CN110929787B - Apple objective grading system based on image - Google Patents
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Abstract
The invention provides an image-based apple objective grading method, which comprises the following steps: disease feature extraction is carried out on a single fruit image shot by a camera so as to judge whether the single fruit belongs to an inedible category or not; and carrying out classification feature extraction on the single fruits of the edible categories, and grading the single fruits according to multi-feature indexes generated by the feature extraction results. The invention replaces manpower with computer vision, replaces a lot of labor force and avoids subjectivity and fatigue of manpower; the inedible apples are eliminated before grading work, and a more complete system is realized; the detection classification is carried out by adopting a plurality of characteristic indexes, and compared with the traditional single characteristic, the quality and the efficiency are improved.
Description
Technical Field
The invention relates to the technical field of image recognition and classification, in particular to an image-based apple objective grading system.
Background
With the development of the high and new technology, the combination of the high and new technology and the agricultural field is deeper and deeper, and plays an important promotion role in the development of agriculture. Machine vision technology was proposed as a representative of advanced technology in the 60 s of the 20 th century, and related research is also started in China by the 80 s. In recent years, the machine vision technology also has abundant results in the field of fine agriculture, but China is a large country of world fruit trees, has long cultivation history and abundant resources, has more than 50 fruits and dried fruits, and is one of the countries with earliest provenance and most varieties of world fruit trees.
Apples are fruits with the largest yield in China, the export amount is large, but the export price is low, and the apple quality is correctly classified as the technology for detecting and classifying apples after picking in China is behind and the competitive power in the international market is lacking, so that the apple quality is an important link in the commercialization treatment of apples. Traditional fruit classification relies on manual operation and judgement, and classification speed is low, the precision is low, has great subjectivity, and degree of automation is low, and the human cost also begins to rise, can not satisfy international requirements. Therefore, how to quickly, objectively and accurately classify apples becomes an important research point in the current fruit classification field.
Disclosure of Invention
According to the technical problems of low fruit grading efficiency, poor precision and serious missing detection phenomenon, the image-based apple objective grading system is provided, the defect types of unqualified apples can be detected before grading and eliminated, and then the apples are automatically graded according to the 'fresh apple quality grade requirement' issued by China and combining with various external characteristics of apples.
The invention adopts the following technical means:
an image-based apple objective grading method is characterized by comprising the following steps: disease feature extraction is carried out on a single fruit image shot by a camera so as to judge whether the single fruit belongs to an inedible category or not; and carrying out classification feature extraction on the single fruits of the edible categories, and grading the single fruits according to multi-feature indexes generated by the feature extraction results.
Further, before determining whether the single fruit belongs to the inedible category, further comprises: and calibrating the shooting position of the camera by using a standard reference object, wherein the calibrated shooting position is set to be the shooting distance with the smallest influence on the actual size of the single fruit.
Further, the classifying feature extraction of the single fruits of the edible category comprises extracting size features, fruit shape features, color features and defect features of the single fruits.
Further, the extracting the disease features of the single fruit image shot by the camera includes: and reading an input single-fruit image, converting the single-fruit image into an LAB color space from an RGB color space, classifying color features by using Kmeans clusters, marking each pixel in the image as a result of Kmeans, generating an image segmented according to colors, and obtaining a color space range containing diseases.
Further, the determining whether the single fruit belongs to the inedible category includes: carrying out disease region identification on the color space range containing the disease obtained after segmentation by utilizing a gray level co-occurrence matrix and a Gabor filter, and carrying out dimension reduction on the obtained characteristic parameters by adopting a principal component analysis method; the diseases are distinguished through the characteristic parameters of different diseases, and the characteristic parameters are input into an SVM classifier to generate a classification model.
Further, calculating the diameter of the longitudinal section of the single fruit by using a minimum circumcircle method to serve as an extracted size characteristic; calculating the ratio of the cross-sectional area of a sample of a single fruit to the minimum circumcircle area by using a minimum circumcircle method to obtain an extracted fruit shape characteristic; using the color degree of the red component extracted in the HSV space as the extracted color feature; the area of the defect region obtained by the morphological operation of the image processing is used as the extracted defect feature.
Further, the single fruit image shot by the camera comprises a single fruit image shot by the camera directly and single fruit angle images shot by the camera and reflected by the plane mirror.
Compared with the prior art, the invention has the following advantages:
the invention replaces manpower with computer vision, replaces a lot of labor force and avoids subjectivity and fatigue of manpower; according to the invention, inedible apples are eliminated before grading work, so that a complete system is realized; the invention adopts multiple characteristic indexes to carry out detection classification, and improves the quality and the efficiency compared with the traditional single characteristic; the invention simulates the operation on the actual production line, acquires the whole surface of the apple, and improves the accuracy; the invention classifies according to the standard of fresh apple quality grade requirement, and has more scientificity and basis.
Based on the reasons, the invention can be widely popularized in the field of fruit classification.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to the drawings without inventive effort to a person skilled in the art.
FIG. 1 is a flow chart of the grading method of the present invention.
FIG. 2 is a schematic diagram of a grading device according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
As shown in fig. 1, the invention provides an image-based apple objective grading method, which comprises the following steps: disease feature extraction is carried out on a single fruit image shot by a camera so as to judge whether the single fruit belongs to an inedible category or not; and carrying out classification feature extraction on the single fruits of the edible categories, and grading the single fruits according to multi-feature indexes generated by the feature extraction results.
Further, before determining whether the single fruit belongs to the inedible category, further comprises: and calibrating the shooting position of the camera by using a standard reference object, wherein the calibrated shooting position is set to be the shooting distance with the smallest influence on the actual size of the single fruit. Specifically, as one implementation mode of calibration, a USB camera is used to take a photograph of a single coin, and image processing is performed on the photograph: firstly, gray scale processing is carried out, then, after binarization processing, the pixel number occupied by the diameter of the coin is calculated, the actual diameter of the unitary coin is 25mm, and then the ratio of the actual diameter to the pixel number occupied by the diameter can be obtained:
wherein M is the actual diameter length of the coin; n is the number of pixels occupied by the diameter of the coin. Through multiple tests, the measurement of the diameter of the same apple under different heights is calculated, and the effect of the height on the actual size of an object is minimal when the height of the camera is 32 cm.
According to the 'apple and pear standards', once the surface of the apple has rot and insect damage, the apple is not included in the grading standard, so that a large number of rotten apple and insect damage apple data sets are manufactured, and the data sets are segmented by using a Kmeans algorithm, so that a disease area part is extracted. The method specifically comprises the following steps:
a) Reading an input single-effect image, converting the image into an LAB color space from RGB, classifying colors in an 'a' space and an 'b' space by using Kmeans clusters, marking each pixel in the image as a result of Kmeans, generating an image of dividing the image according to the colors, and selecting a color space range containing diseases.
The Kmeans algorithm uses the euclidean distance formula:
the distance between every two pairs of data objects can be calculated through a formula, and clustering is carried out according to the distance to the specified category number K. Taking k data randomly as class centers, and carrying out a class center iteration process:
wherein, center k Is a heart-like body; c (C) k Is the k-th class.
There are generally two conditions for the iteration to terminate: 1) Reaching the appointed iteration times; 2) The heart-like does not change any more.
b) And (3) carrying out disease region identification on the segmented disease region by utilizing a gray level co-occurrence matrix and a Gabor filter, and carrying out dimension reduction on the obtained characteristic parameters by adopting a principal component analysis method to remove redundant information. The diseases are distinguished through the characteristic parameters of different diseases, the characteristic parameters are input into an SVM classifier, and a training model is generated through multiple tests.
The texture features are analyzed by using a gray level co-occurrence matrix and a Gabor filter, wherein the gray level co-occurrence matrix reflects comprehensive information of image textures in the directions, adjacent intervals, variation amplitudes and speeds; the gray level co-occurrence matrix is obtained by counting the states that two pixel points of an image, which keep a certain fixed distance in a certain direction, respectively have a certain gray level.
The elements in the gray level co-occurrence matrix are represented by probability values, namely, the values of the elements are divided by the sum of the elements to obtain normalized values with the elements smaller than 1Namely:
wherein i and j respectively represent the gray scale of two pixels; delta is the spatial positional relationship between two pixels; s is the sum of elements in the gray level co-occurrence matrix.
The surface image texture characteristic value table is three measures, namely energy, entropy and contrast, obtained by an apple surface gray level image based on a gray level co-occurrence matrix:
wherein ASM, ENT, CON is the energy, entropy and contrast of the surface texture features, respectively; l represents the gray level of the image.
In order to acquire complete surface information, the invention additionally considers the direction parameter theta reflecting the spatial position relation and the distance parameter d reflecting the spatial position relation, calculates the gray level co-occurrence matrix under the conditions that theta=0 °, 45 °, 90 ° and 135 °, and d=1, and obtains the mean value and the variance of the obtained characteristic index as the characteristic vector.
At this time, the function of the two-dimensional Gabor transform is expressed as:
parameters affecting the Gabor filter include the center frequency f, the direction angle θ, and the bandwidth of the filter determined by σ. The Gabor filter in the invention adopts 40 Gabor filters composed of 5 center frequencies and 8 directions to process the image, and the sampling interval is set to be pi/8. The Gabor filters with different directions and scales are used for filtering the two-dimensional signals, so that the characteristics of the two-dimensional signals in the frequency domain space can be comprehensively reflected.
The principal component analysis method described above uses a new set of low-dimensional eigenvectors to represent the original samples as accurately as possible, i.e., uses m-dimensional eigenvectors q= [ Q1, Q2,..qm ] T to replace the original n-dimensional eigenvectors p= [ P1, P2,..pn ] T (m < n).
The main component analysis method is adopted to remove the correlation among samples and realize the compression of data, and the dimension reduction method is adopted in the invention to reduce the dimension of the Gabor feature vector of the image from 40 dimension to 10 dimension.
The classification method specifically comprises the following steps: libsvm is used for classification into 3 categories, respectively rot, insect injury and others. The input vectors respectively correspond to the texture feature vectors of the surface images, and training accuracy and class labels are output; training repeatedly by using enough samples to obtain a mode with proper classification; and inputting the texture feature vector of the apple surface image to be detected into the texture feature vector to obtain a final classification result.
Further, the classifying feature extraction of the single fruits of the edible category comprises extracting size features, fruit shape features, color features and defect features of the single fruits. Specifically:
when an apple sample is actually detected, as a preferred implementation mode of the invention, two LED illuminating lamps are placed at two sides of a camera in a closed space by using the device shown in fig. 2; four plane mirrors are symmetrically and obliquely placed so as to reflect the whole surface of the apple; several pictures of apples can be obtained, the pictures are segmented by a physical method, the whole surfaces of the apples can be detected, the pictures are segmented by Kmeans and input into a training model, and whether the apples are rotten fruits, insect-damaged fruits or other apples is judged. If the output is diseased apples, the apples are eliminated.
Further, extracting size characteristics, fruit shape characteristics, color characteristics and defect characteristics of the apples without diseases, and classifying the obtained characteristic standard reference 'fresh apple quality grade requirement' into special grade fruits, first grade fruits, second grade fruits, external fruits and the like.
The extraction size features are that the diameter of the longitudinal section of the apple is calculated by using a minimum circumcircle method, and the fruit shape index is expressed as follows:
wherein S0 is the area of the cross section of the apple sample; s1 is the area of the smallest circumscribed circle.
The color features are that the color of the HSV space is less affected by illumination, so that the color related features are selected to be researched under the HSV space, the H and S spaces are selected to extract the red component, and the glossiness is expressed as follows:
wherein S is the number of pixels on the apple surface; s2 is the pixel count of the red component.
The defect feature utilizes morphological operation of image processing to extract defect area of picture, firstly graying, secondly binarizing, extracting edge, filling it to obtain an edge, finally obtaining difference of two edges, namely defect edge, after filling it to obtain defect pixel number. The area of the defect is expressed as:
S d ＝N*k
wherein N is the number of defective pixels; k is the ratio of the actual size to the pixel size.
The image-based apple objective grading method disclosed by the invention replaces manpower by computer vision, replaces a large amount of labor force and avoids subjectivity and fatigue of manpower; the inedible apples are eliminated before grading work, and a more complete system is realized; the detection classification is carried out by adopting a plurality of characteristic indexes, so that the quality and the efficiency are improved compared with the traditional single characteristic; the operation on the actual production line is simulated, the whole surface of the apples is obtained, and the accuracy is improved; the fresh apples are classified according to the standard of the quality grade requirement of fresh apples, and are scientific and basic.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (4)
1. An image-based apple objective grading method is characterized by comprising the following steps:
calibrating the shooting position of the camera by using a standard reference object, wherein the calibrated shooting position is set to be the shooting distance with the least influence on the actual size of the single fruit;
disease feature extraction is carried out on a single fruit image shot by a camera so as to judge whether the single fruit belongs to an inedible category, and the method comprises the following steps:
reading an input single fruit image, converting the single fruit image into an LAB color space from an RGB color space, classifying color features by using Kmeans clusters, marking each pixel in the image as a Kmeans result, generating an image segmented by colors to obtain a color space range containing diseases, carrying out disease area identification on the segmented color space range containing the diseases by using a gray level symbiotic matrix and a Gabor filter, carrying out dimensionality reduction on the obtained feature parameters by using a principal component analysis method to distinguish diseases by using the feature parameters of different diseases, inputting the feature parameters into an SVM classifier to generate a classification model, carrying out statistics on the condition that two pixel points of the image with a certain fixed distance in a certain direction respectively have a certain gray level, obtaining elements in the gray level symbiotic matrix by using probability values, obtaining the probability values by dividing each element value by the sum of each element,
obtaining a surface image texture characteristic value through an apple surface gray level image based on a gray level co-occurrence matrix, wherein the surface image texture characteristic value comprises three measures of energy, entropy and contrast,
taking the direction parameter theta representing the spatial position relation and the distance parameter d representing the spatial position relation into consideration, calculating gray level co-occurrence matrix under the conditions that theta=0 °, 45 °, 90 ° and 135 ° and d=1, obtaining the mean value and variance of the obtained characteristic indexes as characteristic vectors,
disease region identification was performed based on the following two-dimensional Gabor filter:
wherein f is the center frequency, θ is the direction angle, σ is the bandwidth of the filter, x is the pixel abscissa, and y is the pixel ordinate;
reducing the Gabor feature vector dimension of the image from 40 to 10 by adopting a principal component analysis method;
and carrying out classification feature extraction on the single fruits of the edible categories, and grading the single fruits according to multi-feature indexes generated by the feature extraction results.
2. The image-based apple objective grading method according to claim 1, wherein the classifying feature extraction of the single fruits of the edible category comprises extracting size features, fruit shape features, color features and defect features of the single fruits.
3. The image-based apple objective grading method according to claim 2, wherein,
calculating the diameter of the longitudinal section of the single fruit by using a minimum circumcircle method to serve as an extracted size characteristic;
calculating the ratio of the cross-sectional area of a sample of a single fruit to the minimum circumcircle area by using a minimum circumcircle method to obtain an extracted fruit shape characteristic;
using the color degree of the red component extracted in the HSV space as the extracted color feature;
the area of the defect region obtained by the morphological operation of the image processing is used as the extracted defect feature.
4. The image-based apple objective grading method according to claim 1, wherein the single fruit images shot by the camera include single fruit images shot directly by the camera and single fruit angle images shot by the camera and reflected by the plane mirror.
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| CN110148122A (en) * | 2019-05-17 | 2019-08-20 | 南京东奇智能制造研究院有限公司 | Apple presentation quality stage division based on deep learning |
| CN110276386A (en) * | 2019-06-11 | 2019-09-24 | 济南大学 | A kind of apple grading method and system based on machine vision |
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| WO2012039597A2 (en) * | 2010-09-23 | 2012-03-29 | Universiti Putra Malaysia | Fruit ripeness grading system |
| CN110148122A (en) * | 2019-05-17 | 2019-08-20 | 南京东奇智能制造研究院有限公司 | Apple presentation quality stage division based on deep learning |
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