A method and device for evaluating butter crab grade based on spectral information

Abstract

The present invention provides a method and device for evaluating the grade of butter crabs based on spectral information, and relates to the field of engineering optical technology. The present invention uses a spectrometer to perform spectral measurement on the butter crabs to be evaluated, obtains their spectral data, extracts characteristic parameters from these data, and then calculates the similarity, and selects the quality grade corresponding to the maximum similarity value as the preliminary quality grade of the butter crabs to be evaluated, and the grades include top-grade, special-grade, first-grade and unqualified. These characteristic parameters are further combined to calculate the yellow-blue value and light-transmitting area ratio of the butter crabs to be evaluated, while considering the fatness and ovarian index to form a comprehensive evaluation index. Finally, the evaluation index is compared with a preset quality interval, and the quality grade to be determined is corrected according to the comparison result, and the actual quality grade of the butter crabs to be evaluated is finally determined. The present invention realizes a comprehensive evaluation of the quality of butter crabs through spectral measurement technology and 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:

[0005] 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.

[0006] 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.

[0007] 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.

[0008] 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

[0009] 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.

[0010] To achieve the above object, the present invention provides the following technical solutions:

[0011] A method for evaluating the grade of butter crab based on spectral information, the specific steps comprising:

[0012] 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;

[0013] Step 2: Extract characteristic parameters from the spectral data, including reflectance, absorption peak intensity, color index and transmittance;

[0014] 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;

[0015] 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;

[0016] 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.

[0017] Furthermore, characteristic parameters including reflectance, absorption peak intensity, color index and transmittance are extracted from the spectral data according to the following method:

[0018] The characteristic parameters, including reflectance, absorption peak intensity, color index and transmittance, are extracted from the spectral data according to the following method:

[0019] 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:

[0020]

[0021] Where R 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, Ir is the total intensity of reflected light, I i is the total intensity of the incident light, and the wavelength of the light emitted by the spectrometer ranges from [400nm, 750nm];

[0022] 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:

[0023]

[0024] Where, T(λ) is the transmittance, which is the ratio of the total intensity of the transmitted light to the total intensity of the incident light. t is the total intensity of transmitted light;

[0025] The absorption peak intensity is calculated from the absorbance spectrum in the spectral data according to the following formula:

[0026] A=1-RT

[0027] Where A represents the absorption peak intensity;

[0028] 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 L, a, and b * value, L is the brightness coefficient, which is used to measure the brightness of the color value, a 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, and b * 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.

[0029] 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:

[0030] Extract characteristic parameters from the butter crab to be evaluated and the butter crab of known quality level to form a vector representation. Let the characteristic parameter vector of the butter crab to be evaluated be P 待评估 =[f1,f2,f3,…,f k ,…,f n ], it is known that the characteristic parameter vector of the butter crab to be evaluated is Where j is the index of the butter crab with known quality grade, and j∈[1,M], M is the number of butter crabs with known quality grade, k is the index of the feature parameter, and k∈[1,n], n is the number of feature parameters;

[0031] The comparison is performed by calculating the similarity between the characteristic parameter vector of the butter crab to be evaluated and the characteristic parameter vector of each known quality grade of butter crab. Specifically, the similarity between the characteristic parameter vector of the butter crab to be evaluated and the characteristic parameter vector of each known quality grade of butter crab is represented by the Euclidean distance, and the formula based on it is:

[0032]

[0033] in, Represents the characteristic parameter vector P of the butter crab to be evaluated 待评估 and the characteristic parameter vector of the j-th known quality grade butter crab The Euclidean distance between the two is used to measure the similarity between the butter crab to be evaluated and the butter crab of known quality grade. The smaller the distance, the more similar it is. k is the value of the kth feature parameter in the feature parameter vector of the butter crab to be evaluated, is the value of the kth characteristic parameter in the characteristic parameter vector of the jth known quality grade butter crab, S j represents the similarity between the butter crab to be evaluated and the butter crab of the jth known quality grade, and the similarity value ranges from 0 to 1, where 1 represents complete similarity and 0 represents complete dissimilarity;

[0034] After calculating the similarity between all the butter crabs to be evaluated and the butter crabs of known quality grades, find the maximum similarity S max , the quality level of the known quality level of the butter crab corresponding to the maximum similarity is taken as the undetermined quality level L of the butter crab to be evaluated 待定 .

[0035] 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:

[0036] 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:

[0037]

[0038] Among them, HL represents the yellow-blue value of the butter crab to be evaluated;

[0039] The formula used to calculate the light-transmitting area of ​​the butter crab to be evaluated is based on reflectivity and transmittance:

[0040]

[0041] Among them, S is the light-transmitting area of ​​the butter crab to be evaluated, S1 is the serrated edge area of ​​the cephalothorax of the butter crab to be evaluated, and S2 is the total area of ​​the cephalothorax of the butter crab to be evaluated;

[0042] 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:

[0043]

[0044] Wherein, F represents the fatness of the butter crab to be evaluated, W represents the body weight of the butter crab to be evaluated, and l represents the length of the head and chest carapace of the butter crab to be evaluated;

[0045] Obtain an X-ray source, and obtain images of different sections of the butter crab to be evaluated based on the principle of X-ray imaging. Use volume reconstruction technology 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 butter crab ovarian density data and the body mass of the crab to be evaluated, calculate the ovarian index based on the following formula:

[0046]

[0047] Among them, O represents the ovarian index of the butter crab to be evaluated, V lc is the ovarian volume of the butter crab to be evaluated, ρ lc The ovarian density of butter crab was to be evaluated.

[0048] 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:

[0049]

[0050] Among them, PJZS represents the evaluation index of the butter crab to be evaluated, K1, K2, K3, and K4 correspond to the weight ratios of their respective parameters, α, β, and γ are exponential coefficients, L is the brightness value of the butter crab to be evaluated, T is the transmittance, and δ is the adjustment coefficient used to control the slope of the exponential function.

[0051] 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:

[0052] According to the pending quality grade corresponding to the maximum similarity value, the evaluation index interval [A, B] is set. If the evaluation index of the butter crab to be evaluated is within the interval, that is, A≤PJZS≤B, the pending quality grade of the butter crab to be evaluated is selected 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 interval, that is, PJZS≥A, the pending quality grade of the butter crab to be evaluated is selected to be increased by one level as the final evaluation quality grade, and no upgrading is required when the pending quality grade is in the special grade; if the evaluation index of the butter crab to be evaluated is lower than the lower limit of the evaluation index interval, that is, PJZS≤B, the pending quality grade of the butter crab to be evaluated is selected to be decreased by one level as the final evaluation quality grade, and no downgrade is required when the pending quality grade is unqualified.

[0053] 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:

[0054] 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;

[0055] A characteristic parameter extraction module, which is used to extract characteristic parameters from spectral data, including reflectivity, absorption peak intensity, color index and transmittance;

[0056] 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;

[0057] 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;

[0058] 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.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] The butter crab grade assessment 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 the spectral data of the butter crab to be assessed. It uses visible spectrum and X-rays to measure and collect specific parts of the butter crab, and combines the extraction of characteristic parameters with the comparison of the spectral database, so that the identification and quality grade classification of the butter crab to be assessed are more accurate. At the same time, through the calculation and classification mechanism of similarity scores, the butter crab to be assessed can be efficiently classified as top-grade, special-grade or first-grade, which effectively improves the accuracy of the assessment;

[0061] 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;

[0062] The present invention uses spectral measurement technology in combination, utilizes visible spectrum and X-ray to accurately collect spectral data and extract characteristic parameters of specific parts of butter crab, thereby realizing efficient evaluation of the quality grade of butter crab. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a schematic diagram of the overall method flow of the present invention.

[0064] Figure 2 It is a schematic diagram of the system module flow of the present invention. DETAILED DESCRIPTION

[0065] 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.

[0066] 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.

[0067] Example:

[0068] See also Figure 1 , a butter crab grade evaluation method based on spectral information, the specific steps include:

[0069] 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;

[0070] Step 2: Extract characteristic parameters from the spectral data, including reflectance, absorption peak intensity, color index and transmittance;

[0071] 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;

[0072] 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;

[0073] 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.

[0074] 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.

[0075] Furthermore, characteristic parameters including reflectance, absorption peak intensity, color index and transmittance are extracted from the spectral data according to the following method:

[0076] 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:

[0077]

[0078] Where R 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, I r is the total intensity of reflected light, Ii is the total intensity of the incident light, and the wavelength of the light emitted by the spectrometer ranges from [400nm, 750nm];

[0079] 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:

[0080]

[0081] Where, T(λ) is the transmittance, which is the ratio of the total intensity of the transmitted light to the total intensity of the incident light. t is the total intensity of transmitted light;

[0082] The absorption peak intensity is calculated from the absorbance spectrum in the spectral data according to the following formula:

[0083] A=1-RT

[0084] Wherein, A represents the absorption peak intensity; in the above calculation formula, the wavelength range of the light emitted by the spectrometer is between [400nm, 750nm]. When light of various wavelengths is irradiated on the butter crab to be evaluated, the total intensity of the reflected light, the total intensity of the incident light and the total intensity of the transmitted light are collected by the photoelectric sensor.

[0085] The process of calculating the color index first converts the color information of the butter crab to be evaluated into the color space of yellow, blue and green 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 L, a and b * value, L is the brightness coefficient, which is used to measure the brightness of the color value, a 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, and b * 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.

[0086] 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;

[0087] 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.

[0088] 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:

[0089] Extract characteristic parameters from the butter crab to be evaluated and the butter crab of known quality level to form a vector representation. Let the characteristic parameter vector of the butter crab to be evaluated be P 待评估 =[f1,f2,f3,…,f k ,…,f n ], it is known that the characteristic parameter vector of the butter crab to be evaluated is Where j is the index of the butter crab with known quality grade, and j∈[1,M], M is the number of butter crabs with known quality grade, k is the index of the feature parameter, and k∈[1,n], n is the number of feature parameters;

[0090] It should be noted that k is the index of the characteristic parameter, where the characteristic parameters of the butter crab specifically include reflectance, absorption peak intensity, color index and transmittance. When k is different, the combination of characteristic parameter types in the characteristic parameter vector of the butter crab to be evaluated is also different. For example, when k=1, the characteristic parameter type in the characteristic parameter vector is reflectance; when k=2, the characteristic parameter type in the characteristic parameter vector is a combination of reflectance and absorption peak intensity; when k=3, the characteristic parameter type in the characteristic parameter vector is a combination of reflectance, absorption peak intensity and color index.

[0091] The comparison is performed by calculating the similarity between the characteristic parameter vector of the butter crab to be evaluated and the characteristic parameter vector of each known quality grade of butter crab. Specifically, the similarity between the characteristic parameter vector of the butter crab to be evaluated and the characteristic parameter vector of each known quality grade of butter crab is represented by the Euclidean distance, and the formula based on it is:

[0092]

[0093] in, Represents the characteristic parameter vector P of the butter crab to be evaluated 待评估 and the characteristic parameter vector of the j-th known quality grade butter crab The Euclidean distance between the two is used to measure the similarity between the butter crab to be evaluated and the butter crab of known quality grade. The smaller the distance, the more similar it is. k is the value of the kth feature parameter in the feature parameter vector of the butter crab to be evaluated, is the value of the kth characteristic parameter in the characteristic parameter vector of the jth known quality grade butter crab, S j represents the similarity between the butter crab to be evaluated and the butter crab of the jth known quality grade, and the similarity value ranges from 0 to 1, where 1 represents complete similarity and 0 represents complete dissimilarity;

[0094] After calculating the similarity between all the butter crabs to be evaluated and the butter crabs of known quality grades, find the maximum similarity S max , the quality level of the known quality level of the butter crab corresponding to the maximum similarity is taken as the undetermined quality level L of the butter crab to be evaluated 待定 .

[0095] 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.

[0096] 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:

[0097] 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:

[0098]

[0099] Wherein, HL represents 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. * When the value of a increases, HL increases, indicating that the crab roe color is close to red and yellow, which means that the crab roe color is healthy. When the brightness value L increases and the transmittance T increases, the HL value decreases, indicating that under the conditions of high brightness and high transmittance, the crab roe has a strong ability to transmit light and the color saturation of the crab roe is low.

[0100] The formula used to calculate the light-transmitting area of ​​the butter crab to be evaluated is based on reflectivity and transmittance:

[0101]

[0102] Among them, S is the light-transmitting area of ​​the butter crab to be evaluated, S1 is the serrated edge area of ​​the carapace of the butter crab to be evaluated, and S2 is the total area of ​​the carapace of the butter crab to be evaluated; in the above formula, the larger the S1, the larger the serrated edge area, which will affect the scattering and reflection of light, causing the light transmittance to change; the larger the R, the more light is reflected rather than transmitted, and the high reflectivity may indicate the "empty shell" phenomenon, indicating that the butter crab is less full; the decrease in S2 indicates that the overall structure is good, and the light transmittance will be negatively affected, while the decrease in T will make it difficult for light to pass through, which means that the butter crab is full and the crab meat and crab roe are plump;

[0103] 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:

[0104]

[0105] Among them, F represents the fatness of the butter crab to be evaluated, W is the crab body mass of the butter crab to be evaluated, and l is the value of the carapace length of the butter crab to be evaluated; in the above formula, the smaller l is, the smaller the proportion of the crab shell to the total mass of the crab is, which means that under the same mass W, the carapace length l is smaller and the fatness is higher;

[0106] Obtain an X-ray source, and obtain images of different sections of the butter crab to be evaluated based on the principle of X-ray imaging. Use volume reconstruction technology 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. Combine the ovarian density of the butter crab with the body mass of the butter crab to be evaluated to calculate the ovarian index. The formula is:

[0107]

[0108] Among them, O represents the ovarian index of the butter crab to be evaluated, V lc is the ovarian volume of the butter crab to be evaluated, ρ lc 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, V lc * lc It is the mass of the ovary. The greater the density and volume of the ovary, the greater the mass. The larger the proportion of the ovary mass in the total mass of the crab, the higher the nutritional value. The wavelength range of X-rays is [0.01nm, 10nm].

[0109] 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.

[0110] 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:

[0111]

[0112] Among them, PJZS represents the evaluation index of the butter crab to be evaluated, k1, k2, k3, and k4 correspond to the weight ratio of their respective parameters, α, β, and γ are index coefficients, L is the brightness value of the butter crab to be evaluated, T is the transmittance of the spectrometer emission light wavelength λ, and δ is the adjustment coefficient used to control the slope of the exponential function; in the above formula, the weight ratio is set to k1>k3>k4>k2>0, because for the evaluation of butter crab, the most intuitive judgment method is to detect the crab roe content in the butter crab to be evaluated, so the yellow-blue value indicating the crab roe content is given the highest weight ratio k1; secondly, the fullness directly affects the taste and economic value of the 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 the second weight k2 is given; relatively speaking, the weights of the ovarian index k4 and the light-transmitting area k2 are relatively low, mainly because they have less intuitive influence on consumers when choosing ingredients. Although the light-transmitting area can reflect freshness, its influence is not as obvious as crab roe and fullness. In addition, the ovarian index provides information on reproductive status, and although its importance for fishing timing cannot be ignored, its direct influence in the overall assessment is relatively weak;

[0113] In the formula for calculating PJZS, the four exponential coefficients α, β, γ, and δ are all constants. 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. The exponential form is used for F, O, and S to enhance their influence on the evaluation index, so as to balance the impact brought by setting the weight ratio to the maximum k1.

[0114] In k1*HL, when the HL value increases, it means that the crab roe content increases, the evaluation index will increase significantly, and the PJZS value will increase, reflecting the high quality of the crab; in k3*Fβ, the higher the fullness, the more significant the impact on the evaluation index, the larger the PJZS value, reflecting the taste and economic value of the crab meat, on the contrary, if the fullness decreases, the evaluation index will decrease; in k4*Oγ, the larger the ovarian index O, the larger the evaluation index PJZS, reflecting the high nutritional value of the crab; k2*S α In the figure, the larger the proportion of light-transmitting area, the smaller the evaluation index PJZS will be, which reflects the large degree of empty shell of crab; e -δ*[L+T] In the figure, the higher the brightness L and the stronger the transmittance T, the smaller the evaluation index PJZS will be, which reflects that there are fewer nutrients in the crab body.

[0115] It should be noted that by setting a clear evaluation index interval [A, B], an objective and standardized basis is provided for the quality evaluation of butter crab. This method can reduce the influence of subjective factors on quality evaluation, ensure that different evaluators make judgments under the same standards, thereby improving the consistency and repeatability of the evaluation, and based on the undetermined quality level corresponding to the maximum similarity value, combined with the scientific indicators of optical properties, the calculation can ensure the scientificity and rationality of the evaluation results.

[0116] 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:

[0117] According to the pending quality grade corresponding to the maximum similarity value, the evaluation index interval [A, B] is set. If the evaluation index of the butter crab to be evaluated is within the interval, that is, A≤PJZS≤B, the pending quality grade of the butter crab to be evaluated is selected 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 interval, that is, PJZS≥A, the pending quality grade of the butter crab to be evaluated is selected to be increased by one level as the final evaluation quality grade, and no upgrading is required when the pending quality grade is in the special grade; if the evaluation index of the butter crab to be evaluated is lower than the lower limit of the evaluation index interval, that is, PJZS≤B, the pending quality grade of the butter crab to be evaluated is selected to be decreased by one level as the final evaluation quality grade, and no downgrade is required when the pending quality grade is unqualified.

[0118] 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:

[0119] 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;

[0120] A characteristic parameter extraction module, which is used to extract characteristic parameters from spectral data, including reflectivity, absorption peak intensity, color index and transmittance;

[0121] 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;

[0122] 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;

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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 evaluation index of the butter crab with the preset range, and modify the quality grade to be determined according to the comparison result to obtain the final quality grade; 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: Where R 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, I r is the total intensity of reflected light, I i is the total intensity of the incident light, and the wavelength of the light emitted by the spectrometer ranges from [400nm, 750nm]; 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: Where, T(λ) is the transmittance, which is the ratio of the total intensity of the transmitted light to the total intensity of the incident light. t 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: A=1-RT Where A represents the absorption peak intensity; 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 L, a, and b * value, L is the brightness coefficient, which is used to measure the brightness of the color value, a 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, and b * is the blue-yellow coefficient, which is used to indicate the change from blue to yellow, with negative values ​​representing blue and positive values ​​representing yellow; 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: Among them, HL represents 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: Among them, S is the light-transmitting area of ​​the butter crab to be evaluated, S1 is the serrated edge area of ​​the cephalothorax of the butter crab to be evaluated, and S2 is the total area of ​​the cephalothorax 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: Wherein, F represents the fatness of the butter crab to be evaluated, W represents the body weight of the butter crab to be evaluated, and l represents the length of the head and chest carapace of the butter crab to be evaluated; Obtain an X-ray source, and obtain images of different sections of the butter crab to be evaluated based on the principle of X-ray imaging. Use volume reconstruction technology 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. Combine the ovarian density of the butter crab with the body mass of the butter crab to be evaluated to calculate the ovarian index. The formula is: Among them, O represents the ovarian index of the butter crab to be evaluated, V lc is the ovarian volume of the butter crab to be evaluated, ρ lc The ovary density of butter crab.

2. 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. Let the characteristic parameter vector of the butter crab to be evaluated be P 待评估 =[f1,f2,f3,…,f k ,…,f n ], the characteristic parameter vector of the known quality grade of butter crab is Where j is the index of the butter crab with known quality grade, and j∈[1,M], M is the number of butter crabs with known quality grade, k is the index of the feature parameter, and k∈[1,n], n is the number of feature parameters; The comparison is performed by calculating the similarity between the characteristic parameter vector of the butter crab to be evaluated and the characteristic parameter vector of each known quality grade of butter crab. Specifically, the similarity between the characteristic parameter vector of the butter crab to be evaluated and the characteristic parameter vector of each known quality grade of butter crab is represented by the Euclidean distance, and the formula based on it is: in, Represents the characteristic parameter vector P of the butter crab to be evaluated 待评估 and the characteristic parameter vector of the j-th known quality grade butter crab The Euclidean distance between the two is used to measure the similarity between the butter crab to be evaluated and the butter crab of known quality grade. The smaller the distance, the more similar it is. k is the value of the kth feature parameter in the feature parameter vector of the butter crab to be evaluated, is the value of the kth characteristic parameter in the characteristic parameter vector of the jth known quality grade butter crab, S j represents the similarity between the butter crab to be evaluated and the butter crab of the jth known quality grade, and the similarity value ranges from 0 to 1, where 1 represents complete similarity and 0 represents complete dissimilarity; After calculating the similarity between all the butter crabs to be evaluated and the butter crabs of known quality grades, find the maximum similarity S max , the quality level of the known quality level of the butter crab corresponding to the maximum similarity is taken as the undetermined quality level L of the butter crab to be evaluated 待定 .

3. The method for evaluating the grade of butter crab based on spectral information according to claim 1, 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: Among them, PJZS represents the evaluation index of the butter crab to be evaluated, k1, k2, k3, and k4 correspond to the weight ratios of their respective parameters, α, β, and γ are exponential coefficients, L is the brightness value of the butter crab to be evaluated, T is the transmittance, and δ is the adjustment coefficient used to control the slope of the exponential function.

4. The method for evaluating the grade of butter crab based on spectral information according to claim 3 is 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 pending quality grade corresponding to the maximum similarity value, the evaluation index interval [A, B] is set. If the evaluation index of the butter crab to be evaluated is within the interval, that is, A≤PJZS≤B, the pending quality grade of the butter crab to be evaluated is selected 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 interval, that is, PJZS≥A, the pending quality grade of the butter crab to be evaluated is selected to be increased by one level as the final evaluation quality grade, and no upgrading is required when the pending quality grade is in the special grade; if the evaluation index of the butter crab to be evaluated is lower than the lower limit of the evaluation index interval, that is, PJZS≤B, the pending quality grade of the butter crab to be evaluated is selected to be decreased by one level as the final evaluation quality grade, and no downgrade is required when the pending quality grade is unqualified.

5. 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 4, 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.

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