A method, device and system for detecting a cell wall breaking rate of tea leaves

Abstract

The present application relates to the technical field of image region detection, and particularly relates to a tea leaf broken wall rate detection method, device and system. The method obtains different angle leaf gray scale images after each batch of rolling, quantizes the broken wall condition through the initial area of pixel value difference, and obtains an initial broken wall index; determines a suspected crack edge through the consistent degree of edge direction and the width smoothing condition, obtains a broken wall sufficient evaluation index according to the length position distribution uniformity of the suspected crack, and adjusts the initial index by analyzing the boundary complexity to obtain an adjusted broken wall index; obtains a confidence degree through the consistency of the adjusted index at different angles and the similarity with historical batches, and determines the final broken wall rate based on the confidence degree. The present application considers the influence of the leaf vein and tearing characteristics to adjust the broken wall evaluation, and analyzes the correlation historical process reference in real time after rolling to find abnormal conditions caused by process drift in time, improves the broken wall evaluation accuracy in the factory production, and guarantees the quality stability of the tea production.

Description

Technical Field

[0001] This invention relates to the field of image region detection technology, specifically to a method, device, and system for detecting the cell wall breakage rate of tea leaves. Background Technology

[0002] During traditional tea processing—specifically withering, fixation, rolling, and drying—cell wall disruption occurs to varying degrees. This disruption enhances flavor release; polyphenols and amino acids within the cells dissolve more easily after the walls are broken, influencing the taste and aroma of the tea. It also improves the dissolution rate; moderate disruption allows for faster release of active ingredients, making brewing easier. Furthermore, it enhances functional utilization, especially in products like tea powder and health drinks, where the degree of cell wall disruption directly determines the bioavailability of nutrients.

[0003] In actual factory production, manual judgment relies on experience, lacks unified standards, is inefficient, struggles to cover large batches of samples, and lacks quantitative indicators. While high-precision microscopic detection in laboratories is accurate, it requires complex processes, has long cycles, and is costly, making it unsuitable for real-time production line operations. Existing image processing detection quantitative indicators, although suitable for production line scenarios, ignore the influence of the characteristics of the tea leaves themselves, leading to misjudgments of cell wall breakage and increasing estimation and quantification errors, making it difficult to meet the quality control requirements of tea production. Summary of the Invention

[0004] To address the problem that existing image processing detection methods neglect the influence of tea leaf characteristics, leading to misjudgments of cell wall breakage and increased estimation and quantification errors, thus failing to meet the technical requirements for quality control in tea production, this invention aims to provide a method, device, and system for detecting the cell wall breakage rate of tea leaves. The specific technical solution adopted is as follows:

[0005] The first aspect of this application provides a method for detecting the cell wall breakage rate of tea leaves, the method comprising:

[0006] After each batch of rolling and processing, grayscale images of the tea leaves were obtained from different angles.

[0007] On the grayscale image of the blade at each angle, the initial wall breaking index of the blade grayscale image is obtained based on the degree of difference in pixel value area; suspected crack edges are identified based on the consistency of edge direction and the smoothness of edge width in the blade grayscale image; the wall breaking sufficient evaluation index of the blade grayscale image is obtained based on the uniformity of the length and position distribution of the suspected crack edges; the boundary complexity of the blade in the blade grayscale image is analyzed, and the initial wall breaking index is adjusted in combination with the wall breaking sufficient evaluation index to obtain the adjusted wall breaking index of the blade grayscale image.

[0008] Based on the consistency of the adjusted cell wall breaking index of the current sample tea leaves at different angles and the similarity of the adjusted cell wall breaking index between the current batch and historical batches, the cell wall breaking confidence of the current batch of tea samples is obtained; the final cell wall breaking rate of the current batch is determined based on the cell wall breaking confidence.

[0009] Furthermore, the method for obtaining the initial cell wall disruption index includes:

[0010] For any grayscale image of a leaf, the Otsu thresholding method is used to obtain connected components. The ratio between the total area of ​​all connected components and the total area of ​​the leaf in the grayscale image is used as the initial cell wall breaking index.

[0011] Furthermore, the method for determining the suspected crack edge includes:

[0012] For any grayscale image of a leaf, obtain the edge line of the grayscale image of the leaf; for any edge line, calculate the gradient direction difference between every two adjacent pixels on the edge line, and then perform negative correlation mapping on the mean of all gradient direction differences as the direction consistency index of the edge line.

[0013] After calculating the gradient value difference between every two adjacent pixels on the edge line, the standard deviation of all gradient value differences is negatively correlated to obtain the width smoothness index of the edge line; the product of the direction consistency index and the width smoothness index of the edge line is used as the edge feature value of the edge line.

[0014] When the edge feature value is less than the preset feature threshold, the corresponding edge line is regarded as a suspected crack edge.

[0015] Furthermore, the method for obtaining the cell wall disruption assessment indicators includes:

[0016] For any given leaf grayscale image, divide the leaf grayscale image into a preset number of sub-regions; use the proportion of pixels with suspected crack edges in each sub-region as the damage distribution degree of each sub-region; perform negative correlation mapping on the variance of the damage distribution degree of all sub-regions as the local distribution stability of the leaf grayscale image.

[0017] The cumulative distribution degree of the blade's grayscale image is calculated by multiplying the mean gradient value of all pixels on the suspected crack edges in the grayscale image by the total length of the suspected crack edges.

[0018] By combining the local distribution stability and cumulative distribution of the leaf grayscale image, a sufficient evaluation index for the wall breaking of the leaf grayscale image is obtained.

[0019] Furthermore, the method for obtaining the cell wall breaking index includes:

[0020] The ratio of the total length of the leaf boundary to the total area of ​​the leaf in each leaf grayscale image is used as the boundary tearing feature value of each leaf grayscale image.

[0021] The product of the negative correlation mapping of the boundary tearing feature value of each leaf grayscale image and the initial cell wall breakage index is used as the cell wall breakage area evaluation index for each leaf grayscale image.

[0022] The sum of the cell wall breakage area evaluation index and the cell wall breakage sufficiency evaluation index of each leaf grayscale image is normalized to obtain the adjusted cell wall breakage index for each leaf grayscale image.

[0023] Furthermore, the method for obtaining the cell wall disruption confidence level includes:

[0024] The average cell wall breaking index of the sample tea at all angles in each batch is taken as the average cell wall breaking index of the sample tea; the average cell wall breaking index of each historical batch with the same processing parameters as the current batch is obtained.

[0025] After calculating the difference between the average cell wall breaking index of the current batch and the average cell wall breaking index of each historical batch, the sum of the differences of all average cell wall breaking indices is negatively correlated and used as the historical confidence level of the current batch.

[0026] By negatively correlated mapping of the variances of the cell wall breaking index under all angles for the current batch, and combined with historical confidence levels, the cell wall breaking confidence level of the current batch of tea samples is obtained.

[0027] Furthermore, determining the final cell disruption rate of the current batch based on cell disruption confidence includes:

[0028] When the cell wall breakage confidence level is greater than the preset confidence threshold, the final cell wall breakage rate of the finished product after processing is obtained through image analysis; otherwise, the final cell wall breakage rate of the finished product after processing is obtained through a spectrometer.

[0029] Furthermore, after obtaining the confidence level of cell wall disruption for the current batch of tea samples, the process also includes:

[0030] When the confidence level of cell wall breaking is greater than the preset confidence threshold, if the adjusted cell wall breaking index is less than the preset kneading cell wall breaking threshold, a cell wall breaking adjustment alarm will be triggered.

[0031] Secondly, this application provides a tea cell wall breakage rate detection system, the system comprising:

[0032] The data acquisition module is used to acquire grayscale images of tea leaves from different angles after each batch of kneading and processing.

[0033] The cell wall breaking adjustment analysis module is used to obtain the initial cell wall breaking index of the leaf grayscale image at each angle based on the degree of difference in pixel value area; identify suspected crack edges based on the consistency of edge direction and the smoothness of edge width in the leaf grayscale image; obtain the cell wall breaking sufficient evaluation index of the leaf grayscale image based on the uniformity of the length and position distribution of the suspected crack edges; analyze the boundary complexity of the leaf in the leaf grayscale image, and adjust the initial cell wall breaking index in combination with the cell wall breaking sufficient evaluation index to obtain the adjusted cell wall breaking index of the leaf grayscale image.

[0034] The final cell wall breakage rate analysis module is used to obtain the cell wall breakage confidence of the current sample tea based on the consistency of the adjusted cell wall breakage index of the leaf grayscale images of the current sample tea at different angles, and the similarity of the adjusted cell wall breakage index between the current batch and historical batches; the final cell wall breakage rate is obtained by judging the cell wall breakage confidence.

[0035] Thirdly, this application provides a tea cell wall breakage rate detection device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the method as described in the first aspect of this application or any embodiment of the first aspect.

[0036] Fourthly, this application provides a computer program product comprising computer program code, which, when executed, performs the method as described in the first aspect of this application or any embodiment thereof.

[0037] Fifthly, this application provides a computer-readable storage medium that stores computer program code, which, when executed, performs the method as described in the first aspect of this application or any embodiment thereof.

[0038] The present invention has the following beneficial effects:

[0039] This invention acquires grayscale images of the blades at different angles after each batch of kneading, enabling comprehensive analysis of the cell wall breakage characteristics on all sides of the blade despite limitations in image quality. Based on the macroscopic disruption of leaf tissue continuity caused by cell wall breakage, the initial cell wall breakage status can be quantified by the area of ​​pixel value differences, yielding an initial cell wall breakage index. Considering that damage features such as strong creases or fractures can be affected by the inherent veins of the blade, leading to errors in cell wall breakage assessment, this invention utilizes the characteristics of more uniform vein direction and smoother width gradients, contrasting with cracks, to determine suspected crack edges based on edge direction consistency and width smoothness, improving the accuracy of cell wall breakage area identification. Furthermore, the uniform distribution of suspected cracks characterizes the degree of cell wall breakage; the uniformity of suspected crack length and location distribution yields a cell wall breakage sufficiency assessment index, further improving the accuracy and comprehensiveness of the assessment. Adjusting the initial index by analyzing boundary complexity yields an adjusted cell wall breakage index, and further refining the cell wall breakage assessment based on the tearing condition of the blade edges makes the assessment of cell wall breakage at a single angle more reliable. By adjusting the consistency of indicators and their similarity to historical batches from different perspectives to obtain confidence levels, the reliability of multi-angle data is verified, and historical process patterns are correlated, thus improving the credibility of the results. Determining the final cell wall breakage rate based on the confidence level allows for the screening of reliable results, ensuring that the final value accurately reflects the actual cell wall breakage situation. This invention considers the influence of leaf veins and tearing characteristics to adjust the cell wall breakage assessment, and through real-time detection after rolling and correlation with historical process reference analysis, it promptly detects anomalies caused by process drift, improving the accuracy of cell wall breakage assessment in factory production while ensuring the stability of tea production quality. Attached Figure Description

[0040] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a flowchart of a method for detecting the cell wall breakage rate of tea leaves according to an embodiment of the present invention;

[0042] Figure 2 This is a partial schematic diagram of a leaf grayscale image provided in one embodiment of the present invention;

[0043] Figure 3 This is a structural diagram of a tea cell wall breakage rate detection system provided in one embodiment of the present invention;

[0044] Figure 4 This is a schematic diagram of a tea cell wall breakage rate testing device provided in one embodiment of the present invention. Detailed Implementation

[0045] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a tea cell wall breakage rate detection method, device, and system proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0047] The following description, in conjunction with the accompanying drawings, details the specific solution of the tea cell wall breakage rate detection method, equipment, and system provided by this invention.

[0048] Please see Figure 1 The diagram illustrates a flowchart of a method for detecting the cell wall breakage rate of tea leaves according to an embodiment of the present invention. The method includes the following steps:

[0049] S1: After each batch of rolling and processing, obtain grayscale images of the tea leaves from different angles.

[0050] Rolling is a crucial initial cell wall breaking process in tea processing. This embodiment of the application monitors the initial cell wall breaking after rolling to prevent situations where subsequent processes cannot correct the damage, thus improving tea quality. Therefore, in this embodiment, an automatic sampler, such as a small diversion sampling device, is installed at the outlet of the conveyor belt of the rolling machine for the current batch. It periodically picks up a small amount of tea leaves and sends them to the testing port to obtain sample tea leaves. Each leaf of the sample tea leaves is placed on a non-woven fabric or matte black tray, gently pulled apart with tweezers to allow it to naturally unfold, and then lightly touched and fixed with a soft silicone pad and a transparent sheet to prevent displacement without crushing the cells.

[0051] Then, images of the blades from different angles are acquired using an industrial camera, and these images are preprocessed to obtain grayscale images of the blades. In this embodiment of the invention, grayscale images of the blades are acquired from several angles, including frontal view, 45° side view (left and right), and 45° top and bottom view, to provide a more comprehensive image analysis of possible curling and occlusion. The image preprocessing process may specifically include image grayscale conversion, filtering and denoising, and background removal. It should be noted that image preprocessing is a technique well-known to those skilled in the art; specific methods such as grayscale weighting for grayscale conversion, bilateral filtering for filtering and denoising, and background subtraction for background removal can be used, etc., which will not be elaborated upon here. Please refer to [link to relevant documentation]. Figure 2 This illustration shows a partial schematic diagram of a leaf grayscale image provided in an embodiment of the present invention.

[0052] It is understandable that the sampling setup implementer can adjust it according to the specific implementation scenario. For example, the operator can manually sample tea leaves at the outlet of the production line kneading machine at a fixed sampling time. There are no specific limitations on the sampling process here.

[0053] S2: On the grayscale image of the blade at each angle, obtain the initial wall breaking index of the blade grayscale image based on the degree of difference in pixel value area; determine the suspected crack edges based on the consistency of edge direction and the smoothness of edge width in the blade grayscale image; obtain the wall breaking sufficient evaluation index of the blade grayscale image based on the uniformity of the length and position distribution of the suspected crack edges; analyze the boundary complexity of the blade in the blade grayscale image, and adjust the initial wall breaking index in combination with the wall breaking sufficient evaluation index to obtain the adjusted wall breaking index of the blade grayscale image.

[0054] The macroscopic aspect of cell wall damage mainly manifests as the disruption of leaf tissue continuity. Image-representing phenomena include: cracks or breaks, holes, edge tears, abnormal light transmission, abnormal color spots, exudation or bruising-like appearance, and abrupt texture changes accompanied by strong creases. Under normal circumstances, the color of the damaged area will differ from the normal color due to the damage; therefore, a preliminary quantitative analysis of the cell wall damage can be performed by examining pixel value differences.

[0055] In this embodiment of the invention, the method for obtaining the initial cell wall breakage index includes: for any leaf grayscale image, using the Otsu thresholding method to obtain connected components, directly performing thresholding based on the pixel differences where damage occurs, and analyzing the overall degree of damage. The ratio between the total area of ​​all connected components and the total area of ​​the leaf in the leaf grayscale image is used as the initial cell wall breakage index. It should be noted that the Otsu thresholding method is a well-known technique familiar to those skilled in the art and will not be described in detail here.

[0056] In a specific embodiment of the present invention, the total number of pixels in the connected domain is taken as the total area of ​​the connected domain. At the same time, in the grayscale image of the leaf after removing the background, the total number of all pixels is taken as the total area of ​​the leaf. The larger the ratio, that is, the larger the initial cell wall breaking index, the higher the proportion of damaged parts and the higher the cell wall breaking rate.

[0057] However, during the processing of tea, there is a certain degree of damage caused by the curling of the tea leaves. The total area may not be able to fully represent the damage, resulting in errors. Furthermore, when the creases in the tea leaves are heavy but the area of ​​their connected regions is small, the cell wall breakage rate is relatively high, but the accuracy of the assessed cell wall breakage rate is not high due to the small crease area.

[0058] Therefore, damage features such as strong creases or fractures are considered to correct for cell wall breakage assessment. However, since the leaves themselves also have certain vein characteristics, vein interference is first eliminated when analyzing crack edges with textured distribution. The veins of tea leaves are branched, radiating outward from the main vein, with smooth local directions over a long range and good consistency in direction, and the width changes slowly along the vein direction. In contrast, cell rupture caused by rolling produces irregular fractures or branches, so cracks often have asymmetrical strong gradient cross-sections and high dispersion with random changes in direction.

[0059] By filtering out vein information edges based on edge direction distribution and width smoothness, crack edges are determined. In this embodiment of the invention, the method for obtaining suspected crack edges includes:

[0060] First, for any grayscale image of a leaf, the edge line of the leaf grayscale image is obtained. For any edge line, the gradient direction difference between every two adjacent pixels on the edge line is calculated. The mean of all gradient direction differences is negatively correlated and mapped as the direction consistency index of the edge line. Through continuous direction approximation analysis, when the overall difference is lower, it indicates that there are fewer random changes on the edge, the direction consistency is better, and the probability of veins is higher.

[0061] It should be noted that negative correlation mapping and edge detection are well-known techniques familiar to those skilled in the art. Negative correlation mapping can take the form of an inverse proportional value or a negative exponent with the natural constant as the base, and edge detection can take the form of the Canny algorithm, etc., which will not be elaborated or limited here.

[0062] Furthermore, after calculating the gradient value difference between every two adjacent pixels on the edge line, the standard deviation of all gradient value differences is negatively correlated and mapped to obtain the width smoothness index of the edge line. The smaller the standard deviation, the more gradual the width change of adjacent points, the higher the smoothness, and the higher the probability of veins.

[0063] Finally, the product of the direction consistency index and the width smoothness index of the edge line is used as the edge feature value of the edge line. The smooth distribution of edge direction and width reflects the significance of the vein features. The larger the edge feature value, the more significant the vein edge information. Therefore, when the edge feature value is less than the preset feature threshold, the edge is considered to be a texture that may be damaged, and the corresponding edge line is regarded as a suspected crack edge. In this embodiment of the invention, the preset feature threshold can be set to 0.68. The specific value can be adjusted by the implementer and is not limited here.

[0064] The distribution of suspected crack edges can be used to assess the degree of cell wall damage, thus improving the accuracy of cell wall damage assessment. The larger the proportion of crack damage in the overall length and width of the blade, the greater the impact of cell wall damage. At the same time, the more uniform the distribution of damage on the blade, the better the effect of crack cell wall damage, indicating that the cell wall damage is more complete.

[0065] Therefore, in this embodiment of the invention, the method for obtaining the evaluation index for sufficient cell wall disruption includes:

[0066] First, for any given leaf grayscale image, the image is divided into a predetermined number of sub-regions. The distribution uniformity is determined by the distribution within these sub-regions. The proportion of pixels suspected to be crack edges in each sub-region is used as the damage distribution degree of that sub-region, quantifying its distribution extent within each region. The variances of the damage distribution degrees of all sub-regions are negatively correlated and mapped as the local distribution stability of the leaf grayscale image. A smaller variance indicates a more similar distribution degree across sub-regions, signifying better distribution uniformity. In this embodiment, the predetermined number can be set to 30; however, the specific value can be adjusted by the implementer and is not limited here.

[0067] Furthermore, the product of the average gradient value of all pixels on the suspected crack edges in the grayscale image of the blade and the total length of the suspected crack edges is used as the cumulative distribution degree of the grayscale image of the blade. Through the overall significant width level and length, it reflects the degree of damage of the crack in the overall distribution. The larger the cumulative distribution degree, the higher the distribution degree and the higher the degree of wall damage.

[0068] Finally, by combining the local distribution stability and cumulative distribution of the blade grayscale image, an evaluation index for the sufficient wall breaking of the blade grayscale image is obtained. In this embodiment of the invention, the product of the local distribution stability and cumulative distribution of the blade grayscale image is used as the evaluation index for the sufficient wall breaking of the blade grayscale image. The greater the local distribution stability and cumulative distribution, the more numerous and uniform the crack edges are, indicating that the wall breaking is more sufficient.

[0069] After area analysis and edge analysis, a more comprehensive assessment of cell wall damage can be obtained. At this point, the edge tearing of the blade is considered. The more severe the tearing at the blade edge, the more severe the actual area damage. The cell wall damage result reflected by the area is less reliable. Therefore, the initial cell wall damage index of the area analysis is limited by the edge tearing condition.

[0070] In this embodiment of the invention, the method for obtaining the cell wall disruption index includes:

[0071] First, the ratio of the total length of the leaf boundary to the total area of ​​the leaf in each leaf grayscale image is used as the boundary tearing feature value of each leaf grayscale image. The boundary complexity is reflected by the ratio of the total length of the outer contour boundary to the total area in the leaf grayscale image. The larger the ratio, that is, the larger the boundary tearing feature value, the higher the outer contour complexity, the more serious the tearing of the boundary, and the lower the accuracy of its area calculation.

[0072] Therefore, the product of the negative correlation mapping of the boundary tear feature value of each leaf grayscale image and the initial wall breakage index is used as the wall breakage area evaluation index for each leaf grayscale image. The boundary tear feature value is corrected. The larger the boundary tear feature value, the lower the credibility of the wall breakage analysis, and thus the lower the wall breakage area evaluation index will be.

[0073] By combining area analysis and edge analysis, the cell wall breaking situation can be evaluated more comprehensively. The sum of the cell wall breaking area evaluation index and the cell wall breaking full evaluation index of each leaf grayscale image is normalized to obtain the adjusted cell wall breaking index of each leaf grayscale image. The larger the adjusted cell wall breaking index, the higher and more significant the cell wall breaking rate.

[0074] It should be noted that normalization is a technique well known to those skilled in the art. The choice of normalization method can be hyperbolic function, linear normalization, or standard normalization, etc. The specific normalization method is not limited here.

[0075] S3: Based on the consistency of the adjusted cell wall breaking index of the current sample tea leaves at different angles and the similarity of the adjusted cell wall breaking index between the current batch and historical batches, obtain the cell wall breaking confidence of the current batch of tea samples; determine the final cell wall breaking rate of the current batch based on the cell wall breaking confidence.

[0076] For actual factory processing, the production quality under the same processing technology should be relatively consistent. Therefore, the adjustment of cell wall breaking index estimated by image recognition after kneading should also be relatively consistent. When there is a high deviation from historical conditions, it indicates that the evaluation results of image analysis are not reliable enough, and the cell wall breaking situation may be more complex. It may be due to residues or wear from long-term processing, which may cause processing drift and evaluation deviation. Therefore, the reliability of the current cell wall breaking analysis can be quantified by analyzing historical batches and multi-angle results.

[0077] Preferably, in this embodiment of the invention, the method for obtaining the confidence level of cell wall disruption includes:

[0078] The average value of all adjusted cell wall breaking parameters at all angles in each batch of tea samples is taken as the average cell wall breaking parameter of the tea samples, representing the cell wall breaking situation of each batch. Then, the average cell wall breaking parameter of each historical batch with the same processing parameters as the current batch is obtained. In this embodiment of the invention, the average cell wall breaking parameter of historical batches can be the cell wall breaking rate result under multiple experimental processes, or it can include the final cell wall breaking rate of iterative batches. Processing parameters include rolling time, rotation speed, pressure, moisture content, and baking temperature, etc. With the same processing parameters, the cell wall breaking rate of the tea should also be approximately the same.

[0079] Then, after calculating the difference between the average cell wall breaking index of the current batch and the average cell wall breaking index of each historical batch, the sum of the differences of all average cell wall breaking indices is negatively correlated and mapped as the historical confidence level of the current batch. The smaller the overall difference, the more reliable the evaluation results of the current image analysis are after historical correlation analysis.

[0080] Simultaneously, considering that within the current batch, a high degree of difference in cell wall disruption assessment from different angles indicates a potentially higher error rate and reduced reliability, this invention employs a negative correlation mapping method on the variances of adjusted cell wall disruption indices across all angles for the current batch. Combined with historical confidence levels, this yields the cell wall disruption confidence score for the current batch of tea samples. In this embodiment, the product of the negatively correlated variances of adjusted cell wall disruption indices across all angles for the current batch and the historical confidence scores is normalized to obtain the cell wall disruption confidence score for the current batch of tea samples. A higher cell wall disruption confidence score indicates higher reliability of the image assessment and analysis results, and more accurate adjustment of the cell wall disruption indices.

[0081] The cell wall breakage rate of the finished tea product after processing is the final required cell wall breakage rate result. Considering that the drying process does not affect the cell wall breakage trend, when the reliability of image analysis is high, the cell wall breakage rate can also be obtained through image analysis for the processed finished product to improve the efficiency of actual cell wall breakage rate analysis.

[0082] In this embodiment of the invention, when the confidence level of cell wall breakage is greater than a preset confidence threshold, it indicates that the image analysis results are highly reliable, and the final analysis results of the dried product are more reliable. Obtaining the final cell wall breakage rate of the finished product after processing through image analysis can improve the efficiency of cell wall breakage assessment in the factory production process. Otherwise, it indicates that the cell wall breakage status of the sample tea will produce a large cell wall breakage assessment error during image analysis. Therefore, multiple verifications are performed using a spectrometer to obtain the final cell wall breakage rate of the finished product after processing, making the cell wall breakage rate analysis more accurate and providing samples for subsequent cell wall breakage analysis. In a specific embodiment of the invention, the method for obtaining the cell wall breakage rate of the finished tea through image analysis is the same as the method for analyzing and adjusting cell wall breakage indicators after rolling, and will not be elaborated further here. The preset confidence threshold can be set to 0.6, and the specific value can be adjusted by the implementer.

[0083] Considering the long and irreversible nature of the drying process, adjusting and analyzing abnormal cell wall breakage rates would result in significant product waste. An assessment can be made by evaluating the cell wall breakage index and its reliability after kneading, thus improving the efficiency of problem traceability. In this embodiment, if the cell wall breakage confidence level is greater than a preset confidence threshold, and the adjusted cell wall breakage index is less than the preset kneading cell wall breakage threshold, it indicates that cell wall breakage has not been met during kneading, and subsequent processing quality may be even lower. Therefore, a cell wall breakage adjustment alarm is triggered, requiring kneading adjustments or rework, thereby improving the quality stability of the actual production line. The preset kneading cell wall breakage threshold can be set to 15%, and the specific value can be adjusted by the implementer.

[0084] In summary, this invention acquires grayscale images of the blades at different angles after each batch of kneading, enabling comprehensive analysis of the cell wall breakage characteristics on all sides of the blade despite limitations in image quality. Based on the macroscopic disruption of leaf tissue continuity caused by cell wall breakage, the initial cell wall breakage status can be quantified by the area of ​​pixel value differences, yielding an initial cell wall breakage index. Considering that damage features such as strong creases or fractures can be affected by the inherent veins of the blade, leading to errors in cell wall breakage assessment, the invention utilizes the characteristics of more uniform vein direction and smoother width gradients, contrasting with cracks, to determine suspected crack edges based on edge direction consistency and width smoothness, improving the accuracy of cell wall breakage area identification. Furthermore, the uniform distribution of suspected cracks characterizes the degree of cell wall breakage; the uniform distribution of suspected crack length positions yields a cell wall breakage sufficiency assessment index, further improving the accuracy and comprehensiveness of the assessment. Adjusting the initial index by analyzing boundary complexity yields an adjusted cell wall breakage index, and further refining the cell wall breakage assessment based on the tearing condition of the blade edges makes the assessment of cell wall breakage indices at individual angles more reliable. By adjusting the consistency of indicators and their similarity to historical batches from different perspectives to obtain confidence levels, the reliability of multi-angle data is verified, and historical process patterns are correlated, thus improving the credibility of the results. Determining the final cell wall breakage rate based on the confidence level allows for the screening of reliable results, ensuring that the final value accurately reflects the actual cell wall breakage situation. This invention considers the influence of leaf veins and tearing characteristics to adjust the cell wall breakage assessment, and through real-time detection after rolling and correlation with historical process reference analysis, it promptly detects anomalies caused by process drift, improving the accuracy of cell wall breakage assessment in factory production while ensuring the stability of tea production quality.

[0085] This application also provides a tea cell wall breakage rate detection system; please refer to [link / reference]. Figure 3 The diagram shows a structural diagram of a tea cell wall breakage rate detection system provided in an embodiment of the present invention. The system includes: a data acquisition module 201, a cell wall breakage adjustment and analysis module 202, and a final cell wall breakage rate analysis module 203.

[0086] The data acquisition module 201 is used to acquire grayscale images of tea leaves from different angles after each batch of rolling and processing.

[0087] The cell wall breaking adjustment analysis module 202 is used to obtain the initial cell wall breaking index of the leaf grayscale image based on the degree of difference in pixel value area at each angle; to identify suspected crack edges based on the consistency of edge direction and the smoothness of edge width in the leaf grayscale image; to obtain the cell wall breaking sufficient evaluation index of the leaf grayscale image based on the uniformity of the length and position distribution of the suspected crack edges; and to analyze the boundary complexity of the leaf in the leaf grayscale image, and adjust the initial cell wall breaking index in combination with the cell wall breaking sufficient evaluation index to obtain the adjusted cell wall breaking index of the leaf grayscale image.

[0088] The final cell wall breakage rate analysis module 203 is used to obtain the cell wall breakage confidence of the current sample tea based on the consistency of the adjusted cell wall breakage index of the leaf grayscale image of the current sample tea at different angles, and the similarity of the adjusted cell wall breakage index between the current batch and historical batches; and to obtain the final cell wall breakage rate by judging the cell wall breakage confidence.

[0089] It should be noted that the system provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above. In addition, the tea cell wall breakage rate detection system and the tea cell wall breakage rate detection method embodiment provided in the above embodiments belong to the same concept, and their specific implementation process can be found in the method embodiment, which will not be repeated here.

[0090] This application also provides a tea cell wall breakage rate testing device. Please refer to [link / reference]. Figure 4 The diagram shows a schematic of a tea cell wall breakage detection device according to an embodiment of the present invention. The computer device includes a memory 301, a processor 302, and a computer program 303 stored in the memory 301 and running on the processor 302. When the processor 302 executes the computer program 303, the computer device can execute any of the tea cell wall breakage detection methods described above.

[0091] This application also provides a computer program product that, when run on a computer device, enables the computer device to execute any of the aforementioned methods for detecting the cell wall breakage rate of tea leaves.

[0092] This application also provides a computer-readable storage medium storing computer program code. When the computer program code is run on a computer device, the computer device can execute any of the aforementioned methods for detecting the cell wall breakage rate of tea leaves.

[0093] In the embodiments provided in this application, it should be understood that the computer device, computer program product and computer-readable storage medium provided are all used to perform the corresponding methods provided above, and therefore the beneficial effects they can achieve can be referred to the beneficial effects of the methods provided above, which will not be repeated here.

[0094] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0095] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for detecting the cell wall breakage rate of tea leaves, characterized in that, The method includes: After each batch of rolling and processing, grayscale images of the tea leaves were obtained from different angles. On the grayscale image of the blade at each angle, the initial wall breaking index of the blade grayscale image is obtained based on the degree of difference in pixel value area; suspected crack edges are identified based on the consistency of edge direction and the smoothness of edge width in the blade grayscale image; the wall breaking sufficient evaluation index of the blade grayscale image is obtained based on the uniformity of the length and position distribution of the suspected crack edges; the boundary complexity of the blade in the blade grayscale image is analyzed, and the initial wall breaking index is adjusted in combination with the wall breaking sufficient evaluation index to obtain the adjusted wall breaking index of the blade grayscale image. Based on the consistency of the adjusted cell wall breaking index of the current sample tea leaves at different angles and the similarity of the adjusted cell wall breaking index between the current batch and historical batches, the cell wall breaking confidence of the current batch of tea samples is obtained; the final cell wall breaking rate of the current batch is determined based on the cell wall breaking confidence. The methods for obtaining the full evaluation indicators of cell wall disruption include: For any given leaf grayscale image, divide the leaf grayscale image into a preset number of sub-regions; use the proportion of pixels with suspected crack edges in each sub-region as the damage distribution degree of each sub-region; perform negative correlation mapping on the variance of the damage distribution degree of all sub-regions as the local distribution stability of the leaf grayscale image. The cumulative distribution degree of the blade's grayscale image is calculated by multiplying the mean gradient value of all pixels on the suspected crack edges in the grayscale image by the total length of the suspected crack edges. By combining the local distribution stability and cumulative distribution of the leaf grayscale image, the evaluation index of the leaf grayscale image for sufficient cell wall breaking is obtained. The method for obtaining the cell wall breaking index includes: The ratio of the total length of the leaf boundary to the total area of ​​the leaf in each leaf grayscale image is used as the boundary tearing feature value of each leaf grayscale image. The product of the negative correlation mapping of the boundary tearing feature value of each leaf grayscale image and the initial cell wall breakage index is used as the cell wall breakage area evaluation index for each leaf grayscale image. The sum of the cell wall breakage area evaluation index and the cell wall breakage sufficiency evaluation index of each leaf grayscale image is normalized to obtain the adjusted cell wall breakage index for each leaf grayscale image.

2. The method for detecting the cell wall breakage rate of tea leaves according to claim 1, characterized in that, The method for obtaining the initial cell wall disruption index includes: For any grayscale image of a leaf, the Otsu thresholding method is used to obtain connected components. The ratio between the total area of ​​all connected components and the total area of ​​the leaf in the grayscale image is used as the initial cell wall breaking index.

3. The method for detecting the cell wall breakage rate of tea leaves according to claim 1, characterized in that, The method for determining the suspected crack edge includes: For any grayscale image of a leaf, obtain the edge line of the grayscale image of the leaf; for any edge line, calculate the gradient direction difference between every two adjacent pixels on the edge line, and then perform negative correlation mapping on the mean of all gradient direction differences as the direction consistency index of the edge line. After calculating the gradient value difference between every two adjacent pixels on the edge line, the standard deviation of all gradient value differences is negatively correlated to obtain the width smoothness index of the edge line; the product of the direction consistency index and the width smoothness index of the edge line is used as the edge feature value of the edge line. When the edge feature value is less than the preset feature threshold, the corresponding edge line is regarded as a suspected crack edge.

4. The method for detecting the cell wall breakage rate of tea leaves according to claim 1, characterized in that, The method for obtaining the cell wall breaking confidence level includes: The average cell wall breaking index of the sample tea at all angles in each batch is taken as the average cell wall breaking index of the sample tea; the average cell wall breaking index of each historical batch with the same processing parameters as the current batch is obtained. After calculating the difference between the average cell wall breaking index of the current batch and the average cell wall breaking index of each historical batch, the sum of the differences of all average cell wall breaking indices is negatively correlated and used as the historical confidence level of the current batch. By negatively correlated mapping of the variances of the cell wall breaking index under all angles for the current batch, and combined with historical confidence levels, the cell wall breaking confidence level of the current batch of tea samples is obtained.

5. The method for detecting the cell wall breakage rate of tea leaves according to claim 1, characterized in that, The determination of the final cell wall disruption rate of the current batch based on cell wall disruption confidence includes: When the cell wall breakage confidence level is greater than the preset confidence threshold, the final cell wall breakage rate of the finished product after processing is obtained through image analysis; otherwise, the final cell wall breakage rate of the finished product after processing is obtained through a spectrometer.

6. The method for detecting the cell wall breakage rate of tea leaves according to claim 1, characterized in that, After obtaining the confidence level of cell wall breakage for the current batch of tea samples, the process also includes: When the confidence level of cell wall breaking is greater than the preset confidence threshold, if the adjusted cell wall breaking index is less than the preset kneading cell wall breaking threshold, a cell wall breaking adjustment alarm will be triggered.

7. A tea cell wall breakage rate detection system, characterized in that, The system includes: The data acquisition module is used to acquire grayscale images of tea leaves from different angles after each batch of kneading and processing. The cell wall breaking adjustment analysis module is used to obtain the initial cell wall breaking index of the leaf grayscale image at each angle based on the degree of difference in pixel value area; identify suspected crack edges based on the consistency of edge direction and the smoothness of edge width in the leaf grayscale image; obtain the cell wall breaking sufficient evaluation index of the leaf grayscale image based on the uniformity of the length and position distribution of the suspected crack edges; analyze the boundary complexity of the leaf in the leaf grayscale image, and adjust the initial cell wall breaking index in combination with the cell wall breaking sufficient evaluation index to obtain the adjusted cell wall breaking index of the leaf grayscale image. The methods for obtaining the full evaluation indicators of cell wall disruption include: For any given leaf grayscale image, divide the leaf grayscale image into a preset number of sub-regions; use the proportion of pixels with suspected crack edges in each sub-region as the damage distribution degree of each sub-region; perform negative correlation mapping on the variance of the damage distribution degree of all sub-regions as the local distribution stability of the leaf grayscale image. The cumulative distribution degree of the blade's grayscale image is calculated by multiplying the mean gradient value of all pixels on the suspected crack edges in the grayscale image by the total length of the suspected crack edges. By combining the local distribution stability and cumulative distribution of the leaf grayscale image, the evaluation index of the leaf grayscale image for sufficient cell wall breaking is obtained. The method for obtaining the cell wall breaking index includes: The ratio of the total length of the leaf boundary to the total area of ​​the leaf in each leaf grayscale image is used as the boundary tearing feature value of each leaf grayscale image. The product of the negative correlation mapping of the boundary tearing feature value of each leaf grayscale image and the initial cell wall breakage index is used as the cell wall breakage area evaluation index for each leaf grayscale image. The sum of the cell wall breakage area evaluation index and the cell wall breakage sufficiency evaluation index of each leaf grayscale image is normalized to obtain the adjusted cell wall breakage index of each leaf grayscale image. The final cell wall breakage rate analysis module is used to obtain the cell wall breakage confidence of the current sample tea based on the consistency of the adjusted cell wall breakage index of the leaf grayscale images of the current sample tea at different angles, and the similarity of the adjusted cell wall breakage index between the current batch and historical batches; the final cell wall breakage rate is obtained by judging the cell wall breakage confidence.

8. A tea cell wall breakage rate detection device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements a method for detecting the cell wall breakage rate of tea leaves as described in any one of claims 1 to 6.

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