CN112949663A - Tobacco leaf baking control system and method based on image recognition - Google Patents
Tobacco leaf baking control system and method based on image recognition Download PDFInfo
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Abstract
The invention discloses a tobacco leaf baking control system and method based on image recognition, wherein the method comprises the following steps: acquiring an image of the baked tobacco leaves; preprocessing the tobacco leaf image to obtain a preprocessed image; performing tobacco leaf feature extraction on the preprocessed image to obtain a tobacco leaf feature value, wherein the tobacco leaf feature value at least comprises a tobacco leaf color feature value, a main vein color feature value, a tobacco leaf texture feature value and an area ratio feature value; inputting the tobacco leaf characteristic value into a tobacco leaf baking stage identification model, and calculating a measured value of the current tobacco leaf baking stage; and comparing the measured value of the current tobacco leaf curing stage with a set threshold value of the current tobacco leaf curing stage, if the measured value of the current tobacco leaf curing stage is greater than the set threshold value of the current tobacco leaf curing stage, triggering the tobacco leaf curing process to enter the next stage, and controlling the curing equipment to cure according to a tobacco leaf curing process curve set by the next stage. The invention can realize semi-intelligent production of tobacco leaves and reduce cost.
Description
Technical Field
The invention relates to a tobacco leaf baking control system and method based on image recognition, and belongs to the technical field of tobacco leaf baking.
Background
Tobacco leaf curing is an important step in the tobacco production process, and aims to promote yellowing and drying of tobacco leaves. The baking process generally divides the tobacco leaf baking into three stages of a yellowing stage, a color fixing stage and a stem drying stage, and each stage is further divided into a plurality of small stages. In the baking process, a baking engineer adjusts the temperature and the humidity inside the baking room through observation, so that the baking process is strictly followed to ensure the baking quality of the tobacco leaves. However, the problems of large control difficulty, time and labor waste, more required personnel and uneven baking quality exist in baking through the experience of each baking engineer.
Disclosure of Invention
Based on the above, the invention provides an intelligent tobacco leaf baking control system and method based on image recognition.
The technical scheme of the invention is as follows: the tobacco leaf curing control method based on image recognition comprises the following steps:
acquiring an image of the baked tobacco leaves;
preprocessing the tobacco leaf image to obtain a preprocessed image;
performing tobacco leaf feature extraction on the preprocessed image to obtain a tobacco leaf feature value, wherein the tobacco leaf feature value at least comprises a tobacco leaf color feature value, a main vein color feature value, a tobacco leaf texture feature value and an area ratio feature value;
inputting the tobacco leaf characteristic value into a tobacco leaf baking stage identification model, and calculating a measured value of the current tobacco leaf baking stage;
and comparing the measured value of the current tobacco leaf curing stage with a set threshold value of the current tobacco leaf curing stage, if the measured value of the current tobacco leaf curing stage is greater than the set threshold value of the current tobacco leaf curing stage, triggering the tobacco leaf curing process to enter the next stage, and controlling the curing equipment to cure according to a tobacco leaf curing process curve set by the next stage.
Optionally, the tobacco flue-curing stage identification model is as follows:
b＝a1*x1+a2*x2+a3*x3+a4*x4
in the formula, b is an actual measurement value of the current tobacco leaf baking stage, a1 is a tobacco leaf color characteristic value, x1 is a tobacco leaf color weight of the current stage, a2 is a main vein color characteristic value, x2 is a main vein color weight of the current stage, a3 is a tobacco leaf texture characteristic value, x3 is a tobacco leaf texture characteristic weight of the current stage, a4 is an area ratio characteristic value, and x4 is an area ratio characteristic weight of the current stage.
Optionally, according to a tobacco leaf curing process curve, the tobacco leaf curing stage is divided into an initial yellowing stage, an early yellowing stage, a middle yellowing stage, a later yellowing stage, an early fixing stage, a middle fixing stage, a later fixing stage, an early drying stem stage, a middle drying stem stage and a later drying stem stage, and each characteristic weight in the tobacco leaf curing stage identification model in each period is different.
Optionally, the pretreatment comprises the following steps:
carrying out image denoising on the tobacco leaf image to obtain a denoised image;
carrying out color correction on the denoised image to obtain a corrected image;
carrying out foreground thinning on the corrected image to obtain a thinned image;
and carrying out foreground segmentation on the thinned image to obtain a foreground image.
Optionally, the method for extracting the color characteristic value of the tobacco leaves comprises the following steps:
converting the preprocessed image from an RGB space to an HSV space;
and calculating the average value of each channel in the RGB space and the HSV space to obtain six values of R1, G1, B1, H1, S1 and V1, wherein the six values serve as the overall tobacco leaf color characteristic value.
Optionally, the method for extracting the characteristic value of the main vein tobacco leaf comprises the following steps:
segmenting the preprocessed image to obtain a stem image;
converting the stem image from an RGB space to an HSV space;
and calculating the average value of the tobacco leaf main vein in each channel in the RGB space and the HSV space to obtain six values of R2, G2, B2, H2, S2 and V2 as the main vein color characteristic value.
Optionally, the method for extracting the tobacco leaf texture features comprises the following steps: and extracting texture characteristic values of the tobacco leaves by adopting a gray level co-occurrence matrix algorithm, wherein the texture characteristic values comprise contrast, entropy, autocorrelation and energy.
Optionally, the method for extracting the tobacco leaf area ratio characteristic value comprises the following steps:
calculating the tobacco leaf area value at the current tobacco leaf baking time as the current tobacco leaf area value;
calculating the tobacco leaf area value at the initial tobacco leaf baking time as an initial tobacco leaf area value;
and calculating the ratio of the current tobacco leaf area value to the initial tobacco leaf area value, namely the tobacco leaf area ratio characteristic value.
The invention also provides a tobacco leaf baking control system based on image recognition, wherein the system comprises:
an image acquisition module to: acquiring an image of the baked tobacco leaves;
an image processing module to: preprocessing the tobacco leaf image to obtain a preprocessed image;
a feature extraction module to: performing tobacco leaf feature extraction on the preprocessed image to obtain a tobacco leaf feature value, wherein the tobacco leaf feature value at least comprises a tobacco leaf color feature value, a main vein color feature value, a tobacco leaf texture feature value and an area ratio feature value;
a phase calculation module to: inputting the tobacco leaf characteristic value into a tobacco leaf baking stage identification model, and calculating a measured value of the current tobacco leaf baking stage;
a control processing module to: and comparing the measured value of the current tobacco leaf curing stage with a set threshold value of the current tobacco leaf curing stage, if the measured value of the current tobacco leaf curing stage is greater than the set threshold value of the current tobacco leaf curing stage, triggering the tobacco leaf curing process to enter the next stage, and controlling the curing equipment to cure according to a tobacco leaf curing process curve set by the next stage.
Optionally, the tobacco flue-curing stage identification model is as follows:
b＝a1*x1+a2*x2+a3*x3+a4*x4
in the formula, b is an actual measurement value of the current tobacco leaf baking stage, a1 is a tobacco leaf color characteristic value, x1 is a tobacco leaf color weight of the current stage, a2 is a main vein color characteristic value, x2 is a main vein color weight of the current stage, a3 is a tobacco leaf texture characteristic value, x3 is a tobacco leaf texture characteristic weight of the current stage, a4 is an area ratio characteristic value, and x4 is an area ratio characteristic weight of the current stage.
The invention has the beneficial effects that: according to the method, the main characteristics of the tobacco leaves, such as the color characteristic, the main vein color characteristic, the texture characteristic and the area ratio characteristic of the tobacco leaves, are extracted, and the identification model of the tobacco leaf baking stage in the baking process is established, so that the baking stage of the tobacco leaves is predicted, and a basis is provided for semi-intelligent baking of the tobacco leaves. The invention can effectively solve the problems of great control difficulty, time and labor waste, more required personnel and uneven baking quality in the conventional baking method due to the experience of each baking engineer.
Drawings
FIG. 1 is a flow chart of a method of an embodiment of the present invention;
fig. 2 is a system configuration diagram according to an embodiment of the present invention.
Detailed Description
The present invention will be described in detail with reference to the following embodiments in order to make the aforementioned objects, features and advantages of the invention more comprehensible. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein, but rather should be construed as broadly as the present invention is capable of modification in various respects, all without departing from the spirit and scope of the present invention.
Example one
Referring to fig. 1, in an embodiment of the present invention, a tobacco flue-curing control method based on image recognition is provided, where the method includes:
s1, acquiring the baked tobacco leaf image.
In the tobacco leaf baking process, the baking image of the tobacco leaf can be shot through the camera. Specifically, the tobacco leaf images may be collected every half minute, one minute, and so on. After the tobacco leaf image is collected, the tobacco leaf image can be transmitted to the tobacco leaf baking intelligent terminal.
The tobacco leaf baking intelligent terminal is a control center of tobacco leaf baking equipment, and a tobacco leaf baking process curve can be input into the tobacco leaf baking intelligent terminal, so that the tobacco leaf baking is carried out according to the tobacco leaf baking process curve. Specifically, in the tobacco leaf baking process, the tobacco leaf baking intelligent terminal can detect the actual temperature and humidity in the baking room through the dry-wet ball, can control the heating of the baking room through the heating equipment, and can control the dehumidifying of the baking room through the dehumidifying equipment such as a dehumidifying window.
S2, preprocessing the tobacco leaf image to obtain a preprocessed image.
In image analysis, the quality of image quality directly affects the precision of the design and effect of recognition algorithms, and therefore, preprocessing is required before image analysis (feature extraction, recognition, and the like). The main purposes of image preprocessing are to eliminate irrelevant information in images, recover useful real information, enhance the detectability of relevant information, and simplify data to the maximum extent, thereby improving the reliability of feature extraction, image segmentation, matching and recognition. Aiming at the field conditions of tobacco leaf baking and the acquired real picture data, the embodiment provides an image preprocessing scheme which comprises the main steps of image denoising, color correction, foreground refinement and foreground segmentation.
Specifically, the image preprocessing comprises the following steps:
s21, carrying out image denoising on the tobacco leaf image to obtain a denoised image.
Image denoising refers to the process of reducing noise in a digital image. In reality, digital images are often affected by interference of imaging equipment and external environment noise during digitization and transmission, and are called noisy images or noisy images. Common image denoising algorithms include mean filtering, median filtering, and gaussian filtering. According to the embodiment, the salt and pepper noise in the tobacco leaf image is removed by using a median filtering algorithm according to the actual situation of the collected picture, so that a clearer picture is obtained.
S22, carrying out color correction on the de-noised image to obtain a corrected image;
the imaging environment of the curing barn is affected by factors such as light, a camera and the like, and the same tobacco leaves can have different colors in different curing barns and are obviously different from the colors seen by naked eyes. In order to prevent the influence of color distortion on image identification, each curing barn uses standard color card color correction software to perform color correction and obtain correction parameters before use. And correcting the color of the tobacco leaf image by using the correction parameters.
S23, foreground thinning is carried out on the corrected image to obtain a thinned image;
and foreground refinement, namely, the foreground region refinement mainly performs refinement segmentation on the extracted effective region, and eliminates unnecessary interference information in the effective region, such as some overexposed information and the like. In the embodiment, difference calculation and statistics are firstly carried out on pixel values of each channel, some overexposed pixels are filtered, then the pixel values and the pixel difference values are counted in the neighborhood by means of the algorithm ideas of an LBP algorithm and a VIBE, extreme points in a statistical result are filtered according to automatic threshold segmentation, and finally each Blob is independently analyzed in a corresponding binary image, and small regions and interference regions are filtered.
And S24, performing foreground segmentation on the thinned image to obtain a foreground image.
The key area of the tobacco leaves in the picture is defined as a foreground area, and the characteristics are extracted on the basis of the foreground area, so that the interference of the background can be eliminated, and the robustness of the algorithm is improved. In this embodiment, a k-means clustering algorithm is used to cluster image pixels, the class with the largest number of clustered pixels is used as the class to which the foreground image belongs, a pixel segmentation threshold value is dynamically determined according to the difference of a plurality of clustering centers, and the original image pixels are segmented by using the segmentation threshold value to obtain the foreground image.
S3, performing tobacco leaf feature extraction on the preprocessed image to obtain a tobacco leaf feature value, wherein the tobacco leaf feature value at least comprises a tobacco leaf color feature value, a main vein color feature value, a tobacco leaf texture feature value and an area ratio feature value.
In the tobacco curing process, the whole color of the tobacco leaves changes from green to yellow, and the whole color value of the tobacco leaves can be extracted as a main characteristic for identification. Specifically, the RGB mean value of each channel of the foreground map may be used as a feature value of a foreground map color, and meanwhile, considering that different color spaces have different levels of expression capability to colors, the RGB space of the original map is converted into HSV space, the mean value of each channel in the HSV space is calculated, and the six calculated values of R, G, B, H, S, and V are used as an overall color feature value of the foreground map.
Optionally, the method for extracting the color characteristic value of the tobacco leaves comprises the following steps: converting the preprocessed image from an RGB space into an HSV space; and calculating the average value of each channel in the RGB space and the HSV space to obtain six values of R1, G1, B1, H1, S1 and V1, wherein the six values serve as the overall tobacco leaf color characteristic value.
The color of the main part of the tobacco leaves is changed (from blue to white and then from brown) all the time in the tobacco curing process, and the color value of the local position of the tobacco leaves can be extracted to be used as a main characteristic for identification. And (3) segmenting each image after the preprocessing operation by using an image semantic segmentation technology to obtain a stem image, and then extracting the main vein tobacco leaf characteristics by using a method the same as the tobacco leaf color characteristic value.
Optionally, the method for extracting the characteristic value of the main vein tobacco leaves comprises the following steps: segmenting the preprocessed image to obtain a stem image; converting the stem image from an RGB space to an HSV space; calculating the average value of the tobacco leaf main vein in each channel in RGB space and HSV space to obtain six values of R2, G2, B2, H2, S2 and V2 as the main vein color characteristic value.
During the tobacco leaf baking process, the leaf surface texture is continuously changed due to dehydration and curling of the leaf surface. When the tobacco leaves are baked, the leaves of the tobacco leaves are smooth and unfolded, the wrinkles in the tobacco leaves are less, the longer the baking time is, the more the leaves are curled, and the more wrinkles in the leaves are increased. The above change process can be described by using texture features, the texture feature extraction mode is various, and the texture feature value of the tobacco leaf is extracted by using a gray level co-occurrence matrix algorithm in the embodiment, and the texture feature value includes contrast, entropy, autocorrelation and energy.
The gray level co-occurrence matrix is described as follows:
the joint distribution among pixels with some spatial relationship is called gray level co-occurrence matrix, which is denoted as P delta, and the gray level is L, so that P delta is an L multiplied by L matrix. The spatial position relationship of a certain element P δ (i, j) is δ (Dx, Dy), and the horizontal direction is selected, that is, δ is 0. A number of parameters describing texture features may be computed on the gray level co-occurrence matrix.
Contrast ratio: reflecting the definition of the image and the depth of the texture grooves. The deeper the texture grooves, the greater its contrast and the clearer the visual effect. The contrast of the target image can be calculated as follows.
Entropy: the amount of information contained in an image reflects the complexity of the texture of the image, and the larger the value of the amount of information, the more complex the texture.
The entropy of the target image can be calculated as follows.
Self-correlation: reflecting the consistency of the image texture, when the matrix element values are uniform and equal, the correlation value is large.
The autocorrelation of the target image can be calculated as follows.
Energy: the sum of squares of the gray level co-occurrence matrix element values reflects the thickness degree of the image texture, and the larger the value is, the thicker the texture is. The energy of the target image can be calculated as follows.
In the baking process, the water in the leaves is gradually lost, and the whole volume of the tobacco leaves is continuously reduced to be stable. The ratio of the area of the current tobacco leaf foreground to the area of the initial tobacco leaf foreground can express the water loss condition and the wrinkle degree of the tobacco leaves. In this embodiment, the method for extracting the tobacco leaf area ratio characteristic value includes: calculating the tobacco leaf area value at the current tobacco leaf baking time as the current tobacco leaf area value; calculating the tobacco leaf area value at the initial tobacco leaf baking time as an initial tobacco leaf area value; and calculating the ratio of the current tobacco leaf area value to the initial tobacco leaf area value, namely the tobacco leaf area ratio characteristic value.
S4, inputting the tobacco leaf characteristic value into a tobacco leaf baking stage identification model, and calculating the measured value of the current tobacco leaf baking stage.
After the tobacco leaf color feature, the main vein color feature, the tobacco leaf texture feature and the area ratio feature value are obtained, the feature values can be input into a recognition model of the tobacco leaf baking stage, the current tobacco leaf baking stage is calculated, and a basis is provided for the action of baking equipment.
The tobacco leaf baking stage identification model comprises the following steps:
b＝a1*x1+a2*x2+a3*x3+a4*x4
in the formula, b is an actual measurement value of the current tobacco leaf baking stage, a1 is a tobacco leaf color characteristic value, x1 is a tobacco leaf color weight of the current stage, a2 is a main vein color characteristic value, x2 is a main vein color weight of the current stage, a3 is a tobacco leaf texture characteristic value, x3 is a tobacco leaf texture characteristic weight of the current stage, a4 is an area ratio characteristic value, and x4 is an area ratio characteristic weight of the current stage.
In a baking test, the applicant finds that the color characteristics, the main vein color characteristics, the texture characteristics, the shape characteristics (area ratio) and the like of the tobacco leaves can well reflect the baking stage of the tobacco leaves. The following describes in detail the construction of the tobacco flue-curing stage identification model:
at the initial stage of the test, three batches of available data were collected using a simulated flue-curing barn, wherein each batch of data contained 700 tobacco leaf images for a total of 2100 tobacco leaf images. The 2100 pictures are divided into 10 types according to actual temperature values, and the 10 types correspond to 10 types of final classification and respectively correspond to 33 degrees in the early stage of yellowing, 38 degrees in the early stage of yellowing, 40 degrees in the middle stage of yellowing, 42 degrees in the later stage of yellowing, 45 degrees in the early stage of fixation, 48 degrees in the middle stage of fixation, 51 degrees in the later stage of fixation, 54 degrees in the early stage of dry tendons, 60 degrees in the middle stage of dry tendons and 68 degrees in the later stage of dry tendons. Randomly extracting pictures from each category in each batch of classified data according to a proportion of 10%, wherein each batch obtains 70 pictures, a total of 210 pictures obtained from three batches are used as a test set of the algorithm, a total of 630 pictures obtained from the same method are used for each residual batch, and a total of 1890 pictures are used as a training set of the algorithm. All references to training sets and test sets hereinafter refer to the training sets and test sets described herein.
1. Testing on tobacco leaf color characteristics
Processing all pictures in the training set and the test set by using an image preprocessing algorithm to obtain a training set and a test set after preprocessing operation; all the pictures in the training set and the testing set use RGB color spaces, the total RGB mean values of all the tobacco leaf pictures in the category are obtained through calculation according to the category of the pictures in the training set, the total HSV mean values of the training set pictures in the HSV color space are obtained through calculation by using a conversion formula from the RGB color spaces to the HSV color spaces on the basis of the obtained total RGB mean values, and the obtained calculated total R1, G1, B1, H1, S1 and V1 mean values are used as the integral color feature vectors of all the categories of the tobacco leaf pictures. The overall color characteristics of the obtained tobacco leaves are shown in the following table.
TABLE 1 Overall color characteristics of tobacco leaves
| Integral color feature | R1 | G1 | B1 | H1 | S1 | V1 |
| Yellowing (33 degree) | 28.98559 | 137.6638 | 124.4221 | 33.68539 | 211.0334 | 140.683 |
| Yellowing (38 degree) | 38.57855 | 140.9432 | 141.5237 | 29.62675 | 212.5255 | 146.98 |
| Yellowing (40 degree) | 78.55272 | 155.6312 | 167.4217 | 26.26303 | 148.1976 | 168.1518 |
| Yellowing (42 degree) | 107.3094 | 154.2366 | 154.3205 | 32.4003 | 96.32923 | 157.6146 |
| Fixation (45 degree) | 106.5534 | 148.5321 | 151.1236 | 31.14159 | 93.39858 | 153.5185 |
| Fixation (48 degree) | 104.6428 | 145.0961 | 149.0159 | 30.37808 | 95.19472 | 150.9826 |
| Fixation (51 degree) | 103.4023 | 146.3611 | 151.2007 | 29.61248 | 99.35446 | 152.8581 |
| Dry muscle (54 degree) | 102.2867 | 146.7036 | 151.5612 | 29.418 | 101.4612 | 153.1209 |
| Dry muscle (60 degree) | 102.0156 | 146.9476 | 151.9219 | 29.22505 | 102.0616 | 153.2709 |
| Dry muscle (68 degree) | 95.815 | 141.6778 | 148.2334 | 28.22364 | 109.2188 | 149.2211 |
As can be seen from Table 1, the RGB and HSV color space values are significantly changed in the stages from yellowing (33 degrees) to yellowing (40 degrees). And in the stages of yellowing (42 degrees) to dry ribs (68 degrees), RGB and HSV color space values tend to be stable. Therefore, the overall color can be an important feature for distinguishing the stages of yellowing (33 degrees) to yellowing (40 degrees).
2. Testing for pulse-taking color characteristics
And segmenting each picture in the training set after preprocessing operation by using an image semantic segmentation technology to obtain a stem image, wherein all the obtained stem images form a stem image training set, and then calculating R, G, B, H, S, V total mean values corresponding to different temperature stages on the stem image training set by using the same method as that for calculating the overall color feature as local color feature vectors of each category. The color characteristics of the obtained pulses are shown in the following table.
TABLE 2 subjective pulse color characteristics
| Local color features | R2 | G2 | B2 | H2 | S2 | V2 |
| Yellowing (33 degree) | 105.93025 | 154.492 | 136.6434 | 35.12191 | 97.3493 | 154.7116 |
| Yellowing (38 degree) | 104.33102 | 150.4674 | 142.0018 | 32.68671 | 93.7153 | 151.8783 |
| Yellowing (40 degree) | 108.145 | 156.4141 | 153.3125 | 32.9744 | 91.05542 | 160.7706 |
| Yellowing (42 degree) | 116.8869 | 154.2861 | 138.2176 | 38.59089 | 87.30432 | 155.7851 |
| Fixation (45 degree) | 140.2987 | 167.4704 | 152.1403 | 53.1951 | 52.93957 | 168.6926 |
| Fixation (48 degree) | 142.2516 | 168.5237 | 156.8918 | 49.38877 | 49.23513 | 169.7507 |
| Fixation (51 degree) | 139.4277 | 169.2676 | 160.1516 | 45.20694 | 53.06079 | 170.7832 |
| Dry muscle (54 degree) | 128.7576 | 159.7216 | 153.4286 | 41.24326 | 58.02462 | 161.6962 |
| Dry muscle (60 degree) | 108.4789 | 132.7844 | 134.1655 | 34.86294 | 60.69841 | 137.4458 |
| Dry muscle (68 degree) | 97.17059 | 122.5966 | 127.7227 | 30.35075 | 70.78837 | 129.7092 |
As can be seen from Table 2, the RGB and HSV color space values have small changes in the stages of yellowing (33 degrees) to yellowing (42 degrees); in the stages of fixing color (45 degrees) to drying ribs (68 degrees), the color numerical value changes of RGB and HSV color space are obvious, and the change corresponds to the change of the rib color from blue to white to brown in the actual tobacco in the stages of fixing color and drying ribs, so that the main vein color can be used as an important characteristic for distinguishing the stages of fixing color (45 degrees) to drying ribs (68 degrees).
3. Testing for tobacco leaf textural features
And extracting the texture characteristics of the tobacco leaves by adopting a classical gray level co-occurrence matrix algorithm. And extracting four characteristic values (contrast, entropy, autocorrelation and energy) describing textures from pictures in all training data by using a gray level co-occurrence matrix algorithm, and calculating the mean value of the four characteristic values of the textures in each category according to the category to which the tobacco leaves belong, wherein the mean value is used as the characteristic value of the texture in each category. The texture feature values for each class are obtained as shown in the following table.
TABLE 3 tobacco leaf texture characteristics
As can be seen from Table 3, the characteristic values of the texture change from yellow (33 degrees) to dry (68 degrees) to have obvious changes, and the texture characteristics can be used as the characteristics for distinguishing all stages of the flue-cured tobacco.
4. Testing for area ratio features
Because the calculation of the area ratio is related to the initial image of each flue-cured tobacco batch, the data in the training set is divided according to batches, the foreground area ratio of each batch of data is respectively calculated to obtain the shape characteristics of different batches of tobacco leaves, and then the value range of the area ratio of the data in the same stage in the training data is counted according to the state stage to which the tobacco leaves belong. The resulting shape feature data is shown in the table below.
TABLE 4 area bit characterization
| Area bit characterization | Area ratio |
| Yellowing (33 degree) | 1.0-0.95 |
| Yellowing (38 degree) | 0.94-0.85 |
| Yellowing (40 degree) | 0.87-0.81 |
| Yellowing (42 degree) | 0.80-0.75 |
| Fixation (45 degree) | 0.76-0.74 |
| Fixation (48 degree) | 0.76-0.75 |
| Fixation (51 degree) | 0.76-0.74 |
| Dry muscle (54 degree) | 0.75-0.74 |
| Dry muscle (60 degree) | 0.76-0.74 |
| Dry muscle (68 degree) | 0.76-0.73 |
From table 4, it can be obtained that the area ratio of the tobacco leaves tends to be stable (0.76-0.74) when the tobacco leaves are yellowed (42 degrees) to fixed color (45 degrees), and each stage before the tobacco leaves are yellowed (42 degrees) is changed all the time, so that the area ratio can be used as the characteristic for identifying the stages from yellowing (33 degrees) to yellowing (42 degrees).
Through the test, the tobacco color characteristic, the main vein color characteristic, the tobacco texture characteristic and the area ratio characteristic can identify different baking stages of the tobacco. And because the occupation ratio of each characteristic in each baking stage is different, the weight occupied by each characteristic in different stages can be set to be different so as to identify and distinguish each baking stage more accurately. Meanwhile, the judgment threshold values of all stages are different, and the specific formula is as described above.
Optionally, according to the tobacco flue-curing process curve, the tobacco flue-curing stages are divided into a yellowing initial stage, a yellowing early stage, a yellowing middle stage, a yellowing later stage, a color fixing early stage, a color fixing middle stage, a color fixing later stage, a dry rib early stage, a dry rib middle stage and a dry rib later stage, and the characteristic weights in the tobacco flue-curing stage identification model in each period are different. In one example, applicants make statistics based on multiple sets of tests and experience as follows:
TABLE 5 weight and threshold values for each stage for each feature
| Temperature of | Tobacco leaf color weight | Governing pulse color weight | Texture weights | Area weight | Threshold value |
| Yellowing (33 degree) | 0.5 | 0 | 0.25 | 0.25 | 0.6 |
| Yellowing (38 degree) | 0.5 | 0 | 0.25 | 0.25 | 0.67 |
| Yellowing (40 degree) | 0.5 | 0 | 0.25 | 0.25 | 0.76 |
| Yellowing (42 degree) | 0.5 | 0 | 0.25 | 0.25 | 0.79 |
| Fixation (45 degree) | 0.5 | 0 | 0.25 | 0.25 | 0.85 |
| Fixation (48 degree) | 0.4 | 0.1 | 0.4 | 0.1 | 0.85 |
| Fixation (51 degree) | 0.4 | 0.1 | 0.4 | 0.1 | 0.86 |
| Dry muscle (54 degree) | 0.4 | 0.1 | 0.4 | 0.1 | 0.87 |
| Dry muscle (60 degree) | 0.4 | 0.1 | 0.4 | 0.1 | 0.91 |
| Dry muscle (68 degree) | 0.4 | 0.1 | 0.4 | 0.1 | 0.92 |
Remarking: the threshold values in the table are the set threshold values in step S5.
S5, comparing the measured value of the current tobacco leaf roasting stage with the set threshold value of the current tobacco leaf roasting stage, if the measured value of the current tobacco leaf roasting stage is larger than the set threshold value of the current tobacco leaf roasting stage, triggering the tobacco leaf roasting process to enter the next stage, and controlling the roasting equipment to roast according to the tobacco leaf roasting process curve set by the next stage.
Specifically, after the measured value of the current tobacco leaf curing stage is obtained through calculation of the tobacco leaf curing stage identification model, the measured value is compared with the set threshold value of the previous tobacco leaf curing stage. If the preset threshold value is not reached, baking according to the process of the current stage. If the set value is exceeded, the baking equipment is controlled to bake according to the tobacco leaf baking process curve set in the next stage, such as temperature rise setting, humidity discharge and the like.
For the same batch of tobacco, the applicant used the same curing process (existing flue-cured tobacco intensive curing technique), one of which was observed by the curing engineer to identify and adjust the different curing stages, and the other of which was used by the method of the present invention to identify the different curing stages (the stage parameters are shown in table 5). After the baking is finished, the quality of the two kang tobacco leaves is compared with the quality of the main chemical components and the smoking quality, and the difference between the two qualities is found.
According to the method, the main characteristics of the tobacco leaves, such as the color characteristic, the main vein color characteristic, the texture characteristic and the area ratio characteristic of the tobacco leaves, are extracted, and the identification model of the tobacco leaf baking stage in the baking process is established, so that the baking stage of the tobacco leaves is predicted, and a basis is provided for intelligent baking of the tobacco leaves. The invention can effectively solve the problems of great control difficulty, time and labor waste, more required personnel and uneven baking quality in the conventional baking method due to the experience of each baking engineer.
Example two
Referring to fig. 2, a second embodiment of the present invention provides a tobacco flue-curing control system based on image recognition, wherein the system includes: an image acquisition module to: acquiring an image of the baked tobacco leaves; an image processing module to: preprocessing the tobacco leaf image to obtain a preprocessed image; a feature extraction module to: performing tobacco leaf feature extraction on the preprocessed image to obtain a tobacco leaf feature value, wherein the tobacco leaf feature value at least comprises a tobacco leaf color feature value, a main vein color feature value, a tobacco leaf texture feature value and an area ratio feature value; a phase calculation module to: inputting the tobacco leaf characteristic value into a tobacco leaf baking stage identification model, and calculating a measured value of the current tobacco leaf baking stage; a control processing module to: and comparing the measured value of the current tobacco leaf curing stage with a set threshold value of the current tobacco leaf curing stage, if the measured value of the current tobacco leaf curing stage is greater than the set threshold value of the current tobacco leaf curing stage, triggering the tobacco leaf curing process to enter the next stage, and controlling the curing equipment to cure according to a tobacco leaf curing process curve set by the next stage.
Since the apparatus described in the second embodiment of the present invention is an apparatus used for implementing the method of the first embodiment of the present invention, based on the method described in the first embodiment of the present invention, a person skilled in the art can understand the specific structure and the deformation of the apparatus, and thus the details are not described herein. All the devices adopted in the method of the first embodiment of the present invention belong to the protection scope of the present invention.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (10)
1. The tobacco leaf curing control method based on image recognition comprises the following steps:
acquiring an image of the baked tobacco leaves;
preprocessing the tobacco leaf image to obtain a preprocessed image;
performing tobacco leaf feature extraction on the preprocessed image to obtain a tobacco leaf feature value, wherein the tobacco leaf feature value at least comprises a tobacco leaf color feature value, a main vein color feature value, a tobacco leaf texture feature value and an area ratio feature value;
inputting the tobacco leaf characteristic value into a tobacco leaf baking stage identification model, and calculating a measured value of the current tobacco leaf baking stage;
and comparing the measured value of the current tobacco leaf curing stage with a set threshold value of the current tobacco leaf curing stage, if the measured value of the current tobacco leaf curing stage is greater than the set threshold value of the current tobacco leaf curing stage, triggering the tobacco leaf curing process to enter the next stage, and controlling the curing equipment to cure according to a tobacco leaf curing process curve set by the next stage.
2. The control method according to claim 1, wherein the tobacco flue-curing stage identification model is:
b＝a1*x1+a2*x2+a3*x3+a4*x4
in the formula, b is an actual measurement value of the current tobacco leaf baking stage, a1 is a tobacco leaf color characteristic value, x1 is a tobacco leaf color weight of the current stage, a2 is a main vein color characteristic value, x2 is a main vein color weight of the current stage, a3 is a tobacco leaf texture characteristic value, x3 is a tobacco leaf texture characteristic weight of the current stage, a4 is an area ratio characteristic value, and x4 is an area ratio characteristic weight of the current stage.
3. The control method according to claim 2, wherein the tobacco flue-curing stage is divided into an early yellowing stage, a middle yellowing stage, a late yellowing stage, an early fixing stage, a middle fixing stage, a late fixing stage, a early drying stage, a middle drying stage, and a late drying stage according to a tobacco flue-curing process curve, and each characteristic weight in the tobacco flue-curing stage identification model in each stage is different.
4. The control method according to claim 1, wherein the preprocessing includes the steps of:
carrying out image denoising on the tobacco leaf image to obtain a denoised image;
carrying out color correction on the denoised image to obtain a corrected image;
carrying out foreground thinning on the corrected image to obtain a thinned image;
and carrying out foreground segmentation on the thinned image to obtain a foreground image.
5. The control method according to claim 1, wherein the method for extracting the tobacco leaf color characteristic value comprises the following steps:
converting the preprocessed image from an RGB space to an HSV space;
and calculating the average value of each channel in the RGB space and the HSV space to obtain six values of R1, G1, B1, H1, S1 and V1, wherein the six values serve as the overall tobacco leaf color characteristic value.
6. The control method according to claim 1, wherein the main vein tobacco leaf characteristic value extraction method comprises the following steps:
segmenting the preprocessed image to obtain a stem image;
converting the stem image from an RGB space to an HSV space;
and calculating the average value of the tobacco leaf main vein in each channel in the RGB space and the HSV space to obtain six values of R2, G2, B2, H2, S2 and V2 as the main vein color characteristic value.
7. The control method according to claim 1, wherein the tobacco leaf textural features are extracted by a method comprising the following steps: and extracting texture characteristic values of the tobacco leaves by adopting a gray level co-occurrence matrix algorithm, wherein the texture characteristic values comprise contrast, entropy, autocorrelation and energy.
8. The control method according to claim 1, wherein the extraction method of the tobacco leaf area ratio characteristic value is as follows:
calculating the tobacco leaf area value at the current tobacco leaf baking time as the current tobacco leaf area value;
calculating the tobacco leaf area value at the initial tobacco leaf baking time as an initial tobacco leaf area value;
and calculating the ratio of the current tobacco leaf area value to the initial tobacco leaf area value, namely the tobacco leaf area ratio characteristic value.
9. Tobacco curing control system based on image recognition, wherein, the system includes:
an image acquisition module to: acquiring an image of the baked tobacco leaves;
an image processing module to: preprocessing the tobacco leaf image to obtain a preprocessed image;
a feature extraction module to: performing tobacco leaf feature extraction on the preprocessed image to obtain a tobacco leaf feature value, wherein the tobacco leaf feature value at least comprises a tobacco leaf color feature value, a main vein color feature value, a tobacco leaf texture feature value and an area ratio feature value;
a phase calculation module to: inputting the tobacco leaf characteristic value into a tobacco leaf baking stage identification model, and calculating a measured value of the current tobacco leaf baking stage;
a control processing module to: and comparing the measured value of the current tobacco leaf curing stage with a set threshold value of the current tobacco leaf curing stage, if the measured value of the current tobacco leaf curing stage is greater than the set threshold value of the current tobacco leaf curing stage, triggering the tobacco leaf curing process to enter the next stage, and controlling the curing equipment to cure according to a tobacco leaf curing process curve set by the next stage.
10. The control system of claim 9, wherein the tobacco flue-curing stage identification model is:
b＝a1*x1+a2*x2+a3*x3+a4*x4
in the formula, b is an actual measurement value of the current tobacco leaf baking stage, a1 is a tobacco leaf color characteristic value, x1 is a tobacco leaf color weight of the current stage, a2 is a main vein color characteristic value, x2 is a main vein color weight of the current stage, a3 is a tobacco leaf texture characteristic value, x3 is a tobacco leaf texture characteristic weight of the current stage, a4 is an area ratio characteristic value, and x4 is an area ratio characteristic weight of the current stage.
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