Rapid soil trace element monitoring method based on crop multi-growth-period spectral effect

Abstract

The invention discloses a rapid soil trace element monitoring method based on a crop multi-growth-period spectral effect. The rapid soil trace element monitoring method comprises the following steps: acquiring target crop information; the growth period of the target crops is extracted, and if the growth period of the target crops is not in a preset growth period range, the corresponding target crops are deleted; extracting soil trace elements to be measured; determining a soil trace element estimation model corresponding to the target crop according to the growth period of the target crop and the to-be-measured soil trace elements; extracting the spectral absorption depth of the spectral absorption position of the canopy leaf of the target crop; and inputting the spectral absorption depth of the spectral absorption position of the canopy leaf of the target crop into the soil trace element estimation model of the corresponding target crop to obtain the to-be-measured trace element content of the soil environment where the target crop is located. By monitoring different growth periods of different target crops, rapid, efficient and large-area soil trace element monitoring can be realized, and convenience is provided for soil element investigation.

Description

Technical Field

[0001] The present invention relates to the technical field of soil element content detection, and more specifically, to a rapid monitoring method for soil trace elements based on the spectral effects of crops in multiple growth periods. Background Art

[0002] Soil trace element monitoring is an important technical means to ensure food security and sustainable agricultural development; currently, traditional laboratory detection methods mainly rely on soil sampling and analysis at discrete points. Although the accuracy is relatively high, its detection cycle is long, the cost is high, and the spatial coverage ability is limited, making it difficult to meet the needs of large-scale dynamic monitoring of soil elements. Summary of the Invention

[0003] In order to solve at least one of the above technical problems, the purpose of the present invention is to provide a rapid monitoring method for soil trace elements based on the spectral effects of crops in multiple growth periods. By combining the growth change characteristics of vegetation in different growth periods under different element concentrations, the rapid and efficient monitoring of soil trace elements is improved.

[0004] The present invention provides a rapid monitoring method for soil trace elements based on the spectral effects of crops in multiple growth periods, including:

[0005] Obtain target crop information;

[0006] Extract the growth period of the target crop. If the growth period of the target crop is not within the preset growth period range, delete the corresponding target crop;

[0007] Extract the soil trace elements to be measured;

[0008] According to the growth period of the target crop and the soil trace elements to be measured, determine the soil trace element estimation model for the corresponding target crop;

[0009] Extract the spectral absorption depth at the spectral absorption position of the canopy leaves of the target crop;

[0010] Input the spectral absorption depth at the spectral absorption position of the canopy leaves of the target crop into the soil trace element estimation model of the corresponding target crop to obtain the content of the trace elements to be measured in the soil environment where the target crop is located.

[0011] In this solution, it further includes:

[0012] Obtain the crop information within the target area;

[0013] Extract any one crop information within the target area and compare it with the preset crop information in turn to obtain a set of crop similarity values;

[0014] If any crop similarity value in the crop similarity value set is less than or equal to a preset crop similarity value threshold, the corresponding crop information is deleted;

[0015] If any crop similarity value in the crop similarity value set is greater than the preset crop similarity value threshold, the corresponding crop is set as the target crop.

[0016] In this solution, the steps for obtaining the soil trace element estimation model specifically include:

[0017] Based on a preset experimental area, obtain the spectral data sets of the canopy leaves of different target crops at different growth stages;

[0018] Preprocess the spectral data in the spectral data sets of the canopy leaves of different target crops at different growth stages to obtain a data set of the effective spectral absorption positions and the corresponding spectral absorption depths of the canopy leaves of different target crops at different growth stages;

[0019] Divide the data set of the effective spectral absorption positions and the corresponding spectral absorption depths of the canopy leaves of different crops at different growth stages according to different crops and growth stages to obtain multiple data sets of the effective spectral absorption positions and the corresponding spectral absorption depths of the canopy leaves of the same target crop and at the same growth stage;

[0020] Extract the content values of the target trace elements in the experimental area;

[0021] Based on the multiple linear regression algorithm, use the content values of the target trace elements in the experimental area as independent variables and the spectral absorption depths corresponding to the effective spectral absorption positions of the canopy leaves of the same target crop and at the same growth stage as independent variables to construct a soil trace element estimation model.

[0022] In this solution, the steps for preprocessing the spectral data in the spectral data sets of the canopy leaves of different target crops at different growth stages specifically include:

[0023] Extract the spectral absorption positions and the spectral absorption parameters at the corresponding spectral absorption positions in the spectral data;

[0024] Extract the content values of the target trace elements in the canopy parts of the corresponding target crops;

[0025] Compare and analyze the spectral absorption parameters and the content values of the target trace elements in the canopy parts of the target crops to obtain the correlation index between the spectral absorption parameters at the corresponding spectral absorption positions and the content of the target trace elements in the canopy parts;

[0026] If the correlation index is greater than a preset index threshold, delete the corresponding spectral data;

[0027] The spectral absorption parameters at least include spectral absorption depth and spectral absorption area.

[0028] In this solution, the steps for obtaining the spectral absorption depth specifically include:

[0029] Perform envelope removal processing on the spectral data to obtain normalized spectral data;

[0030] Construct a spectral curve based on the normalized spectral data, and extract the spectral absorption valley position in the spectral curve and the spectral absorption valley reflectance corresponding to the spectral absorption valley position;

[0031] Set the spectral absorption depth as D, and its formula is ; where represents the spectral absorption depth at the spectral absorption valley position ; represents the spectral absorption valley position of the envelope reflectance.

[0032] In this solution, the steps for obtaining the spectral absorption area specifically include:

[0033] Obtain the wavelength range where the spectral absorption valley position is located, and set it as ;

[0034] Set the spectral absorption area as S, and its formula is , where represents the envelope reflectance at the spectral absorption position and the spectral absorption position is within the wavelength band .

[0035] In this solution, the steps for constructing a soil trace element estimation model based on the multiple linear regression algorithm, with the content value of the target trace element in the experimental area as the independent variable and the effective spectral absorption position and the corresponding spectral absorption depth of the canopy leaves of the same crop and the same growth period as the independent variables, specifically include:

[0036] According to the multiple linear regression algorithm, design the initial weight coefficients and the initial residual terms of the independent variables;

[0037] Preprocess the content value of the target trace element in the experimental area and the effective spectral absorption position and the corresponding spectral absorption depth of the canopy leaves of the same crop and the same growth period to obtain the training sample set and the test sample set for the corresponding soil trace element estimation model;

[0038] Input the training sample set of the soil trace element estimation model into the multiple linear regression equation with the initial weight coefficients and the initial residual terms for training to obtain the trained multiple linear regression equation;

[0039] Send the test samples in the test sample set to the trained multiple linear regression equation in sequence to obtain the test accuracy rate.

[0040] If the test accuracy rate is greater than the preset accuracy threshold, stop the training, output the weight coefficients and residual terms of the trained multiple linear regression equation, and obtain the soil trace element estimation model, whose formula is ; where represents the spectral absorption depth corresponding to the spectral absorption valley position , represents the corresponding weight coefficient, represents the residual term, n represents the set of spectral absorption valley positions, and m belongs to n.

[0041] In this solution, the step of obtaining the correlation index between the spectral absorption parameters at the corresponding spectral absorption positions and the target trace element content in the canopy part specifically includes:

[0042] According to the spectral absorption parameters at the spectral absorption positions and the target trace element content in the canopy part, obtain the corresponding linear index r, whose formula is , where represents the spectral absorption parameter value of the spectral absorption parameter i at the corresponding spectral absorption position, represents the mean value of the corresponding spectral absorption parameter i, represents the target trace element content in the corresponding target crop canopy part; represents the mean value of the target trace element content in the target crop canopy part;

[0043] Obtain the quantity value of this sample, and obtain the t value according to the quantity value of the sample and the corresponding linear index, , where N is the quantity value of the sample.

[0044] Based on the preset distribution table, determine the correlation index at the position where the t value is located.

[0045] A rapid monitoring method for soil trace elements based on the spectral effects of crops during multiple growth periods disclosed by the present invention includes: obtaining target crop information; extracting the growth period of the target crop, and if the growth period of the target crop is not within the preset growth period range, deleting the corresponding target crop; extracting the soil trace elements to be measured; determining the soil trace element estimation model corresponding to the target crop according to the growth period of the target crop and the soil trace elements to be measured; extracting the spectral absorption depth at the spectral absorption position of the canopy leaves of the target crop; inputting the spectral absorption depth at the spectral absorption position of the canopy leaves of the target crop into the soil trace element estimation model corresponding to the target crop to obtain the content of the trace elements to be measured in the soil environment where the target crop is located. By monitoring different growth periods of different target crops, the present invention can achieve rapid, efficient, and large-area monitoring of soil trace elements, providing convenience for soil element investigation. Description of the Drawings

[0046] Figure 1 Shows a flowchart of a rapid monitoring method for soil trace elements based on the spectral effects of crops during multiple growth periods of the present invention;

[0047] Figure 2 Shows the spectral data normalized by the present invention;

[0048] Figure 3 Shows a linear schematic diagram of molybdenum in soil trace elements and molybdenum in different parts of crops of the present invention. Detailed Embodiments

[0049] In order to more clearly understand the above objects, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the drawings and specific embodiments. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

[0050] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0051] Figure 1 Shows a flowchart of a rapid monitoring method for soil trace elements based on the spectral effects of crops during multiple growth periods of the present invention.

[0052] As Figure 1 shown, the present invention discloses a rapid monitoring method for soil trace elements based on the spectral effects of crops during multiple growth periods, including:

[0053] S101, obtaining target crop information;

[0054] S102. Extract the growth period of the target crop. If the growth period of the target crop is not within the preset growth period range, delete the corresponding target crop.

[0055] S103. Extract the soil trace elements to be measured.

[0056] S104. Determine the soil trace element estimation model for the corresponding target crop according to the growth period of the target crop and the soil trace elements to be measured.

[0057] S105. Extract the spectral absorption depth at the spectral absorption position of the canopy leaves of the target crop.

[0058] S106. Input the spectral absorption depth at the spectral absorption position of the canopy leaves of the target crop into the soil trace element estimation model for the corresponding target crop to obtain the content of the trace elements to be measured in the soil environment where the target crop is located.

[0059] According to the embodiments of the present invention, the trace elements contained in the canopy leaves corresponding to different growth periods of different target crops are different. Therefore, different soil trace element estimation models are set according to the different growth periods of different target crops, the soil trace element estimation model is matched according to the growth period of the target crop, and the spectral absorption depth at the spectral absorption position of the canopy leaves of the corresponding target crop is input to determine the content of the trace elements to be measured in the soil environment where the corresponding target crop is located, such as the trace element molybdenum.

[0060] According to the embodiments of the present invention, it further includes:

[0061] Obtain the crop information within the target area.

[0062] Extract any crop information within the target area and compare and analyze it with the preset crop information in turn to obtain a set of crop similarity values.

[0063] If any crop similarity value in the set of crop similarity values is less than or equal to the preset crop similarity value threshold, delete the corresponding crop information.

[0064] If any crop similarity value in the set of crop similarity values is greater than the preset crop similarity value threshold, set the corresponding crop as the target crop.

[0065] It should be noted that there may be multiple crops in the target area, and the soil depths that the roots of different crops can reach are different. For example, when detecting the content of trace elements within 20 cm of the soil surface, the crop with its main roots at the 20 cm position of the soil surface is used as the preset crop. The crop information includes the shape, size, and color of the canopy leaves of the crop.

[0066] According to an embodiment of the present invention, the steps for obtaining the soil trace element estimation model specifically include:

[0067] Based on a preset experimental area, obtain spectral data sets of canopy leaves of different target crops at different growth stages;

[0068] Preprocess the spectral data in the spectral data sets of canopy leaves of different target crops at different growth stages to obtain a data set of effective spectral absorption positions and corresponding spectral absorption depths of canopy leaves of different target crops at different growth stages;

[0069] Divide the data set of effective spectral absorption positions and corresponding spectral absorption depths of canopy leaves of different crops at different growth stages according to different crops and growth stages to obtain multiple data sets of effective spectral absorption positions and corresponding spectral absorption depths of canopy leaves of the same target crop and at the same growth stage;

[0070] Extract the content values of target trace elements in the experimental area;

[0071] Based on the multiple linear regression algorithm, use the content values of target trace elements in the experimental area as independent variables, and the spectral absorption depths corresponding to the effective spectral absorption positions of canopy leaves of the same target crop and at the same growth stage as independent variables to construct a soil trace element estimation model.

[0072] It should be noted that the spectral data of the canopy leaves of one growth stage of each target crop is set as a data set, and a soil trace element estimation model is constructed according to the corresponding data set, so as to ensure that the trace elements in the soil can be monitored at each stage.

[0073] According to an embodiment of the present invention, the steps for preprocessing the spectral data in the spectral data sets of canopy leaves of different target crops at different growth stages specifically include:

[0074] Extract the spectral absorption positions in the spectral data and the spectral absorption parameters at the corresponding spectral absorption positions;

[0075] Extract the content values of target trace elements in the canopy parts of the corresponding target crops;

[0076] Compare and analyze the spectral absorption parameters and the content values of target trace elements in the canopy parts of the target crops to obtain the correlation index between the spectral absorption parameters at the corresponding spectral absorption positions and the content of target trace elements in the canopy parts;

[0077] If the correlation index is greater than a preset index threshold, delete the corresponding spectral data;

[0078] The spectral absorption parameters at least include spectral absorption depth and spectral absorption area.

[0079] It should be noted that the accumulation and migration characteristics of trace elements in different parts of different target crops are different. Therefore, based on the characteristics of different targets, the element accumulation parts, that is, the parts for spectral modeling, are preferentially determined. First, the element contents of different canopy parts of the target crop at different growth stages are obtained, and the best canopy accumulation parts are selected from the perspective of the accumulation amount. To ensure the effectiveness of subsequent modeling, therefore, a correlation analysis needs to be carried out with the available state content of the corresponding element in the soil. For example, if the preset index threshold is 0.05, when the correlation index is less than or equal to 0.05, there is a significant relationship between the spectral absorption parameters at the corresponding spectral absorption position and the target trace element content of the canopy part.

[0080] Figure 2 The normalized spectral data of the present invention are shown.

[0081] As Figure 2 shown, the steps for obtaining the spectral absorption depth specifically include:

[0082] The spectral data are subjected to an envelope removal process to obtain the normalized spectral data;

[0083] A spectral curve is constructed based on the normalized spectral data, and the spectral absorption valley position and the spectral absorption valley reflectance at the corresponding spectral absorption valley position in the spectral curve are extracted;

[0084] The spectral absorption depth is set as D, and its formula is ; where represents the spectral absorption depth at the spectral absorption valley position ; represents the envelope reflectance at the spectral absorption valley position ;

[0085] According to an embodiment of the present invention, the steps for obtaining the spectral absorption area specifically include:

[0086] Obtain the band range where the spectral absorption valley position is located, and set it as ;

[0087] The spectral absorption area is set as S, and its formula is , where represents the envelope reflectance at the spectral absorption position and the spectral absorption position is within the band ;

[0088] It should be noted that after normalizing the spectral data of the target crop, the normalized spectral data is obtained, and the normalized spectral data is plotted into a spectral curve, and the corresponding spectral absorption valley position is found according to the spectral curve, and then the corresponding absorption depth and absorption area are further determined.

[0089] According to the embodiments of the present invention, the steps of constructing a soil trace element estimation model based on the multiple linear regression algorithm, with the content value of the target trace element in the experimental area as the independent variable, and the effective spectral absorption position and the corresponding spectral absorption depth of the canopy leaves of the same crop and the same growth period as the independent variables, specifically include:

[0090] According to the multiple linear regression algorithm, design the initial weight coefficient and the initial residual term of the independent variable;

[0091] Preprocess the content value of the target trace element in the experimental area, the effective spectral absorption position of the canopy leaves of the same crop and the same growth period, and the corresponding spectral absorption depth to obtain the training sample set and the test sample set of the corresponding soil trace element estimation model;

[0092] Input the training sample set of the soil trace element estimation model into the multiple linear regression equation of the initial weight coefficient and the initial residual term for training to obtain the trained multiple linear regression equation;

[0093] Send the test samples in the test sample set to the trained multiple linear regression equation in turn to obtain the test accuracy rate;

[0094] If the test accuracy rate is greater than the preset accuracy threshold, stop training, output the weight coefficient and the residual term of the trained multiple linear regression equation, and obtain the soil trace element estimation model, and its formula is ; where represents the spectral absorption depth corresponding to the spectral absorption valley position , represents the corresponding weight coefficient, represents the residual term, n represents the set of spectral absorption valley positions, and m belongs to n.

[0095] It should be noted that according to the different growth periods of different target crops, the key growth period observations of different target crops are determined, and crop and soil samples are collected. In this example, wheat is taken as an example. If the key growth period of wheat is determined to be the jointing stage, the main part of the canopy at the jointing stage is the leaf. When collecting, select the target crop growing in each experimental pot. At the jointing stage, cut 20 leaves 20 cm long with scissors. For the soil, select the surface soil of 0-20 cm near the sampling point. After picking out large impurities or large lumps, mix well and put them into a sample bag; A total of 216 soil samples and 216 wheat leaf samples at the jointing stage are collected. Each test sample or training sample includes the spectral data of a wheat leaf at the jointing stage and the soil trace element data corresponding to the wheat.

[0096] According to an embodiment of the present invention, the step of obtaining the correlation index between the spectral absorption parameter at the corresponding spectral absorption position and the target trace element content of the canopy part specifically includes:

[0097] According to the spectral absorption parameter at the spectral absorption position and the target trace element content of the canopy part, obtain the corresponding linear index r, and its formula is , where represents the spectral absorption parameter value of the spectral absorption parameter i at the corresponding spectral absorption position, represents the mean value of the corresponding spectral absorption parameter i, represents the target trace element content of the corresponding target crop canopy part; represents the mean value of the target trace element content of the target crop canopy part;

[0098] Obtain the quantity value of this sample, and obtain the t value according to the quantity value of the sample and the corresponding linear index, , where N is the quantity value of the sample.

[0099] Based on a preset distribution table, determine the correlation index at the position where the t value is located.

[0100] It should be noted that taking the same target crop in the same growth period as the dividing line, for example, the spectral data of 216 same target crops in the same growth period, then the corresponding sample quantity value N is 216.

[0101] Figure 3 shows the linear schematic diagram of soil trace element molybdenum and trace element molybdenum in different parts of crops of the present invention.

[0102] As Figure 3 shown, taking wheat as an example, it shows the linear relationship diagram of trace element molybdenum in different parts and trace element molybdenum in the soil at different growth periods, and the effect of the canopy leaves is the most obvious.

[0103] A rapid monitoring method for soil trace elements based on the spectral effects of crops in multiple growth stages, disclosed by the present invention, includes: obtaining target crop information; extracting the growth period of the target crop, and if the growth period of the target crop is not within the preset growth period range, deleting the corresponding target crop; extracting the soil trace elements to be measured; determining the soil trace element estimation model for the corresponding target crop according to the growth period of the target crop and the soil trace elements to be measured; extracting the spectral absorption depth at the spectral absorption position of the canopy leaves of the target crop; and inputting the spectral absorption depth at the spectral absorption position of the canopy leaves of the target crop into the soil trace element estimation model for the corresponding target crop to obtain the content of the trace elements to be measured in the soil environment where the target crop is located. By monitoring different growth stages of different target crops, the present invention can achieve rapid, efficient, and large-area monitoring of soil trace elements, providing convenience for soil element investigation.

[0104] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the couplings, direct couplings, or communication connections between the various components shown or discussed can be through some interfaces, and the indirect couplings or communication connections of devices or units can be electrical, mechanical, or other forms.

[0105] The units described as separate components above may or may not be physically separated, and the components shown as units may or may not be physical units; they can be located in one place or distributed to multiple network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0106] In addition, in each embodiment of the present invention, the various functional units can all be integrated in one processing unit, or each unit can be separately used as a unit, or two or more units can be integrated in one unit; the above integrated units can be implemented in the form of hardware, or in the form of a combination of hardware and software functional units.

[0107] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps including those of the above method embodiments. The foregoing storage medium includes: various media that can store program codes, such as removable storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0108] Alternatively, if the above integrated unit of the present invention is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiments of the present invention, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media that can store program codes, such as removable storage devices, ROM, RAM, magnetic disks, or optical discs.

Claims

1. A rapid monitoring method for soil trace elements based on the spectral effects of multiple growth stages of crops, characterized in that, Including: Obtain target crop information; Extract the growth period of the target crop. If the growth period of the target crop is not within the preset growth period range, delete the corresponding target crop; Extract the soil trace elements to be measured; Determine the soil trace element estimation model corresponding to the target crop according to the growth period of the target crop and the soil trace elements to be measured; Extract the spectral absorption depth at the spectral absorption position of the canopy leaves of the target crop; Input the spectral absorption depth at the spectral absorption position of the canopy leaves of the target crop into the soil trace element estimation model corresponding to the target crop to obtain the content of the trace elements to be measured in the soil environment where the target crop is located.

2. The rapid soil trace element monitoring method based on the spectral effects of multiple crop growth stages according to claim 1, wherein It also includes: Obtain crop information within the target area; Extract any crop information within the target area and compare it with the preset crop information in sequence to obtain a set of crop similarity values; If any crop similarity value in the set of crop similarity values is less than or equal to the preset crop similarity value threshold, delete the corresponding crop information; If any crop similarity value in the set of crop similarity values is greater than the preset crop similarity value threshold, set the corresponding crop as the target crop.

3. The rapid monitoring method for soil trace elements based on the spectral effects of crops in multiple growth periods according to claim 1, wherein, The acquisition steps of the soil trace element estimation model specifically include: Based on the preset experimental area, obtain the spectral data sets of the canopy leaves of different target crops at different growth periods; Preprocess the spectral data in the spectral data sets of the canopy leaves of different target crops at different growth periods to obtain a data set of the effective spectral absorption positions and the corresponding spectral absorption depths of the canopy leaves of different target crops at different growth periods; Divide the data set of the effective spectral absorption positions and the corresponding spectral absorption depths of the canopy leaves of different crops at different growth periods according to different crops and growth periods to obtain multiple data sets of the effective spectral absorption positions and the corresponding spectral absorption depths of the canopy leaves of the same target crop and the same growth period; Extract the content values of the target trace elements in the experimental area; Based on the multiple linear regression algorithm, use the content value of the target trace element in the experimental area as the independent variable, and the spectral absorption depth corresponding to the effective spectral absorption position of the canopy leaves of the same target crop and the same growth period as the independent variable to construct a soil trace element estimation model.

4. The rapid soil trace element monitoring method based on the spectral effects of multiple crop growth stages according to claim 3, wherein, The step of preprocessing the spectral data in the spectral data sets of the canopy leaves of different target crops at different growth periods specifically includes: Extract the spectral absorption position and the spectral absorption parameters at the corresponding spectral absorption position in the spectral data; Extract the content value of the target trace element in the canopy part of the corresponding target crop; Compare and analyze the spectral absorption parameters and the content value of the target trace element in the canopy part of the target crop to obtain the correlation index between the spectral absorption parameters at the corresponding spectral absorption position and the content of the target trace element in the canopy part; If the correlation index is greater than the preset index threshold, delete the corresponding spectral data; The spectral absorption parameters at least include spectral absorption depth and spectral absorption area.

5. The rapid soil trace element monitoring method based on the spectral effect during multiple growth stages of crops according to claim 4, wherein, The acquisition steps of the spectral absorption depth specifically include: Perform envelope removal processing on the spectral data to obtain normalized spectral data; Construct a spectral curve based on the normalized spectral data, and extract the spectral absorption valley positions in the spectral curve and the spectral absorption valley reflectance at the corresponding spectral absorption valley positions; Set the spectral absorption depth as D, and its formula is ; where represents the spectral absorption valley position of the spectral absorption depth; represents the spectral absorption valley position of the envelope reflectance.

6. The rapid soil trace element monitoring method based on the spectral effects of multiple crop growth stages according to claim 4, characterized in that The steps for obtaining the spectral absorption area specifically include: Obtain the position of the spectral absorption valley The wavelength range it is in, denoted as ; Let the spectral absorption area be S, and its formula is , where represents the spectral absorption position is the envelope reflectivity at the spectral absorption position, and the spectral absorption position is within the wavelength band .

7. The rapid soil trace element monitoring method based on the spectral effects of crops in multiple growth periods according to claim 3, characterized in that, The steps for constructing a soil trace element estimation model based on the multiple linear regression algorithm, with the content values of the target trace elements in the experimental area as independent variables and the effective spectral absorption positions and corresponding spectral absorption depths of the canopy leaves of the same crop and at the same growth stage as independent variables, specifically include: According to the multiple linear regression algorithm, design the initial weight coefficients and initial residual terms of the independent variables; Preprocess the content values of the target trace elements in the experimental area and the effective spectral absorption positions and corresponding spectral absorption depths of the canopy leaves of the same crop and at the same growth stage to obtain the training sample set and test sample set for the corresponding soil trace element estimation model; Input the training sample set of the soil trace element estimation model into the multiple linear regression equation with the initial weight coefficients and initial residual terms for training to obtain the trained multiple linear regression equation; Send the test samples in the test sample set to the trained multiple linear regression equation in sequence to obtain the test accuracy rate; If the test accuracy rate is greater than the preset accuracy rate threshold, stop the training, output the weight coefficients and residual terms of the trained multiple linear regression equation, and obtain the soil trace element estimation model, whose formula is ; where represents the spectral absorption depth corresponding to the spectral absorption valley position , represents the corresponding weight coefficient, represents the residual term, n represents the set of spectral absorption valley positions, and m belongs to n.

8. The rapid soil trace element monitoring method based on the spectral effects during multiple growth stages of crops according to claim 4, wherein The steps for obtaining the correlation index between the spectral absorption parameters at the corresponding spectral absorption positions and the content of the target trace elements in the canopy part specifically include: Based on the spectral absorption parameters at the spectral absorption positions and the target trace element contents in the canopy parts, the corresponding linear index r is obtained, and its formula is , where represents the spectral absorption parameter value of the spectral absorption parameter i at the corresponding spectral absorption position, represents the mean value of the spectral absorption parameter i corresponding to it, represents the target trace element content in the corresponding target crop canopy part; represents the mean value of the target trace element content in the target crop canopy part; Obtain the quantity value of this sample, and obtain the t value according to the quantity value of the sample and the corresponding linear index, , where N is the quantity value of the sample; Based on the preset distribution table, determine the correlation index at the position where the t value is located.

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