Method for dividing agricultural area, storage medium, and processor

Abstract

Embodiments of the present application provide a method for dividing an agricultural area, a storage medium and a processor. The method comprises: obtaining soil data and a remote sensing image of the agricultural area; determining a first distribution map of the agricultural area according to the soil data; determining a second distribution map of a vegetation index of the agricultural area according to the remote sensing image; determining a parameter weight of each soil nutrient parameter, a parameter weight of each soil physical parameter and an image weight of the remote sensing image according to a target management type; determining a number of the first distribution maps and a number of the second distribution maps according to the parameter weight of the soil nutrient parameter, the parameter weight of the soil physical parameter and the image weight; and performing cluster analysis on all the first distribution maps and the second distribution maps to divide the agricultural area and obtain N management partitions under the target management type. The above technical solution can manage crops in different management partitions under different target management types, and improve the refinement degree of partition management.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of agriculture, in particular to a method for dividing an agricultural area, a storage medium and a processor. BACKGROUND

[0002] At present, when an agricultural area is managed and divided, soil nutrient parameters and vegetation indexes are often used as division indexes, all division indexes are regarded as the same importance for clustering analysis, so as to obtain a division result of the corresponding agricultural area, and the crops in the agricultural area are managed according to the division result.

[0003] However, in actual application, the influence degree of different division indexes on different management measures is obviously different. If clustering analysis is performed on all division indexes with the same importance, the obtained division result is relatively fixed, and the pertinence of division management is not strong. In addition, the physical properties of soil have a great influence on the growth of crops. If the influence of the physical properties of soil on the growth of crops cannot be considered, the management effect of crops will be affected, and it is difficult to manage the crops in the agricultural area more accurately. SUMMARY

[0004] The purpose of the embodiments of the present application is to provide a method, device, storage medium and processor for dividing an agricultural area.

[0005] In order to achieve the above purpose, the first aspect of the present application provides a method for dividing an agricultural area, comprising:

[0006] obtaining soil data and a remote sensing image of the agricultural area, the soil data comprising nutrient data and physical data of soil, the nutrient data comprising a plurality of soil nutrient parameters, and the physical data comprising a plurality of soil physical parameters;

[0007] determining a first distribution map of the agricultural area according to the soil data;

[0008] determining a second distribution map of vegetation indexes of the agricultural area according to the remote sensing image;

[0009] determining a parameter weight of each soil nutrient parameter, a parameter weight of each soil physical parameter and an image weight of the remote sensing image according to a target management type;

[0010] determining the number of the first distribution maps and the number of the second distribution maps according to the parameter weight of the soil nutrient parameter, the parameter weight of the soil physical parameter and the image weight, respectively;

[0011] performing clustering analysis on all the first distribution maps and the second distribution maps to divide the agricultural area, so as to obtain N management divisions of the agricultural area under the target management type, wherein N is a natural number, and is a pre-set expected division number.

[0012] In the embodiments of the present application, the determining of the parameter weight of each soil nutrient parameter, the parameter weight of each soil physical parameter and the image weight of the remote sensing image according to the target management type comprises: obtaining growth data of crops in the agricultural area; determining a target management type currently matched by the crops according to the growth data; determining importance levels of the soil nutrient parameters, the soil physical parameters and the remote sensing image under the target management type; and determining the parameter weight of each soil nutrient parameter, the parameter weight of each soil physical parameter and the image weight of the remote sensing image according to the importance levels, wherein the higher the importance level is, the greater the value of the parameter weight and the image weight is.

[0013] In the embodiments of the present application, the determining of the number of the first distribution maps and the number of the second distribution maps according to the parameter weight of the soil nutrient parameters, the parameter weight of the soil physical parameters and the image weight respectively comprises: determining a minimum value among the parameter weight of the soil nutrient parameters, the parameter weight of the soil physical parameters and the image weight; determining a first ratio between each parameter weight and the minimum value; determining a second ratio between each image weight and the minimum weight; and determining the number of the first distribution maps and the number of the second distribution maps according to the first ratio and the second ratio respectively.

[0014] In the embodiments of the present application, the determining of the number of the first distribution maps and the number of the second distribution maps according to the first ratio and the second ratio respectively comprises: rounding up and summing all the first ratios to obtain the number of the first distribution maps of the agricultural area; and rounding up and summing all the second ratios to obtain the number of the second distribution maps of the agricultural area.

[0015] In the embodiments of the present application, the division method further comprises: after the determining of the second distribution map of the vegetation index of the agricultural area according to the remote sensing image, determining size data of the first distribution map; and resampling the second distribution map so that the size data of the second distribution map is consistent with the size data of the first distribution map.

[0016] In the embodiments of the present application, the division method further comprises: after the obtaining of the N management subareas of the agricultural area under the target management type, determining a type of material required to be applied to the crops in each management subarea, and an application amount and an application time of the material; and for the crops in each management subarea, determining a management priority of each management subarea according to the application amount and the application time of the material required to be applied to the crops, so as to apply the material to the crops in each management subarea in sequence according to the management priority.

[0017] In the embodiment of the present application, the management priority of each management partition is determined according to the application amount and application time of the material required to be applied to the crops in each management partition, which comprises: determining the application amount interval of the material required to be applied to the crops in each management partition; determining the time interval of the application time of the material required to be applied to the crops in each management partition; and determining the management priority of each management partition according to the application amount interval and the time interval of each management partition.

[0018] In the embodiment of the present application, the soil nutrient parameters include at least one of organic matter, alkali-hydrolyzable nitrogen, available phosphorus, available potassium and soil pH value, and the soil physical parameters at least include bulk density and particle composition.

[0019] The second aspect of the present application provides a machine readable storage medium, which stores instructions, and the instructions, when executed by a processor, cause the processor to be configured to perform the above-mentioned method for dividing an agricultural area.

[0020] The third aspect of the present application provides a processor configured to perform the above-mentioned method for dividing an agricultural area.

[0021] Through the above technical solution, the division of the agricultural area under each target management type can be determined, the crops in different management partitions can be managed under different target management types, the fine degree of partition management is maximized, unnecessary management cost is reduced, and the efficiency of partition management is improved. Meanwhile, the physical data of the soil is used as an influencing factor for dividing the agricultural area, which can better reflect the actual growth environment of the crops and further improve the accuracy of the division of the agricultural area.

[0022] Other features and advantages of the embodiments of the present application will be described in detail in the following specific implementation part. BRIEF DESCRIPTION OF DRAWINGS

[0023] The accompanying drawings are included to provide a further understanding of the embodiments of the present application, and constitute a part of the specification, and are used together with the following specific implementation part to explain the embodiments of the present application, but do not constitute a limitation on the embodiments of the present application. In the drawings:

[0024] Figure 1 The flowchart of the method for dividing an agricultural area according to the embodiments of the present application is schematically shown;

[0025] Figure 2 The flowchart of the method for dividing an agricultural area according to another embodiment of the present application is schematically shown;

[0026] Figure 3Fig. 1 schematically shows an internal structure diagram of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0027] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. It should be understood that the specific embodiments described herein are merely used to explain and illustrate the embodiments of the present application and should not be used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0028] Figure 1 Fig. 2 schematically shows a flowchart for division of an agricultural area according to an embodiment of the present application. As shown in the figure, Figure 1 in an embodiment of the present application, a method for division of an agricultural area is provided, comprising the following steps:

[0029] Step 101, acquiring soil data and a remote sensing image of an agricultural area, the soil data comprising nutrient data and physical data of soil, the nutrient data comprising a plurality of soil nutrient parameters, and the physical data comprising a plurality of soil physical parameters.

[0030] Step 102, determining a first distribution map of the agricultural area according to the soil data.

[0031] Step 103, determining a second distribution map of a vegetation index of the agricultural area according to the remote sensing image.

[0032] Step 104, determining a parameter weight of each soil nutrient parameter, a parameter weight of each soil physical parameter and an image weight of the remote sensing image according to a target management type.

[0033] Step 105, respectively determining a number of the first distribution maps and a number of the second distribution maps according to the parameter weight of the soil nutrient parameter, the parameter weight of the soil physical parameter and the image weight.

[0034] Step 106, performing cluster analysis on all the first distribution maps and the second distribution maps to divide the agricultural area, to obtain N management subareas of the agricultural area under the target management type, wherein N is a natural number and is a pre-set expected number of subareas.

[0035] The planting area of the crop can include a plurality of soil sampling points. Each soil sampling point includes corresponding latitude and longitude information. The area formed by the plurality of soil sampling points is an agricultural area. The agricultural area can refer to the planting area of the crop. The crop can refer to various plants planted in agriculture. For example, the crop can be rice. When the agricultural area is divided, the processor can obtain soil data and remote sensing images of the agricultural area. The soil data can include soil nutrient data and soil physical data. The soil physical data can refer to physical factors that can change the degree of soil characteristics, formation process, and acidity or alkalinity. The soil physical data can include a plurality of soil physical parameters. The soil nutrient data can include a plurality of soil nutrient parameters.

[0036] In one embodiment, the soil nutrient parameters include at least one of organic matter, alkali-hydrolyzable nitrogen, available phosphorus, available potassium, and soil pH value, and the soil physical parameters include at least bulk density and particle composition. The soil nutrient parameters can refer to nutrient parameters required for crop growth provided by the soil. The soil nutrient parameters can include at least one of organic matter, alkali-hydrolyzable nitrogen, available phosphorus, available potassium, and soil pH value. The organic matter can refer to organic matter containing life functions. For example, the organic matter can include saccharide compounds, cellulose, and lignin, etc. The alkali-hydrolyzable nitrogen can reflect the recent supply of nitrogen in the soil. For example, the alkali-hydrolyzable nitrogen can include inorganic nitrogen and easily hydrolyzed organic nitrogen. The available phosphorus can refer to the total of phosphorus that can be absorbed and utilized by crops in the soil. For example, the available phosphorus can include all water-soluble phosphorus and part of the adsorbed phosphorus, etc. The available potassium can reflect the recent supply of potassium in the soil. The available potassium can refer to potassium that can be easily absorbed and utilized by crops in the soil. For example, the available potassium can include soil solution potassium and soil exchangeable potassium. The soil pH value can refer to the acidity and alkalinity of the soil, and can be used to measure the strength of the reflection of soil acidity and alkalinity. The bulk density can refer to the ratio of the mass or weight of the soil per unit volume to the weight of the same volume of water in the natural state of the field. The bulk density can be determined by soil washing and the amount of soil solids. The bulk density can reflect the degree of soil maturation. The particle composition can refer to the composition of various particles of different sizes and shapes in the soil, and different components and properties.

[0037] The processor can acquire remote sensing images by the unmanned aerial vehicle carrying the multispectral camera. The remote sensing images can include multiple images. Each remote sensing image can be an image of crops in the same growth period in the agricultural area, or an image not in the same growth period. The time for acquiring remote sensing images can be set in advance according to actual needs. Further, the image size data of the multispectral camera can be set in advance before the multispectral camera acquires remote sensing images of the agricultural area. Thus, the processor can acquire remote sensing images of a preset size through the multispectral camera. For example, if the image size data of the multispectral camera is 4cm*4cm, the processor can acquire remote sensing images of 4cm*4cm.

[0038] In the case of acquiring soil data of the agricultural area, the processor can determine a first distribution map of the agricultural area according to the soil data. Specifically, the processor can process each soil sampling point in the agricultural area according to the soil data of the agricultural area and by Kriging interpolation method to determine a first distribution map of the agricultural area of a preset size. The first distribution map can refer to a distribution map of each soil data in the agricultural area. For example, if the soil data of the agricultural area includes organic matter and alkali-hydrolyzable nitrogen, the corresponding first distribution map of the agricultural area includes a distribution map of organic matter and a distribution map of alkali-hydrolyzable nitrogen. The distribution of organic matter in the agricultural area can be determined by the distribution map of organic matter, and the distribution of alkali-hydrolyzable nitrogen in the agricultural area can be determined by the distribution map of alkali-hydrolyzable nitrogen. Further, the size data of the first distribution map can be determined by selecting a pixel size when performing Kriging interpolation. For example, the size data of the first distribution map can be 12cm*12cm.

[0039] In the case of acquiring remote sensing images of the agricultural area, the processor can determine a second distribution map of vegetation index of the agricultural area according to the remote sensing images. Specifically, the processor can process the remote sensing images to determine the vegetation index (NDVI value) of the remote sensing images, thereby determining the second distribution map of the vegetation index. The vegetation index can reflect the vegetation coverage of crops in the agricultural area. That is, the second distribution map can refer to a distribution map of the vegetation coverage of crops in the agricultural area. The vegetation coverage of crops in the agricultural area can be determined by the second distribution map. If the size data of the remote sensing images is 4cm*4cm, the size data of the second distribution map corresponds to 4cm*4cm.

[0040] In one embodiment, the division method further includes: after determining the second distribution map of the vegetation index of the agricultural area according to the remote sensing images, determining the size data of the first distribution map; and resampling the second distribution map to make the size data of the second distribution map consistent with the size data of the first distribution map.

[0041] After determining the second distribution map of the vegetation index of the agricultural region according to the remote sensing image, the processor can determine the size data of the first distribution map, and can further resample the second distribution map so that the size data of the second distribution map is consistent with the size data of the first distribution map. For example, if the size data of the first distribution map is 12 cm*12 cm and the size data of the second distribution map is 4 cm*4 cm, the processor can resample the second distribution map to convert the size data of the second distribution map to 12 cm*12 cm, so that the size data of the second distribution map is consistent with the size data of the first distribution map.

[0042] The processor can determine the parameter weight of each soil nutrient parameter, the parameter weight of each soil physical parameter, and the image weight of the remote sensing image according to the target management type. The target management type can refer to the type of measure currently required for the crop to be managed. For example, it can be actual farm operation such as fertilization, irrigation, and pesticide application. According to different target management types, the corresponding parameter weight of each soil nutrient parameter, the parameter weight of each soil physical parameter, and the image weight of the remote sensing image can be different.

[0043] In one embodiment, determining the parameter weight of each soil nutrient parameter, the parameter weight of each soil physical parameter, and the image weight of the remote sensing image according to the target management type includes: obtaining growth data of the crop in the agricultural region; determining the target management type currently matched by the crop according to the growth data; determining the importance level of the soil nutrient parameter, the soil physical parameter, and the remote sensing image under the target management type; and determining the parameter weight of each soil nutrient parameter, the parameter weight of each soil physical parameter, and the image weight of the remote sensing image according to the importance level, wherein the higher the importance level, the greater the value of the parameter weight and the image weight.

[0044] The processor can obtain growth data of the crop in the agricultural region, and can determine the target management type currently matched by the crop according to the growth data. The growth data can include growth parameters of the crop itself and growth parameters outside the crop. The growth parameters of the crop itself can include leaf emergence time, heading time, and fruiting time of the crop. The growth parameters outside the crop can include temperature and humidity of the environment in which the crop is located. For example, if the temperature at which the crop is currently located exceeds the temperature threshold and the humidity thereof is lower than the humidity threshold, the growth of the crop will be hindered if the agricultural region where the crop is located is not managed. Therefore, the processor can determine that the target management type currently matched by the crop is irrigation according to the growth data of the crop at this time, so as to increase the water content in the agricultural region at this time and ensure the normal growth of the crop.

[0045] In a case that the target management type currently matched by the crop is determined, the processor can determine the importance levels of the soil nutrient parameters, the soil physical parameters, and the remote sensing image under the target management type. The processor can further determine the parameter weights of each soil parameter, the parameter weights of each soil physical parameter, and the image weight of the remote sensing image according to the importance levels. Wherein, the higher the importance level is, the greater the value of the parameter weight and the image weight is. For example, for the target management type of applying pesticide, the area and the amount of pesticide need to be determined according to the actual growth of the crop. The actual growth of the crop can be reflected by the vegetation index, which can be determined by the remote sensing image. In this case, the importance level of the remote sensing image is greater than that of the soil nutrient parameters and the soil physical parameters, and thus the image weight of the remote sensing image is greater than that of the soil nutrient parameters and the soil physical parameters.

[0046] The processor can determine the parameter weights of the soil nutrient parameters, the parameter weights of the soil physical parameters, and the image weight of the remote sensing image. Then, the processor can determine the number of the first distribution maps and the number of the second distribution maps according to the parameter weights of the soil nutrient parameters, the parameter weights of the soil physical parameters, and the image weight, respectively. Specifically, the corresponding weight can be reflected by increasing the number of the same distribution map. For example, if the soil nutrient parameter is organic matter and the parameter weight of the organic matter is 3, the processor can determine that the number of the distribution maps of the organic matter is 3.

[0047] In an embodiment, determining the number of the first distribution maps and the number of the second distribution maps according to the parameter weights of the soil nutrient parameters, the parameter weights of the soil physical parameters, and the image weight respectively includes: determining the minimum value among the parameter weights of the soil nutrient parameters, the parameter weights of the soil physical parameters, and the image weight; determining the first ratio between each parameter weight and the minimum value; determining the second ratio between each image weight and the minimum weight; and determining the number of the first distribution maps and the number of the second distribution maps according to the first ratio and the second ratio respectively.

[0048] In an embodiment, determining the number of the first distribution maps and the number of the second distribution maps according to the first ratio and the second ratio respectively includes: rounding up and summing all the first ratios to obtain the number of the first distribution maps of the agricultural region; and rounding up and summing all the second ratios to obtain the number of the second distribution maps of the agricultural region.

[0049] The processor can determine a minimum value among the parameter weight of the soil nutrient parameter, the parameter weight of the soil physical parameter, and the image weight of the remote sensing image. Then, the processor can determine a first ratio between each parameter weight and the minimum value, and can determine a second ratio between each image weight and the minimum weight. In the case of determining the first ratio and the second ratio, the processor can determine the number of the first distribution maps and the number of the second distribution maps according to the first ratio and the second ratio. Further, if the first ratio or the second ratio is a non-positive integer, the processor can round up all the first ratios to obtain the number of the first distribution maps of the agricultural region. The processor can round up all the second ratios to obtain the number of the second distribution maps of the agricultural region.

[0050] For example, if the minimum value among the parameter weight of the soil nutrient parameter, the parameter weight of the soil physical parameter, and the image weight of the remote sensing image is W min , the processor can determine a first ratio between each parameter weight and the minimum value by . Wherein, J x is the first ratio, J x is a positive integer, is the parameter weight of the xth type of soil parameter in W m , W m is m weights, that is, the total number of the parameter weight and the image weight is m. W min is the minimum value among the parameter weight of the soil nutrient parameter, the parameter weight of the soil physical parameter, and the image weight of the remote sensing image. The processor can determine a second ratio between each image weight and the minimum weight by . Wherein, K y is the second ratio, K y is a positive integer, is the image weight of the yth remote sensing image in W m .

[0051] In the case of determining the number of the first distribution maps and the number of the second distribution maps, the processor can perform cluster analysis on all the first distribution maps and the second distribution maps to divide the agricultural region to obtain N management partitions of the agricultural region under the target management type. Specifically, the processor can perform fuzzy c-means cluster analysis on all the first distribution maps and the second distribution maps by ArcGIS software to divide the agricultural region. Wherein, N is a natural number, which is a pre-set expected partition number. Specifically, N can be a natural number greater than 2. For example, the pre-set expected partition number N can be 5.

[0052] In one embodiment, the dividing method further comprises: after obtaining the N management sub-zones of the agricultural region under the target management type, determining the type of material required to be applied to the crops in each management sub-zone, and the application amount and application time of the material; determining, for the crops in each management sub-zone, a management priority of each management sub-zone according to the application amount and application time of the material required to be applied to the crops, so as to sequentially apply the material to the crops in each management sub-zone according to the management priority.

[0053] After obtaining the N management sub-zones of the agricultural region under the target management type, the processor can determine the type of material required to be applied to the crops in each management sub-zone, and the application amount and application time of the material. The type of material can correspond to the target management type. For example, if the target management type is fertilization, the type of material required to be applied to the crops is fertilizer. If the target management type is pesticide application, the type of material required to be applied to the crops can be pesticide. Specifically, the pesticide can be for disease prevention, the pesticide can be for pest prevention, and the pesticide can be for weed prevention. For the crops in each management sub-zone, the processor can determine a management priority of each management sub-zone according to the application amount and application time of the material required to be applied to the crops, so as to sequentially apply the material to the crops in each management sub-zone according to the management priority. The management priority can refer to the priority order of applying the material to the crops in each management sub-zone. The application amount and application time of the material required to be applied to the crops can be determined according to the growth data of the crops.

[0054] In one embodiment, determining, for the crops in each management sub-zone, a management priority of each management sub-zone according to the application amount and application time of the material required to be applied to the crops comprises: determining, for the crops in each management sub-zone, a dosage interval in which the application amount of the material required to be applied to the crops falls; determining, for the crops in each management sub-zone, a time interval in which the application time of the material required to be applied to the crops falls; and determining the management priority of each management sub-zone according to the dosage interval and the time interval of each management sub-zone.

[0055] For the crops in each management sub-zone, the processor can determine a dosage interval in which the application amount of the material required to be applied to the crops falls, and the processor can determine a time interval in which the application time of the material required to be applied to the crops falls. Then, the processor can further determine the management priority of each management sub-zone according to the dosage interval and the time interval of each management sub-zone.

[0056] Table 1

[0057]

[0058] For example, as shown in Table 1, the dosage range of pesticides can include 0ml-5ml, 5ml-10ml, and 10ml-15ml, and the application time range can include 5min-10min, 10min-15min, and 15min-20min. If the material to be applied to crops in management zone A is pesticide, and the corresponding application amount is 5ml with an application time of 12min, then the processor can determine the management priority of management zone A as ⑧. If the material to be applied to crops in management zone B is pesticide, and the corresponding application amount is 14ml with an application time of 15min, then the processor can determine the management priority of management zone B as ②. Therefore, for both management zones A and B, pesticides can be applied to crops in management zone B first, and then pesticides can be applied to crops in management zone A.

[0059] In one embodiment, such as Figure 2 The diagram illustrates another method for dividing agricultural areas. The processor acquires soil nutrient data, soil physical data, and remote sensing image data from sampling points. Then, the processor uses interpolation to determine x soil parameter distribution maps corresponding to the soil nutrient and physical data of the sampling points. Here, x soil parameter distribution maps represent x types of soil parameters. Each soil parameter corresponds to one soil parameter distribution map. Each soil parameter distribution map corresponds to one parameter weight. For example, if there are 3 types of soil parameters, then there can be 3 corresponding soil parameter distribution maps, and 3 parameter weights corresponding to all soil parameter distribution maps. The processor can determine y vegetation index distribution maps based on the remote sensing image data. For example, if there are 2 types of remote sensing image data, then there can be 2 corresponding vegetation index distribution maps.

[0060] The processor can determine the appropriate target management type for crops based on crop growth data within an agricultural area. The processor can then determine the parameter weights for each soil nutrient parameter and each soil physical parameter based on the target management type. The processor can determine the image weights of remote sensing image data based on the target management type, i.e. The processor can adjust the parameters based on their weights. The number of soil parameter distribution maps can be determined based on image weights. Determine the number of vegetation index distribution maps. For example, the number of distribution maps for soil parameter 1 can be determined as J2, the number of distribution maps for soil parameter 2 as J4, and the number of distribution maps for soil parameter x as J. xThe number of vegetation index distribution maps for period 1 is K1, for period 2 it is K2, and for period 3 it is K3. Then, the processor can perform fuzzy c-means clustering on all soil parameter distribution maps and vegetation index distribution maps to divide the agricultural region, obtaining the management partitioning results of the agricultural region under the target management type, i.e., N management partitions.

[0061] The above technical solutions enable the determination of agricultural zone divisions under each target management type, allowing for the management of crops within different management zones under different target management types. This maximizes the precision of zone management, reduces unnecessary management costs, and improves the efficiency of zone management. Furthermore, incorporating soil physical data as an influencing factor in agricultural zone division better reflects the actual crop growth environment, further enhancing the accuracy of agricultural zone division.

[0062] Figure 1 This is a flowchart illustrating a method for dividing agricultural areas in one embodiment. It should be understood that, although... Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0063] This application provides a storage medium storing a program that, when executed by a processor, implements the above-described method for dividing agricultural areas.

[0064] This application provides a processor for running a program, wherein the program executes the above-described method for dividing agricultural areas.

[0065] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 3As shown in the figure. The computer device includes a processor A01, a network interface A02, a memory (not shown in the figure) and a database (not shown in the figure) connected through a system bus. Among them, the processor A01 of the computer device is used to provide computing and control capabilities. The memory of the computer device includes an 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 in the figure). The internal memory A03 provides an environment for the operating system B01 and the computer program B02 in the non-volatile storage medium A04 to run. The database of the computer device is used to store soil data and remote sensing images and other data. The network interface A02 of the computer device is used to communicate with external terminals through network connection. The computer program B02 is executed by the processor A01 to implement a division method for an agricultural area.

[0066] Those skilled in the art can understand that, Figure 3 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0067] The embodiment of the present application provides a device, the device includes a processor, a memory and a program stored in the memory and executable on the processor, and the processor implements the following steps when executing the program: acquiring soil data and a remote sensing image of an agricultural area, the soil data including nutrient data of soil and physical data of soil, the nutrient data including a plurality of soil nutrient parameters, and the physical data including a plurality of soil physical parameters; determining a first distribution map of the agricultural area according to the soil data; determining a second distribution map of a vegetation index of the agricultural area according to the remote sensing image; determining a parameter weight of each soil nutrient parameter, a parameter weight of each soil physical parameter and an image weight of the remote sensing image according to a target management type; respectively determining a number of the first distribution maps and a number of the second distribution maps according to the parameter weight of the soil nutrient parameter, the parameter weight of the soil physical parameter and the image weight; performing cluster analysis on all the first distribution maps and the second distribution maps to divide the agricultural area, and obtaining N management subareas of the agricultural area under the target management type, wherein N is a natural number, and is a pre-set expected number of subareas.

[0068] In one embodiment, determining the parameter weight of each soil nutrient parameter, the parameter weight of each soil physical parameter, and the image weight of the remote sensing image according to the target management type comprises: obtaining growth data of the crops in the agricultural area; determining a target management type currently matched by the crops according to the growth data; determining an importance level of the soil nutrient parameters, the soil physical parameters, and the remote sensing image under the target management type; and determining the parameter weight of each soil nutrient parameter, the parameter weight of each soil physical parameter, and the image weight of the remote sensing image according to the importance level, wherein the higher the importance level, the greater the value of the parameter weight and the image weight.

[0069] In one embodiment, determining the number of the first distribution maps and the number of the second distribution maps according to the parameter weight of the soil nutrient parameters, the parameter weight of the soil physical parameters, and the image weight respectively comprises: determining a minimum value among the parameter weight of the soil nutrient parameters, the parameter weight of the soil physical parameters, and the image weight; determining a first ratio between each parameter weight and the minimum value; determining a second ratio between each image weight and the minimum weight; and determining the number of the first distribution maps and the number of the second distribution maps according to the first ratio and the second ratio respectively.

[0070] In one embodiment, determining the number of the first distribution maps and the number of the second distribution maps according to the first ratio and the second ratio respectively comprises: rounding up and summing all the first ratios to obtain the number of the first distribution maps of the agricultural area; and rounding up and summing all the second ratios to obtain the number of the second distribution maps of the agricultural area.

[0071] In one embodiment, the division method further comprises: after determining the second distribution map of the vegetation index of the agricultural area according to the remote sensing image, determining size data of the first distribution map; and resampling the second distribution map so that the size data of the second distribution map is consistent with the size data of the first distribution map.

[0072] In one embodiment, the division method further comprises: after obtaining the N management sub-zones of the agricultural area under the target management type, determining a type of material required to be applied to the crops in each management sub-zone, and an application amount and an application time of the material; and determining a management priority of each management sub-zone according to the application amount and the application time of the material required to be applied to the crops in each management sub-zone, so as to apply the material to the crops in each management sub-zone in sequence according to the management priority.

[0073] In one embodiment, determining the management priority of each management partition for the crop in each management partition according to the application amount and the application time of the material required to be applied to the crop comprises: determining, for the crop in each management partition, an amount interval in which the application amount of the material required to be applied to the crop falls; determining, for the crop in each management partition, a time interval in which the application time of the material required to be applied to the crop falls; and determining the management priority of each management partition according to the amount interval and the time interval of each management partition.

[0074] In one embodiment, the soil nutrient parameters include at least one of organic matter, alkali hydrolysis nitrogen, available phosphorus, available potassium, and soil pH value, and the soil physical parameters include at least bulk density and particle composition.

[0075] The present application also provides a computer program product adapted to execute the program of the following method steps when executed on a data processing device: obtaining soil data and remote sensing images of an agricultural area, the soil data including nutrient data of the soil and physical data of the soil, the nutrient data including a plurality of soil nutrient parameters, and the physical data including a plurality of soil physical parameters; determining a first distribution map of the agricultural area according to the soil data; determining a second distribution map of a vegetation index of the agricultural area according to the remote sensing images; determining a parameter weight of each soil nutrient parameter, a parameter weight of each soil physical parameter, and an image weight of the remote sensing images according to a target management type; determining a number of the first distribution maps and a number of the second distribution maps according to the parameter weight of each soil nutrient parameter, the parameter weight of each soil physical parameter, and the image weight of the remote sensing images, respectively; and performing cluster analysis on all the first distribution maps and the second distribution maps to divide the agricultural area to obtain N management partitions of the agricultural area under the target management type, wherein N is a natural number and is a pre-set expected number of partitions.

[0076] In one embodiment, determining the parameter weight of each soil nutrient parameter, the parameter weight of each soil physical parameter, and the image weight of the remote sensing images according to the target management type comprises: obtaining growth data of crops in the agricultural area; determining a target management type currently matched by the crops according to the growth data; determining an importance level of the soil nutrient parameters, the soil physical parameters, and the remote sensing images under the target management type; and determining the parameter weight of each soil nutrient parameter, the parameter weight of each soil physical parameter, and the image weight of the remote sensing images according to the importance level, wherein the higher the importance level, the greater the values of the parameter weight and the image weight.

[0077] In an embodiment, determining the number of first distribution maps and the number of second distribution maps according to the parameter weight of the soil nutrient parameters, the parameter weight of the soil physical parameters, and the image weight respectively comprises: determining a minimum value among the parameter weight of the soil nutrient parameters, the parameter weight of the soil physical parameters, and the image weight; determining a first ratio between each parameter weight and the minimum value; determining a second ratio between each image weight and the minimum weight; determining the number of first distribution maps and the number of second distribution maps according to the first ratio and the second ratio respectively.

[0078] In an embodiment, determining the number of first distribution maps and the number of second distribution maps according to the first ratio and the second ratio respectively comprises: rounding up and summing all the first ratios to obtain the number of first distribution maps of the agricultural region; rounding up and summing all the second ratios to obtain the number of second distribution maps of the agricultural region.

[0079] In an embodiment, the partitioning method further comprises: after determining the second distribution map of the vegetation index of the agricultural region according to the remote sensing image, determining size data of the first distribution map; resampling the second distribution map so that the size data of the second distribution map is consistent with the size data of the first distribution map.

[0080] In an embodiment, the partitioning method further comprises: after obtaining the N management partitions of the agricultural region under the target management type, determining the type of material required to be applied to the crops in each management partition, and the application amount and application time of the material; for the crops in each management partition, determining the management priority of each management partition according to the application amount and application time of the material required to be applied to the crops, so as to sequentially apply the material to the crops in each management partition according to the management priority.

[0081] In an embodiment, for the crops in each management partition, determining the management priority of each management partition according to the application amount and application time of the material required to be applied to the crops comprises: for the crops in each management partition, determining the application amount interval in which the application amount of the material required to be applied to the crops falls; for the crops in each management partition, determining the time interval in which the application time of the material required to be applied to the crops falls; determining the management priority of each management partition according to the application amount interval and the time interval of each management partition.

[0082] In an embodiment, the soil nutrient parameters comprise at least one of organic matter, alkali-hydrolyzable nitrogen, available phosphorus, available potassium, and soil pH value, and the soil physical parameters at least comprise bulk density and particle composition.

[0083] Those skilled in the art will appreciate that embodiments of the application can be readily used as software, hardware, or a combination of software and hardware. In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0084] The present application is described in reference to the flowchart illustrations and / or block diagrams according to the embodiments of the 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 processing system, 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, create means for implementing the functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams.

[0085] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams.

[0086] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams.

[0087] In one typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0088] The memory can include non-persistent memory, random access memory (RAM), and / or non-volatile memory, such as read only memory (ROM) or flash memory, among others. The memory is an example of computer-readable media.

[0089] Computer-readable media includes permanent and non-permanent, movable and non-movable media that can be implemented by any method or technology to store information. The 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 disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer-readable media does not include transitory media such as modulated data signals and carriers.

[0090] It should also be noted that the terms "comprising", "containing", or any other variant thereof are intended to cover non-exclusive inclusions, so that a process, method, article or apparatus that includes a list of elements does not only include those elements, but also includes other elements not explicitly listed, or further includes elements inherent in such a process, method, article or apparatus. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, method, article or apparatus that includes the element.

[0091] 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 can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the scope of claims of the present application.

Claims

1. A method for dividing an agricultural area, characterized in that, The division method comprises: obtaining soil data and remote sensing images of the agricultural area, wherein the soil data comprises nutrient data and physical data of the soil, the nutrient data comprises a plurality of soil nutrient parameters, and the physical data comprises a plurality of soil physical parameters; determining a first distribution map of the agricultural area according to the soil data; determining a second distribution map of vegetation indexes of the agricultural area according to the remote sensing images; determining parameter weights of each soil nutrient parameter, parameter weights of each soil physical parameter, and an image weight of the remote sensing images according to a target management type; determining a number of the first distribution maps and a number of the second distribution maps respectively according to the parameter weights of the soil nutrient parameters, the parameter weights of the soil physical parameters, and the image weight; performing cluster analysis on all the first distribution maps and the second distribution maps to divide the agricultural area, so as to obtain N management sub-zones of the agricultural area under the target management type, wherein N is a natural number, and is a pre-set expected number of sub-zones; wherein the determining of the parameter weights of each soil nutrient parameter, the parameter weights of each soil physical parameter, and the image weight of the remote sensing images according to the target management type comprises: obtaining growth data of crops in the agricultural area; determining a target management type currently matched by the crops according to the growth data; determining importance levels of the soil nutrient parameters, the soil physical parameters, and the remote sensing images under the target management type; and determining the parameter weights of each soil nutrient parameter, the parameter weights of each soil physical parameter, and the image weight of the remote sensing images according to the importance levels, wherein the higher the importance level is, the greater the values of the parameter weights and the image weight are.

2. The method for dividing an agricultural field according to claim 1, wherein, The determining of the number of the first distribution maps and the number of the second distribution maps respectively according to the parameter weights of the soil nutrient parameters, the parameter weights of the soil physical parameters, and the image weight comprises: determining a minimum value among the parameter weights of the soil nutrient parameters, the parameter weights of the soil physical parameters, and the image weight; determining a first ratio between each parameter weight and the minimum value; determining a second ratio between each image weight and the minimum value; determining the number of the first distribution maps and the number of the second distribution maps respectively according to the first ratio and the second ratio.

3. The method for dividing an agricultural field according to claim 2, wherein, The determining of the number of the first distribution maps and the number of the second distribution maps respectively according to the first ratio and the second ratio comprises: summing all the first ratios to obtain the number of the first distribution maps of the agricultural area; summing all the second ratios to obtain the number of the second distribution maps of the agricultural area.

4. The method for dividing an agricultural field according to claim 1, wherein, The division method further comprises: determining size data of the first distribution map after determining the second distribution map of the vegetation indexes of the agricultural area according to the remote sensing images; resampling the second distribution map to make the size data of the second distribution map consistent with the size data of the first distribution map.

5. The method for dividing an agricultural field according to claim 1, wherein, The division method further comprises: After the N management partitions of the agricultural region under the target management type are obtained, a type of material required to be applied to crops in each management partition is determined, as well as an application amount and an application time of the material; For crops in each management partition, a management priority of each management partition is determined according to the application amount and the application time of the material required to be applied to the crops, so as to sequentially apply the material to the crops in each management partition according to the management priority.

6. The method for dividing an agricultural field according to claim 5, wherein, The determining, for the crops in each management partition, of the management priority of each management partition according to the application amount and the application time of the material required to be applied to the crops includes: For the crops in each management partition, an application amount interval in which the application amount of the material required to be applied to the crops is located is determined; For the crops in each management partition, a time interval in which the application time of the material required to be applied to the crops is located is determined; The management priority of each management partition is determined according to the application amount interval and the time interval of each management partition.

7. The method of dividing an agricultural area according to any one of claims 1 to 6, characterized in that, The soil nutrient parameters include at least one of organic matter, alkali-hydrolyzable nitrogen, available phosphorus, available potassium, and soil pH value, and the soil physical parameters at least include bulk density and particle composition.

8. A machine-readable storage medium having stored thereon instructions, the instructions being executable by a machine to cause the machine to: The instructions, when executed by a processor, cause the processor to be configured to perform the partitioning method for an agricultural region according to any one of claims 1 to 7.

9. A device for dividing an agricultural area, characterized by comprise: a memory configured to store instructions; a processor configured to perform the partitioning method for an agricultural region according to any one of claims 1 to 7.

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