CN115684107B - Early quantitative monitoring method for rice salt stress based on sunlight-induced chlorophyll fluorescence index - Google Patents
Early quantitative monitoring method for rice salt stress based on sunlight-induced chlorophyll fluorescence index Download PDFInfo
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Abstract
The invention provides a rice salt stress early quantitative monitoring method based on sunlight-induced chlorophyll fluorescence indexes, which comprises the steps of firstly, collecting single-leaf sunlight-induced chlorophyll fluorescence (Sun-induced chlorophyll Fluorescence, SIF) spectral curve information between 10:00 and 14:00 on a sunny day, calculating 9 SIF yield indexes (FY), and synchronously measuring the net photosynthetic rate of leaves, the maximum photochemical efficiency of a photosystem II (PSII) and the chlorophyll content; then respectively calculating a weight value and a membership value of the physiological and biochemical parameters through a PCA algorithm and a membership function algorithm, and constructing a salt Stress Response Index (SRI) based on the weight value and the membership value; and finally, screening FY with good correlation with the SRI by using a gray correlation analysis algorithm, and constructing a support vector machine (SVR) regression estimation model of the SRI based on the screened FY. The SRI constructed by the invention is sensitive and quick to respond to early salt stress of rice, can reflect the growth state of rice plants within 1-2 days after salt stress starts, and can be expanded to monitoring early salt stress of rice in satellite and unmanned aerial vehicle scale.
Description
Technical Field
The invention relates to a near-ground scale rapid nondestructive quantitative monitoring method for crop growth conditions based on sunlight-induced chlorophyll fluorescence spectrum, in particular to a method for early quantitative monitoring of rice salt stress based on sunlight-induced chlorophyll fluorescence index, and belongs to the technical field of intelligent agriculture.
Background
Soil salinization caused by improper irrigation measures and the like is one of the main barriers of the current agricultural production, and more than 50% of arable land is expected to be subjected to serious salinization in 2050, which forms a serious threat to the production of crops such as rice. Along with the prolongation of the salt stress time, the normal growth and development process of the rice can be seriously affected, including osmotic water loss, nutrition imbalance, damage to a photosynthetic system and the like, and finally the yield of the rice is reduced. Therefore, the method can detect the salt stress of the rice in a nondestructive, rapid and accurate way before the salt stress causes substantial damage to the rice, and has important positive significance for timely adjusting the counter measures of the field production of the rice and reducing or even preventing the influence of the salt stress on the rice production. At present, with the continuous development of remote sensing technology means, a powerful technical support is provided for realizing nondestructive, rapid and accurate diagnosis of the growth state of rice under salt stress. Therefore, the salt stress of the rice can be found in time, the rice production measures under the salt stress can be quickly adjusted, and the accurate implementation is possible.
To date, many traditional methods for detecting salt stress of crops are destructive sampling researches based on physiological and biochemical analysis, and the responses of crops to the salt stress are generally reflected by quantitatively analyzing the change of physiological and biochemical parameters of the crops so as to achieve the aim of detecting the salt stress. The common conditions include response of the photosynthesis process of crops to salt stress, changes in osmotic adjusting substances in the leaves of crops, changes in ion content of the leaves of crops, and the like. In summary, the response of the crop to salt stress can be reflected to a certain extent by quantitatively analyzing the change of the physiological and biochemical parameters of the crop. However, these conventional research methods are time-consuming and laborious, require destructive sampling, and cannot efficiently detect rice salt stress, so an efficient, accurate and nondestructive method is required to monitor salt stress.
With the development of scientific technology in recent years, nondestructive digital imaging technology and non-imaging spectrum technology are applied to the rapid acquisition of salt stress biological parameters. For example, the total biomass, the total leaf area and the aging leaf area of the rice under different salt stress degrees in the seedling stage are obtained through calculation of an RGB image and a fluorescence imaging technology, and the rice biomass obtained through calculation of the RGB image is found to have good correlation (R 2 is more than 0.9) with the actually measured biomass, and the difference between the total leaf area and the aging leaf area calculated by the fluorescence image is obvious. The RGB image is combined with the whole genome association research analysis method and different model construction analysis methods, so that the influence of salt stress on the relative growth rate, the transpiration rate and the transpiration utilization efficiency of rice can be quantified. In addition, some scholars take eggplants, wheat and the like as research objects, and find out that the normalized vegetation index (NDVI), photochemical Reflectance Index (PRI), water Index (WI), optimized Soil Adjustment Vegetation Index (OSAVI) and other vegetation indexes have different linear correlations with physiological and biochemical parameters such as effective quantum yield (delta F/F'm), pigment content, fresh weight of overground biomass, water content of leaves and the like of a vegetation optical system II under salt stress through linear regression analysis. However, the above-described nondestructive detection method also has some limitations: (1) Non-imaging spectroscopic techniques, some vegetation indices have poor response sensitivity when biomass is not significantly reduced, poor ability to capture early photosynthesis, and weak early sensitivity to stress; (2) The digital imaging technology is only suitable for detecting the physiological and biochemical parameters of salt stress in a small indoor area, and is difficult to flexibly apply under different environmental conditions. Therefore, a novel method is needed for identifying and detecting salt stress and timely acquiring early growth states of rice under salt stress.
In recent years, sunlight-induced chlorophyll fluorescence has been considered as an emerging scientific method for monitoring early stress due to its close correlation with photosynthesis. Unlike vegetation reflectivity, chlorophyll fluorescence is generated in the vegetation photosynthesis process, after chlorophyll molecules in the leaves of a general plant absorb light energy (solar radiation), electrons in the chlorophyll molecules are transited from a ground state to an excited state, and due to the short service life of the electrons in the excited state, the electrons are quickly returned from the excited state to the ground state, most of energy released in the process is consumed by photosynthesis or photochemical reaction, only a small part of the energy is consumed in the form of heat dissipation and chlorophyll fluorescence emission (chlorophyll fluorescence, CF), three different energy consumption modes are eliminated, different energy consumption ratios of the three represent that the plant is in different physiological and stress states, and the photosynthetic physiological state of the plant can be reflected through the change of the CF of the plant. The former research shows that when the plant is under different illumination and stress conditions, chlorophyll fluorescence and photosynthesis can show different correlation relations, and when the plant is stressed, the change of CF is earlier than the biological indexes such as chlorophyll content, and the change of CF can be detected before the visible symptoms of the plant occur, so that the state of plant photosynthetic physiology can be reflected earlier, and early diagnosis of plant stress is realized. The early influence of the salt stress on the plants is mainly represented by osmotic stress caused by salt ion aggregation, so that plant cells are difficult to draw water, and therefore, plant leaf stomata shrink to prevent water from scattering and losing, further, the fixation and assimilation of carbon dioxide are reduced for the plants, finally, the photosynthesis is inhibited, chlorophyll fluorescence radiation is influenced, and therefore, the monitoring and diagnosis of the salt stress can be realized by detecting early change of chlorophyll fluorescence on the salt stress. At present, the sunlight-induced chlorophyll fluorescence remote sensing technology has made remarkable progress in the aspects of diagnosis and monitoring of crop nitrogen, high-temperature drought, disease and other stress, traffic environment pollution monitoring, estimation of total primary productivity (GPP) of an ecological system, photosynthesis and the like. Compared with the traditional vegetation index, the sunlight-induced chlorophyll fluorescence (SIF) has obvious change in the early stage of stress, is sensitive and rapid to response to stress, and can realize early monitoring of crop stress.
Disclosure of Invention
The invention aims to provide a rice salt stress early quantitative monitoring method based on a sunlight-induced chlorophyll fluorescence index, which is used for realizing early quantitative rapid nondestructive monitoring of rice salt stress by constructing a salt Stress Response Index (SRI) capable of quantitatively reflecting the rice early salt stress and constructing an SRI support vector machine (SVR) regression estimation model based on the sunlight-induced chlorophyll fluorescence technology.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows: a rice salt stress early-stage quantitative monitoring method based on sunlight-induced chlorophyll fluorescence index comprises the following steps:
step one, data acquisition: collecting fluorescence spectrum information of rice single leaf sunlight-induced chlorophyll, and synchronously measuring net photosynthetic rate of rice leaves, maximum photochemical efficiency of a light system II and chlorophyll content in the same day to obtain estimated model training data and verification data;
step two, calculating fluorescent yield index of sunlight-induced chlorophyll;
Step three, determining a physiological and biochemical parameter weight value W i based on a principal component method;
Step four, calculating a physiological and biochemical parameter membership value x i×j based on a membership function;
Step five, constructing a salt stress response index: calculating a salt stress response index SRI based on the physiological and biochemical parameter weight value and the membership value determined in the third step and the fourth step:
Wherein, p represents the number of the physiological and biochemical parameters to be evaluated, x i×j represents the membership value of different parameters, W i represents the weight value of different parameters, the value of SRI is between 0 and 1, and the larger the value is, the better the growth state of the corresponding sample plant is;
step six, constructing a rice salt stress response index SRI estimation model;
Step seven, checking a rice SRI estimation model: and verifying the rice SRI estimation model based on the randomly partitioned verification data set: according to the rice leaf SRI estimation model, verifying that a rice SRI predicted value is obtained according to the screened FY; and then, verifying the SRI predicted value by verifying the SRI measured value in the data, and selecting a decision coefficient R 2, a Root Mean Square Error (RMSE) and a Relative Root Mean Square Error (RRMSE) to evaluate the quality of the model:
where N represents the number of samples in the dataset, SRI p,n、SRIo,n and The SRI predicted value, the SRI measured value, and the average value of the SRI measured value are shown, respectively.
Further, in the first step, the data collection is from the same year and different growth periods, and the collected sample data is randomly divided into a training data set and a verification data set according to a certain proportion.
Further, in the first step:
a. Acquiring fluorescence spectrum curve information of sunlight-induced chlorophyll from 10:00 to 14:00 of sunny days, and measuring the reflection radiance spectrum and transmission radiance spectrum of rice leaves; recording the blade uplink radiance spectrum and the downlink radiance spectrum of the light with the wavelength of 650nm which is not filtered and the light with the wavelength of 650nm which is filtered respectively;
b. measuring the net photosynthetic rate of the leaves on a sunny day from 9:00 a to 11:00 a.m. using a photosynthetic tester;
c. Measuring the maximum photochemical efficiency of the optical system II by using an active fluorescence tester, performing dark adaptation on the blade for a period of time before each test, and then measuring the minimum fluorescence Fo and the maximum fluorescence Fm to obtain Fv/Fm= (Fm-Fo)/Fm;
d. Using a chlorophyll meter, 1/3,1/2 and 2/3 of the 3-point test was performed on each test leaf, and the 3-point data for each leaf was averaged to obtain the chlorophyll content of the leaf.
Further, in the second step, the SIF yield is calculated by normalizing the SIF using the absorbed photosynthetically active radiation.
In the third step, a physiological and biochemical parameter weight value is obtained by calculation based on the full data set, and the steps are as follows:
a. after the original matrix is standardized, calculating a characteristic value and a corresponding characteristic vector by using a correlation coefficient matrix;
b. Calculating the variance contribution rate of the principal components, and determining the first k principal components with the accumulated contribution rate being more than 85% as effective principal components;
c. calculating the comprehensive weight of the evaluation index, multiplying the coefficient of the corresponding parameter in each principal component expression by the variance contribution rate of the corresponding principal component, dividing by the accumulated contribution rate of the extracted principal component, and finally carrying out accumulated summation; after normalization, the weight corresponding to each parameter is obtained, and the sum of the weight values is 1:
Wherein k represents the number of selected principal components, p represents the number of physiological and biochemical parameters, E i×j represents coefficients corresponding to different parameters in the principal component expression, and V j represents the contribution rate of each principal component.
Further, in the fourth step, the membership value x i×j is calculated according to the following formula:
Wherein x min represents the minimum value of the different physiological and biochemical parameters, x max represents the maximum value of the different physiological and biochemical parameters, and x represents the original test value of the different physiological and biochemical parameters.
Further, in the fifth step, the response performance of the SRI in the early stage of salt stress is determined based on analysis of variance and sensitivity analysis.
Further, in the sixth step, the model is constructed as follows:
a. Screening out chlorophyll fluorescence yield indexes with good correlation degree with salt stress response indexes based on a gray correlation analysis algorithm;
b. Randomly dividing a training data set and a verification data set according to the ratio of 3:1, and constructing a salt stress response index support vector machine regression estimation model by using the screened chlorophyll fluorescence yield index based on the training data.
Further, a Gaussian kernel function is selected as a kernel function of the support vector regression machine, a grid search algorithm is used for searching for a parameter combination of a punishment coefficient C and an internal parameter g of the kernel function, a K-fold cross validation method is adopted for parameter combination validation, and finally a combination which can minimize a cross validation error of a training set is selected as an optimal parameter combination.
Compared with the prior art, the invention has the advantages that:
By constructing the SRI and the estimation model thereof, the early quantitative monitoring of the salt stress of the rice can be realized, the salt stress of the rice can be detected nondestructively, rapidly and accurately before the salt stress causes substantial damage to the rice, and conditions are created for a decision maker to timely adjust the counter measures of the field production of the rice so as to reduce or even prevent the influence of the salt stress on the production of the rice.
The invention avoids some technical limitations in the traditional method, the nondestructive digital imaging technology and the nondestructive spectrum technology, and can be widely applied to real-time and nondestructive monitoring of early growth states of rice under salt stress in the area range of satellite and unmanned aerial vehicle platforms.
Drawings
FIG. 1 is a detailed illustration of the implementation of the method of the present invention.
FIG. 2 is a graph showing the dynamic behavior of physiological and biochemical parameters under analysis of variance according to the present invention; wherein, A-C: a jointing period; D-E: booting stage.
FIG. 3 is a graph showing the dynamic behavior of SRI under analysis of variance according to the present invention; wherein A: a jointing period; b: booting stage.
FIG. 4 is a spectrum of SIF of rice leaf under different salt stress levels according to the present invention; taking booting stage as an example: a1 DAT; b, 2DAT; c, 6DAT; d8 DAT.
FIG. 5 is a graph showing the results of the test of the SRI support vector machine (SVR) regression estimation model of the present invention on different data sets; wherein A: full dataset estimation results; b: a booting stage estimation result; c: and estimating the jointing period.
Detailed Description
The technical scheme of the invention is further described through the specific embodiments.
This example was based on time series survey data of different growth periods of the same year, as shown in table 1:
TABLE 1 time sequence survey data acquisition of different growth periods
After calculation processing, the measured data of the survey are randomly divided into a model training set and a model verification set according to the proportion of 3:1. The data set has the requirements of good systematicness, as many samples as possible, and the like, so that the obtained model has good stability. For weather reasons, the influence of overlarge data size difference in different birth periods on the performance of the constructed model exists. "" represents that data was acquired on the same day and "-" represents that data was not acquired for weather reasons.
Referring to fig. 1, a method for early quantitative monitoring of salt stress of rice based on sunlight-induced chlorophyll fluorescence index specifically comprises the following steps:
Step one, data acquisition:
a. And (3) acquiring spectral curve information of sunlight-induced chlorophyll fluorescence (Sun-induced chlorophyll Fluorescence, SIF) by selecting a range from 10:00 to 14:00 on sunny days, and measuring the reflection radiance spectrum and the transmission radiance spectrum of rice leaves. Recording the blade uplink radiance spectrum and the downlink radiance spectrum of the light with the wavelength of 650nm which is not filtered and the light with the wavelength of 650nm which is filtered respectively;
b. The net photosynthetic rate of leaves (Net photosynthetic rate, pn) was measured on a sunny day from 9:00 a.m. to 11:00 a.m. using a photosynthetic tester (Li-6400 XT). During the test, she Shina light intensity was set to 1200. Mu. Mol.m -2·s-1,CO2 concentration was set to 400. Mu. Mol.mol -1, and constant flow rate was 500. Mu. Mol.s -1 (FIG. 2);
c. The maximum photochemical efficiency (Fv/Fm) of photosystem II (PSII) was determined using an active fluorescence tester. Prior to each test, the leaves required dark adaptation for 20 minutes, and then minimum fluorescence (Fo) and maximum fluorescence (Fm) were measured to give Fv/Fm= (Fm-Fo)/Fm (FIG. 2);
d. Taking 3 points at 1/3,1/2 and 2/3 of each test leaf using a chlorophyll meter, and taking the average value of 3 points of each leaf as chlorophyll content (Chlorophyll content, chl) of the leaf (fig. 2);
step two, calculating fluorescent yield index (Sun-induced chlorophyll Fluorescence YIELD INDICES, FY) of the sunlight-induced chlorophyll:
due to the influence of factors such as solar altitude, the solar radiation intensity at different time points is different, and the SIF output needs to be normalized and calculated by using Absorption Photosynthetically Active Radiation (APAR) so as to eliminate the influence of the solar radiation intensity on the fluorescence intensity. APAR corresponds to the integral of the product of the absorption rate and the incident solar radiation in the Photosynthetically Active Radiation (PAR) region (400-700 nm) (equations (1) - (5)). The total fluorescence yield (totFY) is equal to sum of downstream fluorescence yields (totFY = ≡FY) +.gtoreq.FY).
fAPAR＝(1-R-T) (2)
Wherein PAR is photosynthetically active radiation, APAR is photosynthetically active radiation absorbed, fAPAR is photosynthetically active radiation absorptivity, F is fluorescence intensity, R is reflectance, T is transmittance, and I is solar irradiance. The peaks of the red and far-red light regions of the chlorophyll fluorescence yield curve (fig. 4) were close to normal distribution in distribution, and the former found that the frequencies at 687nm and 739nm were highest as peaks by statistical analysis of the wavelength distribution where the peaks of the two regions were located. The present invention thus calculates 9 fluorescence yield indices as shown in table 2 based on the fluorescence yield at the two bands:
TABLE 2 SIF yield index calculated according to the invention
Step three, determining weight values of physiological and biochemical parameters based on a PCA algorithm:
a. After the normalization of the original matrix, computing eigenvalues and corresponding eigenvectors (Eigenvector, E) using the correlation coefficient matrix; the feature vector is the coefficients in the expression of the principal component, which represents the importance degree of the original physiological and biochemical parameters to the principal component;
b. Calculating a principal component variance contribution ratio (Variance contribution rate, V), and determining the first k principal components with the accumulated contribution ratio being more than 85% as effective principal components;
c. calculating an evaluation index comprehensive weight (Comprehensive weight, W); and multiplying the coefficient of the corresponding parameter in each principal component expression by the variance contribution rate of the corresponding principal component, dividing by the accumulated contribution rate of the extracted principal component, and finally carrying out accumulated summation. After normalization, the weight corresponding to each parameter is obtained (table 3), and the sum of the weight values is 1:
Wherein k represents the number of selected principal components, p represents the number of physiological and biochemical parameters, E i×j represents coefficients corresponding to different parameters in the principal component expression, and V j represents the contribution rate of each principal component.
TABLE 3 comprehensive weights of physiological and biochemical parameters
Step four, calculating membership value of each physiological and biochemical parameter based on membership function:
The membership value is a standard value after the parameter is dimensionless, and aims to eliminate the influence of different unit backgrounds. Since three physiological and biochemical parameters are positively correlated with the growth state of rice under salt stress, the method is calculated according to the following formula:
Wherein x min represents the minimum value of the different physiological and biochemical parameters, x max represents the maximum value of the different physiological and biochemical parameters, and x represents the original test value of the different physiological and biochemical parameters;
Step five, constructing a salt stress Response Index (SALT STRESS Response Index, SRI): calculating a salt stress response index based on the physiological and biochemical parameter weight value and the membership value determined in the third step and the fourth step:
Wherein, p represents the number of the physiological and biochemical parameters to be evaluated, x i×j represents the membership value of different parameters, and W i represents the weight value of different parameters. SRI values between [ 0-1 ], with larger values indicating better growth status for the corresponding sample plants (FIG. 3);
step six, constructing a rice salt stress response index SRI estimation model:
a. Based on a gray correlation analysis algorithm, FY (table 4) with good correlation degree with the salt stress response index is screened out: FY of the first five of the association degree ranks is selected: totFY739, +.fwy 739, +. totFY687, and +.fwy 687;
TABLE 4 analysis of the correlation of fluorescence yield index of sunlight-induced chlorophyll with SRI Gray
B. The training data set and the verification data set are randomly divided according to the ratio of 3:1, and a salt stress response index support vector machine (SVR) regression estimation model is constructed by using the screened FY based on the training data. The invention selects the Gaussian kernel functions (Radial Basis Function, RBF) with most use, less parameter setting, low complexity and easy calculation and good adaptability to samples with different dimensions and numbers as the kernel functions of the support vector regression machine, searches the parameter combination of the punishment coefficient C and the kernel function internal parameter g by using a grid search algorithm, performs parameter combination verification by adopting a K-fold cross verification method, and finally selects the parameter combination which can minimize the cross verification error of the training set as the optimal parameter combination (table 5);
Step seven, checking a rice SRI estimation model: and verifying the rice SRI estimation model based on the randomly partitioned verification data set: according to the rice leaf SRI estimation model, verifying that a rice SRI predicted value is obtained according to the screened FY; then, the SRI predicted value is checked by checking the actual SRI value in the data (fig. 5), and the model is evaluated for the quality by using the decision coefficient (R 2), root Mean Square Error (RMSE) and Relative Root Mean Square Error (RRMSE) (table 5):
where N represents the number of samples in the dataset, SRI p,n、SRIo,n and The SRI predicted value, the SRI measured value, and the average value of the SRI measured value are shown, respectively.
TABLE 5 salt stress response index estimation model modeling results based on FYs
The test results are shown in table 5 and fig. 5, and the test results show that the model is estimated to have the best and most stable performance on the booting stage data set, and then the full data set and the jointing stage data set are obtained. The SRI constructed by the embodiment and the estimation model thereof can realize the real-time monitoring of early salt stress of rice.
While the preferred embodiments of the present invention have been described in detail, the present invention is not limited to the above embodiments and modes, and the present invention can be modified accordingly by those skilled in the art without departing from the spirit of the present invention, so as to be applied to other crop stress monitoring.
The invention is not related in part to the same as or can be practiced with the prior art.
Claims (7)
1. The early quantitative monitoring method for the salt stress of the rice based on the fluorescence index of the sunlight-induced chlorophyll is characterized by comprising the following steps:
step one, data acquisition: collecting fluorescence spectrum information of rice single leaf sunlight-induced chlorophyll, and synchronously measuring net photosynthetic rate of rice leaves, maximum photochemical efficiency of a light system II and chlorophyll content in the same day to obtain estimated model training data and verification data;
step two, calculating fluorescent yield index of sunlight-induced chlorophyll;
Step three, determining a physiological and biochemical parameter weight value W i based on a principal component method; specifically, the physiological and biochemical parameter weight value is obtained by calculation based on a full data set, and the method comprises the following steps:
a. after the original matrix is standardized, calculating a characteristic value and a corresponding characteristic vector by using a correlation coefficient matrix;
b. Calculating the variance contribution rate of the principal components, and determining the first k principal components with the accumulated contribution rate being more than 85% as effective principal components;
c. calculating the comprehensive weight of the evaluation index, multiplying the coefficient of the corresponding parameter in each principal component expression by the variance contribution rate of the corresponding principal component, dividing by the accumulated contribution rate of the extracted principal component, and finally carrying out accumulated summation; after normalization, the weight corresponding to each parameter is obtained, and the sum of the weight values is 1:
Wherein k represents the number of selected main components, p represents the number of physiological and biochemical parameters, E i×j represents coefficients corresponding to different parameters in the main component expression, and V j represents the contribution rate of each main component;
Step four, calculating a physiological and biochemical parameter membership value x i×j based on a membership function; the membership value x i×j is calculated according to the following formula:
Wherein x min represents the minimum value of the different physiological and biochemical parameters, x max represents the maximum value of the different physiological and biochemical parameters, and x represents the original test value of the different physiological and biochemical parameters;
Step five, constructing a salt stress response index: calculating a salt stress response index SRI based on the physiological and biochemical parameter weight value and the membership value determined in the third step and the fourth step:
Wherein, p represents the number of the physiological and biochemical parameters to be evaluated, x i×j represents the membership value of different parameters, W i represents the weight value of different parameters, the value of SRI is between 0 and 1, and the larger the value is, the better the growth state of the corresponding sample plant is;
step six, constructing a rice salt stress response index SRI estimation model;
Step seven, checking a rice SRI estimation model: and verifying the rice SRI estimation model based on the randomly partitioned verification data set: according to the rice leaf SRI estimation model, verifying that a rice SRI predicted value is obtained according to the screened FY; and then, verifying the SRI predicted value by verifying the SRI measured value in the data, and selecting a decision coefficient R 2, a Root Mean Square Error (RMSE) and a Relative Root Mean Square Error (RRMSE) to evaluate the quality of the model:
where N represents the number of samples in the dataset, SRI p,n、SRIo,n and The SRI predicted value, the SRI measured value, and the average value of the SRI measured value are shown, respectively.
2. The method for quantitative early monitoring of salt stress in rice based on fluorescence index of chlorophyll induced by sunlight according to claim 1, wherein in the first step, data collection is performed from different growth periods in the same year, and the collected sample data are randomly divided into training data sets and verification data sets according to a certain proportion.
3. The method for quantitative early monitoring of salt stress in rice based on fluorescence index of sunlight-induced chlorophyll according to claim 1 or 2, wherein in the first step:
a. Acquiring fluorescence spectrum curve information of sunlight-induced chlorophyll from 10:00 to 14:00 of sunny days, and measuring the reflection radiance spectrum and transmission radiance spectrum of rice leaves; recording the blade uplink radiance spectrum and the downlink radiance spectrum of the light with the wavelength of 650nm which is not filtered and the light with the wavelength of 650nm which is filtered respectively;
b. measuring the net photosynthetic rate of the leaves on a sunny day from 9:00 a to 11:00 a.m. using a photosynthetic tester;
c. Measuring the maximum photochemical efficiency of the optical system II by using an active fluorescence tester, performing dark adaptation on the blade for a period of time before each test, and then measuring the minimum fluorescence Fo and the maximum fluorescence Fm to obtain Fv/Fm= (Fm-Fo)/Fm;
d. Using a chlorophyll meter, 1/3,1/2 and 2/3 of the 3-point test was performed on each test leaf, and the 3-point data for each leaf was averaged to obtain the chlorophyll content of the leaf.
4. The method for quantitative early monitoring of salt stress in rice based on fluorescence index of sunlight-induced chlorophyll according to claim 1, wherein in the second step, SIF is normalized to calculate SIF yield using absorbed photosynthetically active radiation.
5. The method for quantitative early monitoring of salt stress in rice based on fluorescence index of sunlight-induced chlorophyll according to claim 1, wherein in the fifth step, response expression of SRI in early salt stress is determined based on analysis of variance and sensitivity analysis.
6. The method for quantitative early monitoring of salt stress in rice based on fluorescence index of sunlight-induced chlorophyll according to claim 1, wherein in the sixth step, a model is constructed as follows:
a. Screening out chlorophyll fluorescence yield indexes with good correlation degree with salt stress response indexes based on a gray correlation analysis algorithm;
b. Randomly dividing a training data set and a verification data set according to the ratio of 3:1, and constructing a salt stress response index support vector machine regression estimation model by using the screened chlorophyll fluorescence yield index based on the training data.
7. The method for early quantitative monitoring of salt stress of rice based on sunlight-induced chlorophyll fluorescence index according to claim 1 or 6, wherein a Gaussian kernel function is selected as a kernel function of a support vector regression machine, a grid search algorithm is used for searching a parameter combination of a punishment coefficient C and an internal parameter g of the kernel function, a K-fold cross validation method is adopted for parameter combination validation, and finally a parameter combination which can minimize a cross validation error of a training set is selected as an optimal parameter combination.
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