CN105651713B - A kind of green vegetables leaf chlorophyll quantitative detecting method based on computer image analysis - Google Patents

A kind of green vegetables leaf chlorophyll quantitative detecting method based on computer image analysis Download PDF

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CN105651713B
CN105651713B CN201511024044.9A CN201511024044A CN105651713B CN 105651713 B CN105651713 B CN 105651713B CN 201511024044 A CN201511024044 A CN 201511024044A CN 105651713 B CN105651713 B CN 105651713B
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chlorophyll content
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王德海
孙宇露
朱国建
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Zhejiang University of Technology ZJUT
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    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
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    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
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    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/314—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry with comparison of measurements at specific and non-specific wavelengths
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    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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Abstract

A kind of green vegetables leaf chlorophyll quantitative detecting method based on computer image analysis, including the following steps: to be changed according to green vegetables chlorophyll content in leaf blades causes leaf color to change, green vegetables leaf image is obtained using digital camera or scanner, color parameter value (the L of blade in image is obtained with computer image technology, a, b, Δ E), and the chlorophyll content of corresponding blade is measured using traditional spectrophotometer method, the relationship between the color parameter value and chlorophyll content of blade is fitted by using different function models, to construct the chlorophyll content prediction model based on color parameter value, as long as obtaining the color value of blade, input model, the measurement to chlorophyll content in leaf blades can be realized.

Description

A kind of green vegetables leaf chlorophyll quantitative detecting method based on computer image analysis
Technical field
The present invention relates to a kind of green vegetables blade Determination of Chlorophyll content quantitative detection methods, belong to the test of crops physical signs Method.
Background technique
The common method of measuring chlorophyll content is spectrophotometer method at present, i.e., shreds blade weighing, with largely having Solvent such as acetone, ethyl alcohol extract leaf chlorophyll for a long time, then with spectrophotometric determination extracting solution in 645nm and Then the OD value of 663nm calculates chlorophyll content using Arnon formula.
Still an alternative is that directly reading the value of chlorophyll content in leaf blades with chlorophyll meter measurement blade.Most It is the SPAR-502 that Japanese Minolta company produces, it is that have absorption in visible light region specific wavelength position according to leaf chlorophyll The characteristics of paddy and reflection peak, the light that a branch of known strength is emitted by the instrument of production are radiated at the blade position that need to be measured, Tested blade specific wavelength absorptivity and reflectivity are detected to calculate chlorophyll content in leaf blades.Using traditional spectrophotometer Measurement chlorophyll content need to be extracted with organic solvent, not only break up sample, and process is cumbersome, relatively time-consuming.Chlorophyll It counts expensive, is not suitable for the research of some bases and measurement of the production unit to gardening product chlorophyll.
Since plant leaf color is the external manifestation of blade Determination of Chlorophyll content, pass through building number using the color parameter of plant It learns model and calculates chlorophyll content as a kind of new method.Reported document has following feature: a. uses colour difference meter Carry out color parameter measurement.Since colour difference meter has certain want to the size of sample, shape, color uniformity, surface smoothness Ask, and colour difference meter volume is larger, instrument price is expensive, suitable for scientific research but be not suitable for base research and production unit make With.B. current chlorophyll content detection model is based on the color systems such as RGB more, and precision of prediction is not high.C. utilization reported in the literature Computer picture establish chlorophyll content be based on more the crops such as detection model cotton foundation because green vegetables plant type, leaf morphology, There are larger differences with above-mentioned crop for surface texture etc., so the prediction model of existing chlorophyll content is not particularly suited for The measurement of green vegetables chlorophyll content in leaf blades.
Summary of the invention
The present invention will overcome problems of the prior art, provide a kind of based on the quick, lossless of color parameter value Green vegetables blade method for measuring chlorophyll content.
The purpose of the present invention is achieved through the following technical solutions: being caused according to the variation of green vegetables chlorophyll content in leaf blades Leaf color variation obtains green vegetables leaf image using digital camera or scanner, is obtained in image with computer image technology The color parameter value (L*, a*, b*, Δ E) of blade, CIE-LAB color specification system is international standard colour system.It uses space coordinate L ﹑ a ﹑ b value indicates.Origin is L=50, a=0, b=0.L* represent brightness (0-100) 0 be black, 100 be white.Coordinate A* indicates red (+) and green (-), and b* indicates yellow (+) and blue (-), Δ E=(Δ L*2+Δa*2+Δb*2)1/2, concentrate Colour system three elements are embodied, can more fully reflect the variation of color three-dimensional space.And use traditional spectrophotometric Meter method measures the chlorophyll content of corresponding blade, by using different function models to the color parameter value and chlorophyll of blade Relationship between content is fitted, so that the chlorophyll content prediction model based on color parameter value is constructed, as long as obtaining The color value of blade is taken, the measurement to chlorophyll content in leaf blades can be realized in input model.
The specific implementation step of the method for the present invention is described in further detail below:
1. leaf image obtains.Leaf digital image is obtained by scanner or digital camera, uses Canon PowerShotA610 digital camera shoot green vegetable leaf direct picture when, using smooth blank sheet of paper as background, camera lens vertically downward, Camera lens is 20cm or so with a distance from target object, and digital machine flash lamp is set to closed state, and pixel is 5,000,000, and phase is arranged Machine is under M-mode, acquisition parameters 1/400, F8.0, ISO100.White balance is automatic, and adjustment lens focus is 20cm.Using JPG Format stores image and incoming computer.
2. leaf image color parameter measures.Leaf digital image image processing software (such as Photoshop that will acquire CS6 etc.) color value that extracts entire blade, does using magic wand tool in Adobe Photoshop CS6 image processing software Green vegetables blade constituency (not comprising the stem arteries and veins part in dish leaf), setting pen tip size are 8 pixels.Then it executes in filter tool Fuzzy averaging order equalizes all pixels parameter value in blade constituency, is read in messagewindow using the Eyedropper tool L*, a*, b* value, and color difference Δ E=(Δ L*2+Δa*2+Δb*2)1/2。
3. the experimental determination of chlorophyll content in leaf blades.After obtaining green vegetables leaf image, sample is shredded immediately, point It does not weigh 0.20g on an electronic balance, is then placed in and fills 25ml mixed liquor (1:1 is prepared by volume for acetone, dehydrated alcohol) Tool plug test tube in, when being placed in that derect seething to leaf tissue bleaches completely under dark condition, with ultraviolet-visible spectrophotometric Meter is scanned different chlorophyll extracting solutions within the scope of 600-700nm, wavelength accuracy 0.2nm.In 645nm and 663nm Place measures and records its OD value, and makees Duplicate Samples, is averaged.
Chlorophyll content, which calculates, utilizes Arnon formula In formula: V is the final volume of leaching liquor；W is fresh weight, and D645, D663 are 645nm and 663nm OD value.
4. the building of chlorophyll content prediction model.Using different functions model y=Ax+B, y=A/x+B, y=Alnx+B With ln (lny)=Alnx+B, A, B be model of fit coefficient, respectively in step 2 measure each color parameter (x) of blade (a*, B*, Δ E) and step 3 in measure chlorophyll content (y) between relationship analysis is fitted by origin8.0 software, pass through Compare the coefficient of determination R of fit equation2, it is as follows to construct 2 prediction models:
Ln (lnC chl)=- 4.0547 ln b*+12.6085 (n=10, R2=0.9925**) --- --- --- (1)
C chl=-0.1412 Δ E+3.4516 (n=10, R2=0.9940) --- --- --- --- (2)
Wherein C chl is chlorophyll content (mg/g), and n is sample size, R2The coefficient of determination, * * indicate that model of fit has pole Significant correlation.
5. the measurement of sample to be tested chlorophyll content.Method measurement leaf image color ginseng is described according to above-mentioned steps 1-2 Color parameter b* or Δ E, are substituted into the chlorophyll content prediction model constructed in step 4, i.e., exportable leaf by number b*, Δ E respectively Chlorophyll contents.
The invention has the advantages that 1. is quick, easy, time saving, laborsaving, flexible operation is simple, does not need to destroy sample, to leaf Piece is not damaged, is not required to chemical reagent, save the cost.2. method proposed by the present invention need to only shoot one completely to a blade The image of blade both can get blade part chlorophyll content, also can get entire chlorophyll content in leaf blades, reduce because of a measurement The low problem of caused measurement accuracy；Evaluated error caused by due to operator's measuring point chooses difference is reduced, precision is higher.
Detailed description of the invention
Fig. 1 is the building schematic diagram of the invention based on b* chlorophyll content prediction model.
Fig. 2 is the building schematic diagram of the invention based on Δ E chlorophyll content prediction model.
Fig. 3 is the inspection schematic diagram of prediction model in embodiment 1
Fig. 4 is the inspection schematic diagram of prediction model in embodiment 2
Specific embodiment
Below by specific embodiment, the present invention is further illustrated, but protection scope of the present invention is not limited in This.
Embodiment 1
On October 10th, 2015,10:30, took 10 groups of blueness that the city Desheng road market of farm produce is bought under the city of Hangzhou, Zhejiang province Dish leaf piece sample, green vegetables kind are " May is slow (WYM, Shanghai local varieties) ", utilize Canon PowerShotA610 number phase When machine shoots green vegetable leaf direct picture, green vegetable leaf to be measured is placed in camera bellows using blank sheet of paper as background, digital camera is placed in camera bellows On the peephole of top, camera bellows standard lamp source (D65) to be opened by digital machine flash lamp and is set to closed state, pixel is 5,000,000, Camera is set under M-mode, acquisition parameters 1/400, F8.0, ISO100.White balance is automatic, and adjustment lens focus is 20cm. It is carried out under the weather condition of ceiling unlimited when taking pictures, camera is shot perpendicular to blade, and shadow-free on blade.Using JPG lattice Formula stores image and incoming computer, the leaf digital image Adobe Photoshop CS6 image processing software that will acquire Middle magic wand tool is done green vegetable leaf piece constituency (not comprising the stem arteries and veins part in dish leaf), and setting pen tip size is 8 pixels.Then it holds All pixels parameter value in blade constituency is equalized, is read using the Eyedropper tool by the fuzzy averaging order in row filter tool Take the b value in colouring information window.
Input model built:
Ln (lnC chl)=- 4.0547ln b*+12.6085 (n=10, R2=0.9925**)
Obtain respectively 10 groups of green vegetables blade sample chlorophyll content predicted values be 3.45,2.92,2.57,2.07,1.57, 3.49,2.77,2.16,1.78,1.63mg/g；It is real that 10 groups of green vegetables blade sample chlorophyll contents are obtained using spectrophotometer method Measured value is 3.48,2.89,2.54,2.11,1.59,3.6,2.66,2.19,1.78,1.63mg/g.
Embodiment 2
On October 15th, 2015,10:30, took 10 groups of green vegetables blade samples, and green vegetables kind is " short anti-blueness ", utilizes Canon When PowerShotA610 digital camera shoots green vegetable leaf direct picture, green vegetable leaf to be measured is placed in camera bellows using blank sheet of paper as back Scape, digital camera are placed on the peephole above camera bellows, open camera bellows standard lamp source (D65), digital machine flash lamp is set to Closed state, pixel are 5,000,000, and camera is arranged under M-mode, acquisition parameters 1/400, F8.0, ISO100.White balance is certainly Dynamic, adjustment lens focus is 20cm.It being carried out under the weather condition of ceiling unlimited when taking pictures, camera is shot perpendicular to blade, and Shadow-free on blade.Image and incoming computer, the leaf digital image Adobe that will acquire are stored using JPG format Magic wand tool does green vegetable leaf piece constituency (not comprising the stem arteries and veins part in dish leaf) in Photoshop CS6 image processing software, Setting pen tip size is 8 pixels.Then the fuzzy averaging order in filter tool is executed, all pixels in blade constituency are joined Digital average reads L*, a*, b* value in colouring information window, and color difference Δ E using the Eyedropper tool.
Input model built:
C chl=-0.1412 Δ E+3.4516 (n=10, R2=0.9940)
Obtain respectively 10 groups of green vegetables blade sample chlorophyll content predicted values be 2.34,2.78,2.36,3.56,1.34, 2.67,1.67,4.54,3.11,2.49mg/g；And 10 groups of green vegetables blade sample chlorophyll contents are obtained using spectrophotometer method Measured value is 2.45,2.72,2.5,3.47,1.36,2.70,1.81,4.6,3.15,2.61mg/g.

Claims (1)

1. a kind of green vegetables leaf chlorophyll quantitative detecting method based on computer image analysis, includes the following steps:
(1) leaf image obtains；Leaf digital image is obtained by scanner or digital camera, uses Canon PowerShotA610 digital camera shoot green vegetable leaf direct picture when, using smooth blank sheet of paper as background, camera lens vertically downward, Camera lens is 20cm with a distance from target object, and digital machine flash lamp is set to closed state, and pixel is 5,000,000, and camera is arranged in M Under mode, acquisition parameters 1/400, F8.0, ISO100；White balance is automatic, and adjustment lens focus is 20cm；Using JPG format Store image and incoming computer；
(2) leaf image color parameter measures；The leaf digital image that will acquire extracts entire blade with image processing software Color value L*, a*, b*, CIE-LAB color specification system are international standard colour systems；It is indicated with space coordinate L ﹑ a ﹑ b value；Origin Coordinate is L=50, a=0, b=0；L* represents brightness, and the value range of L* is 0-100, wherein 0 be black, 100 be white；It sits Marking a* indicates red+and green-, and b* indicates yellow+and blue-, using in Adobe Photoshop CS6 image processing software Magic wand tool does green vegetable leaf piece constituency, and not comprising the stem arteries and veins part in dish leaf, setting pen tip size is 8 pixels；Then it executes All pixels parameter value in blade constituency is equalized, is read using the Eyedropper tool by the fuzzy averaging order in filter tool L*, a*, b* in messagewindow, and calculate color difference Δ E=(Δ L*2+Δa*2+Δb*2)1/2；
(3) experimental determination of chlorophyll content in leaf blades；After obtaining green vegetables leaf image, sample is shredded immediately, respectively Weigh 0.20g on an electronic balance, be then placed in the tool plug test tube for filling 25ml mixed liquor, the mixed liquor by acetone, 1:1 is prepared dehydrated alcohol by volume, when being placed in that derect seething to leaf tissue bleaches completely under dark condition, uses UV, visible light Light spectrophotometer is scanned different chlorophyll extracting solutions within the scope of 600-700nm, wavelength accuracy 0.2nm；? Its OD value is measured and recorded at 645nm and 663nm, and makees Duplicate Samples, is averaged；
Chlorophyll content, which calculates, utilizes Arnon formula: In formula: V is the final volume of leaching liquor, and W is fresh weight, and D645, D663 are 645nm and 663nm OD value, the unit of total chlorophyll content is mg/g；
(4) building of chlorophyll content prediction model；Using different functions model y=Ax+B, y=A/x+B, y=Alnx+B and Ln (lny)=Alnx+B, A, B are the coefficient of model of fit, respectively to measurement each color parameter (x) of blade and step 3 in step 2 Relationship between middle measurement chlorophyll content (y) is fitted analysis, each color parameter (x) of blade by origin8.0 software It is a*, b*, Δ E, by comparing the coefficient of determination R of fit equation2, it is as follows to construct 2 prediction models:
Ln (lnC chl)=- 4.0547ln b*+12.6085, n=10, R2=0.9925**--------------- (1)
C chl=-0.1412 Δ E+3.4516, n=10, R2=0.9940 --- --- --- --- (2)
Wherein C chl is chlorophyll content, and unit is mg/g, and n is sample size, R2The coefficient of determination, * * indicate that model of fit has pole Significant correlation；
(5) measurement of sample to be tested chlorophyll content；Method measurement leaf image color parameter is described according to above-mentioned steps 1-2 Color parameter b* or Δ E, are substituted into the chlorophyll content prediction model constructed in step 4 by b*, Δ E respectively, i.e., exportable leaf is green Cellulose content.
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