Soil organic matter prediction method, device, equipment and storage medium

Abstract

The present invention relates to the field of soil organic matter analysis, and in particular to a soil organic matter prediction method, the method comprising: obtaining reflection spectrum data and organic matter content data of soil in a target area, wherein the reflection spectrum data comprises reflection spectrum curves of several wavelength ranges; extracting the reflection spectrum curve of the target wavelength range from the reflection spectrum data, and calculating the spectrum interval area data of the reflection spectrum curve of the target wavelength range according to the reflection spectrum data and the organic matter content data; constructing a soil organic matter prediction model according to the organic matter content data and the spectrum interval area data of the soil in the target area, and performing accuracy evaluation on the soil organic matter prediction model; responding to a prediction instruction, the prediction instruction comprising the reflection spectrum data of soil in a to-be-predicted area, and obtaining the organic matter content data of the soil in the to-be-predicted area according to the reflection spectrum data of the soil in the to-be-predicted area and the soil organic matter prediction model.

Description

Technical Field

[0001] The present invention relates to the field of soil organic matter analysis, and in particular to a soil organic matter prediction method, device, equipment and storage medium. Background Art

[0002] Soil organic matter refers to carbon-containing organic compounds that exist in various forms in the soil. It refers to substances in the soil that come from life. It is the substance in the soil other than soil minerals. It is the most active part of the soil, the basis of soil fertility, and one of the important indicators for measuring soil fertility. Measuring soil organic matter is of great significance for guiding the planting of crops.

[0003] With the rapid development of spectral characteristics research, in recent years, a series of soil organic matter spectral prediction indices have been constructed based on the spectral characteristics of soil organic matter in the visible light range or near-infrared range. However, this index is mainly for soils with high soil organic matter content data. Because it will be affected by iron oxide absorption, the soil spectrum in the visible light range is not conducive to spectral modeling of organic matter content data. Therefore, for soils with low soil organic matter content data and relatively high iron oxide content, the measurement feasibility is poor and the measurement effect is not accurate enough. Summary of the invention

[0004] Based on this, the purpose of the present invention is to provide a soil organic matter prediction method, device, equipment and storage medium, which constructs a soil organic matter prediction model based on the reflectance spectrum data and organic matter content data of the soil in the target area, and can predict the organic matter content data and the organic matter content data of soil with high iron oxide content efficiently and quickly.

[0005] In a first aspect, an embodiment of the present application provides a soil organic matter prediction method, comprising the following steps:

[0006] Acquire the reflectance spectrum data and organic matter content data of the soil in the target area, wherein the reflectance spectrum data includes reflectance spectrum curves in several wavelength ranges;

[0007] Extracting a reflection spectrum curve of a target wavelength range from the reflection spectrum data, and calculating spectrum interval area data of the reflection spectrum curve of the target wavelength range according to the reflection spectrum data and organic matter content data;

[0008] Constructing a soil organic matter prediction model based on the soil organic matter content data and the spectral interval area data of the target area, and evaluating the accuracy of the soil organic matter prediction model;

[0009] In response to a prediction instruction, the prediction instruction includes the reflectance spectrum data of the soil in the area to be predicted, and the organic matter content data of the soil in the area to be predicted is obtained according to the reflectance spectrum data of the soil in the area to be predicted and a soil organic matter prediction model.

[0010] In a second aspect, an embodiment of the present application provides a litchi orchard soil organic matter prediction device, comprising:

[0011] An acquisition module, used to acquire the reflectance spectrum data and organic matter content data of the soil in the target area, wherein the reflectance spectrum data includes reflectance spectrum curves in several wavelength ranges;

[0012] A spectrum interval area data calculation module is used to extract a reflection spectrum curve of a target wavelength range from the reflection spectrum data, and calculate the spectrum interval area data of the reflection spectrum curve of the target wavelength range according to the reflection spectrum data and organic matter content data;

[0013] A construction module is used to construct a soil organic matter prediction model based on the soil organic matter content data and the spectral interval area data of the target area, and to evaluate the accuracy of the soil organic matter prediction model;

[0014] The measurement module is used to respond to a prediction instruction, which includes the reflectance spectrum data of the soil in the area to be predicted, and obtain the organic matter content data of the soil in the area to be predicted based on the reflectance spectrum data of the soil in the area to be predicted and a soil organic matter prediction model.

[0015] In a third aspect, an embodiment of the present application provides a computer device, comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor; when the computer program is executed by the processor, the steps of the soil organic matter prediction method described in the first aspect are implemented.

[0016] In a fourth aspect, an embodiment of the present application provides a storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the soil organic matter prediction method as described in the first aspect are implemented.

[0017] In an embodiment of the present application, a soil organic matter prediction method, device, equipment and storage medium are provided. Based on the reflectance spectrum data and organic matter content data of the soil in the target area, a soil organic matter prediction model is constructed, which can predict the organic matter content data and the organic matter content data of soil with high iron oxide content efficiently and quickly.

[0018] For better understanding and implementation, the present invention is described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A schematic diagram of the process of predicting soil organic matter provided in the first embodiment of the present application;

[0020] Figure 2 A schematic diagram of a flow chart of a soil organic matter prediction method provided in the second embodiment of the present application;

[0021] Figure 3 A schematic diagram of the process of S2 in the soil organic matter prediction method provided in the first embodiment of the present application;

[0022] Figure 4 A schematic diagram of a process flow of a soil organic matter prediction method provided in the third embodiment of the present application;

[0023] Figure 5 A schematic diagram of the process of S6 in the soil organic matter prediction method provided in the third embodiment of the present application;

[0024] Figure 6 A schematic diagram of the process of S3 in the soil organic matter prediction method provided in the first embodiment of the present application;

[0025] Figure 7 A schematic diagram of the structure of a device for predicting soil organic matter in a litchi orchard provided in the fourth embodiment of the present application;

[0026] Figure 8 A schematic diagram of the structure of a computer device provided in the fifth embodiment of the present application. DETAILED DESCRIPTION

[0027] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0028] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0029] It should be understood that, although the terms first, second, third, etc. may be used in the present application to describe various information, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" / "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determination".

[0030] See also Figure 1 , Figure 1 The present invention provides a flow chart of a soil organic matter prediction method according to the first embodiment of the present invention. The method comprises the following steps:

[0031] S1: Acquire the reflectance spectrum data and organic matter content data of the soil in the target area, wherein the reflectance spectrum data includes reflectance spectrum curves in several wavelength ranges.

[0032] The executor of the soil organic matter prediction method is a prediction device of the soil organic matter prediction method (hereinafter referred to as the prediction device). In an optional embodiment, the prediction device can be a computer device, a server, or a server cluster composed of multiple computer devices.

[0033] The prediction device can obtain the reflectance spectrum data and organic matter content data of the soil in the target area through sensors, or by downloading from the database.

[0034] In this embodiment, the prediction device uses ASD Field Spec@3, a portable ground spectrometer, to scan and analyze the soil in the target area after drying and grinding, and obtains the reflection spectrum data of the soil in the target area, wherein the reflection spectrum data includes reflection spectrum curves in several wavelength ranges, and the wavelength range is 2450nm-2500nm.

[0035] The reflection spectrum data includes the reflectance value of each wavelength of the reflection spectrum curve and the envelope-removed spectrum value of each wavelength.

[0036] See also Figure 2 , Figure 2 The schematic flow chart of the soil organic matter prediction method provided in the second embodiment of the present application includes step S5, which is before step S2 and is specifically as follows:

[0037] S5: preprocessing the reflection spectrum data to obtain preprocessed reflection spectrum data, wherein the preprocessing step includes band removal processing and band averaging processing.

[0038] Iron oxides have significant absorption characteristics in the 400nm-1000nm range of visible near-infrared light, and organic matter also has absorption characteristics in this range. Since the organic matter content data of the soil in the litchi orchard is low and the iron oxide content is high, when the organic matter content data is inverted using the reflectance spectrum data of the litchi orchard soil, the strong absorption of iron oxides in the above spectral range will mask the absorption characteristics of organic matter in the above spectral range, and the inversion accuracy will be greatly reduced. However, organic matter also has absorption characteristics in the near-infrared range of 1000nm-2500nm.

[0039] Therefore, in order to better accurately predict the organic matter content data of the litchi orchard soil, in this embodiment, the prediction device performs band removal processing and band averaging processing on the acquired reflectance spectrum data of the soil in the target area to obtain the pre-processed reflectance spectrum data. Specifically, the prediction device removes the reflectance spectrum curves in the wavelength ranges of 350nm-399nm, 1350nm-1450nm, 1860nm-1960nm, and 2450nm-2500nm in the reflectance spectrum data of the soil in the target area through corresponding filters, and performs band averaging processing on the removed reflectance spectrum data to obtain the processed reflectance spectrum data, and uses the processed reflectance spectrum data as the reflectance spectrum data, thereby achieving a higher-precision prediction of the organic matter content data of the litchi orchard soil, reducing data redundancy, and improving prediction efficiency.

[0040] S2: extracting a reflection spectrum curve in a target wavelength range from the reflection spectrum data, and calculating spectrum interval area data of the reflection spectrum curve in the target wavelength range according to the reflection spectrum data and the organic matter content data.

[0041] The spectral interval area data is the area data enclosed by the reflective spectrum curve and the preset coordinate axis within the target wavelength range.

[0042] In order to avoid the adverse consequences of accuracy caused by the absorption characteristics of soil organic matter and iron oxides and to better predict the organic matter content data of litchi orchard soil, in this embodiment, the prediction device sets the target wavelength range to 2140nm-2240nm.

[0043] The prediction device extracts a reflection spectrum curve of a target wavelength range from the reflection spectrum data, and calculates spectrum interval area data of the reflection spectrum curve of the target wavelength range according to the reflection spectrum data and the organic matter content data.

[0044] See also Figure 3 , Figure 3The schematic flow chart of S2 in the soil organic matter prediction method provided in the first embodiment of the present application includes steps S201 to S202, which are as follows:

[0045] S201: Acquire a band depth value of the reflection spectrum curve according to the reflection spectrum data and a band depth calculation algorithm.

[0046] The band depth calculation algorithm is:

[0047]

[0048]

[0049] BD (λi) =1-R′ (λi)

[0050] In the formula, R λ(i) is the reflectivity value at wavelength λ(i) in the reflection spectrum curve; λ start is the wavelength of the starting position in the reflection spectrum curve, λ end is the wavelength of the end position in the reflection spectrum curve; R c(λi) is the removed spectrum value of the envelope line of wavelength λ(i); BD (λi) is the band depth value of wavelength λ(i);

[0051] In this embodiment, the prediction device obtains the reflectance value of each wavelength in the reflection spectrum data and the envelope-removed spectrum value of each wavelength, and inputs them into the band depth calculation algorithm to obtain the band depth value of the reflection spectrum curve.

[0052] S202: Acquire spectral interval area data of the reflection spectrum curve within the target wavelength range according to the band depth value of the reflection spectrum curve within the target wavelength range and a spectral area data calculation algorithm.

[0053] The spectral area data calculation algorithm is:

[0054]

[0055] Wherein, TA is the area data of the spectral interval, λ left is the wavelength of the starting position of the reflection spectrum curve in the target wavelength range, λ right The wavelength of the end position of the reflection spectrum curve of the target wavelength range.

[0056] In this embodiment, the prediction device obtains the spectral interval area data of the reflection spectrum curve in the target wavelength range according to the band depth value of the reflection spectrum curve in the target wavelength range and the spectral area data calculation algorithm.

[0057] See also Figure 4 , Figure 4 The schematic flow chart of the soil organic matter prediction method provided in the third embodiment of the present application includes step S6, which is before step S3 and is specifically as follows:

[0058] S6: Perform correlation detection on the organic matter content data and spectral interval area data of the soil in the target area to obtain detection results, and based on the detection results, extract sample organic matter content data from the organic matter content data of the soil in the target area, and extract sample spectral interval area data from the spectral interval area data.

[0059] In this embodiment, the prediction device performs a correlation test on the organic matter content data and the spectral interval area data of the soil in the target area to obtain a test result, and based on the test result, extracts sample organic matter content data from the organic matter content data of the soil in the target area, and extracts sample spectral interval area data from the spectral interval area data, wherein the number of the organic matter content data is consistent with the number of the sample spectral interval area data.

[0060] See also Figure 5 , Figure 5 The schematic flow chart of S6 in the soil organic matter prediction method provided in the third embodiment of the present application includes steps S601 to S602, which are specifically as follows:

[0061] S601: According to the spectral interval area data, the organic matter content data and the Pearson correlation coefficient algorithm, a Pearson correlation coefficient is obtained as a spectral correlation coefficient.

[0062] The Pearson correlation coefficient algorithm is:

[0063]

[0064] In the formula, ρ x,y is the Pearson correlation coefficient, x is the spectral interval area data, y is the organic matter content data; μ x is the mean value of the area data of the spectral interval, μ y is the mean value of the organic matter content data; σ x The standard deviation of the spectral interval area data, σ y is the standard value of the organic matter content data; E() is the expected function;

[0065] In this embodiment, the prediction device obtains the mean and standard value of the spectral interval area data, and the mean and standard value of the organic matter content data, and inputs them into a preset Pearson correlation coefficient algorithm to obtain the Pearson correlation coefficient associated with the spectral interval area data and the organic matter content data.

[0066] S602: According to the Pearson correlation coefficient and the linear correlation coefficient algorithm, a linear correlation coefficient is obtained, and according to the linear correlation coefficient and a preset linear correlation coefficient control value, a correlation test is performed on the spectral interval area data and the organic matter content data to obtain a test result.

[0067] The linear correlation coefficient is used to perform a significance test on the degree of correlation between the spectral interval area data and the organic matter content data.

[0068] The linear correlation coefficient algorithm is:

[0069]

[0070] Wherein, t is the linear correlation coefficient, n is the number of the spectral interval area data and the number of the organic matter content data;

[0071] The prediction device inputs the Pearson correlation coefficient into a preset linear correlation coefficient algorithm to obtain the linear correlation coefficient, and obtains the probability P corresponding to the linear correlation coefficient according to a preset distribution probability distribution table. 0 , according to the preset linear correlation coefficient control value P and compare, when P 0 ≤P, obtain a successful test result, extract sample spectral interval area data from the spectral interval area data, and extract sample organic matter content data from the organic matter content data, so as to realize correlation detection of the spectral interval area data and the organic matter content data. Generally, the linear correlation coefficient control value P is 0.05.

[0072] S3: constructing a soil organic matter prediction model based on the soil organic matter content data and the spectral interval area data of the target area, and performing accuracy evaluation on the soil organic matter prediction model.

[0073] The soil organic matter prediction model is:

[0074] a=-0.08+0.33b

[0075] Wherein, a is the output vector of the soil organic matter prediction model, and b is the input vector of the soil organic matter prediction model.

[0076] In this embodiment, the prediction device uses the organic matter content data as the output vector of the soil organic matter prediction model and the spectral area data as the input vector of the soil organic matter prediction model, performs linear regression, constructs a soil organic matter prediction model, and performs accuracy evaluation on the soil organic matter prediction model.

[0077] See also Figure 6 , Figure 6 The schematic flow chart of S3 in the soil organic matter prediction method provided in the first embodiment of the present application includes steps S301 to S302, which are specifically as follows:

[0078] S301: Input the spectral interval area data into the soil organic matter prediction model, obtain the organic matter content prediction data output by the soil organic matter prediction model, and obtain the evaluation parameters of the soil organic matter prediction model according to the organic matter content prediction data, sample organic matter content data and evaluation parameter algorithm, wherein the evaluation parameters include the determination coefficient, the root mean square error and the residual prediction deviation.

[0079] The evaluation parameter algorithm is:

[0080]

[0081]

[0082]

[0083] In the formula, R 2 is the determination coefficient, RMSE is the root mean square error, RPD is the residual prediction deviation, y i is the organic matter content data of the sample, is the predicted data of organic matter content, is the mean of the sample organic matter content data, m is the number of sample organic matter content data; SD is the standard deviation of the sample organic matter content data.

[0084] In this embodiment, the prediction device inputs the sample organic matter content data, the mean of the sample organic matter content data, the standard deviation of the sample organic matter content data and the organic matter content prediction data into a preset evaluation parameter algorithm to obtain the determination coefficient, root mean square error and residual prediction deviation of the soil organic matter prediction model.

[0085] S302: Compare the preset determination coefficient control value, root mean square error control value and residual prediction deviation control value with the corresponding evaluation parameters of the soil organic matter prediction model to obtain a comparison result, and based on the comparison result, use the soil organic matter prediction model as the soil organic matter prediction model for prediction.

[0086] The comparison results include a determination coefficient comparison success result, a root mean square error comparison success result, and a residual prediction deviation comparison success result.

[0087] The prediction device compares the determination coefficient of the soil organic matter prediction model with the determination coefficient of the soil organic matter prediction model according to a preset determination coefficient control value. Generally, the determination coefficient control value is 0.80. When the determination coefficient of the soil organic matter prediction model is greater than the preset determination coefficient control value, a determination coefficient comparison success result is obtained.

[0088] In an optional embodiment, the prediction device inputs the verification set into the soil organic matter prediction model to obtain organic matter content verification data, wherein the verification set includes the reflectance spectrum data and organic matter content data of the soil measured in the laboratory.

[0089] According to the verification set, organic matter content verification data and evaluation parameter algorithm, a root mean square error corresponding to the verification set is obtained as a root mean square error control value, and the root mean square error control value is compared with the root mean square error of the soil organic matter prediction model. When the root mean square error of the soil organic matter prediction model is greater than the root mean square error control value, a root mean square error comparison success result is obtained;

[0090] When the comparison success result, the root mean square error comparison success result and the residual prediction deviation comparison success result are obtained at the same time, the soil organic matter prediction model is used as the soil organic matter prediction model for prediction.

[0091] S4: In response to a prediction instruction, the prediction instruction includes the reflectance spectrum data of the soil in the area to be predicted, and acquiring the organic matter content data of the soil in the area to be predicted according to the reflectance spectrum data of the soil in the area to be predicted and a soil organic matter prediction model.

[0092] The prediction instruction is issued by the user and received by the prediction device.

[0093] The prediction device obtains the prediction instruction sent by the user, inputs the reflectance spectrum data of the soil in the area to be predicted into the soil organic matter prediction model, and obtains the organic matter content data of the soil in the area to be predicted.

[0094] Please refer to Figure 7 , Figure 7 This is a schematic diagram of the structure of a litchi orchard soil organic matter prediction device provided in the fourth embodiment of the present application. The device can implement all or part of the litchi orchard soil organic matter prediction device through software, hardware or a combination of both. The device 7 includes:

[0095] An acquisition module 71 is used to acquire the reflectance spectrum data and organic matter content data of the soil in the target area, wherein the reflectance spectrum data includes reflectance spectrum curves in several wavelength ranges;

[0096] A spectrum interval area data calculation module 72 is used to extract a reflection spectrum curve of a target wavelength range from the reflection spectrum data, and calculate the spectrum interval area data of the reflection spectrum curve of the target wavelength range according to the reflection spectrum data and organic matter content data;

[0097] A construction module 73 is used to construct a soil organic matter prediction model based on the soil organic matter content data and the spectral interval area data of the target area, and to evaluate the accuracy of the soil organic matter prediction model;

[0098] The measurement module 74 is used to respond to a measurement instruction, which includes the reflectance spectrum data of the soil in the area to be predicted, and obtain the organic matter content data of the soil in the area to be predicted based on the reflectance spectrum data of the soil in the area to be predicted and a soil organic matter prediction model.

[0099] In an embodiment of the present application, the acquisition module is used to acquire the reflectance spectrum data and organic matter content data of the soil in the target area, wherein the reflectance spectrum data includes reflectance spectrum curves of several wavelength ranges; the reflectance spectrum curve of the target wavelength range is extracted from the reflectance spectrum data by the spectrum interval area data calculation module, and the spectrum interval area data of the reflectance spectrum curve of the target wavelength range is calculated according to the reflectance spectrum data and the organic matter content data; the construction module is used to construct a soil organic matter prediction model according to the organic matter content data and the spectrum interval area data of the soil in the target area, and the accuracy of the soil organic matter prediction model is evaluated; the measurement module is used to respond to the measurement instruction, and the measurement instruction includes the reflectance spectrum data of the soil in the area to be predicted, and the organic matter content data of the soil in the area to be predicted is acquired according to the reflectance spectrum data and the soil organic matter prediction model of the soil in the area to be predicted. Based on the reflectance spectrum data and the organic matter content data of the soil in the target area, a soil organic matter prediction model is constructed, which can be used to predict the organic matter content data and the organic matter content data of the soil with high iron oxide content, which is efficient and fast.

[0100] Please refer to Figure 8 , Figure 8 The computer device 8 is a schematic diagram of the structure of the computer device provided in the fifth embodiment of the present application. The computer device 8 includes: a processor 81, a memory 82, and a computer program 83 stored in the memory 82 and executable on the processor 81; the computer device may store multiple instructions, which are suitable for being loaded and executed by the processor 81. Figures 1 to 6The method steps of the embodiment shown in the figure can be found in the specific implementation process. Figures 1 to 6 The specific description of the illustrated embodiment will not be repeated here.

[0101] Among them, the processor 81 may include one or more processing cores. The processor 81 uses various interfaces and lines to connect various parts in the server, and executes various functions and processes data of the litchi garden soil organic matter prediction device 6 by running or executing instructions, programs, code sets or instruction sets stored in the memory 82, and calling the data in the memory 82. Optionally, the processor 81 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programble Logic Array, PLA). The processor 81 can integrate one or more combinations of a central processing unit 81 (Central Processing Unit, CPU), an image processor 81 (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the touch display; the modem is used to process wireless communication. It can be understood that the above-mentioned modem may not be integrated into the processor 81, and it can be implemented by a single chip.

[0102] Among them, the memory 82 may include a random access memory 82 (Random Access Memory, RAM), and may also include a read-only memory 82 (Read-Only Memory). Optionally, the memory 82 includes a non-transitory computer-readable storage medium. The memory 82 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 82 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch instructions, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 82 may also be optionally at least one storage device located away from the aforementioned processor 81.

[0103] The present application also provides a storage medium that can store multiple instructions, which are suitable for the processor to load and execute the above-mentioned Figures 1 to 6The method steps of the embodiment shown in the figure can be found in the specific implementation process. Figures 1 to 6 The specific description of the illustrated embodiment will not be repeated here.

[0104] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0105] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0106] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraint algorithm of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0107] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0108] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0109] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0110] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc.

[0111] The present invention is not limited to the above-mentioned embodiments. If various changes or modifications to the present invention do not depart from the spirit and scope of the present invention, and if these changes and modifications fall within the scope of the claims and equivalent technologies of the present invention, the present invention is also intended to include these changes and modifications.

Claims

1. A soil organic matter prediction method, characterized in that: The following steps are involved: Acquire the reflectance spectrum data and organic matter content data of the soil in the target area, wherein the reflectance spectrum data includes reflectance spectrum curves in several wavelength ranges; A reflection spectrum curve of a target wavelength range is extracted from the reflection spectrum data, and a band depth value of the reflection spectrum curve of the target wavelength range is obtained according to the reflection spectrum data and a band depth calculation algorithm, wherein the band depth calculation algorithm is: BD (λi) =1-R′ (λi) In the formula, R λ(i) is the reflectivity value at wavelength λ(i) in the reflection spectrum curve; λ start is the wavelength of the starting position in the reflection spectrum curve, λ end is the wavelength of the end position in the reflection spectrum curve; R c(λi) is the removed spectrum value of the envelope line of wavelength λ(i); BD (λi) is the band depth value of wavelength λ(i); According to the band depth value of the reflection spectrum curve within the target wavelength range and the spectrum area data calculation algorithm, the spectrum interval area data of the reflection spectrum curve within the target wavelength range is obtained, wherein the spectrum area data calculation algorithm is: Wherein, TA is the area data of the spectral interval, λ left is the wavelength of the starting position of the reflection spectrum curve in the target wavelength range, λ right The wavelength of the end position of the reflection spectrum curve of the target wavelength range; Constructing a soil organic matter prediction model based on the soil organic matter content data and the spectral interval area data of the target area, and evaluating the accuracy of the soil organic matter prediction model; In response to a prediction instruction, the prediction instruction includes the reflectance spectrum data of the soil in the area to be predicted, and the organic matter content data of the soil in the area to be predicted is obtained according to the reflectance spectrum data of the soil in the area to be predicted and a soil organic matter prediction model.

2. The soil organic matter prediction method according to claim 1, characterized in that: Before extracting the reflection spectrum curve of the target wavelength range from the reflection spectrum data, the method comprises the following steps: The reflection spectrum data is preprocessed to obtain preprocessed reflection spectrum data, wherein the preprocessing step includes band removal processing and band averaging processing.

3. The soil organic matter prediction method according to claim 1, characterized in that: The method comprises the following steps before constructing a soil organic matter prediction model based on the soil organic matter content data and the spectral interval area data of the target area and evaluating the accuracy of the soil organic matter prediction model: A correlation test is performed on the organic matter content data and the spectral interval area data of the soil in the target area to obtain a test result. Based on the test result, sample organic matter content data is extracted from the organic matter content data of the soil in the target area, and sample spectral interval area data is extracted from the spectral interval area data.

4. The soil organic matter prediction method according to claim 3, characterized in that: The method of performing correlation detection on the organic matter content data and the spectral interval area data of the soil in the target area to obtain the detection result, and extracting the sample organic matter content data from the organic matter content data of the soil in the target area and extracting the sample spectral interval area data from the spectral interval area data according to the detection result, comprises the steps of: According to the spectral interval area data, the organic matter content data and the Pearson correlation coefficient algorithm, the Pearson correlation coefficient is obtained as the spectral correlation coefficient, wherein the Pearson correlation coefficient algorithm is: In the formula, ρ x,y is the Pearson correlation coefficient, x is the spectral interval area data, y is the organic matter content data; μ x is the mean value of the area data of the spectral interval, μ y is the mean value of the organic matter content data; σ x The standard deviation of the spectral interval area data, σy is the standard value of the organic matter content data; E() is the expected function; According to the Pearson correlation coefficient and the linear correlation coefficient algorithm, a linear correlation coefficient is obtained, and according to the linear correlation coefficient and a preset linear correlation coefficient control value, a correlation test is performed on the spectral interval area data and the organic matter content data to obtain a test result, wherein the linear correlation coefficient algorithm is: Wherein, t is the linear correlation coefficient, and n is the number of the spectral interval area data and the number of the organic matter content data.

5. The soil organic matter prediction method according to claim 4, characterized in that: The method of constructing a soil organic matter prediction model based on the soil organic matter content data and the spectral interval area data of the target area comprises the following steps: The sample organic matter content data of the soil in the target area is used as the output vector, the sample spectral interval area data is used as the input vector, and a linear regression method is used to construct a soil organic matter prediction model, wherein the soil organic matter prediction model is: a=-0.08+0.33b Wherein, a is the output vector of the soil organic matter prediction model, and b is the input vector of the soil organic matter prediction model.

6. The soil organic matter prediction method according to claim 1, characterized in that: The accuracy evaluation of the soil organic matter prediction model comprises the following steps: Inputting the spectral interval area data into the soil organic matter prediction model, obtaining the organic matter content prediction data output by the soil organic matter prediction model, and obtaining the evaluation parameters of the soil organic matter prediction model according to the organic matter content prediction data, the sample organic matter content data and the evaluation parameter algorithm, wherein the evaluation parameters include the determination coefficient, the root mean square error and the residual prediction deviation; According to the preset determination coefficient control value, root mean square error control value and residual prediction deviation control value, comparison is performed with the corresponding evaluation parameters of the soil organic matter prediction model to obtain a comparison result, and based on the comparison result, the soil organic matter prediction model is used as the soil organic matter prediction model for prediction.

7. A litchi orchard soil organic matter prediction device, characterized in that: include: An acquisition module, used to acquire the reflectance spectrum data and organic matter content data of the soil in the target area, wherein the reflectance spectrum data includes reflectance spectrum curves in several wavelength ranges; The spectrum interval area data calculation module is used to extract the reflection spectrum curve of the target wavelength range from the reflection spectrum data, and obtain the band depth value of the reflection spectrum curve of the target wavelength range according to the reflection spectrum data and the band depth calculation algorithm, wherein the band depth calculation algorithm is: BD (λi) =1-R′ (λi) In the formula, R λ(i) is the reflectivity value at wavelength λ(i) in the reflection spectrum curve; λ start is the wavelength of the starting position in the reflection spectrum curve, λ end is the wavelength of the end position in the reflection spectrum curve; R c(λi) is the removed spectrum value of the envelope line of wavelength λ(i); BD (λi) is the band depth value of wavelength λ(i); According to the band depth value of the reflection spectrum curve within the target wavelength range and the spectrum area data calculation algorithm, the spectrum interval area data of the reflection spectrum curve within the target wavelength range is obtained, wherein the spectrum area data calculation algorithm is: Wherein, TA is the area data of the spectral interval, λ left is the wavelength of the starting position of the reflection spectrum curve in the target wavelength range, λ right The wavelength of the end position of the reflection spectrum curve of the target wavelength range; A construction module is used to construct a soil organic matter prediction model based on the soil organic matter content data and the spectral interval area data of the target area, and to evaluate the accuracy of the soil organic matter prediction model; The measurement module is used to respond to a prediction instruction, which includes the reflectance spectrum data of the soil in the area to be predicted, and obtain the organic matter content data of the soil in the area to be predicted based on the reflectance spectrum data of the soil in the area to be predicted and a soil organic matter prediction model.

8. A computer device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and executable on the processor; when the computer program is executed by the processor, the steps of the soil organic matter prediction method as described in any one of claims 1 to 6 are implemented.

9. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the soil organic matter prediction method according to any one of claims 1 to 6 are implemented.

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