CN107463942B - Method for grading quality of juicy peaches based on anti-noise support vector machine with boundary points - Google Patents

Abstract

The invention discloses a juicy peach quality grading method based on an anti-noise support vector machine with boundary points, which comprises the following steps: (1) collecting visible/near infrared spectrum data of the juicy peaches with different quality grades, and preprocessing the visible/near infrared spectrum data and extracting PCA (principal component analysis) characteristics; (2) combining the visible/near infrared spectrum sample sets of the juicy peaches with different quality grades pairwise to establish a plurality of groups of visible/near infrared spectrum training sets of the juicy peaches; (3) training the training set by using an anti-noise support vector machine based on boundary points to obtain a plurality of honey peach quality grade classifiers; (4) and detecting the visible/near infrared spectrum of the honey peaches to be classified by using a honey peach quality grade classifier. The method uses the anti-noise support vector machine based on the boundary points to detect the visible/near infrared spectrum of the honey peaches, has the advantages of high detection speed, strong anti-noise capability, high classification accuracy and the like, and can realize the grading of the quality grade of the honey peaches in a noise detection environment.

Description

Method for grading quality of juicy peaches based on anti-noise support vector machine with boundary points

Technical Field

The invention relates to the field of quality grade identification of juicy peaches, in particular to a juicy peach quality grade grading method based on an anti-noise support vector machine of boundary points.

Background

The peaches are native to China, have good ornamental value and very good edibility, and are rich in protein, fat, sugar, calcium, phosphorus, iron and various vitamins, particularly the iron content of the fruits named as the top cogongrass. In particular to honey peaches which are soft and tender in pulp, sweet and juicy, and strong in fragrance and are reputed to be inside and outside the sea. Meanwhile, from international trade, southeast Asia is taken as one of two fresh peach markets in the world, and the good peach consumption habit and the wide market of the southeast Asia become the power for promoting the development of the honey peach industry in China. However, the production technology level of honey peaches in China is low, a great distance is left between standard production and standard management, and the quality grading operation of honey peaches is basically processed in a manual mode. However, the honey peaches containing crude fiber, little meat, fine and soft texture, big head, enough water and soft skin have the following defects in the manual quality grade identification: 1) the manual method often leaves fingerprints on the honey peaches, and due to oxidation, the fingerprints become dark in color, so that the appearance and quality of the honey peaches are affected; 2) the manual quality grading method has strong subjectivity and low standardization degree; 3) the time for the honey peaches to mature and come into the market is concentrated and is not easy to store, and the manual quality grading method is long in time consumption and not beneficial to the circulation of the honey peaches from the production link to the logistics link. Therefore, the research on the simple, rapid and nondestructive automatic identification method for grading the quality of the honey peaches not only has great economic value, but also has important significance for the healthy and continuous development of the honey peach industry.

The visible/near infrared spectrum analysis technology is qualitative and quantitative analysis technology according to the absorption characteristic of a certain substance component to electromagnetic waves, and has the advantages of high speed, high efficiency, stable result, good test reproducibility, no damage and the like. Pan Lei Qing et al establish a method for detecting sugar content of honey peaches in the shelf life without damage by using a near infrared spectrum technology and a partial least square method in near infrared spectrum detection of sugar content of honey peaches in the shelf life (university of Nanjing agriculture, 4 months in 2013); lixiaoli et al, in New method for discriminating varieties of honey peaches based on principal component analysis and multi-class discriminant analysis (academic newspaper of infrared and millimeter waves, 12 months in 2009), measure spectral curves of honey peaches by using a visible-near infrared spectrometer, perform cluster analysis on samples of different varieties by using a principal component analysis method, and establish a model for discriminating varieties of honey peaches by combining a multi-class discriminant analysis technology.

The juicy peach identification models of Panyiqing, Liaoli and the like are all established under the condition that spectral data are noiseless, and are not suitable for the noisy juicy peach spectral data. However, the visible/near infrared spectrum belongs to an absolute measurement technology, and is easily affected by noise, and the noise generally has a large influence on the measurement result. The Zhao-Huan-huan-et al in the article "influence of noise on near infrared spectroscopy analysis and corresponding mathematical processing methods" (spectroscopy and spectral analysis, 4 months 2013) take corn kernels as an example, analyze feasibility of near infrared analysis on a near infrared spectrum analyzer, and point out that certain software technology is needed for quantitative analysis of spectral data acquired by a visible/near infrared spectrum analyzer with low signal-to-noise ratio. Furthermore, Rukshan Batuwita et al, FSVM-CIL, Fuzzy Support vectors for ClassImbalance Learning (IEEE TRANSACTIONS ON FUZZY SYSTEMS, 6.2010), noted that the data collected in real world environments was largely more or less noisy.

Aiming at the current situation and a plurality of defects of the honey peach quality grading method and effectively being applicable to grading detection of daily production links, the invention provides the honey peach quality grading method based on the anti-noise support vector machine of the boundary points.

Disclosure of Invention

The invention mainly aims to provide a method for grading the quality of honey peaches based on a boundary point anti-noise support vector machine, which is lossless, easy to operate, high in reliability and high in quality, and mainly relieves the influence of errors caused by noise on the existing visible/near infrared qualitative analysis model, and improves the accuracy and the anti-noise performance of the method for grading the quality of the honey peaches.

The technical scheme of the invention is as follows: in order to achieve the purpose, spectral data of the honey peaches are obtained through a visible/near infrared spectrum, the spectral data are subjected to preprocessing and feature extraction by a PCA method to obtain feature data of the spectral data to form training samples, an anti-noise support vector machine based on boundary points is used for training a training sample set to construct a plurality of honey peach quality grade classifiers, and finally a voting mechanism is adopted to count detection results to realize automatic detection of the honey peach quality grades.

According to the conception, the invention adopts the following technical scheme.

(1) Collecting n visible/near infrared spectrum data of the juicy peaches with different quality grades, and preprocessing the visible/near infrared spectrum data and extracting PCA (principal component analysis) characteristics to obtain visible/near infrared spectrum sample sets of the juicy peaches with different quality grades;

(2) combining the visible/near infrared spectrum sample sets of the juicy peaches with different quality grades pairwise to establish

Grouping a visible/near infrared spectrum training set of the juicy peaches;

(3) mixing the above

Inputting the visible/near infrared spectrum training set of the juicy peaches into an anti-noise support vector machine based on boundary points for training to obtain

A honey peach quality grade classifier;

(4) detecting a honey peach sample to be graded by using a honey peach quality grade classifier;

the construction steps of the juicy peach quality grade classifier in the step (3) are as follows:

(31) juicy peach visible/near infrared spectrum sample set X with two quality grades in each group of juicy peach visible/near infrared spectrum training set₁And X₂The sphere center c of the minimum sphere space closed region of the two types of sample sets under the feature space is obtained by respectively using a support vector field description (SVDD) algorithm₁And c₂And a set of sample points Z distributed over two minimum spherical spatial closed regions₁And Z₂And is provided with X₁And X₂Set of boundary points B in feature space₁And B₂Are each Z₁And Z₂；

(32) At X₁Each sample x is calculated_1,iTo the center of the sphere c₁Euclidean distance d in feature space_i：

d_i＝||φ(x_1,i)-φ(c₁)||²， (1)

Wherein x_1,iSatisfy x_1,i∈X₁And is

Phi () represents the mapping function of the samples from the original space to the kernel space;

at X₂Calculate each sample x_2,iTo the center of the sphere c₂Euclidean distance e in feature space_iWherein x is_2,iSatisfy x_2,i∈X₂And is

e_i＝||φ(x_2,i)-φ(c₂)||²； (2)

(33) According to d_iValue descending order X₁Sample x in (1)_1,iWherein x is_1,iSatisfy x_1,i∈X₁And is

Sequentially mixing the samples x_1,iSolving the vector mu by substituting formula (3), and then calculating x_1,iUpdating the set of boundary points B together with the obtained μ values for formula (4)₁This process continues through traversal X₁All of the samples in (1) that satisfy the condition,

wherein | B₁I represents B₁The number of the middle samples, the threshold value is a normal number;

according to e_iValue descending order X₂Sample x in (1)_2,iWherein x is_2,iSatisfy x_2,i∈X₂And is

Sequentially mixing the samples x_2,iSolving for vector λ by substituting equation (5), and then calculating x_2,iSubstituting the obtained lambda value into equation (6) to update the boundary point set B₂This process continues through traversal X₂All of the samples in (1) that satisfy the condition,

(34) calculating X₁Middle set of boundary points B₁Weight α of each boundary point in_i：

Calculating X₂Middle set of boundary points B₂Weight β of each boundary point in_i：

(35) B is to be₁Lumped weight α_iRemoving samples with square value smaller than threshold value₁Collecting:

wherein

The threshold value is a normal number of times,

b is to be₂Lumped weight β_iRemoving samples with square value smaller than threshold value₂Collecting:

wherein

(36) B obtained in the step (35)₁And B₂Substituting the set with its class label into a median loss function support vector machine:

wherein w is a weight vector, C is a penalty parameter,

is a relaxation vector, y_i∈ { +1, -1} respectively represents the class labels of two classes of quality grade honey peach visible/near infrared spectrum samples, b is the offset of the classification hyperplane, and tau is one in [0,1 { ]]Real numbers in between;

introducing a Lagrangian function, the above equation can be converted into a quadratic programming form as follows:

wherein γ and ν are lagrangian coefficients;

solving the equations (11) and (12) to obtain the optimal solution w of w and b^*，b^*And obtaining a juicy peach quality grade classifier of the anti-noise support vector machine based on the boundary points:

f(x)＝sign(1-(w^*·φ(x)+b^*))， (13)

wherein sign () is a sign function;

the method for detecting the juicy peach sample to be graded by using the juicy peach quality grade classifier in the step (4) comprises the following specific steps:

(41) acquiring visible/near infrared spectrum data of the juicy peaches to be detected;

(42) preprocessing the acquired visible/near infrared spectrum data and extracting features by using a PCA method;

(43) inputting the extracted feature data into step (3)Is/are as follows

Obtaining in a honey peach quality grade classifier

Determining a result;

(44) statistics by voting mechanism

And (4) judging the result, and taking the grade which accounts for the most in all the results as the quality grade of the juicy peaches to be detected.

The invention has the beneficial effects that:

1) analyzing the appearance quality information of the juicy peaches by using a visible/near infrared spectrum technology, and having the advantages of no use of any chemical reagent, no environmental pollution, no damage to the juicy peaches and no influence on the sale of the juicy peaches;

2) quality grade identification is carried out on the visible/near infrared spectrum data of the juicy peaches by using an anti-noise support vector machine based on boundary points, the identification process is not more than 0.1 second, the identification speed is high, and the method is suitable for the field of large-scale standardized production;

3) the designed anti-noise support vector machine based on the boundary points maps the sample set to a high-dimensional feature space by using a kernel technology, noise samples are effectively removed by identifying the geometric outline of the sample set in the feature space, and a quantile loss function insensitive to noise is used in the selection of the loss function in the support vector machine, so that the accuracy and the anti-noise performance of the honey peach quality grading method are improved.

Drawings

FIG. 1 is a general flow chart of the classification of honey peach quality of an anti-noise support vector machine based on boundary points of the present invention;

FIG. 2 is a graph of the visible/near infrared spectra of honey peaches in the examples;

fig. 3 is a flow chart illustrating the construction of the quality grade classifier for peaches in step (3) of fig. 1.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings 1 in conjunction with the specific examples.

In this embodiment, Yanghu honey peaches produced in Changzhou city of Jiangsu province are selected as research objects, and 490 Yanghu honey peaches of different quality grades are collected according to 8 quality indexes of single fruit weight, fruit shape index, skin color, meat quality, sugar content, soluble solid content, titratable acid and edibility, wherein 80 super-grade peaches, 150 first-grade peaches, 160 second-grade peaches and 100 third-grade peaches are collected.

(1) Collecting the visible/near infrared spectrum data of n juicy peaches with different quality grades, and performing feature extraction on the visible/near infrared spectrum data by adopting a PCA method to obtain visible/near infrared spectrum sample sets of the juicy peaches with different quality grades, wherein the method comprises the following specific steps:

(11) collecting visible/near infrared spectrum data of 490 juicy peaches in total from 4 different quality grades: in this embodiment, a Handheld field spec spectrometer from american application testing device (asd) is used, the Spectral measurement range is 325-1075 nm, the sampling interval is 1.5nm, the resolution is 3.5nm, the probe field angle is 20 degrees, the light source adopts a 14.5V halogen lamp, the Spectral data is sampled after the Spectral scanning is stable, each honey peach is scanned 50 times along the equatorial track, the visible/near infrared Spectral data of the honey peach and the quality grade information thereof are stored, and the average value thereof is taken as the final experimental data, wherein the visible/near infrared Spectral curves of the honey peach with 4 different quality grades are shown in fig. 2.

(12) Preprocessing the acquired visible/near infrared spectrum data of the juicy peaches: in order to eliminate or reduce the interference of high frequency random noise, baseline drift, light scattering and the like on the spectral data, in the embodiment, the spectral data of the 350-1050nm waveband is intercepted and analyzed, and the ASD View SpecPro, UnscambleBlerV 9.6 and DPS (data processing system for reactive stability) are adopted as analysis software. Firstly, adopting a Savitzky-Golay convolution smoothing method, selecting a smoothing point number as 3, carrying out SNV (standard normal variance) processing, and then using a first-order Savitzky-Golay derivative method for processing, wherein a derivative interval is 3. And finally, processing the spectral data by using vector normalization.

(13) Under the condition that the accumulated credibility of the main components is more than or equal to 99.95%, the dimensionality reduction treatment is carried out on 490 juicy peach visible/near infrared spectrum samples by adopting the PCA method, the number of the obtained main factors is 8, and therefore the dimensionality of the juicy peach visible/near infrared spectrum data is reduced to 8 dimensions.

(2) Combining the visible/near infrared spectrum sample sets of the juicy peaches with different quality grades pairwise to establish

The method comprises the following steps of:

in this embodiment, there are 4 kinds of quality grades of honey peaches, and the combination of each two honey peaches is 6 combinations: constructing 6 groups of visible/near infrared spectrum training sets of the juicy peaches according to the 6 combinations, wherein each group of training sets consists of corresponding visible/near infrared spectrum samples of the juicy peaches with two quality grades;

(3) mixing the above

Inputting the visible/near infrared spectrum training set of the juicy peaches into an anti-noise support vector machine based on boundary points for training to obtain

The honey peach quality grade classifier comprises the following specific steps:

(31) juicy peach visible/near infrared spectrum sample set X with two quality grades in each juicy peach visible/near infrared spectrum training set₁And X₂The sphere center c of the minimum sphere space closed region of the two types of sample sets under the feature space is obtained by respectively using a support vector field description (SVDD) algorithm₁And c₂And a set of sample points Z distributed over two spherical closed-space regions₁And Z₂And is provided with X₁And X₂In a feature spaceSet of boundary points B₁And B₂Is Z as an initial value₁And Z₂Wherein, the expression of the SVDD algorithm is as follows:

in the formula (1)

A certain quality grade honey peach visible/near infrared spectrum sample set, c is the sphere center of the minimum spherical space closed region under the characteristic space, r is the sphere radius of the minimum spherical space, x_iIs the visible/near infrared spectrum sample of the ith juicy peach,

represents the mapping function of the sample from the original space to the kernel space, the mapping function used by phi () in this embodiment is a gaussian kernel function, and the value range of the kernel parameter is {0.001,0.1,0.1,1,10,100,1000 };

(32) at X₁Each sample x is calculated_1,iTo the center of the sphere c₁Euclidean distance d in feature space_i：

d_i＝||φ(x_1,i)-φ(c₁)||²， (2)

Wherein x_1,iSatisfy x_1,i∈X₁And is

Phi () represents the mapping function of the samples from the original space to the kernel space;

at X₂Calculate each sample x_2,iTo the center of the sphere c₂Euclidean distance e in feature space_iWherein x is_2,iSatisfy x_2,i∈X₂And is

e_i＝||φ(x_2,i)-φ(c₂)||²； (3)

(33) According to d_iValue descending order X₁Sample x in (1)_1,iWherein x is_1,iSatisfy x_1,i∈X₁And is

Sequentially mixing the samples x_1,iSolving the vector mu by substituting formula (4), and then calculating x_1,iUpdating the set of boundary points B together with the obtained μ values for formula (5)₁This process continues through traversal X₁All of the samples in (1) that satisfy the condition,

wherein | B₁I represents B₁The number of the middle samples, the threshold value is a normal number;

according to e_iValue descending order X₂Sample x in (1)_2,iWherein x is_2,iSatisfy x_2,i∈X₂And is

Sequentially mixing the samples x_2,iSolving for vector λ by substituting equation (6), and then calculating x_iUpdating the set of boundary points B together with the resulting lambda value substituting equation (7)₂This process continues through traversal X₂All of the samples in (1) that satisfy the condition,

(34) calculating X₁Middle set of boundary points B₁Weight α of each boundary point in_i：

Calculating X₂Middle set of boundary points B₂Weight β of each boundary point in_i：

(35) B is to be₁Lumped weight α_iRemoving samples with square value smaller than threshold value₁Collecting:

wherein

The threshold value is a normal number of times,

b is to be₂Lumped weight β_iRemoving samples with square value smaller than threshold value₂Collecting:

wherein

(36) B obtained in the step (35)₁And B₂The pooled samples are substituted into a median loss function support vector machine along with their class labels:

wherein x_iIs B₁And B₂Union of (A) and (B)₁∪B₂W is a weight vector, the penalty parameter C obtains an optimum value by optimizing in a grid {0.001,0.1,0.1,1,10,100,1000},

is a relaxation vector, y_i∈ { +1, -1} respectively represents the class labels of two classes of quality grade honey peach visible/near infrared spectrum samples, b is the offset of the classification hyperplane, and tau is one in [0,1 { ]]The real number between, in this embodiment, τ is 0.05, Φ (x)_i) The middle mapping function uses a Gaussian kernel function, and uses the same kernel parameters as the formula (1);

introducing a Lagrangian function, the above equation can be converted into a quadratic programming form as follows:

wherein γ and ν are lagrangian coefficients;

obtaining w and b optimal solution w by solving equations (12) and (13)^*，b^*Obtaining a juicy peach quality grade classifier of the anti-noise support vector machine based on the boundary points:

f(x)＝sign(1-|w^*·φ(x)+b^*|)， (14)

wherein sign () is a sign function;

(4) as shown in fig. 3, the method for detecting the juicy peach sample to be classified and detected by using the juicy peach quality classification classifier comprises the following specific steps:

(41) in the embodiment, 30 samples of each of the super-grade, first-grade, second-grade and third-grade honey peaches are collected again, and visible/near infrared spectrum data of the honey peaches to be detected in a grading manner are obtained by using a visible/near infrared spectrometer;

(42) preprocessing the acquired visible/near infrared spectrum data, and performing feature extraction on the visible/near infrared spectrum data by using a PCA (principal component analysis) method to obtain 8-dimensional feature data;

(43) respectively inputting the obtained 8-dimensional feature vectors into the 6 juicy peach quality grade classifiers obtained in the step (3) to obtain 6 detection results,

for example: when the feature data of a super juicy peach is input into the super classifier and the first classifier, the detection result is as follows: a special grade; when the test result is input into the super classifier and the second classifier, the test result is as follows: a special grade; when the detection result is input into the super classifier and the third classifier, the detection result is as follows: a special grade; when the signals are input into the first-stage classifier and the second-stage classifier, the detection result is as follows: a first stage; when the signals are input into the first-level classifier and the third-level classifier, the detection results are as follows: a first stage; when the detection result is input into the second-level classifier and the third-level classifier, the detection result is as follows: second-stage;

(44) and counting 6 detection results by adopting a voting mechanism, wherein the highest occupation rate of the special grade in the 6 results is the grade, and the quality grade of the juicy peaches is judged to be the special grade.

The determination accuracy (%) of this example is shown in table 2, and the results of the method of the present invention were compared with those obtained by using a hinge loss function support vector machine (L1-SVM), a partial least squares method, and a three-layer BP neural network method, and the experimental platform was MATLAB 2009 (a).

Table 2: the method, the support vector machine, the partial least square method and the three-layer BP neural network of the invention have the classification judgment accuracy rate (%)

Quality grade of honey peach	The method of the invention	Support vector machine	Partial least squares method	Three-layer BP neural network
					Special class	100	96.67	93.33	93.33
First stage	100	93.33	90.00	93.33
					Second stage	100	90.00	86.76	93.33
Three-stage	100	90.00	86.76	90.00

The above examples are intended to be illustrative of the present invention and are not to be construed as limiting the invention. Those skilled in the art can make various other modifications and alterations without departing from the spirit of the invention in light of the teachings of the present disclosure, and such modifications and alterations are intended to be included within the scope of the invention.

Claims (1)

1. A juicy peach quality grading method based on an anti-noise support vector machine of boundary points is characterized by comprising the following steps:

(1) collecting n visible/near infrared spectrum data of the juicy peaches with different quality grades, and preprocessing the visible/near infrared spectrum data and extracting PCA (principal component analysis) characteristics to obtain visible/near infrared spectrum sample sets of the juicy peaches with different quality grades;

(2) combining the visible/near infrared spectrum sample sets of the juicy peaches with different quality grades pairwise to establish

Grouping a visible/near infrared spectrum training set of the juicy peaches;

(3) mixing the above

Inputting the visible/near infrared spectrum training set of the juicy peaches into an anti-noise support vector machine based on boundary points for training to obtain

A honey peach quality grade classifier;

(4) detecting a honey peach sample to be graded by using a honey peach quality grade classifier;

the construction steps of the juicy peach quality grade classifier in the step (3) are as follows:

(31) juicy peach visible/near infrared spectrum sample set X with two quality grades in each group of juicy peach visible/near infrared spectrum training set₁And X₂The sphere center c of the minimum sphere space closed region of the two types of sample sets under the feature space is obtained by respectively using a support vector field description (SVDD) algorithm₁And c₂And a set of sample points Z distributed over two minimum spherical spatial closed regions₁And Z₂And is provided with X₁And X₂Set of boundary points B in feature space₁And B₂Are each Z₁And Z₂；

(32) At X₁Each sample x is calculated_1,iTo the center of the sphere c₁Euclidean distance d in feature space_i：

d_i＝||φ(x_1,i)-φ(c₁)||²， (1)

Wherein x_1,iSatisfy x_1,i∈X₁And is

Phi () represents the mapping function of the samples from the original space to the kernel space,

at X₂Calculate each sample x_2,iTo the center of the sphere c₂Euclidean distance e in feature space_i：

e_i＝||φ(x_2,i)-φ(c₂)||²， (2)

Wherein x_2,iSatisfy x_2,i∈X₂And is

(33) According to d_iValue descending order X₁Sample x in (1)_1,iWherein x is_1,iSatisfy x_1,i∈X₁And is

Sequentially mixing the samples x_1,iSolving the vector mu by substituting formula (3), and then calculating x_1,iUpdating the set of boundary points B together with the obtained μ values for formula (4)₁This process continues through traversal X₁All of the samples in (1) that satisfy the condition,

wherein | B₁I represents B₁The number of the middle samples, the threshold value is a normal number;

according to e_iValue descending order X₂Sample x in (1)_2,iWherein x is_2,iSatisfy x_2,i∈X₂And is

Sequentially mixing the samples x_2,iSolving for vector λ by substituting equation (5), and then calculating x_2,iSubstituting the obtained lambda value into equation (6) to update the boundary point set B₂This process continues through traversal X₂All of the samples in (1) that satisfy the condition,

wherein | B₂I represents B₂The number of the middle samples;

(34) calculating X₁Middle set of boundary points B₁Weight α of each boundary point in_i：

Calculating X₂Middle set of boundary points B₂Weight β of each boundary point in_i：

(35) B is to be₁Lumped weight α_iRemoving samples with square value smaller than threshold value₁Collecting:

wherein

The threshold value is a normal number of times,

b is to be₂Lumped weight β_iRemoving samples with square value smaller than threshold value₂Collecting:

wherein

(36) B obtained in the step (35)₁And B₂Substituting the set with the class label of its sample into a median loss function support vector machine:

wherein w is a weight vector, C is a penalty parameter,

is a relaxation vector, y_i∈ { +1, -1} respectively represents the class labels of two classes of quality grade honey peach visible/near infrared spectrum samples, b is the offset of the classification hyperplane, and tau is one in [0,1 { ]]Real numbers in between;

introducing a Lagrangian function, the above equation can be converted into a quadratic programming form as follows:

wherein γ and ν are lagrangian coefficients;

solving the equations (11) and (12) to obtain the optimal solution w of w and b^*，b^*And obtaining a juicy peach quality grade classifier of the anti-noise support vector machine based on the boundary points:

f(x)＝sign(1-(w^*·φ(x)+b^*))， (13)

wherein sign () is a sign function;

the method for detecting the juicy peach sample to be graded by using the juicy peach quality grade classifier in the step (4) comprises the following specific steps:

(41) acquiring visible/near infrared spectrum data of the juicy peaches to be detected;

(42) preprocessing the acquired visible/near infrared spectrum data and extracting features by using a PCA method;

(43) inputting the extracted feature data into step (3)

Obtaining in a honey peach quality grade classifier

Determining a result;

(44) statistics by voting mechanism

And (4) judging the result, and taking the grade which accounts for the most in all the results as the quality grade of the juicy peaches to be detected.

Images