A nut quality detection and analysis method based on visual technology

Abstract

The application relates to the technical field of nut quality detection, and specifically discloses a nut quality detection and analysis method based on visual technology, which comprises the following steps: collecting external information of nuts, evaluating the external quality of nuts, screening nuts according to the weight of a detection group, analyzing the internal quality of nuts, and evaluating the comprehensive quality of nuts. The comprehensive quality of nuts is analyzed from two aspects of external quality and internal quality, so that the appearance, internal quality and overall quality of nuts can be comprehensively evaluated, the quality of nuts can be accurately classified and judged through multi-dimensional feature extraction and analysis, the automation of nut quality detection is realized, the detection efficiency is improved, the labor cost and subjective error of manual detection are reduced, nuts with unqualified external quality are sorted through a mechanical arm, nuts with unqualified weight are screened, nuts with abnormal quality are screened in time, and effective data basis is provided for subsequent comprehensive quality analysis of nuts.

Description

Technical Field

[0001] This invention relates to the field of nut quality detection technology, and more specifically, to a nut quality detection and analysis method based on visual technology. Background Technology

[0002] Currently, the quality inspection of nuts during production, processing, and sales mainly relies on manual sampling. This method suffers from low efficiency, high subjectivity, fatigue, and difficulty in ensuring consistent quality. Furthermore, existing automated inspection technologies are insufficiently accurate and unable to comprehensively assess the quality of nuts, a product with diverse surface colors and a complex internal structure requiring comprehensive evaluation. Therefore, a highly efficient, accurate, and comprehensive vision-based method for nut quality inspection and analysis is needed.

[0003] The existing technology still has the following problems: 1. The existing technology only assesses the quality of nuts based on their external physical characteristics, without combining the external and internal characteristics of nuts to assess the overall quality, which reduces the accuracy and rationality of the comprehensive quality assessment of nuts and cannot effectively protect the legitimate rights and interests of consumers.

[0004] 2. Existing technologies simply rely on the overall color of the nut shell to determine maturity, which cannot accurately assess the degree of nut maturity and reduces the objectivity and consistency of nut maturity detection. Summary of the Invention

[0005] In view of this, in order to solve the problems mentioned in the background technology, a nut quality detection and analysis method based on visual technology is proposed.

[0006] The objective of this invention can be achieved through the following technical solution: This invention provides a nut quality detection and analysis method based on visual technology, including the following steps: S1, Nut external information collection: The target batch of nuts is divided into several detection groups according to a preset ratio, the nuts of each detection group are transported through a detection conveyor belt, and the perimeter, area and surface image data of all nuts in each detection group are obtained by a visual acquisition device.

[0007] S2. Nut external quality assessment: Based on the surface image data, the external quality of the nuts is judged by a preset shape parameter threshold. If there are nuts with unqualified appearance, the sorting device is triggered to remove the unqualified products.

[0008] S3. Nut weight screening for each test group: Count the number of remaining nuts in each test group, obtain the total mass of each test group through a weighing device, and compare it with the set standard nut weight range. If the total mass does not meet the standard, the nuts in that test group will be processed.

[0009] S4. Internal quality analysis of nuts: The internal structure of the target nuts after screening is imaged, and the internal defect parameters of each target nut are calculated using image analysis to generate the internal quality pass coefficient and count the number of internally unqualified nuts.

[0010] S5. Comprehensive Quality Assessment of Nuts: Assess whether the overall quality of nuts in the target batch is up to standard based on the number of non-conforming nuts. If not, provide immediate feedback.

[0011] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) The present invention analyzes whether the overall quality of nuts is qualified from the two levels of external quality and internal quality. It can comprehensively evaluate the appearance, internal and overall quality of nuts. Through multi-dimensional feature extraction and analysis, the quality of nuts can be accurately classified and judged, realizing the automation of nut quality detection, improving detection efficiency, reducing labor costs and subjective errors of manual detection. At the same time, quality analysis from two levels will make the implementation of quality standards clear and effectively protect the legitimate rights and interests of consumers.

[0012] (2) This invention sorts nuts with substandard external quality and sieves nuts with substandard weight by using a robotic arm to promptly sieve nuts with abnormal quality, providing effective data for the comprehensive quality analysis of subsequent target batches of nuts. At the same time, automated sieving can improve the quality and production efficiency of nut products.

[0013] (3) This invention calculates the maturity of each nut in each detection group in the target batch by combining the RGB values ​​of each pixel in the surface image of the nut. By analyzing the RGB values ​​of each pixel, rich color information can be obtained, rather than simply judging maturity based on the overall color. This allows for a more accurate assessment of the maturity of the nuts, adapts to complex surface color changes, and improves the objectivity and consistency of nut maturity detection.

[0014] (4) This invention reduces the amount of nuts entering the internal quality testing stage by weighing the nuts before the internal quality testing, thereby improving the efficiency of internal quality testing and reducing the testing cost. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1This is a schematic diagram of the method steps of the present invention.

[0017] Figure 2 This is a flowchart of the nut external quality assessment process of the present invention.

[0018] Figure 3 This is a flowchart of the nut weight evaluation process of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Please see Figure 1 As shown, the present invention provides a nut quality detection and analysis method based on visual technology, including: S1, nut external information collection: the target batch of nuts is divided into several detection groups according to a preset ratio, the nuts of each detection group are transported through a detection conveyor belt, and the perimeter, area and surface image data of all nuts in each detection group are acquired by a visual acquisition device.

[0021] It should be noted that the acquisition methods for the perimeter, area, and surface images of each nut in each detection group of the target batch are as follows: 1) Image acquisition process: When the nut moves with the conveyor belt to the imaging area below the camera, the camera is triggered to acquire images. The triggering mechanism can be implemented through a photoelectric sensor. When the nut blocks the light of the sensor, the sensor sends a signal to the camera, and the camera immediately takes a picture. This ensures that each nut can be accurately captured, and the captured image can accurately reflect the actual shape of the nut, thereby obtaining the surface image of the nut. 2) Image processing and perimeter and area calculation: First, the acquired image is converted to grayscale, and then filtered to remove noise from the image. An edge detection algorithm is used to extract the outline of the nut. After obtaining the outline of the nut, mathematical methods in the image processing software can be used to calculate its perimeter and area.

[0022] S2. Nut external quality assessment: Based on the surface image data, the external quality of the nuts is judged by a preset shape parameter threshold. If there are nuts with unqualified appearance, the sorting device is triggered to remove the unqualified products.

[0023] The specific process for determining whether the appearance quality of the nuts is qualified is as follows: the circumference and area of ​​each nut in each test group are analyzed for roundness, and the roundness compliance coefficient of each nut is determined based on the roundness.

[0024] In a specific embodiment of the present invention, the specific process of calculating the roundness compliance coefficient of each nut is as follows: the perimeter and area of ​​each nut in each detection group are calculated using the roundness formula to obtain the roundness of each nut; the standard roundness of the target nut is extracted from the database; the deviation between the roundness of each nut and the corresponding standard roundness is analyzed to obtain the roundness compliance coefficient of each nut.

[0025] The roundness formula is: Where π represents the mathematical constant pi, which has a value of approximately 3.14, and C and S represent the circumference and area, respectively.

[0026] The formula for analyzing the roundness compliance coefficient of each nut is as follows: Where Δε represents the roundness deviation of the nut as a reference, ε 标 ε represents the standard roundness of the target nut. ij This represents the roundness of each nut in each test group. Where Δε - |ε ij -ε 标 | Part: Calculate the absolute deviation between the actual roundness and the standard value, subtract it from the maximum permissible deviation, reflect the degree of conformity of the shape, and perform normalization processing to limit the result range to [0,1].

[0027] The average RGB channel color value is located from the images of each nut in each detection group, and compared with the RGB channel color range value when the nut is ripe to obtain the ripeness of each nut.

[0028] The specific process for determining the maturity of each nut is as follows: the R, G, and B values ​​corresponding to each pixel are located from the surface images of each nut in each detection group, and the average color values ​​of the R, G, and B channels are obtained by averaging.

[0029] The average values ​​of the R, G, and B channels are compared with the range values ​​of the R, G, and B channels when the target nut is mature, stored in the database. If the average values ​​of the R, G, and B channels of a certain nut are all within the range values ​​of the R, G, and B channels when the target nut is mature, then the maturity of the nut is recorded as τ1.

[0030] If the average values ​​of the R, G, and B channels of a nut are not within the range of R, G, and B channel colors when the nut is mature, then the maturity of the nut is denoted as τ2.

[0031] If any of the R, G, and B channel color averages of a nut are not within the range of R, G, and B channel color values ​​when the nut is mature, then the maturity of the nut is denoted as τ3.

[0032] The maturity of each nut in each test group was statistically analyzed.

[0033] In one specific embodiment of the present invention, τ1 is set to 1, τ3 is set to 0.5, and τ2 is set to 0.

[0034] It should also be noted that in the RGB color space, each pixel is represented by the values ​​of three channels: red (R), green (G), and blue (B). For mature nuts, the values ​​of their R, G, and B channels will have certain ranges. For example, in the RGB color space, the R channel value of a mature hazelnut shell might be between 120 and 150, the G channel value between 90 and 120, and the B channel value between 60 and 90.

[0035] This invention calculates the maturity of each nut in each detection group of the target batch by combining the RGB values ​​of each pixel in the surface image of the nut. By analyzing the RGB value of each pixel, rich color information can be obtained, rather than simply judging maturity based on the overall color. This allows for a more accurate assessment of the nut's maturity, adapts to complex surface color changes, and improves the objectivity and consistency of nut maturity detection.

[0036] The external quality coefficient of each nut in each test group within the target batch is obtained by weighting and averaging the roundness conformity coefficient and maturity degree of each nut. For example, the external quality coefficient δ of each nut in each test group within the target batch is... ij , Where a1 and a2 represent the set roundness compliance coefficient and maturity corresponding to the external quality assessment weights, respectively, a1 + a2 = 1, χ ij The maturity of each nut in each testing group.

[0037] In a specific embodiment of the present invention, the value of a1 is set to 0.5, the value of a2 is set to 0.5, the external quality assessment corresponding to the roundness conformity coefficient and maturity is not independent of each other, but complementary to each other. When comprehensively evaluating the external quality coefficient of nuts, it is necessary to take both factors into account and weigh their relative importance according to the specific evaluation purpose and application scenario.

[0038] Please see Figure 2 As shown, the external quality coefficient of each nut in each test group in the target batch is compared with the external quality coefficient of the set reference. If the external quality coefficient of a nut in a certain test group is greater than the external quality coefficient of the set reference, it indicates that the external quality of the nut in that test group is qualified; otherwise, it indicates that the external quality of the nut in that test group is unqualified.

[0039] The specific method for triggering the sorting device to remove defective products is as follows: once a nut in each inspection group is detected to have an unqualified external quality, the system will send a signal to the robotic arm sorting device. The system will send the location information of the unqualified nut to the robotic arm control system. The robotic arm control system will control the robotic arm to perform a grabbing operation at a suitable position based on the received location information, so that the unqualified nut in each inspection group is removed from the inspection conveyor belt and placed in the unqualified external quality area.

[0040] S3. Nut weight screening for each test group: Count the number of remaining nuts in each test group, obtain the total mass of each test group through a weighing device, and compare it with the set standard nut weight range. If the total mass does not meet the standard, the nuts in that test group will be processed.

[0041] It should be noted that the total weight of nuts in each testing group in the target batch is obtained by a weight sensor placed below the testing conveyor belt.

[0042] The specific process of step S3 is as follows: multiply the number of remaining nuts in each test group by the weight range corresponding to a single target nut extracted from the database to obtain the standard nut weight range corresponding to each test group.

[0043] Please see Figure 3 As shown, the total weight of nuts in each test group in the target batch is compared with the standard nut weight range corresponding to each test group in the target batch. If the total weight of nuts in a certain test group in the target batch is within the standard nut weight range corresponding to that test group, it indicates that the nut weight of that test group in the target batch meets the standard; otherwise, it indicates that the nut weight of that test group in the target batch does not meet the standard.

[0044] It's important to note that the quality of the nuts is indirectly assessed by precisely weighing them. Plump nuts are generally heavier, while shriveled or less plump nuts are lighter. This method is simple and straightforward, but it only provides a general assessment; therefore, a more in-depth analysis of the internal details of nut flesh will follow.

[0045] In a specific embodiment of the present invention, the specific method for processing the nuts in the detection group is as follows: weigh each nut in the nut weight substandard detection group in turn to obtain the weight of each nut in the nut weight substandard detection group, and compare it with the weight range corresponding to a single target nut. If the weight of a nut in a nut weight substandard detection group is not within the weight range corresponding to a single target nut, then the nut in the nut weight substandard detection group is screened.

[0046] This invention reduces the amount of nuts entering the internal quality testing stage by weighing them before the internal quality testing, thereby improving the efficiency of internal quality testing and reducing testing costs.

[0047] This invention utilizes a robotic arm to sort nuts that fail to meet external quality standards and to screen nuts that do not meet weight standards. This timely screening of nuts with quality abnormalities provides effective data for the comprehensive quality analysis of subsequent target batches of nuts. At the same time, automated screening can improve the quality and production efficiency of nut products.

[0048] S4. Internal quality analysis of nuts: The internal structure of the target nuts after screening is imaged, and the internal defect parameters of each target nut are calculated using image analysis to generate the internal quality pass coefficient and count the number of internally unqualified nuts.

[0049] It should be noted that the internal images of each target nut in each detection group of the target batch were acquired by an X-ray detector placed near the detection conveyor belt.

[0050] The process of generating the intrinsic quality qualification coefficient is as follows: taking the sifted test group nuts as target nuts, the number of gray-scale regions and the gray-scale value corresponding to each gray-scale region are located from the internal image of each target nut in each test group.

[0051] The gray values ​​corresponding to each gray area of ​​each target nut in each test group are compared with the normal gray value range of the fruit pulp of the target nut stored in the database. If the gray value corresponding to a gray area of ​​a target nut in a certain test group is within the normal gray value range of the fruit pulp of the target nut, then the gray area is recorded as the normal fruit pulp area. The number of normal fruit pulp areas of each target nut in each test group in the target batch is counted.

[0052] The internal quality qualification coefficient of each target nut is obtained by performing a ratio deviation analysis between the number of normal pulp areas and the number of gray areas of the corresponding nut.

[0053] The analytical formula for the intrinsic quality pass coefficient of each target nut is as follows: Where K represents the percentage of normal fruit pulp areas used as a reference, e represents the natural constant, and σ ig This represents the number of normal pulp areas in each target nut of each testing group. This represents the number of grayscale regions for each target nut in each testing group. This formula is used to quantify the intrinsic quality pass rate of the target nut. The core logic is to compare the proportion of normal flesh area with the set standard, and then use an exponential function to highlight the difference, reflecting the degree of quality deviation.

[0054] In a specific embodiment of the present invention, the method for counting the number of inherently substandard nuts is as follows: the inherent quality pass coefficient of each target nut in each test group is compared with the set reference inherent quality pass coefficient. If the inherent quality pass coefficient of a target nut in a certain test group is less than the set reference inherent quality pass coefficient, then the target nut is recorded as an inherently substandard nut, and the number of inherently substandard nuts in each test group is counted.

[0055] S5. Comprehensive Quality Assessment of Nuts: Assess whether the overall quality of nuts in the target batch is up to standard based on the number of non-conforming nuts. If not, provide immediate feedback.

[0056] In a specific embodiment of the present invention, the specific process for evaluating whether the overall quality of nuts in the target batch is qualified is as follows: count the number of nuts with unqualified external quality, the number of nuts with substandard weight, and the number of nuts with unqualified internal quality in each test group, and sum them up to obtain the number of nuts with abnormal quality in each test group in the target batch.

[0057] If the number of nuts with abnormal quality in a certain testing group is 0, then that testing group is recorded as a normal quality testing group. The number of normal quality testing groups in the target batch is counted and recorded as follows.

[0058] The ratio of the number of normal quality test groups to the total number of test groups in the target batch is analyzed to obtain the overall quality coefficient θ of the nuts in the target batch. The formula for calculating the overall quality coefficient of the nuts in the target batch is as follows: Where K1 represents the percentage of the number of normal quality test groups set as a reference, and n represents the number of test groups. This formula is used to quantify the overall quality level of a target batch of nuts. The core logic is to compare the actual percentage of normal quality test groups with the set standard, and then apply an exponential function... By amplifying the differences, the numerical range is finally adjusted using the natural logarithm to reflect the overall quality of the batch.

[0059] The overall quality coefficient of the target batch of nuts is compared with the overall quality coefficient of the set reference nuts. If the overall quality coefficient of the target batch of nuts is greater than or equal to the overall quality coefficient of the set reference nuts, it indicates that the overall quality of the nuts in the target batch is qualified; otherwise, it indicates that the overall quality of the nuts in the target batch is unqualified.

[0060] This invention analyzes the overall quality of nuts from both external and internal perspectives to determine whether they meet standards. It comprehensively assesses the appearance, internal structure, and overall quality of nuts. Through multi-dimensional feature extraction and analysis, it accurately classifies and judges the quality of nuts, automating nut quality testing, improving testing efficiency, reducing labor costs and subjective errors in manual testing. Furthermore, quality analysis from both perspectives clarifies the implementation of quality standards and effectively protects consumers' legitimate rights and interests.

[0061] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, and all such modifications and additions should fall within the protection scope of the present invention.

Claims

1. A method for quality detection and analysis of nuts based on visual technology, characterized in that, Includes the following steps: S1. Nut External Information Collection: The target batch of nuts is divided into several inspection groups according to a preset ratio. Nuts in each inspection group are transported through an inspection conveyor belt, and visual acquisition equipment is used to acquire the perimeter, area and surface image data of all nuts in each inspection group. S2. Nut external quality assessment: Based on the surface image data, the external quality of the nuts is judged by a preset shape parameter threshold. If there are nuts with unqualified appearance, the sorting device is triggered to remove the unqualified products. S3. Nut weight screening for each test group: Count the number of remaining nuts in each test group, obtain the total mass of each test group through a weighing device, and compare it with the set standard nut weight range. If the total mass does not meet the standard, the nuts in that test group will be processed. S4. Internal quality analysis of nuts: The internal structure of the target nuts after screening is imaged, and the internal defect parameters of each target nut are calculated using image analysis to generate the internal quality pass coefficient and count the number of internally unqualified nuts. S5. Comprehensive Quality Assessment of Nuts: Assess whether the comprehensive quality of nuts in the target batch is up to standard based on the number of non-conforming nuts. If not, provide immediate feedback. The specific process for determining whether the appearance quality of the nuts is up to standard is as follows: The circumference and area of ​​each nut in each test group were analyzed for roundness, and the roundness compliance coefficient of each nut was determined based on the roundness. The average RGB channel color value is located from the images of each nut in each detection group, and compared with the RGB channel color range value when the nut is ripe to obtain the ripeness of each nut. The roundness conformity coefficient and maturity of each nut were weighted and averaged to obtain the following result. External quality coefficients of each nut in each testing group within the target batch; The external quality coefficient of each nut in each test group in the target batch is compared with the external quality coefficient of the set reference. If the external quality coefficient of a nut in a certain test group is greater than the external quality coefficient of the set reference, it indicates that the external quality of the nut in that test group is qualified; otherwise, it indicates that the external quality of the nut in that test group is unqualified. The specific process for determining the maturity of each nut is as follows: The R, G, and B values ​​corresponding to each pixel are located from the surface images of each nut in each detection group, and the average color values ​​of the R, G, and B channels are obtained by averaging. The mean values ​​of the R, G, and B channels are compared with the range values ​​of the R, G, and B channels for the target nut at maturity stored in the database. If the mean values ​​of the R, G, and B channels for a nut all fall within the range values ​​for the target nut at maturity, then the maturity of the nut is recorded as _____. ; If the average values ​​of the R, G, and B channels of a nut are not within the range of R, G, and B channel colors at the point of maturity for that nut, then the maturity level of the nut is recorded as _____. ; If any of the mean values ​​of the R, G, and B channels of a nut are not within the range of R, G, and B channel colors required for the nut to be considered mature, then the maturity of the nut is recorded as follows: ; The maturity of each nut in each test group was statistically analyzed.

2. The method for nut quality detection and analysis based on visual technology according to claim 1, characterized in that: The specific process for calculating the roundness compliance coefficient of each nut is as follows: The circumference and area of ​​each nut in each test group are calculated using the roundness formula to obtain the roundness of each nut. The standard roundness of the target nut is extracted from the database. The deviation between the roundness of each nut and the corresponding standard roundness is analyzed to obtain the roundness compliance coefficient of each nut.

3. The method for nut quality detection and analysis based on visual technology according to claim 2, characterized in that: The specific method for triggering the sorting device to remove defective products is as follows: once a nut in each inspection group is detected to have an unqualified external quality, the system will send a signal to the robotic arm sorting device. The system will send the location information of the unqualified nut to the robotic arm control system. The robotic arm control system will control the robotic arm to perform a grabbing operation at a suitable position based on the received location information, so that the unqualified nut in each inspection group is removed from the inspection conveyor belt and placed in the unqualified external quality area.

4. The method for nut quality detection and analysis based on visual technology according to claim 3, characterized in that: The specific process of step S3 is as follows: Multiply the remaining number of nuts in each test group by the weight range corresponding to a single target nut extracted from the database to obtain the standard nut weight range for each test group. The total weight of nuts in each test group is compared with the corresponding standard nut weight range. If the total weight of nuts in a test group is within the corresponding standard nut weight range, it indicates that the weight of nuts in that test group meets the standard; otherwise, it indicates that the weight of nuts in that test group does not meet the standard.

5. The method for nut quality detection and analysis based on visual technology according to claim 4, characterized in that: The specific method for processing the nuts in the test group is as follows: weigh each nut in the test group whose weight does not meet the standard in turn to obtain the weight of each nut in the test group whose weight does not meet the standard, and compare it with the weight range corresponding to a single target nut. If the weight of a nut in the test group whose weight does not meet the standard is not within the weight range corresponding to a single target nut, then the nut in the test group whose weight does not meet the standard is screened.

6. The method for nut quality detection and analysis based on visual technology according to claim 5, characterized in that: The process for generating the intrinsic quality pass rate is as follows: Using the sifted nuts from the detection group as target nuts, the number of grayscale regions and the corresponding grayscale values ​​of each grayscale region are located from the internal images of each target nut in each detection group. The gray values ​​corresponding to each gray area of ​​each target nut in each test group are compared with the normal gray value range of the pulp of the target nut stored in the database. If the gray value corresponding to a gray area of ​​a target nut in a certain test group is within the normal gray value range of the pulp of the target nut, then the gray area is recorded as the normal pulp area. The number of normal pulp areas of each target nut in each test group in the target batch is counted. The internal quality qualification coefficient of each target nut is obtained by performing a ratio deviation analysis between the number of normal pulp areas and the number of gray areas of the corresponding nut.

7. The method for nut quality detection and analysis based on visual technology according to claim 6, characterized in that: The method for counting the number of inherently substandard nuts is as follows: the inherent quality pass coefficient of each target nut in each test group is compared with the set reference inherent quality pass coefficient. If the inherent quality pass coefficient of a target nut in a certain test group is less than the set reference inherent quality pass coefficient, then the target nut is recorded as an inherently substandard nut, and the number of inherently substandard nuts in each test group is counted.

8. The method for nut quality detection and analysis based on visual technology according to claim 7, characterized in that: The specific process for assessing whether the overall quality of the nuts in the target batch is up to standard is as follows: The number of nuts with substandard external quality, the number of nuts with substandard weight, and the number of nuts with substandard internal quality in each testing group were counted and summed to obtain the number of nuts with abnormal quality in each testing group. If the number of abnormal nuts in a certain testing group is 0, then the testing group is recorded as a normal quality testing group, and the number of normal quality testing groups in the target batch is counted. The ratio of the number of normal quality test groups to the number of test groups in the target batch is analyzed to obtain the comprehensive quality coefficient of nuts in the target batch. The overall quality coefficient of the target batch of nuts is compared with the overall quality coefficient of the set reference nuts. If the overall quality coefficient of the target batch of nuts is greater than or equal to the overall quality coefficient of the set reference nuts, it indicates that the overall quality of the nuts in the target batch is qualified; otherwise, it indicates that the overall quality of the nuts in the target batch is unqualified.

Images