Method and device for evaluating grade of butter crab based on spectral information

Abstract

The invention provides a method and a device for evaluating the grade of a butter crab based on spectral information, and relates to the technical field of engineering optics. The method comprises the following steps of: performing spectral measurement on the butter crab to be evaluated by using a spectrograph to obtain spectral data, extracting characteristic parameters from the data, and then calculating the similarity to obtain the grade of the butter crab; and selecting the quality grade corresponding to the maximum similarity value as the initial quality grade of the butter crabs to be evaluated, wherein the grades comprise top-grade products, special-grade products, first-grade products and unqualified products. And further combining the characteristic parameters, calculating the yellow-blue value and the light transmission ratio area of the to-be-evaluated butter crab, and meanwhile, considering the fatness and ovary index to form a comprehensive evaluation index. And finally, comparing the evaluation index with a preset quality interval, correcting the to-be-determined quality grade according to a comparison result, and finally determining the actual quality grade of the to-be-evaluated butter crab. According to the method, comprehensive evaluation of the quality of the butter crabs is realized through a spectral measurement technology and a characteristic parameter extraction method.

Description

Technical Field

[0001] The invention relates to the technical field of engineering optics, and in particular to a method and device for evaluating the grade of butter crabs based on spectral information. Background Art

[0002] Butter crabs are marine crustaceans that usually live in coastal sandy or muddy environments. They are tender and rich in crab roe, especially during the breeding season, when their oil content and meat flavor are particularly outstanding. With the expansion of the seafood consumption market, the demand for butter crabs continues to grow, and the preferences for their cooking and taste in different regions have also promoted a variety of cooking styles. The evaluation method for butter crab grades is also moving towards intelligence, standardization and sustainable development. At present, traditional sensory evaluation and physical and chemical testing are gradually being replaced by advanced technologies such as machine vision and deep learning, achieving more efficient and objective evaluation. At the same time, combined with big data analysis, the scientific nature and market adaptability of the evaluation are improved. With the improvement of consumers' requirements for food quality, this field has broad technical prospects and market potential.

[0003] In the prior art, publication number CN109883967A discloses a method for distinguishing the quality grade of Chinese mitten crab based on information fusion, which collects and corrects the hyperspectral image of the visible and near-infrared bands of the crab shell of the Chinese mitten crab; then divides the region of interest, extracts RGB information, and then extracts the spectral information of the visible and near-infrared bands of the region of interest, and screens the characteristic wavelengths; filters the hyperspectral image corrected in step one, obtains the main component image, and obtains the texture information of the region of interest in advance; finally, fuses the characteristic wavelengths, texture information, and RGB information, and establishes a grading model based on the fused information through an algorithm.

[0004] Insufficient existing technology: Traditional image processing and feature extraction methods may not be able to fully capture the subtle features of the crab shell surface, resulting in insufficient ability to distinguish between different quality grades. The ant colony optimization algorithm does not give the specific characteristic parameters of the butter crab to be evaluated, and does not set the grade range. It is impossible to accurately judge the quality grade of the butter crab to be evaluated based on the model output results.

[0005] Secondly, the diversity and scale of the data set are insufficient, and may not cover the quality differences of Chinese mitten crabs in different growth environments and breeding conditions. This may lead to overfitting problems in the training and verification process of the model, limiting its effectiveness in practical applications.

[0006] Therefore, it is necessary to provide a method and device for evaluating the grade of butter crab based on spectral information to solve the problem.

[0007] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0008] The object of the present invention is to provide a method and device for evaluating the grade of butter crab based on spectral information to solve the problems raised in the above-mentioned background technology.

[0009] To achieve the above object, the present invention provides the following technical solutions: A method for evaluating the grade of butter crab based on spectral information, the specific steps comprising: Step 1: Use a spectrometer to perform spectral measurement on the butter crab to be evaluated to obtain spectral data of the butter crab to be evaluated; Step 2: Extract characteristic parameters from the spectral data, including reflectance, absorption peak intensity, color index and transmittance; Step 3: Compare the extracted characteristic parameters with the characteristic parameters of butter crabs of known quality grades, calculate the similarity between the characteristic parameters of the butter crab to be evaluated and the butter crabs of each known quality grade, and take the quality grade corresponding to the maximum similarity as the undetermined quality grade of the butter crab to be evaluated. The quality grades include top grade, special grade, first grade and unqualified; Step 4: The extracted characteristic parameters are combined and arranged to calculate the yellow-blue value and the light transmittance ratio of the butter crab to be evaluated, and the butter crab evaluation index is comprehensively generated by combining the fatness and ovarian index of the butter crab to be evaluated; Step 5: Compare the butter crab evaluation index with the preset range, and revise the pending quality grade according to the comparison result to obtain the final quality grade.

[0010] Furthermore, characteristic parameters including reflectance, absorption peak intensity, color index and transmittance are extracted from the spectral data according to the following method: The characteristic parameters, including reflectance, absorption peak intensity, color index and transmittance, are extracted from the spectral data according to the following method: Get the ratio of the total intensity of reflected light to the total intensity of incident light in the spectral data to calculate the reflectance: ; in, is the reflectivity, which represents the ratio of the total intensity of reflected light to the total intensity of incident light of the butter crab to be evaluated. is the total intensity of reflected light, is the total intensity of the incident light, and the wavelength range of the light emitted by the spectrometer is ; The transmittance is obtained by calculating the ratio of the total intensity of the transmitted light to the total intensity of the incident light, based on the formula: ; in, is the transmittance, which is the ratio of the total intensity of the transmitted light to the total intensity of the incident light. is the total intensity of transmitted light; The absorption peak intensity is calculated from the absorbance spectrum in the spectral data according to the following formula: ; in, represents the absorption peak intensity; The process of calculating the color index first converts the color information of the butter crab to be evaluated into a yellow, blue, and green color space through the reflectance spectrum data. In this process, the reflectance of the butter crab to be evaluated is combined with the spectral distribution of the standard light source to generate the corresponding color value, and the corresponding color value is normalized with the standard white point to obtain the corresponding , and value, is the brightness coefficient, which is used to measure the brightness of the color value. is the red-green coefficient, which is used to measure the color change from green to red, where negative values ​​represent green and positive values ​​represent red. It is the blue-yellow coefficient, which is used to indicate the change from blue to yellow. Negative values ​​are blue and positive values ​​are yellow.

[0011] Furthermore, the similarity between the characteristic parameters of the butter crab to be evaluated and the butter crabs of each known quality grade is calculated, and the quality grade corresponding to the maximum similarity value is taken as the undetermined quality grade of the butter crab to be evaluated, and the method is based on: Extract characteristic parameters from the butter crab to be evaluated and the butter crab of known quality level to form a vector representation. Suppose the characteristic parameter vector of the butter crab to be evaluated is , it is known that the characteristic parameter vector of the butter crab to be evaluated is ,in is the index of the butter crab to be evaluated with a known quality grade, and , is the number of butter crabs to be evaluated with known quality grade, is the index of the feature parameter, and , is the number of characteristic parameters; The comparison is performed by calculating the similarity between the feature parameter vector of the butter crab to be evaluated and each known feature parameter vector of the butter crab to be evaluated. Specifically, the distance between the feature parameter vector of the butter crab to be evaluated and each known feature parameter vector of the butter crab to be evaluated is represented by the Euclidean distance, and the formula based on it is: ; ; in, Represents the characteristic parameter vector of the butter crab to be evaluated With The characteristic parameter vector of butter crabs with known quality grades The Euclidean distance between the two butter crabs is used to measure the similarity between the two butter crabs to be evaluated. The smaller the distance, the more similar they are. is the first parameter in the characteristic parameter vector of the butter crab to be evaluated. The value of the characteristic parameter, For the The first of the characteristic parameter vectors of butter crabs with known quality grades The value of the characteristic parameter, Indicates that the butter crab and the The similarity value between the known butter crabs to be evaluated ranges from 0 to 1, where 1 indicates complete similarity and 0 indicates complete dissimilarity; After calculating the similarities of all known butter crabs to be evaluated, find the maximum similarity , the quality grade of the known butter crab to be evaluated corresponding to the maximum similarity is taken as the undetermined quality grade of the butter crab to be evaluated .

[0012] Furthermore, the extracted characteristic parameters are combined and arranged to calculate the yellow-blue value, light transmittance ratio, fatness and ovarian index of the butter crab to be evaluated, and the method is based on: The yellow-blue value of the butter crab to be evaluated is calculated based on the color index and the absorption peak intensity, and the formula is: ; in, Indicates the yellow-blue value of the butter crab to be evaluated; The formula used to calculate the light transmittance ratio of the butter crab to be evaluated is based on the reflectivity and transmittance: ; in, is the light-transmitting area of ​​the butter crab to be evaluated, To evaluate the serrated edge area of ​​the carapace of the butter crab, The entire area of ​​the carapace of the butter crab to be evaluated; The crab body mass data and the carapace length of the butter crab to be evaluated are obtained to calculate the fatness, and the formula is as follows: ; in, Indicates the fatness of the butter crab to be evaluated. To evaluate the body quality of butter crabs, is the value of the carapace length of the butter crab to be evaluated; Get The source of the rays, and based on The optical imaging principle is used to obtain images of different sections of the butter crab to be evaluated. The volume reconstruction technology is used to synthesize the image data of different sections into a three-dimensional model to obtain the volume data of the butter crab to be evaluated. Based on the historical ovarian density data of the butter crab and the body mass of the crab to be evaluated, the ovarian index is calculated. The formula is: ; in, Indicates the ovarian index of the butter crab to be evaluated. To evaluate the ovarian volume of butter crab, The ovarian density of butter crab was to be evaluated.

[0013] Furthermore, a comprehensive evaluation index of the butter crab to be evaluated is generated to verify the evaluation results to ensure the accuracy and reliability of the evaluation. The formula is: ; in, Indicates the evaluation index of the butter crab to be evaluated. , , , Corresponding to the weight ratio of each parameter, , , is the exponential coefficient, is the brightness value of the butter crab to be evaluated, is the transmittance, It is the adjustment coefficient used to control the slope of the exponential function.

[0014] Furthermore, the evaluation index of the butter crab to be evaluated is compared with the preset range to determine the quality grade of the butter crab to be evaluated, and the method is based on: According to the undetermined quality level corresponding to the maximum similarity value, the evaluation index interval is set , if the butter crab evaluation index to be evaluated is within the interval, that is , select the pending quality grade of the butter crab to be evaluated as the final evaluation quality grade; if the evaluation index of the butter crab to be evaluated exceeds the upper limit of the evaluation index range, that is , choose to raise the pending quality grade of the butter crab to be evaluated by one level as the final evaluation quality grade. When the pending quality grade is at the premium grade, there is no need to upgrade; if the evaluation index of the butter crab to be evaluated is lower than the lower limit of the evaluation index range, that is , choose to lower the pending quality grade of the butter crab to be evaluated by one level as the final assessment quality grade. When the pending quality grade is unqualified, there is no need to downgrade it.

[0015] The present invention further provides a butter crab grade assessment device based on spectral information, characterized in that the grade assessment device is used to execute the above-mentioned butter crab grade assessment method based on spectral information, comprising: A spectral measurement module, wherein the spectral measurement module is used to perform spectral measurement on the butter crab to be evaluated using a spectrometer to obtain spectral data of the butter crab to be evaluated; A characteristic parameter extraction module, which is used to extract characteristic parameters from spectral data, including reflectivity, absorption peak intensity, color index and transmittance; A similarity comparison module, wherein the similarity comparison module is used to compare the extracted characteristic parameters with the characteristic parameters of butter crabs of known quality grades, calculate the similarity between the characteristic parameters of the butter crab to be evaluated and the butter crabs of each known quality grade, and use the quality grade corresponding to the maximum similarity as the undetermined quality grade of the butter crab to be evaluated, and the quality grades include top grade, special grade, first grade and unqualified; An evaluation index generation module, which is used to combine and arrange the extracted characteristic parameters to calculate the yellow-blue value and the light transmittance ratio of the butter crab to be evaluated, and to comprehensively generate the butter crab evaluation index by combining the fatness and ovarian index of the butter crab to be evaluated; The grade verification and determination module is used to compare the butter crab evaluation index with a preset interval, and to correct the pending quality grade according to the comparison result to obtain the final quality grade.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The butter crab grade evaluation method based on spectral information of the present invention solves the problem of insufficient capture of crab shell subtle features in the prior art by comprehensively collecting and measuring spectral data of the butter crab to be evaluated. The X-ray is used to measure and collect specific parts of the butter crab, and the extraction of characteristic parameters and comparison with the spectral database make the identification and quality classification of the butter crab to be evaluated more accurate. At the same time, through the calculation and classification mechanism of similarity scores, the butter crab to be evaluated can be efficiently classified as top-grade, special-grade or first-grade, effectively improving the accuracy of the evaluation; In addition, the synthetically generated evaluation index of butter crab not only takes into account the spectral characteristics, but also combines multiple factors such as fatness and ovarian index, making the evaluation result more reliable. Through the application of weight distribution and adjustment coefficients, the applicability of the present invention under different characteristics of butter crabs to be evaluated is enhanced, thereby meeting the market demand for high-quality butter crabs; The present invention uses the spectrum measurement technology comprehensively to utilize the visible spectrum and The X-rays can accurately collect spectral data and extract characteristic parameters of specific parts of the butter crab, thus achieving efficient evaluation of the quality grade of the butter crab. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the overall method flow of the present invention.

[0018] Figure 2 It is a schematic diagram of the system module flow of the present invention. DETAILED DESCRIPTION

[0019] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0020] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0021] Example: See also Figure 1 , a butter crab grade evaluation method based on spectral information, the specific steps include: Step 1: Use a spectrometer to perform spectral measurement on the butter crab to be evaluated to obtain spectral data of the butter crab to be evaluated; Step 2: Extract characteristic parameters from the spectral data, including reflectance, absorption peak intensity, color index and transmittance; Step 3: Compare the extracted characteristic parameters with the characteristic parameters of butter crabs of known quality grades, calculate the similarity between the characteristic parameters of the butter crab to be evaluated and the butter crabs of each known quality grade, and take the quality grade corresponding to the maximum similarity as the undetermined quality grade of the butter crab to be evaluated. The quality grades include top grade, special grade, first grade and unqualified; Step 4: The extracted characteristic parameters are combined and arranged to calculate the yellow-blue value and the light transmittance ratio of the butter crab to be evaluated, and the butter crab evaluation index is comprehensively generated by combining the fatness and ovarian index of the butter crab to be evaluated; Step 5: Compare the butter crab evaluation index with the preset range, and revise the pending quality grade according to the comparison result to obtain the final quality grade.

[0022] It should be noted that it is crucial to extract characteristic parameters such as reflectance, absorption peak intensity, color index and transmittance from spectral data. The calculation of reflectance and transmittance can quantify the optical response of the butter crab to be evaluated at a specific wavelength, reflecting its surface and internal characteristics, which is helpful to evaluate the quality of the butter crab. The absorption peak intensity reveals the chemical composition and freshness of the butter crab to be evaluated, while the color index quantifies the color characteristics of the butter crab to be evaluated, which is directly related to the market value and consumer perception. Through these calculations, the optical behavior of the butter crab to be evaluated can be fully understood, providing a scientific basis for accurately evaluating the quality grade of the butter crab and ensuring the reliability and effectiveness of the evaluation process.

[0023] Furthermore, characteristic parameters including reflectance, absorption peak intensity, color index and transmittance are extracted from the spectral data according to the following method: Get the ratio of the total intensity of reflected light to the total intensity of incident light in the spectral data to calculate the reflectance: ; in, is the reflectivity, which represents the ratio of the total intensity of reflected light to the total intensity of incident light of the butter crab to be evaluated. is the total intensity of reflected light, is the total intensity of the incident light, and the wavelength range of the light emitted by the spectrometer is ; The transmittance is obtained by calculating the ratio of the total intensity of the transmitted light to the total intensity of the incident light, based on the formula: ; in, is the transmittance, which is the ratio of the total intensity of the transmitted light to the total intensity of the incident light. is the total intensity of transmitted light; The absorption peak intensity is calculated from the absorbance spectrum in the spectral data according to the following formula: ; in, Indicates the absorption peak intensity; in the above calculation formula, the wavelength range of the light emitted by the spectrometer is When light of various wavelengths is irradiated on the butter crab to be evaluated, the total intensity of reflected light, the total intensity of incident light and the total intensity of transmitted light are collected by the photoelectric sensor.

[0024] The process of calculating the color index first converts the color information of the crab to be evaluated into a yellow, blue, and green color space through the reflectance spectrum data. In this process, the reflectance of the crab to be evaluated is combined with the spectral distribution of the standard light source to generate the corresponding color value, and the corresponding color value is normalized with the standard white point to obtain the corresponding , and value, is the brightness coefficient, which is used to measure the brightness of the color value. is the red-green coefficient, which is used to measure the color change from green to red, where negative values ​​represent green and positive values ​​represent red. It is the blue-yellow coefficient, which is used to indicate the change from blue to yellow. Negative values ​​are blue and positive values ​​are yellow.

[0025] It should be noted that the similarity measurement method provides a fast and systematic way to evaluate the similarity between the butter crab to be tested and evaluated and the known butter crabs to be evaluated in the database. This method can process a large amount of data in a short time, thereby improving the evaluation efficiency, especially in a commercial environment that requires rapid grading, and by setting different similarity thresholds, the butter crab to be tested and evaluated can be accurately classified into the appropriate quality grade. This grading method helps to eliminate the interference of subjective factors and ensure that the evaluation results are based on objective spectral features. This method allows the system to automatically determine the similarity between the butter crab to be tested and evaluated and the known butter crab to be evaluated, and classify them. This automated processing not only reduces the complexity of manual operations, but also reduces the risk of human errors, making the entire evaluation process more efficient; Using spectral characteristic parameters for similarity calculation represents an advanced quality assessment method. Traditional quality judgment often relies on visual or sensory evaluation, while spectral analysis can provide more comprehensive and objective data support, and spectral characteristic parameters can capture the physical and chemical changes of the butter crab to be evaluated at the microscopic level, thus more sensitively reflecting the freshness and quality fluctuations of the butter crab. By combining the similarity score with the preset quality grade, it can provide producers and consumers with an intuitive basis for quality judgment.

[0026] Furthermore, the similarity between the characteristic parameters of the butter crab to be evaluated and the butter crabs of each known quality grade is calculated, and the quality grade corresponding to the maximum similarity value is taken as the undetermined quality grade of the butter crab to be evaluated, and the method is based on: Extract characteristic parameters from the butter crab to be evaluated and the butter crab of known quality level to form a vector representation. Suppose the characteristic parameter vector of the butter crab to be evaluated is , it is known that the characteristic parameter vector of the butter crab to be evaluated is ,in is the index of the butter crab to be evaluated with a known quality grade, and , is the number of butter crabs to be evaluated with known quality grade, is the index of the feature parameter, and , is the number of characteristic parameters; It should be noted that is the index of characteristic parameters, among which the characteristic parameters of butter crab include reflectivity, absorption peak intensity, color index and transmittance. When the value is different, the combination of feature parameter types in the feature parameter vector of the butter crab to be evaluated is also different, for example When , the characteristic parameter type in the characteristic parameter vector is reflectivity; when When , the characteristic parameter type in the characteristic parameter vector is a combination of reflectivity and absorption peak intensity; when When , the characteristic parameter types in the characteristic parameter vector are a combination of reflectivity, absorption peak intensity and color index.

[0027] The comparison is performed by calculating the similarity between the feature parameter vector of the butter crab to be evaluated and each known feature parameter vector of the butter crab to be evaluated. Specifically, the distance between the feature parameter vector of the butter crab to be evaluated and each known feature parameter vector of the butter crab to be evaluated is represented by the Euclidean distance, and the formula based on it is: ; ; in, Represents the characteristic parameter vector of the butter crab to be evaluated With The characteristic parameter vector of butter crabs with known quality grades The Euclidean distance between the two butter crabs is used to measure the similarity between the two butter crabs to be evaluated. The smaller the distance, the more similar they are. is the first parameter in the characteristic parameter vector of the butter crab to be evaluated. The value of the characteristic parameter, For the The first of the characteristic parameter vectors of butter crabs with known quality grades The value of the characteristic parameter Indicates that the butter crab and the The similarity value between the known butter crabs to be evaluated ranges from 0 to 1, where 1 indicates complete similarity and 0 indicates complete dissimilarity; After calculating the similarities of all known butter crabs to be evaluated, find the maximum similarity , the quality grade of the known butter crab to be evaluated corresponding to the maximum similarity is taken as the undetermined quality grade of the butter crab to be evaluated .

[0028] It should be noted that the calculation of the yellow-blue value, light-transmitting area, fatness and ovarian index of the butter crab by combining and arranging the extracted characteristic parameters has important practical value. The yellow-blue value can directly reflect the color characteristics of the crab and affect its market appeal; the light-transmitting area helps to evaluate its freshness and quality; the fatness is a key indicator to measure the fullness of the crab meat and is directly related to its economic value; the ovarian index provides information about the reproductive status and helps determine the timing of crab fishing.

[0029] Furthermore, the extracted characteristic parameters are combined and arranged to calculate the yellow-blue value, light transmittance ratio, fatness and ovarian index of the butter crab to be evaluated, and the method is based on: The yellow-blue value of the butter crab to be evaluated is calculated based on the color index and the absorption peak intensity, and the formula is: ; in, Indicates the yellow-blue value of the butter crab to be evaluated; in the above formula, the larger the yellow-blue value, the higher the crab roe content in the butter crab to be evaluated. , The increase in the value of Increased, indicating that the crab roe color is close to red and yellow, which means that the crab roe color is healthy, and the brightness value Increase, transmittance When enhanced, it will lead to The value decreases, indicating that under the conditions of high brightness and high transmittance, the crab roe has a strong light transmittance and a low color saturation. The formula used to calculate the light transmittance ratio of the butter crab to be evaluated is based on the reflectivity and transmittance: ; in, is the light-transmitting area of ​​the butter crab to be evaluated, To evaluate the serrated edge area of ​​the carapace of the butter crab, is the total area of ​​the cephalothorax of the butter crab to be evaluated; in the above formula, The larger the value, the larger the jagged edge area will be, which will affect the scattering and reflection of light and change the light transmittance. The larger it is, the more light is reflected rather than transmitted. High reflectivity may indicate an "empty shell" phenomenon, indicating that the butter crab is less full. The decrease indicates that the overall structure is good, and the light transmittance will be negatively affected. The reduction will make it difficult for light to penetrate, which means that the crab is fuller and has more meat and roe. The crab body mass data and the carapace length of the butter crab to be evaluated are obtained to calculate the fatness, and the formula is as follows: ; in, Indicates the fatness of the butter crab to be evaluated. To evaluate the body quality of butter crabs, is the value of the carapace length of the butter crab to be evaluated; in the above formula, The smaller the crab, the smaller the proportion of the crab shell in the total crab mass, which means that Lower, head and chest carapace length Small ones tend to be fatter; Get The source of the rays, and based on The optical imaging principle is used to obtain images of different sections of the butter crab to be evaluated. The volume reconstruction technology is used to synthesize the image data of different sections into a three-dimensional model to obtain the ovarian volume of the butter crab to be evaluated. The ovarian index is calculated by combining the ovarian density of the butter crab with the body mass of the butter crab to be evaluated. The formula is: ; in, Indicates the ovarian index of the butter crab to be evaluated. To evaluate the ovarian volume of butter crab, is the ovarian density of butter crab. The ovarian density data of butter crab can be obtained by referring to the relevant historical biological data of butter crab. In the above formula, The quality of the ovary. The greater the density and volume of the ovary, the greater the quality. The greater the proportion of the ovary quality to the total crab quality, the higher the nutritional value. The wavelength range of the radiation is .

[0030] It should be noted that by integrating multiple key quality indicators into a unified evaluation index, the evaluation error caused by the fluctuation of a single indicator can be effectively reduced, providing a more comprehensive and objective quality measurement standard. This index not only helps to standardize the evaluation process and enhance the comparability between different butter crabs to be evaluated, but also provides a clear quality reference for producers and consumers, thereby building trust. In addition, using this comprehensive index to verify the results can help to promptly identify potential quality problems, enhance the market's overall reputation and competitiveness for butter crabs, and promote the sustainable development of the industry.

[0031] Furthermore, a comprehensive evaluation index of the butter crab to be evaluated is generated to verify the evaluation results to ensure the accuracy and reliability of the evaluation. The formula is: ; in, Indicates the evaluation index of the butter crab to be evaluated. , , , Corresponding to the weight ratio of each parameter, , , is the exponential coefficient, is the brightness value of the butter crab to be evaluated, The wavelength of the light emitted by the spectrometer is The transmittance, is the adjustment coefficient used to control the slope of the exponential function; in the above formula, the weight ratio is set to The reason is that the most intuitive way to evaluate butter crabs is to detect the crab roe content in the butter crabs to be evaluated. Therefore, the yellow-blue value indicating the crab roe content is given the highest weight ratio. ; Secondly, the fullness directly affects the taste and economic value of crab meat. By observing the fullness of the butter crab to be evaluated, it can also reflect the quality of the butter crab to be evaluated to a large extent, so it is given the second weight ; Relatively speaking, ovarian index and light transmission area The weights of the following are low, mainly because they have less direct influence on consumers' choice of ingredients. Although the light-transmitting area ratio can reflect freshness, its influence is not as obvious as crab roe and fullness. In addition, the ovarian index provides information about reproductive status. Although its importance to the timing of fishing cannot be ignored, its direct influence in the overall evaluation is relatively weak; In calculation In the formula, , , , The four index coefficients are all fixed values. They are set in exponential form because many biological characteristics, especially those related to quality, often have nonlinear relationships. The use of exponential form can better capture these nonlinear characteristics, reflecting that the impact of certain parameters on the evaluation index within a specific range may show an accelerating or decelerating trend, and , , The index form is used to enhance its influence on the evaluation index, so as to balance the weight ratio. The biggest impact.

[0032] In, when When the value becomes larger, it means that the crab roe content increases, and the evaluation index will increase significantly. The value increases, reflecting the high quality of the crab; The higher the fullness, the more significant the impact on the evaluation index. The larger the value, the more it reflects the taste and economic value of the crab meat. On the contrary, if the fullness decreases, the evaluation index will decrease. Ovarian index The larger the evaluation index The bigger it is, the higher the nutritional value of the crab is; The larger the light-transmitting area, the higher the evaluation index. Decreased, reflecting the greater degree of crab shell emptying; Medium, Brightness The higher the light transmittance The stronger it is, the more evaluation index The decrease reflects the less nutrients in the crab's body.

[0033] It should be noted that by setting a clear evaluation index range , providing an objective and standardized basis for the quality assessment of butter crab. This method can reduce the influence of subjective factors on quality assessment, ensure that different assessors make judgments under the same standards, thereby improving the consistency and repeatability of the assessment, and calculate the undetermined quality grade corresponding to the maximum similarity value combined with scientific indicators of optical properties to ensure the scientificity and rationality of the assessment results.

[0034] Furthermore, the evaluation index of the butter crab to be evaluated is compared with the preset range to determine the quality grade of the butter crab to be evaluated, and the method is based on: According to the undetermined quality level corresponding to the maximum similarity value, the evaluation index interval is set , if the butter crab evaluation index to be evaluated is within the interval, that is , select the pending quality grade of the butter crab to be evaluated as the final evaluation quality grade; if the evaluation index of the butter crab to be evaluated exceeds the upper limit of the evaluation index range, that is , choose to raise the pending quality grade of the butter crab to be evaluated by one level as the final evaluation quality grade. When the pending quality grade is at the premium grade, there is no need to upgrade; if the evaluation index of the butter crab to be evaluated is lower than the lower limit of the evaluation index range, that is , choose to lower the pending quality grade of the butter crab to be evaluated by one level as the final assessment quality grade. When the pending quality grade is unqualified, there is no need to downgrade it.

[0035] See also Figure 2 The present invention further provides a butter crab grade assessment device based on spectral information, the grade assessment device is used to perform the above-mentioned butter crab grade assessment method based on spectral information, comprising: A spectral measurement module, wherein the spectral measurement module is used to perform spectral measurement on the butter crab to be evaluated using a spectrometer to obtain spectral data of the butter crab to be evaluated; A characteristic parameter extraction module, which is used to extract characteristic parameters from spectral data, including reflectivity, absorption peak intensity, color index and transmittance; A similarity comparison module, wherein the similarity comparison module is used to compare the extracted characteristic parameters with the characteristic parameters of butter crabs of known quality grades, calculate the similarity between the characteristic parameters of the butter crab to be evaluated and the butter crabs of each known quality grade, and use the quality grade corresponding to the maximum similarity as the undetermined quality grade of the butter crab to be evaluated, and the quality grades include top grade, special grade, first grade and unqualified; An evaluation index generation module, which is used to combine and arrange the extracted characteristic parameters to calculate the yellow-blue value and the light transmittance ratio of the butter crab to be evaluated, and to comprehensively generate the butter crab evaluation index by combining the fatness and ovarian index of the butter crab to be evaluated; The grade verification and determination module is used to compare the butter crab evaluation index with a preset interval, and to correct the pending quality grade according to the comparison result to obtain the final quality grade.

[0036] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0037] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0038] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0039] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for evaluating the grade of butter crab based on spectral information, characterized in that: The specific steps include: Step 1: Use a spectrometer to perform spectral measurement on the butter crab to be evaluated to obtain spectral data of the butter crab to be evaluated; Step 2: Extract characteristic parameters from the spectral data, including reflectance, absorption peak intensity, color index and transmittance; Step 3: Compare the extracted characteristic parameters with the characteristic parameters of butter crabs of known quality grades, calculate the similarity between the characteristic parameters of the butter crab to be evaluated and the butter crabs of each known quality grade, and take the quality grade corresponding to the maximum similarity as the undetermined quality grade of the butter crab to be evaluated. The quality grades include top grade, special grade, first grade and unqualified; Step 4: The extracted characteristic parameters are combined and arranged to calculate the yellow-blue value and the light transmittance ratio of the butter crab to be evaluated, and the butter crab evaluation index is comprehensively generated by combining the fatness and ovarian index of the butter crab to be evaluated; Step 5: Compare the butter crab evaluation index with the preset range, and revise the pending quality grade according to the comparison result to obtain the final quality grade.

2. The method for evaluating the grade of butter crab based on spectral information according to claim 1, characterized in that: The characteristic parameters, including reflectance, absorption peak intensity, color index and transmittance, are extracted from the spectral data according to the following method: Get the ratio of the total intensity of reflected light to the total intensity of incident light in the spectral data to calculate the reflectance: ； in, is the reflectivity, which represents the ratio of the total intensity of reflected light to the total intensity of incident light of the butter crab to be evaluated. is the total intensity of reflected light, is the total intensity of the incident light, and the wavelength range of the light emitted by the spectrometer is ; The transmittance is obtained by calculating the ratio of the total intensity of the transmitted light to the total intensity of the incident light, based on the formula: ； in, is the transmittance, which is the ratio of the total intensity of the transmitted light to the total intensity of the incident light. is the total intensity of transmitted light; The absorption peak intensity is calculated from the absorbance spectrum in the spectral data according to the following formula: ； in, represents the absorption peak intensity; The process of calculating the color index first converts the color information of the butter crab to be evaluated into a yellow, blue, and green color space through the reflectance spectrum data. In this process, the reflectance of the butter crab to be evaluated is combined with the spectral distribution of the standard light source to generate the corresponding color value, and the corresponding color value is normalized with the standard white point to obtain the corresponding , and value, is the brightness coefficient, which is used to measure the brightness of the color value. is the red-green coefficient, which is used to measure the color change from green to red, where negative values ​​represent green and positive values ​​represent red. It is the blue-yellow coefficient, which is used to indicate the change from blue to yellow. Negative values ​​are blue and positive values ​​are yellow.

3. The method for evaluating the grade of butter crab based on spectral information according to claim 1, characterized in that: The similarity between the characteristic parameters of the butter crab to be evaluated and the butter crabs of each known quality grade is calculated, and the quality grade corresponding to the maximum similarity value is taken as the undetermined quality grade of the butter crab to be evaluated. The method is as follows: Extract characteristic parameters from the butter crab to be evaluated and the butter crab of known quality level to form a vector representation. Suppose the characteristic parameter vector of the butter crab to be evaluated is , it is known that the characteristic parameter vector of the butter crab to be evaluated is ,in is the index of the butter crab to be evaluated with a known quality grade, and , is the number of butter crabs to be evaluated with known quality grade, is the index of the feature parameter, and , is the number of characteristic parameters; The comparison is performed by calculating the similarity between the feature parameter vector of the butter crab to be evaluated and each known feature parameter vector of the butter crab to be evaluated. Specifically, the distance between the feature parameter vector of the butter crab to be evaluated and each known feature parameter vector of the butter crab to be evaluated is represented by the Euclidean distance, and the formula based on it is: ； ； in, Represents the characteristic parameter vector of the butter crab to be evaluated With The characteristic parameter vector of butter crabs with known quality grades The Euclidean distance between the two butter crabs is used to measure the similarity between the two butter crabs to be evaluated. The smaller the distance, the more similar they are. is the first parameter in the characteristic parameter vector of the butter crab to be evaluated. The value of the characteristic parameter, For the The first of the characteristic parameter vectors of butter crabs with known quality grades The value of the characteristic parameter, Indicates that the butter crab is to be evaluated The similarity value between the known butter crabs to be evaluated ranges from 0 to 1, where 1 indicates complete similarity and 0 indicates complete dissimilarity; After calculating the similarities of all known butter crabs to be evaluated, find the maximum similarity , the quality grade of the known butter crab to be evaluated corresponding to the maximum similarity is taken as the undetermined quality grade of the butter crab to be evaluated .

4. The method for evaluating the grade of butter crab based on spectral information according to claim 2, characterized in that: The extracted characteristic parameters are combined and arranged to calculate the yellow-blue value, light transmittance ratio, fatness and ovarian index of the butter crab to be evaluated, based on the following method: The yellow-blue value of the butter crab to be evaluated is calculated based on the color index and the absorption peak intensity, and the formula is: ； in, Indicates the yellow-blue value of the butter crab to be evaluated; The formula used to calculate the light-transmitting area of ​​the butter crab to be evaluated is based on reflectivity and transmittance: ； in, is the light-transmitting area of ​​the butter crab to be evaluated, To evaluate the serrated edge area of ​​the carapace of the butter crab, The entire area of ​​the carapace of the butter crab to be evaluated; The crab body mass data and the carapace length of the butter crab to be evaluated are obtained to calculate the fatness, and the formula is as follows: ； in, Indicates the fatness of the butter crab to be evaluated. To evaluate the body quality of butter crabs, is the value of the carapace length of the butter crab to be evaluated; Get The source of the rays, and based on The optical imaging principle is used to obtain images of different sections of the butter crab to be evaluated. The volume reconstruction technology is used to synthesize the image data of different sections into a three-dimensional model to obtain the ovarian volume of the butter crab to be evaluated. The ovarian index is calculated by combining the ovarian density of the butter crab with the body mass of the butter crab to be evaluated. The formula is: ； in, Indicates the ovarian index of the butter crab to be evaluated. To evaluate the ovarian volume of butter crab, The ovary density of butter crab.

5. The method for evaluating the grade of butter crab based on spectral information according to claim 4 is characterized in that: The evaluation index of the butter crab to be evaluated is comprehensively generated to verify the evaluation results to ensure the accuracy and reliability of the evaluation. The formula is: ； in, Indicates the evaluation index of the butter crab to be evaluated. , , , Corresponding to the weight ratio of each parameter, , , is the exponential coefficient, is the brightness value of the butter crab to be evaluated, is the transmittance, It is the adjustment coefficient used to control the slope of the exponential function.

6. The method for evaluating the grade of butter crab based on spectral information according to claim 5, characterized in that: The evaluation index of the butter crab to be evaluated is compared with the preset range to determine the quality grade of the butter crab to be evaluated. The method is based on: According to the undetermined quality level corresponding to the maximum similarity value, the evaluation index interval is set , if the butter crab evaluation index to be evaluated is within the interval, that is , select the pending quality grade of the butter crab to be evaluated as the final evaluation quality grade; if the evaluation index of the butter crab to be evaluated exceeds the upper limit of the evaluation index range, that is , choose to raise the pending quality grade of the butter crab to be evaluated by one level as the final evaluation quality grade. When the pending quality grade is at the premium grade, there is no need to upgrade; if the evaluation index of the butter crab to be evaluated is lower than the lower limit of the evaluation index range, that is , choose to lower the pending quality grade of the butter crab to be evaluated by one level as the final assessment quality grade. When the pending quality grade is unqualified, there is no need to downgrade it.

7. A butter crab grade assessment device based on spectral information, characterized in that: The grade assessment device is used to execute the butter crab grade assessment method based on spectral information according to any one of claims 1 to 6, comprising: A spectral measurement module, wherein the spectral measurement module is used to perform spectral measurement on the butter crab to be evaluated using a spectrometer to obtain spectral data of the butter crab to be evaluated; A characteristic parameter extraction module, which is used to extract characteristic parameters from spectral data, including reflectivity, absorption peak intensity, color index and transmittance; A similarity comparison module, wherein the similarity comparison module is used to compare the extracted characteristic parameters with the characteristic parameters of butter crabs of known quality grades, calculate the similarity between the characteristic parameters of the butter crab to be evaluated and the butter crabs of each known quality grade, and use the quality grade corresponding to the maximum similarity as the undetermined quality grade of the butter crab to be evaluated, and the quality grades include top grade, special grade, first grade and unqualified; An evaluation index generation module, which is used to combine and arrange the extracted characteristic parameters to calculate the yellow-blue value and the light transmittance ratio of the butter crab to be evaluated, and to comprehensively generate the butter crab evaluation index by combining the fatness and ovarian index of the butter crab to be evaluated; The grade verification and determination module is used to compare the butter crab evaluation index with a preset interval, and to correct the pending quality grade according to the comparison result to obtain the final quality grade.

Images