CN114580511B - Sulfur-smoked dried ginger identification method based on image brightness information and voting mechanism - Google Patents
Sulfur-smoked dried ginger identification method based on image brightness information and voting mechanism Download PDFInfo
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- 241000234314 Zingiber Species 0.000 title claims abstract description 80
- 235000006886 Zingiber officinale Nutrition 0.000 title claims abstract description 80
- 235000008397 ginger Nutrition 0.000 title claims abstract description 80
- 238000000034 method Methods 0.000 title claims abstract description 26
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 23
- 238000012706 support-vector machine Methods 0.000 claims abstract description 5
- 238000013528 artificial neural network Methods 0.000 claims abstract description 3
- 238000007637 random forest analysis Methods 0.000 claims abstract description 3
- 229910052717 sulfur Inorganic materials 0.000 claims description 48
- 239000011593 sulfur Substances 0.000 claims description 48
- NINIDFKCEFEMDL-UHFFFAOYSA-N Sulfur Chemical compound [S] NINIDFKCEFEMDL-UHFFFAOYSA-N 0.000 claims description 34
- RAHZWNYVWXNFOC-UHFFFAOYSA-N Sulphur dioxide Chemical compound O=S=O RAHZWNYVWXNFOC-UHFFFAOYSA-N 0.000 claims description 28
- 239000013598 vector Substances 0.000 claims description 22
- 238000012549 training Methods 0.000 claims description 13
- 238000001514 detection method Methods 0.000 claims description 9
- 238000003958 fumigation Methods 0.000 claims description 9
- 238000000605 extraction Methods 0.000 claims description 6
- 239000003153 chemical reaction reagent Substances 0.000 claims description 4
- 238000005987 sulfurization reaction Methods 0.000 claims description 4
- 238000012360 testing method Methods 0.000 claims description 3
- 238000010801 machine learning Methods 0.000 claims description 2
- 230000000877 morphologic effect Effects 0.000 claims description 2
- 238000012795 verification Methods 0.000 claims description 2
- 239000000463 material Substances 0.000 description 8
- 239000003814 drug Substances 0.000 description 3
- 238000006243 chemical reaction Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000002329 infrared spectrum Methods 0.000 description 2
- 238000010998 test method Methods 0.000 description 2
- 238000010792 warming Methods 0.000 description 2
- UCKMPCXJQFINFW-UHFFFAOYSA-N Sulphide Chemical compound [S-2] UCKMPCXJQFINFW-UHFFFAOYSA-N 0.000 description 1
- 244000273928 Zingiber officinale Species 0.000 description 1
- 241000234299 Zingiberaceae Species 0.000 description 1
- 229910052785 arsenic Inorganic materials 0.000 description 1
- RQNWIZPPADIBDY-UHFFFAOYSA-N arsenic atom Chemical compound [As] RQNWIZPPADIBDY-UHFFFAOYSA-N 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 230000007797 corrosion Effects 0.000 description 1
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- 238000001035 drying Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 229910001385 heavy metal Inorganic materials 0.000 description 1
- 238000004128 high performance liquid chromatography Methods 0.000 description 1
- 210000004072 lung Anatomy 0.000 description 1
- QSHDDOUJBYECFT-UHFFFAOYSA-N mercury Chemical compound [Hg] QSHDDOUJBYECFT-UHFFFAOYSA-N 0.000 description 1
- 229910052753 mercury Inorganic materials 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 229940126680 traditional chinese medicines Drugs 0.000 description 1
- 238000001195 ultra high performance liquid chromatography Methods 0.000 description 1
- 239000001841 zingiber officinale Substances 0.000 description 1
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Abstract
The invention discloses a sulfur-smoked dried ginger identification method based on image brightness information and a voting mechanism, which comprises the following steps: (1) sample preparation and image data acquisition; (2) extracting image features; (3) Respectively using a Support Vector Machine (SVM), a BP neural network (BPNN) and a random forest algorithm (RF) to establish a sulfur-smoked dried ginger identification model; (4) And establishing a set of identification model based on a voting mechanism according to the results of the three models. The invention adopts the sulfur-smoked dried ginger identification method based on the image brightness information and the voting mechanism for the first time, can accurately predict the degree of dryness Jiang Liuxun, and has the advantages of rapidness, no damage, high identification accuracy and strong stability.
Description
Technical Field
The invention belongs to the technical field of medicinal material detection; in particular to a sulfur-smoked dried ginger identification method based on image brightness information and a voting mechanism.
Background
Rhizoma Zingiberis (Zingiber officinale Roscoe) is dried rhizome of rhizoma Zingiberis recens of the genus zingiber of the family Zingiberaceae, and has effects of warming middle warmer, dispelling cold, restoring yang, dredging collaterals, warming lung, and resolving fluid retention. The market demand for dried ginger is enormous and huge populations worldwide, especially asia, africa and europe, have a habit of using dried ginger (including fresh ginger) on a daily basis. The sulfur fumigation of ginger can achieve the effects of corrosion resistance, mildew resistance and worm damage resistance, and is favorable for drying, color enhancement and the like. Although the sulfur fumigation plays a certain positive role in processing and storing the traditional Chinese medicine materials, modern researches prove that the chemical properties of the traditional Chinese medicine materials are changed after the sulfur fumigation, the property, taste and efficacy of the medicine materials are affected, heavy metals such as sulfide, arsenic, mercury and the like are remained, and a large amount of traditional Chinese medicines can cause damage to human bodies after being taken for a long time.
The existing detection technology of the sulfur fumigation medicinal material comprises an ultra-high performance liquid chromatography method, a near infrared spectrum detection method and the like. But existing methods require expensive equipment to collect the information. Therefore, it is not convenient to identify sulfur-free and sulfur-containing dried ginger using existing methods. With the development of machine learning algorithms, image recognition technology is becoming mature. The identification method of the sulfur-smoked dried ginger based on the image has the advantages of rapidness, no pollution, no damage to materials and the like. Therefore, how to quickly identify the sulfur-smoked dried ginger through image information is a key problem to be solved currently.
Disclosure of Invention
The invention aims to: in order to solve the technical problems, the invention provides a sulfur-smoked dried ginger identification method based on image brightness information and a voting mechanism. Compared with the traditional identification method, the method can accurately predict the sulfuring degree of the dried ginger, and has the advantages of low cost, rapidness, no damage, high identification accuracy and strong stability.
The technical scheme is as follows: in order to achieve the above object, the technical scheme provided by the invention is as follows: a sulfur-smoked dried ginger identification method based on image brightness information and voting mechanism comprises the following steps:
(1) Sample collection: selecting dried ginger with different sulfur contents;
(2) Sample classification: marking the sulphitation degree of the sample by using a sulfur dioxide detection reagent;
(3) Acquisition of image data: respectively using image acquisition equipment to acquire image information of the dried ginger, and marking the sulfur fumigation degree (high sulfur, low sulfur and no sulfur) of each image;
(4) LBP feature extraction: let the RGB value of the ith row and jth column pixel point (i, j) of the image be (R ij,Gij,Bij), and convert the RGB color information of the image into Lab color space three-channel information. Let Lab value of Lab pixel (i, j) after conversion be (L ij,aij,bij), then luminance value of pixel (i, j) be L ij. The brightness values of 8 adjacent pixels around the pixel are respectively L 0,L1,...,L7. Let L p be the luminance value of the p-th pixel point, and the luminance relation value Lr ij of the pixel point can be obtained by using the formula (1) and the formula (2). The luminance relation value L ij is an integer of [1,10 ]. Lr ij of each pixel of the whole L channel is calculated, and then the ratio of the occurrence times of each integer to the total number of brightness relation values is counted, so that a ten-dimensional vector is formed. This vector is denoted as f L1. The feature vector values for the a-channel and the b-channel were calculated in the same manner and denoted as f a1 and f b1. The concatenation f L1,fa1 and f b1 forms a vector f 1. And (3) downsampling the original image, and repeating the feature extraction process of the step (4) on the downsampled image to obtain a vector f 2. The downsampling is repeated again to obtain a vector f 3. And splicing the vectors f 1,f2 and f 3 to obtain the final image characteristic f.
(5) And (3) establishing a model: dividing the dried ginger image sample into a training set and a verification set, and learning the characteristics of the dried ginger training set samples with different sulfuration degrees by using a support vector machine, a BP neural network and a random forest algorithm. Model training is carried out by adopting LibSVM software packages, RF software packages and BPNN software packages, and 3 different dry Jiang Liuxun degree prediction models are constructed;
(6) Establishing a voting mechanism: let the weight occupied by the SVM algorithm be w SVM_M, the weight occupied by the BPNN algorithm be w BPNN_M, and the weight occupied by the RF algorithm be w RF_M. V i is calculated according to equations (3), (4), (5), (6). Calculating the maximum value of v 1,v2,v3, and if v 1 is the maximum value, then the sample is the sulfur-free dried ginger; if v 2 is the maximum, then this sample is low sulfur dried ginger; if v 3 is the largest, then this sample is high sulfur dried ginger. And for the new test sample, after the characteristic value is extracted, the prediction model can determine the type of the prediction result.
vi＝ci_SVM×wSVM_M+ci_BPNN×wBPNN_M+ci_RF×wRF_M (3)
Compared with the prior art, the invention has the beneficial effects that:
1. compared with the traditional experience identification, such as high performance liquid chromatography, a Fourier transform near infrared spectrum method and the like, the method has the advantages of rapidness, no damage, convenience and low cost.
2. The invention provides a new identification method for the sulfuration degree of the dried ginger, provides scientific basis for evaluating the sulfuration degree of the dried ginger in the market, and has wide application prospect.
Drawings
Fig. 1: the sulfur dioxide detection kit of Beijing Zhiyun reaches the technology Co., ltd used in the embodiment 1 of the invention;
fig. 2: the embodiment 1 of the invention is a sulfur-free, low-sulfur and high-sulfur dried ginger photo collected by a P30 ELEAL mobile phone;
fig. 3: the invention relates to a sulfur-free, low-sulfur and high-sulfur dried ginger photo collected by an apple XRA2108 mobile phone in an embodiment 1;
fig. 4: lab three-channel image of example 1 of the present invention: (a) an L channel (b) an a channel (c) a b channel;
Fig. 5: lab Chart of the invention embodiment 1: (a) an L-channel luminance map (b) an a-channel value map (c) a b-channel value map;
fig. 6: lab three channel histogram of embodiment 1 of the present invention: (a) an L-channel histogram (b) an a-channel value histogram (c) a b-channel value histogram;
fig. 7: the identification method of the embodiment 1 of the invention predicts the accuracy under different training set occupation ratios on different mobile phones;
Fig. 8 is a flowchart of a method for identifying sulfur-smoked dried ginger based on image brightness information and voting mechanism.
Detailed Description
The present invention will be described by way of specific examples, to facilitate understanding and grasping of the technical solution of the present invention, but the present invention is not limited thereto. The experimental methods described in the following examples are all conventional methods unless otherwise specified; the reagents and materials, unless otherwise specified, are commercially available.
The invention uses the instrument: hua is a sulfur dioxide detection kit of P30 ELEAL and apple XRA2108, beijing Zhiyun reaches the technology Co., ltd.
Example 1
A sulfur-smoked dried ginger identification method based on image brightness information and voting mechanism comprises the following steps:
1. collecting a medicinal material sample: 123 batches of dried ginger with different sulfur contents are purchased from the market, and different batches of dried ginger are respectively measured by using a sulfur dioxide rapid detection kit. According to the fumigation degree of the dried Jiang Liu in different batches, the dried ginger can be divided into 3 types, namely low-sulfur dried ginger, high-sulfur dried ginger and sulfur-free dried ginger. Wherein, the total amount of the low-sulfur dried ginger is 27 batches, the total amount of the high-sulfur dried ginger is 66 batches, and the total amount of the sulfur-free dried ginger is 30 batches. The three types of classification standards are that the sulfur dioxide content of the low-sulfur dried ginger is 50-150mg/kg, the sulfur dioxide content of the high-sulfur dried ginger is more than 150mg/kg, and the sulfur dioxide content of the sulfur-free dried ginger is 0mg/kg. FIG. 1 shows the results of the color cards obtained with different amounts of dioxide in dried ginger. The final classification is shown in Table 1, where NS represents no sulfur, LS represents low sulfur, and HS represents high sulfur.
TABLE 1
2. Collecting image data of a sample: and acquiring the images of the stems Jiang Gaoqing of different producing areas through an image acquisition device, and marking the information of the sulfuring degree of each image. In order to ensure the integrity of the morphological detail information of the dried ginger, the data acquisition of the images is carried out by adopting a Hua P30 ELEAL mobile phone and an apple XRA2108 mobile phone under the same condition. And collecting positive and negative images of each dried ginger. The number of images of the low sulfur ginger, the high sulfur ginger and the sulfur-free ginger are 54, 132 and 60 respectively. Fig. 2 (a), (b) and (c) show the image data of sulfur-free, low-sulfur and high-sulfur dried ginger photographed by a mobile phone of P30 ELEAL. Fig. 3 (a), (b) and (c) are sulfur-free, low-sulfur, high-sulfur ginger image data captured by an apple XRA2108 cell phone.
3. LBP feature extraction: let the RGB value of the ith row and jth column pixel point (i, j) of the image be (R ij,Gij,Bij), and convert the RGB color information of the image into Lab color space three-channel information. Let Lab value of Lab pixel (i, j) after conversion be (L ij,aij,bij), then luminance value of pixel (i, j) be L ij. The brightness values of 8 adjacent pixels around the pixel are respectively L 0,L1,...,L7. Let L p be the luminance value of the p-th pixel point, and the luminance relation value Lr ij of the pixel point can be obtained by using the formula (1) and the formula (2). The luminance relation value L ij is an integer of [1,10 ]. Lr ij of each pixel of the whole L channel is calculated, and then the ratio of the occurrence times of each integer to the total number of brightness relation values is counted, so that a ten-dimensional vector is formed. This vector is denoted as f L1. The feature vector values for the a-channel and the b-channel were calculated in the same manner and denoted as f a1 and f b1. The concatenation f L1,fa1 and f b1 forms a vector f 1. And (3) downsampling the original image, and repeating the feature extraction process of the step (4) on the downsampled image to obtain a vector f 2. The downsampling is repeated again to obtain a vector f 3. And splicing the vectors f 1,f2 and f 3 to obtain the final image characteristic f. Fig. 4 (a), 4 (b) and 4 (c) show images of Lab space L channel, a channel and b channel, respectively. Fig. 5 (a), 5 (b) and 5 (c) show the L-channel luminance map, the a-channel value map and the b-channel value map, respectively. Fig. 6 (a), 6 (b) and 6 (c) show an L-channel histogram, an a-channel histogram and a b-channel histogram, respectively.
4. Establishing a voting mechanism: let the weight occupied by the SVM algorithm be w SVM_M, the weight occupied by the BPNN algorithm be w BPNN_M, and the weight occupied by the RF algorithm be w RF_M. V i is calculated according to equations (3), (4), (5), (6). Calculating the maximum value of v 1,v2,v3, and if v 1 is the maximum value, then the sample is the sulfur-free dried ginger; if v 2 is the maximum, then this sample is low sulfur dried ginger; if v 3 is the largest, then this sample is high sulfur dried ginger. And for the new test sample, after the characteristic value is extracted, the prediction model can determine the type of the prediction result.
vi＝ci_SVM×wSVM_M+ci_BPNN×wBPNN_M+ci_RF×wRF_M (3)
5. Establishing a sulfur content identification model: to avoid experimental errors due to random sampling, the train-test procedure was repeated 200 times per prediction. Each time, the representative characteristic value is taken as a characteristic vector, a support vector machine is used for learning the sample type, and LibSVM software packages, BPNN software packages and RF software packages are adopted for model training. And (3) setting the accuracy of the weight of each algorithm in the voting mechanism according to the accuracy of each algorithm, which is obtained by different methods shown in the step (5), wherein the accuracy of the SVM algorithm is larger than that of the BPNN algorithm and larger than that of the RF algorithm according to the accuracy of the SVM algorithm obtained in the table 2. Therefore, three weights are respectively set as w SVM_M＝0.4,wBPNN_M＝0.35,wRF_M =0.25, and finally a prediction model is constructed to realize the prediction of the sulfur content of the dried ginger.
Table 2 shows the average prediction accuracy of the images taken by the P30ELEAL mobile phone and the apple XRA2108 mobile phone
| Accuracy% | ACCSVM | ACCBPNN | ACCRF |
| Hua is P30 | 80.8939 | 71.5648 | 79.9622 |
| Apple XR | 92.3797 | 87.1865 | 88.0789 |
6. Model training accuracy and stability: the size of the training set affects the accuracy of the final prediction, with the training set duty cycle being incrementally taken from between 0.1-0.9, 0.1 increments each time. Training was repeated 200 times with the train-test procedure. The experimental results are shown in table 3 and table 4, and among the prediction accuracy rates of 9 different training set occupation ratios, the accuracy rate based on the voting mechanism is highest, and the accuracy rate of the apple XR shooting image reaches 93.44%. The accuracy of the P30 shot image is 82.98%. In summary, the ideas presented in this patent have high accuracy and high practicality.
Table 3 accuracy of images taken by apple XRA2108 cell phone
Table 4 shows the accuracy of the images taken by the P30 ELE AL00 mobile phone
The foregoing detailed description is directed to one of the possible embodiments of the present invention, which is not intended to limit the scope of the invention, but is to be accorded the full scope of all such equivalents and modifications so as not to depart from the scope of the invention.
Claims (6)
1. The sulfur-smoked dried ginger identification method based on the image brightness information and the voting mechanism is characterized by comprising the following steps of:
(1) Sample collection: selecting dried ginger with different sulfur dioxide contents;
(2) Sample classification: marking the sulphitation degree of the sample by using a sulfur dioxide detection reagent;
(3) Acquisition of image data: respectively using image acquisition equipment to acquire image information of the dried ginger, and marking the sulfur fumigation degree of each image according to high sulfur, low sulfur and no sulfur;
(4) LBP feature extraction: setting RGB values of the ith row and the jth column of the image as (R ij,Gij,Bij), and converting RGB color information of the image into three-channel information of a Lab color space; setting the Lab value of the converted Lab pixel point (i, j) as (L ij,aij,bij), and setting the brightness value of the pixel point (i, j) as L ij; the brightness values of 8 adjacent pixel points around the pixel point are respectively L 0,L1,...,L7; let L p be the luminance value of the p-th pixel point, and use formula (1) and formula (2) to obtain the luminance relation value Lr ij of the pixel point; the luminance relation value L ij is an integer of [1,10 ]; calculating Lr ij of each pixel of the whole L channel, and counting the ratio of the occurrence times of each integer to the total number of brightness relation values to form a ten-dimensional vector; this vector is denoted as f L1, and the characteristic vector values for the a-channel and the b-channel are calculated in the same manner as f a1 and f b1; splicing f L1,fa1 and f b1 to form a vector f 1, downsampling the original image, and repeating the feature extraction process of the step (4) on the downsampled image to obtain a vector f 2; then, repeating downsampling to obtain a vector f 3, and splicing the vectors f 1,f2 and f 3 to obtain the final image characteristic f;
(5) And (3) establishing a model: dividing the dried ginger image sample into a training set and a verification set, and learning the characteristics of the dried ginger training set samples with different sulfuration degrees by using a support vector machine, a BP neural network and a random forest algorithm; model training is carried out by adopting LibSVM software packages, RF software packages and BPNN software packages, and 3 different dry Jiang Liuxun degree prediction models are constructed;
(6) Establishing a voting mechanism: let the weight occupied by the SVM algorithm be w SVM_M, the weight occupied by the BPNN algorithm be w BPNN_M, and the weight occupied by the RF algorithm be w RF_M; v i is calculated according to formulas (3), (4), (5), (6); calculating the maximum value of v 1,v2,v3, and if v 1 is the maximum value, then the sample is the sulfur-free dried ginger; if v 2 is the maximum, then this sample is low sulfur dried ginger; if v 3 is the maximum, then this sample is high sulfur dried ginger; for the new test sample, after the characteristic value is extracted, the prediction model can judge the type of the prediction result;
vi＝ci_SVM×wSVM_M+ci_BPNN×wBPNN_M+ci_RF×wRF_M (3)
2. The method for identifying sulfur-smoked dried ginger based on brightness information and voting mechanism according to claim 1, wherein the dried ginger with different sulfur contents in the step (1) is respectively low sulfur dried ginger, high sulfur dried ginger and sulfur-free dried ginger; the criteria for three categories of division are: the sulfur dioxide content of the low sulfur dried ginger is 50-150mg/kg, the sulfur dioxide content of the high sulfur dried ginger is more than 150mg/kg, and the sulfur dioxide content of the sulfur-free dried ginger is 0mg/kg.
3. The method for identifying sulfur-smoked dried ginger based on image brightness information and voting mechanism according to claim 1, wherein the reagent for marking the degree of fumigation of the dried Jiang Liu in the step (2) is a sulfur dioxide rapid detection kit of Beijing Zhiyun reaches science and technology Co., ltd, which detects the sulfur dioxide content range of the dried ginger.
4. The method for authenticating a sulfur-smoked dried ginger having an image based on luminance information and voting scheme as claimed in claim 1, wherein in step (3), in order to ensure the integrity of the morphological detail information of the dried ginger, the data acquisition of the image is respectively carried out by using a Hua P30 ELE AL00 mobile phone and an apple XRA2108 mobile phone under the same condition.
5. The method for identifying the sulfur-smoked dried ginger based on the image brightness information and the voting mechanism according to claim 1, wherein the prediction model in the step (5) is used for collecting pictures of the dried ginger with different sulfur contents, establishing a dried ginger image database, calculating the brightness value relation information among pixels of the dried ginger image, extracting Lab three-channel characteristic information of the dried ginger image, modeling through a machine learning algorithm and the voting mechanism, and finally realizing the prediction of the degree of dry Jiang Liu fumigation.
6. The method for identifying sulfur-smoked dried ginger based on image brightness information and voting mechanism according to claim 1, wherein in step (5), three weights of SVM algorithm, BPNN algorithm and RF algorithm are respectively set to w SVM_M＝0.4,wBPNN_M＝0.35,wRF_M =0.25.
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| 基于局部自适应色差阈值的彩色图像边缘检测;杨娴;电子与信息学报;20120930;第34卷(第9期);参见第2058-2063页 * |
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