Method for dividing agricultural area management partitions, storage medium and processor

Abstract

Embodiments of the present application provide a method for dividing agricultural area management partitions, a storage medium and a processor. The method comprises: obtaining crop information of a current crop planted in an agricultural area, the crop information comprising a crop variety, a planting method of the crop variety and rice type information; dividing the agricultural area into a plurality of first-level management partitions according to the crop information; determining soil information and ground elevation information of each first-level management partition; determining a previous crop variety of each first-level management partition; establishing a plurality of grids corresponding to each first-level management partition and extracting grid data of each grid, the grid data comprising the previous crop variety, the soil information and the ground elevation information; and clustering and classifying the grid data of each first-level partition based on a preset clustering algorithm to obtain second-level management partitions of the agricultural area, so as to formulate corresponding farm work according to the second-level management partitions. The agricultural area is divided into two management partitions, and the fine degree of partition management is improved.

Description

Technical Field

[0001] This application relates to the agricultural field, specifically to a method, apparatus, storage medium, and processor for dividing agricultural area management zones. Background Technology

[0002] The yield of food crops is closely related to national food security and individual living standards. Therefore, it is particularly important to implement precise zoning management of farmland in order to maximize production benefits with minimal production input.

[0003] Existing technical solutions typically use soil nutrient data and remote sensing data to manage and zone farmland. However, this management and zoning method fails to take into account the actual situation, such as the current crop varieties, rice types, planting methods, and previous crops. As a result, it often fails to truly guide farmers in actual agricultural operations, such as the quantitative input of water, fertilizer, and pesticides, and the unified arrangement of sowing, transplanting, and harvesting operations for each management zone, thereby improving the overall coordination and efficiency of farm operations. Summary of the Invention

[0004] The purpose of this application is to provide a method, apparatus, storage medium, and processor for dividing agricultural area management zones.

[0005] To achieve the above objectives, the first aspect of this application provides a method for dividing agricultural regional management zones, comprising:

[0006] The process involves: acquiring information on currently planted crops within the agricultural area, including crop varieties, planting methods, and rice cultivation types; dividing the agricultural area into multiple primary management zones based on this information; determining the soil and ground elevation information for each primary management zone; identifying the previous crop variety for each primary management zone; establishing multiple grids corresponding to each primary management zone and extracting grid data for each grid, including the previous crop variety, soil information, and ground elevation information; and clustering and classifying the grid data for each primary management zone using a pre-defined clustering algorithm to obtain secondary management zones for the agricultural area, thereby enabling the development of corresponding agricultural operations based on these secondary management zones.

[0007] In the embodiments of this application, determining the previous crop variety for each primary management zone includes: determining the target historical time period for each primary management zone based on the crop variety currently planted in each primary management zone; acquiring historical remote sensing images of each primary management zone during the corresponding target historical time period; determining the remote sensing vegetation index for each primary management zone based on the historical remote sensing images; and determining the previous crop variety for each primary management zone based on the remote sensing vegetation index.

[0008] In the embodiments of this application, the target value range of the remote sensing vegetation index corresponding to the same crop type in different target historical time periods is different. Determining the previous crop variety of each first-level management zone based on the remote sensing vegetation index includes: for each target historical time period, determining the actual vegetation index of each first-level management zone in the target historical time period; for each first-level management zone, determining the crop type corresponding to the target value range of the actual vegetation index of the first-level management zone as the previous crop variety of the first-level management zone.

[0009] In the embodiments of this application, determining the soil information and ground elevation information of each primary management zone includes: establishing a boundary file for each primary management zone using a geographic information system; and determining the soil information and ground elevation information of each primary management zone based on the boundary file.

[0010] In the embodiments of this application, determining the soil information of each primary management zone includes: obtaining soil samples from each primary management zone; measuring the nutrient parameters and physical parameters of the soil samples to determine the soil information of each primary management zone. The nutrient parameters include organic matter, available nitrogen, available phosphorus, available potassium, and soil pH value, and the physical parameters include bulk density and particle composition.

[0011] In embodiments of this application, the method further includes: acquiring three-dimensional point cloud data of agricultural areas collected by drones; determining field elevation information of agricultural areas based on the three-dimensional point cloud data; and determining ground elevation information of each primary management zone based on the field elevation information.

[0012] In the embodiments of this application, clustering and classifying the grid data of each primary management partition based on a preset clustering algorithm to obtain the secondary management partitions of the agricultural area includes: clustering and classifying the grid data of each primary management partition based on the k-means algorithm to obtain the secondary management partitions of the agricultural area, wherein the value of k in the k-means algorithm is determined using the elbow rule.

[0013] A second aspect of this application provides a processor configured to perform the method described above for dividing agricultural area management zones.

[0014] A third aspect of this application provides an apparatus for dividing agricultural area management zones, including a processor configured to perform the above-described method for dividing agricultural area management zones.

[0015] A fourth aspect of this application provides a machine-readable storage medium storing instructions that, when executed by a processor, configure the processor to perform the aforementioned method for dividing agricultural area management zones.

[0016] The above technical solution enables precise zoning of agricultural areas, allowing farmers to perform quantitative operations within each zone, thereby improving the accuracy of agricultural operations and the overall coordination and efficiency of agricultural operations. Furthermore, by first dividing the area into primary management zones based on current crop information, and then further subdividing each primary zone into secondary management zones, the solution takes into account information such as the current crop variety, rice type, planting method, and previous crop variety. This zoning method, combined with current practical factors, makes the zoning more precise and provides greater guidance.

[0017] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description

[0018] The accompanying drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the following detailed description to explain the embodiments of this application, but do not constitute a limitation on the embodiments of this application. In the drawings:

[0019] Figure 1 The illustration shows a flowchart of a method for dividing agricultural area management zones according to an embodiment of this application;

[0020] Figure 2 The diagram illustrates the relationship between k and the cost function according to an embodiment of this application.

[0021] Figure 3 The diagram illustrates the internal structure of a computer device according to an embodiment of this application. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this application and are not intended to limit the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0023] Figure 1 The illustration schematically depicts a flowchart of a method for dividing agricultural area management zones according to an embodiment of this application. For example... Figure 1 As shown in one embodiment of this application, a method for dividing agricultural area management zones is provided, comprising the following steps:

[0024] Step 101: Obtain information on the crops currently planted within the agricultural area. The crop information includes crop varieties, planting methods, and rice cultivation types.

[0025] Step 102: Based on crop information, divide the agricultural area into multiple primary management zones.

[0026] Step 103: Determine the soil information and ground elevation information for each primary management zone.

[0027] Step 104: Determine the preceding crop variety for each primary management zone.

[0028] Step 105: Establish multiple grids corresponding to each primary management zone, and extract grid data for each grid. The grid data includes the previous crop variety, soil information, and ground elevation information.

[0029] Step 106: Cluster and classify the grid data of each primary management zone based on a preset clustering algorithm to obtain secondary management zones of agricultural areas, and formulate corresponding agricultural operations based on the secondary management zones.

[0030] Crops refer to agricultural crops, such as rice and wheat. In this technical solution, the currently planted crop can refer to rice. Taking rice as an example, the crop variety in the crop information refers to the variety of rice. Transplanting method refers to the cultivation method; the transplanting method for crop varieties can refer to the transplanting method for rice. Generally, rice transplanting methods include direct seeding, transplanting, and broadcasting. Direct seeding refers to a cultivation method where seeds are sown directly in the field without seedling raising or transplanting. Direct seeding can be divided into direct seeding in paddy fields and direct seeding in dry fields. Based on the degree of mechanization, direct seeding can also be divided into manual sowing and mechanical sowing. Transplanting refers to planting seedlings into paddy fields, or transplanting seedlings from nursery beds to paddy fields; transplanting provides more growing space for the seedlings. Broadcasting refers to throwing seeds that have not yet fully grown into seedlings into the field by hand, or throwing seedlings into the paddy field before they have grown large enough. Rice cropping type refers to the rice cropping system, which is the system of classifying the same farmland according to the number of times it can be planted and harvested in a year. Rice cropping types can be divided into single-season mid-season rice, single-season late-season rice, double-season early-season rice, double-season late-season rice, ratooning rice, etc. After obtaining the information on the currently planted crop, the agricultural area is divided into multiple primary management zones based on the crop information. That is, after obtaining the variety, planting method, and rice cropping type of the currently planted rice, the agricultural area is divided into multiple primary management zones according to: Variety Type 1 * Planting Method * Rice Cropping Type + ... + Variety Type N * Planting Method * Rice Cropping Type. For example, an agricultural area is planted with three varieties of rice, including Meixiangzhan 2, Quanyou Huazhan, and Quanyou Simiao, all of which are single-season mid-season rice. Among them, Meixiangzhan 2 is planted by direct seeding, while Quanyou Huazhan and Quanyou Simiao are planted by transplanting. Therefore, the farm can be divided into first-level management zones according to the following sequence: Meixiangzhan No. 2 * transplanting * single-season medium-grain rice + Quanyou Huazhan * transplanting * single-season medium-grain rice + Quanyou Simiao * transplanting * single-season medium-grain rice. The number of first-level zones is 1*1*1 + 1*1*1 + 1*1*1 = 3, meaning a total of 3 first-level management zones. Management zoning, which utilizes multi-source data for scientific farmland management, is a crucial foundation for precision agriculture. Precise management zoning allows for the control of variable inputs into farmland. Scientific and rational zoning guides farmers in applying fertilizers according to the zoning units, effectively improving agricultural economic efficiency. It not only enhances the productive potential of arable land but also accelerates the efficient and sustainable use of arable land, which is of great significance for increasing farmers' income and ensuring food security.

[0031] After dividing the land into primary management zones, it is necessary to determine the soil information and ground elevation information for each zone. Ground elevation refers to the vertical height of a point on the ground relative to the Yellow Sea level; this information is equivalent to altitude. Next, the preceding crop variety for each primary management zone needs to be determined. In crop rotation, different types and varieties of crops are planted on the same plot of land to ensure a reasonable combination and connection. For example, if soybeans are planted after wheat, wheat is the preceding crop, and soybeans are the following crop.

[0032] After determining the soil information, ground elevation information, and previous crop variety for each primary management zone, a grid corresponding to each primary management zone is established, and grid data for each grid is extracted. This grid data includes the previous crop variety, soil information, and ground elevation information. The grid can be established using GIS software (Geographic Information System), and the grid size can be set according to requirements. In this technical solution, the grid size can be set to 5m*5m. Then, the grid data is clustered and classified based on a preset clustering algorithm to obtain the secondary management zones of the agricultural area. Smaller sub-zones can be merged into larger sub-zones after classification, rather than being treated as separate sub-zones, to avoid operational difficulties due to fragmented sub-zones. The definition of whether a sub-zone area is too small depends on the specific area of ​​the agricultural region and the availability of agricultural machinery. For example, a sub-zone area less than 100 square meters can be considered too small. Finally, corresponding agricultural operations are formulated based on the secondary management zones, such as fertilization, irrigation, and pesticide application, including specific operation times, application rates, and application methods.

[0033] In this application, based on the crop varieties, planting methods, and rice cultivation types currently planted in the agricultural area, the agricultural area is divided into primary management zones. Then, based on the primary management zones, the agricultural area is further divided into secondary management zones according to the soil information, ground elevation information, and previous crop varieties of each primary management zone. This two-stage management zoning method can combine more practical factors of the agricultural area, and divide areas with similar factors into one management zone as much as possible. This makes the management zoning of the agricultural area more precise, and farmers can carry out corresponding agricultural operations under this refined management zoning, which can effectively improve the overall production efficiency and benefits of the agricultural area.

[0034] In one embodiment, determining the previous crop variety for each primary management zone includes: determining the target historical time period for each primary management zone based on the currently planted crop variety; and acquiring historical remote sensing images of each primary management zone during the corresponding target historical time period. The target historical time period is selected according to requirements; for example, it could be mid-August to early September of the previous year. Remote sensing images refer to films or photographs that record the electromagnetic wave magnitudes of various ground features. They are mainly divided into aerial photographs and satellite photographs. Furthermore, remote sensing images that can be processed by computers must be digital images, and they can be acquired using remote sensing (RS) technology. Acquiring historical remote sensing images of a primary management zone during the corresponding target historical time period can mean acquiring historical remote sensing images of the primary management zone from mid-August to early September of the previous year using remote sensing technology.

[0035] Then, based on historical remote sensing images, the remote sensing vegetation index of each primary management zone is determined. The vegetation index is an important parameter for crop growth analysis. In the field of remote sensing, the vegetation index is a simple and effective measurement parameter used to characterize the surface vegetation cover and growth status. It is a parameter composed of reflectance factors of different band vegetation-soil systems in a certain form. Its functional relationship with vegetation characteristic parameters is more stable and reliable than that of a single band value. This combination of multi-band reflectance factors is collectively referred to as the vegetation index (or vegetation spectral parameter). Remote sensing vegetation indices include, but are not limited to, NDVI normalized difference vegetation index, NDRE normalized difference red edge vegetation index, and EVI enhanced vegetation index. Among them, the normalized difference vegetation index refers to the quantitative measurement of vegetation growth status by calculating the difference between the near-infrared band and the red band. This index can reflect the health status and growth of vegetation. Its calculation formula (1) is:

[0036] NDVI=(NIR–RED) / (NIR+RED)(1)

[0037] The Normalized Difference Red Edge Vegetation Index (NDRI) is commonly used to monitor crops that have reached maturity. Its calculation formula (2) is as follows:

[0038] NDRE=(NIR–REDEDGE) / (NIR+REDEDGE)(2)

[0039] The enhanced vegetation index is commonly used to analyze regions of Earth with abundant chlorophyll. Since the normalized difference vegetation index is easily affected by soil background and atmospheric interference, the enhanced vegetation index is used to adjust the results of the normalized difference vegetation index in order to reduce these interferences. This index is adapted to atmospheric and soil noise, especially in densely vegetated areas. Its calculation formula (3) is as follows:

[0040] EVI=2.5*((NIR–RED) / ((NIR)+(6*RED)–(7.5*BLUE)+1))(3)

[0041] Wherein, NIR represents the near-infrared reflectance, RED represents the red light reflectance, REDEDGE represents the red-edge reflectance, and BLUE represents the blue light reflectance; all parameter data can be obtained from remote sensing imagery. Based on historical remote sensing imagery of the target for a given period and the above calculation formula, the remote sensing vegetation index for that period can be determined.

[0042] After determining the remote sensing vegetation index, the preceding crop variety for each primary management zone can be identified based on the index. However, because the target range of the remote sensing vegetation index for the same crop type varies across different historical time periods, determining the preceding crop variety for each primary management zone requires: first, determining the actual vegetation index for each primary management zone during the target historical time period; and then, for each primary management zone, identifying the crop variety corresponding to the target range of the actual vegetation index as the preceding crop variety for that zone. Specifically, assuming the current crop is rice, the preceding crops for rice can be determined using the remote sensing vegetation index as winter wheat, rapeseed, and green manure. However, the vegetation index ranges for winter wheat, rapeseed, and green manure differ across different historical time periods throughout the year. Therefore, before calculating the remote sensing vegetation index, it is essential to first determine the target historical time period and the range of vegetation index values ​​for each preceding crop within that period. Specifically, this includes the range of vegetation index values ​​for winter wheat, rapeseed, and green manure within the target historical time period. For example, from November of the previous year to January of the following year, the NDVI value for green manure is approximately 0.3-0.35, for rapeseed it is approximately 0.20-0.25, and for bare land it is approximately 0.2. At this time, the NDVI... 冬小麦 >NDVI 绿肥 >NDVI 油菜 ≥NDVI 裸地 In late March to early April of that year, the NDVI value for winter wheat was approximately 0.65-0.8, for green manure it was approximately 0.65-0.8, for rapeseed it was 0.3-0.6, and for bare soil it was approximately 0.2. 冬小麦 ≈NDVI 绿肥 >NDVI 油菜 >NDVI 裸地Assuming the target historical period is from November of the previous year to January of the following year, the remote sensing vegetation index of each primary zone during this period is calculated. For example, if the NDVI of one primary zone is 0.33, then it can be determined that the previous crop variety for this primary zone was green manure.

[0043] In one embodiment, determining the soil information and ground elevation information of each primary management zone includes: establishing a boundary file for each primary management zone using a Geographic Information System (GIS), and determining the soil information and ground elevation information of each primary management zone based on the boundary file. GIS is a specific and crucial spatial information system that, with the support of computer hardware and software, can collect, store, manage, process, analyze, display, and describe geographic distribution data across the entire or part of the Earth's surface (including the atmosphere). Establishing a boundary file for each primary management zone using a GIS can refer to creating a shapefile boundary file for each primary management zone using GIS. The shapefile boundary file allows for the extraction of soil information and ground elevation information for each primary management zone. The shapefile can be used to describe geometric objects such as points, polylines, and polygons. For example, a shapefile can store the geometric location of spatial objects such as wells, rivers, and lakes. Besides geometric location, the shapefile can also store the attributes of these spatial objects, such as the name of a river or the temperature of a city. The soil information comes from the basic attribute dataset of the China High-Resolution National Soil Information Grid, including data such as organic matter content, cation exchange capacity, total nitrogen content, total phosphorus content, total potassium content, soil texture type, and soil thickness at a 90m resolution.

[0044] Furthermore, to determine the soil information for each primary management zone, soil samples can be manually collected. This involves obtaining soil samples from each zone and measuring their nutrient and physical parameters. The soil sample selection criteria can be determined based on actual needs. In this technical solution, soil parameters can be obtained by collecting soil samples at a density of 1 soil sample / 5 mu (approximately 0.33 hectares) to determine the soil information for each primary management zone. Nutrient parameters include organic matter, available nitrogen, available phosphorus, available potassium, and soil pH. Physical parameters include bulk density and particle size distribution. After determining the soil parameters, GIS can be used to perform spatial interpolation on each primary management zone to obtain a soil nutrient map of the agricultural area. Simultaneously, drones can be used to determine the ground elevation information for each primary management zone. This involves acquiring 3D point cloud data of the agricultural area collected by the drone, determining the field elevation information based on the 3D point cloud data, and then determining the ground elevation information for each primary management zone based on the field elevation information. The 3D point cloud refers to a dataset of 3D coordinate points arranged in a regular grid. In the absence of drones, ground elevation information for agricultural areas can be constructed using some commercial satellites available online.

[0045] In one embodiment, such as Figure 2 As shown, a schematic diagram illustrating the relationship between k and the cost function according to an embodiment of this application is presented, including:

[0046] The algorithm uses a pre-defined clustering algorithm to cluster and classify grid data to obtain secondary management zones for agricultural areas. This includes using the k-means algorithm to cluster and classify the grid data to obtain secondary management zones for agricultural areas. The k-means algorithm is an indirect clustering method based on the similarity measure between samples, belonging to unsupervised learning. This algorithm uses k as a parameter to divide n objects into k clusters, ensuring high similarity within clusters and low similarity between clusters. The similarity is calculated based on the average value of objects in a cluster (considered the centroid of the cluster). The algorithm first randomly selects k objects, each representing the centroid of a cluster. For each remaining object, it is assigned to the cluster with the highest similarity based on its distance from the centroids of each cluster. Then, the new centroid of each cluster is calculated, and this process is repeated until the criterion function converges. In this technical solution, the value of k in the k-means algorithm is determined using the elbow rule. The elbow rule is calculated based on the cost function (SSE), which is the sum of the distortion levels of each class. The distortion level of each class is equal to the sum of the squared distances from each variable point to its class center; this function is also known as the squared error function. Regarding the selection of the number of classes, the elbow rule plots the cost function values ​​for different values. As the value increases, the number of samples in each class decreases, thus the samples are closer to their centroid, and the average distortion level decreases. As the value continues to increase, the improvement in the average distortion level continues to decrease. The position corresponding to the largest decrease in the improvement in distortion level during the process of increasing the value is the elbow. Figure 2 As shown, the squared error function decreases rapidly at the beginning, until it begins to level off at the elbow (k=3). Therefore, the value at the elbow can be determined as k=3. The specific calculation formula (4) for the squared error function is as follows:

[0047]

[0048] Among them, C k It is the k-th category, where K is the maximum number of categories (usually around 8-10), and p is C. k The sample points in the data, μ k It is C k The center (C k (mean of all samples)

[0049] This application provides a processor for running a program, wherein the program executes the above-described method for dividing agricultural area management zones.

[0050] This application provides an apparatus for dividing agricultural area management zones, including a processor configured to perform the above-described method for dividing agricultural area management zones.

[0051] This application provides a storage medium storing a program that, when executed by a processor, implements the above-described method for dividing agricultural area management zones.

[0052] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor A01, a network interface A02, memory (not shown), and a database (not shown) connected via a system bus. The processor A01 provides computing and control capabilities. The memory includes internal memory A03 and a non-volatile storage medium A04. The non-volatile storage medium A04 stores an operating system B01, a computer program B02, and a database (not shown). The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 stored in the non-volatile storage medium A04. The database stores data for methods of dividing agricultural area management zones. The network interface A02 communicates with external terminals via a network connection. When the computer program B02 is executed by the processor A01, it implements a method for dividing agricultural area management zones.

[0053] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0054] This application provides an apparatus including a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs the following steps: acquiring information about currently planted crops within an agricultural area, including crop variety, planting method, and rice cultivation type; dividing the agricultural area into multiple primary management zones based on the crop information; determining soil information and ground elevation information for each primary management zone; determining the previous crop variety for each primary management zone; establishing multiple grids corresponding to each primary management zone and extracting grid data for each grid, including the previous crop variety, soil information, and ground elevation information; clustering and classifying the grid data of each primary management zone based on a preset clustering algorithm to obtain secondary management zones for the agricultural area, and formulating corresponding agricultural operations based on the secondary management zones.

[0055] In one embodiment, determining the previous crop variety for each primary management zone includes: determining the target historical time period for each primary management zone based on the crop variety currently planted in each primary management zone; acquiring historical remote sensing images of each primary management zone during the corresponding target historical time period; determining the remote sensing vegetation index for each primary management zone based on the historical remote sensing images; and determining the previous crop variety for each primary management zone based on the remote sensing vegetation index.

[0056] In one embodiment, the target value range of the remote sensing vegetation index for the same crop type differs across different target historical time periods. Determining the previous crop variety for each primary management zone based on the remote sensing vegetation index includes: determining the actual vegetation index of each primary management zone for each target historical time period; and determining the crop type corresponding to the target value range of the actual vegetation index of each primary management zone as the previous crop variety for that primary management zone.

[0057] In one embodiment, determining the soil information and ground elevation information of each primary management zone includes: establishing a boundary file for each primary management zone using a geographic information system; and determining the soil information and ground elevation information of each primary management zone based on the file.

[0058] In one embodiment, determining the soil information for each primary management zone includes: acquiring soil samples from each primary management zone; and measuring the nutrient and physical parameters of the soil samples to determine the soil information for each primary management zone. The nutrient parameters include organic matter, available nitrogen, available phosphorus, available potassium, and soil pH. The physical parameters include bulk density and particle composition.

[0059] In one embodiment, the method further includes: acquiring three-dimensional point cloud data of the agricultural area collected by a drone; determining the field elevation information of the agricultural area based on the three-dimensional point cloud data; and determining the ground elevation information of each primary management zone based on the field elevation information.

[0060] In one embodiment, clustering and classifying the grid data of each primary management partition based on a preset clustering algorithm to obtain the secondary management partitions of the agricultural area includes: clustering and classifying the grid data based on the k-means algorithm to obtain the secondary management partitions of the agricultural area, wherein the value of k in the k-means algorithm is determined using the elbow rule.

[0061] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program with the following method steps: obtaining information on currently planted crops within an agricultural area, including crop variety, planting method, and rice cultivation type; dividing the agricultural area into multiple primary management zones based on the crop information; determining soil information and ground elevation information for each primary management zone; determining the previous crop variety for each primary management zone; establishing multiple grids corresponding to each primary management zone and extracting grid data for each grid, including the previous crop variety, soil information, and ground elevation information; clustering and classifying the grid data of each primary management zone based on a preset clustering algorithm to obtain secondary management zones for the agricultural area, and formulating corresponding agricultural operations based on the secondary management zones.

[0062] In one embodiment, determining the previous crop variety for each primary management zone includes: determining the target historical time period for each primary management zone based on the crop variety currently planted in each primary management zone; acquiring historical remote sensing images of each primary management zone during the corresponding target historical time period; determining the remote sensing vegetation index for each primary management zone based on the historical remote sensing images; and determining the previous crop variety for each primary management zone based on the remote sensing vegetation index.

[0063] In one embodiment, the target value range of the remote sensing vegetation index for the same crop type differs across different target historical time periods. Determining the previous crop variety for each primary management zone based on the remote sensing vegetation index includes: determining the actual vegetation index of each primary management zone for each target historical time period; and determining the crop type corresponding to the target value range of the actual vegetation index of each primary management zone as the previous crop variety for that primary management zone.

[0064] In one embodiment, determining the soil information and ground elevation information of each primary management zone includes: establishing a boundary file for each primary management zone using a geographic information system; and determining the soil information and ground elevation information of each primary management zone based on the boundary file.

[0065] In one embodiment, determining the soil information for each primary management zone includes: acquiring soil samples from each primary management zone; and measuring the nutrient and physical parameters of the soil samples to determine the soil information for each primary management zone. The nutrient parameters include organic matter, available nitrogen, available phosphorus, available potassium, and soil pH. The physical parameters include bulk density and particle composition.

[0066] In one embodiment, the method further includes: acquiring three-dimensional point cloud data of the agricultural area collected by a drone; determining the field elevation information of the agricultural area based on the three-dimensional point cloud data; and determining the ground elevation information of each primary management zone based on the field elevation information.

[0067] In one embodiment, clustering and classifying the grid data of each primary management partition based on a preset clustering algorithm to obtain secondary management partitions of the agricultural region includes: clustering and classifying the grid data of each primary management partition based on the k-means algorithm to obtain secondary management partitions of the agricultural region, wherein the value of k in the k-means algorithm is determined using the elbow rule.

[0068] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0069] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0070] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0071] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0072] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0073] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, like read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0074] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, 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 technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0075] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0076] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for dividing agricultural regional management zones, characterized in that, The method includes: Obtain information on the crops currently planted within the agricultural area, including crop varieties, planting methods for the crop varieties, and rice cultivation type information; Based on the crop information, the agricultural area is divided into multiple primary management zones; Determine the soil information and ground elevation information for each primary management zone; Determine the preceding crop variety for each primary management zone; Multiple grids are established corresponding to each primary management zone, and grid data for each grid is extracted. The grid data includes the previous crop variety, soil information, and ground elevation information. The grid data of each primary management zone is clustered and classified based on the k-means algorithm to obtain the secondary management zones of the agricultural area, so as to formulate corresponding agricultural operations according to the secondary management zones. The value of k in the k-means algorithm is determined using the elbow rule. The determination of the preceding crop variety for each primary management zone includes: The target historical time period for each primary management zone is determined based on the crop varieties currently being planted in each zone. For each primary management zone, acquire the historical remote sensing image of the primary management zone in the corresponding target historical time period; The remote sensing vegetation index of each primary management zone is determined based on the historical remote sensing images. The target value range of the remote sensing vegetation index for the same crop type varies in different target historical time periods. For each target historical time period, determine the actual vegetation index of each primary management zone during that target historical time period; For each primary management zone, the crop type corresponding to the target value range of the actual vegetation index of the primary management zone is determined as the previous crop variety of the primary management zone.

2. The method for dividing agricultural regional management zones according to claim 1, characterized in that, The determination of soil information and ground elevation information for each primary management zone includes: Use a geographic information system to create boundary files for each primary management zone; Soil information and ground elevation information for each primary management zone are determined based on the boundary file.

3. The method for dividing agricultural regional management zones according to claim 1, characterized in that, The determination of soil information for each primary management zone includes: Obtain soil samples from each primary management zone; Nutrient and physical parameters of the soil samples were measured to determine soil information for each primary management zone. The nutrient parameters included organic matter, available nitrogen, available phosphorus, available potassium, and soil pH. The physical parameters included bulk density and particle composition.

4. The method for dividing agricultural regional management zones according to claim 1, characterized in that, The method further includes: Acquire three-dimensional point cloud data of the agricultural area collected by the drone; The field elevation information of the agricultural area is determined based on the three-dimensional point cloud data. The ground elevation information of each primary management zone is determined based on the field elevation information.

5. A processor, characterized in that, It is configured to perform the method for dividing agricultural area management zones as described in any one of claims 1 to 4.

6. A device for dividing agricultural area management zones, characterized in that, Includes the processor according to claim 5.

7. A machine-readable storage medium storing instructions thereon, characterized in that, When executed by a processor, this instruction causes the processor to be configured to perform the method for dividing agricultural area management zones according to any one of claims 1 to 4.

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