A soil zinc content monitoring method and system based on hyperspectral remote sensing
Through hyperspectral remote sensing technology and machine classification model optimization, the problem of rapid and accurate monitoring of soil zinc content in ecologically fragile areas has been solved, and efficient and low-cost soil zinc content monitoring has been achieved.
Patent Information
- Application Number
- CN202510005858.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-03
- Publication Date
- 2025-09-23
- Estimated Expiration
- 2045-01-03
AI Technical Summary
Existing technologies make it difficult to quickly and accurately monitor manganese content in the soil of ecologically fragile areas. Traditional methods require a lot of manpower and material resources and are expensive, and there is insufficient research on the use of hyperspectral technology to monitor zinc content in ecologically fragile areas.
Hyperspectral remote sensing technology is used to enhance the spectral reflectance data of soil samples in ecologically fragile areas, build a machine classification model, optimize characteristic bands, and use the optimal spectral form to monitor soil zinc content.
It has achieved rapid and accurate monitoring of soil zinc content in ecologically fragile areas, improved the applicability and accuracy of monitoring, and reduced manpower, material resources and costs.
Smart Images
Figure CN119715410B_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of soil pollution monitoring in ecologically fragile areas, and in particular to a method and system for monitoring soil zinc content based on hyperspectral remote sensing. Background Art
[0002] Soil is a vital support for agricultural production and a major component of the ecological environment, and its quality directly impacts people's daily lives. Because the zinc content in soil is relatively hidden and its manifestation is delayed, it has received little attention. However, when zinc in soil exceeds a certain level, it can affect the normal growth of plants and even accumulate in humans and animals through the food chain, posing a significant threat to human health. Therefore, how to quickly and effectively detect the zinc content and spatial distribution in soil is crucial for soil remediation and management.
[0003] The traditional method for monitoring soil zinc levels involves collecting large numbers of samples and bringing them back to the laboratory for measurement using methods such as atomic absorption spectroscopy, ultraviolet-visible spectrophotometry, atomic fluorescence spectroscopy, inductively coupled plasma-mass spectrometry, and inductively coupled plasma-emission spectrometry. This method is labor-intensive and expensive, especially for mapping elemental concentrations over large areas, which is typically achieved using spatial interpolation. This results are affected by factors such as the number of sampling points and the spatial distribution of the samples. Although studies have used hyperspectral technology to estimate soil heavy metal concentrations, most have been conducted in areas with high soil metal concentrations. Studies have not specifically examined soil zinc concentrations in ecologically fragile regions. It is also unclear which spectral form and characteristic band optimization methods are suitable for zinc monitoring in these areas. Summary of the Invention
[0004] In view of the shortcomings of existing methods and the needs of practical applications, in order to solve the problem of rapid and accurate monitoring of soil zinc content in ecologically fragile areas, the present invention provides a soil zinc content monitoring method based on hyperspectral remote sensing, comprising the following steps:
[0005] Hyperspectral remote sensing technology is used to obtain spectral reflectance data and corresponding zinc content of soil samples in ecologically fragile areas. The spectral reflectance data is then enhanced, and the enhanced spectral data set is combined with the enhanced zinc content to obtain an enhanced spectral dataset. An optimal spectral form is obtained from the enhanced spectral dataset. Based on the optimal spectral form, characteristic bands of the enhanced spectral dataset are optimized, and soil zinc content monitoring is completed based on the prioritized results. This method, based on test data from soil samples in ecologically fragile areas, monitors soil zinc content by processing the data and constructing a classification learning model. This method solves the problem of rapid and accurate soil zinc content monitoring in ecologically fragile areas, improving the applicability of soil zinc content monitoring in ecologically fragile areas.
[0006] Optionally, performing data enhancement on the spectral reflectance data comprises the following steps:
[0007] The spectral reflectance data is first enhanced to obtain a first enhanced result; the spectral reflectance data is second enhanced to obtain a second enhanced result; and the spectral reflectance data is third enhanced to obtain a third enhanced result. The present invention is beneficial to improving the accuracy of the present invention by performing multiple data enhancements on the spectral reflectance data.
[0008] Optionally, the first enhancement is performed on the spectral reflectance data to obtain a first enhancement result, which satisfies the following formula:
[0009]
[0010] in, represents the first enhancement result, Indicates band The wavelength, represents spectral reflectance data, Indicates the interval between two adjacent wavelengths.
[0011] Optionally, the second enhancement is performed on the spectral reflectance data to obtain a second enhancement result, which satisfies the following formula:
[0012]
[0013] in, represents the second enhancement result, Indicates band The wavelength, represents the first enhancement result, Indicates the interval between two adjacent wavelengths.
[0014] Optionally, obtaining an optimal spectral form by enhancing the spectral dataset comprises the following steps:
[0015] The enhanced spectral dataset is split to obtain a spectral data training set and a spectral data validation set; a first machine classification model is constructed, the first machine classification model is trained using the spectral data training set, and the spectral data validation set and the trained first machine classification model are combined to obtain a first validation result; the first validation result is comprehensively analyzed using the coefficient of determination, relative root mean square error, and relative analytical error to obtain the optimal spectral form. The present invention utilizes multiple error analysis methods to facilitate rapid and accurate determination of the optimal spectral form.
[0016] Optionally, the method of performing characteristic band optimization on the enhanced spectral dataset based on the optimal spectral form and completing soil zinc content monitoring according to the priority results includes the following steps:
[0017] Based on the optimal spectral form, the enhanced spectral dataset is screened to obtain an optimal spectral dataset; the optimal spectral dataset is divided to obtain an optimal spectral training dataset and an optimal spectral verification dataset; the optimal band in the optimal spectral training dataset is obtained using multiple band selection algorithms; a second machine classification model is constructed, and the second machine classification model is trained using the optimal band. The second machine classification model is combined with the optimal spectral verification dataset and the trained second machine classification model to obtain a second verification result; the second verification result is comprehensively analyzed using the coefficient of determination, relative root mean square error, and relative analytical error to obtain an optimal band selection algorithm; and soil zinc content monitoring is completed using the trained second machine classification model based on the optimal band selection algorithm. The present invention utilizes data in the optimal spectral form for comprehensive analysis, further improving the accuracy of the present invention.
[0018] Optionally, the band selection algorithm includes a least absolute shrinkage and selection operator algorithm, a genetic algorithm, and a gradient boosting decision tree algorithm.
[0019] Optionally, the coefficient of determination satisfies the following formula:
[0020]
[0021] in, represents the coefficient of determination;
[0022] The relative root mean square error satisfies the following formula:
[0023]
[0024] in, represents the relative root mean square error;
[0025] The relative analysis error satisfies the following formula:
[0026]
[0027] in, represents the relative analytical error, 、 and The samples are The true value, predicted value and average value of .
[0028] Optionally, the machine classification model is constructed based on the leave-one-out method. The present invention uses the leave-one-out method to construct the machine classification model, which can fully utilize the data and has high accuracy.
[0029] In a second aspect, to efficiently implement the hyperspectral remote sensing-based soil zinc content monitoring method provided by the present invention, the present invention also provides a hyperspectral remote sensing-based soil zinc content monitoring system, comprising a processor, an input device, an output device, and a memory, wherein the processor, input device, output device, and memory are interconnected, wherein the memory is used to store a computer program, the computer program including program instructions, and the processor is configured to call the program instructions to execute the hyperspectral remote sensing-based soil zinc content monitoring method as described in the first aspect of the present invention. The hyperspectral remote sensing-based soil zinc content monitoring system of the present invention has a compact structure and stable performance, and is capable of stably implementing the hyperspectral remote sensing-based soil zinc content monitoring method provided by the present invention, further enhancing the overall applicability and practical application capabilities of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS
[0030] Figure 1 A flow chart of a soil zinc content monitoring method based on hyperspectral remote sensing provided by an embodiment of the present invention;
[0031] Figure 2 A framework diagram of a soil zinc content monitoring system based on hyperspectral remote sensing provided by an embodiment of the present invention;
[0032] Figure 3 A schematic diagram of the structure of a soil zinc content monitoring device based on hyperspectral remote sensing provided by an embodiment of the present invention. DETAILED DESCRIPTION
[0033] Specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are for illustrative purposes only and are not intended to limit the present invention. In the following description, numerous specific details are set forth to provide a thorough understanding of the present invention. However, it will be apparent to one of ordinary skill in the art that these specific details are not necessarily required to practice the present invention. In other instances, well-known circuits, software, or methods are not specifically described to avoid obscuring the present invention.
[0034] Throughout this specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment of the present invention. Therefore, appearances of the phrases "in one embodiment," "in an embodiment," "an example," or "an example" in various places throughout this specification are not necessarily all referring to the same embodiment or example. Furthermore, the particular features, structures, or characteristics may be combined in any suitable combinations and / or subcombinations in one or more embodiments or examples. Furthermore, those of ordinary skill in the art will appreciate that the figures provided herein are for illustrative purposes only and are not necessarily drawn to scale.
[0035] See also Figure 1 In order to apply to soil zinc content monitoring in ecologically fragile areas and solve the problem of rapid and accurate soil zinc content monitoring, the present invention provides a soil zinc content monitoring method based on hyperspectral remote sensing, such as Figure 1 As shown, in one embodiment, the method includes the following steps:
[0036] S1. Use hyperspectral remote sensing technology to obtain spectral reflectance data and corresponding zinc content of soil samples in ecologically fragile areas.
[0037] In the embodiment, soil samples from ecologically fragile areas are brought back indoors for drying and impurity removal, and then ground into 200-mesh powder samples using a grinder model RS200. Based on hyperspectral remote sensing technology, a FieldSpec4 portable spectrometer is used to perform spectral measurement of the obtained powder samples in an indoor darkroom environment to obtain spectral reflectance data of the soil samples from ecologically fragile areas.
[0038] Furthermore, the zinc content of each sample after measuring the spectral reflectance data was measured using a Niton XL3t 950 portable X-ray fluorescence spectrometer. During the measurement, the instrument working mode was set to the mineral (Cu / Zn) mode, and the measurement time for each sample was set to 120 s.
[0039] S2. Perform data enhancement on the spectral reflectance data, and combine the enhancement result with the zinc content to obtain an enhanced spectral data set.
[0040] In the embodiment, the step S2 of performing data enhancement on the spectral reflectance data includes the following steps:
[0041] S21. Perform a first enhancement on the spectral reflectance data to obtain a first enhancement result.
[0042] Specifically, the first enhancement is performed on the spectral reflectance data to obtain a first enhancement result that satisfies the following formula:
[0043]
[0044] in, represents the first enhancement result, Indicates band The wavelength, represents spectral reflectance data, Indicates the interval between two adjacent wavelengths.
[0045] S22: Perform a second enhancement on the spectral reflectance data to obtain a second enhancement result.
[0046] Specifically, the second enhancement is performed on the spectral reflectance data to obtain a second enhancement result that satisfies the following formula:
[0047]
[0048] in, represents the second enhancement result, Indicates band The wavelength, represents the first enhancement result, Indicates the interval between two adjacent wavelengths.
[0049] S23: Perform a third enhancement on the spectral reflectance data to obtain a third enhancement result.
[0050] Specifically, the third enhancement of the spectral reflectance data is performed by connecting the peak points on the spectral curve point by point with straight lines, and making the outer angle of the broken line at the peak point greater than 180°, thereby forming an envelope. Then, the value on the original spectral curve is divided by the corresponding value on the envelope to perform spectral de-envelope processing. In this embodiment, this can be achieved through the Continuum Removed function under the Specrral module in ENVI.
[0051] Furthermore, various enhancement results and the corresponding zinc content are combined to obtain enhanced spectral data corresponding to various spectral forms, and then aggregated to form an enhanced spectral data set.
[0052] S3. Obtaining an optimal spectral form through the enhanced spectral dataset.
[0053] In the embodiment, step S3 of obtaining the optimal spectral form by enhancing the spectral dataset comprises the following steps:
[0054] S31 , splitting the enhanced spectral data set to obtain a spectral data training set and a spectral data validation set.
[0055] The enhanced spectral dataset was randomly divided into 2:1 to obtain the spectral data training set and the spectral data validation set.
[0056] S32: Construct a first machine classification model, use the spectral data training set to train the first machine classification model, and combine the spectral data validation set and the trained first machine classification model to obtain a first verification result.
[0057] Specifically, a first machine classification model is constructed based on the leave-one-out method, and the first machine classification model is trained using the spectral data training set. Then, the spectral data validation set and the trained first machine classification model are combined to obtain a first verification result.
[0058] S33. Comprehensively analyze the first verification result using the coefficient of determination, relative root mean square error, and relative analytical error to obtain the optimal spectral form.
[0059] Specifically, based on the performance of the corresponding data of each spectral form in the validation set, the spectral form with the best model effect is comprehensively determined in combination with the determination coefficient, relative root mean square error and relative analytical error. The larger the determination coefficient and relative analytical error, and the smaller the relative root mean square error, the better the model effect.
[0060] Furthermore, the coefficient of determination satisfies the following formula:
[0061]
[0062] The relative root mean square error satisfies the following formula:
[0063]
[0064] The relative analysis error satisfies the following formula:
[0065]
[0066] in, 、 and The samples are The true value, predicted value and average value of .
[0067] S4. Based on the optimal spectral form, perform characteristic band optimization on the enhanced spectral dataset, and complete soil zinc content monitoring according to the priority results.
[0068] In an embodiment, the step S4 of performing characteristic band optimization on the enhanced spectral dataset based on the optimal spectral form and completing soil zinc content monitoring according to the priority result includes the following steps:
[0069] S41 . Based on the optimal spectral form, the enhanced spectral dataset is screened to obtain an optimal spectral dataset.
[0070] Specifically, the enhanced spectral data sets in non-optimal spectral form in the above steps are removed from the enhanced spectral data sets to obtain the optimal spectral data sets.
[0071] S42 , dividing the optimal spectral data set to obtain an optimal spectral training data set and an optimal spectral verification data set.
[0072] The optimal spectral dataset was randomly divided into 2:1 to obtain the optimal spectral training dataset and the optimal spectral verification dataset.
[0073] S43. Obtaining the optimal band in the optimal spectrum training data set through multiple band selection algorithms.
[0074] In an embodiment, the band selection algorithm includes a least absolute shrinkage and selection operator algorithm, a genetic algorithm, and a gradient boosting decision tree algorithm.
[0075] Specifically, obtaining the optimal band in the optimal spectral training data set by the least absolute shrinkage and selection operator algorithm includes the following steps:
[0076] All variables, i.e., bands, are preliminarily trained using the Lasso class in the linear_model module of the Scikit-learn machine learning library to obtain the initial model.
[0077] The importance of each variable is obtained and ranked through the coef_ parameter, and then the top N features are added to the initial model for re-verification. The number of features when the initial model is optimal is determined by the performance of the initial model in the optimal spectral training data set, and the optimal band corresponding to the minimum absolute shrinkage and selection operator algorithm is obtained.
[0078] Furthermore, obtaining the optimal wavelength band in the optimal spectrum training data set by a genetic algorithm includes the following steps:
[0079] All variables are preliminarily trained using the RandomForestRegressor class in the ensemble module of the Scikit-learn machine learning library to obtain the initial model;
[0080] Based on the ranking_ parameter, the RFECV class in the feature_selection module of the Scikit-learn machine learning library is used to automatically select features and obtain the optimal band corresponding to the genetic algorithm.
[0081] Furthermore, obtaining the optimal band in the optimal spectral training data set by the gradient boosting decision tree algorithm includes the following steps:
[0082] All variables are trained using the GradientBoostingRegress class in the ensemble module of the Scikit-learn machine learning library to obtain the initial model;
[0083] The importance of each variable is obtained and ranked through the feature_importances_ parameter, and the top N features are added to the initial model. The number of features when the initial model is optimal is determined by the performance of the initial model in the optimal spectral training data set, and the optimal band corresponding to the gradient boosting decision tree algorithm is obtained.
[0084] In other embodiments, other model algorithms that can extract band features, such as neural networks, etc., may also be adopted.
[0085] S44: Construct a second machine classification model, use the optimal wavelength band to train the second machine classification model, and combine the optimal spectrum verification data set and the trained second machine classification model to obtain a second verification result.
[0086] Specifically, a second machine classification model is constructed based on the leave-one-out method, and the second machine classification model is trained using the optimal band data obtained by various band selection algorithms. Then, the second verification result is obtained by combining the optimal spectral verification data set and the trained second machine classification model.
[0087] S45. Comprehensively analyze the second verification result using the coefficient of determination, the relative root mean square error, and the relative analysis error to obtain an optimal band selection algorithm.
[0088] Specifically, based on the performance of the corresponding data of each band selection algorithm in the validation set, the band selection algorithm with the best model effect is comprehensively determined in combination with the determination coefficient, relative root mean square error and relative analysis error. The larger the determination coefficient and relative analysis error, and the smaller the relative root mean square error, the better the model effect.
[0089] Furthermore, the coefficient of determination satisfies the following formula:
[0090]
[0091] The relative root mean square error satisfies the following formula:
[0092]
[0093] The relative analysis error satisfies the following formula:
[0094]
[0095] in, 、 and The samples are The true value, predicted value and average value of .
[0096] S46. Based on the optimal band selection algorithm, soil zinc content monitoring is completed through the trained second machine classification model.
[0097] In the embodiment, the optimal band is extracted from the spectral data of the soil in the monitored area in the ecologically fragile area by using the optimal band selection algorithm, and the soil zinc content monitoring is completed using the trained second machine classification model.
[0098] See also Figure 2In an embodiment, to efficiently implement the hyperspectral remote sensing-based soil zinc content monitoring method provided by the present invention, the present invention also provides a hyperspectral remote sensing-based soil zinc content monitoring system, comprising: an input device, an output device, a processor, and a memory, wherein the input device, output device, processor, and memory are interconnected, and the memory contains program instructions for performing the steps of the hyperspectral remote sensing-based soil zinc content monitoring method. The hyperspectral remote sensing-based soil zinc content monitoring system of the present invention has a compact structure and stable performance, capable of stably implementing the hyperspectral remote sensing-based soil zinc content monitoring method of the present invention, further enhancing the overall applicability and practical application capabilities of the present invention.
[0099] In an embodiment, the processor may be a central processing unit (CPU), which may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc. The input device may be used to obtain data information. The output device may be used to output the results obtained by storing the program instructions contained in the computer program in the memory provided by the present invention. The memory may include a read-only memory and a random access memory, and provides instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory.
[0100] In yet another alternative embodiment, see Figure 3 In order to efficiently implement the soil zinc content monitoring method based on hyperspectral remote sensing provided by the present invention, this embodiment also provides a soil zinc content monitoring device based on hyperspectral remote sensing, such as Figure 3 As shown, including:
[0101] The memory 10 is used to store computer programs, and the processor 20 is used to execute the computer programs to implement the aforementioned soil zinc content monitoring method based on hyperspectral remote sensing. The memory 10, processor 20, communication interface 31, and communication bus 32 all communicate with each other via the communication bus 32.
[0102] In an embodiment, the memory 10 is used to store one or more program instructions. The memory 10 may store program instructions for implementing the following functions:
[0103] Hyperspectral remote sensing technology is used to obtain spectral reflectance data and corresponding zinc content of soil samples in ecologically fragile areas. The spectral reflectance data is enhanced, and the enhanced spectral dataset is obtained by combining the enhancement results and the zinc content. The optimal spectral form is obtained from the enhanced spectral dataset. Based on the optimal spectral form, the characteristic bands of the enhanced spectral dataset are optimized, and soil zinc content monitoring is completed according to the priority results.
[0104] In one possible implementation, the memory 10 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function, etc.; the data storage area may store data created during use. In addition, the memory 10 may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include NVRAM. The memory stores an operating system and operating instructions, executable modules or data structures, or a subset thereof, or an extended set thereof, wherein the operating instructions may include various operating instructions for implementing various operations. The operating system may include various system programs for implementing various basic tasks and processing hardware-based tasks.
[0105] The processor 20 may be a central processing unit (CPU), an application-specific integrated circuit, a digital signal processor, a field programmable gate array, or other programmable logic device. The processor 20 may be a microprocessor or any conventional processor. The processor 20 may call programs stored in the memory 10. The communication interface 31 may be an interface of a communication module for connecting to other devices or systems.
[0106] Of course, it needs to be explained that Figure 3 The structure shown does not constitute a limitation on the soil zinc content monitoring device based on hyperspectral remote sensing in this embodiment. In actual applications, the soil zinc content monitoring device based on hyperspectral remote sensing may include Figure 3 More or fewer components than shown, or combinations of certain components.
[0107] The embodiment further provides a storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the above-mentioned soil zinc content monitoring method based on hyperspectral remote sensing are implemented.
[0108] The storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc., which can store program codes.
[0109] In summary, the present invention is based on the detection data of soil samples in ecologically fragile areas. By processing the data and constructing a classification learning model, it completes soil zinc content monitoring, solves the problem of rapid and accurate monitoring of soil zinc content in ecologically fragile areas, and improves the applicability of soil zinc content monitoring in ecologically fragile areas.
[0110] Therefore, the present invention effectively overcomes various shortcomings of the prior art and has high industrial utilization value.
[0111] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope described in the present invention.
Claims
1. A soil zinc content monitoring method based on hyperspectral remote sensing, characterized in that: The soil zinc content monitoring method based on hyperspectral remote sensing comprises the following steps: Using hyperspectral remote sensing technology, we obtained spectral reflectance data and corresponding zinc content of soil samples in ecologically fragile areas; performing data enhancement on the spectral reflectance data, and combining the enhancement result with the zinc content to obtain an enhanced spectral data set; Obtaining an optimal spectral form through the enhanced spectral dataset; Based on the optimal spectral form, the enhanced spectral data set is optimized for characteristic bands, and soil zinc content monitoring is completed according to the priority results; The data enhancement of the spectral reflectance data comprises the following steps: Performing a first enhancement on the spectral reflectance data to obtain a first enhancement result; performing a second enhancement on the spectral reflectance data to obtain a second enhancement result; performing a third enhancement on the spectral reflectance data to obtain a third enhancement result; The first enhancement is performed on the spectral reflectance data to obtain a first enhancement result, which satisfies the following formula: in, represents the first enhancement result, Indicates band The wavelength, represents spectral reflectance data, Indicates the interval between two adjacent wavelengths; The second enhancement is performed on the spectral reflectance data to obtain a second enhancement result, which satisfies the following formula: in, represents the second enhancement result, Indicates band The wavelength, represents the first enhancement result, Indicates the interval between two adjacent wavelengths; Performing a third enhancement on the spectral reflectance data to obtain a third enhancement result, including: connecting peak points on the spectral curve with straight lines point by point, and making the outer angle of the broken line at the peak point greater than 180 degrees, thereby forming an envelope line, and then dividing the value on the original spectral curve by the corresponding value on the envelope line to perform spectral de-envelope processing; Obtaining the optimal spectral form by enhancing the spectral dataset comprises the following steps: Splitting the enhanced spectral data set to obtain a spectral data training set and a spectral data validation set; Constructing a first machine classification model, training the first machine classification model using the spectral data training set, and combining the spectral data validation set and the trained first machine classification model to obtain a first verification result; Comprehensively analyzing the first verification result using the coefficient of determination, relative root mean square error, and relative analytical error to obtain the optimal spectral form; The method of performing characteristic band optimization on the enhanced spectral dataset based on the optimal spectral form and completing soil zinc content monitoring according to the priority results includes the following steps: Based on the optimal spectral form, the enhanced spectral dataset is screened to obtain an optimal spectral dataset; Dividing the optimal spectral data set to obtain an optimal spectral training data set and an optimal spectral verification data set; Obtaining the optimal band in the optimal spectrum training data set through a variety of band selection algorithms; Constructing a second machine classification model, training the second machine classification model using the optimal wavelength band, and combining the optimal spectrum verification data set and the trained second machine classification model to obtain a second verification result; Comprehensively analyzing the second verification result using the coefficient of determination, relative root mean square error, and relative analytical error to obtain an optimal band selection algorithm; Based on the optimal band selection algorithm, soil zinc content monitoring is completed through the trained second machine classification model.
2. The soil zinc content monitoring method based on hyperspectral remote sensing according to claim 1, characterized in that: The band selection algorithm includes a least absolute shrinkage and selection operator algorithm, a genetic algorithm, and a gradient boosting decision tree algorithm.
3. The soil zinc content monitoring method based on hyperspectral remote sensing according to claim 1, characterized in that: The coefficient of determination satisfies the following formula: in, represents the coefficient of determination; The relative root mean square error satisfies the following formula: in, represents the relative root mean square error; The relative analysis error satisfies the following formula: in, represents the relative analytical error, 、 and The samples are The true value, predicted value and average value of .
4. The soil zinc content monitoring method based on hyperspectral remote sensing according to claim 1, characterized in that: The machine classification model is built based on the leave-one-out method.
5. A soil zinc content monitoring system based on hyperspectral remote sensing, characterized in that: The soil zinc content monitoring system based on hyperspectral remote sensing includes: an input device, an output device, a processor, and a memory. The input device, output device, processor, and memory are interconnected. The memory includes program instructions, and the program instructions are used to execute the soil zinc content monitoring method based on hyperspectral remote sensing according to any one of claims 1 to 4.