Fruit quality detection method and system based on fruit appearance parameters

Abstract

The invention relates to the technical field of size measurement, and provides a fruit quality detection method and system based on fruit appearance parameters, and the method comprises the steps: obtaining a plurality of radius representative points and circumferential structure points of nectarine structure distribution; obtaining a nectarine contour probability according to the circumferential structure points; obtaining a corrected local abnormal factor according to the radius of the radius representative point and the nectarine contour probability; obtaining a size representation degree according to the nectarine contour probability and the corrected local anomaly factor; obtaining the overall radius of the nectarine according to the radius and the size representation degree of the radius representation point; and carrying out quality grade division on the nectarine according to the whole radius of the nectarine to obtain a nectarine quality detection result. According to the method, the accurate whole radius of the nectarine is obtained, and the quality grade of the nectarine is divided, so that a more accurate nectarine quality detection result is obtained.

Description

Technical Field

[0001] The invention relates to the technical field of size measurement, and in particular to a fruit quality detection method and system based on fruit appearance parameters. Background Art

[0002] In fruit quality testing, appearance parameters are important indicators for evaluating fruit quality. Generally, analysis of fruit appearance characteristics such as color, shape, size, and texture can effectively evaluate the quality and market adaptability of the fruit.

[0003] In the process of nectarine appearance quality inspection, in order to shorten the overall inspection time, maintain high production efficiency and reduce manual operations, a batch of nectarines need to be inspected for appearance at one time. The size of the nectarine is an important indicator that directly affects the fullness of its flesh and its water content. Larger nectarines usually have richer flesh and higher sugar content, and taste better; while small fruits may be relatively dry and have a lighter taste. In addition, the appearance size of nectarines is also closely related to factors such as its growth environment and nutritional supply, reflecting the level of planting management, which makes preliminary screening through size inspection very intuitive and effective.

[0004] When measuring the size of nectarines, since the edge contour of nectarines is approximately circular, it is necessary to obtain the edge radius of the approximate circle of the edge contour of nectarines, so as to estimate the size of nectarines. At present, the Hough circle detection method is often used to extract the radius of nectarines, but the extracted nectarine radius has different representativeness in the size of nectarines in batches, that is, the circular contour presented by the nectarine radius is not necessarily the true contour of the nectarine, thereby reducing the accuracy of the fruit quality detection results. Summary of the invention

[0005] The present invention provides a fruit quality detection method and system based on fruit appearance parameters to solve the problem that the representativeness of the nectarine radius extracted by the existing Hough circle detection is different, thereby reducing the accuracy of the fruit quality detection result. The technical scheme adopted is specifically as follows: The present invention proposes a fruit quality detection method based on fruit appearance parameters, the method comprising the following steps: Obtaining a nectarine structure distribution; obtaining a number of radius representative points for the nectarine structure distribution; the radius representative points represent a number of circular contours in the nectarine structure distribution, the radius representative points represent the position and radius size of the circular contour, and the circular contour is a possible contour shape of the nectarine; Obtain several circumferential structure points of each radius representative point; obtain the nectarine outline probability of each radius representative point according to the color change and difference of the structure points near the corresponding position of each circumferential structure point in the nectarine structure distribution; According to the radius of each radius representative point and the probability of the nectarine contour, the modified local anomaly factor of each radius representative point is obtained; According to the nectarine contour probability of each radius representative point and the corrected local anomaly factor, the size representative degree of each radius representative point is obtained; according to the radius and size representative degree of all radius representative points, the overall radius of the nectarine is obtained; The quality grades of nectarines were divided according to their overall radius, and the quality test results of nectarines were obtained.

[0006] Furthermore, the method of obtaining the nectarine contour probability of each radius representative point according to the color change and difference of the structure points near the corresponding position of each circular structure point in the nectarine structure distribution includes the following specific methods: For any circumferential structure point, obtain the characteristic change direction of the circumferential structure point; obtain a number of color structure points of the nectarine structure distribution in the RGB space, record the color structure point corresponding to the circumferential structure point in the RGB space as the color reference point of the circumferential structure point, and record the absolute value of the difference between the G channel values ​​of the two color structure points adjacent to each other in the characteristic change direction of the circumferential structure point as the green mutation degree of the circumferential structure point; record the absolute value of the difference between the R channel values ​​of the two color structure points adjacent to each other in the characteristic change direction of the circumferential structure point as the red mutation degree of the circumferential structure point; According to the difference in the red-green mutation degree of the circular structure points, the probability of the nectarine outline at each radius representative point is obtained.

[0007] Furthermore, the specific method of obtaining the nectarine contour probability of each radius representative point according to the difference in red-green mutation degree of the circular structure points includes: In the formula, For the The radius represents the contour probability of the nectarine at each point; For the The number of circular structure points represented by each radius; For the The radius represents the point The red mutation degree of each circular structure point; For the The radius represents the point The degree of green mutation of each circular structure point; represents a hyperparameter; represents the linear normalization function.

[0008] Furthermore, the modified local anomaly factor of each radius representative point is obtained according to the radius of each radius representative point and the nectarine contour probability, including the specific method of: The radius of all radius representative points is used for anomaly detection using the LOF anomaly detection algorithm. During the detection process, The calculation method of the corrected local anomaly factor of the representative point of radius is: In the formula, For the The radius represents the point Corrected local anomaly factor when away from the neighborhood; is the initial parameter of the LOF anomaly detection algorithm; express For the The radius represents the point A radius representative point within the distance neighborhood; The radius represents the point The probability of the nectarine outline; The radius represents the point of - Local reachability density, obtained by the LOF anomaly detection algorithm; For the The radius represents the contour probability of the nectarine at each point; For the The radius represents the point - local reachability density; is the weight normalization function.

[0009] Furthermore, the size representativeness of each radius representative point is obtained according to the nectarine contour probability of each radius representative point and the corrected local anomaly factor, including the specific method of: In the formula, For the The radius represents the size of the point, representing the degree; For the The radius represents the contour probability of the nectarine at each point; For the The radius represents the point Corrected local anomaly factor when away from the neighborhood; is a linear normalization function.

[0010] Furthermore, the whole radius of the nectarine is obtained according to the radius and size representative degree of all radius representative points, and the specific method includes: In the formula, represents the overall radius of the nectarine; is the number of points representing the radius; For the The radius represents the size of the point, representing the degree; For the The radius represents the radius of the point; is the weight normalization function.

[0011] Furthermore, the specific method of obtaining a plurality of radius representative points for the nectarine structure distribution includes: Extracting a number of nectarine contours from the nectarine structure distribution, inputting the several nectarine contours into the Hough circle transform, and obtaining a number of curves in the Hough space; The Hough space exceeds The intersection of the curves is recorded as a radius representative point, where is a preset intersection threshold; the abscissa of the radius representative point in the Hough space is the abscissa of the center of the circle, the ordinate is the ordinate of the center of the circle, and the ordinate is the radius.

[0012] Furthermore, the specific method of obtaining a number of circular structure points of each radius representative point includes: In Hough space, for any curve passing through any radius representative point, several structural points corresponding to the curve in the nectarine structure distribution are recorded as several circular structural points of the radius representative point.

[0013] Furthermore, the quality grade of nectarines is classified according to the overall radius of nectarines to obtain the nectarine quality test result, which includes the specific method of: Will of nectarines are classified as special grade; The nectarines are classified into the first grade; Nectarines are divided into two levels; among them, For the preset super standard size, is a preset first-level standard size, and , is the overall radius of the nectarine.

[0014] The present invention also proposes a fruit quality detection system based on fruit appearance parameters, which includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the above method are implemented.

[0015] The beneficial effects of the present invention are as follows: a number of radius representative points are extracted from the same batch of nectarines through their possible circular contours, and some of the radius representative points may be caused by the red-green dividing line on the surface of the nectarine. The present invention constructs the green mutation degree and red mutation degree of the circumferential structure point of the radius representative point through the changes in the R channel value and the G channel value of the red-green dividing line position, and judges the possibility that the radius representative point corresponds to the true contour of the nectarine; due to the large number of structural points reflecting the contour in the nectarine structure distribution, the circular contour constructed by the radius representative point may not be the nectarine contour. The present invention calculates the corrected local anomaly factor of each radius representative point through the similar properties of the size of the nectarines in the same batch, combined with the nectarine contour probability of the radius representative point, and judges the abnormal situation of the radius corresponding to each radius representative point; using the different representativeness of the radius representative point in the nectarine size of the batch, the present invention obtains the size representative degree of each radius representative point through the nectarine contour probability and the corrected local anomaly factor of the radius representative point, and then obtains the overall radius of the nectarine, and detects the quality of the whole batch of nectarines. The present invention obtains the accurate overall radius of the nectarine, classifies the quality of the nectarine, and obtains a more accurate nectarine quality detection result. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 creative labor.

[0017] Figure 1 A schematic flow chart of a method for detecting fruit quality based on fruit appearance parameters provided by one embodiment of the present invention; Figure 2 This is an RGB channel separation diagram of a nectarine structure distribution provided in this embodiment. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0019] See also Figure 1 , which shows a flow chart of a fruit quality detection method based on fruit appearance parameters provided by an embodiment of the present invention, the method comprising the following steps: Step S001, obtaining nectarine structure distribution; obtaining a number of radius representative points for the nectarine structure distribution.

[0020] It should be noted that the purpose of this embodiment is to analyze the circular contours of nectarines from the same batch. Since the collected nectarine structure distribution contains a large number of circular contours composed of structural points, it is necessary to screen the circular contours that can truly reflect the nectarine contours. Therefore, it is necessary to first extract several circular contours from the nectarine structure distribution through Hough circle detection.

[0021] Specifically, a light-colored conveyor belt is arranged on a fruit quality inspection line, a batch of nectarines to be inspected are spread on the conveyor belt, and the nectarines are prevented from overlapping during the spreading process. An industrial camera is arranged directly above the conveyor belt, and the lens of the industrial camera is aimed at the conveyor belt. During the conveying process of the conveyor belt, the industrial camera is used to collect nectarine images as nectarine structural distribution; and a number of nectarine contours are extracted from the nectarine structural distribution, and the several nectarine contours are input into Hough circle transformation to obtain a number of curves in Hough space; The Hough space exceeds The intersection of the curves is recorded as a radius representative point, where To preset the intersection threshold, this embodiment adopts Take as an example to describe; the horizontal coordinate of the radius representative point in the Hough space is the horizontal coordinate of the center of the circle, the vertical coordinate is the vertical coordinate of the center of the circle, and the vertical coordinate is the radius; in the Hough space, for any curve passing through any radius representative point, the several structural points corresponding to the curve in the nectarine structure distribution are recorded as several circular structural points of the radius representative point.

[0022] Optionally, in one embodiment of the present invention, the nectarine contour is extracted for the nectarine structural distribution. In this embodiment, the nectarine structural distribution is processed, that is, the nectarine image is grayscaled to obtain a nectarine grayscale image; since the Hough circle detection is sensitive to noise, the nectarine grayscale image is median filtered to obtain a nectarine filtered image; the nectarine filtered image is Canny edge detected to obtain several edge lines of the nectarine filtered image, and the edge lines are several nectarine contours in the nectarine structural distribution; wherein grayscale, median filtering, Canny edge detection and Hough circle transform are well-known technologies, and the specific methods are not introduced here.

[0023] It should be noted that since the nectarine contour is usually nearly circular, most of the structural points on the nectarine contour are transformed into curves in the Hough space that intersect at one point. Because there are many structural points on the nectarine contour, there are also many curves on the real nectarine contour that intersect at one point when transformed into the Hough space.

[0024] Step S002, obtaining a number of circular structure points of each radius representative point; obtaining the nectarine contour probability of each radius representative point according to the color change and difference of the structure points near the corresponding position of each circular structure point in the nectarine structure distribution.

[0025] It should be noted that during the growth of nectarines, the surface of the fruit is exposed to sunlight. The ultraviolet and visible light in the sunlight have a direct impact on the synthesis of pigments in the peel. The surface of mature nectarines usually changes from green to red. However, during the growth of nectarines, some areas of the surface of the nectarine may be blocked by leaves, etc., which reduces the synthesis of the peel pigment in the blocked area, resulting in the color of the blocked area still being green instead of red. When the nectarines are ripe and harvested, there will be an obvious red-green dividing line on the surface of the nectarine, and the dividing line may also be in the shape of an arc. In the process of obtaining the radius representative point using Hough space, since the red-green dividing line is more obvious, the red-green dividing line may also be judged as the radius representative point, so it is necessary to distinguish the radius representative points caused by the red-green dividing line on the surface of the nectarine.

[0026] It should be further explained that the red-green dividing line has different degrees of obviousness in the RGB channels of the nectarine structure distribution. The dividing effect of the red-green dividing line in the R channel is not obvious, that is, the R channel values ​​of the pixels on both sides of the red-green dividing line are not much different, while the dividing effect in the G channel is more obvious, that is, the G channel values ​​of the pixels on both sides of the red-green dividing line are more different. The separation effect of the red-green dividing line in the RGB channels of the nectarine structure distribution is as follows: Figure 2 As shown. Therefore, the radius representative points caused by the red and green dividing lines are distinguished.

[0027] Specifically, for any curve passing through any radius representative point in the Hough space, the pixel point corresponding to the curve in the nectarine filter image is recorded as a circumferential structure point of the radius representative point; For any circular structure point, the characteristic change direction of the circular structure point is obtained; a number of color structure points of the nectarine structure distribution in the RGB space are obtained, and the color structure point corresponding to the circular structure point in the RGB space is recorded as the color reference point of the circular structure point, and the absolute value of the difference between the G channel values ​​of the two color structure points adjacent to the circular structure point in the characteristic change direction of the color reference is recorded as the green mutation degree of the circular structure point; the absolute value of the difference between the R channel values ​​of the two color structure points adjacent to the circular structure point in the characteristic change direction of the color reference point is recorded as the red mutation degree of the circular structure point.

[0028] Optionally, in one embodiment of the present invention, the Sobel operator is used to obtain the gradient direction of the circular structure point and used as the characteristic change direction of the circular structure point, wherein using the Sobel operator to obtain the gradient direction of the pixel point is a well-known technology, and the specific method is not introduced here; and the nectarine structure is distributed in a number of color structure points in the RGB space, which is, in this embodiment, the RGB image of the nectarine image and the RGB three-channel value of each pixel point therein.

[0029] Further, The probability of a nectarine contour with a radius representative point is calculated as: In the formula, For the The radius represents the contour probability of the nectarine at each point; For the The number of circular structure points represented by each radius; For the The radius represents the point The red mutation degree of each circular structure point; For the The radius represents the point The degree of green mutation of each circular structure point; represents a hyperparameter; Represents a linear normalization function, the normalized object is all radius representative points .

[0030] Step S003: Obtain a corrected local anomaly factor for each radius representative point according to the radius of each radius representative point and the nectarine contour probability.

[0031] It should be noted that since the resolution of the nectarine structure distribution captured by the industrial camera is high and the number of edge pixels obtained by edge detection is large, there are a large number of curves in the Hough space, resulting in the radius representative point formed by the intersection of the curves may not be the nectarine outline. Since the same batch of nectarines are planted with the same variety and consistent management measures, the sizes of the nectarines in the same batch are relatively similar. In the Hough space, the vertical axis coordinates of the radius representative points are relatively concentrated, that is, the radii are relatively similar.

[0032] Specifically, the radius of all radius representative points is used for anomaly detection using the LOF anomaly detection algorithm. During the detection process, since some radius representative points belong to the red-green dividing line, it is necessary to reduce the weight of these radius representative points in anomaly detection. The calculation method of the corrected local anomaly factor of the representative point of radius is: In the formula, For the The radius represents the point Corrected local anomaly factor when away from the neighborhood; is the initial parameter of the LOF anomaly detection algorithm; express For the The radius represents the point A radius representative point within the distance neighborhood; The radius represents the point The probability of the nectarine outline; The radius represents the point of - Local reachability density, obtained by the LOF anomaly detection algorithm; For the The radius represents the contour probability of the nectarine at each point; For the The radius represents the point - local reachability density; is the weight normalization function, and the normalization object is The radius represents the point The probability of a nectarine outline for all radius representative points within the distance neighborhood.

[0033] It should be noted that The larger the value, the The radius of the point is more abnormal, that is, The smaller the radius representative point, the less likely it is to represent the radius size of the entire batch of nectarines.

[0034] Step S004: according to the nectarine outline probability of each radius representative point and the corrected local anomaly factor, the size representative degree of each radius representative point is obtained; according to the radius and size representative degree of all radius representative points, the overall radius of the nectarine is obtained.

[0035] It should be noted that among all the radius representative points, the radius of some radius representative points is not the radius of the actual nectarine contour edge, so it is necessary to reduce the weight of these radius representative points when obtaining the radius size of the entire batch of nectarines.

[0036] It should be further explained that when analyzing the weights of the radius representative points, the weights of the radius representative points with lower probability of nectarine outline and the weights of the radius representative points with abnormal radius are mainly reduced.

[0037] Specifically, The calculation method of the size representation degree of each radius representative point is: In the formula, For the The radius represents the size of the point, representing the degree; For the The radius represents the contour probability of the nectarine at each point; For the The radius represents the point Corrected local anomaly factor when away from the neighborhood; is a linear normalization function, and the normalization object is all the radius representative points in Corrected local outlier factor when moving away from the neighborhood.

[0038] It should be noted that the larger the size representativeness of the radius representative point, the better it can represent the size of the entire batch of nectarines, so the overall radius of the nectarines is calculated based on the size representativeness of the radius representative point.

[0039] Specifically, the calculation method of the overall radius of nectarine is: In the formula, represents the overall radius of the nectarine; is the number of points representing the radius; For the The radius represents the size of the point, representing the degree; For the The radius represents the radius of the point; is the weight normalization function, and the normalization object is the size representation degree of all radius representative points.

[0040] Step S005: classify the quality grades of the nectarines according to the overall radius of the nectarines to obtain the nectarine quality detection results.

[0041] It should be noted that the size of nectarines is generally described by their diameter. Nectarines with larger diameters have richer flesh and higher sugar content, so the quality grades of nectarines are divided according to their size.

[0042] Specifically, of nectarines are classified as special grade; Will Nectarines are classified as Grade 1; Will The nectarines are classified into two grades; in, For the preset super standard size, is a preset first-level standard size, and , is the overall radius of the nectarine; , This embodiment is described using this as an example.

[0043] It should be noted that, so far, the quality of nectarines has been detected by grading each batch of nectarines according to the overall radius of the nectarines.

[0044] Another embodiment of the present invention provides a fruit quality detection system based on fruit appearance parameters, the system comprising a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, the above method steps S001 to S005 are implemented.

[0045] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A fruit quality detection method based on fruit appearance parameters, characterized in that: The method comprises the following steps: Obtaining a nectarine structure distribution; obtaining a number of radius representative points for the nectarine structure distribution; the radius representative points represent a number of circular contours in the nectarine structure distribution, the radius representative points represent the position and radius size of the circular contour, and the circular contour is a possible contour shape of the nectarine; Obtain several circumferential structure points of each radius representative point; obtain the nectarine outline probability of each radius representative point according to the color change and difference of the structure points near the corresponding position of each circumferential structure point in the nectarine structure distribution; According to the radius of each radius representative point and the probability of the nectarine contour, the modified local anomaly factor of each radius representative point is obtained; According to the nectarine contour probability of each radius representative point and the corrected local anomaly factor, the size representative degree of each radius representative point is obtained; according to the radius and size representative degree of all radius representative points, the overall radius of the nectarine is obtained; The quality grades of nectarines were divided according to their overall radius, and the quality test results of nectarines were obtained.

2. A method for detecting fruit quality based on fruit appearance parameters according to claim 1, characterized in that: The method of obtaining the nectarine contour probability of each radius representative point according to the color change and difference of the structure points near the corresponding position of each circumferential structure point in the nectarine structure distribution includes: For any circumferential structure point, obtain the characteristic change direction of the circumferential structure point; obtain a number of color structure points of the nectarine structure distribution in the RGB space, record the color structure point corresponding to the circumferential structure point in the RGB space as the color reference point of the circumferential structure point, and record the absolute value of the difference between the G channel values ​​of the two color structure points adjacent to each other in the characteristic change direction of the circumferential structure point as the green mutation degree of the circumferential structure point; record the absolute value of the difference between the R channel values ​​of the two color structure points adjacent to each other in the characteristic change direction of the circumferential structure point as the red mutation degree of the circumferential structure point; According to the difference in the red-green mutation degree of the circular structure points, the probability of the nectarine outline at each radius representative point is obtained.

3. A method for detecting fruit quality based on fruit appearance parameters according to claim 2, characterized in that: The specific method of obtaining the nectarine contour probability of each radius representative point according to the difference in red-green mutation degree of the circumferential structure points is as follows: In the formula, For the The radius represents the contour probability of the nectarine at each point; For the The number of circular structure points represented by each radius; For the The radius represents the point The red mutation degree of each circular structure point; For the The radius represents the point The degree of green mutation of each circular structure point; represents a hyperparameter; represents the linear normalization function.

4. The method for detecting fruit quality based on fruit appearance parameters according to claim 1, characterized in that: The method of obtaining the corrected local anomaly factor of each radius representative point according to the radius of each radius representative point and the nectarine contour probability includes the following specific methods: The radius of all radius representative points is used for anomaly detection using the LOF anomaly detection algorithm. During the detection process, The calculation method of the corrected local anomaly factor of the representative point of radius is: In the formula, For the The radius represents the point Corrected local anomaly factor when away from the neighborhood; is the initial parameter of the LOF anomaly detection algorithm; express For the The radius represents the point A radius representative point within the distance neighborhood; The radius represents the point The probability of the nectarine outline; The radius represents the point of - Local reachability density, obtained by the LOF anomaly detection algorithm; For the The radius represents the contour probability of the nectarine at each point; For the The radius represents the point - local reachability density; is the weight normalization function.

5. The method for detecting fruit quality based on fruit appearance parameters according to claim 1, characterized in that: The specific method of obtaining the size representative degree of each radius representative point according to the nectarine contour probability of each radius representative point and the corrected local anomaly factor is as follows: In the formula, For the The radius represents the size of the point, representing the degree; For the The radius represents the contour probability of the nectarine at each point; For the The radius represents the point Corrected local anomaly factor when away from the neighborhood; is a linear normalization function.

6. The method for detecting fruit quality based on fruit appearance parameters according to claim 1, characterized in that: The method of obtaining the overall radius of the nectarine according to the radius and size representative degree of all radius representative points includes: In the formula, represents the overall radius of the nectarine; is the number of points representing the radius; For the The radius represents the size of the point, representing the degree; For the The radius represents the radius of the point; is the weight normalization function.

7. The method for detecting fruit quality based on fruit appearance parameters according to claim 1, characterized in that: The specific method of obtaining a plurality of radius representative points for the nectarine structure distribution includes: Extracting a number of nectarine contours from the nectarine structure distribution, inputting the several nectarine contours into the Hough circle transform, and obtaining a number of curves in the Hough space; The Hough space exceeds The intersection of the curves is recorded as a radius representative point, where is a preset intersection threshold; the abscissa of the radius representative point in the Hough space is the abscissa of the center of the circle, the ordinate is the ordinate of the center of the circle, and the ordinate is the radius.

8. The method for detecting fruit quality based on fruit appearance parameters according to claim 7, characterized in that: The specific method of obtaining a number of circular structure points of each radius representative point includes: In Hough space, for any curve passing through any radius representative point, several structural points corresponding to the curve in the nectarine structure distribution are recorded as several circular structural points of the radius representative point.

9. The method for detecting fruit quality based on fruit appearance parameters according to claim 1, characterized in that: The method of classifying the quality of nectarines according to the overall radius of the nectarines to obtain the nectarine quality test results includes the following specific methods: Will of nectarines are classified as special grade; The nectarines are classified into the first grade; Nectarines are divided into two levels; among them, For the preset super standard size, is a preset first-level standard size, and , is the overall radius of the nectarine.

10. A fruit quality detection system based on fruit appearance parameters, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of a fruit quality detection method based on fruit appearance parameters as described in any one of claims 1 to 9 are implemented.

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