A soil organic matter content detection device and detection method thereof

Abstract

The present application provides a soil organic matter content detection device and a detection method thereof, which determines the fluctuation difference of the vertical diffusion distribution of organic matter in the soil through spectral data, and then performs multi-scale information decomposition on the spectral data to obtain horizontal spectral characteristic bands and vertical spectral characteristic bands; determines the content distribution entropy of organic matter in the horizontal direction and the sampling point distribution information according to the horizontal spectral characteristic bands to determine the content change gradient of organic matter in soils of different blocks, and hierarchically couples the horizontal spectral characteristic bands and the vertical spectral characteristic bands according to the content change gradient and the fluctuation difference to obtain coupling characteristics; determines the band disturbance amount in the spectral measurement process; performs spectral compensation on the coupling characteristics according to the band disturbance amount, and then realizes soil organic matter content detection based on the compensated coupling characteristics. The scheme of the present application can realize hierarchical coupling of soil spectral data at different sampling points, thereby improving the confidence of soil organic matter content detection.

Description

Technical Field

[0001] The present application relates to the field of soil detection technology, and more specifically, to a soil organic matter content detection device and a detection method thereof. Background Art

[0002] Soil testing is a key technology to ensure agricultural production, environmental protection and sustainable development. By testing indicators such as nutrients, organic matter content, pH value, etc. in the soil, scientific fertilizer application can be carried out to improve the yield and quality of crops and avoid soil degradation and environmental pollution caused by excessive fertilization. At the same time, soil testing can also detect and evaluate soil pollution, prevent the spread of pollution, and protect the ecological environment. Rational use of soil testing technology will help guide land management, prevent soil erosion and desertification, and provide a scientific basis for ecological restoration, promote the sustainable development of agriculture and ecosystems, and achieve harmonious coexistence between man and nature.

[0003] Spectroscopic technology is a commonly used technology for detecting soil organic matter content. Its advantages are non-destructive, high-precision, economical and environmentally friendly. It is suitable for large-scale sample analysis and on-site real-time detection. In the process of using spectral technology to detect the organic matter content in the soil, the moisture, minerals and other organic matter in the soil will interfere with the near-infrared spectral signal of the organic matter, resulting in random errors in the spectral technology detection of soil organic matter content. By hierarchically coupling the spectral data of the soil at different sampling points in the target soil (hierarchical coupling is the integration of soil spectral data at different sampling points and different depths in the target soil), the random errors in the process of spectral technology in detecting soil organic matter content can be reduced, thereby improving the accuracy of soil organic matter content detection. Therefore, how to achieve hierarchical coupling of soil spectral data at different sampling points to improve the confidence in soil organic matter content detection has become a difficult problem faced by the industry. Summary of the invention

[0004] The present application provides a soil organic matter content detection device and a detection method thereof, which can realize hierarchical coupling of soil spectral data at different sampling points, thereby improving the confidence of soil organic matter content detection.

[0005] In a first aspect, the present application provides a method for detecting soil organic matter content, comprising the following steps:

[0006] Based on spectral technology, the organic matter content of the topsoil layer at different sampling points in the target soil is measured, and the spectral data of the topsoil layer is obtained;

[0007] Determine the fluctuation difference of the vertical diffusion distribution of organic matter in the target soil through the spectral data, and then perform multi-scale information decomposition on the spectral data based on the fluctuation difference to obtain horizontal spectral characteristic bands and vertical spectral characteristic bands;

[0008] Determine the content distribution entropy of organic matter in the topsoil layer in the horizontal direction according to the horizontal spectral characteristic band, and then determine the content change gradient of organic matter in the topsoil layer of different blocks according to the content distribution entropy and the horizontal distribution information of the sampling points, and hierarchically couple the horizontal spectral characteristic band and the vertical spectral characteristic band through the content change gradient and the fluctuation difference to obtain the coupling characteristics of the organic matter content in the topsoil layer;

[0009] Collect soil moisture information of the topsoil layer at different sampling points, determine the diffusion form of organic matter when the spectrum at different sampling points fluctuates according to all the soil moisture information, and then determine the band disturbance amount of the spectrum during the determination of organic matter content according to all the diffusion forms and the spectral data;

[0010] The coupling characteristics of the organic matter content in the topsoil layer are spectrally compensated according to the band disturbance amount to obtain the coupling compensation characteristics of the organic matter content in the target soil, and then the organic matter content in the target soil is coupled detected based on the coupling compensation characteristics.

[0011] Preferably, determining the fluctuation difference of the vertical diffusion distribution of organic matter in the target soil through the spectral data specifically includes:

[0012] Determine the fluctuation degree of the diffusion of organic matter in the vertical direction at different sampling points according to the spectral data;

[0013] The fluctuation difference of vertical diffusion distribution of organic matter in the target soil is determined by the difference information between all fluctuations.

[0014] Preferably, determining the content distribution entropy of organic matter in the topsoil layer in the horizontal direction according to the horizontal spectral characteristic band specifically includes:

[0015] Determining a horizontal distribution diagram of organic matter content in the topsoil layer based on the horizontal spectral characteristic band;

[0016] The content distribution entropy of the organic matter in the topsoil layer in the horizontal direction is determined according to the content distribution diagram of the organic matter in the horizontal direction.

[0017] Preferably, determining the content change gradient of organic matter in the topsoil layer of different blocks based on the content distribution entropy and the horizontal distribution information of the sampling points specifically includes:

[0018] Acquire horizontal distribution information of sampling points, and then divide the target soil according to the horizontal distribution information to obtain different blocks of soil;

[0019] Determine the gradient factor of the change in organic matter content in the soil of each block;

[0020] The content variation gradient of organic matter in the topsoil layer of different blocks is determined by all the gradient factors and the content distribution entropy.

[0021] Preferably, the horizontal spectral characteristic band and the vertical spectral characteristic band are hierarchically coupled by the content change gradient and the fluctuation difference to obtain the coupling characteristics of the organic matter content in the topsoil layer, which specifically includes:

[0022] Acquire all spectral features in the horizontal spectral feature band and all spectral features in the vertical spectral feature band;

[0023] Determine the spectral variance of the horizontal level through all spectral features in the horizontal spectral feature band;

[0024] Determine the spectral variance of the vertical level through all spectral features in the vertical spectral feature band;

[0025] Determining a fusion weight of the horizontal level and a fusion weight of the vertical level according to the spectral variance of the horizontal level, the spectral variance of the vertical level, the content change gradient and the fluctuation difference;

[0026] The horizontal spectral characteristic band and the vertical spectral characteristic band are weightedly coupled through the fusion weight of the horizontal level and the fusion weight of the vertical level to obtain the coupling characteristics of the organic matter content in the topsoil layer.

[0027] Preferably, determining the diffusion form of organic matter when the spectrum fluctuates at different sampling points based on all soil moisture information specifically includes:

[0028] For each sampling point, the soil moisture information of the topsoil layer at the sampling point is obtained;

[0029] Obtaining soil moisture data at each sampling time in the soil moisture information;

[0030] Determine the fluctuation value of soil moisture at each sampling point at each group of adjacent sampling times according to each soil moisture data;

[0031] The diffusion form of organic matter when the spectrum fluctuates at the sampling point is determined based on the fluctuation values ​​of all soil moisture conditions, and then the diffusion form of organic matter when the spectrum fluctuates at each sampling point is obtained.

[0032] Preferably, the coupling characteristics of the organic matter content in the topsoil layer are spectrally compensated according to the band disturbance amount to obtain the coupling compensation characteristics of the organic matter content in the target soil, which specifically includes:

[0033] Determine the correction parameters for spectrum compensation according to the waveband disturbance amount;

[0034] Performing anomaly detection on the spectral features in the coupled features of the organic matter content in the topsoil layer to obtain an abnormal spectral feature;

[0035] The abnormal spectral characteristics are compensated by the correction parameters, thereby obtaining the coupled compensation characteristics of the organic matter content in the target soil.

[0036] In a second aspect, the present application provides a soil organic matter content detection device, comprising:

[0037] An acquisition module is used to determine the organic matter content of the topsoil layer at different sampling points in the target soil based on spectral technology, and then obtain spectral data of the topsoil layer;

[0038] A processing module, used to determine the fluctuation difference of the vertical diffusion distribution of organic matter in the target soil through the spectral data, and then perform multi-scale information decomposition on the spectral data according to the fluctuation difference to obtain horizontal spectral characteristic bands and vertical spectral characteristic bands;

[0039] The processing module is further used to determine the content distribution entropy of organic matter in the topsoil layer in the horizontal direction according to the horizontal spectral characteristic band, and then determine the content change gradient of organic matter in the topsoil layer of different blocks according to the content distribution entropy and the horizontal distribution information of the sampling points, and hierarchically couple the horizontal spectral characteristic band and the vertical spectral characteristic band through the content change gradient and the fluctuation difference to obtain the coupling characteristics of the organic matter content in the topsoil layer;

[0040] The processing module is also used to collect soil moisture information of the topsoil layer at different sampling points, determine the diffusion form of organic matter when the spectrum fluctuates at different sampling points based on all the soil moisture information, and then determine the band disturbance amount of the spectrum during the determination of organic matter content based on all the diffusion forms and the spectral data;

[0041] An execution module is used to perform spectral compensation on the coupling characteristics of the organic matter content in the topsoil layer according to the band disturbance amount, obtain the coupling compensation characteristics of the organic matter content in the target soil, and then perform coupling detection on the organic matter content in the target soil based on the coupling compensation characteristics.

[0042] In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory stores codes, and the processor is configured to obtain the codes and execute the above-mentioned soil organic matter content detection method.

[0043] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned soil organic matter content detection method is implemented.

[0044] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects:

[0045] In the embodiment of the present application, the organic matter content of the topsoil layer at different sampling points in the target soil is measured based on spectral technology, and then the spectral data of the topsoil layer is obtained; the fluctuation difference of the vertical diffusion distribution of the organic matter in the target soil is determined by the spectral data, and then the spectral data is subjected to multi-scale information decomposition by the fluctuation difference to obtain horizontal spectral characteristic bands and vertical spectral characteristic bands; the content distribution entropy of the organic matter in the topsoil layer in the horizontal direction is determined according to the horizontal spectral characteristic band, and then the content distribution entropy and the horizontal distribution information of the sampling points are used to determine the content change gradient of the organic matter in the topsoil layer of different blocks, and the content change gradient and the fluctuation entropy are used to determine the content distribution entropy of the organic matter in the topsoil layer of different blocks. The horizontal spectral characteristic band and the vertical spectral characteristic band are hierarchically coupled according to the dynamic difference degree to obtain the coupling characteristics of the organic matter content in the topsoil layer; the soil moisture information of the topsoil layer at different sampling points is collected, and the diffusion form of the organic matter when the spectrum fluctuates at different sampling points is determined according to all the soil moisture information, and then the band disturbance amount of the spectrum in the process of measuring the organic matter content is determined according to all the diffusion forms and the spectral data; the coupling characteristics of the organic matter content in the topsoil layer are spectrally compensated according to the band disturbance amount to obtain the coupling compensation characteristics of the organic matter content in the target soil, and then the organic matter content in the target soil is coupled detected based on the coupling compensation characteristics.

[0046] It can be seen that the present application performs spectral compensation on the coupling characteristics of the organic matter content in the topsoil layer through the band disturbance amount, obtains the coupling compensation characteristics of the organic matter content in the target soil, and then determines the organic matter content in the target soil based on the coupling compensation characteristics; firstly, by hierarchically coupling the spectral characteristic bands of different levels (spectral characteristic bands in the horizontal direction and spectral characteristic bands in the vertical direction), the coupling characteristics of the organic matter content in the topsoil layer are obtained, and the coupling characteristics can comprehensively reflect the organic matter content of soils at different levels, thereby improving the detection accuracy of the organic matter content in the soil; secondly, the soil moisture information and spectral data of the topsoil layer at different sampling points are used to determine the organic matter content in the target soil due to the coupling compensation characteristics. Changes in environmental factors (such as changes in soil moisture) lead to disturbances in spectral data on specific bands, and then the disturbances are spectrally compensated to obtain the coupling compensation characteristics of the organic matter content in the target soil. By performing interference compensation on the spectral characteristic bands of organic matter in the soil, the random errors of spectral technology in the process of detecting soil organic matter content can be reduced, thereby improving the confidence in the detection of soil organic matter content. Finally, the content of organic matter in the soil is obtained based on the coupling compensation characteristic analysis, thereby realizing the accurate coupling evaluation of soil organic matter content. In summary, the present application scheme can realize the hierarchical coupling of soil spectral data at different sampling points, thereby improving the confidence in the detection of soil organic matter content. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 is an exemplary flow chart of a method for detecting soil organic matter content according to some embodiments of the present application;

[0048] Figure 2 is a schematic diagram of a process for determining a fluctuation difference according to some embodiments of the present application;

[0049] Figure 3 is a schematic diagram of a process for determining a content change gradient according to some embodiments of the present application;

[0050] Figure 4 is a schematic diagram of an exemplary software module of a soil organic matter content detection device according to some embodiments of the present application;

[0051] Figure 5 It is a structural schematic diagram of a computer device for implementing a soil organic matter content detection method according to some embodiments of the present application. DETAILED DESCRIPTION

[0052] The core of this application is to determine the fluctuation difference of the vertical diffusion distribution of organic matter in the soil through spectral data, and then perform multi-scale information decomposition on the spectral data to obtain horizontal spectral characteristic bands and vertical spectral characteristic bands; determine the content distribution entropy of organic matter in the horizontal direction and the sampling point distribution information according to the horizontal spectral characteristic bands to determine the content change gradient of organic matter in the soil of different blocks, and hierarchically couple the horizontal spectral characteristic bands and the vertical spectral characteristic bands according to the content change gradient and the fluctuation difference to obtain coupling characteristics; determine the band disturbance amount in the spectral measurement process; perform spectral compensation on the coupling characteristics according to the band disturbance amount, and then realize soil organic matter content detection based on the compensated coupling characteristics. The scheme of this application can realize the hierarchical coupling of soil spectral data at different sampling points, thereby improving the confidence of soil organic matter content detection.

[0053] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. Figure 1 , which is an exemplary flow chart of a soil organic matter content detection method according to some embodiments of the present application. The soil organic matter content detection method 100 mainly includes the following steps:

[0054] In step 101, the organic matter content of the topsoil layer at different sampling points in the target soil is measured based on spectral technology, thereby obtaining spectral data of the topsoil layer.

[0055] It should be noted that the principle of using spectral technology to test soil organic matter content is based on the interaction between light and soil organic matter molecules. Specifically, the spectral technology uses a spectral instrument to measure the reflection or absorption characteristics of light by a soil sample within a specific wavelength range, thereby inferring the content of organic matter in the soil. In this application, the organic matter content in the topsoil layer is mainly detected. In other embodiments, the organic matter content of other soil layers can also be detected.

[0056] In specific implementation, the organic matter content of the topsoil layer at different sampling points in the target soil is determined based on spectral technology, and then the spectral data of the topsoil layer is obtained. The following method can be used, namely: a near-infrared spectrometer can be used to test the organic matter content of the topsoil layer at different sampling points in the target soil, so that the test data obtained by the test is used as the spectral data of the topsoil layer, wherein the spectral data is composed of multiple element data, and the element data in the spectral data is composed of the type, wavelength, and intensity of the element, wherein the type represents the type of the element in the soil sample, the wavelength represents the wavelength of the element in the soil sample in the spectral signal, and the intensity represents the relative intensity of the element in the soil sample in the spectral signal; it should be noted that in the present application, when performing spectral detection on the target soil, the detection sampling frequency can be set between 20-50Hz, and the detection sampling time can be set between 1 and 5 minutes. The longer the collection time, the more data is collected, so that the collected data has better representativeness.

[0057] It should be noted that in the present application, the target soil is divided into grids, and sampling points are set at each grid intersection to obtain multiple different sampling points. In other embodiments, the sampling points can also be set by random sampling, which is not limited here. In addition, in the present application, the topsoil at each sampling point can be collected by the vertical profile collection method, wherein the vertical profile collection method refers to collecting soil samples from the surface downward to obtain soil samples at different depths in the vertical direction. In other embodiments, other soil collection methods can also be used to collect soil samples, which are not limited here.

[0058] In step 102, the fluctuation difference of the vertical diffusion distribution of organic matter in the target soil is determined by the spectral data, and then the spectral data is subjected to multi-scale information decomposition based on the fluctuation difference to obtain horizontal spectral characteristic bands and vertical spectral characteristic bands.

[0059] In some embodiments, reference Figure 2 As shown, this figure is a schematic diagram of the process of determining the fluctuation difference in some embodiments of the present application. In this embodiment, the fluctuation difference of the vertical diffusion distribution of organic matter in the target soil can be determined by the spectral data by the following steps:

[0060] In step 1021, the fluctuation degree of diffusion of organic matter in the vertical direction at different sampling points is determined according to the spectral data;

[0061] In step 1022, the fluctuation difference of the vertical diffusion distribution of organic matter in the target soil is determined through the difference information between all fluctuations.

[0062] In specific implementation, determining the fluctuation degree of vertical diffusion of organic matter at different sampling points based on the spectral data can be achieved in the following manner, namely: selecting a sampling point from all sampling points, using the spectral data of the organic matter in the vertical direction at the sampling point as the vertical spectral data, and then using the variance of the vertical spectral data as the fluctuation degree of vertical diffusion of organic matter at the sampling point, repeating the above steps to obtain the fluctuation degree of vertical diffusion of organic matter at the remaining sampling points.

[0063] It should be noted that the volatility in this application is an indicator for measuring the degree of fluctuation in the vertical diffusion distribution of organic matter in the soil. The larger the volatility, the greater the degree of fluctuation in the vertical diffusion distribution of organic matter in the soil. The smaller the volatility, the smaller the degree of fluctuation in the vertical diffusion distribution of organic matter in the soil.

[0064] In specific implementation, determining the fluctuation difference of the vertical diffusion distribution of organic matter in the target soil through the difference information between all fluctuations can be achieved in the following way, that is: the average difference between all fluctuations can be used as the fluctuation difference of the vertical diffusion distribution of organic matter in the target soil, and preferably, the average absolute difference between all fluctuations can be used as the fluctuation difference of the vertical diffusion distribution of organic matter in the target soil.

[0065] It should be noted that the fluctuation difference in the present application is an indicator to measure the difference in the distribution change of soil organic matter content at different sampling points. The larger the fluctuation difference, the greater the difference in the distribution change of soil organic matter content at different sampling points. The smaller the fluctuation difference, the smaller the difference in the distribution change of soil organic matter content at different sampling points.

[0066] In some embodiments, performing multi-scale information decomposition on the spectral data based on the fluctuation difference to obtain horizontal spectral feature bands and vertical spectral feature bands can be achieved by using the following steps:

[0067] Dividing the spectral data scale into horizontal spectral data segments and vertical spectral data segments according to the fluctuation difference;

[0068] The horizontal spectrum data segment and the vertical spectrum data segment are subjected to characteristic band decomposition to obtain a horizontal spectrum characteristic band and a vertical spectrum characteristic band.

[0069] In specific implementation, the scale division of the spectral data into horizontal spectral data segments and vertical spectral data segments by using the fluctuation difference can be achieved in the following manner, namely: the hyperplane used for scale division can be determined by using the fluctuation difference, and then the spectral data can be divided into horizontal spectral data segments and vertical spectral data segments. As a preferred embodiment, the hyperplane used for scale division can be determined by using the fluctuation difference, namely: the fluctuation difference can be used as an adjustment parameter of the hyperplane of a support vector machine, that is, the position of the hyperplane in the coordinate axis of the support vector machine can be adjusted according to the size of the fluctuation difference. For example, the position of the hyperplane on the Y-axis can be adjusted upward by the fluctuation difference unit scale, and the unit scale is 1 cm.

[0070] It should be noted that in the present application, dividing the spectral data scale into horizontal spectral data segments and vertical spectral data segments by the fluctuation difference of the vertical diffusion distribution of organic matter helps to reduce the error caused by the distribution heterogeneity of the organic matter content in the horizontal and vertical directions, thereby improving the division accuracy of the horizontal spectral data segments and the vertical spectral data segments.

[0071] In specific implementation, the horizontal spectral data segment and the vertical spectral data segment are subjected to characteristic band decomposition to obtain the horizontal spectral characteristic band and the vertical spectral characteristic band, which can be achieved in the following manner, namely: the horizontal spectral characteristic band can be decomposed into spectral features of different characteristic wavelengths by using the wavelet transform in the prior art, and then the spectral features related to the organic matter content are screened out from all the spectral features, and the set of all the screened spectral features is used as the horizontal spectral characteristic band; the vertical spectral characteristic band can be decomposed into spectral features of different characteristic wavelengths by using the wavelet transform in the prior art, and then the spectral features related to the organic matter content are screened out from all the spectral features, and the set of all the screened spectral features is used as the vertical spectral characteristic band; it should be noted that in other embodiments, other existing decomposition algorithms can also be used to perform characteristic decomposition on the spectral data segment, which is not limited here.

[0072] It should be noted that the spectral characteristic band in the present application is the spectral characteristic related to the organic matter content extracted from the spectral data, the horizontal spectral characteristic band refers to the spectral characteristic related to the organic matter content in the horizontal direction, and the vertical spectral characteristic band refers to the spectral characteristic related to the organic matter content in the vertical direction.

[0073] In step 103, the content distribution entropy of organic matter in the topsoil layer in the horizontal direction is determined according to the horizontal spectral characteristic band, and then the content change gradient of organic matter in the topsoil layer of different blocks is determined according to the content distribution entropy and the horizontal distribution information of the sampling points. The horizontal spectral characteristic band and the vertical spectral characteristic band are hierarchically coupled through the content change gradient and the fluctuation difference to obtain the coupling characteristics of the organic matter content in the topsoil layer.

[0074] In some embodiments, determining the content distribution entropy of organic matter in the topsoil layer in the horizontal direction according to the horizontal spectral characteristic band can be achieved by the following steps:

[0075] Determining a horizontal distribution diagram of organic matter content in the topsoil layer based on the horizontal spectral characteristic band;

[0076] The content distribution entropy of the organic matter in the topsoil layer in the horizontal direction is determined according to the content distribution diagram of the organic matter in the horizontal direction.

[0077] In specific implementation, the content distribution map of organic matter in the topsoil layer in the horizontal direction can be determined based on the horizontal spectral characteristic band. The following method can be used, namely: initialize a quantitative correction model, use all spectral features in the horizontal spectral characteristic band as the input of the quantitative correction model, and then use the quantitative correction model to convert the spectral data into predicted values ​​of organic matter content, so as to obtain the predicted value of organic matter content in the topsoil layer at each sampling point, and form a content distribution map of organic matter in the horizontal direction according to the positions of the corresponding sampling points. It should be noted that the quantitative correction model in this application is used to extract the organic matter content. A regression model, whose quantitative correction model can be pre-trained by a large amount of organic matter detection data; determining the content distribution entropy of organic matter in the topsoil layer in the horizontal direction according to the organic matter content distribution diagram in the horizontal direction can be achieved in the following way, namely: obtaining the predicted value of the organic matter content in the topsoil layer at each sampling point from the content distribution diagram, performing information entropy description on all content prediction values, obtaining content information entropy, and then using the content information entropy as the content distribution entropy of organic matter in the topsoil layer in the horizontal direction. In other embodiments, the wavelet entropy in the prior art can also be used to perform information entropy description on all content prediction values, which is not limited here.

[0078] It should be noted that the content distribution entropy in the present application is an indicator to measure the degree of disorder in the horizontal distribution of organic matter in the topsoil layer. The larger the content distribution entropy, the more disordered the horizontal distribution of organic matter in the topsoil layer is; the smaller the content distribution entropy, the more orderly the horizontal distribution of organic matter in the topsoil layer is.

[0079] In some embodiments, reference Figure 3 As shown, this figure is a schematic diagram of the process of determining the content change gradient in some embodiments of the present application. In this embodiment, the content change gradient of organic matter in the topsoil layer of different blocks can be determined by the content distribution entropy and the horizontal distribution information of the sampling points, which can be achieved by the following steps:

[0080] In step 1031, horizontal distribution information of the sampling points is obtained, and then the target soil is divided according to the horizontal distribution information to obtain different blocks of soil;

[0081] In step 1032, the gradient factor of the change in organic matter content in the soil of each block is determined;

[0082] In step 1033, the content change gradient of organic matter in the topsoil layer of different blocks is determined by all the gradient factors and the content distribution entropy.

[0083] It should be noted that the horizontal distribution information of the sampling points in the present application refers to the distribution of each sampling point in the horizontal direction in the target soil, and the horizontal distribution information can be represented by a sampling point distribution map; in specific implementation, the target soil is divided by the horizontal distribution information, and different block soils can be obtained in the following manner, namely: the sampling point distribution map of the target soil can be divided into blocks of the same size according to the number of sampling points, wherein the number of sampling points distributed in each block is the same, and then each divided block is used as block soil.

[0084] In specific implementation, the gradient factor for the change in organic matter content in the soil of each block can be determined in the following manner, namely: select a block of soil, obtain the predicted value of the organic matter content in the topsoil layer at each sampling point in the block of soil, and then calculate the absolute difference between the predicted values ​​of the organic matter content at adjacent sampling points, and then use the average of all absolute differences as the gradient factor for the change in organic matter content in the soil of the block, repeat the above steps to obtain the gradient factor for the change in organic matter content in the soil of the remaining blocks.

[0085] It should be noted that the gradient factor in the present application is an indicator for measuring the intensity of the gradient change of the organic matter content at different sampling points in the block soil. The gradient factor can reflect the distribution differences of the organic matter content in the block soil. The larger the gradient factor, the greater the intensity of the gradient change of the organic matter content at different sampling points in the block soil. The smaller the gradient factor, the smaller the intensity of the gradient change of the organic matter content at different sampling points in the block soil.

[0086] In specific implementation, the content change gradient of organic matter in the topsoil layer of different blocks can be determined by all gradient factors and the content distribution entropy, which can be achieved in the following way, namely: each gradient factor can be adjusted by the content distribution entropy to obtain the adjustment value of each gradient factor, and then the average value of the adjustment value of all gradient factors is used as the content change gradient of organic matter in the topsoil layer of different blocks. As a preferred embodiment, each gradient factor is adjusted by the content distribution entropy to obtain the adjustment value of each gradient factor, which can be achieved in the following way, namely: the natural exponent of the opposite number of the content distribution entropy can be used as the adjustment coefficient, and then the product of the adjustment coefficient and each gradient factor corresponds to the adjustment value of the gradient factor respectively; it should be noted that in the present application, the adjustment of each gradient factor by the content distribution entropy is beneficial to reduce the influence of abnormal values ​​of organic matter content on the content change gradient, thereby improving the calculation accuracy of the content change gradient.

[0087] It should be noted that the content change gradient in the present application is an indicator to measure the degree of change in the spatial distribution of soil organic matter in different blocks. The larger the content change gradient, the greater the degree of change in the spatial distribution of soil organic matter in different blocks. The smaller the content change gradient, the smaller the degree of change in the spatial distribution of soil organic matter in different blocks.

[0088] In some embodiments, the horizontal spectral characteristic band and the vertical spectral characteristic band are hierarchically coupled by the content change gradient and the fluctuation difference to obtain the coupling characteristics of the organic matter content in the topsoil layer. The following steps can be used to achieve this:

[0089] Acquire all spectral features in the horizontal spectral feature band and all spectral features in the vertical spectral feature band;

[0090] Determine the spectral variance of the horizontal level through all spectral features in the horizontal spectral feature band;

[0091] Determine the spectral variance of the vertical level through all spectral features in the vertical spectral feature band;

[0092] Determining a fusion weight of the horizontal level and a fusion weight of the vertical level according to the spectral variance of the horizontal level, the spectral variance of the vertical level, the content change gradient and the fluctuation difference;

[0093] The horizontal spectral characteristic band and the vertical spectral characteristic band are weightedly coupled through the fusion weight of the horizontal level and the fusion weight of the vertical level to obtain the coupling characteristics of the organic matter content in the topsoil layer.

[0094] In specific implementation, determining the spectral variance of the horizontal level through all the spectral features in the horizontal spectral feature band can be achieved in the following manner, namely: the variance of all the spectral features in the horizontal spectral feature band can be used as the spectral variance of the horizontal level; determining the spectral variance of the vertical level through all the spectral features in the vertical spectral feature band can be achieved in the following manner, namely: the variance of all the spectral features in the vertical spectral feature band can be used as the spectral variance of the vertical level; it should be noted that in the present application, the spectral variance of the horizontal level refers to the variance of the spectral features in the horizontal direction, and the spectral variance of the vertical level refers to the variance of the spectral features in the vertical direction.

[0095] In specific implementation, determining the fusion weight of the horizontal level and the fusion weight of the vertical level according to the spectral variance of the horizontal level, the spectral variance of the vertical level, the content change gradient and the fluctuation difference can be achieved in the following manner, namely: first, the product of the spectral variance of the horizontal level and the content change gradient can be used as the fusion weight in the horizontal direction, and the product of the spectral variance of the vertical level and the fluctuation difference can be used as the fusion weight in the vertical direction; then, the sum of the fusion weight in the horizontal direction and the fusion weight in the vertical direction is used as the total fusion weight; finally, the ratio of the fusion weight in the horizontal direction to the total fusion weight is used as the fusion weight of the horizontal level, and the ratio of the fusion weight in the vertical direction to the total fusion weight is used as the fusion weight of the vertical level.

[0096] It should be noted that the fusion weight in the present application is a numerical value used to balance and integrate the importance of horizontal spectral feature bands and vertical spectral feature bands when performing hierarchical coupling.

[0097] In specific implementation, the horizontal spectral feature band and the vertical spectral feature band are weightedly coupled through the fusion weight of the horizontal level and the fusion weight of the vertical level, and the coupling characteristics of the organic matter content in the topsoil layer are obtained. The following method can be used, namely: initialize a coupling model, use the fusion weight of the horizontal level and the fusion weight of the vertical level as the weight parameters of the coupling model, use the horizontal spectral feature band and the vertical spectral feature band as the initialization parameters of the coupling model, and then realize the weighted coupling of the horizontal spectral feature band and the vertical spectral feature band through the coupling model, so as to obtain the coupling characteristics of the organic matter content in the topsoil layer.

[0098] It should be noted that the coupling characteristics in the present application refer to the significant spectral characteristics in the horizontal spectral characteristic band and the vertical spectral characteristic band, and the significant spectral characteristics can comprehensively reflect the content of organic matter in the target soil; in addition, the present application can couple and screen the spectral characteristics in the horizontal spectral characteristic band and the vertical spectral characteristic band by weighted coupling of the horizontal spectral characteristic band and the vertical spectral characteristic band, thereby obtaining significant spectral characteristics that can comprehensively reflect the content of organic matter in the target soil.

[0099] In step 104, soil moisture information of the topsoil layer at different sampling points is collected, and the diffusion form of organic matter when the spectrum fluctuates at different sampling points is determined based on all the soil moisture information, and then the band disturbance amount of the spectrum during the organic matter content determination process is determined based on all the diffusion forms and the spectral data.

[0100] It should be noted that soil moisture information refers to the moisture data in the soil at each sampling point; in specific implementation, the collection of soil moisture information of the topsoil layer at different sampling points can be achieved in the following manner, namely: select a sampling point, and collect the soil moisture and soil water content of the topsoil layer at the sampling point within a preset time period (the default is 24 hours) according to a preset sampling frequency (the sampling frequency has a value range of 20-50Hz). The collection of soil moisture and soil water content can be used as the soil moisture data of the topsoil layer at the sampling point at the corresponding sampling time, and the collection of all soil moisture data can be used as soil moisture information. Repeat the above steps to obtain the soil moisture information of the topsoil layer at different sampling points.

[0101] In some embodiments, determining the diffusion form of organic matter when the spectrum fluctuates at different sampling points based on all soil moisture information can be achieved by the following steps:

[0102] For each sampling point, the soil moisture information of the topsoil layer at the sampling point is obtained;

[0103] Obtaining soil moisture data at each sampling time in the soil moisture information;

[0104] Determine the fluctuation value of soil moisture at each sampling point at each group of adjacent sampling times according to each soil moisture data;

[0105] The diffusion form of organic matter when the spectrum fluctuates at the sampling point is determined based on the fluctuation values ​​of all soil moisture conditions, and then the diffusion form of organic matter when the spectrum fluctuates at each sampling point is obtained.

[0106] In specific implementation, the fluctuation value of the soil moisture at the sampling point at each group of adjacent sampling moments can be determined according to each soil moisture data. The following method can be used, namely: select a group of adjacent sampling moments, obtain two soil moisture data at the adjacent sampling moments, use the difference between the soil moisture in the two soil moisture data as the humidity fluctuation amount, use the difference between the soil water content in the two soil moisture data as the water content fluctuation amount, and then use the sum of the humidity fluctuation amount and the water content fluctuation amount as the fluctuation value of the soil moisture at the sampling point at the adjacent sampling moments, repeat the above steps, and obtain the fluctuation values ​​of the soil moisture at the sampling points at the remaining groups of adjacent sampling moments. value; determining the diffusion form of organic matter when the spectrum fluctuates at the sampling point according to the fluctuation values ​​of all soil moisture conditions, and then obtaining the diffusion form of organic matter when the spectrum fluctuates at each sampling point can be achieved in the following way, namely: initializing a spectral analysis model, obtaining the spectral data at the sampling point, using the spectral data at the sampling point as the initialization parameter of the spectral analysis model, using the fluctuation values ​​of all soil moisture conditions as the analysis parameters of the spectral analysis model, and using the analysis result of the spectral analysis model as the diffusion form of organic matter when the spectrum fluctuates at the sampling point, repeating the above steps to obtain the diffusion form of organic matter when the spectrum fluctuates at each sampling point.

[0107] It should be noted that the diffusion morphology in this application refers to the distribution and migration patterns of organic matter in the soil under the influence of changes in environmental factors (such as fluctuations in soil moisture). It describes how organic matter moves, diffuses or concentrates in the soil with spatial changes. By analyzing the diffusion morphology, the impact of environmental factors (such as fluctuations in soil moisture) on the distribution of organic matter can be evaluated, which is of great reference value for optimizing agricultural management measures such as fertilization and irrigation.

[0108] In some embodiments, determining the band perturbation amount of the spectrum during the determination of organic matter content from all diffusion forms and the spectral data can be achieved by the following steps:

[0109] Determine the spectrum fluctuation value at each group of adjacent sampling moments through the spectrum data;

[0110] Determine the spectral fluctuation amount during the determination of organic matter content based on all spectral fluctuation values;

[0111] The diffusion interference value of the spectrum during the determination of organic matter content is determined by all diffusion forms;

[0112] The band disturbance amount of the spectrum during the determination of the organic matter content is determined according to the spectrum fluctuation amount and the diffusion interference value.

[0113] In specific implementation, determining the spectral fluctuation value at each group of adjacent sampling moments through the spectral data can be achieved in the following manner, namely: selecting a group of adjacent sampling moments as, obtaining two spectral values ​​at the group of adjacent sampling moments in the spectral data, and the absolute value of the difference between the two spectral values ​​can be used as the spectral fluctuation value at the group of adjacent sampling moments, repeating the above steps to obtain the spectral fluctuation value at each group of adjacent sampling moments; determining the spectral fluctuation amount in the process of determining the organic matter content according to all spectral fluctuation values ​​can be achieved in the following manner, namely: the mean of all spectral fluctuation values ​​can be used as the spectral fluctuation amount in the process of determining the organic matter content; determining the diffusion interference value of the spectrum in the process of determining the organic matter content according to all diffusion forms can be achieved in the following manner, namely: initializing a diffusion model, taking all diffusion forms as the diffusion interference parameters of the diffusion model, and taking the interference value in the generation result of the diffusion model as the diffusion interference value of the spectrum in the process of determining the organic matter content; determining the band disturbance amount of the spectrum in the process of determining the organic matter content according to the spectral fluctuation amount and the diffusion interference value can be achieved in the following manner, namely: the product of the spectral fluctuation amount and the diffusion interference value can be used as the band disturbance amount of the spectrum in the process of determining the organic matter content.

[0114] It should be noted that the diffusion interference value in the present application is an indicator for measuring the degree of interference of changes in soil moisture on organic matter diffusion detection. The larger the diffusion interference value, the greater the interference of changes in soil moisture on organic matter diffusion detection, and the smaller the diffusion interference value, the smaller the interference of changes in soil moisture on organic matter diffusion detection. In addition, it should be noted that the band disturbance amount in the present application refers to the degree of disturbance of spectral data in a specific band due to changes in environmental factors (such as changes in soil moisture) during the determination of soil organic matter content. The band disturbance amount can quantify the spectral interference caused by environmental changes, thereby providing a compensation basis for subsequent spectral compensation.

[0115] In step 105, spectral compensation is performed on the coupling characteristics of the organic matter content in the topsoil layer according to the band disturbance amount to obtain the coupling compensation characteristics of the organic matter content in the target soil, and then coupling detection is performed on the organic matter content in the target soil based on the coupling compensation characteristics.

[0116] In some embodiments, spectral compensation is performed on the coupling characteristics of the organic matter content in the topsoil layer according to the band disturbance amount, and the coupling compensation characteristics of the organic matter content in the target soil are obtained by the following steps:

[0117] Determine the correction parameters for spectrum compensation according to the waveband disturbance amount;

[0118] Performing anomaly detection on the spectral features in the coupled features of the organic matter content in the topsoil layer to obtain an abnormal spectral feature;

[0119] The abnormal spectral characteristics are compensated by the correction parameters, thereby obtaining the coupled compensation characteristics of the organic matter content in the target soil.

[0120] It should be noted that the spectral compensation in the present application refers to compensating for the interference information in the spectral characteristics to improve the accuracy of the spectral characteristics. In the detection of soil organic matter content, spectral compensation is usually used to eliminate the measurement error caused by band disturbance, so as to obtain more reliable detection results; in addition, the correction parameter in the present application is a quantitative indicator used to correct the anomaly in the spectral data. In spectral analysis, the band disturbance amount causes the spectral characteristics to deviate from their actual values, and the correction parameter corrects these deviations so that the spectral data more accurately reflects the actual content of organic matter in the soil. The correction parameter is mainly used to correct the abnormal characteristics in the spectral data and reduce the error caused by the band disturbance; in specific implementation, the correction parameter for spectral compensation determined according to the band disturbance amount can be implemented in the following way, that is: the natural exponential of the opposite number of the band disturbance amount can be used as the correction parameter for spectral compensation.

[0121] In a specific implementation, anomaly detection is performed on the spectral features in the coupling features of the organic matter content in the topsoil layer, and the abnormal spectral features can be obtained by the following method, namely: the abnormal spectral features in the coupling features can be detected based on the box plot anomaly detection method in the prior art, and the detected abnormal spectral features are used as abnormal spectral features; the abnormal spectral features are compensated by the correction parameters, and then the coupling compensation features of the organic matter content in the target soil are obtained. The following method can be used, namely: a compensation model is initialized, the correction parameters are used as the constraint parameters of the compensation model, and then all abnormal spectral features are used as As the input of the compensation model, the compensation characteristics output by the compensation model can be obtained, and then the set consisting of the compensation characteristics and the normal spectral characteristics in the coupling characteristics is used as the coupling compensation characteristics; it should be noted that the compensation relationship in the compensation model can be determined by the following formula, namely: compensation value = abnormal spectral characteristics * correction parameter + dynamic correction factor, wherein the dynamic correction factor can be dynamically adjusted according to the abnormality degree of the abnormal spectral characteristics, the greater the abnormality degree of the abnormal spectral characteristics, the greater the dynamic correction factor, and the smaller the abnormality degree of the abnormal spectral characteristics, the smaller the dynamic correction factor.

[0122] It should be noted that the coupled detection of the organic matter content in the target soil based on the coupled compensation feature refers to the coupled evaluation of the vertical and horizontal distribution of organic matter in the soil through the coupled compensation feature, so as to achieve a comprehensive evaluation of the organic matter content in the soil. In specific implementation, the coupled detection of the organic matter content in the target soil based on the coupled compensation feature can be achieved in the following manner, namely: initializing the quantitative correction model in the above embodiment, and then using the coupled compensation feature as the input of the quantitative correction model, and then through the quantitative correction model, the coupled compensation feature can be converted into a predicted value of the organic matter content, and then the predicted value of the organic matter content is used as the organic matter content in the target soil, and the organic matter content is a comprehensive evaluation value of the organic matter content in the target soil.

[0123] On the other hand, in some embodiments, the present application provides a soil organic matter content detection device, referring to Figure 4 , which is a schematic diagram of an exemplary software module of a soil organic matter content detection device according to some embodiments of the present application, the soil organic matter content detection device 400 includes: an acquisition module 401, a processing module 402 and an execution module 403, which are respectively described as follows:

[0124] Acquisition module 401, in the present application, acquisition module 401 is mainly used to determine the organic matter content of the topsoil layer at different sampling points in the target soil based on spectral technology, and then obtain spectral data of the topsoil layer;

[0125] Processing module 402, in the present application, the processing module 402 is used to determine the fluctuation difference of the vertical diffusion distribution of organic matter in the target soil through the spectral data, and then perform multi-scale information decomposition on the spectral data according to the fluctuation difference to obtain horizontal spectral characteristic bands and vertical spectral characteristic bands;

[0126] The processing module 402 in the present application is also used to determine the content distribution entropy of organic matter in the topsoil layer in the horizontal direction according to the horizontal spectral characteristic band, and then determine the content change gradient of organic matter in the topsoil layer of different blocks according to the content distribution entropy and the horizontal distribution information of the sampling point, and hierarchically couple the horizontal spectral characteristic band and the vertical spectral characteristic band through the content change gradient and the fluctuation difference to obtain the coupling characteristics of the organic matter content in the topsoil layer;

[0127] The processing module 402 in the present application is also used to collect soil moisture information of the topsoil layer at different sampling points, determine the diffusion form of organic matter when the spectrum fluctuates at different sampling points according to all the soil moisture information, and then determine the band disturbance amount of the spectrum during the determination of organic matter content according to all the diffusion forms and the spectral data;

[0128] Execution module 403. In the present application, execution module 403 is mainly used to perform spectral compensation on the coupling characteristics of the organic matter content in the topsoil layer according to the band disturbance amount, obtain the coupling compensation characteristics of the organic matter content in the target soil, and then perform coupling detection on the organic matter content in the target soil based on the coupling compensation characteristics.

[0129] In addition, the present application also provides a computer device, which includes a memory and a processor, the memory stores a code, and the processor is configured to obtain the code and execute the above-mentioned soil organic matter content detection method.

[0130] In some embodiments, reference Figure 5 , which is a schematic diagram of the structure of a computer device for implementing a soil organic matter content detection method according to some embodiments of the present application. The soil organic matter content detection method in the above embodiment can be Figure 5 The computer device 500 shown in the figure is implemented, and the computer device 500 includes at least one processor 501, a communication bus 502, a memory 503 and at least one communication interface 504.

[0131] The processor 501 may be a general-purpose central processing unit (CPU) or an application specific integrated circuit (ASIC).

[0132] The communication bus 502 may be used to transmit information between the above-mentioned components.

[0133] The memory 503 may be a read only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read only memory (EEPROM), a compact disc read only memory (CD ROM) or other optical disc storage, an optical disc storage (including a compressed optical disc, a laser disc, an optical disc, a digital versatile disc, a Blu-ray disc, etc.), a magnetic disk or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 503 may exist independently and be connected to the processor 501 via the communication bus 502. The memory 503 may also be integrated with the processor 501.

[0134] The memory 503 is used to store the program code for executing the solution of the present application, and the execution is controlled by the processor 501. The processor 501 is used to execute the program code stored in the memory 503. The program code may include one or more software modules. The soil organic matter content detection method in the above embodiment can be implemented by the processor 501 and one or more software modules in the program code in the memory 503.

[0135] The communication interface 504 uses any transceiver or other device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0136] In a specific implementation, as an embodiment, a computer device may include multiple processors, each of which may be a single-core (single CPU) processor or a multi-core (multi CPU) processor. The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (such as computer program instructions).

[0137] The above-mentioned computer device may be a general-purpose computer device or a special-purpose computer device. In a specific implementation, the computer device may be a desktop computer, a portable computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device or an embedded device. The embodiment of the present application does not limit the type of computer device.

[0138] In addition, the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned soil organic matter content detection method is implemented.

[0139] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0140] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for detecting soil organic matter content, characterized in that: The steps include: Based on spectral technology, the organic matter content of the topsoil layer at different sampling points in the target soil is measured, and the spectral data of the topsoil layer is obtained; Determine the fluctuation difference of the vertical diffusion distribution of organic matter in the target soil through the spectral data, and then perform multi-scale information decomposition on the spectral data based on the fluctuation difference to obtain horizontal spectral characteristic bands and vertical spectral characteristic bands; Determine the content distribution entropy of organic matter in the topsoil layer in the horizontal direction according to the horizontal spectral characteristic band, and then determine the content change gradient of organic matter in the topsoil layer of different blocks according to the content distribution entropy and the horizontal distribution information of the sampling points, and hierarchically couple the horizontal spectral characteristic band and the vertical spectral characteristic band through the content change gradient and the fluctuation difference to obtain the coupling characteristics of the organic matter content in the topsoil layer; Collect soil moisture information of the topsoil layer at different sampling points, determine the diffusion form of organic matter when the spectrum at different sampling points fluctuates according to all the soil moisture information, and then determine the band disturbance amount of the spectrum during the determination of organic matter content according to all the diffusion forms and the spectral data; The coupling characteristics of the organic matter content in the topsoil layer are spectrally compensated according to the band disturbance amount to obtain the coupling compensation characteristics of the organic matter content in the target soil, and then the organic matter content in the target soil is coupled detected based on the coupling compensation characteristics.

2. The method according to claim 1, characterized in that Determining the fluctuation difference of vertical diffusion distribution of organic matter in the target soil through the spectral data specifically includes: Determine the fluctuation degree of the diffusion of organic matter in the vertical direction at different sampling points according to the spectral data; The fluctuation difference of vertical diffusion distribution of organic matter in the target soil is determined by the difference information between all fluctuations.

3. The method according to claim 1, characterized in that Determining the content distribution entropy of organic matter in the topsoil layer in the horizontal direction according to the horizontal spectral characteristic band specifically includes: Determining a horizontal distribution diagram of organic matter content in the topsoil layer based on the horizontal spectral characteristic band; The content distribution entropy of the organic matter in the topsoil layer in the horizontal direction is determined according to the content distribution diagram of the organic matter in the horizontal direction.

4. The method according to claim 1, characterized in that Determining the content change gradient of organic matter in the topsoil layer of different blocks based on the content distribution entropy and the horizontal distribution information of the sampling points specifically includes: Acquire horizontal distribution information of sampling points, and then divide the target soil according to the horizontal distribution information to obtain different blocks of soil; Determine the gradient factor of the change in organic matter content in the soil of each block; The content variation gradient of organic matter in the topsoil layer of different blocks is determined by all the gradient factors and the content distribution entropy.

5. The method according to claim 1, characterized in that The horizontal spectral characteristic band and the vertical spectral characteristic band are hierarchically coupled by the content change gradient and the fluctuation difference, and the coupling characteristics of the organic matter content in the topsoil layer are obtained, which specifically include: Acquire all spectral features in the horizontal spectral feature band and all spectral features in the vertical spectral feature band; Determine the spectral variance of the horizontal level through all spectral features in the horizontal spectral feature band; Determine the spectral variance of the vertical level through all spectral features in the vertical spectral feature band; Determining a fusion weight of the horizontal level and a fusion weight of the vertical level according to the spectral variance of the horizontal level, the spectral variance of the vertical level, the content change gradient and the fluctuation difference; The horizontal spectral characteristic band and the vertical spectral characteristic band are weightedly coupled through the fusion weight of the horizontal level and the fusion weight of the vertical level to obtain the coupling characteristics of the organic matter content in the topsoil layer.

6. The method according to claim 1, characterized in that Based on all the soil moisture information, the diffusion forms of organic matter when the spectrum fluctuates at different sampling points are determined, including: For each sampling point, the soil moisture information of the topsoil layer at the sampling point is obtained; Obtaining soil moisture data at each sampling time in the soil moisture information; Determine the fluctuation value of soil moisture at each sampling point at each group of adjacent sampling times according to each soil moisture data; The diffusion form of organic matter when the spectrum fluctuates at the sampling point is determined based on the fluctuation values ​​of all soil moisture conditions, and then the diffusion form of organic matter when the spectrum fluctuates at each sampling point is obtained.

7. The method according to claim 1, characterized in that The coupling characteristics of the organic matter content in the topsoil layer are spectrally compensated according to the band disturbance amount to obtain the coupling compensation characteristics of the organic matter content in the target soil, which specifically include: Determine the correction parameters for spectrum compensation according to the waveband disturbance amount; Performing anomaly detection on the spectral features in the coupled features of the organic matter content in the topsoil layer to obtain an abnormal spectral feature; The abnormal spectral characteristics are compensated by the correction parameters, thereby obtaining the coupled compensation characteristics of the organic matter content in the target soil.

8. A soil organic matter content detection device, characterized in that: Included are: An acquisition module is used to determine the organic matter content of the topsoil layer at different sampling points in the target soil based on spectral technology, and then obtain spectral data of the topsoil layer; A processing module, used to determine the fluctuation difference of the vertical diffusion distribution of organic matter in the target soil through the spectral data, and then perform multi-scale information decomposition on the spectral data according to the fluctuation difference to obtain horizontal spectral characteristic bands and vertical spectral characteristic bands; The processing module is further used to determine the content distribution entropy of organic matter in the topsoil layer in the horizontal direction according to the horizontal spectral characteristic band, and then determine the content change gradient of organic matter in the topsoil layer of different blocks according to the content distribution entropy and the horizontal distribution information of the sampling points, and hierarchically couple the horizontal spectral characteristic band and the vertical spectral characteristic band through the content change gradient and the fluctuation difference to obtain the coupling characteristics of the organic matter content in the topsoil layer; The processing module is also used to collect soil moisture information of the topsoil layer at different sampling points, determine the diffusion form of organic matter when the spectrum fluctuates at different sampling points based on all the soil moisture information, and then determine the band disturbance amount of the spectrum during the determination of organic matter content based on all the diffusion forms and the spectral data; An execution module is used to perform spectral compensation on the coupling characteristics of the organic matter content in the topsoil layer according to the band disturbance amount, obtain the coupling compensation characteristics of the organic matter content in the target soil, and then perform coupling detection on the organic matter content in the target soil based on the coupling compensation characteristics.

9. A computer device, comprising a memory and a processor, wherein the memory stores a code, characterized in that: The processor is configured to obtain the code and execute the soil organic matter content detection method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the soil organic matter content detection method according to any one of claims 1 to 7 is implemented.

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