CN106092955B - The measuring method of awns genus plant leaf blade moisture content - Google Patents
The measuring method of awns genus plant leaf blade moisture content Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 30
- 238000001228 spectrum Methods 0.000 claims abstract description 11
- 238000002329 infrared spectrum Methods 0.000 claims abstract description 7
- 238000004364 calculation method Methods 0.000 claims abstract description 6
- 238000006467 substitution reaction Methods 0.000 claims description 4
- 241000196324 Embryophyta Species 0.000 description 35
- 230000003595 spectral effect Effects 0.000 description 27
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 8
- 238000005259 measurement Methods 0.000 description 6
- 241000878006 Miscanthus sinensis Species 0.000 description 4
- 230000000052 comparative effect Effects 0.000 description 4
- 238000001035 drying Methods 0.000 description 4
- 238000004611 spectroscopical analysis Methods 0.000 description 4
- 235000014676 Phragmites communis Nutrition 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 244000273256 Phragmites communis Species 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 238000009499 grossing Methods 0.000 description 2
- 238000012417 linear regression Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 241000607479 Yersinia pestis Species 0.000 description 1
- 230000032683 aging Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000003851 biochemical process Effects 0.000 description 1
- 230000006735 deficit Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 201000010099 disease Diseases 0.000 description 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 230000008641 drought stress Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 229920002521 macromolecule Polymers 0.000 description 1
- 235000015097 nutrients Nutrition 0.000 description 1
- 235000016709 nutrition Nutrition 0.000 description 1
- 230000035764 nutrition Effects 0.000 description 1
- 238000010238 partial least squares regression Methods 0.000 description 1
- 230000037039 plant physiology Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000005096 rolling process Methods 0.000 description 1
- 239000002689 soil Substances 0.000 description 1
- 230000035882 stress Effects 0.000 description 1
- 238000010408 sweeping Methods 0.000 description 1
- 238000005303 weighing Methods 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3563—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing solids; Preparation of samples therefor
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Abstract
The invention discloses a kind of measuring methods of awns genus plant leaf blade moisture content, successively the following steps are included: 1), acquisition awns genus plant leaf blade to be measured；2), awns genus plant leaf blade is scanned in the near infrared spectrum wave band of 400nm-2500nm, to obtain the reflected light spectrum of following 23 characteristic wave strong points: 486nm, 554nm, 624nm, 676nm, 694nm, 714nm, 772nm, 912nm, 958nm, 1098nm, 1322nm, 1392nm, 1432nm, 1520nm, 1642nm, 1870nm, 1900nm, 2038nm, 2122nm, 2214nm, 2274nm, 2316nm and 2404nm；3) above-mentioned data, are substituted into moisture content calculation formula, to obtain the moisture content of awns genus plant leaf blade to be measured.
Description
Technical field
The present invention relates to a kind of measuring methods of awns genus plant leaf blade moisture content.
Background technique
Awns genus plant has been classified as global important biomolecule matter energy crop, often plants awns genus using water shortage or salt-soda soil etc. at present
Plant, the awns genus plant in these regions often will appear water deficit, directly affect its biochemical process and morphosis.Moisture
Content directly displays Nutrient Stress in awns genus growing process, drought stress, disease pest harm and ageing process.Monitor awns genus
The moisture content of plant leaf blade is of great significance in terms of its growth, nutrition and Resistance detecting.
The common detection method of plant leaf blade moisture content is drying constant weight method (national standard), this process needs picking leaves piece and dried
It does to constant weight, not only destroy blade but also takes long time, be not suitable for Fast nondestructive evaluation water content in plant leaf.
It is increasingly mature with near-infrared spectrum technique, plant physiology and biochemistry index determining is established using the technology corresponding
Model it is more and more common.There are a large amount of related patents for utilizing near infrared ray plant leaf blade moisture content at present.Such as
Publication No.: the disclosure of the invention of the CN103592229A measuring method of tealeaves blade moisture content, and its characteristic peak carried out
Analysis, establishes corresponding model.
Summary of the invention
The technical problem to be solved in the present invention is to provide a kind of measuring method of awns genus plant leaf blade moisture content, this method is
A method of the lossless quick detection awns genus plant leaf blade moisture content based on 23 characteristic wavelengths, can accelerate finding speed,
Reduce cost of determination.
In order to solve the above technical problem, the present invention provides a kind of measuring methods of awns genus plant leaf blade moisture content, successively
The following steps are included:
1) awns genus plant leaf blade to be measured, is acquired；
2), awns genus plant leaf blade is scanned in the near infrared spectrum wave band of 400nm-2500nm, to obtain following 23
The reflected light spectrum of a characteristic wave strong point: 486nm, 554nm, 624nm, 676nm, 694nm, 714nm, 772nm, 912nm,
958nm、1098nm、1322nm、1392nm、1432nm、1520nm、1642nm、1870nm、1900nm、2038nm、 2122nm、
2214nm, 2274nm, 2316nm and 2404nm；
3), by above-mentioned steps 2) the following moisture content calculation formula of the data obtained substitution, to obtain awns genus plant to be measured
The moisture content of blade；
YMoisture content=0.897541+0.02111 λ486+0.009529λ554+0.006118λ624+0.02277λ676-0.01889
λ694 +0.01661λ714+0.007454λ772-0.009413λ912+0.013440λ958+0.004891λ1098+0.022500λ1322+
0.035300λ1392-0.048630λ1432+0.022810λ1520+0.004899λ1642+0.02370λ1870-0.066660λ1900+
0.055620λ2038-0.0257 20λ2122+0.037900λ2214-0.062270λ2274-0.057590λ2316+0.010390λ2404。
Invention process of the invention is specific as follows:
(1) 678 parts of awns genus plant samples, packet are scanned in the near infrared spectrum wave band that spectral region is 400nm-2500nm
187 parts of Chinese silvergrasses, 189 portions of reeds, 180 parts of southern reeds, 120 parts of Caulis Miscanthis floridulis and 2 parts of huge awns are included.Material is shown in Table 1.
The moisture content range of 1 five awns genus plants of table
| Kind | Number (part) | Range | Average value | Root mean square |
| Chinese silvergrass | 187 | 0.6578-0.8554 | 0.7635 | 0.0283 |
| Reed | 189 | 0.7294-0.9561 | 0.787 | 0.0231 |
| Nan Di | 180 | 0.6102-0.8044 | 0.7091 | 0.0343 |
| Caulis Miscanthis floriduli | 120 | 0.6658-0.8628 | 0.768 | 0.0226 |
| Huge awns | 2 | 0.7622-0.7912 | 0.7767 | 0.0205 |
(2) the spectrum diffuse reflectance value for obtaining sample, scans 30 times altogether, takes the average value of each spectrum segment.
(3) water cut value of 678 parts of awns genus plant samples is obtained using drying constant weight method, specific moisture content is shown in Table 1.
(4) in order to eliminate the disturbing factors such as original spectral data medium-high frequency random noise, baseline drift to model built
It influences, research uses a variety of preprocess methods such as Smoothing, Normalize.Various pretreated spectroscopic datas are made
For independent variable X, Chinese silvergrass leaf water content establishes Partial Least Squares Regression (partial least squares as Y variable
Regression, PLS) model, various preprocess methods are evaluated by comparing the prediction effect of each model,
It finally determines the preprocess method that step-length is 3 rolling average exponential smoothing and establishes corresponding model.
(5) ratio with 2:1 in 678 parts of samples is randomly divided into modeling collection and forecast set, wherein 452 parts of Chinese silvergrass samples
For establishing model, 226 parts of samples are verified for model prediction.
(6) and the characteristic wave (Fig. 1) closely related with awns genus material blade moisture content is obtained
The spectrum that diffuses using sample based on 23 characteristic wavelengths is to answer with the moisture content of the sample of measurement as independent variable
Variable fits spectroscopic data and measurement of water-content coefficient value using PLS (Partial Least Squares), establishes based on 23 characteristic wavelengths
Multiple linear regression model, X-axis indicates the practical measurement moisture content of sample in Fig. 2, and Y-axis is indicated with 23 characteristic light spectrum predictions
The sample moisture content of acquisition.Relative coefficient between the two reaches 0.9896, and the coefficient of determination has reached 0.9793.
(7) corresponding moisture content model is established.Specific formula is as follows:
YMoisture content=0.897541+0.02111 λ486+0.009529λ554+0.006118λ624+0.02277λ676-0.01889
λ694 +0.01661λ714+0.007454λ772-0.009413λ912+0.013440λ958+0.004891λ1098+0.022500λ1322+
0.035300λ1392-0.048630λ1432+0.022810λ1520+0.004899λ1642+0.02370λ1870-0.066660λ1900+
0.055620λ2038-0.0257 20λ2122+0.037900λ2214-0.062270λ2274-0.057590λ2316+0.010390λ2404。
Wherein YMoisture contentFor the prediction moisture content of awns genus plant to be measured, λ486The spectral reflectance light number for being the sample at 486nm
Value, λ554The spectral reflectance light numerical value for being the sample at 554nm, λ624The spectral reflectance light numerical value for being the sample at 624nm,
λ676The spectral reflectance light numerical value for being the sample at 676nm, λ694The spectral reflectance light numerical value for being the sample at 694nm, λ714
The spectral reflectance light numerical value for being the sample at 714nm, λ772The spectral reflectance light numerical value for being the sample at 772nm, λ912For
Spectral reflectance light numerical value of the sample at 912nm, λ958The spectral reflectance light numerical value for being the sample at 958nm, λ1098For this
Spectral reflectance light numerical value of the sample at 1098nm, λ1322The spectral reflectance light numerical value for being the sample at 1322nm, λ1392For this
Spectral reflectance light numerical value of the sample at 1392nm, λ1432The spectral reflectance light numerical value for being the sample at 1432nm, λ1520For this
Spectral reflectance light numerical value of the sample at 1520nm, λ1642The spectral reflectance light numerical value for being the sample at 1642nm, λ1870For this
Spectral reflectance light numerical value of the sample at 1870nm, λ1900The spectral reflectance light numerical value for being the sample at 1900nm, λ2038For this
Spectral reflectance light numerical value of the sample at 2038nm, λ2122The spectral reflectance light numerical value for being the sample at 2122nm, λ2214For this
Spectral reflectance light numerical value of the sample at 2214nm, λ2274The spectral reflectance light numerical value for being the sample at 2274nm, λ2316For this
Spectral reflectance light numerical value of the sample at 2316nm, λ2404The spectral reflectance light numerical value for being the sample at 2404nm.
(8) reflected light spectrum of the scanning awns genus plant leaf blade in 23 characteristic wavelengths from, specific wavelength includes 486,
554、 624、676、694、714、772、912、958、1098、1322、1392、1432、1520、1642、1870、1900、
2038,2122,2214,2274,2316nm and 2404nm.With it is existing in relation to the characteristic peak of plant leaf blade moisture content compared with, remove
Go a few characteristic peak with known plant water content characteristic peak relatively, such as in the feature of tealeaves blade moisture content
There is the characteristic peak of 538,673,710 and 969nm in peak, 554,676, the 714 and 958nm of characteristic peak with awns genus plant leaf blade,
His characteristic peak is new characteristic peak, thus it is speculated that the combining form of awns genus plant moisture and macromolecular substances and other plant have one
Fixed difference.
(9) 226 parts of awns genus plant leaf blade samples are calculated in the substitution formula of the reflected light spectrum of 23 characteristic wave strong points
Obtain leaf water content.The true value of forecast set and the relative coefficient of predicted value are 0.9886, the coefficient of determination 0.9776.
The present invention has following technical advantage:
(1) this method step is simple, can directly take awns genus plant fresh leaf to be scanned, by fitting multiple linear regression
Model directly obtains leaf water content；
(2) this method quick nondestructive needs not move through the weighing drying and repeatedly weigh step that time-consuming, and does not destroy plant
Reset condition；
(3) this method has chosen the near infrared spectrum of 23 characteristic peak closely related with awns genus plant leaf blade moisture content
Value, surface sweeping time are shorter.
(4), the testing result accuracy of moisture content is high.
Detailed description of the invention
Specific embodiments of the present invention will be described in further detail with reference to the accompanying drawing.
Fig. 1 is the regression coefficient figure of awns genus plant leaf blade moisture content spectral model；X-axis is spectral value, and Y-axis is to return correlation
Property coefficient.
The actual value and predicted value scatter diagram of the moisture content of Fig. 2 forecast set sample；The practical measurement of X-axis expression sample
Moisture content, Y-axis indicate the sample moisture content obtained with 23 characteristic light spectrum predictions.
Specific embodiment
The present invention is described further combined with specific embodiments below, but protection scope of the present invention is not limited in
This.
Embodiment 1, a kind of measuring method of awns genus plant leaf blade moisture content, successively follow the steps below:
1) awns genus plant leaf blade to be measured, is acquired；
2), awns genus plant leaf blade is scanned in the near infrared spectrum wave band of 400nm-2500nm, to obtain following 23
The reflected light spectrum of a characteristic wave strong point: 486nm, 554nm, 624nm, 676nm, 694nm, 714nm, 772nm, 912nm,
958nm、1098nm、1322nm、1392nm、1432nm、1520nm、1642nm、1870nm、1900nm、2038nm、 2122nm、
2214nm, 2274nm, 2316nm and 2404nm；
3), by above-mentioned steps 2) the following moisture content calculation formula of the data obtained substitution, to obtain awns genus plant to be measured
The moisture content of blade；
YMoisture content=0.897541+0.02111 λ486+0.009529λ554+0.006118λ624+0.02277λ676-0.01889
λ694 +0.01661λ714+0.007454λ772-0.009413λ912+0.013440λ958+0.004891λ1098+0.022500λ1322+
0.035300λ1392-0.048630λ1432+0.022810λ1520+0.004899λ1642+0.02370λ1870-0.066660λ1900+
0.055620λ2038-0.0257 20λ2122+0.037900λ2214-0.062270λ2274-0.057590λ2316+0.010390λ2404。
Experiment 1, by following sample, the method is detected in accordance with the above-mentioned embodiment 1, and acquired results are as shown in table 2 below.
Above-mentioned sample is detected, the comparison of gained and result of the present invention according to conventional " drying constant weight method (national standard) "
As described in Table 2.
Remarks explanation: every kind of sample takes 3 repetitions, is averaged.
The moisture content comparison that table 2, two methods measure
" 1870m " of 23 characteristic wavelengths of embodiment 1 is changed to " 1700nm " by comparative example 1, still (partially minimum with PLS
Square law) spectroscopic data and measurement of water-content coefficient value are fitted, to obtain corresponding moisture content calculation formula.With this 1 institute of comparative example
Method is stated to detect sample described in table 1.Described in testing result table 2 as above.
" 554nm " of 23 characteristic wavelengths of embodiment 1 is changed to " 538nm " by comparative example 2, still (partially minimum with PLS
Square law) spectroscopic data and measurement of water-content coefficient value are fitted, to obtain corresponding moisture content calculation formula.With this 2 institute of comparative example
Method is stated to detect sample described in table 1.Described in testing result table 2 as above.
The above list is only a few specific embodiments of the present invention for finally, it should also be noted that.Obviously, this hair
Bright to be not limited to above embodiments, acceptable there are many deformations.Those skilled in the art can be from present disclosure
All deformations for directly exporting or associating, are considered as protection scope of the present invention.
Claims (1)
1. the measuring method of awns genus plant leaf blade moisture content, it is characterized in that successively the following steps are included:
1) awns genus plant leaf blade to be measured, is acquired；
2), awns genus plant leaf blade is scanned in the near infrared spectrum wave band of 400nm-2500nm, to obtain following 23 spies
Levy wavelength at reflected light spectrum: 486nm, 554nm, 624nm, 676nm, 694nm, 714nm, 772nm, 912nm, 958nm,
1098nm、1322nm、1392nm、1432nm、1520nm、1642nm、1870nm、1900nm、2038nm、2122nm、2214nm、
2274nm, 2316nm and 2404nm；
3), by above-mentioned steps 2) the following moisture content calculation formula of the data obtained substitution, to obtain awns genus plant leaf blade to be measured
Moisture content；
YMoisture content=0.897541+0.02111 λ486+0.009529λ554+0.006118λ624+0.02277λ676-0.01889λ694+
0.01661λ714+0.007454λ772-0.009413λ912+0.013440λ958+0.004891λ1098+0.022500λ1322+
0.035300λ1392-0.048630λ1432+0.022810λ1520+0.004899λ1642+0.02370λ1870-0.066660λ1900+
0.055620λ2038-0.025720λ2122+0.037900λ2214-0.062270λ2274-0.057590λ2316+0.010390λ2404。
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