Salicornia europaea lithium stress time sequence spectrum characteristic database construction method

Abstract

The invention relates to the technical field of salicornia europaea spectral feature extraction, and discloses a salicornia europaea lithium stress time sequence spectral feature database construction method which comprises the following steps: culturing salicornia europaea in a greenhouse by utilizing water culture vessels containing different lithium concentrations to obtain a plurality of salicornia europaea water culture vessels containing different lithium concentrations; measuring hyperspectral images of the salicornia europaea in the sampling intervals to obtain hyperspectral images of the salicornia europaea and the reference white board in each sampling interval; extracting a hyperspectral reflectivity image and hyperspectral data of the salicornia europaea in each sampling interval so as to obtain absorption characteristic parameters and red edge positions of each continuum removal spectrum under different set wavelengths, and calculating the hyperspectral reflectivity of the salicornia europaea according to the hyperspectral reflectivity image of the salicornia europaea in each sampling interval. Respectively constructing a time sequence spectrum library and a standard spectrum library of salicornia europaea at different sampling intervals under the stress of different lithium concentrations; according to the method, the spectral feature precision of salicornia europaea is improved, and meanwhile, the spectral database of salicornia europaea is enriched.

Description

Technical Field

[0001] The invention relates to the technical field of Salicornia herba feature extraction, and in particular to a method for constructing a time-series spectral feature database of Salicornia herba under lithium stress. Background Art

[0002] Rare metal lithium occupies an extremely important position in today's new energy field and is widely valued and applied. It mainly includes three types: brine type, hard rock type and clay type, among which the brine type has the largest reserves. Some typical plants grow near most salt lakes, such as salt horn grass and reeds. Studies have shown that salt horn grass has a strong tolerance to lithium and can be used as an indicator plant for brine-type lithium prospecting. How to quickly extract the salt horn grass around the salt lake and its lithium content has important reference value for evaluating whether the salt lake has the ability to extract lithium resources, and is also of great significance to accelerating lithium mineral exploration.

[0003] Although there is a spectral library of Salicornia herbacea, the existing spectral library only has spectra at a certain time point and does not take into account the characteristics of the plant production cycle. Therefore, this method is difficult to distinguish plants with smaller differences. Secondly, the plant standard spectra used are mostly based on portable hyperspectral acquisition or endmember spectrum extraction from satellite hyperspectral images. The leaf spectrum or canopy spectrum obtained based on portable hyperspectral acquisition cannot well reflect the spectrum of the entire plant, and the endmember spectrum extracted from satellite hyperspectral images is also difficult to accurately reflect the plant spectrum due to its low spatial resolution. The existing spectral features are also mostly obtained based on the spectrum of a period, and the spectral characteristics of the plant growth cycle are not considered. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method for constructing a time-series spectral feature database of Salicornia herba under lithium stress, which is used to solve the problem of low Salicornia herba identification accuracy caused by the singleness of time point spectra in the existing Salicornia herba spectral library.

[0005] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is:

[0006] A method for constructing a time-series spectral feature database of lithium stress of Salicornia herba, comprising the following steps:

[0007] S1. Cultivating Salicornia herbacea in hydroponic vessels containing different lithium concentrations in a greenhouse to obtain a number of Salicornia herbacea hydroponic vessels containing different lithium concentrations;

[0008] S2, measuring the hyperspectral images of Salicornia in several Salicornia hydroponic vessels containing different lithium concentrations during the sampling interval, and obtaining the hyperspectral images of Salicornia and the reference white plate during each sampling interval;

[0009] S3, based on the hyperspectral images of Salicornia herba and the reference white plate at each sampling interval, extract the hyperspectral reflectance images and hyperspectral data of Salicornia herba at each sampling interval to obtain the absorption characteristic parameters and red edge positions of each continuum removal spectrum at different set wavelengths;

[0010] S4. Based on the hyperspectral reflectance images of Salicornia herba at each sampling interval and the absorption characteristic parameters and red edge positions of each continuum-removed spectrum at different set wavelengths, a time series spectral library and a standard spectral library of Salicornia herba at different sampling intervals under different lithium concentration stress were constructed.

[0011] Furthermore, step S1 specifically includes:

[0012] S11, selecting a number of hydroponic vessels, uniformly adding a NaCl solution of the same concentration into each hydroponic vessel, and adding lithium solutions of different concentrations into a number of hydroponic vessels, to obtain a number of hydroponic vessels containing different lithium concentrations;

[0013] S12, placing Salicornia seeds in a number of hydroponic vessels containing different lithium concentrations to obtain a number of Salicornia hydroponic vessels containing different lithium concentrations, and moving them to a greenhouse for cultivation;

[0014] S13. Add water to several Salicornia hydroponic containers containing different lithium concentrations once every week to keep the total amount of aqueous solution unchanged.

[0015] Furthermore, step S2 specifically includes:

[0016] S21, setting a sampling interval, wrapping a number of Salicornia hydroponic vessels containing different lithium concentrations with black flannel, with only the Salicornia leaking out, and placing the Salicornia hydroponic vessels containing different lithium concentrations wrapped with black flannel at a fixed position on the hyperspectral scanning platform;

[0017] The sampling intervals were the seedling stage, growth stage, vigorous stage, and decay stage of Salicornia herba;

[0018] S22. Measure the hyperspectral images of Salicornia in several Salicornia hydroponic vessels containing different lithium concentrations during the sampling interval, and obtain the hyperspectral images of Salicornia and the reference white plate during each sampling interval.

[0019] Furthermore, step S3 specifically includes:

[0020] S31, calculating the hyperspectral reflectance image data of the Salicornia herb at each sampling interval according to the hyperspectral images of the Salicornia herb and the reference white plate at each sampling interval;

[0021] S32, setting a first band threshold, using the first band threshold to distinguish the background and the Salicornia area of ​​the hyperspectral reflectance image data of Salicornia in each sampling interval, extracting the area of ​​Salicornia, and obtaining a hyperspectral reflectance image of Salicornia in each sampling interval;

[0022] S33, calculating the average hyperspectral data of Salicornia herba in each sampling interval, and using the average hyperspectral data of Salicornia herba in each sampling interval;

[0023] S34, performing an envelope removal operation according to the hyperspectral data of Salicornia herba in each sampling interval to obtain a continuum-removed spectrum in each sampling interval;

[0024] S35, based on the continuum removal spectrum of each sampling interval, extracting absorption characteristic parameters of each continuum removal spectrum at different set wavelengths;

[0025] S36, calculating the first-order derivative of the hyperspectral data of Salicornia herba in each sampling interval according to the absorption characteristic parameters of each continuum removal spectrum at different set wavelengths;

[0026] S37, calculating the maximum value of the first-order derivative of the hyperspectral data of Salicornia herbacea in a set wavelength range according to the first-order derivative of the hyperspectral data of Salicornia herbacea in each sampling interval, and using the maximum value as the red edge position.

[0027] Furthermore, the formula for calculating the hyperspectral reflectance image data of Salicornia herba in each sampling interval in step S31 is:

[0028]

[0029] Where, λ represents the wavelength, ρ(λ) represents the reflectance of Salicornia at wavelength λ, L(λ), L s (λ) represents the radiance value of Salicornia and the reference white plate at wavelength λ, ρ s (λ) represents the reflectance of the reference white plate at wavelength λ.

[0030] Furthermore, the absorption characteristic parameters in step S35 include absorption depth and absorption width, wherein the absorption depth is 1 minus the spectral value of the continuum of the absorption position removed from the spectrum, and the absorption width is the wavelength difference of the spectral value at each absorption position interval equal to the spectral value at half the absorption depth.

[0031] Furthermore, the formula for calculating the first-order derivative of the hyperspectral data of Salicornia herba in each sampling interval in step S36 is:

[0032]

[0033] Among them, λ i represents the wavelength of band i, R′(λi ) represents the wavelength λ of band i i The first-order derivative of i ) represents the wavelength λ of band i i The reflectivity at i-1 ) represents the wavelength λ of band i-1 i-1 The reflectivity at , Δλ represents the interval between two adjacent wavelengths.

[0034] Furthermore, in step S37, the maximum value of the first-order derivative of the hyperspectral data of Salicornia herba in the set wavelength range is calculated and used as the formula for the red edge position:

[0035] λ R =max(R′(λ j )), j∈(A, B)

[0036] Among them, λ R Indicates the red edge position, max indicates the maximum value, λ j represents the wavelength of band j, (A, B) represents the set wavelength range, R′(λ j ) represents the wavelength λ of band j j The first derivative of .

[0037] Furthermore, step S4 specifically includes:

[0038] S41, removing the absorption characteristic parameters and red edge position combination of the spectrum at different set wavelengths from each continuum, obtaining time series spectrum characteristic data, and constructing a time series spectrum library of different sampling intervals under different lithium concentration stresses of Salicornia herba;

[0039] S42. Based on the hyperspectral reflectance images of Salicornia herba at each sampling interval, a standard spectral library of Salicornia herba at different sampling intervals under different lithium concentration stress is constructed.

[0040] The present invention has the following beneficial effects:

[0041] 1. A method for constructing a time-series spectral feature database of lithium stress of Salicornia herba proposed in the present invention. The constructed Salicornia herba standard spectral library includes spectra of different growth periods, which enriches the spectral database of Salicornia herba and can also provide reference spectral data for extracting Salicornia herba information from portable spectrometers or images in the field;

[0042] 2. The generated time series spectral feature data integrates the spectral features of a growth cycle, which is richer than the previous spectral feature information of only one period, and the results of information extraction and physical and chemical feature inversion are more effective;

[0043] 3. The spectrum of the whole plant of Salicornia herb is obtained using the standard spectral library and the time-series spectral library of Salicornia herb. This is more accurate than the canopy spectrum obtained by ASD in field experiments or the endmember spectrum extracted by images, and can avoid other interfering information. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a schematic flow chart of a method for constructing a time-series spectral feature database of lithium stress of Salicornia herba proposed in the present invention. DETAILED DESCRIPTION

[0045] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0046] like Figure 1 As shown, a method for constructing a time-series spectral feature database of lithium stress of Salicornia herba includes the following steps S1-S4:

[0047] S1. Cultivating Salicornia herbacea in hydroponic vessels containing different lithium concentrations in a greenhouse to obtain a number of Salicornia herbacea hydroponic vessels containing different lithium concentrations.

[0048] In this embodiment, potted Salicornia herba is cultivated in a greenhouse, the purpose of which is to hydroponically cultivate Salicornia herba using different lithium concentrations so as to obtain the time series spectral characteristics of Salicornia herba under lithium concentration stress in subsequent steps. The operation process is as follows:

[0049] Specifically, step S1 includes S11-S13:

[0050] S11. Select a number of hydroponic vessels, evenly add a NaCl solution of the same concentration into each hydroponic vessel, and simultaneously add lithium solutions of different concentrations into a number of hydroponic vessels to obtain a number of hydroponic vessels containing different lithium concentrations.

[0051] S12. Place Salicornia seeds in a number of hydroponic vessels containing different lithium concentrations to obtain a number of Salicornia hydroponic vessels containing different lithium concentrations, and move them to a greenhouse for cultivation.

[0052] S13. Add water to several Salicornia hydroponic containers containing different lithium concentrations once every week to keep the total amount of aqueous solution unchanged.

[0053] In this embodiment, four hydroponic vessels can be selected for the experiment, specifically: NaCl solution of the same concentration is uniformly added to each hydroponic vessel, specifically 200mM NaCl solution is uniformly added to each hydroponic vessel, and lithium solutions of different concentrations are added to the four hydroponic vessels at the same time to obtain four hydroponic vessels containing different lithium concentrations; wherein the total solution of each of the four groups of hydroponic vessels is 2L, and lithium solutions of different concentrations of 0mM, 10mM, 100mM and 200mM are added to the four hydroponic vessels as the experimental group, and the Salicornia hydroponic vessel with a lithium concentration of 0 is used as the control group, the purpose of which is to observe at what concentration lithium has a promoting and stressing effect on Salicornia; at the same time, Salicornia seeds are placed in the four hydroponic vessels containing different lithium concentrations to obtain four Salicornia hydroponic vessels containing different lithium concentrations, and they are moved to a greenhouse for cultivation, and water is added to the four Salicornia hydroponic vessels containing different lithium concentrations every other week to keep the total amount of the aqueous solution unchanged, that is, the total amount of the aqueous solution is always kept at 2L.

[0054] S2. Measure the hyperspectral images of Salicornia in several Salicornia hydroponic vessels containing different lithium concentrations during the sampling interval, and obtain the hyperspectral images of Salicornia and the reference white plate during each sampling interval.

[0055] Specifically, step S2 specifically includes S21-S22:

[0056] S21. Setting a sampling interval, wrapping a number of Salicornia hydroponic vessels containing different lithium concentrations with black velvet, with only the Salicornia leaking out, and placing the number of Salicornia hydroponic vessels containing different lithium concentrations with the Salicornia leaking out and wrapped with black velvet at a fixed position on the hyperspectral scanning platform; wherein the sampling interval is the seedling stage, growth stage, vigorous stage and decay stage of the Salicornia.

[0057] S22. Measure the hyperspectral images of Salicornia in several Salicornia hydroponic vessels containing different lithium concentrations during the sampling interval, and obtain the hyperspectral images of Salicornia and the reference white plate during each sampling interval.

[0058] In this embodiment, Lumo Scanner control software is used to connect the AisaFenix ​​imaging hyperspectrometer to collect the hyperspectral image of Salicornia herba. The performance parameters of the AisaFenix ​​imaging hyperspectrometer are shown in Table 1:

[0059] Table 1 Performance parameters of AisaFenix ​​imaging hyperspectrometer

[0060]

[0061]

[0062] Using the performance parameters given in Table 1, the hyperspectral images of several groups of Salicornia in hydroponic vessels containing different lithium concentrations are measured at each sampling interval, thereby obtaining the hyperspectral images of Salicornia and the reference white board at each sampling interval, so as to extract the spectral features of Salicornia in the subsequent steps. Among them, the reference white board is also called a standard white board or a reflectance standard white board or a reflectance reference white board, which is an optical transfer standard, that is, a calibrated standard white board with spectral reflectance data provided by the National Institute of Metrology, and is also the most ideal Lambertian body. The reflected light in any direction follows the cosine theorem and can provide a reflectance of more than 98% of the full spectrum. Therefore, the reference white board can be used to accurately extract the hyperspectral reflectance image of Salicornia at each sampling interval in the subsequent step.

[0063] S3. Based on the hyperspectral images of Salicornia herba and the reference white plate at each sampling interval, the hyperspectral reflectance images and hyperspectral data of Salicornia herba at each sampling interval are extracted to obtain the absorption characteristic parameters and red edge positions of each continuum removal spectrum at different set wavelengths.

[0064] Specifically, step S3 specifically includes S31-S37:

[0065] S31. Calculate the hyperspectral reflectance image data of Salicornia herba at each sampling interval based on the hyperspectral images of Salicornia herba at each sampling interval and the reference white plate, that is:

[0066]

[0067] Where, λ represents the wavelength, ρ(λ) represents the reflectance of Salicornia at wavelength λ, L(λ), L s (λ) represents the radiance value of Salicornia and the reference white plate at wavelength λ, ρ s (λ) represents the reflectance of the reference white plate at wavelength λ.

[0068] S32, setting a first band threshold, using the first band threshold to distinguish the background and the Salicornia area of ​​the hyperspectral reflectance image data of Salicornia in each sampling interval, extracting the Salicornia area, and obtaining a hyperspectral reflectance image of Salicornia in each sampling interval.

[0069] In this embodiment, the first band is band 224 (754 nm), and the threshold of the first band is 0.15, that is, when the threshold of band 224 is greater than 0.15, it is Salicornia herbacea, and when it is less than 0.15, it is background, so as to accurately obtain the Salicornia herbacea area, thereby obtaining a high-spectral reflectance image of Salicornia herbacea in each sampling interval.

[0070] S33, calculating the average hyperspectral data of Salicornia herba in each sampling interval, and using the average hyperspectral data of Salicornia herba in each sampling interval.

[0071] In this embodiment, the average hyperspectral data of Salicornia herba in each sampling interval is calculated, that is, the average value of the hyperspectral reflectance image of Salicornia herba in each sampling interval is calculated.

[0072] S34, performing an envelope removal operation according to the hyperspectral data of Salicornia herba in each sampling interval to obtain a continuum-removed spectrum in each sampling interval.

[0073] In this embodiment, the hyperspectral data of Salicornia herba at each sampling interval is imported into the ENVI software, and the Continuum Removed function in the spectral analysis of the software is used to obtain the envelope-removed data to highlight the changes in spectral characteristics; this processing method is widely used in remote sensing, geological exploration and other fields, especially when processing hyperspectral data, it can more clearly identify and analyze spectral characteristics.

[0074] S35. Based on the continuum removal spectrum of each sampling interval, the absorption characteristic parameters of each continuum removal spectrum at different set wavelengths are extracted; wherein the absorption characteristic parameters include absorption depth and absorption width, wherein the absorption depth is 1 minus the spectral value of the continuum removal spectrum at the absorption position, and the absorption width is the wavelength difference at which the spectral value of each absorption position interval is equal to the spectral value at half the absorption depth.

[0075] In this embodiment, the different wavelengths set are 450nm, 650nm, 970nm, 1400nm and 1.9nm respectively; the purpose of obtaining the absorption characteristic parameters of each continuum removal spectrum at these wavelengths is that the spectral characteristics of the absorption position at these wavelengths are directly related to the chlorophyll, carotene and total moisture content of Salicornia herbacea, which can better highlight the spectral characteristics of Salicornia herbacea.

[0076] S36, according to the absorption characteristic parameters of each continuum removal spectrum at different set wavelengths, calculate the first-order derivative of the hyperspectral data of Salicornia herba in each sampling interval, that is:

[0077]

[0078] Among them, λ i represents the wavelength of band i, R′(λ i ) represents the wavelength λ of band i i The first-order derivative of i ) represents the wavelength λ of band i i The reflectivity at i-1 ) represents the wavelength λ of band i-1 i-1 The reflectivity at , Δλ represents the interval between two adjacent wavelengths.

[0079] S37, according to the first-order derivative of the hyperspectral data of Salicornia herba in each sampling interval, calculate the maximum value of the first-order derivative of the hyperspectral data of Salicornia herba in the set wavelength range, and use it as the red edge position, that is:

[0080] λ R =max(R′(λ j )), j∈(A, B)

[0081] Among them, λ R Indicates the red edge position, max indicates the maximum value, λ j represents the wavelength of band j, (A, B) represents the set wavelength range, R′(λ j ) represents the wavelength λ of band j j The first derivative of .

[0082] In this embodiment, the wavelength range (A, B) is set to 680nm-760nm, because the plant spectrum curve in this area has the fastest change rate, can reflect the health of the plant, and is highly correlated with the growth status of the plant.

[0083] S4. Based on the hyperspectral reflectance images of Salicornia herba at each sampling interval and the absorption characteristic parameters and red edge positions of each continuum-removed spectrum at different set wavelengths, a time series spectral library and a standard spectral library of Salicornia herba at different sampling intervals under different lithium concentration stress were constructed.

[0084] Specifically, step S4 includes S41-S42:

[0085] S41. The absorption characteristic parameters and red edge position combinations of the spectrum at different set wavelengths are removed from each continuum to obtain time series spectrum characteristic data, and a time series spectrum library of different sampling intervals under different lithium concentration stresses of Salicornia herba is constructed.

[0086] S42. Based on the hyperspectral reflectance images of Salicornia herba at each sampling interval, a standard spectral library of Salicornia herba at different sampling intervals under different lithium concentration stress is constructed.

[0087] In this embodiment, the time series spectral feature data is input into the ENVI software to generate a time series spectral library of different sampling intervals under different lithium concentration stress of Salicornia herba, and at the same time, the hyperspectral reflectance image of Salicornia herba at each sampling interval is input into the ENVI software to generate a standard spectral library of different sampling intervals under different lithium concentration stress of Salicornia herba; wherein, the standard spectral library can be used in the field or in images to extract information about Salicornia herba and its spatial distribution, and can enrich the Salicornia herba hyperspectral database, and the time series spectral feature database can also improve the recognition of Salicornia herba, and provide a technical reference for the rapid identification of salt lakes with lithium anomalies using aerospace hyperspectral images.

[0088] In summary, the method for constructing a time-series spectral feature database of lithium stress of Salicornia herba proposed in the present invention controls and realizes the hydroponic experiment of Salicornia herba under different lithium concentration stress through temperature experiment, measures the imaging hyperspectral image data of Salicornia herba in multiple periods through the scanning platform of the loaded AisaFenix ​​imaging hyperspectrometer, extracts the hyperspectral data of Salicornia herba in each period and its spectral features one by one, and finally constructs a standard spectral database of Salicornia herba under different lithium stresses; wherein, the standard spectral library of Salicornia herba constructed by the present invention includes spectra of different growth periods, which can enrich the spectral database of Salicornia herba and can also The invention provides reference spectral data for extracting information of Salicornia herbacea from a portable spectrometer or an image in the field; and the obtained time-series spectral characteristic data integrates the spectral characteristics of a growth cycle, which is richer than the spectral characteristic information of only one period in the past, so the results of information extraction and physical and chemical characteristic inversion are more effective; therefore, the standard spectral library and time-series spectral library of Salicornia herbacea constructed by the present invention are used to obtain the spectrum of the whole plant of Salicornia herbacea, which is more accurate than the canopy spectrum obtained by ASD in the field experiment or the end-member spectrum extracted by image, can avoid other interference information, and provide basic data and technical reference for the extraction of lithium anomalies in salt lakes.

[0089] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

[0090] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A method for constructing a time series spectral feature database of lithium stress of Salicornia herba, characterized in that: The following steps are involved: S1. Cultivating Salicornia herbacea in hydroponic vessels containing different lithium concentrations in a greenhouse to obtain a number of Salicornia herbacea hydroponic vessels containing different lithium concentrations; S2, measuring the hyperspectral images of Salicornia in several Salicornia hydroponic vessels containing different lithium concentrations during the sampling interval, and obtaining the hyperspectral images of Salicornia and the reference white plate during each sampling interval; S3, based on the hyperspectral images of Salicornia herba and the reference white plate at each sampling interval, extract the hyperspectral reflectance images and hyperspectral data of Salicornia herba at each sampling interval to obtain the absorption characteristic parameters and red edge positions of each continuum removal spectrum at different set wavelengths; S4. Based on the hyperspectral reflectance images of Salicornia herba at each sampling interval and the absorption characteristic parameters and red edge positions of each continuum-removed spectrum at different set wavelengths, a time series spectral library and a standard spectral library of Salicornia herba at different sampling intervals under different lithium concentration stress were constructed.

2. The method for constructing a time series spectral feature database of lithium stress of Salicornia herba according to claim 1, characterized in that: Step S1 specifically includes: S11, selecting a number of hydroponic vessels, uniformly adding a NaCl solution of the same concentration into each hydroponic vessel, and adding lithium solutions of different concentrations into a number of hydroponic vessels, to obtain a number of hydroponic vessels containing different lithium concentrations; S12, placing Salicornia seeds in a number of hydroponic vessels containing different lithium concentrations to obtain a number of Salicornia hydroponic vessels containing different lithium concentrations, and moving them to a greenhouse for cultivation; S13. Add water to several Salicornia hydroponic containers containing different lithium concentrations once every week to keep the total amount of aqueous solution unchanged.

3. The method for constructing a time series spectral feature database of lithium stress of Salicornia herba according to claim 2, characterized in that: Step S2 specifically includes: S21, setting a sampling interval, wrapping a number of Salicornia hydroponic vessels containing different lithium concentrations with black flannel, with only the Salicornia leaking out, and placing the Salicornia hydroponic vessels containing different lithium concentrations wrapped with black flannel at a fixed position on the hyperspectral scanning platform; The sampling intervals were the seedling stage, growth stage, vigorous stage, and decay stage of Salicornia herba; S22. Measure the hyperspectral images of Salicornia in several Salicornia hydroponic vessels containing different lithium concentrations during the sampling interval, and obtain the hyperspectral images of Salicornia and the reference white plate during each sampling interval.

4. The method for constructing a time series spectral feature database of lithium stress of Salicornia herba according to claim 3, characterized in that: Step S3 specifically includes: S31, calculating the hyperspectral reflectance image data of the Salicornia herb at each sampling interval according to the hyperspectral images of the Salicornia herb and the reference white plate at each sampling interval; S32, setting a first band threshold, using the first band threshold to distinguish the background and the Salicornia area of ​​the hyperspectral reflectance image data of Salicornia in each sampling interval, extracting the area of ​​Salicornia, and obtaining a hyperspectral reflectance image of Salicornia in each sampling interval; S33, calculating the average hyperspectral data of Salicornia herba in each sampling interval, and using the average hyperspectral data of Salicornia herba in each sampling interval; S34, performing an envelope removal operation according to the hyperspectral data of Salicornia herba in each sampling interval to obtain a continuum-removed spectrum in each sampling interval; S35, based on the continuum removal spectrum of each sampling interval, extracting absorption characteristic parameters of each continuum removal spectrum at different set wavelengths; S36, calculating the first-order derivative of the hyperspectral data of Salicornia herba in each sampling interval according to the absorption characteristic parameters of each continuum removal spectrum at different set wavelengths; S37, calculating the maximum value of the first-order derivative of the hyperspectral data of Salicornia herbacea in a set wavelength range according to the first-order derivative of the hyperspectral data of Salicornia herbacea in each sampling interval, and using the maximum value as the red edge position.

5. The method for constructing a time series spectral feature database of lithium stress of Salicornia herba according to claim 4, characterized in that: The formula for calculating the hyperspectral reflectance image data of Salicornia herba in each sampling interval in step S31 is: Where, λ represents the wavelength, ρ(λ) represents the reflectance of Salicornia at wavelength λ, L(λ), L s (λ) represents the radiance value of Salicornia and the reference white plate at wavelength λ, ρ s (λ) represents the reflectance of the reference white plate at wavelength λ.

6. The method for constructing a time series spectral feature database of lithium stress of Salicornia herba according to claim 5, characterized in that: The absorption characteristic parameters in step S35 include absorption depth and absorption width, wherein the absorption depth is 1 minus the spectrum value of the continuum of the absorption position removed from the spectrum, and the absorption width is the wavelength difference of the spectrum value at each absorption position interval equal to the spectrum value at half the absorption depth.

7. The method for constructing a time series spectral feature database of lithium stress of Salicornia herba according to claim 6, characterized in that: The formula for calculating the first-order derivative of the hyperspectral data of Salicornia herba at each sampling interval in step S36 is: Among them, λ i represents the wavelength of band i, R ′ (λ i ) represents the wavelength λ of band i i The first-order derivative of i ) represents the wavelength λ of band i i The reflectivity at i-1 ) represents the wavelength λ of band i-1 i-1 The reflectivity at , Δλ represents the interval between two adjacent wavelengths.

8. The method for constructing a time series spectral feature database of lithium stress of Salicornia herba according to claim 7, characterized in that: In step S37, the maximum value of the first-order derivative of the hyperspectral data of Salicornia herba in the set wavelength range is calculated and used as the formula for the red edge position: l R =max(R′(λ j )),j∈(A,B) Among them, λ R Indicates the red edge position, max indicates the maximum value, λ j represents the wavelength of band j, (A, B) represents the set wavelength range, R′(λ j ) represents the wavelength λ of band j j The first derivative of .

9. The method for constructing a time series spectral feature database of lithium stress of Salicornia herba according to claim 8, characterized in that: Step S4 specifically includes: S41, removing the absorption characteristic parameters and red edge position combination of the spectrum at different set wavelengths from each continuum, obtaining time series spectrum characteristic data, and constructing a time series spectrum library of different sampling intervals under different lithium concentration stresses of Salicornia herba; S42. Based on the hyperspectral reflectance images of Salicornia herba at each sampling interval, a standard spectral library of Salicornia herba at different sampling intervals under different lithium concentration stress is constructed.

Images