highlighted, and the wolfberry is placed tightly without overlapping as much as possible;
the number of samples of the wolfberry fruits is more than or equal to 100;
performing the spectral scanning with a hyperspectral imager;
the conditions for the spectral scan are as follows: the distance between a lens of the hyperspectral imager and the wolfberry fruit is 20-30 cm; the moving speed of the platform is 1.5 mm/s; when a 1000-2400nm lens is used, the integration time is 4500 mus, and the frame time is 46928;
2) RAD correction is carried out on the original hyperspectral data of the sample;
RAD is corrected to Radiometric calibration in step 2);
3) performing black and white correction on the data subjected to RAD correction in the step 2) to process the data into relative reflectivity data;
the black and white correction formula in step 3) is as follows:
wherein R represents the relative reflectivity of the corrected image, IRRepresenting the energy value, I, of the original imageWEnergy value, I, representing a whiteboard imageBAn energy value representing a blackboard image;
4) carrying out threshold segmentation on the relative reflectivity data, and deleting small-area operation;
step 4) adopting MATLAB software to carry out threshold segmentation and deleting small-area operation;
5) extracting the region of interest from the data processed in the step 4), and then calculating to obtain an average spectral value of the region of interest;
in the step 5), MATLAB software is adopted to extract the region of interest and calculate an average spectrum;
6) dividing the average spectral value of the region of interest obtained after the data of the spectral scanning is processed in the step 5) into three parts, and recording the three parts as the spectral data of a training set, a verification set and a test set;
the operation of dividing the average spectrum value of the interest area into three parts in the step 6) is as follows: by using randderm function in matlab software, the data of the spectrum scanning is divided into three parts on average, wherein the two parts are calculated according to the following formula of 2: 1, training and verifying the random grouping of spectrum data in proportion, and using the remaining part of collected data as a test set;
7) processing the spectral data of the training set, the verification set and the test set obtained by processing in the step 6) by using ZCA whitening;
8) modeling main spectral information and producing area information obtained by the training spectral data processed in the step 7) by using partial least squares regression to obtain a wolfberry producing area identification model, and debugging the model by using the spectral data verification set and the test spectral data; and identifying the producing area of the medlar by the finally established producing area identification model.
2. The method of claim 1, wherein: the number of spectral scans was 3.
3. The method according to claim 1 or 2, characterized in that: and 8) adopting MATLAB software to establish the partial least squares regression model.
4. The method according to claim 1 or 2, characterized in that: the production area of the same variety of the medlar in different production areas is at least one of Xinjiang, inner Mongolia, Gansu, Qinghai and Ningxia;
the variety of the wolfberry fruits of the same variety in different producing areas is Ningxia wolfberry.
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| CN114720420A (en) * | 2022-03-24 | 2022-07-08 | 中国中医科学院中药研究所 | Method and system for identifying production area of Chinese prickly ash based on hyperspectral imaging technology |
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