A multi-feature fusion non-destructive detection method and system for Hami melon quality

Abstract

The present invention provides a multi-feature fusion non-destructive quality detection method and system for Hami melons. The method comprises: obtaining quality characterization parameters of different regions within a Hami melon sampling period and environmental coupling parameters within the sampling period, and dividing quality discrimination criteria; comprehensively processing the quality characterization parameters and environmental coupling parameters to obtain input sets for quality assessment models for different regions; fusing the quality characterization parameters and environmental coupling parameters to construct Hami melon quality assessment models for different regions, and calculating the credible deviation limits of the Hami melon quality assessment model results for different regions; splicing the quality characterization parameters and environmental coupling parameters for different regions to construct a comprehensive quality prediction model for Hami melons, and calculating the credible deviation limits of the comprehensive quality assessment model results. The present invention uses multi-mode feature fusion detection technology to achieve regional and overall comprehensive quality assessment of Hami melons, thereby improving the accuracy and efficiency of non-destructive quality detection of Hami melons.

Description

Technical Field

[0001] The present invention relates to the technical field of non-destructive detection of agricultural product quality, and in particular to a multi-feature fusion non-destructive detection method and system for Hami melon quality. Background Art

[0002] Traditional Hami melon quality testing relies primarily on sensory, physical, and chemical methods. This is not only inefficient but also introduces additional destructive processes, making it impossible to fully assess the overall quality of Hami melons. Currently, non-destructive testing technology has become a key tool for intelligent quality assessment. Using physical, chemical, or biological methods to test agricultural product quality, it has been widely used in quality grading, quality control, and intelligent traceability. It enables rapid and accurate quality assessment, providing consumers with safer, higher-quality products.

[0003] Current nondestructive testing of Hami melon quality primarily relies on spectroscopy, electromagnetic waves, sound waves, and machine vision to capture quality indicators such as shape, size, color, texture, moisture, sugar content, and firmness. These characteristics are then extracted and modeled. However, existing feature extraction methods for Hami melon quality are relatively simple and lack multi-feature fusion. Consequently, the grading accuracy and the confidence intervals of model results need to be further improved. With the advancement of artificial intelligence and machine learning technologies, intelligent modeling using multimodal data fusion is urgently needed to improve the accuracy and efficiency of Hami melon quality testing, thereby enhancing the quality and safety of Hami melons. Therefore, it is crucial to design a multi-feature fusion nondestructive testing method and system for Hami melon quality. Summary of the Invention

[0004] In order to overcome the deficiencies of the prior art, the purpose of the present invention is to provide a multi-feature fusion non-destructive detection method and system for Hami melon quality.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] The present invention provides a multi-feature fusion non-destructive detection method for Hami melon quality, comprising:

[0007] Hami melons were divided into regions, and quality characterization parameters of Hami melons in different regions during the sampling period and environmental coupling parameters during the sampling period were obtained, and quality judgment standards were divided;

[0008] Comprehensively process the quality characterization parameters and environmental coupling parameters of Hami melons in different regions to obtain the input set of the quality evaluation model for Hami melons in different regions;

[0009] The quality characterization parameters and environmental coupling parameters of Hami melon were integrated to construct Hami melon quality assessment models for different regions, and the credible deviation limits of the Hami melon quality assessment model results for different regions were calculated.

[0010] The quality characterization parameters and environmental coupling parameters of Hami melon in different regions were spliced ​​together, and a comprehensive quality prediction model for Hami melon was constructed based on them. The credible deviation limits of the results of the comprehensive quality evaluation model of Hami melon were calculated.

[0011] The model was graded and corrected by determining the credible deviation limits of the Hami melon quality evaluation model results in different regions and the credible deviation limits of the Hami melon comprehensive quality evaluation model results.

[0012] Preferably, the Hami melon is divided into regions to obtain the quality characterization parameters of the Hami melon in different regions within the sampling period and the environmental coupling parameters within the sampling period, specifically:

[0013] From left to right, 1 / 4 of the cantaloupe is the first area, 1 / 4-3 / 4 is the second area, and the remaining 1 / 4 is the third area;

[0014] In each area, data from 6 points are collected at the same time step, and the mean is calculated, which is regarded as the data of one sampling time step in one area.

[0015] The quality characterization parameters are divided into time series data and image data, including appearance texture, sugar content, acidity, moisture content, flesh hardness, electrical conductivity, dielectric constant, acoustic wave data, spectral data, and visual images;

[0016] The environmental coupling parameters are sampled in one group in each area, including temperature and humidity, gas, vibration, and noise.

[0017] Preferably, the quality judgment criteria are divided into:

[0018] Obtain standard reference thresholds for Hami melon quality characterization parameters;

[0019] Obtain the correlation r between the standard reference data of Hami melon quality characterization parameters i :

[0020]

[0021] Where, X i and Y i Respectively represent the standard reference data of different variables of Hami melon quality characterization parameters, and represents the average value of Hami melon standard reference data, i represents the quality parameter variable, r i ∈[-1,1];

[0022] Obtain the weight of Hami melon quality characterization parameters based on the degree of correlation:

[0023]

[0024] Where θ i is the weight of Hami melon quality parameters, θ i ∈[0,1],|r i | represents the correlation degree of quality parameter variable i, σ i The standard deviation of the preset quality parameter standard data;

[0025] Calculate the evaluation index of the comprehensive quality judgment standard of Hami melon, and obtain the quality grade of Hami melon according to the quality evaluation index. The evaluation index of the comprehensive quality judgment standard is:

[0026]

[0027] Where Q i Indicates the comprehensive quality grade of Hami melon, Q i ∈(0,1],θ i Indicates the weight of Hami melon quality characterization parameters, τ i Indicates the reference quality index for each quality characterization parameter.

[0028] Preferably, the quality characterization parameters and environmental coupling parameters of different regions of Hami melon are comprehensively processed, specifically as follows:

[0029] Obtain the discrete coefficient CV of the quality characterization parameters and environmental coupling parameters of each region according to the fluctuation changes of the quality characterization parameters and environmental coupling parameters before and after the data of each region i , calculated as:

[0030]

[0031] Where x i represents the data of parameter i, x i-1 , x i+1 Represents the parameter x i Forward and backward data, represents the mean value of the parameter, σ i represents the standard deviation of parameter i;

[0032] The quality characterization parameters and environmental coupling parameters of Hami melon in different regions were comprehensively processed based on the dispersion coefficient.

[0033] Preferably, the quality characterization parameters and environmental coupling parameters of Hami melon are integrated to construct Hami melon quality assessment models for different regions, and the credible deviation limits of the Hami melon quality assessment model results for different regions are calculated, specifically including:

[0034] Construct Hami melon quality assessment models for the first, second, and third regions;

[0035] Training cantaloupe quality assessment models for the first region, the second region, and the third region based on the input set;

[0036] Based on the trained Hami melon quality assessment model, the quality of the first, second and third areas of Hami melon was identified to obtain the quality discrimination index. The quality grades of the first, second and third areas of Hami melon were obtained according to the quality discrimination index, and the credible deviation limits of the quality assessment model were calculated respectively.

[0037] Preferably, the quality characterization parameters and environmental coupling parameters of Hami melons in different regions are spliced ​​together, and a comprehensive quality prediction model of Hami melons is constructed based on the spliced ​​together quality characterization parameters and environmental coupling parameters. The credible deviation limit of the Hami melon comprehensive quality evaluation model result is calculated, specifically including:

[0038] The dot matrix data of the first area, the second area, and the third area are spliced ​​together, and the mean of the dot matrix data is used as the comprehensive quality data input set of Hami melon;

[0039] Construct a comprehensive quality prediction model for Hami melon;

[0040] Training the comprehensive quality prediction model based on the comprehensive quality data input set;

[0041] The quality of Hami melon was identified based on the trained Hami melon comprehensive quality prediction model to obtain the quality discrimination index. The comprehensive quality grade and credible deviation limit of Hami melon were obtained according to the quality discrimination index.

[0042] Preferably, the Hami melon quality assessment models for the first region, the second region, and the third region are constructed in the same manner as the comprehensive quality prediction model for Hami melon, which is:

[0043] Construct two branch networks, one for processing time series data and the other for processing image data;

[0044] The feature vectors of the two branch networks are extracted respectively, and the feature cascade fusion is performed through the attention mechanism. The feature extraction of time series data and image data is as follows:

[0045]

[0046] Where, Represents the features extracted from time series data, Represents the kth feature map of the l-1th layer in the network structure, They represent the convolution kernel and bias term connected to the k-th input feature map and the m-th feature map of the l-th layer in the temporal branch network, respectively. represents the features extracted from image data, They represent the convolution kernel and bias term connected to the k-th input feature map and the m-th feature map of the l-th layer in the image branch network, respectively. ReLU represents the activation function, where F d,g Represents feature output, concat is a concatenation operation;

[0047] The feature fusion of time series and image data is performed through the attention mechanism, which is:

[0048] F out =Attention(F d,g )+F d,g

[0049] The output layer of the Hami melon quality index obtained based on the fusion features is:

[0050] Y out =softmax((w y F out +b y ))

[0051] Based on the model output probability, the credible deviation limit of the model output is calculated as:

[0052] θ=[αloss-|e 上 |,αloss+|e 下 |]

[0053] In the formula, Attention is the attention mechanism operation, F out Represents the fusion feature output, where w y is the weight of the output layer, b y is the bias term of the output layer, Y out Output result of quality index. loss represents the cross entropy loss function of the model, e 上 and e 下 Represents the preset maximum and minimum errors, θ∈[0,1], and α represents the error correction factor α∈(0,1].

[0054] Preferably, the model is graded and corrected by determining the credible deviation limits of the Hami melon quality evaluation model results in different regions and the credible deviation limits of the Hami melon comprehensive quality evaluation model results, specifically:

[0055] For Hami melon quality assessment models and Hami melon comprehensive quality assessment models in different regions, if the credible deviation limit of the model output is ≤5%, the error correction factor α=1;

[0056] If the credible deviation limit of the model output is >5%, the model is corrected by the error correction factor, α∈(0,1);

[0057] Among them, the comprehensive quality judgment result of Hami melon is consistent with the quality grades of the first region, the second region, and the third region, or is consistent with the judgment result of the lowest quality grade of the first region, the second region, and the third region.

[0058] The present invention also provides a multi-feature fusion non-destructive detection system for Hami melon quality, comprising:

[0059] The acquisition unit is used to obtain the quality characterization parameters of Hami melon in different areas during the sampling period and the environmental coupling parameters during the sampling period:

[0060] A data comprehensive processing unit is used to comprehensively process the quality characterization parameters and environmental coupling parameters of Hami melons from different regions;

[0061] The first model building unit is used to build Hami melon quality evaluation models in different regions;

[0062] The second model building unit is used for the comprehensive quality evaluation model of Hami melon.

[0063] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0064] The present invention provides a multi-feature fusion nondestructive quality detection method and system for Hami melons. The method comprises obtaining quality characterization parameters and environmental coupling parameters for different regions within a Hami melon sampling period, and then classifying quality discrimination criteria; comprehensively processing the quality characterization parameters and environmental coupling parameters to obtain input sets for quality assessment models for different regions; fusing the quality characterization parameters and environmental coupling parameters to construct Hami melon quality assessment models for different regions, and calculating the credible deviation limits of the Hami melon quality assessment model results for different regions; combining the quality characterization parameters and environmental coupling parameters for different regions to construct a comprehensive quality prediction model for Hami melons, and calculating the credible deviation limits of the comprehensive quality assessment model results. The model is then graded and corrected by determining the credible deviation limits of the Hami melon quality assessment model results for different regions and the credible deviation limits of the comprehensive Hami melon quality assessment model results. The system comprises an acquisition unit, a data comprehensive processing unit, a first model construction unit, and a second model construction unit. The present invention uses multi-mode feature fusion detection technology to achieve regional and overall comprehensive evaluation of Hami melon quality, which helps to improve the accuracy and efficiency of non-destructive detection of Hami melon quality, thereby improving the quality and safety of Hami melon. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0066] Figure 1 A schematic flow chart of a method for nondestructive detection of Hami melon quality using multi-feature fusion provided by an embodiment of the present invention;

[0067] Figure 2 This is a schematic diagram of the Hami melon area division in the embodiment provided by the present invention;

[0068] Figure 3 Schematic diagram of the structure of the Hami melon quality non-destructive detection system with multi-feature fusion provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0069] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0070] The purpose of the present invention is to provide a multi-feature fusion non-destructive detection method for Hami melon quality. Through multi-mode feature fusion detection technology, regional and overall comprehensive evaluation of Hami melon quality can be achieved, which helps to improve the accuracy and efficiency of non-destructive detection of Hami melon quality, thereby improving the quality and safety of Hami melon.

[0071] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0072] Figure 1 A flow chart of the method provided in the embodiment of the present invention is shown in FIG. Figure 1 As shown, the present invention provides a multi-feature fusion non-destructive detection method for Hami melon quality, comprising:

[0073] Step 100: Divide the Hami melon into regions, obtain quality characterization parameters of the Hami melon in different regions within a sampling period and environmental coupling parameters within the sampling period, and divide the quality judgment standards;

[0074] Step 200: Comprehensively process the quality characterization parameters and environmental coupling parameters of Hami melons in different regions to obtain an input set of a quality assessment model for Hami melons in different regions;

[0075] Step 300: fusing the Hami melon quality characterization parameters and the environmental coupling parameters to construct Hami melon quality assessment models for different regions, and calculating the credible deviation limits of the Hami melon quality assessment model results for the different regions;

[0076] Step 400: combining the quality characterization parameters and environmental coupling parameters of Hami melons from different regions, constructing a comprehensive quality prediction model for Hami melons based on the combined parameters, and calculating the credible deviation limit of the Hami melon comprehensive quality evaluation model results;

[0077] Step 500: The model is graded and corrected by determining the credible deviation limits of the Hami melon quality evaluation model results in different regions and the credible deviation limits of the Hami melon comprehensive quality evaluation model results.

[0078] In step 100, the Hami melon is divided into regions to obtain the quality characterization parameters of the Hami melon in different regions within the sampling period and the environmental coupling parameters within the sampling period, specifically:

[0079] From left to right, 1 / 4 of the cantaloupe is the first area, 1 / 4-3 / 4 is the second area, and the remaining 1 / 4 is the third area. The specific division diagram is as follows Figure 2 As shown;

[0080] In each area, data from 6 points are collected at the same time step, and the mean is calculated, which is regarded as the data of one sampling time step in one area.

[0081] The method for solving the mean is: remove the maximum and minimum values ​​from the six point matrix data obtained at the same time step in each area, then average them, and use the mean result as the data of one sampling time step in one area;

[0082] The quality characterization parameters are divided into time series data and image data, including but not limited to appearance texture, sugar content, acidity, moisture content, flesh hardness, electrical conductivity, dielectric constant, acoustic wave data, spectral data, visual images, etc.;

[0083] The environmental coupling parameters are sampled in each area, including temperature and humidity, gas, vibration, and noise;

[0084] It should be noted that the present invention provides an embodiment in which the Hami melon timing and image data acquisition conditions are set to two groups, one group with a temperature of 0-5°C and a humidity of 75%-85%, and the other group with a temperature of 5-10°C and a humidity of 80%-90%.

[0085] In step 100, the quality judgment criteria are divided into:

[0086] Obtaining standard reference thresholds for Hami melon quality characterization parameters;

[0087] Obtain the correlation r between the standard reference data of Hami melon quality characterization parameters i :

[0088]

[0089] Where, X i and Y i Respectively represent the standard reference data of different variables of Hami melon quality characterization parameters, and represents the average value of Hami melon standard reference data, i represents the quality parameter variable, r i ∈[-1,1];

[0090] Obtain the weight of Hami melon quality characterization parameters based on the degree of correlation:

[0091]

[0092] Where θ i is the weight of Hami melon quality parameters, θ i ∈[0,1],|r i | represents the correlation degree of quality parameter variable i, σ i The standard deviation of the preset quality parameter standard data;

[0093] Calculate the evaluation index of the comprehensive quality judgment standard of Hami melon, and obtain the quality grade of Hami melon according to the quality evaluation index. The evaluation index of the comprehensive quality judgment standard is:

[0094]

[0095] Where Q i Indicates the comprehensive quality grade of Hami melon, Q i ∈(0,1],θ i Indicates the weight of Hami melon quality characterization parameters, τ i The reference quality index representing each quality characterization parameter;

[0096] Determine the standard reference thresholds of Hami melon quality characterization parameters based on national standards, industry standards, group standards, enterprise standards, etc., and determine the reference quality index of each quality characterization parameter based on the sensory reference thresholds, thereby classifying the quality grades of Hami melons;

[0097] The present invention provides an embodiment in which the quality grade of Hami melon is divided into five grades: A+ grade, A grade, B+ grade, B grade and C grade, specifically:

[0098] If Q i ∈[0.9,1], the quality grade of Hami melon is judged to be A+;

[0099] If Q i ∈[0.8,0.9), the quality grade of Hami melon is judged to be A;

[0100] If Q i ∈[0.7,0.8), the quality grade of Hami melon is B+;

[0101] If Q i ∈[0.5,0.7), the quality grade of Hami melon is judged to be B;

[0102] If Q i ∈(0,0.5), the quality grade of Hami melon is judged to be C.

[0103] In step 200, the quality characterization parameters and environmental coupling parameters of different regions of Hami melon are comprehensively processed, specifically:

[0104] Obtain the discrete coefficient CV of the quality characterization parameters and environmental coupling parameters of each region according to the fluctuation changes of the quality characterization parameters and environmental coupling parameters before and after the data of each region i , calculated as:

[0105]

[0106] Where x i Represents the data of parameter i, x i-1 , x i+1 Represents the parameter x i Forward and backward data, represents the mean value of the parameter, σ i represents the standard deviation of parameter i;

[0107] The quality characterization parameters and environmental coupling parameters of Hami melon in different regions were comprehensively processed based on the dispersion coefficient.

[0108] The present invention provides an embodiment in which the discrete fluctuation interval of the preset quality characterization parameter and environmental coupling parameter reference data is set to [0.1, 0.6]. If the discrete coefficient of the quality characterization parameter and environmental coupling parameter of each region is greater than 0.6, this point is judged as a discrete point. If the proportion of discrete points is greater than 10%, interpolation correction is required.

[0109] In step 300, the quality characterization parameters of Hami melon and the environmental coupling parameters are integrated to construct Hami melon quality assessment models for different regions, and the credible deviation limits of the Hami melon quality assessment model results for different regions are calculated, specifically including:

[0110] Construct Hami melon quality assessment models for the first, second, and third regions;

[0111] Training cantaloupe quality assessment models for the first region, the second region, and the third region based on the input set;

[0112] Based on the trained Hami melon quality assessment model, the quality of the first, second and third areas of Hami melon was identified to obtain the quality discrimination index. The quality grades of the first, second and third areas of Hami melon were obtained according to the quality discrimination index, and the credible deviation limits of the quality assessment model were calculated respectively.

[0113] In step 400, the quality characterization parameters and environmental coupling parameters of Hami melons from different regions are combined, and a comprehensive quality prediction model for Hami melons is constructed based on the combined parameters. The credible deviation limit of the Hami melon comprehensive quality evaluation model result is calculated, specifically including:

[0114] The dot matrix data of the first area, the second area, and the third area are spliced ​​together, and the mean of the dot matrix data is used as the comprehensive quality data input set of Hami melon;

[0115] Construct a comprehensive quality prediction model for Hami melon;

[0116] Training the comprehensive quality prediction model based on the comprehensive quality data input set;

[0117] The quality of Hami melon was identified based on the trained Hami melon comprehensive quality prediction model to obtain the quality discrimination index. The comprehensive quality grade and credible deviation limit of Hami melon were obtained according to the quality discrimination index.

[0118] The Hami melon quality evaluation models for the first, second, and third regions are constructed in the same manner as the comprehensive quality prediction model for Hami melon, which is:

[0119] Construct two branch networks, one for processing time series data and the other for processing image data;

[0120] The feature vectors of the two branch networks are extracted respectively, and the feature cascade fusion is performed through the attention mechanism. The feature extraction of time series data and image data is as follows:

[0121]

[0122]

[0123] Where, Represents the features extracted from time series data, Represents the kth feature map of the l-1th layer in the network structure, They represent the convolution kernel and bias term connected to the k-th input feature map and the m-th feature map of the l-th layer in the temporal branch network, respectively. represents the features extracted from image data, They represent the convolution kernel and bias term connected to the k-th input feature map and the m-th feature map of the l-th layer in the image branch network, respectively. ReLU represents the activation function, where F d,g Represents feature output, concat is a concatenation operation;

[0124] The feature fusion of time series and image data is performed through the attention mechanism, which is:

[0125] F out =Attention(F d,g )+F d,g

[0126] The output layer of the Hami melon quality index obtained based on the fusion features is:

[0127] Y out =softmax((w y F out +b y ))

[0128] Based on the model output probability, the credible deviation limit of the model output is calculated as:

[0129] θ=[αloss-|e 上 |,αloss+|e 下 |]

[0130] In the formula, Attention is the attention mechanism operation, F out Represents the fusion feature output, where w y is the weight of the output layer, b y is the bias term of the output layer, Y out Output result of quality index. loss represents the cross entropy loss function of the model, e 上 and e 下 Represents the preset maximum and minimum errors, and α represents the error correction factor.

[0131] In step 500, the model is graded and corrected by determining the credible deviation limits of the Hami melon quality assessment model results in different regions and the credible deviation limits of the Hami melon comprehensive quality assessment model results, specifically:

[0132] For Hami melon quality assessment models and Hami melon comprehensive quality assessment models in different regions, if the credible deviation limit of the model output is ≤5%, the error correction factor α=1;

[0133] If the credible deviation limit of the model output is >5%, the model is corrected by the error correction factor, α∈(0,1);

[0134] Among them, the comprehensive quality judgment result of Hami melon is consistent with the quality grades of the first region, the second region, and the third region, or is consistent with the judgment result of the lowest quality grade of the first region, the second region, and the third region.

[0135] like Figure 3 As shown, the present invention also provides a multi-feature fusion cantaloupe quality non-destructive detection system, comprising:

[0136] The acquisition unit is used to obtain the quality characterization parameters of Hami melon in different areas during the sampling period and the environmental coupling parameters during the sampling period:

[0137] A data comprehensive processing unit is used to comprehensively process the quality characterization parameters and environmental coupling parameters of Hami melons from different regions;

[0138] The first model building unit is used to build Hami melon quality evaluation models in different regions;

[0139] The second model building unit is used for the comprehensive quality evaluation model of Hami melon.

[0140] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0141] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A multi-feature fusion non-destructive detection method for Hami melon quality, characterized in that: include: Hami melons were divided into regions, and quality characterization parameters of Hami melons in different regions during the sampling period and environmental coupling parameters during the sampling period were obtained, and quality judgment standards were divided; Comprehensively process the quality characterization parameters and environmental coupling parameters of Hami melons in different regions to obtain the input set of the quality evaluation model for Hami melons in different regions; The quality characterization parameters and environmental coupling parameters of Hami melon were integrated to construct Hami melon quality assessment models for different regions. The credible deviation limits of the Hami melon quality assessment model results for different regions were calculated. Specifically, Construct Hami melon quality assessment models for the first, second, and third regions; Training cantaloupe quality assessment models for the first region, the second region, and the third region based on the input set; Based on the trained Hami melon quality assessment model, the quality of the first, second, and third regions of the Hami melon is identified to obtain a quality discrimination index. The quality grades of the first, second, and third regions of the Hami melon are obtained according to the quality discrimination index, and the credible deviation limits of the quality assessment model are calculated respectively. The quality characterization parameters and environmental coupling parameters of Hami melons from different regions were spliced ​​together, and a comprehensive quality prediction model for Hami melons was constructed based on them. The credible deviation limits of the results of the comprehensive quality evaluation model for Hami melons were calculated, specifically: The dot matrix data of the first area, the second area, and the third area are spliced ​​together, and the mean of the dot matrix data is used as the comprehensive quality data input set of Hami melon; Construct a comprehensive quality prediction model for Hami melon; Training the comprehensive quality prediction model based on the comprehensive quality data input set; The quality of Hami melon is identified based on the trained Hami melon comprehensive quality prediction model to obtain the quality discrimination index. The comprehensive quality grade and credible deviation limit of Hami melon are obtained according to the quality discrimination index. The model was modified by grade by determining the credible deviation limits of Hami melon quality evaluation model results in different regions and the credible deviation limits of Hami melon comprehensive quality evaluation model results. Among them, Hami melons are divided into regions to obtain the quality characterization parameters of Hami melons in different regions during the sampling period and the environmental coupling parameters during the sampling period, specifically: From left to right, 1 / 4 of the cantaloupe is the first area, 1 / 4-3 / 4 is the second area, and the remaining 1 / 4 is the third area; In each area, data from 6 points are collected at the same time step, and the mean is calculated, which is regarded as the data of one sampling time step in one area. The quality characterization parameters are divided into time series data and image data, including appearance texture, sugar content, acidity, moisture content, flesh hardness, electrical conductivity, dielectric constant, acoustic wave data, spectral data, and visual images; The environmental coupling parameters are sampled in one group in each area, including temperature and humidity, gas, vibration, and noise.

2. The method according to claim 1, characterized in that The quality judgment criteria are divided into: Obtain standard reference thresholds for Hami melon quality characterization parameters; Obtaining the correlation between standard reference data of Hami melon quality characterization parameters : Where, and Respectively represent the standard reference data of different variables of Hami melon quality characterization parameters, and Represents the average value of Hami melon standard reference data, represents the quality parameter variable, ; Obtain the weight of Hami melon quality characterization parameters based on the degree of correlation: Where, is the weight of Hami melon quality parameters, , Represents the quality parameter variable The correlation degree, The standard deviation of the preset quality parameter standard data; Calculate the evaluation index of the comprehensive quality judgment standard of Hami melon, and obtain the quality grade of Hami melon according to the quality evaluation index. The evaluation index of the comprehensive quality judgment standard is: Where, Indicates the comprehensive quality grade of Hami melon. , Indicates the weight of Hami melon quality characterization parameters, Indicates the reference quality index for each quality characterization parameter.

3. The method according to claim 1, characterized in that The quality characterization parameters and environmental coupling parameters of Hami melon in different regions were comprehensively processed, specifically: Obtain the discrete coefficients of the quality characterization parameters and environmental coupling parameters of each region based on the fluctuations of the quality characterization parameters and environmental coupling parameters before and after the data of each region , calculated as: Where, Representation parameters data, , Represents parameters respectively Forward and backward data, represents the mean value of the parameter, Representation parameters The standard deviation of The quality characterization parameters and environmental coupling parameters of Hami melon in different regions were comprehensively processed based on the dispersion coefficient.

4. The method according to claim 1, wherein The Hami melon quality evaluation models for the first, second, and third regions are constructed in the same manner as the comprehensive quality prediction model for Hami melon, which is: Construct two branch networks, one for processing time series data and the other for processing image data; The feature vectors of the two branch networks are extracted respectively, and the feature cascade fusion is performed through the attention mechanism. The feature extraction of time series data and image data is as follows: Where, Represents the features extracted from time series data, Indicates the network structure Layer feature maps, Respectively represent the first Layer The input feature map and The convolution kernel and bias term connected to the feature map, represents the features extracted from image data, They represent the first Layer The input feature map and The convolution kernel and bias term connected to the feature map, represents the activation function, where represents the feature output, For splicing operation; The feature fusion of time series and image data is performed through the attention mechanism, which is: The output layer of the Hami melon quality index obtained based on the fusion features is: Based on the model output probability, the credible deviation limit of the model output is calculated as: Where, For the attention mechanism operation, Represents the fusion feature output, where is the weight of the output layer, is the bias term of the output layer, Output result for quality index, represents the cross entropy loss function of the model, and Indicates the preset maximum and minimum errors. , Error correction factor .

5. The method according to claim 4, characterized in that The model was graded and corrected by determining the credible deviation limits of the Hami melon quality assessment model results in different regions and the credible deviation limits of the Hami melon comprehensive quality assessment model results. Specifically: For Hami melon quality evaluation models and Hami melon comprehensive quality evaluation models in different regions, if the credible deviation limit of the model output is ≤5%, the error correction factor =1; If the credible deviation limit of the model output is greater than 5%, the model is corrected by the error correction factor. ; Among them, the comprehensive quality judgment result of Hami melon is consistent with the quality grades of the first region, the second region, and the third region, or is consistent with the judgment result of the lowest quality grade of the first region, the second region, and the third region.

6. A Hami melon quality non-destructive detection system using multi-feature fusion, applied to the Hami melon quality non-destructive detection method using multi-feature fusion according to any one of claims 1 to 5, characterized in that: include: The acquisition unit is used to obtain the quality characterization parameters of Hami melon in different areas during the sampling period and the environmental coupling parameters during the sampling period: A data comprehensive processing unit is used to comprehensively process the quality characterization parameters and environmental coupling parameters of Hami melons from different regions; The first model building unit is used to build Hami melon quality evaluation models in different regions; The second model building unit is used for the comprehensive quality evaluation model of Hami melon.

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