Irrigation calculation method and device for low crops and medium

Abstract

The invention discloses an irrigation calculation method and device for low crops and a medium, and relates to the technical field of agricultural irrigation. The method comprises the steps that image data of an irrigation area and meteorological data in a first preset time period are obtained, the image data comprise remote sensing data and LIDAR data, and an intelligent irrigation model comprises a crop sub-model, a soil sub-model and a terrain sub-model; inputting the remote sensing data into the crop sub-model to obtain crop data, inputting the remote sensing data into the soil sub-model to obtain soil data, and inputting the LIDAR data into the terrain sub-model to obtain terrain data; the crop data, the soil data and the meteorological data are divided and calculated according to preset dimensions through MapReduce, and the total water supply amount of the irrigation area is obtained. According to the method, the terrain gradient obtained through LIDAR data calculation is achieved, after the terrain gradient is combined, a more accurate irrigation strategy is formulated, operation such as feature extraction and classification is carried out on multiple nodes in parallel, and therefore the processing time is remarkably shortened.

Description

Technical Field

[0001] The present application relates to the technical field of agricultural irrigation, and in particular to an irrigation calculation method, equipment and medium for low-growing crops. Background Art

[0002] The formulation of irrigation plans requires the use of modern scientific and technological means. Scientific and reasonable irrigation plans can not only improve the utilization efficiency of water resources, but also promote the growth and development of crops, enhance soil fertility, and reduce agricultural production costs.

[0003] However, existing irrigation schemes often ignore the terrain slope, which may lead to unreasonable irrigation system design, making it impossible to evenly distribute irrigation water to farmland, resulting in over-irrigation in low-lying areas and insufficient irrigation in high-slope areas. In addition, using remote sensing data as a data source, the large amount of data calculation may lead to a long time-consuming process of formulating irrigation plans, and the inability to adjust irrigation plans in time according to the growth of crops and climatic conditions; and may cause crops to lack water or have too much water at critical growth stages, affecting their normal growth and yield; in addition, the inability to simulate the next stage means that it is impossible to predict the risks and problems that may arise in the future.

[0004] Through the above analysis, the problems and defects of the prior art are as follows:

[0005] The irrigation schemes in the existing technology ignore the problem of terrain slope, and use remote sensing data as a data source, which requires a large amount of calculation, takes a long time to calculate, and has a low accuracy rate, resulting in water shortage or excessive water for crops at critical growth stages. Summary of the invention

[0006] The embodiments of the present application provide an irrigation calculation method, device and medium for low-lying crops, which can solve the problem that the irrigation scheme in the prior art ignores the terrain slope, and uses remote sensing data as a data source, which has a large amount of calculation, a long calculation time and a low accuracy rate, resulting in the problem of water shortage or excessive water for crops at critical growth stages.

[0007] In a first aspect, an embodiment of the present application provides an irrigation calculation method for low-lying crops, the method comprising: acquiring image data of an irrigation area and meteorological data within a first preset time period, the image data comprising remote sensing data and LIDAR data, the intelligent irrigation model comprising a crop sub-model, a soil sub-model and a terrain sub-model; inputting remote sensing data into the crop sub-model to obtain crop data, inputting remote sensing data into the soil sub-model to obtain soil data, and inputting LIDAR data into the terrain sub-model to obtain terrain data; dividing and calculating the crop data, soil data and meteorological data according to preset dimensions through MapReduce to obtain the total water supply of the irrigation area.

[0008] In one implementation of the present application, based on the LIDAR data being the three-dimensional point cloud data of the irrigation area, the LIDAR data is input into the terrain sub-model to obtain the terrain data, specifically including: preprocessing the three-dimensional point cloud data and converting it into grid data; calculating the slope value of the grid unit according to the elevation information of the grid data; and statistically analyzing the slope values ​​of the grid units to obtain the slope value of the irrigation area.

[0009] In one implementation of the present application, after obtaining the slope value of the irrigation area, the method further includes: when the slope value is lower than a first preset threshold, using a directional irrigation method; when the slope value is higher than the first preset threshold, using a spraying method.

[0010] In one implementation of the present application, remote sensing data is input into a crop sub-model to obtain crop data, specifically including: converting remote sensing data into surface temperature values; extracting and calculating the surface temperature values ​​during the day and at night to obtain surface temperature difference values; obtaining soil thermal inertia based on the full-band albedo and surface temperature difference values; and obtaining soil moisture content based on soil thermal inertia inversion.

[0011] In one implementation of the present application, remote sensing data is input into a soil sub-model to obtain soil data, specifically including: extracting color features, texture features, and shape features of crops in remote sensing data to obtain the type and growth stage of the crops; counting the number of pixels of the same crop type and growth stage to obtain the actual area of ​​the crops.

[0012] In one implementation of the present application, crop data, soil data, and meteorological data are divided and calculated according to preset dimensions through MapReduce to obtain the total water supply of the irrigation area, specifically including: dividing the actual area according to a preset ratio to obtain irrigation units; obtaining the type and growth stage of the crop, soil moisture content, and meteorological data of the irrigation unit in the Map stage to obtain the unit water supply; and summarizing and verifying the unit water supply in the Reduce stage to obtain the total water supply.

[0013] In one implementation of the present application, after irrigating the irrigation area, the method further includes: irrigating the irrigation area if verification is passed; after the irrigation is completed, obtaining the type and growth stage of the crop and the soil moisture content within a second preset time period to provide feedback on the unit water supply; and based on the feedback, simulating the water demand of the crop at different growth stages.

[0014] In one implementation of the present application, after acquiring image data of the irrigation area and meteorological data within a preset time period, the method also includes: aligning and removing noise from the three-dimensional point cloud data; and based on the remote sensing data including the remote sensing image, performing radiation correction, atmospheric correction and geometric correction on the remote sensing image.

[0015] In a second aspect, an embodiment of the present application further provides an irrigation computing device for low-lying crops, the device comprising at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: obtain image data of the irrigation area and meteorological data within a first preset time period, the image data comprising remote sensing data and LIDAR data, the intelligent irrigation model comprising a crop sub-model, a soil sub-model and a terrain sub-model; input the remote sensing data into the crop sub-model to obtain crop data, input the remote sensing data into the soil sub-model to obtain soil data, and input the LIDAR data into the terrain sub-model to obtain terrain data; and divide and calculate the crop data, soil data and meteorological data according to preset dimensions through MapReduce to obtain the total water supply of the irrigation area.

[0016] In a third aspect, an embodiment of the present application further provides a non-volatile computer storage medium for irrigation calculations for low crops, storing computer executable instructions, wherein the computer executable instructions are configured to: obtain image data of the irrigation area and meteorological data within a first preset time period, the image data including remote sensing data and LIDAR data, the intelligent irrigation model including a crop sub-model, a soil sub-model and a terrain sub-model; input remote sensing data into the crop sub-model to obtain crop data, input remote sensing data into the soil sub-model to obtain soil data, input LIDAR data into the terrain sub-model to obtain terrain data; divide and calculate the crop data, soil data and meteorological data according to preset dimensions through MapReduce to obtain the total water supply of the irrigation area.

[0017] The embodiments of the present application provide an irrigation calculation method, device and medium for low-lying crops. After considering the terrain slope, a more accurate irrigation strategy can be formulated to avoid water accumulation in low-lying areas due to poor drainage, while ensuring that highlands can also get enough water, thereby reducing the waste of water resources, improving irrigation efficiency, and reducing agricultural water costs; the terrain slope information calculated through LIDAR data can more accurately understand the terrain characteristics of the irrigation area, thereby formulating an irrigation strategy that better meets actual needs; MapReduce can divide image data into multiple small blocks, and perform feature extraction, classification and other operations in parallel on multiple nodes, thereby significantly reducing processing time. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0019] Figure 1A flowchart of an irrigation calculation method for low-growing crops provided in an embodiment of the present application;

[0020] Figure 2 A schematic diagram of the internal structure of an irrigation computing device for short crops provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. 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 making creative work are within the scope of protection of the present application.

[0022] The embodiments of the present application provide an irrigation calculation method, device and medium for low-growing crops, which solve the problem that the irrigation scheme in the prior art ignores the terrain slope, and the use of remote sensing data as a data source has a large amount of calculation, resulting in the problem of water shortage or excessive water for crops at critical growth stages.

[0023] The technical solution proposed in the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0024] Figure 1 This is a flow chart of an irrigation calculation method for low-growing crops provided in an embodiment of the present application. Figure 1 As shown, an irrigation calculation method for low-growing crops provided in an embodiment of the present application specifically includes the following steps:

[0025] Step 10: Acquire image data of the irrigation area and meteorological data within a first preset time period, the image data includes remote sensing data and LIDAR data, and the intelligent irrigation model includes a crop sub-model, a soil sub-model and a terrain sub-model.

[0026] In the embodiment of the present application, due to the consideration of the terrain slope factor, only the irrigation plan for short crops is optimized and calculated. Tall crops may affect the elevation information of the ground due to their own growth. The amount of remote sensing data and LIDAR data is huge, and MapReduce is used for calculation. In the MapReduce calculation process, the calculation task is first decomposed, and each sub-model can be regarded as a Map. Then, the calculation results of the sub-models are merged and analyzed in the Reduce operation.

[0027] Step 20: Input the remote sensing data into the crop sub-model to obtain crop data, input the remote sensing data into the soil sub-model to obtain soil data, and input the LIDAR data into the terrain sub-model to obtain terrain data.

[0028] In this step, the amount of remote sensing data and LIDAR data is huge, so a distributed parallel computing method is adopted to enable the three sub-models (ie, three MAPs) to perform calculations simultaneously.

[0029] As an optional embodiment, based on the LIDAR data being the three-dimensional point cloud data of the irrigation area, the LIDAR data is input into the terrain sub-model to obtain the terrain data, which may specifically include: Step 2011: preprocessing the three-dimensional point cloud data and converting it into grid data; calculating the slope value of the grid unit according to the elevation information of the grid data; and performing statistics on the slope values ​​of the grid units to obtain the slope value of the irrigation area.

[0030] In this step, LIDAR technology uses laser beams to accurately measure the surface, which can obtain three-dimensional coordinate information of the surface and provide more accurate and detailed terrain data. LIDAR data contains a large amount of discrete three-dimensional point cloud data. It will be very time-consuming to directly calculate the slope of such a large amount of three-dimensional point cloud data. Therefore, the three-dimensional point cloud data can be converted into regular grid data. Each grid cell contains an elevation value. Then, the slope value of the grid cell can be calculated by the elevation difference between adjacent grid cells and the distance between them. The slope values ​​of the grid cells are counted, the highest and lowest values ​​are removed, and the remaining data is averaged to obtain the slope value of the target area, which simplifies the data structure and calculation complexity.

[0031] For example, Point 1: (x = 0, y = 0, z = 100), Point 2: (x = 10, y = 0, z = 101), Point 3: (x = 0, y = 10, z = 99), Point 4: (x = 10, y = 10, z = 100), 10x10 meter grid, Grid Cell 1 (0-10m, 0-10m): z = 100 (based on Point 1), Grid Cell 2 (10-20m, 0-10m): z = 101 (based on Point 2), Grid Cell 3 (0-10m, 10-20m): z = 99 (based on Point 3), Grid Cell 4 (10-20m, 10-20m): z = 100 (based on Point 4); calculate Grid Cell 1 to Grid Cell 2 (along the x-axis direction): Slope = arctan (Δz / Δx), where Δz = 101-100 = 1 meter, Δx = 10 meters, Slope = arctan (101) ≈ 5.71 degrees. The calculations can be performed in sequence to obtain the slope value of the target area.

[0032] Further, after obtaining the slope value of the irrigation area, the method may further include: Step 2012: when the slope value is lower than a first preset threshold, adopting a directional irrigation method;

[0033] In this step, if the slope value is less than 30 degrees, for example, the water outlet of the water pipe can be fixed on the ground and flow directly to the roots of the plants.

[0034] Step 2014: When the slope value is higher than the first preset threshold, a spraying method is adopted.

[0035] In this step, for example, if the slope value is higher than 30 degrees, the water outlet of the water pipe can be set to a spraying shape, and one can be set at a certain distance to form a more uniform surface coverage.

[0036] As another optional embodiment, the remote sensing data is input into the soil sub-model to obtain soil data, which may specifically include: Step 2021: converting the remote sensing data into surface temperature values; Step 2022: extracting and calculating the surface temperature values ​​during the day and at night respectively to obtain the surface temperature difference value; Step 2023: obtaining the soil thermal inertia based on the full-band albedo and the surface temperature difference value; Step 2024: obtaining the soil moisture content based on the soil thermal inertia inversion.

[0037] In this step, the formula of soil thermal inertia is P = (KQc)^1 / 2, where P is thermal inertia, K is thermal conductivity, Q is soil density, and c is specific heat. However, since these parameters are difficult to obtain directly, apparent thermal inertia ATI is usually used instead. The formula of apparent thermal inertia ATI = (1-A) / (Td-Tn) is used, where A is the full-band albedo, Td and Tn are the surface temperatures during the day and at night, respectively. By using the known difference in surface temperature ΔT between day and night and the full-band albedo A, in the embodiment of the present application, the remote sensing image is a corrected remote sensing image, and the pixel value corresponding to the atmospheric corrected image is the reflectivity, so the ATI value can be calculated. The soil with large thermal inertia has high water content, and the soil with small thermal inertia has low water content. Therefore, the soil water content is obtained by inverting the soil thermal inertia. It is worth noting that this method is suitable for bare soil or soil with low vegetation cover.

[0038] For example, the daytime surface temperature Td = 30 degrees, the nighttime surface temperature Tn = 20 degrees, the full-band albedo A = 0.2, the surface temperature difference value is 10 degrees, ATI = (1-A) / (Td-Tn) = 0.08, and the ATI value is 0.08, which is relative to a lower ATI value, that is, the soil moisture content is relatively low.

[0039] As another optional embodiment, the remote sensing data is input into the crop sub-model to obtain the crop data, which may specifically include: Step 2031: extracting the color features, texture features and shape features of the crops in the remote sensing data to obtain the type and growth stage of the crops; Step 2032: counting the number of pixels of the same crop type and growth stage to obtain the actual area of ​​the crops.

[0040] In this step, the spectral information of crops in remote sensing images, namely the color characteristics, texture characteristics and shape characteristics of crops, is used to identify different crop types and their growth stages. Through spectral analysis, different crops and different growth stages have specific reflectivity characteristics in different bands. The crop types, such as wheat, corn, rice, etc., are obtained, and their growth stages, such as seedling stage, growth stage, maturity stage, etc., are estimated. The pixels are counted separately to obtain the area, which is more conducive to calculating the total water supply.

[0041] For example, wheat usually presents a lighter green color in the early stage of growth, and gradually deepens as it grows. In remote sensing images, this can be determined by calculating the reflectivity intensity of the green band (G) to determine whether it belongs to wheat, corn or other categories, and counting the number of pixels classified as wheat and corn. Assuming that the resolution of the remote sensing image is 1 meter x 1 meter, then the actual area represented by each pixel is 1 square meter. Multiply the number of pixels belonging to the same crop type by the area represented by each pixel to get the actual area of ​​the crop type. Assuming that the classification results show that the number of wheat pixels is 10,000 and the number of corn pixels is 8,000, and the remote sensing image resolution is 1 meter x 1 meter, the actual area of ​​wheat = 10,000*1 square meter = 10,000 square meters, and the actual area of ​​corn = 8,000*1 square meter = 8,000 square meters.

[0042] Step 30: Use MapReduce to divide and calculate the crop data, soil data, and meteorological data according to the preset dimensions to obtain the total water supply of the irrigation area.

[0043] In this step, the amount of crop data, soil data and meteorological data is huge, so distributed parallel computing is performed again and divided according to preset dimensions. The preset dimensions can be the size of the crop area, date or temperature, which is further explained below.

[0044] As another optional embodiment, crop data, soil data and meteorological data are divided and calculated according to preset dimensions through MapReduce to obtain the total water supply of the irrigation area, which may specifically include: Step 301: Divide the actual area according to a preset ratio to obtain irrigation units; Step 302: Obtain the type and growth stage of the crop, soil moisture content and meteorological data of the irrigation unit in the Map stage to obtain the unit water supply; Step 303: Summarize and verify the unit water supply in the Reduce stage.

[0045] In this step, the preset dimension is a preset proportion of the actual area. That is to say, in order to reduce the amount of calculation, a large irrigation area is divided into small irrigation units, which are then summarized. At the same time, the unit water supply of similar irrigation units can be compared to verify the accuracy of the water supply.

[0046] Furthermore, after irrigating the irrigation area, the method may further include:

[0047] Step 304: If the verification is passed, irrigate the irrigation area. Step 305: After the irrigation is completed, obtain the type and growth stage of the crop and the soil moisture content in the second preset time period to provide feedback on the water supply; Step 306: Based on the feedback, simulate the water demand of the crop at different growth stages.

[0048] In this step, the collected data is compared with the preset soil moisture to evaluate the irrigation effect. If the irrigation effect is not good, such as the soil moisture is too low or too high, the water supply needs to be adjusted.

[0049] It is understandable that after acquiring the image data of the irrigation area and the meteorological data within a preset time period, the method may further include: Step 01: registering and removing noise from the three-dimensional point cloud data;

[0050] In this step, 3D point cloud data registration is the process of unifying point cloud data obtained from different perspectives, different times or different sensors into the same coordinate system; distance-based filtering can be used to identify noise points, and based on the identification results, the noise points are deleted from the 3D point cloud data or replaced with the average value of the neighboring points.

[0051] Step 02: Based on the remote sensing data including remote sensing images, perform radiation correction, atmospheric correction and geometric correction on the remote sensing images.

[0052] In this step, the remote sensing image may include a main remote sensing image and an auxiliary image of the target irrigation area. The auxiliary image is used to assist in correction. Radiation correction is to eliminate radiation errors in remote sensing images caused by sensors or external environmental factors. Atmospheric correction is to eliminate the influence of the atmosphere on remote sensing images, such as gas absorption and scattering, to obtain the true reflectivity or radiance information of the surface. Geometric correction is to eliminate geometric distortion in remote sensing images. Further cropping can be performed to determine the image of the irrigation area.

[0053] The above is an embodiment of the method proposed in this application. Based on the same inventive concept, this application embodiment also provides an irrigation calculation device for low-growing crops, the structure of which is as follows: Figure 2 shown.

[0054] Figure 2The internal structure diagram of an irrigation computing device for low-growing crops provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, the device includes:

[0055] at least one processor 201;

[0056] and, a memory 202 communicatively connected to the at least one processor;

[0057] Among them, the memory 202 stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor 201 so that the at least one processor 201 can: obtain image data of the irrigation area and meteorological data within a first preset time period, the image data includes remote sensing data and LIDAR data, and the intelligent irrigation model includes a crop sub-model, a soil sub-model and a terrain sub-model; input the remote sensing data into the crop sub-model to obtain crop data, input the remote sensing data into the soil sub-model to obtain soil data, and input the LIDAR data into the terrain sub-model to obtain terrain data; divide and calculate the crop data, soil data and meteorological data according to preset dimensions through MapReduce to obtain the total water supply of the irrigation area.

[0058] Some embodiments of the present application provide corresponding Figure 1 A non-volatile computer storage medium for irrigation calculation of low crops stores computer executable instructions, wherein the computer executable instructions are configured to: obtain image data of an irrigation area and meteorological data within a first preset time period, wherein the image data includes remote sensing data and LIDAR data, and an intelligent irrigation model includes a crop sub-model, a soil sub-model, and a terrain sub-model; input remote sensing data into the crop sub-model to obtain crop data, input remote sensing data into the soil sub-model to obtain soil data, and input LIDAR data into the terrain sub-model to obtain terrain data; and divide and calculate the crop data, soil data, and meteorological data according to preset dimensions through MapReduce to obtain the total water supply of the irrigation area.

[0059] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the IoT device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0060] The system and medium provided in the embodiments of the present application correspond one-to-one to the method. Therefore, the system and medium also have similar beneficial technical effects to the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the system and medium will not be repeated here.

[0061] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0062] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0063] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0064] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0065] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0066] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0067] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0068] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0069] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for calculating irrigation for low-growing crops, characterized in that: The method comprises: Acquire image data of the irrigation area and meteorological data within a first preset time period, wherein the image data includes remote sensing data and LIDAR data, and the intelligent irrigation model includes a crop sub-model, a soil sub-model, and a terrain sub-model; Inputting the remote sensing data into the crop sub-model to obtain crop data, inputting the remote sensing data into the soil sub-model to obtain soil data, and inputting the LIDAR data into the terrain sub-model to obtain terrain data; The crop data, soil data and meteorological data are divided and calculated according to preset dimensions through MapReduce to obtain the total water supply of the irrigation area.

2. The irrigation calculation method for low-growing crops according to claim 1, characterized in that: Based on the LIDAR data being three-dimensional point cloud data of the irrigation area, the LIDAR data is input into the terrain sub-model to obtain terrain data, specifically including: Preprocessing the three-dimensional point cloud data and converting it into grid data; Calculating the slope value of the grid unit according to the elevation information of the grid data; The slope values ​​of the grid cells are counted to obtain the slope value of the irrigation area.

3. The irrigation calculation method for low-growing crops according to claim 2, characterized in that: After obtaining the slope value of the irrigation area, the method further includes: When the slope value is lower than a first preset threshold, a directional irrigation method is adopted; When the slope value is higher than a first preset threshold, a spraying method is adopted.

4. The irrigation calculation method for low-growing crops according to claim 1, characterized in that: Inputting the remote sensing data into the soil sub-model to obtain soil data specifically includes: Converting the remote sensing data into surface temperature values; Extract the surface temperature values ​​during the day and at night respectively and calculate them to obtain the surface temperature difference value; The soil thermal inertia is obtained according to the full-band albedo and the surface temperature difference value; The soil moisture content is obtained by inverting the soil thermal inertia.

5. The irrigation calculation method for low-growing crops according to claim 1, characterized in that: Inputting the remote sensing data into the crop sub-model to obtain crop data specifically includes: Extracting color features, texture features, and shape features of crops from the remote sensing data to obtain the type and growth stage of the crops; The number of pixels of the same crop type and growth stage is counted to obtain the actual area of ​​the crop.

6. The irrigation calculation method for low-growing crops according to claim 5, characterized in that: The method of calculating the crop data, soil data and meteorological data according to preset dimensions by MapReduce to obtain the total water supply of the irrigation area specifically includes: Dividing the actual area according to a preset ratio to obtain irrigation units; In the Map stage, the type and growth stage of the crop in the irrigation unit, soil moisture content and meteorological data are obtained to obtain the unit water supply; In the Reduce phase, the unit water supply is summarized and verified to obtain the total water supply.

7. The irrigation calculation method for low-growing crops according to claim 6, characterized in that: After obtaining the total water supply, the method further comprises: If the verification is passed, irrigating the irrigation area; After the irrigation is completed, obtaining the type and growth stage of the crop and the soil moisture content within a second preset time period to provide feedback on the water supply of the unit; Based on the feedback, the water requirement of the crop at different growth stages is simulated.

8. The irrigation calculation method for low-growing crops according to claim 2, characterized in that: After acquiring the image data of the irrigation area and the meteorological data within a preset time period, the method further includes: Performing registration and noise removal on the three-dimensional point cloud data; Based on the remote sensing data including remote sensing images, radiation correction, atmospheric correction and geometric correction are performed on the remote sensing images.

9. An irrigation calculation device for low-lying crops, characterized in that: The device comprises: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to: Acquire image data of the irrigation area and meteorological data within a first preset time period, wherein the image data includes remote sensing data and LIDAR data, and the intelligent irrigation model includes a crop sub-model, a soil sub-model, and a terrain sub-model; Inputting the remote sensing data into the crop sub-model to obtain crop data, inputting the remote sensing data into the soil sub-model to obtain soil data, and inputting the LIDAR data into the terrain sub-model to obtain terrain data; The crop data, soil data and meteorological data are divided and calculated according to preset dimensions through MapReduce to obtain the total water supply of the irrigation area.

10. A non-volatile computer storage medium for irrigation calculations for low-growing crops, storing computer executable instructions, characterized in that: The computer executable instructions are configured to: Acquire image data of the irrigation area and meteorological data within a first preset time period, wherein the image data includes remote sensing data and LIDAR data, and the intelligent irrigation model includes a crop sub-model, a soil sub-model, and a terrain sub-model; Inputting the remote sensing data into the crop sub-model to obtain crop data, inputting the remote sensing data into the soil sub-model to obtain soil data, and inputting the LIDAR data into the terrain sub-model to obtain terrain data; The crop data, soil data and meteorological data are divided and calculated according to preset dimensions through MapReduce to obtain the total water supply of the irrigation area.

Images