Crop three-dimensional dynamic spectral data simulation method and device

Abstract

The present invention relates to the technical field of crop growth monitoring, and provides a method and device for simulating three-dimensional dynamic spectral data of crops, the method comprising: constructing a growth curve model according to the growth data of crops; the growth data comprises at least one of the potential maximum length of a leaf position, the potential maximum width of a leaf position, the potential maximum height of a crop, the potential maximum stem thickness of a crop, and daily accumulated temperature data, the growth curve model is used to predict the leaf length, width, height, and stem thickness data of the crop at any time to generate a single plant crop model; constructing a dynamic three-dimensional scene based on a plurality of single plant crop models generated according to a first configuration parameter based on the growth curve model, configuring and simulating the spectral properties of crops and corresponding soil in the dynamic three-dimensional scene, and obtaining target spectral data. The method of the present invention improves the accuracy of spectral simulation and spectral acquisition efficiency during crop growth.

Description

Technical Field

[0001] The present invention relates to the technical field of crop growth monitoring, and in particular to a method and device for simulating three-dimensional dynamic spectral data of crops. Background Art

[0002] In the widespread application of remote sensing technology, high-quality crop sample data, especially comprehensive data sets containing ground-measured data and corresponding spectral data, are an indispensable cornerstone for building and optimizing remote sensing models.

[0003] Traditional ground sample data collection methods face many challenges: data acquisition is difficult, costly, and limited in quantity, and it is difficult to fully cover different temporal and spatial conditions. This limitation leads to a lack of representativeness in sample data, which in turn limits the training and verification of remote sensing models in complex environments. In order to overcome these difficulties, simulators can simulate data close to the real world, break free from temporal and spatial constraints, and efficiently generate high-quality sample data covering a wide range of temporal and spatial ranges and complex conditions. Especially driven by advanced technologies such as deep learning and large models, simulators can provide massive data samples, providing strong support for the training and optimization of remote sensing models.

[0004] In the related technologies, the existing radiation transfer spectrum simulation technology uses a static model to simulate the spectral characteristics of crops, which is difficult to reflect the dynamic changes of the spectral characteristics of crops in different growth stages and environmental conditions. At the same time, the related technology usually extracts simple features such as crop leaves to construct the three-dimensional structure of crops. This simplified processing reduces the accuracy and applicability of the simulation results, and it is difficult to meet the needs of remote sensing applications in complex environments. Summary of the invention

[0005] The present invention provides a method and device for simulating three-dimensional dynamic spectral data of crops, which are used to solve the defects that the static model used in the prior art is difficult to simulate the dynamic changes of spectral characteristics during the growth process of crops, and the three-dimensional structure of crops constructed using leaf characteristics reduces the accuracy and applicability of simulation results, thereby improving the accuracy of spectral simulation during the growth process of crops and the efficiency of spectral acquisition.

[0006] The present invention provides a method for simulating three-dimensional dynamic spectral data of crops, comprising:

[0007] A growth curve model is constructed according to the growth data of the crop; the growth data includes at least one of the potential maximum length of a leaf, the potential maximum width of a leaf, the potential maximum height of the crop, the potential maximum stem diameter of the crop and daily accumulated temperature data; the growth curve model is used to predict the leaf length, width, height and stem diameter data of the crop at any time to generate a single plant crop model;

[0008] A dynamic three-dimensional scene is constructed based on multiple single crop models generated according to the growth curve model according to the first configuration parameters, and the spectral properties of the crops and corresponding soil in the dynamic three-dimensional scene are configured and simulated to obtain target spectral data.

[0009] According to a method for simulating three-dimensional dynamic spectral data of crops provided by the present invention, constructing a growth curve model according to the growth data of the crops comprises:

[0010] Collecting crop growth data, the growth data including potential maximum length of a leaf, potential maximum width of a leaf, potential maximum height of the crop, potential maximum stem diameter of the crop and daily accumulated temperature data;

[0011] Taking the daily accumulated temperature data as the time variable, a growth curve model of the crop is constructed according to the potential maximum length of the leaf position, the potential maximum width of the leaf position, the potential maximum height of the crop, the potential maximum stem diameter of the crop and the daily accumulated temperature data.

[0012] According to a method for simulating three-dimensional dynamic spectral data of crops provided by the present invention, the growth curve model includes a leaf length model, a leaf width model, a crop plant height model and a crop stem thickness model;

[0013] The leaf length model is expressed by the following formula:

[0014] ;

[0015] in, For the crop n The length of a leaf, is the potential maximum length of the corresponding leaf position, and is the leaf length model parameter, is the daily accumulated temperature data, For the n The initial accumulated temperature data when a leaf begins to develop; e is the base of natural logarithms;

[0016] The blade width model is expressed by the following formula:

[0017] ;

[0018] in, For the crop n The width of a leaf, is the potential maximum width of the corresponding leaf position, and is the leaf width model parameter;

[0019] The crop height model is expressed by the following formula:

[0020] ;

[0021] in, is the plant height of the crop, is the potential maximum height of the crop, and is the crop height model parameter;

[0022] The crop stem thickness model is expressed by the following formula:

[0023] ;

[0024] in, The crop stems are thick. is the potential maximum stem diameter of the crop, and is the crop stem thickness model parameter.

[0025] According to a method for simulating three-dimensional dynamic spectral data of crops provided by the present invention, the first configuration parameter includes at least one of row spacing, plant spacing, leaf angle and leaf inclination angle between different individual crops.

[0026] According to a method for simulating three-dimensional dynamic spectral data of crops provided by the present invention, the spectral properties of crops and corresponding soils in the dynamic three-dimensional scene are configured and simulated to obtain target spectral data, including:

[0027] The three-dimensional radiation transfer model LESS based on ray tracing simulates the spectral properties or reflectance values ​​of the crops and the corresponding soil in the dynamic three-dimensional scene according to the second configuration parameters to obtain soil spectral data and crop canopy spectral data.

[0028] According to a crop three-dimensional dynamic spectral data simulation method provided by the present invention, the second configuration parameters include: observation zenith angle, observation azimuth angle, solar zenith angle, solar azimuth angle, leaf structure parameters, chlorophyll content, carotenoid content, equivalent water thickness and equivalent dry matter content of LESS.

[0029] The present invention also provides a crop three-dimensional dynamic spectrum data simulation device, comprising:

[0030] A model building module, used to build a growth curve model according to the growth data of the crop; the growth data includes at least one of the potential maximum length of a leaf, the potential maximum width of a leaf, the potential maximum height of the crop, the potential maximum stem diameter of the crop and daily accumulated temperature data; the growth curve model is used to predict the leaf length, width, height and stem diameter data of the crop at any time to generate a single plant crop model;

[0031] The spectral acquisition module is used to construct a dynamic three-dimensional scene based on multiple single crop models generated by the growth curve model according to the first configuration parameters, configure and simulate the spectral properties of the crops and corresponding soil in the dynamic three-dimensional scene, and obtain target spectral data.

[0032] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, any one of the above-mentioned methods for simulating three-dimensional dynamic spectral data of crops is implemented.

[0033] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for simulating three-dimensional dynamic spectral data of crops as described in any one of the above is implemented.

[0034] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements any of the above-mentioned methods for simulating three-dimensional dynamic spectral data of crops.

[0035] The method and device for simulating three-dimensional dynamic spectral data of crops provided by the present invention construct a growth curve model through the growth data of crops, and can predict the leaf length, width, height and stem thickness data of crops at any time to generate a single crop model; then a dynamic three-dimensional scene is constructed by using the growth curve model to generate multiple single crop models according to first configuration parameters, and the spectral properties of crops and corresponding soils in the dynamic three-dimensional scene are configured and simulated to obtain target spectral data, thereby improving the accuracy of spectral simulation and spectral acquisition efficiency during crop growth. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0037] Figure 1 It is a schematic flow chart of the crop three-dimensional dynamic spectral data simulation method provided by the present invention.

[0038] Figure 2 It is a structural schematic diagram of the crop three-dimensional dynamic spectral data simulation device provided by the present invention.

[0039] Figure 3 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] Combine the following Figure 1-Figure 2 The present invention describes a method and device for simulating three-dimensional dynamic spectral data of crops.

[0042] Figure 1 FIG. 1 is one of the flow charts of the method for simulating crop three-dimensional dynamic spectral data provided by the present invention, such as Figure 1 As shown, the crop three-dimensional dynamic spectral data simulation method includes the following steps:

[0043] Step 110, constructing a growth curve model based on crop growth data; the growth data includes at least one of the potential maximum length of a leaf, the potential maximum width of a leaf, the potential maximum height of the crop, the potential maximum stem diameter of the crop and daily accumulated temperature data; the growth curve model is used to predict the leaf length, width, height and stem diameter data of the crop at any time to generate a single crop model.

[0044] In this embodiment, the crops include corn, sorghum, wheat, etc.

[0045] In this step, the growth data includes leaf growth data, for example, the growth data includes the potential maximum length of a leaf and the potential maximum width of a leaf, and the corresponding growth curve model includes a leaf length model and a leaf width model; the above growth data can be extracted from the captured optical remote sensing images.

[0046] In this step, the growth data also includes the crop growth height and root thickness; for example, the growth data also includes: the potential maximum height of the crop and the potential maximum stem thickness of the crop, and the corresponding growth curve model also includes a crop plant height model and a crop stem thickness model.

[0047] In this embodiment, the growth data also includes daily accumulated temperature data (Growing Degree Days, GDD), that is, growing degree days. GDD is an indicator used to measure the accumulated heat during plant growth. GDD represents the total effective temperature experienced by the plant within a certain period of time when the temperature is higher than the base temperature of plant growth.

[0048] In this embodiment, the GDD is usually calculated by taking the average of the daily maximum and minimum temperatures, subtracting the base temperature of the plant's growth (ie, the minimum growth temperature), and finally accumulating all daily GDD values ​​within a period of time.

[0049] In this embodiment, any one of the above-mentioned potential maximum length of a leaf position, potential maximum width of a leaf position, potential maximum height of a crop, and potential maximum stem thickness of a crop can be combined with daily accumulated temperature data to determine the corresponding growth curve model.

[0050] In this embodiment, the growth curve model can be one or more of a leaf length model, a leaf width model, a crop height model and a crop stem thickness model, that is, a corresponding growth curve model is constructed by different phenological indicators (potential maximum length of a leaf, potential maximum width of a leaf, potential maximum height of a crop, potential maximum stem thickness of a crop and daily accumulated temperature data).

[0051] Step 120: construct a dynamic three-dimensional scene based on the multiple single-plant crop models generated according to the first configuration parameters based on the growth curve model, configure and simulate the spectral properties of the crops and corresponding soil in the dynamic three-dimensional scene, and obtain target spectral data.

[0052] In this step, the dynamic three-dimensional scene is driven by the above-mentioned multiple phenological characteristics and GDD, which conforms to the growth and change laws of conventional crops and greatly improves the efficiency of spectral simulation.

[0053] In this embodiment, based on the growth curve model constructed above, the leaf length, width, height and stem diameter data of a single corn plant at any time can be predicted, thereby generating a three-dimensional model of the single plant.

[0054] In this embodiment, the first configuration parameter includes at least one of row spacing, plant spacing, leaf angle and leaf inclination angle between different individual crops.

[0055] Specifically, the above dynamic three-dimensional scene can be constructed using XL language in GroImp software. By configuring the above first configuration parameter in GroImp software, a three-dimensional scene of any planting density (row spacing and plant spacing) can be generated based on the single plant model, and a three-dimensional scene when GDD increases by 100 can also be quickly generated. These scenes accurately reflect the morphological structure changes of corn in different growth stages. The influence of leaf angle changes on the canopy spectrum is lower than that of leaf area changes.

[0056] In this embodiment, the interaction between solar radiation and surface crops can be simulated by a three-dimensional radiative transfer model (3D Radiative Transfer Model), thereby accurately describing the impact of the three-dimensional structure of terrain and vegetation on radiative transfer in the real world; for example, the three-dimensional radiative transfer model includes a discrete anisotropic radiative transfer (DART) model or a ray tracing-based three-dimensional radiative transfer model (Lightweight Efficient Simulator of Scattering, LESS), etc.

[0057] In this embodiment, a large amount of canopy spectral data under different growth periods and different lighting conditions can be simulated through the three-dimensional radiation transfer model, which can be used as the data basis for the simulation spectrum generator; in addition, the three-dimensional radiation transfer model can also be used to change the leaf spectral properties in the scene, such as chlorophyll content, carotenoid content, and equivalent dry matter content, thereby simulating a large amount of spectral data.

[0058] The method for simulating three-dimensional dynamic spectral data of crops provided in an embodiment of the present invention constructs a growth curve model through the growth data of crops, and can predict the leaf length, width, height and stem thickness data of crops at any time to generate a single crop model; then, a dynamic three-dimensional scene is constructed by using the growth curve model to generate multiple single crop models according to the first configuration parameters, and the spectral properties of the crops and the corresponding soil in the dynamic three-dimensional scene are configured and simulated to obtain target spectral data, thereby improving the accuracy of spectral simulation and spectral acquisition efficiency during the crop growth process.

[0059] In some embodiments, constructing a growth curve model based on crop growth data includes: collecting crop growth data, the growth data including the potential maximum length of a leaf, the potential maximum width of a leaf, the potential maximum height of a crop, the potential maximum stem diameter of a crop, and daily accumulated temperature data; using the daily accumulated temperature data as a time variable, and constructing a growth curve model for the crop based on the potential maximum length of a leaf, the potential maximum width of a leaf, the potential maximum height of a crop, the potential maximum stem diameter of a crop, and daily accumulated temperature data.

[0060] In this embodiment, after the growth data is collected, the growth data may be cleaned and sorted to ensure the validity and accuracy of the data.

[0061] In this embodiment, the growth curve model includes four different sub-models, which are used to simulate the changes in spectral data of crop leaf length, width, crop height and crop stem diameter in different growth stages (such as jointing stage, trumpet stage, tasseling stage and filling stage) to reflect the dynamic change characteristics of these spectral data.

[0062] In this embodiment, the growth curve model includes a leaf length model, a leaf width model, a crop height model and a crop stem thickness model; this embodiment constructs a Logistics growth curve model for each leaf length, width, plant height, stem thickness and other parameters based on GDD as a time variable, and constructs a corresponding three-dimensional scene through the measured structural data of the four growth periods.

[0063] Specifically, the leaf length model is expressed by the following formula:

[0064] ;

[0065] in, For the crop n The length of a leaf, is the potential maximum length of the corresponding leaf position, and is the leaf length model parameter, is the daily accumulated temperature data, For the n The initial accumulated temperature data when a leaf begins to develop; e is the base of the natural logarithm; the leaf width model is expressed by the following formula:

[0066] ;

[0067] in, For the crop n The width of a leaf, is the potential maximum width of the corresponding leaf position, and is the leaf width model parameter; this embodiment is based on the leaf length and width growth curve model constructed above, and then uses the leaf data measured in four key growth periods to fit the model to find the unknown parameters, and then obtain the ability to predict any GDD Dynamic model of leaf size under.

[0068] In this embodiment, The calculation method is as follows:

[0069] ;

[0070] in, Necessary for the first leaf to develop GDD , PHYLL is the thermal time accumulated temperature interval between adjacent leaves, and the PHYLL value is related to the genetic characteristics of the variety.

[0071] In this embodiment, when constructing a dynamic three-dimensional scene, not only the leaf size but also the plant height and stem thickness should be considered; this embodiment is also based on GDDThe time variable was used to construct the logistics growth curve model of plant height and stem diameter.

[0072] Specifically, the crop height model is expressed by the following formula:

[0073] ;

[0074] in, is the plant height of the crop, is the potential maximum height of the crop, and is the crop plant height model parameter; the crop stem thickness model is expressed by the following formula:

[0075] ;

[0076] in, The crop stems are thick. is the potential maximum stem diameter of the crop, and is the crop stem thickness model parameter.

[0077] The method for simulating three-dimensional dynamic spectral data of crops provided in an embodiment of the present invention uses daily accumulated temperature data as a time variable, and constructs a crop growth curve model according to the potential maximum length of a leaf position, the potential maximum width of a leaf position, the potential maximum height of a crop, the potential maximum stem diameter of a crop, and daily accumulated temperature data. This method can realize dynamic prediction of the change in effective spectral data of crops in different growth periods, and improve the analysis and calculation efficiency of crop growth data.

[0078] In some embodiments, the spectral properties of crops and corresponding soils in a dynamic three-dimensional scene are configured and simulated to obtain target spectral data, including: a three-dimensional radiation transfer model LESS based on ray tracing simulates the spectral properties or reflectance values ​​of crops and corresponding soils in the dynamic three-dimensional scene according to second configuration parameters to obtain soil spectral data and crop canopy spectral data.

[0079] In this embodiment, the second configuration parameters include: observation zenith angle, observation azimuth angle, solar zenith angle, solar azimuth angle, leaf structure parameters, chlorophyll content, carotenoid content, equivalent water thickness and equivalent dry matter content of the LESS.

[0080] In this embodiment, on the basis of constructing a dynamic three-dimensional scene through a growth curve model, a large amount of canopy spectral data under different growth periods and different lighting conditions can be simulated using a three-dimensional radiation transfer model - LESS, which is used as the data basis for a simulated spectrum generator; LESS can take into account factors such as the optical properties inside the crop canopy, the leaf arrangement, and the leaf spectral characteristics, and configure observation conditions, soil spectra, and leaf spectra to achieve dynamic simulation and prediction of spectral characteristics.

[0081] It should be noted that LESS also has a batch processing function that can process a large number of three-dimensional scenes in batches, and can also configure and modify the spectral properties of crop leaves, such as chlorophyll content, carotenoid content, and equivalent dry matter content, and can simulate a large amount of spectral data. The spectrum simulated based on the three-dimensional radiation transfer model is more accurate and can take into account the spectral changes caused by differences in plant structure of different crop types and varieties.

[0082] In this embodiment, LESS can not only simulate the spectrum within the range of 400-2500 nm, but also simulate the reflectance value of any band. The parameter settings of the blade attributes are shown in Table 1:

[0083] Table 1. Distribution of input variables for generating canopy reflectance using 3D RTM simulation

[0084]

[0085] In Table 1 above, the units of chlorophyll content, carotenoid content, equivalent water thickness and equivalent dry matter content are ug / cm 2 ; This embodiment uses 11 generated corn three-dimensional scenes to simulate the spectral data set under the changes in leaf attributes in Table 1. Users can use the dynamic three-dimensional scene generation method of this study to generate more three-dimensional scenes, and use the LESS model batch processing method to simulate and generate a large amount of hyperspectral or multi-spectral data as the data source basis for the simulated spectrum generator.

[0086] The method for simulating three-dimensional dynamic spectral data of crops provided in an embodiment of the present invention simulates the spectral properties or reflectance values ​​of crops and corresponding soils in a dynamic three-dimensional scene according to a second configuration parameter through a three-dimensional radiation transfer model LESS based on ray tracing, so as to obtain soil spectral data and crop canopy spectral data. By adopting a temperature-driven leaf and stem growth model, a large amount of canopy spectral data in different growth periods and different lighting conditions can be simulated, thereby improving the processing efficiency of crop spectral data and meeting the needs of large-scale data generation.

[0087] The crop three-dimensional dynamic spectral data simulation device provided by the present invention is described below. The crop three-dimensional dynamic spectral data simulation device described below and the crop three-dimensional dynamic spectral data simulation method described above can be referenced to each other.

[0088] Figure 2 1 is a schematic diagram of the structure of the crop three-dimensional dynamic spectrum data simulation device provided by the present invention. The crop three-dimensional dynamic spectrum data simulation device can be a crop three-dimensional dynamic spectrum data simulator. The simulator realizes the functions as described in the above steps 110 to 120; Figure 2As shown, the crop three-dimensional dynamic spectral data simulation device includes: a model building module 210 and a spectral acquisition module 220.

[0089] A model building module 210 is used to build a growth curve model based on the growth data of the crop; the growth data includes at least one of the potential maximum length of a leaf, the potential maximum width of a leaf, the potential maximum height of the crop, the potential maximum stem diameter of the crop and daily accumulated temperature data; the growth curve model is used to predict the leaf length, width, height and stem diameter data of the crop at any time to generate a single plant crop model;

[0090] The spectral acquisition module 220 is used to construct a dynamic three-dimensional scene based on multiple single crop models generated according to the first configuration parameters based on the growth curve model, configure and simulate the spectral properties of the crops and corresponding soil in the dynamic three-dimensional scene, and obtain target spectral data.

[0091] The crop three-dimensional dynamic spectral data simulation device provided by the embodiment of the present invention constructs a growth curve model through the growth data of the crop, and can predict the leaf length, width, height and stem thickness data of the crop at any time to generate a single crop model; then, a dynamic three-dimensional scene is constructed by using the growth curve model to generate multiple single crop models according to the first configuration parameters, and the spectral properties of the crops and the corresponding soil in the dynamic three-dimensional scene are configured and simulated to obtain target spectral data, thereby improving the accuracy of spectral simulation and spectral acquisition efficiency during the crop growth process.

[0092] Figure 3 is a schematic diagram of the structure of the electronic device provided by the present invention, such as Figure 3 As shown, the electronic device may include: a processor 310, a communication interface 320, a memory 330 and a communication bus 340, wherein the processor 310, the communication interface 320 and the memory 330 communicate with each other through the communication bus 340. The processor 310 may call the logic instructions in the memory 330 to execute the crop three-dimensional dynamic spectral data simulation method, the method comprising: constructing a growth curve model according to the growth data of the crop; the growth data includes at least one of the potential maximum length of the leaf position, the potential maximum width of the leaf position, the potential maximum height of the crop, the potential maximum stem thickness of the crop and the daily accumulated temperature data, the growth curve model is used to predict the leaf length, width, height and stem thickness data of the crop at any time to generate a single crop model; constructing a dynamic three-dimensional scene based on the growth curve model according to the first configuration parameters of the multiple single crop models, configuring and simulating the spectral properties of the crop and the corresponding soil in the dynamic three-dimensional scene, and obtaining the target spectral data.

[0093] In addition, the logic instructions in the above-mentioned memory 330 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0094] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the crop three-dimensional dynamic spectral data simulation method provided by the above methods, the method including: constructing a growth curve model based on the growth data of the crop; the growth data includes at least one of the potential maximum length of the leaf, the potential maximum width of the leaf, the potential maximum height of the crop, the potential maximum stem thickness of the crop and daily accumulated temperature data, the growth curve model is used to predict the leaf length, width, height and stem thickness data of the crop at any time to generate a single crop model; based on the growth curve model, a dynamic three-dimensional scene is constructed based on multiple single crop models generated according to the first configuration parameters, the spectral properties of the crops and the corresponding soil in the dynamic three-dimensional scene are configured and simulated to obtain target spectral data.

[0095] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the crop three-dimensional dynamic spectral data simulation method provided by the above-mentioned methods, the method comprising: constructing a growth curve model based on the growth data of the crop; the growth data comprises at least one of the potential maximum length of a leaf, the potential maximum width of a leaf, the potential maximum height of the crop, the potential maximum stem thickness of the crop and daily accumulated temperature data, the growth curve model is used to predict the leaf length, width, height and stem thickness data of the crop at any time to generate a single crop model; constructing a dynamic three-dimensional scene based on multiple single crop models generated according to the first configuration parameters based on the growth curve model, configuring and simulating the spectral properties of the crop and the corresponding soil in the dynamic three-dimensional scene, and obtaining target spectral data.

[0096] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0097] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for simulating three-dimensional dynamic spectral data of crops, characterized in that: include: A growth curve model is constructed according to the growth data of the crop; the growth curve model is used to predict the leaf length, width, height and stem diameter data of the crop at any time to generate a single plant crop model; Constructing a dynamic three-dimensional scene based on a plurality of single-plant crop models generated according to the growth curve model according to the first configuration parameter, configuring and simulating the spectral properties of the crops and corresponding soil in the dynamic three-dimensional scene, and obtaining target spectral data; The step of constructing a growth curve model according to the growth data of the crop comprises: Collecting crop growth data, the growth data including potential maximum length of a leaf, potential maximum width of a leaf, potential maximum height of the crop, potential maximum stem diameter of the crop and daily accumulated temperature data; Taking the daily accumulated temperature data as the time variable, constructing a growth curve model of the crop according to the potential maximum length of the leaf position, the potential maximum width of the leaf position, the potential maximum height of the crop, the potential maximum stem diameter of the crop and the daily accumulated temperature data; the growth curve model includes a leaf length model, a leaf width model, a crop height model and a crop stem diameter model; The leaf length model is expressed by the following formula: ； in, For the crop n The length of a leaf, is the potential maximum length of the corresponding leaf position, and is the leaf length model parameter, is the daily accumulated temperature data, For the n The initial accumulated temperature data when a leaf begins to develop; e is the base of natural logarithms; The blade width model is expressed by the following formula: ； in, For the crop n The width of a leaf, is the potential maximum width of the corresponding leaf position, and is the leaf width model parameter; The crop height model is expressed by the following formula: ； in, is the plant height of the crop, is the potential maximum height of the crop, and is the crop height model parameter; The crop stem thickness model is expressed by the following formula: ； in, The crop stems are thick. is the potential maximum stem diameter of the crop, and is a crop stem thickness model parameter; the first configuration parameter includes at least one of row spacing, plant spacing, leaf angle and leaf inclination angle between different individual crops; the spectral properties of the crops and the corresponding soil in the dynamic three-dimensional scene are configured and simulated to obtain the target spectral data including: The three-dimensional radiation transfer model LESS based on ray tracing simulates the spectral properties or reflectance values ​​of crops and corresponding soil in the dynamic three-dimensional scene according to the second configuration parameters to obtain soil spectral data and crop canopy spectral data; the second configuration parameters include: observation zenith angle, observation azimuth, solar zenith angle, solar azimuth, leaf structure parameters, chlorophyll content, carotenoid content, equivalent water thickness and equivalent dry matter content of LESS.

2. A crop three-dimensional dynamic spectrum data simulation device, used to implement the crop three-dimensional dynamic spectrum data simulation method according to claim 1, characterized in that: include: A model building module, used to build a growth curve model according to the growth data of the crop; the growth data includes at least one of the potential maximum length of a leaf, the potential maximum width of a leaf, the potential maximum height of the crop, the potential maximum stem diameter of the crop and daily accumulated temperature data; the growth curve model is used to predict the leaf length, width, height and stem diameter data of the crop at any time to generate a single plant crop model; A spectral acquisition module, used to construct a dynamic three-dimensional scene based on a plurality of single-plant crop models generated by the growth curve model according to the first configuration parameter, configure and simulate the spectral properties of the crops and the corresponding soil in the dynamic three-dimensional scene, and obtain target spectral data; The model building module is specifically used for: Collecting crop growth data, the growth data including potential maximum length of a leaf, potential maximum width of a leaf, potential maximum height of the crop, potential maximum stem diameter of the crop and daily accumulated temperature data; Taking the daily accumulated temperature data as the time variable, a growth curve model of the crop is constructed according to the potential maximum length of the leaf position, the potential maximum width of the leaf position, the potential maximum height of the crop, the potential maximum stem diameter of the crop and the daily accumulated temperature data.

3. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for simulating three-dimensional dynamic spectral data of crops as claimed in claim 1 is implemented.

4. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for simulating three-dimensional dynamic spectral data of crops as claimed in claim 1 is implemented.

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