CN104568639B - A kind of determination method and apparatus of sugar degree - Google Patents
A kind of determination method and apparatus of sugar degree Download PDFInfo
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- CN104568639B CN104568639B CN201310472798.5A CN201310472798A CN104568639B CN 104568639 B CN104568639 B CN 104568639B CN 201310472798 A CN201310472798 A CN 201310472798A CN 104568639 B CN104568639 B CN 104568639B
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- 238000000034 method Methods 0.000 title claims abstract description 25
- 235000013399 edible fruits Nutrition 0.000 claims abstract description 46
- 241000207199 Citrus Species 0.000 claims description 74
- 235000020971 citrus fruits Nutrition 0.000 claims description 73
- 238000004040 coloring Methods 0.000 claims description 8
- 238000012360 testing method Methods 0.000 claims description 8
- 230000008569 process Effects 0.000 claims description 4
- 241001672694 Citrus reticulata Species 0.000 description 7
- 244000269722 Thea sinensis Species 0.000 description 5
- 235000009508 confectionery Nutrition 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 4
- 239000008103 glucose Substances 0.000 description 4
- WQZGKKKJIJFFOK-GASJEMHNSA-N Glucose Natural products OC[C@H]1OC(O)[C@H](O)[C@@H](O)[C@@H]1O WQZGKKKJIJFFOK-GASJEMHNSA-N 0.000 description 3
- 230000007547 defect Effects 0.000 description 3
- 238000001514 detection method Methods 0.000 description 3
- 230000011218 segmentation Effects 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 240000002319 Citrus sinensis Species 0.000 description 1
- 235000005976 Citrus sinensis Nutrition 0.000 description 1
- 241000032242 Festuca myuros Species 0.000 description 1
- 229930006000 Sucrose Natural products 0.000 description 1
- CZMRCDWAGMRECN-UGDNZRGBSA-N Sucrose Chemical compound O[C@H]1[C@H](O)[C@@H](CO)O[C@@]1(CO)O[C@@H]1[C@H](O)[C@@H](O)[C@H](O)[C@@H](CO)O1 CZMRCDWAGMRECN-UGDNZRGBSA-N 0.000 description 1
- 241000607479 Yersinia pestis Species 0.000 description 1
- 238000010521 absorption reaction Methods 0.000 description 1
- GZCGUPFRVQAUEE-SLPGGIOYSA-N aldehydo-D-glucose Chemical compound OC[C@@H](O)[C@@H](O)[C@H](O)[C@@H](O)C=O GZCGUPFRVQAUEE-SLPGGIOYSA-N 0.000 description 1
- 239000007864 aqueous solution Substances 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 239000003086 colorant Substances 0.000 description 1
- 201000010099 disease Diseases 0.000 description 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 235000015203 fruit juice Nutrition 0.000 description 1
- 238000003703 image analysis method Methods 0.000 description 1
- 238000003709 image segmentation Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 230000000877 morphologic effect Effects 0.000 description 1
- 239000002420 orchard Substances 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 230000035807 sensation Effects 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 239000000243 solution Substances 0.000 description 1
- 241000894007 species Species 0.000 description 1
- 238000010186 staining Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 239000005720 sucrose Substances 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
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- Image Processing (AREA)
- Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The embodiment provides a kind of determination method and apparatus of sugar degree, it is possible to achieve non invasive estimation sugar degree.This method includes：Obtain the coloured image and weight of fruit；Pol parameter is obtained from the coloured image of fruit；Sugar degree is determined using pol model according to pol parameter and weight.Described device includes：Information acquisition device, coloured image and weight for obtaining fruit；Computing unit, for obtaining pol parameter from the coloured image of fruit；Determining unit, for determining sugar degree using pol model according to pol parameter and weight.It according to the present invention, can achieve that the pol of fruit is determined by ordinary camera and computer, therefore, the present invention, which need not increase extras, can estimate sugar degree, be extremely suitable for the fruit grading work of small business.
Description
Technical field
Determine technology the present invention relates to a kind of fruit quality, more particularly to a kind of sugar degree determination method and apparatus.
Background technology
Its price of the fruit of different qualities differs greatly, and fruit quality is classified the attention for being constantly subjected to orchard worker and businessman.With
The sampling check of many article qualities is different, and fruit grading requirement is accomplished to be classified each fruit.Due to fruit quality
Quality often come out with external morphologic appearance, this infers that its internal information is provided for people by fruit external morphology can
Energy.Simplification, the cheap worked with digital image acquisition, has objectively promoted the fruit quality based on image to study,
And it is desirable that realize the automation of fruit quality detection.In such research of fruit quality, because citrus distribution is relatively wide, production
The focus of attention that great achievement is people is measured, fruit quality is studied by taking citrus as an example below.
Research to citrus image is concentrated mainly on two aspects：On the one hand it is to fruit shape, degree of staining, pericarp fold, life
Manage the research of the external sort features such as defect, pest and disease damage；On the other hand it is to pol, acidity, hardness, pulp quality, fruit juice amount
Deng the research of inside quality feature.External sort feature is due to being external, visual, and this to be engaged in research people in this respect
Member external sort can be identified by machine, in practice it has proved that, discrimination is generally also higher.Such as Fernando uses changeable
Measure image analysis method and pericarp defect automatic detection is carried out to the orange from 4 different cultivars, defects detection success rate is
91.5%, failure modes rate reaches 94.2%.And inside quality feature is due to being wrapped in inside pericarp, based in external image
Component matter judges, is assuming that citrus external morphology architectural feature with inside quality parameter there is certain to be related under the premise of this
Row judges, there is certain difficulty.But, due to its lossless characteristic, inside can be found out in the case where not destroying fruit
Information, therefore, by the most attention of people.
In many inside quality parameters of citrus, pol is very important index parameter, is also the important of maturity
One of mark.Pol refers to the sucrose grams dissolved in every 100 grams of aqueous solution in the case of 20 DEG C, the higher sensation given people of pol
More sweet tea.Although sugariness varies with each individual, the too small citrus of pol under normal circumstances, its mouthfeel will not typically allow most people to be expired
Meaning.Because higher pol can more win public praise and the desire to buy of consumer, therefore, generated really in citrus operation cultivation
Protect the lower cultivation rattail fescue sward of sunshine time, tree, do not turn over some specific aim measures that protection tree root etc. is intended to improve citrus pol,
Also deeply concerned degree of the people to citrus pol as can be seen here.
The content of the invention
The embodiment provides a kind of determination method and apparatus of sugar degree, citrus can be obtained with quick nondestructive
Pol content.
The invention provides a kind of determination method of sugar degree, including：
Obtain the coloured image and weight of fruit；
Pol parameter is obtained from the coloured image of fruit；
Sugar degree is determined using pol model according to pol parameter and weight.
The pol parameter includes：Green component average x1, tone average x2, red green two colouring component average x3。
The pol model is any one in following three formula：
Wherein,
y：Pol
x1：Green component average brightness
x2：Tone average value
x3：The average brightness of red and green component
x4：Weight
k1、k2：Dummy variable
a0、a1、a2、a3、b0、b1、b2：Treat scaling parameter；
The undetermined parameter is obtained by testing.
Before the coloured image step of the acquisition fruit, the background of fruit is set to uniform background.
Before sugar degree step is determined using pol model according to pol parameter and weight, methods described also includes treating
The calibration process of scaling parameter, to be determined to pol estimation models parameter.
Present invention also offers a kind of determining device of sugar degree, described device includes：
Information acquisition device, coloured image and weight for obtaining fruit；
Computing unit, for obtaining pol parameter from the coloured image of fruit；
Determining unit, for determining sugar degree using pol model according to pol parameter and weight.
According to the present invention, it can achieve that the pol of fruit is determined by ordinary camera and computer, therefore, the present invention
Extras, which need not be increased, can estimate sugar degree, and the technology of the present invention is extremely suitable for the fruit grading work of small business.
Brief description of the drawings
Fig. 1 shows the flow of the determination sugar degree of the embodiment of the present invention；
Fig. 2 shows the flow of the calculating pol parameter of the embodiment of the present invention；
Fig. 3 shows the flow of the fruit grading of the embodiment of the present invention.
Embodiment
Understand for the ease of persons skilled in the art and realize the present invention, describe the implementation of the present invention in conjunction with accompanying drawing
Example.
Embodiment one
The citrus of different pol contents, in growth course, absorption, reflection to different-waveband electromagnetic wave have differences,
So that the coloring of its outward appearance is also different, so as to form different colors in fruit appearance.Therefore, can by coloured image come
Find out citrus pol content and carry out lossless classification.
As shown in figure 1, present embodiments providing a kind of determination method of sugar degree, comprise the following steps：
Step 11, the coloured image and weight for obtaining fruit.
Coloured image is obtained by camera, and weight can be obtained by weight sensor or electronic scale.
Step 12, pol parameter is obtained from the coloured image of fruit；
Step 13, sugar degree using pol model determined according to pol parameter and weight
In step 12, in order to obtain pol parameter from the coloured image of fruit, first from the background of coloured image
Prospect citrus image is partitioned into, pol parameter is then obtained.According to substantial amounts of result of the test, the pol parameter includes：Green
Component average x1, tone average x2, red green two colouring component average x3And citrus weight x4。
In order to more easily be partitioned into foreground image from the background of coloured image（That is, citrus image）, according to the present invention
Embodiment, it may be preferable that use uniform background in photography, such as color is single and reflective less black background, so can
Prospect is showed to greatest extent.
Because prospect has differences with background image, can by analyze the correlation of prospect and background two parts image come
Judge the relationship of the two, and then realization is partitioned into foreground image from the background of coloured image.
If x=(x1x2…xn) ', y=(y1y2…yn) ' it is stochastic variable, their variance is respectively Var（x）、Var
（y）, covariance is Cov（x,y）, then
Illustrate vector x, y degree of correlation, statistically, commonly referred to as R2To determine index.This concept is incorporated into mandarin orange
In the segmentation of tangerine image, and then realize the separation of citrus foreground and background.Obtain after citrus foreground image, so that it may according to foreground image
Determine pol parameter：Green component, red yellow color component, tone, citrus weight with green syt.As shown in Fig. 2 being below
Solve the arthmetic statement of pol parameter.
Step 21, citrus picture centre is clicked citrus image-region as target point or left mouse button provide target
Point.Aiming spot requires not strict, as long as being exactly being photographed near citrus centre position when absorbing image
Say, image center is citrus internal image；
Step 22, centered on target point, all pixels in 3 × 3 masks are taken, in this, as the mesh that will be compared
Mark region；
Step 23, pointer are moved to the original position of image（0,0）, using original position as current point, covered according to 3 × 3
Film size, traversing graph picture；
Step 24, basis（1）Formula, calculates the determination index in 3 × 3 regions and target area centered on current point, and
This value is compared with certain determination index threshold, if the value is more than threshold value, current pixel is judged as prospect i.e. citrus image,
Otherwise, the pixel is set to background.Due to determining that index threshold and image and target point selection position have relation, therefore it is not
One fixed numerical value, but easily determined by trial and error method according to specific image.From our tests to a large amount of citrus images
As a result see, determine index R2Typically preferable result can be obtained between 0.25~0.50.Further, since citrus is put during photography
In middle position, the image periphery taken the photograph has very big background, therefore, when making actual image segmentation, image most
The boundary pixel of periphery is all processed into background pixel.
Step 25, according to order pointer from top to bottom, from left to right next pixel is moved to, judges whether it is most after image
Element, if not step 24 is transferred to, otherwise performs step 26；
Step 26, extraction foreground part simultaneously calculate 3 pol parameters by foreground image：Green component, tone, it is red with
The yellow color component of green syt, above-mentioned component can represent with average, i.e.,：Green component average (x1), tone average (x2), it is red green
Average (the x of two colouring components3)。
After the pol parameter for trying to achieve all citruses, it is to complete the calibration of pol model then to solve the parameter in pol model
Work, pol is determined finally according to pol model.Pol model can select any one in following (2)-(4) three formula：
Wherein,
y：Pol
x1：Green component average brightness
x2：Tone average value
x3：The average brightness of red and green component
x4：Citrus weight
k1、k2：Dummy variable
a0、a1、a2、a3、b0、b1、b2：Treat scaling parameter；
According to document, pol has certain relation with citrus size, therefore the present invention is introduced when estimating pol
Dummy variable, is divided into several grades by citrus size, for more accurately estimating citrus pol.The specific dummy variable root
Determined according to different pols situation of different sizes, refer to and describe below.
Following features are found according to the outward appearance of citrus：(i) in transformable interval, the bigger citrus of green component values is more not
Sweet tea, can be interpreted as roughly " more green more sweetless "；(ii) yellow color component shows identical trend, i.e. x with green component3More
Big pol is lower, therefore forecast model（2）In parameter a3<0；(iii) tone H constant interval is more between 0.36~0.71, is
The transition of blood orange to yellow is interval, and H increases to yellow convergence, and H reduces to orange red convergence, that is, citrus more tends to be orange red
Sugar colour degree is higher；(iv) the more big then pol of the area percentage that citrus image is occupied is lower, i.e., citrus is more big more sweetless.It can see
Arrive, these conclusions are compared with our experience coincide.
The factors such as citrus pol and species, the place of production are related, if directly using mould in following examples one or embodiment two
Type, which carries out the estimation of citrus pol, can produce larger error, therefore, be carried out using the present invention before pol is determined, it is necessary to extract representative
Property citrus determine treat scaling parameter a0、a1、a2、a3、b0、b1、b2, and according to determination after the pol estimation models after scaling parameter
Carry out glucose prediction.
The picture weight forecast model calibration of citrus pol
Scaling parameter a is treated in order to determine pol prediction model0、a1、a2、a3、b0、b1、b2, it is necessary first to obtain some mandarin oranges
The image and weight of tangerine simultaneously survey pol, are extracted using the method for the present invention after pol parameter and weight, using statistical method
Model parameter, and then the practical application of implementation model are just estimated that, the process of this determination model parameter is exactly to calibrate, it is fixed
Timestamp extracts quantitative requirement >=60 of citrus.As an example, we take citrus 60, are individually placed to graph under fixed tripod
Picture, then weighs, measures pol.The camera type used of photographing is Canon EOS Kiss Digital X, image resolution ratio
3888 × 2592, ISO speed is 800, shutter speed 1/50s, lens aperture F/8.Saccharometer is Atago Portable digital pols
Meter, sugar concentration measurement scope（Brix%）In 0~53, sugar concentration measurement precision（Brix%）0.2nd, minimum scale 0.1.This sample is measured
Pol distribution between 9-18%.
60 citruses are randomly divided into 2 groups, one group 45 another group 15, according to step 21-26 algorithm, calculation is asked respectively
The pol parameter of each citrus, then with one group of calibration model parameter of 45, the group of 15 is used for the quality of testing model.
Pol is divided into y ＜ 11,11≤y≤14, the three-levels of y ＞ 14, and provides corresponding k1And k2Respectivelyk1=1, k2= 0、k1=0, k2=1、k1=1, k2=1.Possesses the total data yx ' of citrus glucose prediction model parameter solutioni=(yi x1i x2i x3i
x4i k1i k1i) after, it is possible to calculate（2）~（4）Middle model parameter, naturally it is also possible to using existing statistical software such as
The solving model parameter such as SAS, SPSS.And detected with other data, 45 groups of the present embodiment models fitting data（i=1,
2,…,45）, 15 groups of model testing data, comprehensive 2 groups of data test results obtain model（2）Precision highest, determine index
For 0.949, it can be used for the prediction classification of citrus pol.Forecast model is as follows：
Corresponding dummy variable estimates model：
Now, pol classification process is：
First according to formula（6）Calculate y0, it is then based on y0K is determined according to the pol rank of setting1、k2, finally by（5）In advance
Estimate pol and be classified.
The present embodiment also discloses a kind of method of fruit quality classification.It includes foregoing pol and determines method, and root
Determine that pol is classified to fruit quality determined by method according to pol.As shown in figure 3, stage division is as follows：
Step 31, determine dummy variable k1、k2.Forecast model based on dummy variable generally comprises a pair of models, wherein one
Individual is the model without dummy variable, for determining dummy variable.To determine dummy variable, first according to no dummy variable
Model calculates pol y0,
If y0＜ T1, then k1=1,k2=0
If T1≤y0≤T2, then k1=0,k2=1
If y0＞ T2, then k1=1,k2=1
Wherein, T1、T2It is the pol rank boundary set according to specific citrus, if citrus pol value is between certain two number
" sweet tea " is defined as between value, then the lower limit of the two numerical value and the upper limit are exactly T1、T2。
Step 32, calculating citrus pol y to be estimated.The image parameter x of acquisition1~x4And just calculate obtain virtual
Variable k1、k2Glucose prediction model is substituted into, calculating obtains y.
Step 33, pol classification.Citrus is divided into according to the pol grade scale of setting by several classes.Due to identical pol each one
Impression can be variant, therefore the standard does not have strict regulation, such as following grade III Standard：
If y ＜ 11, " sweetless "
If 11≤y≤14, " sweet tea "
If y ＞ 14, " very sweet tea "
After being classified by above-mentioned pol, the key factor of pol as quality can be subjected to quality grading, and root to citrus
Citrus is dispensed according to quality grading result.That is, the citrus of different pol ranks is cased respectively, marks different prices.
Embodiment two
A kind of determining device of sugar degree is present embodiments provided, the device includes：
Information acquisition device, coloured image and weight for obtaining fruit；Image acquiring device can be camera and appoint
What equipment with taking pictures, weight weight sensor or electronic scale are obtained；Computing unit, for the coloured image from fruit
Middle acquisition pol parameter；Determining unit, for determining sugar degree using pol model according to pol parameter and weight.
The operation principle of the unit of the present embodiment can be found in the description of embodiment one.
According to the present invention, it can achieve that the pol of fruit is determined by ordinary camera and computer, therefore, the present invention
Extras, which need not be increased, can estimate sugar degree, and the technology of the present invention is extremely suitable for the fruit grading work of small business.
We have used citrus image to account for the pixel hundred of whole image in the citrus pol estimation based on pure graphical method
Divide than this pol parameter, because this is an amount relevant with camera positions, for the sake of convenient application, we put camera
In on photographic car, this ensure that mobile property freely.
Citrus image accounts for why whole image percentage influences pol, it may be possible to which citrus, which varies in weight, to be caused, because mandarin orange
Tangerine image percentage is one of citrus volume to be embodied indirectly, and citrus volume and weight is height correlation, therefore, can
To speculate, citrus weight exists with pol to be contacted.Actual result shows that replacing citrus image with citrus weight accounts for whole image hundred
Estimate accuracy point than after is higher, and it is that the replacement of citrus weight this supposition is that this, which shows that citrus image accounts for whole image percentage,
Correctly.Why had differences both when estimating pol, probably due to：It is only that citrus is placed under camera lens when (i) photographing
Rather than under camera lens on certain fixing point, cause citrus to make citrus image size to the different reasons such as lens distortion in addition of distance of camera lens
Change；(ii) the difference of citrus composition；(iii) the reasons such as error are extracted in segmentation.
If we measure weight, it is not necessary to calculate the percentage that citrus image accounts for whole image again, this way
Advantage is that the operation of acquisition citrus image will be more flexible.The present embodiment is the mandarin orange in the citrus pol estimation pure graphical method
Tangerine image accounts for the pixel percentage of whole image this pol parameter and is substituted for citrus weight, glucose prediction model form and other
Step is identical with pure graphical method, except that being required to measure citrus weight when calibration and prediction.
Due to, as an independent variable, reducing photo distance, segmentation directly using citrus weight and extracting the influence of error etc.,
Precision of prediction should be higher.
Although depicting the present invention by embodiment, it will be appreciated by the skilled addressee that not departing from the present invention's
In the case of spirit and essence, so that it may the present invention is had many deformations and change, the scope of the present invention is by appended claim
To limit.
Claims (4)
1. a kind of determination method of sugar degree, it is characterised in that including：
The coloured image and weight of fruit are obtained, the fruit is citrus；
Pol parameter is obtained from the coloured image of fruit, the pol parameter includes：Green component average x1, tone average x2、
The average x of red green two colouring component3；
Sugar degree is determined using pol model according to pol parameter and weight；
The pol model is any one in following three formula：
Wherein,
y：Pol
x1：Green component average
x2：Tone average
x3：The average of red green two colouring component
x4：Weight
k1、k2：Dummy variable
a0、a1、a2、a3、b0、b1、b2：Undetermined parameter；
The undetermined parameter is obtained by testing.
2. according to the method described in claim 1, it is characterised in that, will before the coloured image step of the acquisition fruit
The background of fruit is set to uniform background.
3. according to the method described in claim 1, it is characterised in that determined according to pol parameter and weight using pol model
Before sugar degree step, methods described also includes the calibration process for treating scaling parameter, to determine pol estimation models parameter.
4. a kind of determining device of sugar degree, it is characterised in that described device includes：
Information acquisition device, coloured image and weight for obtaining fruit, the fruit are citrus；
Computing unit, for obtaining pol parameter from the coloured image of fruit, the pol parameter includes：Green component average
x1, tone average x2, red green two colouring component average x3；
Determining unit, for determining sugar degree using pol model according to pol parameter and weight；
The pol model is any one in following three formula：
Wherein,
y：Pol
x1：Green component average
x2：Tone average
x3：The average of red green two colouring component
x4：Weight
k1、k2：Dummy variable
a0、a1、a2、a3、b0、b1、b2：Undetermined parameter；
The undetermined parameter is obtained by testing.
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| CN110826552A (en) * | 2019-11-05 | 2020-02-21 | 华中农业大学 | Grape nondestructive automatic detection device and method based on deep learning |
| CN111551499B (en) * | 2020-04-28 | 2023-07-04 | 中国农业科学院农业信息研究所 | Method and device for measuring sugar content of fruit, computer equipment and storage medium |
| CN113426693B (en) * | 2021-07-26 | 2022-05-31 | 四川农业大学 | Fruit multistage screening device and screening method |
| CN113655038B (en) * | 2021-08-24 | 2023-09-22 | 南昌航空大学 | Method for nondestructive detection of fruit sugar degree by using smart phone |
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| CN100449301C (en) * | 2006-03-24 | 2009-01-07 | 浙江大学 | Method and apparatus for non-destructive testing of fruit internal quality based on optical properties |
| CN100483109C (en) * | 2007-01-12 | 2009-04-29 | 浙江大学 | Portable fruit sugar content non-destructive detection device capable of weighing |
| US20100233327A1 (en) * | 2009-03-11 | 2010-09-16 | Hersh Seth J | System and method for formulating compositions of concentrated liquid sweeteners for individual servings in recyclable and compostable packaging |
| JP2013156143A (en) * | 2012-01-30 | 2013-08-15 | Seiko Epson Corp | Optical measurement apparatus and optical measurement method |
| CN102890057B (en) * | 2012-10-24 | 2014-10-08 | 华东交通大学 | Portable quality detection device for simultaneously detecting sugar degree and hardness of fruit |
| CN102937575B (en) * | 2012-11-21 | 2015-05-20 | 浙江大学 | Watermelon sugar degree rapid modeling method based on secondary spectrum recombination |
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