A soil nitrogen content dynamic estimation method, device, terminal and storage medium

Abstract

The application provides a soil nitrogen content dynamic estimation method and device, a terminal and a storage medium, and relates to the technical field of soil content analysis. The method comprises the following steps: acquiring a hyperspectral remote sensing image of crops in a to-be-measured area, and calculating spectral characteristic parameters and vegetation indexes according to the hyperspectral remote sensing image; inputting the spectral characteristic parameters and the vegetation indexes into a crop spectrum-nitrogen content inversion model to output plant nitrogen content in the to-be-measured area; calculating a soil nitrogen change amount of the to-be-measured area by using the plant nitrogen content, and dynamically estimating soil nitrogen content of the to-be-measured area by using the soil nitrogen change amount. The application can improve the efficiency and accuracy of soil nitrogen content estimation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of soil content analysis, and particularly relates to a soil nitrogen content dynamic estimation method and device, a terminal and a storage medium. BACKGROUND

[0002] In traditional agriculture, the nitrogen nutrient content information in soil is mainly obtained by manual destructive field sampling combined with indoor manual measurement chemical analysis to obtain relevant indexes, which has the advantages of high measurement accuracy and strong accuracy, but the monitoring method is difficult to apply to large-scale and continuous soil monitoring, and has large workload and high cost. SUMMARY

[0003] The present application provides a soil nitrogen content dynamic estimation method, device, terminal and storage medium to solve the problems of low detection efficiency and inability to realize real-time dynamic monitoring in the prior art.

[0004] In a first aspect, the present application provides a soil nitrogen content dynamic estimation method, comprising:

[0005] Obtaining a hyperspectral remote sensing image of crops in a to-be-measured region, and calculating spectral feature parameters and vegetation indexes according to the hyperspectral remote sensing image;

[0006] Inputting the spectral feature parameters and the vegetation indexes into a crop spectrum-nitrogen content inversion model to output plant nitrogen content in the to-be-measured region;

[0007] Using the plant nitrogen content to calculate a soil nitrogen change amount of the to-be-measured region, and using the soil nitrogen change amount to dynamically estimate soil nitrogen content of the to-be-measured region.

[0008] In a second aspect, the present application provides a soil nitrogen content dynamic estimation device, comprising:

[0009] A data calculation module is configured to obtain a hyperspectral remote sensing image of crops in a to-be-measured region, and calculate spectral feature parameters and vegetation indexes according to the hyperspectral remote sensing image;

[0010] A plant nitrogen content determination module is configured to input the spectral feature parameters and the vegetation indexes into a crop spectrum-nitrogen content inversion model to output plant nitrogen content in the to-be-measured region;

[0011] A nitrogen content estimation module is configured to use the plant nitrogen content to calculate a soil nitrogen change amount of the to-be-measured region, and use the soil nitrogen change amount to dynamically estimate soil nitrogen content of the to-be-measured region.

[0012] In a third aspect, the present application provides a terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method according to the first aspect or any possible implementation manner of the first aspect when executing the computer program.

[0013] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, wherein the computer program is executable by a processor to implement the steps of the method according to the first aspect or any possible implementation manner of the first aspect.

[0014] The present application provides a soil nitrogen content dynamic estimation method, device, terminal and storage medium. The soil nitrogen content dynamic estimation method comprises the following steps: acquiring a hyperspectral remote sensing image of crops in a to-be-measured region; calculating spectral characteristic parameters and vegetation indexes according to the hyperspectral remote sensing image; inputting the spectral characteristic parameters and the vegetation indexes into a crop spectrum-nitrogen content inversion model to output plant nitrogen content in the to-be-measured region; calculating a soil nitrogen change amount of the to-be-measured region by using the plant nitrogen content; and dynamically estimating soil nitrogen content of the to-be-measured region by using the soil nitrogen change amount. The present application acquires the hyperspectral remote sensing image of crops by means of the hyperspectral remote sensing technology, can quickly cover a large area of the to-be-measured region, greatly shortens the time for data acquisition and processing compared with the traditional method of collecting a large number of samples on site and analyzing in a laboratory, realizes rapid estimation of the soil nitrogen content, and does not cause physical damage to the crops and the soil. Moreover, the present application correlates the crop spectrum characteristics and the plant nitrogen content by using the crop spectrum-nitrogen content inversion model, and further deduces the soil nitrogen content, fully utilizes the close nutrient relationship between the crops and the soil nitrogen, reduces the influence of the complex physicochemical properties of the soil itself on direct estimation of the soil nitrogen, and improves the accuracy of the estimation result. Meanwhile, the dynamically estimated soil nitrogen content data can provide a scientific basis for precise fertilization of the crops, reasonably adjusts the nitrogen fertilizer application amount according to the actual situation of the soil nitrogen in different regions, avoids excessive or insufficient nitrogen fertilizer, improves the nitrogen fertilizer utilization efficiency, and reduces the risk of agricultural non-point source pollution. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative labor.

[0016] Figure 1 is the implementation flowchart of the soil nitrogen content dynamic estimation method provided by the embodiments of the present application;

[0017] Figure 2FIG. 1 is a structural schematic diagram of a soil nitrogen content dynamic estimation device provided by an embodiment of the present application;

[0018] Figure 3 FIG. 2 is a schematic diagram of a terminal provided by an embodiment of the present application. DETAILED DESCRIPTION

[0019] In the following description, specific details are set forth, such as particular system configurations, techniques, etc., in order to provide a thorough understanding of the present application. However, persons skilled in the art will understand that the present application can be practiced without these specific details. In other instances, well-known structures, devices, circuits, and methods have not been described in detail in order to avoid obscuring the present application.

[0020] In order to make the objectives, technical solutions and advantages of the present application clearer, the following will be described by specific embodiments in conjunction with the accompanying drawings.

[0021] Since the soil nitrogen content is closely related to the nitrogen content of crop plants, the amount of fertilization, etc., in order to solve the problem of low detection efficiency and inability to achieve real-time dynamic monitoring in the prior art for soil nitrogen detection, the present application provides a soil nitrogen content dynamic estimation method, which uses a UAV to carry a hyperspectral remote sensing device to obtain crop spectral information, and inversely obtains the nitrogen content of plants, and then estimates the soil nitrogen content in combination with the amount of fertilization, fertilizer utilization rate, etc.

[0022] Among them, hyperspectral remote sensing refers to a technology that obtains a lot of narrow and continuous spectral data in the visible light and infrared band range. At the same time, UAVs are widely used in various fields of agricultural monitoring in China, and the use of airborne hyperspectral remote sensing means can obtain fine spectral information of ground objects, and has incomparable advantages over traditional methods such as non-destructive, large-scale rapid monitoring, high spatial and temporal resolution, and flexible operation.

[0023] Figure 1 The implementation flowchart of the soil nitrogen content dynamic estimation method provided by the embodiment of the present application is described in detail as follows:

[0024] In step 101, a hyperspectral remote sensing image of crops in a to-be-detected region is obtained, and spectral feature parameters and vegetation indices are calculated according to the hyperspectral remote sensing image.

[0025] Among them, the to-be-detected region can be a farmland.

[0026] In the embodiment of the present application, the UAV carries the hyperspectral remote sensing device to collect the hyperspectral remote sensing image of the crops in the to-be-detected region according to the flight path and height of the UAV reasonably planned. Then, after collecting the hyperspectral remote sensing image, the spectral feature parameters and vegetation indices are calculated according to the data extracted from the hyperspectral remote sensing image.

[0027] Wherein, the preparation and setting of the unmanned aerial vehicle carrying hyperspectral remote sensing equipment are as follows:

[0028] 1) Equipment preparation: the embodiment of the application utilizes an unmanned aerial vehicle with stable flight performance and sufficient load capacity to carry hyperspectral remote sensing equipment for flight operation. At the same time, auxiliary equipment such as GPS positioning equipment and data transmission module is prepared.

[0029] 2) Flight parameter setting: according to the area, terrain and growth conditions of the crops in the region to be measured, the flight route and height of the unmanned aerial vehicle are reasonably planned. Generally, the flight height can be set between 50-150 meters to ensure that the hyperspectral remote sensing images obtained have appropriate spatial resolution and coverage. The flight speed is controlled at 5-15 meters / second to ensure the stability and accuracy of image acquisition. At the same time, the acquisition parameters of the hyperspectral remote sensing equipment are set, such as the spectral acquisition range and acquisition frequency, and the gray cloth, white board and black board acquisition correction is performed once per flight.

[0030] After the unmanned aerial vehicle carrying the hyperspectral remote sensing equipment is set, the embodiment of the application controls the unmanned aerial vehicle to perform flight operation on the region to be measured according to the flight route and parameters of the equipment under suitable weather conditions (such as sunny and windless day), and obtains the hyperspectral remote sensing images of the crops in the region to be measured. In the flight process, auxiliary data such as geographical position information and flight time is recorded in real time.

[0031] After the flight is completed, the collected data is transmitted to the data processing platform (such as terminal equipment) through the data transmission module. At the same time, the embodiment of the application based on the unmanned aerial vehicle carrying the hyperspectral remote sensing equipment can monitor crops in different growth periods according to actual needs. The embodiment of the application can flexibly perform multiple spectral information acquisition according to different growth stages of crops using the unmanned aerial vehicle, realize dynamic monitoring of the nitrogen status of crops in the growth process, and timely grasp the change trend of soil nitrogen content.

[0032] Compared with the traditional method which usually needs to collect soil samples and bring them back to the laboratory for analysis, the process is complicated and time-consuming, the embodiment of the application utilizes the unmanned aerial vehicle carrying the hyperspectral remote sensing equipment to perform non-contact spectral information acquisition in the air on a large area of detection region, which can quickly obtain spectral data of crops, greatly improve the detection efficiency, and will not cause physical damage to crops and soil.

[0033] In one possible implementation, after obtaining the hyperspectral remote sensing images of the crops in the region to be measured, the method can further include:

[0034] preprocessing the hyperspectral remote sensing images, the preprocessing including radiation correction, geometric correction and atmospheric correction;

[0035] Correspondingly, the spectral characteristic parameters and the vegetation indexes are calculated according to the hyperspectral remote sensing image.

[0036] The spectral characteristic parameters and the vegetation indexes are calculated according to the preprocessed hyperspectral remote sensing image.

[0037] Optionally, after the hyperspectral remote sensing image of the crops in the target area is acquired, the image is preprocessed, including but not limited to radiation correction, geometric correction and atmospheric correction, to eliminate the influence of external factors on the spectral data and improve the accuracy and reliability of the data.

[0038] In a possible implementation, the spectral characteristic parameters and the vegetation indexes are calculated according to the hyperspectral remote sensing image, which can include:

[0039] The characteristic bands of the crops in the target area and the spectral reflectance of the corresponding bands are extracted from the hyperspectral remote sensing image.

[0040] The spectral characteristic parameters and the vegetation indexes are calculated by using the characteristic bands of the crops in the target area and the spectral reflectance of the corresponding bands.

[0041] Optionally, the characteristic bands of the crops in the target area and the spectral reflectance of the corresponding bands are extracted from the acquired hyperspectral remote sensing image. Then, the spectral characteristic parameters and the vegetation indexes of the corresponding bands are calculated by using each characteristic band of the crops in the target area and the spectral reflectance of the corresponding band.

[0042] For the vegetation indexes in the embodiments, the normalized difference vegetation index (NDVI), the relative vegetation index (RVI) and the difference vegetation index (DVI) can be used.

[0043] For example, the calculation formula of the partial vegetation indexes of the summer corn is as follows, and the corresponding vegetation indexes are:

[0044]

[0045] For the spectral characteristic parameters in the embodiments, the parameters are used to describe and quantify the characteristics of the hyperspectral data and are mainly used to analyze the spectral characteristics of the substances.

[0046] In a possible implementation, the spectral characteristic parameters are calculated by using the characteristic bands of the crops in the target area and the spectral reflectance of the corresponding bands, which can include:

[0047] Using the first-order differential spectral analysis method, the characteristic bands of crops in the target area and the spectral reflectance of the corresponding bands are calculated to obtain the spectral characteristic parameters corresponding to each characteristic band.

[0048] Optionally, the spectral characteristic parameters corresponding to each characteristic band are calculated using a first-order differential spectral analysis method. Specifically, the ratio of the difference in spectral reflectance between two adjacent characteristic bands before and after the target characteristic band to the corresponding wavelength difference is used as the derivative spectrum of the target characteristic band. Here, the target characteristic band is any one of all characteristic bands within the target region. The specific calculation formula is as follows:

[0049]

[0050] in, For derivative spectra, Characteristic bands The corresponding spectral reflectance at that location, Characteristic bands The corresponding spectral reflectance at that location, Characteristic bands The corresponding wavelength at that location, Characteristic bands The wavelength corresponding to that location.

[0051] In this embodiment, the spectral feature parameters mainly include spectral location-based feature parameters, spectral area-based feature parameters, and spectral vegetation index-based feature parameters. The specific spectral feature parameters and their corresponding calculation processes are shown in Table 1.

[0052] Table 1 Hyperspectral characteristic parameters

[0053]

[0054] In step 102, the spectral characteristic parameters and vegetation index are input into the crop spectral-nitrogen content inversion model, and the plant nitrogen content in the area to be tested is output.

[0055] In this embodiment of the application, the spectral feature parameters and vegetation index extracted from the hyperspectral remote sensing image of crops in the area to be tested obtained in step 101 are used as inputs and input into the established crop spectral-nitrogen content inversion model to output the plant nitrogen content of crops in the area to be tested.

[0056] The embodiment of the application inputs the spectral feature parameters and the vegetation index into a crop spectrum-nitrogen content inversion model. The model is trained and verified by a large amount of data, and can accurately establish the quantitative relationship between the spectral feature parameters, the vegetation index and the plant nitrogen content. Through the scientific inversion process, the output plant nitrogen content has high accuracy, and provides reliable basic data for subsequent estimation of soil nitrogen content.

[0057] In a possible implementation, the construction process of the crop spectrum-nitrogen content inversion model can be as follows.

[0058] The historical spectral feature parameters, the historical vegetation index and the corresponding historical plant nitrogen content of the crops in the target region are obtained, and the target region is any region.

[0059] The crop spectrum-nitrogen content inversion model is constructed by using a statistical regression method.

[0060] The historical spectral feature parameters and the historical vegetation index are used as inputs, and the historical plant nitrogen content is used as output, to train the crop spectrum-nitrogen content inversion model.

[0061] Optionally, there is a close relationship between the nitrogen content and the hyperspectral data. The embodiment of the application can extract the spectral features related to the nitrogen content by analyzing the crop hyperspectral data and combining the sample data measured by ground sampling, to construct the crop spectrum-nitrogen content inversion model, so as to realize quantitative evaluation and monitoring of the nitrogen content.

[0062] The process of obtaining the historical spectral feature parameters, the historical vegetation index and the corresponding historical plant nitrogen content is as follows.

[0063] The unmanned aerial vehicle carries the hyperspectral remote sensing equipment to obtain a plurality of historical hyperspectral remote sensing images of the crops in the target region according to the set flight route and height, and the plurality of historical hyperspectral remote sensing images are spliced according to the shooting order to obtain a complete historical hyperspectral remote sensing image of the target region.

[0064] The complete historical hyperspectral remote sensing image is preprocessed, and the preprocessing includes radiation correction, geometric correction and atmospheric correction, to eliminate the influence of external factors on the spectral data and improve the accuracy and reliability of the data.

[0065] The historical feature bands of the crops in the target region and the historical spectral reflectance of the corresponding bands are extracted from the preprocessed complete historical hyperspectral remote sensing image, wherein the historical spectral reflectance is the average of the spectral reflectance of all historical hyperspectral remote sensing images.

[0066] The historical plant nitrogen content of the crops in the target region is obtained.

[0067] The construction process of the crop spectrum-nitrogen content inversion model is as follows:

[0068] Optionally, in the embodiment of the application, the spectrum characteristic parameters and vegetation indexes that are significantly correlated with the plant nitrogen content are screened based on the correlation coefficient method, and no less than three spectrum characteristic parameters and vegetation indexes (for example, the normalized difference vegetation index, the ratio vegetation index, and the difference vegetation index mentioned above) with high correlation are selected, the data set is divided into a training set and a validation set according to a ratio of 7:3, and a statistical regression method is used to construct a crop nitrogen content estimation model. The statistical regression method can include linear regression (y=a+bx), logarithmic regression (y=a+blnx), quadratic regression (y=a+bx+cx 2 ), and exponential regression (y=ae bx ), wherein y is a fitted value of a physiological and ecological parameter, x is a vegetation index, and a, b, and c are constants.

[0069] The data set is obtained by using a grid random sampling method to select 20-30 sampling points in the flight area to collect crop samples and soil samples. The soil samples are collected from a 0-50 cm soil layer. Then, the total nitrogen content of the plant and the total nitrogen content of the soil are measured in the laboratory.

[0070] After the construction of the multiple crop nitrogen content estimation models, in order to further screen the best model, that is, the crop spectrum-nitrogen content inversion model required by the embodiment of the application, one regression equation with high coefficients of determination R 2 and variance test quantities (F values) is selected from each spectrum characteristic parameter and vegetation index, the experimental measured data are used to test and verify the accuracy of each model, the root mean square error (RMSE) and the relative error (RE) are tested, and the crop nitrogen content estimation model corresponding to the spectrum characteristic parameter or vegetation index with the smallest RMSE and RE is selected as the crop spectrum-nitrogen content inversion model.

[0071] In step 103, the soil nitrogen content of the to-be-measured area is dynamically estimated by using the plant nitrogen content to calculate the soil nitrogen change amount of the to-be-measured area.

[0072] In the embodiment of the application, the soil nitrogen change amount of the to-be-measured area is calculated by using the plant nitrogen content calculated in step 102, and then the soil nitrogen content of the to-be-measured area is dynamically estimated by using the soil nitrogen change amount.

[0073] The embodiment of the application calculates the soil nitrogen variation of the to-be-measured region by using the plant nitrogen content, and further dynamically estimates the soil nitrogen content, fully considering the dynamic change characteristics of the soil nitrogen in time and space. The soil nitrogen content is not fixed, and is affected by various factors such as crop growth, fertilization, rainfall and the like. The method can reflect the influence of these factors on the soil nitrogen content in real time, so that the estimation result is more in line with the actual situation, and a scientific basis is provided for precision agriculture management.

[0074] In a possible implementation, the soil nitrogen variation of the to-be-measured region is calculated by using the plant nitrogen content, which can include:

[0075] The fertilization amount and the fertilizer utilization rate of the to-be-measured region are obtained.

[0076] The nitrogen loss amount of the to-be-measured region is calculated.

[0077] The product of the fertilization amount and the fertilizer utilization rate is taken as a first product, and the first product is subtracted by the nitrogen loss amount and the plant nitrogen content, to obtain the soil nitrogen variation.

[0078] Optionally, the soil nitrogen variation calculated in the embodiment of the application needs to be combined with the data such as the fertilization amount, the fertilizer utilization rate and the plant nitrogen content of the to-be-measured region obtained by inversion. The fertilizer utilization rate is determined according to factors such as fertilizer type, soil type and climate condition. Therefore, the fertilization amount and the fertilizer utilization rate of the to-be-measured region are first obtained, and then the nitrogen loss amount of the to-be-measured region is calculated. Finally, the soil nitrogen variation is calculated by using the soil nitrogen dynamic balance equation, that is,

[0079]

[0080] wherein, the soil nitrogen variation is kg / ha; the fertilization amount is kg / ha, the fertilizer utilization rate is %, the first product is kg / ha; the plant nitrogen content is kg / ha; the nitrogen loss amount can be estimated by using a leaching model, and is kg / ha.

[0081] The process of obtaining the fertilization amount and the fertilizer utilization rate of the to-be-measured region is as follows:

[0082] At the same time of collecting the hyperspectral remote sensing image, the embodiment of the application collects the ground crop and soil sample data of the to-be-measured region, and records information such as the fertilization amount, the fertilizer type (such as urea, organic fertilizer and the like), the fertilization time and the fertilizer utilization rate.

[0083] In a possible implementation, the calculating the nitrogen loss amount of the to-be-tested region can include:

[0084] obtaining the rainfall of the to-be-tested region;

[0085] calculating the nitrogen loss amount of the to-be-tested region by using the rainfall of the to-be-tested region and the preset soil depth.

[0086] Optionally, the nitrogen loss amount calculated in the embodiments of the present application needs to be corrected in combination with environmental parameters such as soil texture (such as sandy soil and clay), rainfall, and temperature. First, the rainfall, temperature, and soil texture type of the to-be-tested region are obtained, and then the nitrogen loss amount is calculated by using the formula , wherein is the nitrogen loss amount, is the leaching coefficient, is the rainfall, is the nitrogen loss rate parameter, and is the soil depth.

[0087] In a possible implementation, the dynamic estimation of the soil nitrogen content of the to-be-tested region by using the soil nitrogen change amount can include:

[0088] obtaining the initial soil nitrogen content of the to-be-tested region;

[0089] dynamically estimating the soil nitrogen content of the to-be-tested region by using the initial soil nitrogen content and the sum of the soil nitrogen change amounts of each time period in the to-be-tested region.

[0090] Optionally, the embodiments of the present application dynamically estimate the soil nitrogen content of the to-be-tested region by using the following calculation formula, that is,

[0091]

[0092] , wherein is the soil nitrogen content at time , is the initial soil nitrogen content, which is determined by a traditional method, is the soil nitrogen change amount in the first time period, and is the time.

[0093] In a possible implementation, after the dynamic estimation of the soil nitrogen content of the to-be-tested region by using the soil nitrogen change amount, the method can further include:

[0094] visualizing the soil nitrogen content of the to-be-tested region.

[0095] ​Optionally, the soil nitrogen content estimated dynamically is visualized to generate a soil nitrogen content spatial distribution map, and combined with a geographic information system (GIS) technology, the distribution map is superimposed and displayed with information such as topography and crop planting area of the to-be-measured region, to provide the user with intuitive soil nitrogen status information of the to-be-measured region. Meanwhile, according to the distribution of the soil nitrogen content, the growth demand of the crop and the fertilization standard, personalized fertilization suggestions are provided for the user to guide precision fertilization operation. The embodiments of the present application utilize the hyperspectral equipment carried by the unmanned aerial vehicle to record the spatial position information while obtaining the spectral information, to generate a spatial distribution map of the soil nitrogen content, which can intuitively show the distribution difference of the soil nitrogen content in different regions of the farmland, to realize precision agriculture.

[0096] The present application provides a soil nitrogen content dynamic estimation method, which obtains a hyperspectral remote sensing image of crops in a to-be-measured region, and calculates spectral characteristic parameters and vegetation indices according to the hyperspectral remote sensing image; inputs the spectral characteristic parameters and the vegetation indices into a crop spectrum-nitrogen content inversion model to output plant nitrogen content in the to-be-measured region; calculates a soil nitrogen content change amount of the to-be-measured region by using the plant nitrogen content, and dynamically estimates the soil nitrogen content of the to-be-measured region by using the soil nitrogen content change amount. The present application obtains the hyperspectral remote sensing image of crops by means of the hyperspectral remote sensing technology, which can quickly cover a large area of the to-be-measured region, greatly shortens the time for data acquisition and processing compared with the traditional method of collecting a large number of samples in the field and analyzing in the laboratory, realizes rapid estimation of the soil nitrogen content, and does not cause physical damage to the crops and the soil; and the crop spectrum-nitrogen content inversion model is used to correlate the crop spectrum characteristics and the plant nitrogen content, and further deduce the soil nitrogen content, which fully utilizes the close nutrient relationship between the crops and the soil nitrogen, reduces the influence of the interference of the complex physicochemical properties of the soil itself on the direct estimation of the soil nitrogen, and improves the accuracy of the estimation result; meanwhile, the dynamically estimated soil nitrogen content data can provide a scientific basis for precision fertilization of crops, reasonably adjust the nitrogen fertilizer application amount according to the actual situation of the soil nitrogen in different regions, avoid excessive or insufficient nitrogen fertilizer, improve the nitrogen fertilizer utilization efficiency, and reduce the risk of agricultural non-point source pollution.

[0097] It should be understood that the size of the serial number of each step in the above embodiments does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0098] The following is a device embodiment of the present application, and for details not described in detail, reference can be made to the corresponding method embodiments described above.

[0099] Figure 2A structure diagram of the soil nitrogen content dynamic estimation device provided by the embodiment of the present application is shown, only the parts related to the embodiment of the present application are shown for the convenience of description, and the details are as follows:

[0100] As shown in Figure 2 , the soil nitrogen content dynamic estimation device 2 comprises:

[0101] The data calculation module 21 is configured to acquire a hyperspectral remote sensing image of crops in the to-be-measured region, and calculate spectral characteristic parameters and vegetation indices according to the hyperspectral remote sensing image.

[0102] The plant nitrogen content determination module 22 is configured to input the spectral characteristic parameters and the vegetation indices into a crop spectrum-nitrogen content inversion model, and output plant nitrogen content in the to-be-measured region.

[0103] The nitrogen content estimation module 23 is configured to calculate a soil nitrogen change amount of the to-be-measured region by using the plant nitrogen content, and dynamically estimate the soil nitrogen content of the to-be-measured region by using the soil nitrogen change amount.

[0104] The present application provides a soil nitrogen content dynamic estimation device, which acquires a hyperspectral remote sensing image of crops in the to-be-measured region, and calculates spectral characteristic parameters and vegetation indices according to the hyperspectral remote sensing image; inputs the spectral characteristic parameters and the vegetation indices into a crop spectrum-nitrogen content inversion model, and outputs plant nitrogen content in the to-be-measured region; calculates a soil nitrogen change amount of the to-be-measured region by using the plant nitrogen content, and dynamically estimates the soil nitrogen content of the to-be-measured region by using the soil nitrogen change amount. The present application acquires a hyperspectral remote sensing image of crops by means of hyperspectral remote sensing technology, which can quickly cover a large area of the to-be-measured region, greatly shortens the time of data acquisition and processing compared with the traditional method of collecting a large number of samples in the field and analyzing in the laboratory, realizes the rapid estimation of the soil nitrogen content, and does not cause physical damage to the crops and the soil; and further, the crop spectrum-nitrogen content inversion model is used to associate the crop spectrum characteristics with the plant nitrogen content, and then deduce the soil nitrogen content, which fully utilizes the close nutrient relationship between the crops and the soil nitrogen, reduces the influence of the interference of the complex physicochemical properties of the soil itself on the direct estimation of the soil nitrogen, and improves the accuracy of the estimation result; at the same time, the dynamically estimated soil nitrogen content data can provide a scientific basis for the precise fertilization of crops, reasonably adjust the nitrogen fertilizer application amount according to the actual situation of the soil nitrogen in different regions, avoid the excess or deficiency of nitrogen fertilizer, improve the nitrogen fertilizer utilization efficiency, and reduce the risk of agricultural non-point source pollution.

[0105] In a possible implementation manner, the nitrogen content estimation module can be configured to:

[0106] acquire the fertilizer application amount and the fertilizer utilization rate of the to-be-measured region;

[0107] Calculate the nitrogen loss amount of the to-be-tested region;

[0108] The product of the fertilization amount and the fertilizer utilization rate is taken as a first product, and the first product is subtracted by the nitrogen loss amount and the plant nitrogen content to obtain the soil nitrogen change amount.

[0109] In a possible implementation, the nitrogen content estimation module can also be configured to:

[0110] The nitrogen loss amount of the to-be-tested region is calculated by using the rainfall of the to-be-tested region and a preset soil depth.

[0111] In a possible implementation, the nitrogen content estimation module can also be configured to:

[0112] Obtain the initial soil nitrogen content of the to-be-tested region;

[0113] The soil nitrogen content of the to-be-tested region is dynamically estimated by using the initial soil nitrogen content and the sum of the soil nitrogen change amounts of each time period in the to-be-tested region.

[0114] In a possible implementation, the data calculation module can be configured to:

[0115] Extract the characteristic waveband of the crop in the target region and the spectral reflectivity of the corresponding waveband from the hyperspectral remote sensing image;

[0116] The spectral feature parameter and the vegetation index are calculated by using the characteristic waveband of the crop in the target region and the spectral reflectivity of the corresponding waveband.

[0117] In a possible implementation, the data calculation module can also be configured to:

[0118] The spectral feature parameter corresponding to each characteristic waveband is obtained by calculating the spectral reflectivity of the characteristic waveband of the crop in the target region and the corresponding waveband by using the first-order differential spectral analysis method.

[0119] In a possible implementation, the construction process of the crop spectral-nitrogen content inversion model is as follows:

[0120] Obtain the historical spectral feature parameters, the historical vegetation index, and the corresponding historical plant nitrogen content of the crop in the target region, the target region being any region;

[0121] The crop spectral-nitrogen content inversion model is constructed by using a statistical regression method;

[0122] The historical spectral feature parameters and the historical vegetation index are taken as input, and the historical plant nitrogen content is taken as output, to train the crop spectral-nitrogen content inversion model.

[0123] Figure 3 is a schematic diagram of a terminal provided by an embodiment of the present application. As shown inFigure 3 As shown, the terminal 3 of this embodiment includes a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30. The processor 30 implements the steps in each of the above soil nitrogen content dynamic estimation method embodiments when executing the computer program 32, for example Figure 1 As shown, the steps 101 to 103. Alternatively, the processor 30 implements the functions of each module / unit in each of the above apparatus embodiments when executing the computer program 32, for example Figure 2 As shown, the functions of each module.

[0124] Illustratively, the computer program 32 can be segmented into one or more modules / units stored in the memory 31 and executed by the processor 30 to complete the present application. The one or more modules / units can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program 32 in the terminal 3. For example, the computer program 32 can be segmented into Figure 2 As shown, each module.

[0125] The terminal 3 can be a desktop computer, a notebook, a palm computer, and a cloud server, etc. The terminal 3 can include, but is not limited to, the processor 30, the memory 31. Those skilled in the art can understand that Figure 3 The terminal 3 is only an example and does not constitute a limitation on the terminal 3, which can include more or fewer components than shown, or combine some components, or different components, for example, the terminal can also include an input / output device, a network access device, a bus, etc.

[0126] The processor 30 can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0127] The memory 31 can be an internal storage unit of the terminal 3, such as a hard disk or a memory of the terminal 3. The memory 31 can also be an external storage device of the terminal 3, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the terminal 3. Further, the memory 31 can also include both the internal storage unit and the external storage device of the terminal 3. The memory 31 is used to store the computer program and other programs and data required by the terminal. The memory 31 can also be used to temporarily store data that has been output or will be output.

[0128] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional units and modules is exemplified, and in actual application, the above functions can be completed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit, and the integrated unit can be realized in the form of hardware or software. In addition, the specific names of each functional unit and module are only for easy distinction, and do not limit the protection scope of the application. The specific working process of the units and modules in the system can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0129] In the above embodiments, the description of each embodiment has its own emphasis, and the parts not described or recorded in detail in a certain embodiment can refer to the relevant description of other embodiments.

[0130] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the application.

[0131] In the embodiments of the present application, it should be understood that the disclosed apparatus / terminal and method can be implemented in other manners. For example, the described apparatus / terminal embodiments are merely schematic. Taking the division of the modules or units as an example, the division can be changed in actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0132] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiments.

[0133] In addition, each functional unit in the various embodiments of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0134] The integrated module / unit, if realized in the form of a software functional unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on this understanding, all or part of the processes in the above-described embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. When the processor executes the computer program, the steps of the above-described various soil nitrogen content dynamic estimation method embodiments can be implemented. The computer program includes computer program code, which can be in the form of source code, object code, executable file or some intermediate form. The computer readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0135] The above-described embodiments are only used to illustrate the technical solutions of the present application, but not limit them; although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method of dynamically estimating soil nitrogen content, characterized by, The application relates to a method for estimating soil nitrogen content in a to-be-measured region. The method comprises the following steps: acquiring a hyperspectral remote sensing image of crops in the to-be-measured region, and calculating spectral characteristic parameters and vegetation indexes according to the hyperspectral remote sensing image; inputting the spectral characteristic parameters and the vegetation indexes into a crop spectral-nitrogen content inversion model to output plant nitrogen content in the to-be-measured region; calculating soil nitrogen variation in the to-be-measured region by using the plant nitrogen content, and dynamically estimating soil nitrogen content in the to-be-measured region by using the soil nitrogen variation; wherein the calculation of the soil nitrogen variation in the to-be-measured region by using the plant nitrogen content comprises the following steps: acquiring fertilizer application amount and fertilizer utilization rate of the to-be-measured region; calculating nitrogen loss amount of the to-be-measured region; wherein, is the soil nitrogen change amount, in kg / ha; is the fertilizer amount, in kg / ha; is the plant nitrogen content, in kg / ha; is the fertilizer use efficiency, in %; is the nitrogen loss amount, in kg / ha.

2. The method of dynamically estimating soil nitrogen content according to claim 1, characterized by, multiplying the fertilizer application amount and the fertilizer utilization rate to obtain a first product, and subtracting the nitrogen loss amount and the plant nitrogen content from the first product to obtain the soil nitrogen variation, wherein the soil nitrogen variation is calculated by using a soil nitrogen dynamic balance equation, and the soil nitrogen dynamic balance equation is as follows: the calculation of the nitrogen loss amount of the to-be-measured region comprises the following steps: acquiring rainfall amount of the to-be-measured region; 3. The method of claim 1, wherein calculating the nitrogen loss amount of the to-be-measured region by using the rainfall amount and a preset soil depth. the dynamic estimation of the soil nitrogen content in the to-be-measured region by using the soil nitrogen variation comprises the following steps: acquiring initial soil nitrogen content of the to-be-measured region; 4. The method according to claim 1, wherein dynamically estimating the soil nitrogen content in the to-be-measured region by using the initial soil nitrogen content and a sum of soil nitrogen variations of each time period in the to-be-measured region. the calculation of the spectral characteristic parameters and the vegetation indexes according to the hyperspectral remote sensing image comprises the following steps: extracting characteristic wave bands of crops in a target region and spectral reflectivity of corresponding wave bands from the hyperspectral remote sensing image; 5. The method according to claim 4, wherein calculating the spectral characteristic parameters and the vegetation indexes by using the characteristic wave bands of crops in the target region and the spectral reflectivity of corresponding wave bands. the calculation of the spectral characteristic parameters by using the characteristic wave bands of crops in the target region and the spectral reflectivity of corresponding wave bands comprises the following steps:

6. The method of dynamically estimating soil nitrogen content according to claim 1, characterized by, calculating the spectral reflectivity of the characteristic wave bands of crops in the target region and corresponding wave bands by using a first-order differential spectral analysis method to obtain spectral characteristic parameters corresponding to each characteristic wave band. a construction process of the crop spectral-nitrogen content inversion model is as follows: acquiring historical spectral characteristic parameters, historical vegetation indexes and corresponding historical plant nitrogen content of crops in a target region, wherein the target region is any region; constructing a crop spectral-nitrogen content inversion model by using a statistical regression method; 7. A device for dynamically estimating soil nitrogen content, characterized in that, training the crop spectral-nitrogen content inversion model by taking the historical spectral characteristic parameters and the historical vegetation indexes as inputs and taking the historical plant nitrogen content as output. The application further relates to a data processing device for estimating soil nitrogen content in a to-be-measured region. The data processing device comprises a data calculation module for acquiring a hyperspectral remote sensing image of crops in the to-be-measured region, and calculating spectral characteristic parameters and vegetation indexes according to the hyperspectral remote sensing image. A plant nitrogen content determination module is configured to input the spectral feature parameter and the vegetation index into a crop spectrum-nitrogen content inversion model to output plant nitrogen content in the to-be-measured region. A nitrogen content estimation module is configured to calculate soil nitrogen variation in the to-be-measured region by using the plant nitrogen content, and dynamically estimate soil nitrogen content in the to-be-measured region by using the soil nitrogen variation. The nitrogen content estimation module is configured to: obtain fertilization amount and fertilizer utilization rate of the to-be-measured region; calculate nitrogen loss amount of the to-be-measured region; multiply the fertilization amount and the fertilizer utilization rate to obtain a first product, and subtract the nitrogen loss amount and the plant nitrogen content from the first product to obtain the soil nitrogen variation, wherein the soil nitrogen variation is calculated by using a soil nitrogen dynamic balance equation, and the soil nitrogen dynamic balance equation is: wherein, is the amount of change in soil nitrogen, in kg / ha; is the amount of fertilizer application, in kg / ha; is the amount of nitrogen content in the plant, in kg / ha; is the fertilizer use efficiency, in %; is the amount of nitrogen loss, in kg / ha.

8. A terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the steps of the soil nitrogen content dynamic estimation method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 8. The computer program is executed by the processor to implement the steps of the soil nitrogen content dynamic estimation method according to any one of claims 1 to 6.

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