CN104634943B - A kind of On-line Measuring Method of salinized soil salinity - Google Patents

Abstract

A kind of On-line Measuring Method of salinized soil salinity, the present invention relates to the On-line Measuring Method of a kind of salinized soil salinity, it is sampled for the measuring method solving existing salinized soil salinity wastes time and energy, the problem that the on-line measurement precision of salinity is relatively low.On-line Measuring Method: one, obtain cracking soil sample photo, then texture feature extraction by soil cracking experiment, then measure salt content in soil, set up soil salt content inverse model；Two, the textural characteristics value that on-line measurement field soil salinity, calculating field soil Normalized Grey Level co-occurrence matrix and corresponding fit correlation are best, obtains the preliminary on-line measurement result of soil salt content；Three, by maximum and the minima of soil sample salinity, soil salt content measurement result is revised.The present invention, based on the impact on soil drying shrinkage cracking degree of the saline-alkali soil salinity, sets up the texture characteristic amount of cracking sample and the quantitative model of sample salinity, improves certainty of measurement.

Description

A kind of On-line Measuring Method of salinized soil salinity

Technical field

The present invention relates to the On-line Measuring Method of a kind of salinized soil salinity, particularly relate to indoor salinized soil and contain The foundation of salt amount inverse model and realized the On-line Measuring Method of soil salt content by on-line measurement saline-alkali soil textural characteristics.

Background technology

The salinization of soil is the land deterioration process that becomes international, its heavy damage soil property and to a great extent On reduce the yield of crops, ecological environment is also had bigger harm simultaneously.Therefore, soil saliferous is measured fast and accurately Amount level is for determining salinization of soil degree, instructing the improvement of salinized soil to have important practical significance.At present, tradition Salinity measuring method determine mainly for the total content of each salt ion in field sampling, Indoor measurement soil, this The measuring method of soil salt content waste time and energy and can not the field real conditions of real time reaction salinity, although remote sensing tool There is the feature that large area is real-time, but the spatial resolution of remotely-sensed data itself and spectral resolution are limited so that it is be confined to big The determination of the salinity degree of area and drawing application, and the single-point salinity certainty of measurement of little yardstick is very poor.Song-Nen plain Soda type saline-alkali soil be a kind of typical salinized soil, its soil clay particle content is higher, and to have the strongest dilation special Property.Therefore, after precipitation, the dry shrinkage and cracking phenomenon that soil surface occurs with the evaporation of moisture is the most universal.So far, Existing a lot of research pays close attention to the identification of soil crack and measurement and soil physical chemistry parameter, environmental condition etc. to cracking journey The aspects such as the impact of degree.Salt content is as the main chemical characteristic of salinized soil, and dehiscence process and crackle to soil are special Levying also to have affects significantly.

Summary of the invention

The measuring method sampling that the invention aims to solve existing salinized soil salinity is wasted time and energy, saliferous The relatively low problem of on-line measurement precision of amount, and the On-line Measuring Method of a kind of salinized soil salinity is provided.

The On-line Measuring Method of salinized soil salinity of the present invention follows these steps to realize:

One, soil salt content inverse model is set up

1, soil cracking experiment

Select many groups soil sample of different salt content, all of soil sample is air-dried, milled processed mistake The sieve in 2mm aperture, is then divided into soil sample A and soil sample B, by soil therein by the soil sample after often group is sieved Sample A carries out dry shrinkage and cracking experiment, and described dry shrinkage and cracking experiment is first all of soil sample A to be each configured to water content Identical saturated mud, pours the wooden of 50 cm x 50 3 centimetres of sizes of cm x into respectively often assembling the saturated mud put In sample box, place after floating mud and carry out natural drying in the lab to produce cup shake, until sample in sample box Quality when no longer reducing, obtain the soil sample A of season cracking；

Being arranged on by digital camera on fixing experiment porch, digital camera distance ground level is 1m, with digital camera The camera lens center that is projected as on the ground determines that the square area of 50 cm x 50 centimetres is to ensure the soil of all season crackings The take pictures geometric distortion impact of process of earth sample A is identical, the black and white grid calibration plate of 50 cm x 50 centimetres is covered and is being dried The soil sample A surface of cracking, then uses digital camera to take pictures the soil sample A of season cracking, obtains cracking soil Earth sample photo；

2, texture feature extraction

Split soil sample photo carry out geometric distortion correction to often opening, the sample image after geometric distortion correction is carried out Shear to obtain the colored slit region image of sample, then every colored slit region image is converted into gray-scale map, then root According to grey level histogram selected threshold and then carry out binary conversion treatment, then by the crackle figure of each cracking soil sample after inversion operation (receipts of surrounding in fragmented parts and sample box are included as being divided into uncracked piece of district (representing with black picture dot) and constriction zone Contracting part, represents with white picture dot)；

Calculate the gray level co-occurrence matrixes of each cracking soil sample binary image, 40 will be set to apart from step-length, 4 sides To respectively 0 °, 45 °, 90 ° and 135 °, the Normalized Grey Level co-occurrence matrix then four direction obtained carry out arithmetic mean from And to remove the impact in direction, calculate each Statistic Texture value according to each cracking soil sample gray level co-occurrence matrixes, respectively unite The computing formula of meter textural characteristics value is as follows:

1) contrast:

$\mathrm{contra}=\underset{n}{\overset{\mathrm{Ng}-1}{\mathrm{\Σ}}}{n}^2\left\{\underset{i=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\underset{j=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}p(i,j)\right\},|i-j|=n,---\left(1\right)$

2) angle second moment:

$\mathrm{ASM}=\underset{i}{\mathrm{\Σ}}\underset{j}{\mathrm{\Σ}}{\left\{p(i,j)\right\}}^2,---\left(2\right)$

3) entropy:

$\mathrm{Entropy}=-\underset{i=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\underset{j=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}p(i,j)\log \left(p(i,j)\right),---\left(3\right)$

4) concordance:

$\mathrm{Homo}=\underset{i=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\underset{j=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\frac{1}{1+{(i-j)}^2}p(i,j),---\left(4\right)$

5) dependency:

$\mathrm{Cerrelation}=\frac{\underset{i=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\underset{j=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\left(\mathrm{ij}\right)p(i,j)-{\mathrm{\μ}}_x{\mathrm{\μ}}_y}{{\mathrm{\σ}}_x{\mathrm{\σ}}_y},---\left(5\right)$

6) cluster is shady:

$\mathrm{Clustershade}=\underset{i=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\underset{j=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}{((i-{\mathrm{\μ}}_i)+(j-{\mathrm{\μ}}_j))}^{3}p(i,j),---\left(6\right)$

7) maximum of probability:

Maxprobability=max{p (i, j) }, (7)

8) and average:

$\mathrm{SumAverage}=\underset{i=2}{\overset{2\mathrm{Ng}}{\mathrm{\Σ}}}{\mathrm{ip}}_{x+y}\left(i\right),---\left(8\right)$

p_x+yI () represents that the probit of gray level i occur simultaneously in x row and y row；

9) and entropy:

$\mathrm{SumEntropy}=-\underset{i=2}{\overset{2\mathrm{Ng}}{\mathrm{\Σ}}}{p}_{x+y}\left(i\right)\log \left\{p_{x+y}\left(i\right)\right\},---\left(9\right)$

10) and variance:

$\mathrm{SumVariance}=\underset{i=2}{\overset{2\mathrm{Ng}}{\mathrm{\Σ}}}{(i-\mathrm{SumEntropy})}^2{p}_{x+y}\left(i\right),---\left(10\right)$

11) relevant information feature 1:

$\mathrm{Inforofcorrelation}1=\frac{\mathrm{HXY}-\mathrm{HXY}1}{\max (\mathrm{HX},\mathrm{HY})},---\left(11\right)$

12) relevant information feature 2:

Inforofcorrelation2={1-exp [-2* (HXY2-HXY)] }^1/2, (12)

Wherein:

(i is j) that (it is general that i, j) position element value, i.e. image gray levels i, j occur Normalized Grey Level co-occurrence matrix simultaneously to p Rate value, Ng is co-occurrence matrix number of greyscale levels, and i, j are positive integer,

$p_x\left(i\right)=\underset{j=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}p(i,j),---\left(13\right)$

$p_y\left(j\right)=\underset{i=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}p(i,j),---\left(14\right)$

$p_{x-y}\left(k\right)=\underset{i=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\underset{j=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}p(i,j),i+j=k,k=\mathrm{2,3},....2\mathrm{Ng},---\left(15\right)$

$\mathrm{HXY}=-\underset{i=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\underset{j=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}p(i,j)\log \left\{p(i,j)\right\},---\left(16\right)$

$\mathrm{HXY}1=-\underset{i=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\underset{j=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}p(i,j)\log \left\{p_x\left(i\right)p_y\left(j\right)\right\},---\left(17\right)$

$\mathrm{HXY}2=-\underset{i=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\underset{j=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}{p}_x\left(i\right)p_y\left(j\right)\log \left\{p_x\left(i\right)p_y\left(j\right)\right\},---\left(18\right)$

μ_x, μ_yFor p_x(i), p_y(j) average, σ_x, σ_yFor p_x(i), p_yJ () variance, n is gray level；

3, soil salt is measured

Each soil sample B is configured to soil extraction, measures the Ca in soil sample B respectively²⁺、Mg²⁺、Cl^-、CO^2- And HCO₃ ^-Content, calculates Ca in each soil sample B²⁺、Mg²⁺、Cl^-、CO^2-And HCO₃ ^-The summation of content, is soil sample B Salinity；

4, inverse model is set up

The salinity and the calculated each Statistic Texture value that utilize soil sample B set up linear model, its center line Property model in abscissa (independent variable) be texture characteristic amount, vertical coordinate (dependent variable) is salinity, select contain with soil sample B The texture characteristic amount that salt amount fit correlation is best, sets up the normal linearity inverse model of soil salt content field on-line measurement；

Two, on-line measurement field soil salinity

For the on-line measurement of field to be measured salinized soil, digital camera is placed on a mobile platform, adjust platform Highly making digital camera height perpendicular to the ground is 1m, arranges digital camera parameter identical, so with parameter during soil cracking experiment Afterwards the cracking salinized soil in field is taken pictures, the cracking salinized soil photograph in field is carried out geometric distortion correction, According to soil sample A take pictures correspondence 50cm*50cm area size and shearing field, position photo, thus obtain standard crack shine Sheet, subsequently carries out gray-scale map conversion and binary conversion treatment to standard crack photo, calculates Normalized Grey Level co-occurrence matrix and phase Answer the textural characteristics value that fit correlation is best, bring the result of calculation of textural characteristics value best for fit correlation into normal linearity anti- Drill in model, thus realize the preliminary on-line measurement of salinized soil salinity；

Three, soil salt content measurement result is revised

In all of on-line measurement field soil sample, select at soil corresponding to the textural characteristics minima of line computation Soil sample max corresponding with the textural characteristics maximum in line computation for sample min, measure respectively accurately soil sample min and The salinity of soil sample max, if soil sample min salinity on-line measurement result is C1, soil sample max salinity is online Measurement result is C2, and the accurate results of soil sample min salinity is D1, the accurate measurement of soil sample max salinity Result is D2, utilizes (C1, D1), and (C2, D2) sets up linear scaled model, then according to linear scaled model to all online surveys Amount result is modified, thus completes the on-line measurement of soil salt content.

Soil salt content On-line Measuring Method of the present invention obtains the crackle of soil sample first by digital camera Image, calculates the gray level co-occurrence matrixes data of sample crack image, calculates the stricture of vagina of sample crack image according to gray level co-occurrence matrixes Reason characterizing magnitudes, measures the salinity of soil sample the most respectively, thus sets up with eigenvalue as abscissa, be vertical with salinity The linear relationship of coordinate, then obtains its textural characteristics value by the soil sample image in field, is treated according to linear relationship Survey the salinity of soil.

The On-line Measuring Method of salinized soil salinity of the present invention is to soil dry shrinkage and cracking based on saline-alkali soil salinity The impact of degree, first carries out soil split test to the soil of different salinity, sets up texture characteristic amount and the sample of cracking sample The quantitative model of this salinity, and then realize the on-line measurement of soil salt content under the natural conditions of field, measurement result precision R²Reach more than 0.85, show measurement result can for salt-soda soil improvement provide the most in real time, data support accurately.

Detailed description of the invention

Detailed description of the invention one: the On-line Measuring Method of present embodiment salinized soil salinity follows these steps to reality Execute:

One, soil salt content inverse model is set up

1, soil cracking experiment

Select many groups soil sample of different salt content, all of soil sample is air-dried, milled processed mistake The sieve in 2mm aperture, is then divided into soil sample A and soil sample B, by soil therein by the soil sample after often group is sieved Sample A carries out dry shrinkage and cracking experiment, and described dry shrinkage and cracking experiment is first all of soil sample A to be each configured to water content Identical saturated mud, pours the wooden of 50 cm x 50 3 centimetres of sizes of cm x into respectively often assembling the saturated mud put In sample box, place after floating mud and carry out natural drying in the lab to produce cup shake, until sample in sample box Quality when no longer reducing, obtain the soil sample A of season cracking；

Being arranged on by digital camera on fixing experiment porch, digital camera distance ground level is 1m, with digital camera The camera lens center that is projected as on the ground determines that the square area of 50 cm x 50 centimetres is to ensure the soil of all season crackings The take pictures geometric distortion impact of process of earth sample A is identical, the black and white grid calibration plate of 50 cm x 50 centimetres is covered and is being dried The soil sample A surface of cracking, then uses digital camera to take pictures the soil sample A of season cracking, obtains cracking soil Earth sample photo；

2, texture feature extraction

Split soil sample photo carry out geometric distortion correction to often opening, the sample image after geometric distortion correction is carried out Shear to obtain the colored slit region image of sample, then every colored slit region image is converted into gray-scale map, then root According to grey level histogram selected threshold and then carry out binary conversion treatment, then by the crackle figure of each cracking soil sample after inversion operation (receipts of surrounding in fragmented parts and sample box are included as being divided into uncracked piece of district (representing with black picture dot) and constriction zone Contracting part, represents with white picture dot)；

Calculate the gray level co-occurrence matrixes of each cracking soil sample binary image, 40 will be set to apart from step-length, 4 sides To respectively 0 °, 45 °, 90 ° and 135 °, the Normalized Grey Level co-occurrence matrix then four direction obtained carry out arithmetic mean from And to remove the impact in direction, calculate each Statistic Texture value according to each cracking soil sample gray level co-occurrence matrixes, respectively unite The computing formula of meter textural characteristics value is as follows:

1) contrast:

$\mathrm{contra}=\underset{n}{\overset{\mathrm{Ng}-1}{\mathrm{\Σ}}}{n}^2\left\{\underset{i=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\underset{j=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}p(i,j)\right\},|i-j|=n,---\left(1\right)$

2) angle second moment:

$\mathrm{ASM}=\underset{i}{\mathrm{\Σ}}\underset{j}{\mathrm{\Σ}}{\left\{p(i,j)\right\}}^2,---\left(2\right)$

3) entropy:

$\mathrm{Entropy}=-\underset{i=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\underset{j=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}p(i,j)\log \left(p(i,j)\right),---\left(3\right)$

4) concordance:

$\mathrm{Homo}=\underset{i=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\underset{j=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\frac{1}{1+{(i-j)}^2}p(i,j),---\left(4\right)$

5) dependency:

$\mathrm{Cerrelation}=\frac{\underset{i=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\underset{j=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\left(\mathrm{ij}\right)p(i,j)-{\mathrm{\μ}}_x{\mathrm{\μ}}_y}{{\mathrm{\σ}}_x{\mathrm{\σ}}_y},---\left(5\right)$

6) cluster is shady:

$\mathrm{Clustershade}=\underset{i=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\underset{j=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}{((i-{\mathrm{\μ}}_i)+(j-{\mathrm{\μ}}_j))}^{3}p(i,j),---\left(6\right)$

7) maximum of probability:

Maxprobability=max{p (i, j) }, (7)

8) and average:

$\mathrm{SumAverage}=\underset{i=2}{\overset{2\mathrm{Ng}}{\mathrm{\Σ}}}{\mathrm{ip}}_{x+y}\left(i\right),---\left(8\right)$

p_x+yI () represents that the probit of gray level i occur simultaneously in x row and y row；

9) and entropy:

$\mathrm{SumEntropy}=-\underset{i=2}{\overset{2\mathrm{Ng}}{\mathrm{\Σ}}}{p}_{x+y}\left(i\right)\log \left\{p_{x+y}\left(i\right)\right\},---\left(9\right)$

10) and variance:

$\mathrm{SumVariance}=\underset{i=2}{\overset{2\mathrm{Ng}}{\mathrm{\Σ}}}{(i-\mathrm{SumEntropy})}^2{p}_{x+y}\left(i\right),---\left(10\right)$

11) relevant information feature 1:

$\mathrm{Inforofcorrelation}1=\frac{\mathrm{HXY}-\mathrm{HXY}1}{\max (\mathrm{HX},\mathrm{HY})},---\left(11\right)$

12) relevant information feature 2:

Inforofcorrelation2={1-exp [-2* (HXY2-HXY)] }^1/2, (12)

Wherein:

(i is j) that (it is general that i, j) position element value, i.e. image gray levels i, j occur Normalized Grey Level co-occurrence matrix simultaneously to p Rate value, Ng is co-occurrence matrix number of greyscale levels, and i, j are positive integer,

$p_x\left(i\right)=\underset{j=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}p(i,j),---\left(13\right)$

$p_y\left(j\right)=\underset{i=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}p(i,j),---\left(14\right)$

$p_{x-y}\left(k\right)=\underset{i=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\underset{j=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}p(i,j),i+j=k,k=\mathrm{2,3},....2\mathrm{Ng},---\left(15\right)$

$\mathrm{HXY}=-\underset{i=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\underset{j=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}p(i,j)\log \left\{p(i,j)\right\},---\left(16\right)$

$\mathrm{HXY}1=-\underset{i=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\underset{j=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}p(i,j)\log \left\{p_x\left(i\right)p_y\left(j\right)\right\},---\left(17\right)$

$\mathrm{HXY}2=-\underset{i=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\underset{j=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}{p}_x\left(i\right)p_y\left(j\right)\log \left\{p_x\left(i\right)p_y\left(j\right)\right\},---\left(18\right)$

μ_x, μ_yFor p_x(i), p_y(j) average, σ_x, σ_yFor p_x(i), p_yJ () variance, n is gray level；

3, soil salt is measured

Each soil sample B is configured to soil extraction, measures the Ca in soil sample B respectively²⁺、Mg²⁺、Cl^-、CO^2- And HCO₃ ^-Content, calculates Ca in each soil sample B²⁺、Mg²⁺、Cl^-、CO^2-And HCO₃ ^-The summation of content, is soil sample B Salinity；

4, inverse model is set up

The salinity and the calculated each Statistic Texture value that utilize soil sample B set up linear model, its center line Property model in abscissa (independent variable) be texture characteristic amount, vertical coordinate (dependent variable) is salinity, select contain with soil sample B The texture characteristic amount that salt amount fit correlation is best, sets up the normal linearity inverse model of soil salt content field on-line measurement；

Two, on-line measurement field soil salinity

For the on-line measurement of field to be measured salinized soil, digital camera is placed on a mobile platform, adjust platform Highly making digital camera height perpendicular to the ground is 1m, arranges digital camera parameter identical, so with parameter during soil cracking experiment Afterwards the cracking salinized soil in field is taken pictures, the cracking salinized soil photograph in field is carried out geometric distortion correction, According to soil sample A take pictures correspondence 50cm*50cm area size and shearing field, position photo, thus obtain standard crack shine Sheet, subsequently carries out gray-scale map conversion and binary conversion treatment to standard crack photo, calculates Normalized Grey Level co-occurrence matrix and phase Answer the textural characteristics value that fit correlation is best, bring the result of calculation of textural characteristics value best for fit correlation into normal linearity anti- Drill in model, thus realize the preliminary on-line measurement of salinized soil salinity；

Three, soil salt content measurement result is revised

In all of on-line measurement field soil sample, select at soil corresponding to the textural characteristics minima of line computation Soil sample max corresponding with the textural characteristics maximum in line computation for sample min, measure respectively accurately soil sample min and The salinity of soil sample max, if soil sample min salinity on-line measurement result is C1, soil sample max salinity is online Measurement result is C2, and the accurate results of soil sample min salinity is D1, the accurate measurement of soil sample max salinity Result is D2, utilizes (C1, D1), and (C2, D2) sets up linear scaled model, then according to linear scaled model to all online surveys Amount result is modified, thus completes the on-line measurement of soil salt content.

Present embodiment step one is in order to take into full account conforming factor, according to the soil salt content in salinity region The objective scope of soil salt content in classification chart and region, in the range of mulching soil salinity, uniform design difference salinity contains Many groups soil sample of amount.In the cracking experiment of step one soil when using the pedotheque of digital camera shooting cracking, in order to Reduce the bad border change impact on photographic quality of taking pictures, need to carry out with the white color board of standard the white balance calibration of camera, And the intensity of illumination measured according to digital photometer arranges the light sensitivitys of camera, the parameter such as aperture size and shutter speed.

In present embodiment step one, texture feature extraction part is split soil sample photo carry out geometric distortion to often being opened Correction, geometric distortion correction be in order to remove take pictures during the center distortion of the photo that causes of camera lens and camera lens when taking pictures The photo that angular deviation causes distortion on some direction, the true soil sample figure the most identical to obtain all directions ratio Picture.According to grey level histogram selected threshold and then carry out binary conversion treatment, its process is to each soil sample image line gray scale , there is notable difference owing to crackle is in soil surface gray scale in statistics with histogram, therefore grey level histogram presents the most bimodal Form, selects at the lowest point between two gray scale peak values gray level as threshold value, and then gray level image is carried out binary conversion treatment.

The n in contrast formula in present embodiment textural characteristics value represents the difference between two gray levels.Calculate every The gray level co-occurrence matrixes of individual cracking soil sample binary image, the element of gray level co-occurrence matrixes is between image picture elements gray scale Second order hybrid conditional probability density, it reflects the gray scale between the pixel having in image on a certain distance step-length and direction Dependency.For gray level image f known to a width, (x, y), if the gray level of image is defined as L level, then gray level co-occurrence matrixes is i.e. For the matrix of L × L size, the computing formula of its second order hybrid conditional probability density is as follows:

P (i, j)=g{ (x1, y1), (x2, y2) ∈ m × n | f (x1, y1)=i, f (x2, y2)=j},

Wherein (i j) represents that gray level co-occurrence matrixes is (i, j) element value of position, wherein i, j represents gray level image respectively to p F (x, y) gray level at (x1, y1) and (x2, y2) coordinate position.

Detailed description of the invention two: present embodiment is step one texture feature extraction portion unlike detailed description of the invention one Polynomial method is used to split soil sample photo carry out geometric distortion correction to often opening in point.Other step and parameter thereof are with concrete Embodiment one is identical.

Detailed description of the invention three: present embodiment step one unlike detailed description of the invention one or two measures soil salt Branch divides the mass ratio of soil quality and moisture in middle soil extraction to be 1:5.Other step and parameter thereof and specific embodiment party Formula one or two is identical.

Detailed description of the invention four: present embodiment step one unlike one of detailed description of the invention one to three measures soil Earth salinity part use flare photometer measure Na in soil sample B⁺And K⁺Content.Other step and parameter thereof are with concrete One of embodiment one to three is identical.

Detailed description of the invention five: present embodiment step one unlike one of detailed description of the invention one to four measures soil Earth salinity part use EDTA compleximetry measure Ca in soil sample B²⁺And Mg²⁺Content.Other step and parameter thereof with One of detailed description of the invention one to four is identical.

Detailed description of the invention six: present embodiment step one unlike one of detailed description of the invention one to five measures soil Earth salinity part uses AgNO₃Solution titrimetry measures Cl in soil sample B^-Content.Other step and parameter thereof are real with concrete Execute one of mode one to five identical.

Detailed description of the invention seven: present embodiment step one unlike one of detailed description of the invention one to six measures soil Earth salinity part use Dual-indicator neutralisation measure CO in soil sample B^2-And HCO₃ ^-Content.Other step and parameter thereof Identical with one of detailed description of the invention one to six.

Embodiment: the On-line Measuring Method of the present embodiment salinized soil salinity follows these steps to implement:

One, soil salt content inverse model is set up

1, soil cracking experiment

The Daan City the most serious in song-Nen plain soda soil salinization and alkalization is selected according to spatial distribution and salinization and alkalization Select 56 groups of soil samples of different salt content, all of soil sample is air-dried, milled processed the sieve in mistake 2mm aperture Son, is then divided into soil sample A and soil sample B by the soil sample after often group is sieved, is done by soil sample A therein Contracting cracking experiment, described dry shrinkage and cracking experiment is that all of soil sample A is first each configured to identical saturated of water content Mud, in the wooden sample box often assembling the saturated mud put and pouring into respectively 50 cm x 50 3 centimetres of sizes of cm x, smears Place after flat mud and carry out natural drying in the lab to produce cup shake, until the quality of sample no longer subtracts in sample box Time few, obtain the soil sample A of season cracking；

Being arranged on by digital camera on fixing experiment porch, digital camera distance ground level is 1m, with digital camera The camera lens center that is projected as on the ground determines that the square area of 50 cm x 50 centimetres is to ensure the soil of all season crackings The take pictures geometric distortion impact of process of earth sample A is identical, the black and white grid calibration plate of 50 cm x 50 centimetres is covered and is being dried The soil sample A surface of cracking, then uses digital camera to take pictures the soil sample A of season cracking, obtains cracking soil Earth sample photo；

2, texture feature extraction

Polynomial method is used to split soil sample photo carry out geometric distortion correction, after geometric distortion correction to often opening Sample image carries out shearing to obtain the colored slit region image of sample, is then converted into by every colored slit region image Gray-scale map, further according to grey level histogram selected threshold and then carry out binary conversion treatment, then by each cracking soil after inversion operation The crack image of sample is divided into uncracked piece of district (representing with black picture dot) and constriction zone (includes fragmented parts and sample The constriction of surrounding in box, represents with white picture dot)；

Calculate the gray level co-occurrence matrixes of each cracking soil sample binary image, gray level Ng is set to 2, distance step-length Being set to 40,4 directions are respectively 0 °, 45 °, 90 ° and 135 °, the Normalized Grey Level co-occurrence matrix then obtained by four direction Carry out arithmetic mean thus to remove the impact in direction, calculate each statistics stricture of vagina according to each cracking soil sample gray level co-occurrence matrixes Reason eigenvalue, the computing formula of each Statistic Texture value is as follows, wherein n=i-j, 0≤i≤Ng, 0≤j≤Ng:

1) contrast:

$\mathrm{contra}=\underset{n}{\overset{\mathrm{Ng}-1}{\mathrm{\Σ}}}{n}^2\left\{\underset{i=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\underset{j=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}p(i,j)\right\},|i-j|=n,---\left(1\right)$

2) angle second moment:

$\mathrm{ASM}=\underset{i}{\mathrm{\Σ}}\underset{j}{\mathrm{\Σ}}{\left\{p(i,j)\right\}}^2,---\left(2\right)$

3) entropy:

$\mathrm{Entropy}=-\underset{i=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\underset{j=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}p(i,j)\log \left(p(i,j)\right),---\left(3\right)$

4) concordance:

$\mathrm{Homo}=\underset{i=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\underset{j=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\frac{1}{1+{(i-j)}^2}p(i,j),---\left(4\right)$

5) dependency:

$\mathrm{Cerrelation}=\frac{\underset{i=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\underset{j=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\left(\mathrm{ij}\right)p(i,j)-{\mathrm{\μ}}_x{\mathrm{\μ}}_y}{{\mathrm{\σ}}_x{\mathrm{\σ}}_y},---\left(5\right)$

6) cluster is shady:

$\mathrm{Clustershade}=\underset{i=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\underset{j=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}{((i-{\mathrm{\μ}}_i)+(j-{\mathrm{\μ}}_j))}^{3}p(i,j),---\left(6\right)$

7) maximum of probability:

Maxprobability=max{p (i, j) }, (7)

8) and average:

$\mathrm{SumAverage}=\underset{i=2}{\overset{2\mathrm{Ng}}{\mathrm{\Σ}}}{\mathrm{ip}}_{x+y}\left(i\right),---\left(8\right)$

p_x+yI () represents that the probit of gray level i occur simultaneously in x row and y row；

9) and entropy:

$\mathrm{SumEntropy}=-\underset{i=2}{\overset{2\mathrm{Ng}}{\mathrm{\Σ}}}{p}_{x+y}\left(i\right)\log \left\{p_{x+y}\left(i\right)\right\},---\left(9\right)$

10) and variance:

$\mathrm{SumVariance}=\underset{i=2}{\overset{2\mathrm{Ng}}{\mathrm{\Σ}}}{(i-\mathrm{SumEntropy})}^2{p}_{x+y}\left(i\right),---\left(10\right)$

11) relevant information feature 1:

$\mathrm{Inforofcorrelation}1=\frac{\mathrm{HXY}-\mathrm{HXY}1}{\max (\mathrm{HX},\mathrm{HY})},---\left(11\right)$

12) relevant information feature 2:

Inforofcorrelation2={1-exp [-2* (HXY2-HXY)] }^1/2, (12)

Wherein:

(i is j) that (it is general that i, j) position element value, i.e. image gray levels i, j occur Normalized Grey Level co-occurrence matrix simultaneously to p Rate value, Ng is co-occurrence matrix number of greyscale levels, and i, j are positive integer,

$p_x\left(i\right)=\underset{j=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}p(i,j),---\left(13\right)$

$p_y\left(j\right)=\underset{i=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}p(i,j),---\left(14\right)$

$p_{x-y}\left(k\right)=\underset{i=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\underset{j=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}p(i,j),i+j=k,k=\mathrm{2,3},....2\mathrm{Ng},---\left(15\right)$

$\mathrm{HXY}=-\underset{i=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\underset{j=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}p(i,j)\log \left\{p(i,j)\right\},---\left(16\right)$

$\mathrm{HXY}1=-\underset{i=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\underset{j=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}p(i,j)\log \left\{p_x\left(i\right)p_y\left(j\right)\right\},---\left(17\right)$

$\mathrm{HXY}2=-\underset{i=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\underset{j=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}{p}_x\left(i\right)p_y\left(j\right)\log \left\{p_x\left(i\right)p_y\left(j\right)\right\},---\left(18\right)$

μ_x, μ_yFor p_x(i), p_y(j) average, σ_x, σ_yFor p_x(i), p_yJ () variance, Ng is gray level；

3, soil salt is measured

Each soil sample B is configured to soil quality-biodiversity than the soil extraction for 1:5, measures soil respectively Ca in earth sample B²⁺、Mg²⁺、Cl^-、CO^2-And HCO₃ ^-Content, calculates Ca in each soil sample B²⁺、Mg²⁺、Cl^-、CO^2-With HCO₃ ^-The summation of content, is the salinity of soil sample B, measurement result such as table 1；

Table 1 experiment cracking sample salinity

4, inverse model is set up

The salinity and the calculated each Statistic Texture value that utilize soil sample B set up linear model, its center line Abscissa (independent variable) in property model is texture characteristic amount, and vertical coordinate (dependent variable) is salinity, comparative selection degree contra (CON) as texture characteristic amount, the measurement result of contrast contra such as table 2, set up soil salt content field on-line measurement Normal linearity inverse model, contrast and salinity linear model y=79.41x 0.7144；

Table 2 test cracking sample contrast C ON

Two, on-line measurement field soil salinity

Select in Daan City, Jilin Province the soil sample of season cracking under 48 natural conditions carry out soil salt content Line is measured, and for the on-line measurement of field to be measured salinized soil, is placed on a mobile platform by digital camera, adjusts platform high It is 1m that degree makes digital camera height perpendicular to the ground, arranges digital camera parameter identical, then with parameter during soil cracking experiment The cracking salinized soil in field is taken pictures, the cracking salinized soil photograph in field is carried out geometric distortion correction, root According to soil sample A take pictures correspondence 50cm*50cm area size and shearing field, position photo, thus obtain standard crack shine Sheet, subsequently carries out gray-scale map conversion and binary conversion treatment to standard crack photo, calculates Normalized Grey Level co-occurrence matrix and mark The contrast C ON textural characteristics value that quasi-inverse model is corresponding, it is as shown in table 3 that contrast C ON textural characteristics value extracts result, by right Bring in normal linearity inverse model than the result of calculation of degree CON textural characteristics value, thus realize salinized soil salinity Preliminary on-line measurement, preliminary on-line measurement result is as shown in table 4；

Table 3 field crackle sample contrast C ON

Table 4 on-line measurement PRELIMINARY RESULTS

Three, soil salt content measurement result is revised

In all of on-line measurement field soil sample, select at soil corresponding to the textural characteristics minima of line computation Soil sample max corresponding with the textural characteristics maximum in line computation for sample min, measure respectively accurately soil sample min and The salinity of soil sample max, if soil sample min salinity on-line measurement result C1 is 1.45mg/g, soil sample max contains Salt amount on-line measurement result C2 is 44.31mg/g, and the accurate results D1 of soil sample min salinity is 2.87mg/g, soil The accurate results D2 of earth sample max salinity is 21.84mg/g, utilizes (C1, D1), and (C2, D2) sets up linear scaled mould Type y=0.46x+0.78 (wherein x represents salinity first on-line measurement value, and y represents salinity calibration value), then according to linear All on-line measurement results are modified by calibration model, thus complete the on-line measurement of soil salt content, measurement result such as table 5 Shown in, precision R²=0.94.

Table 5 salinity on-line measurement calibration result

Claims (7)

1. the On-line Measuring Method of a salinized soil salinity, it is characterised in that the on-line measurement of salinized soil salinity Method is to follow these steps to realize:

One, soil salt content inverse model is set up

1, soil cracking experiment

Select many groups soil sample of different salt content, all of soil sample is air-dried, milled processed mistake 2mm hole The sieve in footpath, is then divided into soil sample A and soil sample B, by soil sample A therein by the soil sample after often group is sieved Carrying out dry shrinkage and cracking experiment, described dry shrinkage and cracking experiment is that first all of soil sample A to be each configured to water content identical Saturated mud, the wooden sample often assembling the saturated mud put and pouring into respectively 50 cm x 50 3 centimetres of sizes of cm x In box, place after floating mud and carry out natural drying in the lab to produce cup shake, until the matter of sample in sample box When amount no longer reduces, obtain the soil sample A of season cracking；

Being arranged on by digital camera on fixing experiment porch, digital camera distance ground level is 1m, with digital camera lens The center that is projected as on the ground determines that the square area of 50 cm x 50 centimetres is to ensure the soil-like of all season crackings The take pictures geometric distortion impact of process of this A is identical, covers the black and white grid calibration plate of 50 cm x 50 centimetres at season cracking Soil sample A surface, then use digital camera the soil sample A of season cracking is taken pictures, obtain cracking soil-like This photo；

2, texture feature extraction

Split soil sample photo carry out geometric distortion correction to often opening, the sample image after geometric distortion correction is sheared To obtain the colored slit region image of sample, then every colored slit region image is converted into gray-scale map, further according to ash Spend rectangular histogram selected threshold and then carry out binary conversion treatment, then the crack image of each cracking soil sample being divided after inversion operation It is segmented into uncracked piece of district and constriction zone；

Calculating the gray level co-occurrence matrixes of each cracking soil sample binary image, will be set to 40 apart from step-length, 4 directions are divided Be not 0 °, 45 °, 90 ° and 135 °, the Normalized Grey Level co-occurrence matrix then four direction obtained carry out arithmetic mean thus with Remove the impact in direction, calculate each Statistic Texture value according to each cracking soil sample gray level co-occurrence matrixes, respectively add up stricture of vagina The computing formula of reason eigenvalue is as follows:

1) contrast:

$\mathrm{contra}=\underset{n}{\overset{\mathrm{Ng}-1}{\mathrm{\Σ}}}{n}^2\left\{\underset{i=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\underset{j=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}p(i,j)\right\},|i-j|=n---\left(1\right)$

2) angle second moment:

$\mathrm{ASM}=\underset{i}{\mathrm{\Σ}}\underset{j}{\mathrm{\Σ}}{\left\{p(i,j)\right\}}^2,---\left(2\right)$

3) entropy:

$\mathrm{Entropy}=-\underset{i=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\underset{j=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}p(i,j)\log \left(p(i,j)\right),---\left(3\right)$

4) concordance:

$\mathrm{Homo}=\underset{i=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\underset{j=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\frac{1}{1+{(i-j)}^2}p(i,j),---\left(4\right)$

5) dependency:

$\mathrm{Correlation}=\frac{\underset{i=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\underset{j=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\left(\mathrm{ij}\right)p(i,j)-{\mathrm{\μ}}_x{\mathrm{\μ}}_y}{{\mathrm{\σ}}_x{\mathrm{\σ}}_y},---\left(5\right)$

6) cluster is shady:

$\mathrm{Clustershade}=\underset{i=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\underset{j=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}{((i-{\mathrm{\μ}}_i)+(j-{\mathrm{\μ}}_j))}^{3}p(i,j),---\left(6\right)$

7) maximum of probability:

Maxprobability=max{p (i, j) }, (7)

8) and average:

$\mathrm{SumAverage}=\underset{i=2}{\overset{2\mathrm{Ng}}{\mathrm{\Σ}}}{\mathrm{ip}}_{x+y}\left(i\right),---\left(8\right)$

p_x+yI () represents that the probit of gray level i occur simultaneously in x row and y row；

9) and entropy:

$\mathrm{SumEntropy}=-\underset{i=2}{\overset{2\mathrm{Ng}}{\mathrm{\Σ}}}{p}_{x+y}\left(i\right)\log \left\{p_{x+y}\left(i\right)\right\},---\left(9\right)$

10) and variance:

$\mathrm{SumVariance}=\underset{i=2}{\overset{2\mathrm{Ng}}{\mathrm{\Σ}}}{(i-\mathrm{SumEntropy})}^2{p}_{x+y}\left(i\right),---\left(10\right)$

11) relevant information feature 1:

$\mathrm{Inforofcorrelation}1=\frac{\mathrm{HXY}-\mathrm{HXY}1}{\max (\mathrm{HX},\mathrm{HY})},---\left(11\right)$

12) relevant information feature 2:

Inforofcorrelation2={1-exp [-2* (HXY2-HXY)] }^1/2, (12)

Wherein:

(i j) is Normalized Grey Level co-occurrence matrix (i, j) position element value, the probability that i.e. image gray levels i, j occur simultaneously to p Value, Ng is co-occurrence matrix number of greyscale levels, and i, j are positive integer,

$p_x\left(i\right)=\underset{j=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}p(i,j),---\left(13\right)$

$p_y\left(j\right)=\underset{i=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}p(i,j),---\left(14\right)$

$p_{x-y}\left(k\right)=\underset{i=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\underset{j=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}p(i,j),i+j=k,k=\mathrm{2,3},...2\mathrm{Ng},---\left(15\right)$

$\mathrm{HXY}=-\underset{i=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\underset{j=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}p(i,j)\log \left\{p(i,j)\right\},---\left(16\right)$

$\mathrm{HXY}1=-\underset{i=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\underset{j=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}p(i,j)\log \left\{p_x\left(i\right)p_y\left(j\right)\right\},---\left(17\right)$

$\mathrm{HXY}2=-\underset{i=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}\underset{j=1}{\overset{\mathrm{Ng}}{\mathrm{\Σ}}}{p}_x\left(i\right)p_y\left(j\right)\log \left\{p_x\left(i\right)p_y\left(j\right)\right\},---\left(18\right)$

μ_x, μ_yFor p_x(i), p_y(j) average, σ_x, σ_yFor p_x(i), p_yJ () variance, n is gray level；

3, soil salt is measured

Each soil sample B is configured to soil extraction, measures the Ca in soil sample B respectively²⁺、Mg²⁺、Cl^-、CO^2-With HCO₃ ^-Content, calculates Ca in each soil sample B²⁺、Mg²⁺、Cl^-、CO^2-And HCO₃ ^-The summation of content, is soil sample B's Salinity；

4, inverse model is set up

The salinity and the calculated each Statistic Texture value that utilize soil sample B set up linear model, wherein linear mould Abscissa in type is texture characteristic amount, and vertical coordinate is salinity, selects the stricture of vagina best with soil sample B salinity fit correlation Reason characteristic quantity, sets up the normal linearity inverse model of soil salt content field on-line measurement；

Two, on-line measurement field soil salinity

For the on-line measurement of field to be measured salinized soil, digital camera is placed on a mobile platform, adjust podium level Making digital camera height perpendicular to the ground is 1m, arranges digital camera parameter identical with parameter during soil cracking experiment, the most right The cracking salinized soil in field is taken pictures, and the cracking salinized soil photograph in field is carried out geometric distortion correction, according to Soil sample A takes pictures the 50cm*50cm area size of correspondence and shearing field, position photo, thus obtains standard crack photo, Subsequently standard crack photo is carried out gray-scale map conversion and binary conversion treatment, calculates Normalized Grey Level co-occurrence matrix and corresponding plan The textural characteristics value that conjunction relation is best, brings the result of calculation of textural characteristics value best for fit correlation into normal linearity inverting mould In type, thus realize the preliminary on-line measurement of salinized soil salinity；

Three, soil salt content measurement result is revised

In all of on-line measurement field soil sample, select at soil sample corresponding to the textural characteristics minima of line computation Soil sample max corresponding with the textural characteristics maximum in line computation for min, the most accurately measures soil sample min and soil The salinity of sample max, if soil sample min salinity on-line measurement result is C1, soil sample max salinity on-line measurement Result is C2, and the accurate results of soil sample min salinity is D1, the accurate results of soil sample max salinity For D2, utilizing (C1, D1), (C2, D2) sets up linear scaled model, then ties all on-line measurements according to linear scaled model Fruit is modified, thus completes the on-line measurement of soil salt content.

The On-line Measuring Method of a kind of salinized soil salinity the most according to claim 1, it is characterised in that step one Texture feature extraction part use polynomial method split soil sample photo carry out geometric distortion correction to often opening.

The On-line Measuring Method of a kind of salinized soil salinity the most according to claim 1, it is characterised in that step one In measurement soil salt part, in soil extraction, the mass ratio of soil quality and moisture is 1:5.

The On-line Measuring Method of a kind of salinized soil salinity the most according to claim 1, it is characterised in that step one Measure and soil salt part uses flare photometer measure Na in soil sample B⁺And K⁺Content.

The On-line Measuring Method of a kind of salinized soil salinity the most according to claim 1, it is characterised in that step one Measure and soil salt part uses EDTA compleximetry measure Ca in soil sample B²⁺And Mg²⁺Content.

The On-line Measuring Method of a kind of salinized soil salinity the most according to claim 1, it is characterised in that step one Measure and soil salt part uses AgNO₃Solution titrimetry measures Cl in soil sample B^-Content.

The On-line Measuring Method of a kind of salinized soil salinity the most according to claim 1, it is characterised in that step one Measure and soil salt part uses Dual-indicator neutralisation measure CO in soil sample B^2-And HCO₃ ^-Content.