Variable irrigation prescription map generation method and device based on cooperative effect of unmanned aerial vehicle and satellite remote sensing, equipment, medium and product

Abstract

The invention discloses a variable irrigation prescription map generation method and device based on the synergistic effect of an unmanned aerial vehicle and satellite remote sensing, equipment, a medium and a product, and relates to the technical field of agricultural information monitoring and agricultural irrigation, and the method comprises the steps: formulating an irrigation management strategy of a sprinkling irrigation cluster based on a crop key water demand period; according to the unmanned aerial vehicle remote sensing information, constructing a soil moisture content inversion model in different growth stages, and according to a conversion equation between the unmanned aerial vehicle remote sensing information and satellite remote sensing information, predicting and inverting the soil moisture content in different growth stages; determining a soil moisture content spatial distribution diagram in the irrigation area of the sprinkling irrigation cluster; based on the irrigation information, according to the soil moisture content inversion model and the soil moisture content spatial distribution diagram, determining a moisture deficit spatial distribution diagram in the control area of the single sprinkling irrigation equipment; and based on an irrigation management strategy, according to the water deficit spatial distribution map, determining a variable irrigation prescription map in a control area of the single sprinkling irrigation equipment, and according to the application, accurate irrigation management can be realized.

Description

Technical Field

[0001] The present application relates to the field of agricultural information monitoring and agricultural irrigation technology, and in particular to a method, device, equipment, medium and product for generating a variable irrigation prescription map based on the synergy of unmanned aerial vehicle and satellite remote sensing. Background Art

[0002] As the scale of China's high-standard farmland construction continues to expand, the area of ​​flat, concentrated and contiguous arable land of more than 10,000 mu continues to increase, providing basic conditions for the installation and utilization of multiple large and medium-sized sprinkler irrigation equipment. At the same time, managed irrigation services, as a new type of high-level agricultural service, provide the possibility for the continuous, stable and precise operation of sprinkler clusters.

[0003] Considering the characteristics of short critical water demand period for crops and heavy workload, in order to achieve fast and low-cost precise water management of sprinkler irrigation clusters and avoid production reduction caused by untimely irrigation, it is urgently necessary to combine remote sensing information to accurately judge the spatial distribution characteristics of water deficit in the irrigation area of ​​sprinkler irrigation clusters, so as to generate the rotation irrigation system of sprinkler clusters and the variable irrigation prescription map for each sprinkler irrigation plot.

[0004] The method of using drones to obtain remote sensing information is limited by the drone's endurance and the allowed flight altitude (no more than 120m). The flight area of ​​a single drone multispectral system is limited during the allowed flight time period (11:00-16:00) (maximum about 2775 acres). In addition, in order to avoid signal interference, two drones flying at the same time need to be at least 300m away, which increases the purchase cost and management complexity of multiple drones. Although satellite remote sensing can provide spectral information of a large area at the same time for free, it is limited by the satellite transit time period, low resolution and cloudy interference. The timeliness and accuracy of effective data have become the main factors limiting the application of satellite remote sensing technology in precision irrigation management in sprinkler irrigation clusters.

[0005] It can be seen that neither UAV remote sensing information nor satellite remote sensing information can be used for precise irrigation management in sprinkler clusters. Summary of the invention

[0006] The purpose of this application is to provide a method, device, equipment, medium and product for generating a variable irrigation prescription map based on the synergy of unmanned aerial vehicles and satellite remote sensing, so as to solve the problem of being unable to perform precise irrigation management in a sprinkler cluster.

[0007] To achieve the above objectives, this application provides the following solutions:

[0008] In a first aspect, the present application provides a method for generating a variable irrigation prescription map based on the synergy of drones and satellite remote sensing, comprising:

[0009] Formulate irrigation management strategies for sprinkler clusters based on the key water demand period of crops; the key water demand period of crops occurs in five growth stages, including the seedling water stage, winter irrigation stage, greening and jointing water stage, pre-flowering water stage and grain filling water stage; the irrigation management strategy includes whether irrigation is required at each growth stage and the amount of irrigation water; the amount of irrigation water is determined based on the soil moisture content determined by the effective rainfall and crop water consumption in different growth stages and the spatial variation of the degree of soil moisture deficit caused by the spatial variation of soil physical and chemical properties;

[0010] Constructing a soil moisture inversion model at different growth stages based on the UAV remote sensing information, and predicting and inverting the soil moisture content at different growth stages based on a conversion equation between the UAV remote sensing information and the satellite remote sensing information;

[0011] Based on satellite remote sensing information before the crops enter the critical water demand period of the crops, a spatial distribution map of soil moisture content within the irrigation area of ​​the sprinkler irrigation cluster is determined according to the soil moisture inversion model; the satellite remote sensing information includes spectral data;

[0012] Based on the irrigation information, the water deficit spatial distribution map in the control area of ​​a single sprinkler irrigation device is determined according to the soil moisture inversion model and the soil moisture spatial distribution map; the irrigation information includes the water supply capacity of the water source, the irrigation demand, the valid data when the satellite passes by, and the weather factors; the valid data when the satellite passes by is the satellite spectral image when the weather is clear and there is no cloud cover;

[0013] Based on the irrigation management strategy, a variable irrigation prescription map within the control area of ​​a single sprinkler irrigation device is determined according to the water deficit spatial distribution map; the variable irrigation prescription map includes the travel speed and irrigation volume of the sprinkler irrigation device.

[0014] In a second aspect, the present application provides a variable irrigation prescription map generation device based on the synergy of drones and satellite remote sensing, comprising:

[0015] The irrigation management strategy formulation module is used to formulate the irrigation management strategy of the sprinkler cluster based on the critical water demand period of the crop; the critical water demand period of the crop occurs in five growth stages, which include the seedling water stage, winter irrigation stage, greening and jointing water stage, pre-flowering water stage and grain filling water stage; the irrigation management strategy includes whether irrigation is required in each growth stage and the amount of irrigation water; the amount of irrigation water is determined according to the soil moisture content determined by the effective rainfall and crop water consumption in different growth stages and the spatial variation of the soil moisture deficit caused by the spatial variation of soil physical and chemical properties;

[0016] A prediction and inversion module is used to construct an inversion model of soil moisture content at different growth stages based on UAV remote sensing information, and predict and invert the soil moisture content at different growth stages based on a conversion equation between the UAV remote sensing information and satellite remote sensing information;

[0017] A soil moisture content spatial distribution map determination module is used to determine the soil moisture content spatial distribution map within the irrigation area of ​​the sprinkler irrigation cluster according to the soil moisture content inversion model based on satellite remote sensing information before the crop enters the critical water demand period of the crop; the satellite remote sensing information includes spectral data;

[0018] A water deficit spatial distribution map determination module is used to determine the water deficit spatial distribution map within the control area of ​​a single sprinkler irrigation device based on irrigation information, the soil moisture inversion model and the soil moisture spatial distribution map; the irrigation information includes water supply capacity of the water source, irrigation demand, valid data when the satellite passes by, and weather factors; the valid data when the satellite passes by is a satellite spectral image with clear weather and no cloud cover;

[0019] The variable irrigation prescription map determination module is used to determine the variable irrigation prescription map within the control area of ​​a single sprinkler irrigation device based on the irrigation management strategy and the water deficit spatial distribution map; the variable irrigation prescription map includes the travel speed and irrigation volume of the sprinkler irrigation device.

[0020] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the above-mentioned methods for generating a variable irrigation prescription map based on the synergy of drones and satellite remote sensing.

[0021] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-mentioned methods for generating a variable irrigation prescription map based on the synergy of unmanned aerial vehicles and satellite remote sensing.

[0022] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned methods for generating variable irrigation prescription maps based on the synergy of unmanned aerial vehicles and satellite remote sensing.

[0023] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0024] This application is based on the synergy of UAV and satellite remote sensing, combined with the key water-demanding growth period of crops, and fully utilizes the timeliness and accuracy of UAV remote sensing on the basis of large-scale rotation irrigation decision-making based on satellite remote sensing information. By constructing a soil moisture inversion model for different growth stages, a soil moisture spatial distribution map, and a water deficit spatial distribution map, a variable irrigation prescription map is formulated for each sprinkler equipment in the sprinkler cluster. Since the variable irrigation prescription map is obtained by combining UAV remote sensing information and satellite remote sensing information, sprinkler irrigation is carried out according to the variable irrigation prescription map to achieve precise irrigation management. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0026] Figure 1 A flow chart of a method for generating a variable irrigation prescription map based on the synergy of unmanned aerial vehicles and satellite remote sensing provided in an embodiment of the present application;

[0027] Figure 2 A schematic diagram of a computer device provided in accordance with an embodiment of the present application. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0029] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0030] The embodiment of the present application provides a method for generating a variable irrigation prescription map based on the synergy of drones and satellite remote sensing. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, Figure 1 As shown, the method includes the following steps.

[0031] S1: Formulate irrigation management strategies for sprinkler clusters based on the critical water requirement period of crops; the critical water requirement period of crops occurs in five growth stages, which include the seedling water stage, winter irrigation stage, greening and jointing water stage, pre-flowering water stage and grain filling water stage; the irrigation management strategy includes whether irrigation is needed in each growth stage and the amount of irrigation water; the amount of irrigation water is determined based on the soil moisture content determined by the effective rainfall and crop water consumption in different growth stages and the spatial variation of soil moisture deficit caused by the spatial variation of soil physical and chemical properties.

[0032] S2: constructing a soil moisture inversion model at different growth stages based on the UAV remote sensing information, and predicting and inverting the soil moisture content at different growth stages based on the conversion equation between the UAV remote sensing information and the satellite remote sensing information.

[0033] S3: Based on the satellite remote sensing information before the crops enter the critical water demand period of the crops, determine the spatial distribution map of soil moisture content within the irrigation area of ​​the sprinkler cluster according to the soil moisture inversion model; the satellite remote sensing information includes spectral data.

[0034] S4: Based on the irrigation information, the spatial distribution map of water deficit in the control area of ​​a single sprinkler irrigation device is determined according to the soil moisture inversion model and the soil moisture spatial distribution map; the irrigation information includes the water supply capacity of the water source, irrigation demand, valid data when the satellite passes by, and weather factors; the valid data when the satellite passes by is the satellite spectral image when the weather is clear and there is no cloud cover.

[0035] S5: Based on the irrigation management strategy, determine a variable irrigation prescription map within the control area of ​​a single sprinkler irrigation device according to the water deficit spatial distribution map; the variable irrigation prescription map includes the travel speed and irrigation volume of the sprinkler irrigation device.

[0036] In an exemplary embodiment, in order to determine the spatial variation characteristics of soil moisture content and its deficit within an irrigated area of ​​more than 10,000 mu, a soil moisture inversion model based on satellite remote sensing information needs to be constructed. At the same time, considering the poor accuracy of satellite remote sensing information, it is necessary to first construct a conversion equation between drone remote sensing and satellite remote sensing information. Combined with the differences in remote sensing information caused by different coverage at different growth stages of crops, it is necessary to construct the conversion equation between drone remote sensing and satellite remote sensing information and the soil moisture inversion model in stages. S2 can be replaced by the following steps.

[0037] S21: Based on the vegetation coverage at different growth stages, a soil moisture inversion model at different growth stages is constructed according to the UAV remote sensing information.

[0038] S22: Based on the resolution of the satellite remote sensing image, resample the spectral information in the UAV remote sensing image to the resolution scale of the satellite remote sensing image.

[0039] S23: performing linear fitting on the unified UAV spectral information and satellite spectral information to determine a conversion equation between the UAV remote sensing information and the satellite remote sensing information.

[0040] S24: Based on the conversion equation, the satellite remote sensing information is converted to UAV remote sensing accuracy, the soil moisture inversion model is called, and the soil moisture content is predicted and inverted.

[0041] In an exemplary embodiment, during the seedling watering and winter irrigation, because the vegetation coverage is low and the planned wet layer depth is mainly concentrated in the 0-40 cm soil layer, the shallow soil moisture content can be directly inverted using remote sensing information. S21 can be replaced by the following steps.

[0042] S211: During the seedling water stage and the winter irrigation stage, shallow soil moisture content, a first UAV remote sensing image, and a first satellite remote sensing image at multiple points are synchronously acquired on the same day.

[0043] S212: Using the band information in the first UAV remote sensing image as a first independent variable and the shallow soil moisture content as a first dependent variable, first UAV spectral information related to soil moisture content is screened out from the first UAV remote sensing image. The band information includes red light, green light, blue light, red edge, and near infrared bands.

[0044] S213: Using the first independent variable and the first dependent variable as input data and the first UAV spectral information as output data, construct a soil moisture inversion model for the seedling water stage and the winter irrigation stage.

[0045] S214: During the greening and jointing water stage, the pre-flowering water stage and the grain filling water stage, the soil moisture content, the second UAV remote sensing image and the second satellite remote sensing image at multiple points are synchronously acquired on the same day.

[0046] S215: Determine a spectral index representing the moisture content of the plant according to the band information in the second UAV remote sensing image.

[0047] S216: Using the band information in the second UAV remote sensing image and the spectral index characterizing the plant moisture content as the second independent variable, and the soil moisture content as the second dependent variable, the second UAV spectral information related to the soil moisture content is screened out from the second UAV remote sensing image.

[0048] S217: Using the second independent variable and the second dependent variable as input data and the second UAV spectral information as output data, construct a soil moisture inversion model for the greening and jointing water stage, the pre-flowering water stage, and the filling water stage.

[0049] In another exemplary embodiment, S21 may be replaced by the following steps.

[0050] (1) On the same day, shallow soil moisture content, satellite spectral images, and UAV remote sensing images are simultaneously acquired at multiple locations.

[0051] (2) The different band information (red light, green light, blue light, red edge, and near infrared) collected by UAV remote sensing was used as the independent variable, and the measured shallow soil moisture content was used as the dependent variable. The spectral data closely related to the soil moisture content was screened out from the UAV remote sensing images through the LASSO regression algorithm. The screened independent and dependent variables were then used as input data, and a soil moisture inversion model was constructed through a machine learning model.

[0052] (3) Based on the resolution of satellite remote sensing images, the spectral data collected by UAV remote sensing are resampled and unified to the resolution scale of satellite remote sensing images. The i spectral data closely related to soil moisture content obtained from UAV remote sensing selected in step (2) are respectively used as y i , the corresponding spectral data of the same type obtained from satellite remote sensing is taken as x i , by fitting y i With x i The linear equations between them are used to establish the transformation equations of UAV remote sensing and satellite remote sensing information respectively.

[0053] (4) After the established conversion equation is converted to the UAV remote sensing accuracy, the soil moisture inversion model established in (2) can be called to predict and invert the soil moisture content.

[0054] In an exemplary embodiment, when watering the jointing, pre-flowering and filling, because the vegetation coverage is high, there is a strong correlation between the 0-60cm soil moisture content and the plant moisture content, and the soil moisture content can be directly inverted using remote sensing information. S3 can be replaced by the following steps.

[0055] S31: Obtain satellite remote sensing images before the crops enter the critical water demand period of the crops, and select satellite remote sensing images corresponding to the satellite transit date that satisfies the conditions of clear weather and no cloud cover. For example, the transit date of Sentinel-2 that satisfies the conditions of clear weather and no cloud cover is selected, and the spectral data is extracted after the satellite remote sensing images are downloaded from the corresponding website.

[0056] S32: extracting spectral data of the satellite remote sensing image, and transforming the spectral data based on the transformation equation to determine transformed spectral data.

[0057] S33: Inputting the converted spectral data into the soil moisture inversion model to predict the soil moisture content in the irrigation area of ​​the sprinkler irrigation cluster.

[0058] S34: Based on the soil moisture content in the irrigation area of ​​the sprinkler irrigation cluster, a spatial distribution map of the soil moisture content in the irrigation area of ​​the sprinkler irrigation cluster is drawn using ArcGIS software.

[0059] In another exemplary embodiment, S3 may be replaced by the following steps.

[0060] (1)Simultaneously obtain soil moisture content, satellite spectral images, and UAV remote sensing images at multiple locations on the same day.

[0061] (2) The different band information collected by UAV remote sensing and the spectral index representing the plant moisture content composed of these band information (for example, Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Optimization of Soil Regulatory Vegetation Index (OSAVI), Enhanced Vegetation Index (EVI)) are used as independent variables, and the measured soil moisture content is used as the dependent variable. The spectral data closely related to the soil moisture content are screened out through the LASSO regression algorithm. Then, the screened independent variables and dependent variables are used as input data, and a soil moisture inversion model is constructed through a machine learning model.

[0062] (3) Based on the resolution of satellite remote sensing images, the spectral data collected by UAV remote sensing are resampled and unified to the resolution scale of satellite remote sensing images. The i spectral data closely related to soil moisture content obtained from UAV remote sensing selected in step (2) are respectively used as y i , the corresponding spectral data of the same type obtained from satellite remote sensing is taken as x i , by fitting y i With x i The linear equations between them are used to establish the transformation equations of UAV remote sensing and satellite remote sensing information respectively.

[0063] (4) After the satellite remote sensing information is converted to the UAV remote sensing accuracy through the conversion equation established in (3), the soil moisture inversion model established in (2) can be used to predict and invert the soil moisture content.

[0064] In an exemplary embodiment, S4 may be replaced by the following steps.

[0065] S41: If the water supply capacity of the water source cannot simultaneously meet the irrigation needs of all sprinkler irrigation equipment, the average soil moisture content in the control area of ​​a single sprinkler irrigation equipment is calculated based on the spatial distribution map of soil moisture content.

[0066] S42: sorting the average soil moisture content in the area controlled by the single sprinkler irrigation equipment, and formulating a rotation irrigation system for the sprinkler irrigation cluster according to the sorting results.

[0067] S43: Based on the rotation irrigation system, within the area controlled by a single sprinkler equipment that needs to be irrigated, if there is valid data when the satellite passes over on the day of irrigation or two days before irrigation, based on the conversion equation, the spectral data corresponding to the valid data is converted, and the converted spectral data is input into the soil moisture inversion model to predict the first soil moisture content in the inverted irrigation area; the irrigation area is the area controlled by a single irrigation equipment.

[0068] S44: Calculate a first difference between the first soil moisture content and the irrigation upper limit, and generate a water deficit spatial distribution map of the irrigation area according to the first difference.

[0069] S45: If the satellite data is invalid due to weather factors, the required spectral data is screened out based on the spectral data of the soil or canopy obtained by using the drone remote sensing system within a set time period two days before irrigation or on the day of irrigation. The set time period is 11:00-16:00.

[0070] S46: Inputting the required spectral data into the soil moisture inversion model to predict a second soil moisture content in the inversion irrigation area.

[0071] S47: Calculate a second difference between the second soil moisture content and the irrigation upper limit, and generate a water deficit spatial distribution map of the irrigation area according to the second difference.

[0072] S48: If the water supply capacity of the water source can simultaneously meet the irrigation needs of all sprinkler irrigation equipment, the difference between the third soil moisture content and the irrigation upper limit is calculated according to the spatial distribution map of soil moisture content to generate a spatial distribution map of water deficit in the irrigation area; the first soil moisture content, the second soil moisture content and the third moisture content are the soil moisture contents within the planned moist layer.

[0073] In an exemplary embodiment, S5 may be replaced by the following steps.

[0074] S51: Based on the irrigation management strategy, according to the water deficit spatial distribution map and the range of the sprinkler equipment, the control area of ​​each sprinkler equipment is divided into rectangular grids of different lengths or fan-shaped grids of different angles that are greater than the range of the sprinkler head as variable irrigation management areas along the travel direction.

[0075] S52: Based on the water deficit spatial distribution map, calculate the average soil moisture deficit of each variable irrigation management area.

[0076] S53: Determine the irrigation amount of each variable irrigation management area according to the average soil moisture deficit and the planned depth of the moist layer.

[0077] S54: Determine a variable irrigation prescription map within the control area of ​​a single sprinkler irrigation device according to the irrigation amount and the travel speed of each sprinkler irrigation device.

[0078] In another exemplary embodiment, S5 may be replaced by the following steps.

[0079] According to the complexity of the spatial distribution map of water deficit in the irrigation area and taking into account the range of the sprinkler irrigation equipment, the control area of ​​each sprinkler irrigation equipment is divided into rectangular grids of different lengths (translation sprinkler, reel sprinkler) or fan grids of different angles (circular sprinkler) along its travel direction as variable irrigation management areas. Based on the spatial distribution map of water deficit in the control area of ​​a single sprinkler irrigation equipment, the average soil moisture deficit is calculated in each management area, and the irrigation amount W in each management area is calculated by multiplying it by the planned wet layer depth. 灌 :

[0080] W 灌 =(⊙ 上限 -⊙ 土 )×h;

[0081] Among them, W 灌 is the irrigation volume of the management area, mm; 上限 is the upper limit of soil moisture content in the management area, cm 3 / cm 3 ⊙ 土 is the average soil moisture content in the management area, cm 3 / cm 3 ; h is the planned wet layer depth, mm.

[0082] A GPS positioning system and an automatic control system for adjusting the travel speed are installed on each sprinkler irrigation equipment. When entering the boundary of each management area, watering is achieved by automatically adjusting the travel speed of the sprinkler irrigation equipment. The code composed of different travel speeds of sprinkler irrigation equipment in different management areas is the variable irrigation prescription map of the sprinkler irrigation area.

[0083] Based on the same inventive concept, the embodiment of the present application also provides a variable irrigation prescription map generation device based on the synergy of drones and satellite remote sensing for implementing the variable irrigation prescription map generation method based on the synergy of drones and satellite remote sensing. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more embodiments of the variable irrigation prescription map generation device based on the synergy of drones and satellite remote sensing provided below can refer to the above limitations on the variable irrigation prescription map generation method based on the synergy of drones and satellite remote sensing, and will not be repeated here.

[0084] In an exemplary embodiment, a variable irrigation prescription map generation device based on the synergy of drone and satellite remote sensing is provided, comprising:

[0085] The irrigation management strategy formulation module is used to formulate irrigation management strategies for sprinkler clusters based on the critical water demand period of crops; the critical water demand period of crops occurs in five growth stages, and the five growth stages include the seedling water stage, winter irrigation stage, greening and jointing water stage, pre-flowering water stage and grain filling water stage; the irrigation management strategy includes whether irrigation is required in each growth stage and the amount of irrigation water; the amount of irrigation water is determined based on the soil moisture content determined by the effective rainfall and crop water consumption in different growth stages and the spatial variation of the degree of soil moisture deficit caused by the spatial variation of soil physical and chemical properties.

[0086] The prediction and inversion module is used to construct an inversion model of soil moisture content at different growth stages based on UAV remote sensing information, and predict and invert the soil moisture content at different growth stages based on the conversion equation between the UAV remote sensing information and the satellite remote sensing information.

[0087] A soil moisture spatial distribution map determination module is used to determine the soil moisture spatial distribution map within the irrigation area of ​​the sprinkler cluster based on satellite remote sensing information before the crop enters the critical water demand period of the crop and according to the soil moisture inversion model; the satellite remote sensing information includes spectral data.

[0088] A water deficit spatial distribution map determination module is used to determine the water deficit spatial distribution map within the control area of ​​a single sprinkler irrigation device based on irrigation information, according to the soil moisture inversion model and the soil moisture spatial distribution map; the irrigation information includes water supply capacity of the water source, irrigation demand, valid data when the satellite passes by, and weather factors; the valid data when the satellite passes by is a satellite spectral image with clear weather and no cloud cover.

[0089] The variable irrigation prescription map determination module is used to determine the variable irrigation prescription map within the control area of ​​a single sprinkler irrigation device based on the irrigation management strategy and the water deficit spatial distribution map; the variable irrigation prescription map includes the travel speed and irrigation volume of the sprinkler irrigation device.

[0090] This application combines the time period of critical water demand for crops, fully utilizes the timeliness and accuracy of drone remote sensing to make large-scale rotation irrigation decisions based on satellite remote sensing information, and formulates a variable irrigation prescription map for a single sprinkler equipment.

[0091] In an exemplary embodiment, a computer device is provided, such as Figure 2 As shown, the computer device can be a server or a terminal. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store variable irrigation prescription map generation data based on the synergy of unmanned aerial vehicles and satellite remote sensing. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a variable irrigation prescription map generation method based on the synergy of unmanned aerial vehicles and satellite remote sensing is implemented.

[0092] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the above method is implemented when the processor executes the computer program.

[0093] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, which implements the above method when executed by a processor.

[0094] In an exemplary embodiment, a computer program product is provided, including a computer program, which implements the above method when executed by a processor.

[0095] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ReadOnlyMemory, ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (Magnetoresistive RandomAccess Memory, MRAM), ferroelectric random access memory (Ferroelectric RandomAccess Memory, FRAM), phase change memory (Phase Change Memory, PCM), graphene memory, etc. Volatile memory can include random access memory (RandomAccess Memory, RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0096] In this application, all actions to obtain signals, information or data are carried out in compliance with the relevant data protection laws and policies of the country where they are located and with the authorization given by the owner of the corresponding device.

[0097] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0098] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0099] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for generating variable irrigation prescription maps based on the synergy of unmanned aerial vehicles and satellite remote sensing, characterized in that: The variable irrigation prescription map generation method based on the synergy of UAV and satellite remote sensing includes: Formulate irrigation management strategies for sprinkler clusters based on the key water demand period of crops; the key water demand period of crops occurs in five growth stages, including the seedling water stage, winter irrigation stage, greening and jointing water stage, pre-flowering water stage and grain filling water stage; the irrigation management strategy includes whether irrigation is required at each growth stage and the amount of irrigation water; the amount of irrigation water is determined based on the soil moisture content determined by the effective rainfall and crop water consumption in different growth stages and the spatial variation of the degree of soil moisture deficit caused by the spatial variation of soil physical and chemical properties; Constructing a soil moisture inversion model at different growth stages based on the UAV remote sensing information, and predicting and inverting the soil moisture content at different growth stages based on a conversion equation between the UAV remote sensing information and the satellite remote sensing information; Based on satellite remote sensing information before the crops enter the critical water demand period of the crops, a spatial distribution map of soil moisture content within the irrigation area of ​​the sprinkler irrigation cluster is determined according to the soil moisture inversion model; the satellite remote sensing information includes spectral data; Based on the irrigation information, the water deficit spatial distribution map in the control area of ​​a single sprinkler irrigation device is determined according to the soil moisture inversion model and the soil moisture spatial distribution map; the irrigation information includes the water supply capacity of the water source, the irrigation demand, the valid data when the satellite passes by, and the weather factors; the valid data when the satellite passes by is the satellite spectral image when the weather is clear and there is no cloud cover; Based on the irrigation management strategy, a variable irrigation prescription map within the control area of ​​a single sprinkler irrigation device is determined according to the water deficit spatial distribution map; the variable irrigation prescription map includes the travel speed and irrigation volume of the sprinkler irrigation device.

2. The variable irrigation prescription map generation method based on the synergy of unmanned aerial vehicle and satellite remote sensing according to claim 1 is characterized in that: The soil moisture inversion model at different growth stages is constructed based on the UAV remote sensing information, and the soil moisture content at different growth stages is predicted and inverted based on the conversion equation between the UAV remote sensing information and the satellite remote sensing information, specifically including: Based on the vegetation coverage at different growth stages, a soil moisture inversion model at different growth stages is constructed according to the UAV remote sensing information; Based on the resolution of the satellite remote sensing image, the spectral information in the UAV remote sensing image is resampled to the resolution scale of the satellite remote sensing image; Performing linear fitting on the unified UAV spectral information and satellite spectral information to determine a conversion equation between the UAV remote sensing information and the satellite remote sensing information; Based on the conversion equation, the satellite remote sensing information is converted to UAV remote sensing accuracy, and the soil moisture inversion model is called to predict and invert the soil moisture content.

3. The variable irrigation prescription map generation method based on the synergy of unmanned aerial vehicle and satellite remote sensing according to claim 2 is characterized in that: Based on the vegetation coverage at different growth stages, the soil moisture inversion model at different growth stages is constructed according to the UAV remote sensing information, specifically including: During the seedling water stage and the winter irrigation stage, shallow soil moisture content, a first UAV remote sensing image, and a first satellite remote sensing image at multiple points are synchronously acquired on the same day; Using the band information in the first UAV remote sensing image as a first independent variable, using the shallow soil moisture content as a first dependent variable, and screening out the first UAV spectral information related to the soil moisture content from the first UAV remote sensing image; Taking the first independent variable and the first dependent variable as input data and the first UAV spectral information as output data, constructing a soil moisture inversion model in the seedling water stage and the winter irrigation stage; During the greening and jointing water stage, the pre-flowering water stage, and the filling water stage, the soil moisture content, the second UAV remote sensing image, and the second satellite remote sensing image at multiple points are synchronously acquired on the same day; Determine a spectral index representing the moisture content of the plant according to the band information in the second UAV remote sensing image; Using the band information in the second UAV remote sensing image and the spectral index characterizing the plant moisture content as the second independent variable, using the soil moisture content as the second dependent variable, and screening out the second UAV spectral information related to the soil moisture content from the second UAV remote sensing image; Taking the second independent variable and the second dependent variable as input data and the second UAV spectral information as output data, an inversion model of soil moisture content in the greening and jointing water stage, the pre-flowering water stage and the filling water stage is constructed.

4. The variable irrigation prescription map generation method based on the synergy of unmanned aerial vehicle and satellite remote sensing according to claim 2 is characterized in that: Based on the satellite remote sensing information before the crops enter the critical water demand period of the crops, the spatial distribution map of soil moisture content within the irrigation area of ​​the sprinkler irrigation cluster is determined according to the soil moisture inversion model, specifically including: Acquire satellite remote sensing images of crops before they enter a critical water demand period, and select satellite remote sensing images corresponding to the closest satellite transit date that satisfies clear weather and no cloud cover conditions; Extracting spectral data of the satellite remote sensing image, and transforming the spectral data based on the transformation equation to determine the transformed spectral data; Inputting the converted spectral data into the soil moisture inversion model to predict the soil moisture content within the irrigation area of ​​the inversion sprinkler irrigation cluster; Based on the soil moisture content in the irrigation area of ​​the sprinkler irrigation cluster, ArcGIS software is used to draw a spatial distribution map of the soil moisture content in the irrigation area of ​​the sprinkler irrigation cluster.

5. The variable irrigation prescription map generation method based on the synergy of unmanned aerial vehicle and satellite remote sensing according to claim 2 is characterized in that: Based on the irrigation information, the water deficit spatial distribution map in the control area of ​​the single sprinkler irrigation equipment is determined according to the soil moisture inversion model and the soil moisture spatial distribution map, specifically including: If the water supply capacity of the water source cannot simultaneously meet the irrigation needs of all sprinkler irrigation equipment, the average soil moisture content in the control area of ​​a single sprinkler irrigation equipment is calculated based on the spatial distribution map of soil moisture content; Sorting the average soil moisture content in the area controlled by the single sprinkler irrigation equipment, and formulating a rotation irrigation system for the sprinkler irrigation cluster according to the sorting results; Based on the rotation irrigation system, within the area controlled by a single sprinkler irrigation device that needs to be irrigated, if there is valid data when the satellite passes over on the day of irrigation or two days before irrigation, based on the conversion equation, the spectral data corresponding to the valid data is converted, and the converted spectral data is input into the soil moisture inversion model to predict the first soil moisture content within the inverted irrigation area; the irrigation area is the area controlled by a single irrigation device; Calculating a first difference between the first soil moisture content and an upper limit of irrigation, and generating a spatial distribution map of water deficit in the irrigation area according to the first difference; If the satellite data are invalid due to weather factors, the required spectral data are screened out based on the spectral data of the soil or canopy obtained within a set time period using a drone remote sensing system two days before irrigation or on the day of irrigation; Inputting the required spectral data into the soil moisture inversion model to predict a second soil moisture content in the inverted irrigation area; Calculating a second difference between the second soil moisture content and the irrigation upper limit, and generating a water deficit spatial distribution map of the irrigation area according to the second difference; If the water supply capacity of the water source can simultaneously meet the irrigation needs of all sprinkler irrigation equipment, the difference between the third soil moisture content and the irrigation upper limit is calculated according to the spatial distribution map of soil moisture content to generate a spatial distribution map of water deficit in the irrigation area; the first soil moisture content, the second soil moisture content and the third moisture content are the soil moisture contents within the planned moist layer.

6. The variable irrigation prescription map generation method based on the synergy of unmanned aerial vehicle and satellite remote sensing according to claim 1 is characterized in that: Based on the irrigation management strategy, a variable irrigation prescription map within the control area of ​​a single sprinkler irrigation device is determined according to the water deficit spatial distribution map, specifically including: Based on the irrigation management strategy, according to the water deficit spatial distribution map and the range of the sprinkler irrigation equipment, the control area of ​​each sprinkler irrigation equipment is divided into rectangular grids of different lengths or fan-shaped grids of different angles along the travel direction as variable irrigation management areas that are greater than the range of the sprinkler nozzle; Based on the water deficit spatial distribution map, calculating the average soil moisture deficit of each variable irrigation management area; Determining the irrigation amount of each variable irrigation management area according to the average soil moisture deficit and the depth of the planned moist layer; A variable irrigation prescription map within the control area of ​​a single sprinkler irrigation device is determined according to the irrigation amount and the travel speed of each sprinkler irrigation device.

7. A variable irrigation prescription map generation device based on the synergy of unmanned aerial vehicles and satellite remote sensing, characterized in that: The variable irrigation prescription map generation device based on the synergy of unmanned aerial vehicle and satellite remote sensing includes: The irrigation management strategy formulation module is used to formulate the irrigation management strategy of the sprinkler cluster based on the critical water demand period of the crop; the critical water demand period of the crop occurs in five growth stages, which include the seedling water stage, winter irrigation stage, greening and jointing water stage, pre-flowering water stage and grain filling water stage; the irrigation management strategy includes whether irrigation is required in each growth stage and the amount of irrigation water; the amount of irrigation water is determined according to the soil moisture content determined by the effective rainfall and crop water consumption in different growth stages and the spatial variation of the soil moisture deficit caused by the spatial variation of soil physical and chemical properties; A prediction and inversion module is used to construct an inversion model of soil moisture content at different growth stages based on UAV remote sensing information, and predict and invert the soil moisture content at different growth stages based on a conversion equation between the UAV remote sensing information and satellite remote sensing information; A soil moisture content spatial distribution map determination module is used to determine the soil moisture content spatial distribution map within the irrigation area of ​​the sprinkler irrigation cluster according to the soil moisture content inversion model based on satellite remote sensing information before the crop enters the critical water demand period of the crop; the satellite remote sensing information includes spectral data; A water deficit spatial distribution map determination module is used to determine the water deficit spatial distribution map within the control area of ​​a single sprinkler irrigation device based on irrigation information, the soil moisture inversion model and the soil moisture spatial distribution map; the irrigation information includes water supply capacity of the water source, irrigation demand, valid data when the satellite passes by, and weather factors; the valid data when the satellite passes by is a satellite spectral image with clear weather and no cloud cover; The variable irrigation prescription map determination module is used to determine the variable irrigation prescription map within the control area of ​​a single sprinkler irrigation device based on the irrigation management strategy and the water deficit spatial distribution map; the variable irrigation prescription map includes the travel speed and irrigation volume of the sprinkler irrigation device.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the variable irrigation prescription map generation method based on the synergy of unmanned aerial vehicle and satellite remote sensing as described in any one of claims 1 to 6.

9. A 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 generating a variable irrigation prescription map based on the synergy of unmanned aerial vehicle and satellite remote sensing described in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for generating a variable irrigation prescription map based on the synergy of unmanned aerial vehicle and satellite remote sensing described in any one of claims 1 to 6 is implemented.

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