Wheat field grain water content prediction method and system based on PlanetScope image

Abstract

The invention discloses a wheat field grain water content prediction method and system based on a PlanetScope image. The method comprises the following steps: acquiring 8-waveband multispectral surface reflectance data of PlanetScope, screening an image of which the cloud cover is lower than a preset percentage from the 8-waveband multispectral surface reflectance data, and calculating an enhanced vegetation index (EVI) pixel by pixel to obtain an EVI time sequence; obtaining the heading period of the winter wheat based on the preprocessed EVI time sequence; based on the heading period of the winter wheat, predicting the physiological mature period of the winter wheat through an effective accumulated temperature accumulation model; an actually measured wheat grain moisture content sample is obtained, and a field grain drying model combining a moisture diffusion mechanism, saturation vapor pressure difference adjustment and Gaussian function constraint is constructed based on the wheat grain moisture content sample; and by taking the predicted physiological mature period as a priori and combining daily meteorological data and a field grain drying model, simulating the dynamic change of the moisture content of the wheat grains day by day, and generating a spatial distribution diagram of the moisture content of the wheat grains. The method can predict the water content of the wheat grains in the field in a high-time-efficiency and high-precision manner.

Description

Technical Field

[0001] This invention relates to the field of remote sensing agricultural monitoring technology, and in particular to a method, system, storage medium, and electronic device for predicting wheat grain moisture content in the field based on PlanetScope imagery. Background Technology

[0002] Kernel moisture content (KMC) is a key physiological indicator for measuring wheat maturity and quality, directly affecting wheat yield, quality, and the feasibility and economic viability of harvesting, transportation, processing, and trade. According to the National Grain Quality Standard (GB1351-2008), the kernel moisture content of commercial wheat must not exceed 12.5% ​​during acquisition, storage, transportation, processing, and sales. Especially against the backdrop of rapid development of high-standard farmland construction and large-scale, intensive agriculture, the need for accurate prediction of kernel moisture content is increasingly urgent.

[0003] Currently, KMC determination mainly relies on traditional ground-based detection techniques, which have formed relatively mature measurement systems, such as drying loss method, capacitance method, microwave method, dielectric property analysis method, and spectroscopic method. These methods have good accuracy and stability under laboratory or single-point measurement conditions; however, their spatial coverage is limited, making it difficult to meet the needs of dynamic monitoring of wheat KMC and large-scale harvesting decisions at the regional scale.

[0004] Remote sensing technology, with its advantages of wide coverage, non-contact operation, and high timeliness, can efficiently monitor wheat growth status at regional scales, providing a new solution for crop moisture monitoring. Existing research has attempted to utilize remote sensing image analysis of spectral bands or vegetation indices sensitive to grain moisture content (KMC) to construct machine learning models for KMC retrieval, thereby obtaining regional-scale KMC distribution information. However, such remote sensing retrieval methods mainly rely on data at the time of remote sensing observation, resulting in information acquisition and processing lags. They struggle to respond promptly to rapid changes in grain moisture content, especially during the critical stage of rapid KMC decline after winter wheat physiological maturity, making it difficult to support efficient and accurate production decisions. Summary of the Invention

[0005] This invention provides a method, system, storage medium, and electronic device for predicting wheat grain moisture content in the field based on PlanetScope imagery, which can predict wheat grain moisture content in the field with high timeliness and high accuracy.

[0006] This invention provides a method for predicting wheat grain moisture content in the field based on PlanetScope imagery, comprising: Acquire PlanetScope 8-band multispectral surface reflectance data of the target area after the winter wheat growing season greening period, filter out images with cloud cover below a preset percentage, calculate the Enhanced Vegetation Index (EVI) pixel by pixel, and obtain the EVI time series. The EVI time series is preprocessed, and the heading date of winter wheat is obtained based on the preprocessed EVI time series. Based on the heading stage of winter wheat, the physiological maturity stage of winter wheat is predicted using a cumulative effective accumulated temperature model. Obtain measured wheat grain moisture content samples, and construct a field grain drying model based on the wheat grain moisture content samples, which combines moisture diffusion mechanism, saturated water vapor pressure difference regulation and Gaussian function constraint. Using the predicted physiological maturity period as a priori, and combining daily meteorological data with the field grain drying model, the dynamic changes in wheat grain moisture content are simulated daily, and a spatial distribution map of wheat grain moisture content is generated.

[0007] Furthermore, based on the above method for predicting wheat field grain moisture content using PlanetScope imagery, the formula for calculating the EVI time series is as follows:

[0008] in, , and These correspond to Band 8, Band 6, and Band 2 of PlanetScope's 8-band multispectral imagery, respectively.

[0009] Furthermore, according to the above-mentioned method for predicting wheat field grain moisture content based on PlanetScope imagery, the EVI time series is preprocessed, and the heading date of winter wheat is obtained based on the preprocessed EVI time series, including: The missing values ​​in the EVI time series are smoothed and filled using a cubic spline function to obtain a seamless EVI time series. The time corresponding to the maximum value of the seamless EVI time series is taken as the heading stage of winter wheat.

[0010] Furthermore, according to the above-mentioned method for predicting wheat field grain moisture content based on PlanetScope imagery, the prediction of the physiological maturity period of winter wheat based on the heading stage of winter wheat using a cumulative effective accumulated temperature model includes: Obtain the daily maximum and minimum temperatures of winter wheat in the target area after the heading stage; Calculate the number of growth degree days per day after the heading stage of winter wheat based on the highest and lowest temperatures; Calculate the cumulative effective accumulated temperature growth days required from the heading stage to the physiological maturity stage of winter wheat based on the aforementioned growth period days; Starting from the heading stage of each pixel, the number of growth days is accumulated day by day. When the accumulated effective accumulated temperature growth days first exceeds or equals the regional reference value of the accumulated effective accumulated temperature growth days, that day is determined as the physiological maturity stage of wheat.

[0011] Furthermore, according to the above-mentioned method for predicting wheat field grain moisture content based on PlanetScope imagery, the method involves obtaining measured wheat grain moisture content samples, and constructing a field grain drying model based on these samples, which combines moisture diffusion mechanisms, saturated vapor pressure differential regulation, and Gaussian function constraints. This model includes: Obtain actual samples of wheat grain moisture content; Based on daily meteorological data after the physiological maturity of winter wheat, the daily equilibrium moisture content of grain and the saturated water vapor pressure difference were calculated. A field grain drying model combining moisture diffusion mechanism, saturated vapor pressure difference regulation, and Gaussian function constraint was constructed. The parameters of the field grain drying model were fitted based on the wheat grain moisture content samples.

[0012] Furthermore, according to the above-mentioned method for predicting wheat field grain moisture content based on PlanetScope imagery, the field grain drying model is as follows:

[0013] in, This is a water diffusion mechanism. Gaussian function constraint, For saturated water vapor pressure differential regulation; Indicates the first The moisture content of wheat grains in a given day For the first The moisture content of wheat grains in a given day and The first The equilibrium water content and saturated vapor pressure difference of the day This is the position where the drying rate reaches its peak. , and For model fitting parameters, The drying coefficient is... To control the distribution of drying intensity with KMC, This is the saturated water vapor pressure difference adjustment coefficient.

[0014] Furthermore, based on the aforementioned method for predicting wheat field grain moisture content using PlanetScope imagery, the predicted physiological maturity period is used as a priori. Combining daily meteorological data with the field grain drying model, the dynamic changes in wheat grain moisture content are simulated daily, generating a spatial distribution map of wheat grain moisture content. The moisture content is then calculated using the following formula:

[0015] in, Physiological maturity period The moisture content of wheat grains, For the first Daily water loss , The moisture content of wheat grains on day t and day t-1 are respectively.

[0016] This invention also provides a wheat field grain moisture content prediction system based on PlanetScope imagery, comprising: The data acquisition module acquires PlanetScope's 8-band multispectral surface reflectance data after the winter wheat growing season in the target area has turned green. It then filters out images with cloud cover below a preset percentage, calculates the Enhanced Vegetation Index (EVI) pixel by pixel, and obtains the EVI time series. The winter wheat heading date determination module is used to preprocess the EVI time series and obtain the winter wheat heading date based on the preprocessed EVI time series. The winter wheat physiological maturity prediction module is used to predict the physiological maturity of winter wheat based on the heading stage of winter wheat using a cumulative effective accumulated temperature model. The field grain drying model construction module is used to obtain measured wheat grain moisture content samples and construct a field grain drying model based on the wheat grain moisture content samples, which combines moisture diffusion mechanism, saturated water vapor pressure difference regulation and Gaussian function constraint. The wheat grain moisture content calculation module is used to simulate the dynamic changes of wheat grain moisture content on a daily basis, using the predicted physiological maturity period as a priori, combined with daily meteorological data and the field grain drying model, and to generate a spatial distribution map of wheat grain moisture content.

[0017] The present invention also provides a computer-readable storage medium storing a plurality of instructions adapted for loading by a processor to execute any of the above-described methods for predicting wheat field grain moisture content based on PlanetScope imagery.

[0018] The present invention also provides an electronic device, including a processor and a memory, wherein the processor is electrically connected to the memory, the memory is used to store instructions and data, and the processor is used in the steps of the wheat field grain moisture content prediction method based on PlanetScope imagery described in any of the preceding claims.

[0019] The present invention provides a method, system, storage medium, and electronic device for predicting wheat grain moisture content in the field based on PlanetScope imagery. The present invention has the following beneficial effects: (1) Improve the timeliness of prediction and realize early prediction. Compared with the problem of information acquisition and processing lag in traditional KMC estimation methods based on remote sensing inversion, the present invention, based on the phased modeling strategy and time-series remote sensing fusion, can realize dynamic prediction about 15 days in advance before the physiological maturity of wheat, meet the timeliness requirements under the rapid changes of KMC during wheat drying, and improve the practicality and foresight of KMC prediction in serving agricultural management activities such as wheat maturity judgment, harvest arrangement and quality control.

[0020] (2) Introducing physiological mechanism modeling to enhance predictive interpretability. In view of the physiological characteristics of winter wheat at different growth stages, this invention designs a staged modeling framework: in the vegetative growth stage and the reproductive growth stage, the plant growth status is characterized by time-series remote sensing vegetation index, and combined with the accumulated effective temperature, an intra-season prediction model for the physiological maturity period is constructed; in the grain drying stage, combined with the physiological characteristic that grain moisture decreases under the dominant influence of the environment, a process-oriented field drying model is constructed; through the above structured segmented modeling, the KMC prediction model has a clear physical and physiological basis, enhancing the physiological interpretability and cross-regional applicability of the model.

[0021] (3) Improve the drying model structure and enhance the accuracy of KMC simulation. To address the bias problem of "underestimating high values ​​and overestimating low values" in existing drying models, this invention improves the field grain drying model based on the moisture diffusion mechanism between grains and the environment. Specifically, it includes: introducing saturated vapor pressure difference (VPD) as a regulating term to enhance the model's response to meteorological evaporation potential; constraining the drying coefficient's "slow-fast-slow" drying potential change over time using a Gaussian function; and adopting a daily difference equation structure to improve the model's sensitivity to daily meteorological dynamics. This invention improves the field grain drying model by fully integrating process physical mechanisms and meteorological change information, thereby enhancing the accuracy, robustness, and generalization ability of KMC simulation at the regional scale. Attached Figure Description

[0022] The technical solution and other beneficial effects of the present invention will become apparent from the following detailed description of specific embodiments of the invention, in conjunction with the accompanying drawings.

[0023] Figure 1A flowchart illustrating a method for predicting wheat grain moisture content in the field based on PlanetScope imagery, provided in an embodiment of the present invention.

[0024] Figure 2 This is a schematic diagram of the structure of a wheat field grain moisture content prediction system based on PlanetScope imagery provided in an embodiment of the present invention.

[0025] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] This invention provides a method, system, storage medium, and electronic device for predicting wheat grain moisture content in the field based on PlanetScope imagery. The wheat grain moisture content prediction system based on PlanetScope imagery provided by this invention can be integrated into an electronic device, such as a terminal or server. The terminal can include a tablet computer, laptop computer, personal computer (PC), microprocessor box, or other devices.

[0028] Please see Figure 1 , Figure 1 The flowchart illustrates a method for predicting wheat grain moisture content in the field based on PlanetScope imagery, provided in an embodiment of the present invention. This method, applied in electronic devices, includes the following steps: S1. Acquire PlanetScope 8-band multispectral surface reflectance data after the winter wheat growing season in the target area. Select images with cloud cover below a preset percentage and calculate the Enhanced Vegetation Index (EVI) pixel by pixel to obtain the EVI time series.

[0029] Specifically, 8-band multispectral surface reflectance data from PlanetScope (a type of remote sensing data from the Planet Labs Dove constellation) were acquired for the target area after the winter wheat growing season (2021-2022, 2023-2024, and 2024-2025). Images with cloud cover below a preset percentage (10% in this embodiment) were selected, and the Enhanced Vegetation Index (EVI) was calculated pixel-by-pixel to obtain the EVI time series. The EVI time series was calculated using the following formula:

[0030] in, , and The surface reflectance corresponds to Band 8 (near-infrared band), Band 6 (red band), and Band 2 (blue band) of PlanetScope's 8-band multispectral imagery, respectively.

[0031] S2, preprocess the EVI time series, and obtain the heading date of winter wheat based on the preprocessed EVI time series.

[0032] In one embodiment, step S2 includes the following steps: S21, use a cubic spline function to smooth and fill in the missing values ​​in the EVI time series to obtain a seamless EVI time series; S22, the time corresponding to the maximum value of the seamless EVI time series is taken as the heading period of winter wheat.

[0033] S3, based on the heading stage of winter wheat, predicts the physiological maturity stage of winter wheat using a cumulative effective accumulated temperature model.

[0034] In one embodiment, step S3 includes the following steps: S31, obtain the daily maximum and minimum temperatures of winter wheat in the target area after the heading stage.

[0035] Specifically, by using historical meteorological data and weather forecast data, the daily maximum and minimum temperatures after the heading stage of winter wheat in the target area are obtained.

[0036] S32, calculates the number of growth degree days per day after the heading stage of winter wheat based on the highest and lowest temperatures.

[0037] The formula for calculating the daily growing degree days (GDD) after the heading stage of winter wheat is as follows:

[0038]

[0039] in, , These are the daily high and low temperatures, The reference temperature for the start of winter wheat growth (in one specific embodiment, it can be set to...) ), The upper limit temperature for winter wheat growth (in one specific embodiment, it can be set to...) ).

[0040] S33, calculates the cumulative effective accumulated temperature growth days required from the heading stage to the physiological maturity stage of winter wheat based on the growth degree days.

[0041] Specifically, the formula for calculating the Accumulated Growing Degree Days (AGDD) is as follows:

[0042] In one specific embodiment, based on phenological observation data of the target area, Set as .

[0043] S34, starting from the heading stage of each pixel, the number of growth days is accumulated day by day. When the accumulated effective accumulated temperature growth days are greater than or equal to the regional reference value of the accumulated effective accumulated temperature growth days for the first time, that day is determined as the physiological maturity period of wheat.

[0044] Specifically, it is expressed by the following formula:

[0045] in, PMD This is the period of physiological maturity. HD It is the heading stage. t This refers to the number of days from the beginning of the heading stage. This is a reference value for areas with accumulated temperature growth days.

[0046] S4. Obtain measured wheat grain moisture content samples and construct a field grain drying model based on the wheat grain moisture content samples, which combines moisture diffusion mechanism, saturated water vapor pressure difference regulation and Gaussian function constraint.

[0047] In one embodiment, step S4 includes the following steps: S41, Obtain a sample of the measured wheat grain moisture content (KMC).

[0048] Specifically, KMC sample data measured in the field from May 25, 2022 to June 6, 2022 were collected to construct a sample pair set:

[0049] in , The first i Two adjacent sampling dates for a sample point , These are the KMCs for the corresponding sampling.

[0050] S42 calculates the daily equilibrium moisture content of grains and the saturated water vapor pressure difference based on daily meteorological data after the physiological maturity period of winter wheat.

[0051] Based on daily meteorological data after the physiological maturity of winter wheat, including air temperature and relative humidity, the daily equilibrium moisture content of the grain is calculated. ) and saturated vapor pressure difference (VPD).

[0052] in, The GAB model (Guggenheim-Anderson-de-Boer model) is used, and the formula is as follows:

[0053] in, Water activity, expressed as daily relative humidity. Approximate substitution; T Air temperature (unit: K); , A , B and K For model parameters, in a specific embodiment, the values ​​are as follows: , A=0.013 , B= 557.0 , K=0.695 .

[0054] VPD uses the Tetens formula, specifically:

[0055] in, T Air temperature (unit: degrees Celsius). This refers to the relative humidity of the air.

[0056] S43, construct a field grain drying model that combines moisture diffusion mechanism, saturated vapor pressure difference regulation and Gaussian function constraint.

[0057] A difference equation model is constructed and used as a field grain drying model, specifically as follows:

[0058] in, This is a water diffusion mechanism. Gaussian function constraint, For saturated water vapor pressure differential regulation; Indicates the first The moisture content of wheat grains in a given day For the first The moisture content of wheat grains in a given day and The first The equilibrium water content and saturated vapor pressure difference of the day This is the position where the drying rate reaches its peak. , and For model fitting parameters, The drying coefficient is... To control the distribution of drying intensity with KMC, This is the saturated water vapor pressure difference adjustment coefficient.

[0059] S44, Fit the parameters of the field grain drying model based on wheat grain moisture content samples.

[0060] Specifically, based on the sample pair S constructed in step S41, the least squares method is used to fit the parameters of the above difference equation model to obtain the optimal result. , and The parameter values ​​were obtained as k=0.181, σ=0.074 and α=0.014.

[0061] S5 uses the predicted physiological maturity period as a priori, combines daily meteorological data with a field grain drying model, simulates the dynamic changes in wheat grain moisture content day by day, and generates a spatial distribution map of wheat grain moisture content.

[0062] Specifically, it is calculated using the following formula:

[0063] in, Physiological maturity period The moisture content of wheat grains, For the first Daily water loss , The moisture content of wheat grains on day t and day t-1 are respectively.

[0064] Based on the method described in the above embodiments, this embodiment will further describe the wheat field grain moisture content prediction system based on PlanetScope imagery. The wheat field grain moisture content prediction system based on PlanetScope imagery can be implemented as an independent entity or integrated into an electronic device. The electronic device can be a terminal, server, or other device. The terminal can include a tablet computer, laptop computer, personal computer (PC), microprocessor box, or other devices.

[0065] Please see Figure 2 , Figure 2 This invention specifically describes a wheat field grain moisture content prediction system based on PlanetScope imagery, which is applied in electronic devices. The system may include: The data acquisition module acquires PlanetScope's 8-band multispectral surface reflectance data after the winter wheat growing season in the target area has turned green. It then filters out images with cloud cover below a preset percentage, calculates the Enhanced Vegetation Index (EVI) pixel by pixel, and obtains the EVI time series. The winter wheat heading date determination module is used to preprocess the EVI time series and obtain the winter wheat heading date based on the preprocessed EVI time series. The winter wheat physiological maturity prediction module is used to predict the physiological maturity of winter wheat based on the heading stage of winter wheat using a cumulative effective accumulated temperature model. The field grain drying model construction module is used to obtain measured wheat grain moisture content samples and construct a field grain drying model based on the wheat grain moisture content samples, which combines moisture diffusion mechanism, saturated water vapor pressure difference regulation and Gaussian function constraint. The wheat grain moisture content calculation module is used to simulate the dynamic changes of wheat grain moisture content on a daily basis, using the predicted physiological maturity period as a priori, combined with daily meteorological data and the field grain drying model, and to generate a spatial distribution map of wheat grain moisture content.

[0066] In specific implementation, the above modules and / or units can be implemented as independent entities, or they can be arbitrarily combined and implemented as the same or several entities. For the specific implementation of the above modules and / or units, please refer to the previous method embodiments. For the specific beneficial effects that can be achieved, please also refer to the beneficial effects in the previous method embodiments, which will not be repeated here.

[0067] In addition, this embodiment of the invention also provides an electronic device, which may be a computer, tablet computer, or other similar device. This electronic device can implement the steps in any embodiment of the wheat field grain moisture content prediction method based on PlanetScope imagery provided by this invention. Therefore, it can achieve the beneficial effects that any of the wheat field grain moisture content prediction methods based on PlanetScope imagery provided by this invention can achieve, as detailed in the preceding embodiments, and will not be repeated here.

[0068] Figure 3 A specific structural block diagram of an electronic device provided in an embodiment of the present invention is shown. This electronic device can be used to implement the wheat field grain moisture content prediction method based on PlanetScope imagery provided in the above embodiments. The electronic device 500 can be a terminal, server, or other device. The terminal can include a tablet computer, laptop computer, personal computer (PC), microprocessor box, or other devices.

[0069] The memory 520 can be used to store software programs and modules, such as the program instructions / modules corresponding to those in the above embodiments. The processor 580 executes various functional applications and data processing by running the software programs and modules stored in the memory 520. The memory 520 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 520 may further include memory remotely located relative to the processor 580, and these remote memories can be connected to the electronic device 500 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0070] The input unit 530 can be used to receive input numeric or character information, and to generate a keyboard and mouse related to user settings and function control.

[0071] Display unit 540 can be used to display information input by the user or information provided to the user, as well as various graphical user interfaces, which can be composed of graphics, text, icons, video, and any combination thereof. Display unit 540 may include display panel 541, which may optionally be configured in the form of LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode), or other similar forms.

[0072] Electronic device 500, through transmission module 570 (e.g., Wi-Fi module), can help users receive requests, send information, etc., providing users with wireless broadband internet access. Although transmission module 570 is shown in the figure, it is understood that it is not an essential component of electronic device 500 and can be omitted as needed without changing the essence of the invention.

[0073] The processor 580 is the control center of the electronic device 500. It connects to various parts of the phone via various interfaces and lines, and performs various functions and processes data of the electronic device 500 by running or executing software programs and / or modules stored in the memory 520, and by calling data stored in the memory 520, thereby providing overall monitoring of the electronic device. Optionally, the processor 580 may include one or more processing cores; in some embodiments, the processor 580 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may also not be integrated into the processor 580.

[0074] Electronic device 500 also includes a power supply 590 (such as a battery) that supplies power to various components. In some embodiments, the power supply may be logically connected to processor 580 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The power supply 590 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0075] Although not shown, the electronic device 500 also includes cameras (such as front-facing cameras and rear-facing cameras), Bluetooth modules, etc., which will not be described in detail here. Specifically, in this embodiment, the display unit of the electronic device is a touch screen display, and the mobile terminal also includes a memory and one or more programs, wherein one or more programs are stored in the memory and configured to be executed by one or more processors. One or more programs contain instructions for performing the following operations: Acquire PlanetScope 8-band multispectral surface reflectance data of the target area after the winter wheat growing season greening period, filter out images with cloud cover below a preset percentage, calculate the Enhanced Vegetation Index (EVI) pixel by pixel, and obtain the EVI time series. The EVI time series is preprocessed, and the heading date of winter wheat is obtained based on the preprocessed EVI time series. Based on the heading stage of winter wheat, the physiological maturity stage of winter wheat is predicted using a cumulative effective accumulated temperature model. Obtain measured wheat grain moisture content samples, and construct a field grain drying model based on the wheat grain moisture content samples, which combines moisture diffusion mechanism, saturated water vapor pressure difference regulation and Gaussian function constraint. Using the predicted physiological maturity period as a priori, and combining daily meteorological data with the field grain drying model, the dynamic changes in wheat grain moisture content are simulated daily, and a spatial distribution map of wheat grain moisture content is generated.

[0076] In practice, the above modules can be implemented as independent entities or combined in any way to be implemented as the same or several entities. For the specific implementation of the above modules, please refer to the previous method implementation examples, which will not be repeated here.

[0077] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor. Therefore, embodiments of the present invention provide a storage medium storing multiple instructions that can be loaded by a processor to execute the steps of any embodiment of the wheat field grain moisture content prediction method based on PlanetScope imagery provided by the present invention.

[0078] The computer-readable storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0079] Since the instructions stored in the storage medium can execute the steps in any embodiment of the wheat field grain moisture content prediction method based on PlanetScope imagery provided in the embodiments of the present invention, the beneficial effects that any wheat field grain moisture content prediction method based on PlanetScope imagery provided in the embodiments of the present invention can achieve can be realized. For details, please refer to the previous embodiments, which will not be repeated here.

[0080] The foregoing has provided a detailed description of a method, system, storage medium, and electronic device for predicting wheat field grain moisture content based on PlanetScope imagery, as provided in the embodiments of the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for predicting wheat grain moisture content in the field based on PlanetScope imagery, characterized in that, The method includes: Acquire PlanetScope 8-band multispectral surface reflectance data of the target area after the winter wheat growing season greening period, filter out images with cloud cover below a preset percentage, calculate the Enhanced Vegetation Index (EVI) pixel by pixel, and obtain the EVI time series. The EVI time series is preprocessed, and the heading date of winter wheat is obtained based on the preprocessed EVI time series. Based on the heading stage of winter wheat, the physiological maturity stage of winter wheat is predicted using a cumulative effective accumulated temperature model. Obtain measured wheat grain moisture content samples, and construct a field grain drying model based on the wheat grain moisture content samples, which combines moisture diffusion mechanism, saturated water vapor pressure difference regulation and Gaussian function constraint. Using the predicted physiological maturity period as a priori, and combining daily meteorological data with the field grain drying model, the dynamic changes in wheat grain moisture content are simulated daily, and a spatial distribution map of wheat grain moisture content is generated.

2. The method for predicting wheat grain moisture content in the field based on PlanetScope imagery according to claim 1, characterized in that, The formula for calculating the EVI time series is as follows: in, , and The surface reflectance corresponds to Band 8, Band 6, and Band 2 of PlanetScope's 8-band multispectral imagery, respectively.

3. The method for predicting wheat grain moisture content in the field based on PlanetScope imagery according to claim 1, characterized in that, The EVI time series is preprocessed, and the heading date of winter wheat is obtained based on the preprocessed EVI time series, including: The missing values ​​in the EVI time series are smoothed and filled using a cubic spline function to obtain a seamless EVI time series. The time corresponding to the maximum value of the seamless EVI time series is taken as the heading stage of winter wheat.

4. The method for predicting wheat grain moisture content in the field based on PlanetScope imagery according to claim 1, characterized in that, Based on the heading stage of winter wheat, the physiological maturity stage of winter wheat is predicted using a cumulative effective accumulated temperature model, including: Obtain the daily maximum and minimum temperatures of winter wheat in the target area after the heading stage; Calculate the number of growth degree days per day after the heading stage of winter wheat based on the highest and lowest temperatures; Calculate the cumulative effective accumulated temperature growth days required from the heading stage to the physiological maturity stage of winter wheat based on the aforementioned growth period days; Starting from the heading stage of each pixel, the number of growth days is accumulated day by day. When the accumulated effective accumulated temperature growth days first exceeds or equals the regional reference value of the accumulated effective accumulated temperature growth days, that day is determined as the physiological maturity stage of wheat.

5. The method for predicting wheat grain moisture content in the field based on PlanetScope imagery according to claim 1, characterized in that, Obtain measured wheat grain moisture content samples, and construct a field grain drying model based on these samples, incorporating moisture diffusion mechanisms, saturated vapor pressure differential regulation, and Gaussian function constraints. This model includes: Obtain actual samples of wheat grain moisture content; Based on daily meteorological data after the physiological maturity of winter wheat, the daily equilibrium moisture content of grain and the saturated water vapor pressure difference were calculated. A field grain drying model combining moisture diffusion mechanism, saturated vapor pressure difference regulation, and Gaussian function constraint was constructed. The parameters of the field grain drying model were fitted based on the wheat grain moisture content samples.

6. The method for predicting wheat grain moisture content in the field based on PlanetScope imagery according to claim 5, characterized in that, The field grain drying model is as follows: in, This is a water diffusion mechanism. Gaussian function constraint, For saturated water vapor pressure differential regulation; Indicates the first The moisture content of wheat grains in a given day For the first The moisture content of wheat grains in a given day and The first The equilibrium water content and saturated vapor pressure difference of the day This is the position where the drying rate reaches its peak. , and For model fitting parameters, The drying coefficient is... To control the distribution of drying intensity with KMC, This is the saturated water vapor pressure difference adjustment coefficient.

7. The method for predicting wheat grain moisture content in the field based on PlanetScope imagery according to claim 1, characterized in that, Using the predicted physiological maturity period as a priori, and combining daily meteorological data with the field grain drying model, the dynamic changes in wheat grain moisture content are simulated daily, and a spatial distribution map of wheat grain moisture content is generated. The moisture content is then calculated using the following formula: in, Physiological maturity period The moisture content of wheat grains, For the first Daily water loss , The moisture content of wheat grains on day t and day t-1 are respectively.

8. A wheat field grain moisture content prediction system based on PlanetScope imagery, characterized in that, include: The data acquisition module acquires PlanetScope's 8-band multispectral surface reflectance data after the winter wheat growing season in the target area has turned green. It then filters out images with cloud cover below a preset percentage, calculates the Enhanced Vegetation Index (EVI) pixel by pixel, and obtains the EVI time series. The winter wheat heading date determination module is used to preprocess the EVI time series and obtain the winter wheat heading date based on the preprocessed EVI time series. The winter wheat physiological maturity prediction module is used to predict the physiological maturity of winter wheat based on the heading stage of winter wheat using a cumulative effective accumulated temperature model. The field grain drying model construction module is used to obtain measured wheat grain moisture content samples and construct a field grain drying model based on the wheat grain moisture content samples, which combines moisture diffusion mechanism, saturated water vapor pressure difference regulation and Gaussian function constraint. The wheat grain moisture content calculation module is used to simulate the dynamic changes of wheat grain moisture content on a daily basis, using the predicted physiological maturity period as a priori, combined with daily meteorological data and the field grain drying model, and to generate a spatial distribution map of wheat grain moisture content.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a plurality of instructions adapted to be loaded by a processor to execute the method for predicting wheat field grain moisture content based on PlanetScope imagery as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, The method includes a processor and a memory, the processor being electrically connected to the memory, the memory being used to store instructions and data, and the processor being used to execute the steps in the method for predicting wheat field grain moisture content based on PlanetScope imagery as described in any one of claims 1 to 7.

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