Rapid evaluation method and system for soil fertility level

Abstract

The application provides a soil fertility level rapid evaluation method and system, relates to the soil fertility detection technical field, and provides the spectrum data basis through scanning soil samples to obtain the original absorbance spectrum data curve and extracts the water peak value absorbance, the actual peak wavelength and the water combined frequency characteristic peak width; calculates the water salt optical path coupling factor and carries out water salt correction operation to obtain the virtual dry soil spectrum data curve in combination with the volume correction coefficient, quantifies the interference of the salt on the water optical path and eliminates the water background and the salt masking; the characteristic virtual baseline of organic matter and total nitrogen is constructed, and the integral area is calculated to obtain the characteristic intensity of organic matter and total nitrogen, so that physical input variables are provided for quantitative inversion; the water salt optical path coupling factor is introduced to invert the organic matter content and the total nitrogen content, the signal suppression caused by the salt stress is corrected; the effective fertility index is calculated in combination with the organic matter content and the soil total nitrogen content, and is classified, so that the comprehensive evaluation of the actual fertilization capacity of the soil is realized.

Description

Technical Field

[0001] This invention relates to the field of soil fertility testing technology, specifically to a rapid evaluation method and system for soil fertility levels. Background Technology

[0002] Modern precision agriculture relies on real-time monitoring of soil nutrient status, with soil organic matter and total nitrogen content being core indicators for assessing land productivity potential. Traditional detection methods often employ chemical analysis techniques such as potassium dichromate oxidation or the Kjeldahl method, which have limitations including long detection cycles, cumbersome pretreatment, high consumption of chemical reagents, and difficulty in achieving continuous in-situ monitoring. With the development of photoelectric detection technology, near-infrared spectroscopy has been gradually introduced into the field of soil testing due to its non-destructive and rapid characteristics. This technology utilizes the interaction between the vibrational frequencies of hydrogen-containing groups in molecules and light, and inverts the nutrient content in the soil by analyzing the diffuse reflectance characteristics of the spectrum. It has become an important technological path to replace traditional wet chemical analysis and achieve digital soil management.

[0003] In the prior art, CN108169162A discloses a rapid evaluation method for the fertility level of tea garden soil. This method collects near-infrared spectral data of the soil, uses a continuous projection algorithm to screen characteristic wavelength variables related to organic matter, and combines this with machine learning algorithms such as linear discriminant analysis, support vector machine, or extreme learning machine to establish a classification model, thereby determining the soil fertility level. This type of technology focuses on the application of chemometric methods, that is, establishing a probabilistic mapping relationship between the original spectral data and soil nutrient chemical values ​​through mathematical statistical means, and improving the identification accuracy of dried or specifically pretreated soil samples through algorithm model optimization.

[0004] However, the aforementioned existing technologies have significant technical limitations when applied to the detection of fresh, moist soil samples from the field that have not undergone drying treatment. In-situ field soils typically contain varying gradients of moisture and salinity. Moisture exhibits extremely strong combination and overtone absorption peaks in the near-infrared region, with signal intensities far exceeding those of organic matter and nitrogen, resulting in a background masking effect. Simultaneously, changes in salt ion concentration in the soil induce ion hydration, altering the hydrogen bond network structure of water molecules and causing nonlinear physical shifts in spectral absorption peak positions. Existing statistical modeling methods lack independent decoupling and compensation mechanisms for this complex water-salt physical interference. They cannot accurately extract effective nutrient spectral features from the band shifts caused by strong moisture background and salinity. Consequently, in environments with large moisture content fluctuations or salinization, the robustness and detection accuracy of the spectral inversion model decrease significantly, failing to meet the technical requirements for rapid detection of fresh field soil samples.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a rapid evaluation method and system for soil fertility levels to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A rapid evaluation method for soil fertility levels includes the following steps:

[0009] Sampling points were set in the area to be evaluated to collect soil samples. The original absorbance spectral data curve of the soil samples was obtained in the preset target band. The peak absorbance of water, the actual peak wavelength of water, and the characteristic peak width of water in the preset water combination absorption band were extracted.

[0010] Based on the drift of the actual peak wavelength relative to the reference peak wavelength of pure water and the peak width of the combined frequency characteristic of water, a standard unit constant and the peak absorbance of water are introduced for normalization, and the water-salt optical path coupling factor is calculated.

[0011] The water-salt background interference component is calculated using the water-salt optical path coupling factor and subtracted from the original absorbance spectral data curve. The effective concentration ratio of dry soil particles is determined by the peak absorbance of water and the preset volume correction coefficient. Gain compensation is then applied to the spectral data after subtracting the water-salt background interference component to obtain a virtual dry soil spectral data curve.

[0012] On the virtual dry soil spectral data curve, construct the characteristic virtual baselines of organic matter and total nitrogen in the characteristic bands of organic matter and total nitrogen. Calculate the integral area of ​​the virtual dry soil spectral data curve relative to the characteristic virtual baselines of organic matter and total nitrogen in the band frequency region to obtain the characteristic intensity of organic matter and total nitrogen.

[0013] The characteristic intensities of organic matter and total nitrogen were inverted to the initial nutrient content. Nonlinear gain compensation based on water-salt optical path coupling factor was introduced to correct the gain of the initial nutrient content in order to compensate for the suppression of spectral signal caused by salt stress, and the final soil organic matter content and soil total nitrogen content were obtained.

[0014] The effective fertility index is calculated based on the soil organic matter content and total nitrogen content. The effective fertility index is then compared with the preset graded fertility thresholds, and the soil fertility level assessment results are generated based on the comparison results.

[0015] Furthermore, soil samples were randomly collected from the field to be evaluated to obtain soil samples to be tested. Stones and plant debris were removed from the soil samples collected from the field and placed into a sample cup for the spectrometer. The surface was then compacted and leveled using a sample press. A rotating sample stage was used to perform diffuse reflectance scanning at three different physical positions on the same sample to obtain three reflectance curves within the preset target wavelength range. The three reflectance curves were then logarithmically transformed to obtain three spectral absorbance curves. The arithmetic mean of the three spectral absorbance curves was calculated to obtain the original absorbance spectral data curve.

[0016] Furthermore, extract the peak absorbance of water, the actual peak wavelength of water, and the characteristic peak width of water in the original absorbance spectral data curve within the preset water absorption band.

[0017] The extraction methods for moisture peak absorbance are as follows: the peak absorbance value is found within the preset moisture combined absorption band in the original absorbance spectral data curve; the extraction method for actual moisture peak wavelength is found at the wavelength corresponding to the moisture peak absorbance in the original absorbance spectral data curve; and the extraction method for moisture combined characteristic peak width is found by searching along the wavelength axis in the original absorbance spectral data curve, with the actual moisture peak wavelength as the center, in both the short-wavelength and long-wavelength directions, and identifying the first characteristic wavelength in the short-wavelength direction and the second characteristic wavelength in the long-wavelength direction when the absorbance value drops to half of the moisture peak absorbance, and then calculating the difference between the second characteristic wavelength and the first characteristic wavelength.

[0018] Furthermore, based on the theory of ion hydration effect, a formula for calculating the water-salt optical path coupling factor is constructed;

[0019] Formula for calculating water-salt optical path coupling factor:

[0020]

[0021] in, Indicates the water-salt optical path coupling factor. Indicates the peak absorbance at moisture content. This indicates the actual peak wavelength of moisture content. The wavelength representing the theoretical reference peak position for pure water. This represents the preset spectral scale normalization factor. This represents the preset sensitivity coefficient. Represented by natural constant Logarithmic function with base 0. Represents the natural constant. This indicates the width of the characteristic peak of the water combination frequency. This represents the preset standard width normalization factor.

[0022] Furthermore, the water-salt optical path coupling factor is used as an adjustment coefficient and multiplied with the preset standard water-based absorbance spectral data curve to calculate the background spectral response intensity under the current salt stress environment, and then subtracted from the original absorbance spectral data curve.

[0023] The peak absorbance of water content is used to characterize the volume fraction of water in the soil. Combined with a volume correction factor, the effective concentration fraction of dry soil particles is calculated. A virtual dry soil spectral data curve, obtained after water-salt correction calculations, is then constructed. The formula used is as follows:

[0024]

[0025] in, This represents the virtual dry soil spectral data curve. Indicates the spectral wavelength variable. This represents the original absorbance spectral data curve. This represents the preset standard water-based absorbance spectral data curve. Preset background correction factor, This indicates the preset volume correction factor.

[0026] Furthermore, based on the specific absorption bands of organic matter and nitrogen in the near-infrared region, characteristic bands of organic matter and total nitrogen were obtained. The absorbance at both ends of the characteristic bands of organic matter and total nitrogen was used to construct virtual baselines of organic matter first-order overtone region, organic matter combined frequency region, and total nitrogen.

[0027] Feature virtual baseline calculation principle:

[0028]

[0029] in, Indicates the first Wavelength variation in each band The characteristic virtual baseline value at the location, hour, This indicates that the first harmonic region of organic matter is at a wavelength The characteristic virtual baseline value at the location, hour, Indicates the frequency region of organic matter combination in wavelength variation The characteristic virtual baseline value at the location, hour, Indicates the wavelength variation in the full nitrogen frequency region The characteristic virtual baseline value at the location, Indicates the first The absorbance of the starting wavelength of each band on the virtual dry soil spectral data curve. Indicates the first The absorbance of the final wavelength of each band on the virtual dry soil spectral data curve. Indicates the first The starting wavelength of each band, Indicates the first The ending wavelength of each band, Indicates the first Wavelength variations across bands;

[0030] According to the Wavelength variation in each band The characteristic virtual baseline values ​​at the location are used to generate the characteristic virtual baseline of the first harmonic frequency region of organic matter, the characteristic virtual baseline of the combined frequency region of organic matter, and the characteristic virtual baseline of total nitrogen.

[0031] For the virtual baselines of the first harmonic frequency region of organic matter, the virtual baselines of the combined frequency region of organic matter, and the virtual baselines of total nitrogen, the baseline tangent integral method is used to calculate the integral area of ​​the virtual dry soil spectral data curves relative to the virtual baselines of organic matter and total nitrogen to obtain the characteristic intensity of organic matter and the characteristic intensity of total nitrogen.

[0032] Principle of organic matter characteristic strength calculation:

[0033]

[0034] in, Indicates the intensity of organic matter characteristics. This represents the wavelength variation in the first harmonic region of organic matter. This represents the wavelength variation in the organic matter combination frequency region. This indicates that on the virtual dry soil spectral data curve, the wavelength variable... Virtual dry soil spectral data values ​​at the location, This indicates that on the virtual dry soil spectral data curve, the wavelength variable... Virtual dry soil spectral data values ​​at the location, On the virtual baseline representing the characteristics of the first harmonic frequency region of organic matter, the wavelength variable The virtual baseline value of the first-order overtone region characteristic of organic matter at that location. On the virtual baseline representing the frequency characteristics of organic matter assemblage, the wavelength variable Virtual baseline value of organic matter combination frequency region characteristics at the location;

[0035] Principle of calculating the characteristic intensity of total nitrogen:

[0036]

[0037] in, Indicates the characteristic intensity of all nitrogen. This represents the wavelength variable in the full nitrogen frequency region. Indicates the wavelength variable Virtual dry soil spectral data values ​​at the location, On the virtual baseline representing the total nitrogen characteristics, the wavelength variable The virtual baseline value of total nitrogen characteristics at the location.

[0038] Furthermore, the final calculation principle for soil organic matter content is as follows:

[0039]

[0040] in, Indicates the soil organic matter content. This represents the preset organic matter spectral conversion sensitivity coefficient. This represents the preset organic matter reference offset correction constant. This represents the preset salt suppression compensation coefficient for organic matter;

[0041] The final calculation principle for total soil nitrogen content is as follows:

[0042]

[0043] in, Indicates the total nitrogen content of the soil. This represents the preset total nitrogen spectral conversion sensitivity coefficient. This represents the preset total nitrogen reference offset correction constant. This represents the preset salt suppression compensation coefficient for total nitrogen.

[0044] Furthermore, the effective fertility index is calculated based on the soil organic matter content and the soil total nitrogen content;

[0045] Principle of effective fertility index calculation:

[0046]

[0047] in, Indicates the effective fertility index, This indicates the weight of the target crop's requirement for organic matter nutrients. This indicates the weight of the target crop's requirement for total nitrogen nutrients. This indicates the preset baseline value for organic matter nutrient content. This represents the preset benchmark value for total nitrogen nutrient content. This represents the salt sensitivity coefficient of the target crop.

[0048] The effective fertility index is compared with the preset graded fertility thresholds. If the effective fertility index is less than the preset first-level fertility threshold, the soil fertility is judged to be low. If the effective fertility index is greater than or equal to the preset first-level fertility threshold and less than the preset second-level fertility threshold, the soil fertility is judged to be medium. If the effective fertility index is greater than or equal to the preset second-level fertility threshold, the soil fertility is judged to be high.

[0049] A rapid evaluation system for soil fertility levels, the evaluation system being used to implement the above-mentioned evaluation method, comprising:

[0050] Data acquisition and extraction module: used to collect soil samples at sampling points set in the area to be evaluated, obtain the original absorbance spectral data curve of the soil samples in the preset target band, and extract the peak absorbance of water, the actual peak wavelength of water, and the characteristic peak width of water in the preset water combination absorption band.

[0051] Correction factor extraction module: Based on the drift of the actual peak wavelength relative to the reference peak wavelength of pure water and the peak width of the combined frequency characteristic of water, it introduces a standard unit constant and the peak absorbance of water for normalization processing, and calculates the water-salt optical path coupling factor.

[0052] Water and salt interference correction module: It is used to calculate the water and salt background interference component using the water and salt optical path coupling factor and subtract it from the original absorbance spectral data curve. It uses the peak absorbance of water and the preset volume correction coefficient to determine the effective concentration ratio of dry soil particles and performs gain compensation on the spectral data after subtracting the water and salt background interference component to obtain a virtual dry soil spectral data curve.

[0053] Nutrient feature extraction module: used to construct characteristic virtual baselines of organic matter and total nitrogen on the characteristic bands of organic matter and total nitrogen on the virtual dry soil spectral data curve, calculate the integral area of ​​the virtual dry soil spectral data curve relative to the characteristic virtual baselines of organic matter and total nitrogen in the band frequency region, and obtain the characteristic intensity of organic matter and total nitrogen.

[0054] Nutrient content calculation module: It is used to invert the characteristic intensity of organic matter and total nitrogen into the initial nutrient content, introduce nonlinear gain compensation based on water-salt optical path coupling factor, and perform gain correction on the initial nutrient content to compensate for the spectral signal suppression caused by salt stress, so as to obtain the final soil organic matter content and soil total nitrogen content.

[0055] Fertility assessment module: This module calculates the effective fertility index based on soil organic matter content and total nitrogen content, compares the effective fertility index with preset graded fertility thresholds, and generates an assessment result of soil fertility level based on the comparison results.

[0056] Compared with the prior art, the beneficial effects of the present invention are:

[0057] This invention extracts the peak absorbance, actual peak wavelength, and combined frequency characteristic peak width of water from the raw absorbance spectral data curves of undried soil samples collected in the field within a preset target wavelength band. The drift of the actual peak wavelength relative to the reference peak wavelength of pure water is used to characterize the physical binding state of water molecules by salt in the soil. By introducing a standard unit constant and combining it with a water-salt optical path coupling factor calculated through normalization, the nonlinear interference of complex water-salt environments on the optical path can be quantified. This water-salt optical path coupling factor is used to adjust the correction intensity of the water-salt background spectrum, and the peak absorbance of water is substituted as a variable representing the water volume ratio to perform water-salt correction calculations on the raw absorbance spectral data curves. This processing method can separate virtual dry soil spectral data curves reflecting the solid phase characteristics of the soil from a strong moisture background using mathematical methods without physical drying, reducing the interference of spectral baseline drift caused by soil moisture fluctuations and soluble salts on the detection results, and achieving direct analysis of the spectral characteristics of in-situ moist soil samples from the field.

[0058] This invention further constructs a characteristic virtual baseline from the organic matter and total nitrogen characteristic bands of the virtual dry soil spectral data curve, and calculates the integral area of ​​the virtual dry soil spectral data curve relative to the characteristic virtual baseline within the band frequency range to obtain the organic matter and total nitrogen characteristic intensities, thus avoiding the noise influence of irrelevant variables in full-band modeling. During the inversion process, nonlinear gain compensation based on the water-salt optical path coupling factor is introduced to numerically correct the initial nutrient content obtained from the inversion of organic matter and total nitrogen characteristic intensities, compensating for the suppression of organic functional group spectral signals caused by saline stress, thereby obtaining the final soil organic matter and total nitrogen content. Furthermore, this invention calculates an effective fertility index based on the soil organic matter and total nitrogen content, and compares the effective fertility index with a preset graded fertility threshold to generate an assessment result of the soil fertility level. This evaluation method incorporates the limiting effect of salinity on crop nutrient absorption, and the output assessment result reflects the actual fertilization capacity of the soil under the current water and saline environment, overcoming the limitation that a single chemical storage index cannot truly characterize the production performance of saline-alkali soils. Attached Figure Description

[0059] Figure 1 This is a schematic diagram of the overall method flow of the present invention;

[0060] Figure 2 This is a schematic diagram of the overall system structure of the present invention;

[0061] Figure 3 This is the original absorbance spectrum numerical curve of the sample;

[0062] Figure 4 The numerical curve of the virtual dry soil spectrum of the sample. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0064] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0065] Example:

[0066] Please see Figure 1 , Figure 3 and Figure 4 The present invention provides a technical solution:

[0067] A rapid evaluation method for soil fertility levels includes the following steps:

[0068] Step 1: Collect soil samples from sampling points in the area to be evaluated, obtain the original absorbance spectral data curve of the soil samples in the preset target band, and extract the peak absorbance of water, the actual peak wavelength of water, and the characteristic peak width of water in the preset water combination absorption band.

[0069] In this embodiment, soil samples are randomly sampled from the field to be evaluated to obtain soil samples to be tested. Stones and plant debris are removed from the soil samples collected from the field and placed into a sample cup for the spectrometer. The surface is then compacted and leveled using a sample press. The target wavelength range is set to 900 nm to 2500 nm. A rotating sample stage is used to perform diffuse reflectance scanning at three different physical positions of the same sample to obtain three reflectance curves within the preset target wavelength range. The three reflectance curves are then logarithmically transformed to obtain three spectral absorbance curves. The arithmetic mean of the three spectral absorbance curves is calculated to obtain the original absorbance spectral data curve.

[0070] Extract the peak absorbance, actual peak wavelength, and characteristic peak width of water in the original absorbance spectrum data curve within the preset water absorption band. Specifically, the water absorption band is the band range from 1850nm to 2050nm.

[0071] The extraction methods for moisture peak absorbance are as follows: the peak absorbance value is found within the preset moisture combined absorption band in the original absorbance spectral data curve; the extraction method for actual moisture peak wavelength is found at the wavelength corresponding to the moisture peak absorbance in the original absorbance spectral data curve; and the extraction method for moisture combined characteristic peak width is found by searching along the wavelength axis in the original absorbance spectral data curve, with the actual moisture peak wavelength as the center, in both the short-wavelength and long-wavelength directions, and identifying the first characteristic wavelength in the short-wavelength direction and the second characteristic wavelength in the long-wavelength direction when the absorbance value drops to half of the moisture peak absorbance, and then calculating the difference between the second characteristic wavelength and the first characteristic wavelength.

[0072] Because the sample was not physically dried, moisture not only produced a strong spectral absorption background, but the dissolved salts within it also altered the hydrogen bond network structure of water molecules through ion hydration, causing nonlinear physical deformation of the moisture absorption peak. Extracting the moisture peak absorbance to quantify the proportion of water molecules in the optical path serves as a quantitative benchmark for subsequent calculations of volume correction coefficients and removal of the moisture background signal. Simultaneously, extracting the actual peak wavelength of moisture and the combined frequency characteristic peak width captures the spectral fingerprint characteristics under salt stress. This is because the ionic electric field intensity in the soil solution alters the vibrational frequency of OH bonds, causing a regular wavelength shift and peak broadening or sharpening of the absorption peak relative to the theoretical peak position of pure water. This process transforms complex environmental interference into calculable physical variables, providing indispensable data support for subsequent accurate removal of the water-salt background and reconstruction of virtual dry soil spectral data curves reflecting the true characteristics of the soil solid phase. This ensures the signal-to-noise ratio of feature extraction and the accuracy of the detection results without the need for physical drying.

[0073] Step 2: Based on the drift of the actual peak wavelength relative to the reference peak wavelength of pure water and the peak width of the combined frequency characteristic of water, a standard unit constant and the peak absorbance of water are introduced for normalization, and the water-salt optical path coupling factor is calculated.

[0074] In this embodiment, a formula for calculating the water-salt optical path coupling factor is constructed based on the theory of ion hydration effect.

[0075] Formula for calculating water-salt optical path coupling factor:

[0076]

[0077] in, Indicates the water-salt optical path coupling factor. Indicates the peak absorbance at moisture content. This indicates the actual peak wavelength of moisture content. The theoretical reference peak wavelength for pure water is 1940 nm. This represents the preset spectral scale normalization factor, with a value of 1 nm. This represents the preset sensitivity coefficient, used to adjust the response sensitivity of the water-salt optical path coupling factor to the drift of the actual peak wavelength relative to the reference peak wavelength of pure water. The value of 1.5 is used to simulate the nonlinear relationship between the ion electric field intensity and the spectral shift. Represented by natural constant Logarithmic function with base 0. Represents the natural constant. This indicates the width of the characteristic peak of the water combination frequency. This represents the preset standard width normalization factor, with a value of 1nm.

[0078] The water-salt optical path coupling factor calculated based on the ion hydration effect theory can quantify the degree of spectral response distortion caused by the ion electric field of soluble salts under current soil moisture conditions. As a dimensionless dynamic adjustment coefficient, it provides a basis for determining the correction intensity of the water-salt background spectrum. In the calculation logic of the water-salt optical path coupling factor, it increases synchronously with the increase of the drift of the actual peak wavelength of water relative to the reference peak wavelength of pure water. This numerical trend directly reflects the intensity of ion hydration in the soil solution; that is, the greater the drift of the actual peak wavelength of water relative to the reference peak wavelength of pure water, the stronger the binding force of salt on the hydrogen bond network of water molecules. The water-salt optical path coupling factor is correspondingly increased to characterize more severe optical path nonlinear interference, and the weight of this nonlinear response in the model is further strengthened using a preset sensitivity coefficient. Simultaneously, by introducing the peak width of the water combination frequency characteristic into the calculation, the water-salt optical path coupling factor can integrate the broadening or sharpening information of the waveform dimension, supplementing and improving the capture of salt interference characteristics from the perspective of the complexity of hydrogen bond energy level distribution. Furthermore, the peak absorbance of water content plays a reverse normalization role in the calculation of the water-salt optical path coupling factor. This mechanism removes the influence of the total water content represented by the peak absorbance of water content on the assessment of salt stress, ensuring that the calculated water-salt optical path coupling factor purely reflects the qualitative change of water molecules under the influence of salt per unit optical path, rather than a simple superposition of water volume represented by the peak absorbance of water content. This achieves accurate decoupling of complex water-salt interference signals and provides a reliable quantitative basis for subsequent correction calculations.

[0079] Step 3: Calculate the water and salt background interference component using the water-salt optical path coupling factor and subtract it from the original absorbance spectral data curve. Use the peak absorbance of water and the preset volume correction coefficient to determine the effective concentration ratio of dry soil particles and perform gain compensation on the spectral data after subtracting the water and salt background interference component to obtain the virtual dry soil spectral data curve.

[0080] In this embodiment, the water-salt optical path coupling factor is used as an adjustment coefficient, which is multiplied by the preset standard water-based absorbance spectral data curve to calculate the background spectral response intensity under the current salt stress environment, and then subtracted from the original absorbance spectral data curve.

[0081] The peak absorbance of water content is used to characterize the volume fraction of water in the soil. Combined with a volume correction factor, the effective concentration fraction of dry soil particles is calculated. A virtual dry soil spectral data curve, obtained after water-salt correction calculations, is then constructed. The formula used is as follows:

[0082]

[0083] in, This represents the virtual dry soil spectral data curve. This represents the spectral wavelength variable, with values ​​ranging from 900 nm to 2500 nm within the target wavelength band. This represents the original absorbance spectral data curve. This represents the preset standard water-based absorbance spectral data curve, obtained by measuring the standard absorbance curve of pure water across the entire wavelength range in a laboratory setting. The preset background correction coefficients were used to construct a soil calibration set containing different moisture and salinity gradients through gradient mixing experiments. The objective function was to minimize the residual area of ​​the spectrum in the pure water characteristic band after water removal. The best fitting coefficients were obtained by optimization using the least squares method. The preset volume correction coefficient is obtained by setting up a physical and chemical analysis comparison experiment. The true volume moisture content of a group of fresh soil samples is calculated and measured using the drying method, and linearly fitted with the moisture peak absorbance. The resulting slope is the volume correction coefficient.

[0084] Water-salt optical path coupling factor is used to perform water-salt correction calculation on the original absorbance spectral data curve to construct virtual dry soil spectral data curve. The virtual dry soil spectral data curve physically represents the pure spectral response of the soil solid phase material after mathematical means to remove strong water absorption and salt interference. It aims to eliminate the masking effect of the in-situ moist environment in the field on weak nutrient signals from the data level. In the specific computational logic, the numerical generation of the virtual dry soil spectral data curve relies on the dynamic control of the preset standard water-based absorbance spectral data curve by the water-salt optical path coupling factor. Specifically, the water-salt optical path coupling factor is used as a weighting coefficient to quantify the background spectral response intensity under the current salt stress and is subtracted from the original absorbance spectral data curve to specifically correct the spectral baseline drift and waveform distortion caused by the ion hydration effect. At the same time, in order to overcome the problem of reduced effective optical path of solid particles and signal dilution caused by water occupying soil pores, the water peak absorbance is used as a variable to characterize the water volume ratio and is combined with a preset volume correction coefficient to construct a correction denominator. When the water peak absorbance increases, the value of the correction denominator decreases accordingly, and then the virtual dry soil spectral data curve is amplified by the division operation. This reverse compensation mechanism based on volume ratio restores the effective concentration ratio of dry soil particles diluted by water, so that the final virtual dry soil spectral data curve can truly reflect the chemical bond vibration characteristics of soil organic matter and total nitrogen, and realize the accurate reconstruction of soil solid phase spectral information without the need for physical drying.

[0085] Step 4: Construct characteristic virtual baselines for organic matter and total nitrogen on the characteristic bands of organic matter and total nitrogen on the virtual dry soil spectral data curve, calculate the integral area of ​​the virtual dry soil spectral data curve relative to the characteristic virtual baselines of organic matter and total nitrogen in the band frequency region, and obtain the characteristic intensities of organic matter and total nitrogen.

[0086] In this embodiment, the characteristic bands of organic matter and nitrogen in the near-infrared region are obtained for the specific absorption bands of organic matter and nitrogen in the soil. The characteristic bands of organic matter and total nitrogen are used to construct the first-order overtone characteristic virtual baseline of organic matter, the characteristic virtual baseline of organic matter combined frequency region, and the characteristic virtual baseline of total nitrogen using the absorbance at both ends of the characteristic bands of organic matter and total nitrogen.

[0087] Feature virtual baseline calculation principle:

[0088]

[0089] in, Indicates the first Wavelength variation in each band The characteristic virtual baseline value at the location, hour, This indicates that the first harmonic region of organic matter is at a wavelength The characteristic virtual baseline value at that location indicates that the wavelength range of the first-order harmonic region of organic matter is the CH bond first-order harmonic region from 1700 nm to 1750 nm. hour, Indicates the frequency region of organic matter combination in wavelength variation The characteristic virtual baseline value at that location indicates that the mixed frequency region of organic matter is the mixed frequency region of CH and CC bonds, ranging from 2300 nm to 2350 nm. hour, Indicates the wavelength variation in the full nitrogen frequency region The characteristic virtual baseline value at that location, the wavelength range of the full nitrogen frequency region is the amide bond combination frequency region from 2100nm to 2150nm. Indicates the first The absorbance of the starting wavelength of each band on the virtual dry soil spectral data curve. Indicates the first The absorbance of the final wavelength of each band on the virtual dry soil spectral data curve. Indicates the first The starting wavelength of each band, Indicates the first The ending wavelength of each band, Indicates the first Wavelength variations across bands;

[0090] According to the Wavelength variation in each band The characteristic virtual baseline values ​​at the location are used to generate the characteristic virtual baseline of the first harmonic frequency region of organic matter, the characteristic virtual baseline of the combined frequency region of organic matter, and the characteristic virtual baseline of total nitrogen.

[0091] For the virtual baselines of the first harmonic frequency region of organic matter, the virtual baselines of the combined frequency region of organic matter, and the virtual baselines of total nitrogen, the baseline tangent integral method is used to calculate the integral area of ​​the virtual dry soil spectral data curves relative to the virtual baselines of organic matter and total nitrogen to obtain the characteristic intensity of organic matter and the characteristic intensity of total nitrogen.

[0092] Principle of organic matter characteristic strength calculation:

[0093]

[0094] in, Indicates the intensity of organic matter characteristics. This represents the wavelength variation in the first harmonic region of organic matter. This represents the wavelength variation in the organic matter combination frequency region. This indicates that on the virtual dry soil spectral data curve, the wavelength variable... Virtual dry soil spectral data values ​​at the location, This indicates that on the virtual dry soil spectral data curve, the wavelength variable... Virtual dry soil spectral data values ​​at the location, On the virtual baseline representing the characteristics of the first harmonic frequency region of organic matter, the wavelength variable The virtual baseline value of the first-order overtone region characteristic of organic matter at that location. On the virtual baseline representing the frequency characteristics of organic matter assemblage, the wavelength variable Virtual baseline value of organic matter combination frequency region characteristics at the location;

[0095] Principle of calculating the characteristic intensity of total nitrogen:

[0096]

[0097] in, Indicates the characteristic intensity of all nitrogen. This represents the wavelength variable in the full nitrogen frequency region. Indicates the wavelength variable Virtual dry soil spectral data values ​​at the location, On the virtual baseline representing the total nitrogen characteristics, the wavelength variable The virtual baseline value of total nitrogen characteristics at the location.

[0098] After obtaining the virtual dry soil spectral data curve, in order to accurately quantify the chemical reserves of organic matter and total nitrogen simultaneously from the virtual dry soil spectral data curve, characteristic virtual baselines for organic matter and total nitrogen were constructed in the first-order octave region of organic matter, the organic matter combination frequency region, and the total nitrogen frequency region, respectively. The integral area was calculated based on the characteristic virtual baselines of organic matter and total nitrogen to obtain the characteristic intensity of organic matter and total nitrogen. The characteristic virtual baseline value, as a dependent variable reflecting the intensity of the background spectral response, is significant in constructing a reference benchmark that can simulate physical backgrounds such as light scattering from soil particles. The value of the characteristic virtual baseline depends on the absorbance of the starting and ending wavelengths in the first-order octave region of organic matter, the organic matter combination frequency region, and the total nitrogen frequency region on the virtual dry soil spectral data curve. These absorbances at the starting and ending wavelengths on the virtual dry soil spectral data curve serve as key independent variables, directly locking the position intercept and slope of the characteristic virtual baseline value within the corresponding band. A linear correlation is maintained between the wavelength variable and the characteristic virtual baseline value, allowing the characteristic virtual baseline value to exhibit a linear trend that can fit non-specific physical background signals as the wavelength variable changes. After establishing the characteristic virtual baseline values ​​using the above method, the characteristic intensity of organic matter and the characteristic intensity of total nitrogen are used as dependent variables to characterize the net energy absorbed by soil nutrients. The values ​​of the characteristic intensity of organic matter and the characteristic intensity of total nitrogen are determined by the integral of the difference between the virtual dry soil spectral data value and the characteristic virtual baseline value in the corresponding first harmonic frequency region of organic matter, the combined frequency region of organic matter, and the frequency region of total nitrogen. In this calculation process, the virtual dry soil spectral data value represents the actual spectral response containing nutrient information, while the characteristic virtual baseline value represents the non-specific background response. The difference between the virtual dry soil spectral data value and the characteristic virtual baseline value is the effective absorbance. This difference shows a significant positive correlation with the concentration of nutrients in the soil. That is, the higher the content of organic matter or total nitrogen in the soil, the stronger the absorption effect of its molecular functional groups on the characteristic wavelength light, resulting in a deeper dip of the virtual dry soil spectral data curve relative to the characteristic virtual baseline of organic matter and total nitrogen, which in turn leads to a larger value of the characteristic intensity of organic matter and the characteristic intensity of total nitrogen obtained by integral calculation. Compared to single-point sampling, this process using the baseline tangent integration method can effectively smooth random noise on the virtual dry soil spectral data curve. In particular, for the organic matter characteristic intensity, by accumulating the integration areas of the two frequency bands, the first harmonic region of organic matter and the combined frequency region of organic matter, the complementary information of different energy level transitions is utilized to improve the sensitivity and robustness of the organic matter characteristic intensity to changes in organic matter content, laying a solid physical data foundation for the subsequent establishment of a high-precision quantitative inversion model.

[0099] Step 5: The characteristic intensities of organic matter and total nitrogen are inverted to the initial nutrient content. Nonlinear gain compensation based on water-salt optical path coupling factor is introduced to correct the gain of the initial nutrient content to compensate for the suppression of spectral signal caused by salt stress, so as to obtain the final soil organic matter content and soil total nitrogen content.

[0100] In this embodiment, the final soil organic matter content is calculated based on the following principle:

[0101]

[0102] in, Indicates the soil organic matter content. This represents the preset organic matter spectral transformation sensitivity coefficient. By setting up soil samples with the same salinity but different organic matter contents, the organic matter characteristic intensity of each group of samples is calculated. Linear regression analysis is then performed on the organic matter characteristic intensity and organic matter content of multiple groups of samples, and the slope obtained is the organic matter spectral transformation sensitivity coefficient. This represents the preset organic matter baseline shift correction constant. The intercept obtained through linear regression analysis of organic matter characteristic intensity and organic matter content from multiple sample groups is the organic matter baseline shift correction constant. This represents the preset salt suppression compensation coefficient for organic matter. By setting up soil samples with the same organic matter content but different salt contents, the organic matter characteristic intensity of each group of samples is calculated. The initial nutrient content of organic matter is then calculated through inversion. The relative signal deficit of each sample is extracted. The relative signal deficit is obtained by calculating the ratio of the true organic matter content to the inverted initial nutrient content of organic matter and subtracting one. Using the relative signal deficit as the dependent variable, a linear regression is performed to obtain the slope, which is the compensation coefficient for the suppression of organic matter by salt.

[0103] The final calculation principle for total soil nitrogen content is as follows:

[0104]

[0105] in, Indicates the total nitrogen content of the soil. This represents the preset total nitrogen spectral conversion sensitivity coefficient. By setting up soil samples with the same salinity but different total nitrogen contents, the total nitrogen characteristic intensity of each group of samples is calculated. Linear regression analysis is performed on the total nitrogen characteristic intensity and total nitrogen content of multiple groups of samples, and the slope obtained is the total nitrogen spectral conversion sensitivity coefficient. The preset total nitrogen benchmark shift correction constant is represented by the intercept obtained from linear regression analysis of total nitrogen characteristic intensity and total nitrogen content across multiple sample groups. This represents the preset salt suppression compensation coefficient for total nitrogen. By setting up soil samples with the same total nitrogen content but different salt contents, the total nitrogen characteristic intensity of each group of samples is calculated. The initial total nitrogen nutrient content is then calculated through inversion. The relative signal deficit for each sample is extracted. The relative signal deficit is obtained by calculating the ratio of the true total nitrogen content to the inverted initial total nitrogen nutrient content and subtracting one. Using the relative signal deficit as the dependent variable and performing linear regression fitting, the resulting slope is the salt suppression compensation coefficient for total nitrogen.

[0106] After obtaining the organic matter characteristic intensity and total nitrogen characteristic intensity through integral calculation, in order to accurately convert these physical characteristics into chemical indicators reflecting soil fertility levels, the organic matter characteristic intensity and total nitrogen characteristic intensity are converted into the final soil organic matter content and soil total nitrogen content according to the inversion formula. In this calculation process, the soil organic matter content and soil total nitrogen content are used as dependent variables, and their values ​​represent the true mass percentage of nutrients per unit mass of soil after background removal and salt suppression correction. The calculation logic of soil organic matter content and soil total nitrogen content is jointly determined by two parts: linear transformation basis and nonlinear gain compensation. Among them, the organic matter characteristic intensity or total nitrogen characteristic intensity is first multiplied by the preset organic matter spectral transformation sensitivity coefficient or the preset total nitrogen spectral transformation sensitivity coefficient and then added to the preset organic matter reference offset correction constant or the preset total nitrogen reference offset correction constant. This linear part of the calculation reflects the basic physical process of converting light absorption intensity into initial nutrient content according to the Beer-Lambert law. The greater the organic matter characteristic intensity and total nitrogen characteristic intensity, the higher the initial nutrient content obtained by conversion. However, to address the physical inhibition of chemical bonds by salt, the formula further multiplies the initial nutrient content by a gain compensation term that includes a water-salt optical path coupling factor. The physical reason for introducing this step is that high concentrations of salt ions in the soil solution create a strong electric field, restricting the free vibration of chemical bonds in organic matter and total nitrogen molecules. This leads to signal attenuation in the measured characteristic intensities of organic matter and total nitrogen, thus requiring numerical compensation. The specific calculation of this gain compensation term involves the logarithmic function operation of the water-salt optical path coupling factor. There is a non-linear positive gain relationship between soil organic matter content and soil total nitrogen content and the water-salt optical path coupling factor. That is, the larger the value of the water-salt optical path coupling factor, the stronger the ion hydration effect and the more severe the vibrational inhibition of chemical bonds. The gain factor calculated by the compensation coefficient with the natural logarithm of the water-salt optical path coupling factor as the independent variable also increases accordingly, thus performing a larger multiplicative correction on the initial content obtained from the linear transformation. The necessity of this calculation process lies in the fact that, through the multiplication gain mechanism in the mathematical formula, the chemical bond vibration signal suppressed by the salt electric field is reversed, ensuring that the final output of soil organic matter content and soil total nitrogen content still has extremely high detection accuracy and physical authenticity under saline-alkali stress environment.

[0107] Step 6: Calculate the effective fertility index based on the soil organic matter content and total nitrogen content, compare the effective fertility index with the preset graded fertility threshold, and generate the soil fertility level assessment result based on the comparison result.

[0108] In this embodiment, the effective fertility index is calculated based on the soil organic matter content and the soil total nitrogen content.

[0109] Principle of effective fertility index calculation:

[0110]

[0111] in, Indicates the effective fertility index, This indicates the weight of the target crop's requirement for organic matter nutrients. This indicates the weight of the target crop's requirement for total nitrogen nutrients. and The sum is 1, and it is set according to the fertilization needs of relevant personnel for the types of crops being planted. This indicates the preset baseline value for organic matter nutrient content. This represents the preset benchmark value for total nitrogen nutrient content, which is set by querying the corresponding local agricultural industry standards based on the selected crop type and geographical region. The target crop's salt sensitivity coefficient is determined by referring to the slope of the Maas-Hoffman linear model of the crop and salt content recorded by the Food and Agriculture Organization of the United Nations in the "Guidelines for Water Quality in Agricultural Water Use" as the relative yield decline rate. The relative yield decline rate of cotton, which has strong salt tolerance, is used as the benchmark for the salt sensitivity coefficient and set to 1. The ratio between the relative yield decline rates of the target crop and cotton is the salt sensitivity coefficient of the target crop.

[0112] The effective fertility index is compared with the preset graded fertility thresholds. If the effective fertility index is less than the preset first-level fertility threshold, the soil fertility is determined to be low. If the effective fertility index is greater than or equal to the preset first-level fertility threshold and less than the preset second-level fertility threshold, the soil fertility is determined to be medium. If the effective fertility index is greater than or equal to the preset second-level fertility threshold, the soil fertility is determined to be high.

[0113] After calculating the soil organic matter content and total nitrogen content, in order to comprehensively evaluate the actual agricultural production potential of the soil under the current water and salinity environment, this embodiment converts the soil organic matter content and total nitrogen content into an effective fertility index based on the calculation formula and the water-salt-optical path coupling factor, and generates the final evaluation result accordingly. The effective fertility index, as the dependent variable, does not simply represent the sum of soil chemical nutrients, but rather the effective fertilization capacity of the soil that can be actually utilized by the target crop, considering the hindered absorption caused by salinity stress. The calculation logic of the effective fertility index is jointly determined by two parts: potential nutrient supply and salt stress correction coefficient. Soil organic matter content and total nitrogen content are positively contributing independent variables, which are normalized by the ratio of preset organic matter nutrient content benchmark value and preset total nitrogen nutrient content benchmark value, respectively. Then, a weighted sum is calculated based on the preset target crop's demand weight for organic matter nutrient and the preset target crop's demand weight for total nitrogen nutrient. This part reflects the chemical reserve basis of the soil. The higher the soil organic matter content and soil total nitrogen content, the larger the calculated effective fertility index. However, to align with the objective laws of agricultural production in saline-alkali land, the formula introduces a decay term incorporating the water-salt-optical path coupling factor. This is because soil salinity generates osmotic stress, hindering crop roots from absorbing water and nutrients. Therefore, the water-salt-optical path coupling factor, as a negatively limiting independent variable, exhibits a negative correlation with the effective fertility index. That is, the larger the value of the water-salt-optical path coupling factor, the more severe the soil salinization. This, combined with the preset target crop's salt sensitivity coefficient, leads to a decrease in the effective fertility index under the same nutrient reserves. This calculation overcomes the shortcomings of traditional evaluation methods that focus only on chemical reserves while ignoring environmental stress. By introducing a salt correction mechanism, it can identify some pseudo-fertile soils with high nutrient content but which crops cannot absorb due to high salinity, ensuring the agricultural applicability of the evaluation results. Finally, by comparing the calculated effective fertility index with preset primary and secondary fertility thresholds, it is possible to determine whether soil fertility is at a low, medium, or high level, thus providing a practically guiding decision-making basis for precision fertilization and improvement in the field.

[0114] Please see Figure 2 The present invention also provides a rapid evaluation system for soil fertility levels, the evaluation system being used to implement the above-mentioned evaluation method, comprising:

[0115] Data acquisition and extraction module: used to collect soil samples at sampling points set in the area to be evaluated, obtain the original absorbance spectral data curve of the soil samples in the preset target band, and extract the peak absorbance of water, the actual peak wavelength of water, and the characteristic peak width of water in the preset water combination absorption band.

[0116] Correction factor extraction module: Based on the drift of the actual peak wavelength relative to the reference peak wavelength of pure water and the peak width of the combined frequency characteristic of water, it introduces a standard unit constant and the peak absorbance of water for normalization processing, and calculates the water-salt optical path coupling factor.

[0117] Water and salt interference correction module: It is used to calculate the water and salt background interference component using the water and salt optical path coupling factor and subtract it from the original absorbance spectral data curve. It uses the peak absorbance of water and the preset volume correction coefficient to determine the effective concentration ratio of dry soil particles and performs gain compensation on the spectral data after subtracting the water and salt background interference component to obtain a virtual dry soil spectral data curve.

[0118] Nutrient feature extraction module: used to construct characteristic virtual baselines of organic matter and total nitrogen on the characteristic bands of organic matter and total nitrogen on the virtual dry soil spectral data curve, calculate the integral area of ​​the virtual dry soil spectral data curve relative to the characteristic virtual baselines of organic matter and total nitrogen in the band frequency region, and obtain the characteristic intensity of organic matter and total nitrogen.

[0119] Nutrient content calculation module: It is used to invert the characteristic intensity of organic matter and total nitrogen into the initial nutrient content, introduce nonlinear gain compensation based on water-salt optical path coupling factor, and perform gain correction on the initial nutrient content to compensate for the spectral signal suppression caused by salt stress, so as to obtain the final soil organic matter content and soil total nitrogen content.

[0120] Fertility assessment module: This module calculates the effective fertility index based on soil organic matter content and total nitrogen content, compares the effective fertility index with preset graded fertility thresholds, and generates an assessment result of soil fertility level based on the comparison results.

[0121] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0122] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0123] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0124] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A rapid evaluation method for soil fertility levels, characterized in that, The specific steps include: Sampling points were set in the area to be evaluated to collect soil samples. The original absorbance spectral data curve of the soil samples was obtained in the preset target band. The peak absorbance of water, the actual peak wavelength of water, and the characteristic peak width of water in the preset water combination absorption band were extracted. Based on the drift of the actual peak wavelength relative to the reference peak wavelength of pure water and the peak width of the combined frequency characteristic of water, a standard unit constant and the peak absorbance of water are introduced for normalization, and the water-salt optical path coupling factor is calculated. The water-salt background interference component is calculated using the water-salt optical path coupling factor and subtracted from the original absorbance spectral data curve. The effective concentration ratio of dry soil particles is determined by the peak absorbance of water and the preset volume correction coefficient. Gain compensation is then applied to the spectral data after subtracting the water-salt background interference component to obtain a virtual dry soil spectral data curve. On the virtual dry soil spectral data curve, construct the characteristic virtual baselines of organic matter and total nitrogen in the characteristic bands of organic matter and total nitrogen. Calculate the integral area of ​​the virtual dry soil spectral data curve relative to the characteristic virtual baselines of organic matter and total nitrogen in the band frequency region to obtain the characteristic intensities of organic matter and total nitrogen. The characteristic intensities of organic matter and total nitrogen were inverted to the initial nutrient content. Nonlinear gain compensation based on water-salt optical path coupling factor was introduced to correct the gain of the initial nutrient content in order to compensate for the suppression of spectral signal caused by salt stress, and the final soil organic matter content and soil total nitrogen content were obtained. The effective fertility index is calculated based on the soil organic matter content and total nitrogen content. The effective fertility index is compared with the preset graded fertility threshold, and the soil fertility level assessment result is generated based on the comparison result. Based on the theory of ion hydration effect, a formula for calculating the water-salt optical path coupling factor is constructed. Formula for calculating water-salt optical path coupling factor: in, Indicates the water-salt optical path coupling factor. Indicates the peak absorbance at moisture content. This indicates the actual peak wavelength of moisture content. The wavelength representing the theoretical reference peak position for pure water. This represents the preset spectral scale normalization factor. This represents the preset sensitivity coefficient. Represented by natural constant Logarithmic function with base 0. Represents the natural constant. This indicates the width of the characteristic peak of the water content combination frequency. This represents the preset standard width normalization factor; Using the water-salt optical path coupling factor as an adjustment coefficient, multiply it with the preset standard water-based absorbance spectral data curve to calculate the background spectral response intensity under the current salt stress environment, and then subtract it from the original absorbance spectral data curve; The peak absorbance of water content is used to characterize the volume fraction of water in the soil. Combined with a volume correction factor, the effective concentration fraction of dry soil particles is calculated. A virtual dry soil spectral data curve, obtained after water-salt correction calculations, is then constructed. The formula used is as follows: in, This represents the virtual dry soil spectral data curve. Indicates the spectral wavelength variable. This represents the original absorbance spectral data curve. This represents the preset standard water-based absorbance spectral data curve. Preset background correction factor, This represents the preset volume correction factor; Based on the specific absorption bands of organic matter and nitrogen in the near-infrared region, characteristic bands of organic matter and total nitrogen were obtained. The absorbance at both ends of the characteristic bands of organic matter and total nitrogen was used to construct virtual baselines of the first-order overtone region of organic matter, the characteristic virtual baseline of the combined frequency region of organic matter, and the characteristic virtual baseline of total nitrogen. Feature virtual baseline calculation principle: in, Indicates the first Wavelength variation in each band The characteristic virtual baseline value at the location, hour, This indicates that the first harmonic region of organic matter is at a wavelength The characteristic virtual baseline value at the location, hour, Indicates the frequency region of organic matter combination in wavelength variation The characteristic virtual baseline value at the location, hour, Indicates the wavelength variation in the full nitrogen frequency region The characteristic virtual baseline value at the location, Indicates the first The absorbance of the starting wavelength of each band on the virtual dry soil spectral data curve. Indicates the first The absorbance of the final wavelength of each band on the virtual dry soil spectral data curve. Indicates the first The starting wavelength of each band, Indicates the first The ending wavelength of each band, Indicates the first Wavelength variations across bands; According to the Wavelength variation in each band The characteristic virtual baseline values ​​at the location are used to generate the characteristic virtual baseline of the first harmonic frequency region of organic matter, the characteristic virtual baseline of the combined frequency region of organic matter, and the characteristic virtual baseline of total nitrogen. For the virtual baselines of the first harmonic frequency region of organic matter, the virtual baselines of the combined frequency region of organic matter, and the virtual baselines of total nitrogen, the baseline tangent integral method is used to calculate the integral area of ​​the virtual dry soil spectral data curves relative to the virtual baselines of organic matter and total nitrogen to obtain the characteristic intensity of organic matter and the characteristic intensity of total nitrogen. Principle of organic matter characteristic strength calculation: in, Indicates the intensity of organic matter characteristics. This represents the wavelength variation in the first harmonic region of organic matter. This represents the wavelength variation in the organic matter combination frequency region. This indicates that on the virtual dry soil spectral data curve, the wavelength variable... Virtual dry soil spectral data values ​​at the location, This indicates that on the virtual dry soil spectral data curve, the wavelength variable... Virtual dry soil spectral data values ​​at the location, On the virtual baseline representing the characteristics of the first harmonic frequency region of organic matter, the wavelength variable The virtual baseline value of the first-order overtone region characteristic of organic matter at that location. On the virtual baseline representing the frequency characteristics of organic matter assemblage, the wavelength variable Virtual baseline value of organic matter combination frequency region characteristics at the location; Principle of calculating the characteristic intensity of total nitrogen: in, Indicates the characteristic intensity of all nitrogen. This represents the wavelength variable in the full nitrogen frequency region. Indicates the wavelength variable Virtual dry soil spectral data values ​​at the location, On the virtual baseline representing the total nitrogen characteristics, the wavelength variable Virtual baseline value of total nitrogen characteristics at the location; The final calculation principle for soil organic matter content is as follows: in, Indicates the soil organic matter content. This represents the preset organic matter spectral conversion sensitivity coefficient. This represents the preset organic matter reference offset correction constant. This represents the preset salt suppression compensation coefficient for organic matter; The final calculation principle for total soil nitrogen content is as follows: in, Indicates the total nitrogen content of the soil. This represents the preset total nitrogen spectral conversion sensitivity coefficient. This represents the preset total nitrogen reference offset correction constant. This represents the preset salt suppression compensation coefficient for total nitrogen; The effective fertility index is calculated based on soil organic matter content and total nitrogen content. Principle of effective fertility index calculation: in, Indicates the effective fertility index, This indicates the weight of the target crop's requirement for organic matter nutrients. This indicates the weight of the target crop's requirement for total nitrogen nutrients. This indicates the preset baseline value for organic matter nutrient content. This represents the preset benchmark value for total nitrogen nutrient content. This represents the salt sensitivity coefficient of the target crop. The effective fertility index is compared with the preset graded fertility thresholds. If the effective fertility index is less than the preset first-level fertility threshold, the soil fertility is determined to be low. If the effective fertility index is greater than or equal to the preset first-level fertility threshold and less than the preset second-level fertility threshold, the soil fertility is determined to be medium. If the effective fertility index is greater than or equal to the preset second-level fertility threshold, the soil fertility is determined to be high.

2. The rapid evaluation method for soil fertility level according to claim 1, characterized in that: Three reflectance curves within the preset target wavelength range are obtained, and the three reflectance curves are logarithmically transformed to obtain three spectral absorbance curves. The arithmetic mean of the three spectral absorbance curves is calculated to obtain the original absorbance spectral data curve.

3. The rapid evaluation method for soil fertility level according to claim 2, characterized in that: Extract the peak absorbance of water, the actual peak wavelength of water, and the characteristic peak width of water in the original absorbance spectral data curve within the preset water absorption band. The extraction methods for moisture peak absorbance are as follows: the peak absorbance value is found within the preset moisture combined absorption band in the original absorbance spectral data curve; the extraction method for actual moisture peak wavelength is found at the wavelength corresponding to the moisture peak absorbance in the original absorbance spectral data curve; and the extraction method for moisture combined characteristic peak width is found by searching along the wavelength axis in the original absorbance spectral data curve, with the actual moisture peak wavelength as the center, in both the short-wavelength and long-wavelength directions, and identifying the first characteristic wavelength in the short-wavelength direction and the second characteristic wavelength in the long-wavelength direction when the absorbance value drops to half of the moisture peak absorbance, and then calculating the difference between the second characteristic wavelength and the first characteristic wavelength.

4. A rapid evaluation system for soil fertility levels, characterized in that: The evaluation system is used to implement the evaluation method according to any one of claims 1-3, including: Data acquisition and extraction module: used to collect soil samples at sampling points set in the area to be evaluated, obtain the original absorbance spectral data curve of the soil samples in the preset target band, and extract the peak absorbance of water, the actual peak wavelength of water, and the characteristic peak width of water in the preset water combination absorption band. Correction factor extraction module: Based on the drift of the actual peak wavelength relative to the reference peak wavelength of pure water and the peak width of the combined frequency characteristic of water, it introduces a standard unit constant and the peak absorbance of water for normalization processing, and calculates the water-salt optical path coupling factor. Water and salt interference correction module: It is used to calculate the water and salt background interference component using the water and salt optical path coupling factor and subtract it from the original absorbance spectral data curve. It uses the peak absorbance of water and the preset volume correction coefficient to determine the effective concentration ratio of dry soil particles and performs gain compensation on the spectral data after subtracting the water and salt background interference component to obtain the virtual dry soil spectral data curve. Nutrient feature extraction module: used to construct characteristic virtual baselines of organic matter and total nitrogen on the characteristic bands of organic matter and total nitrogen on the virtual dry soil spectral data curve, calculate the integral area of ​​the virtual dry soil spectral data curve relative to the characteristic virtual baselines of organic matter and total nitrogen in the band frequency region, and obtain the characteristic intensity of organic matter and total nitrogen. Nutrient content calculation module: It is used to invert the characteristic intensity of organic matter and total nitrogen into the initial nutrient content, introduce nonlinear gain compensation based on water-salt optical path coupling factor, and perform gain correction on the initial nutrient content to compensate for the spectral signal suppression caused by salt stress, so as to obtain the final soil organic matter content and soil total nitrogen content. Fertility assessment module: This module calculates the effective fertility index based on soil organic matter content and total nitrogen content, compares the effective fertility index with preset graded fertility thresholds, and generates an assessment result of soil fertility level based on the comparison results.

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