Method, processor and storage medium for monitoring vigor based on remote sensing data

Abstract

The application relates to the field of agricultural data, in particular to a method for monitoring growth conditions based on remote sensing data, a processor and a storage medium. The method comprises the following steps: determining remote sensing data of an agricultural area, the agricultural area comprising a plurality of sub-areas; for any one to-be-monitored sub-area, generating a frequency distribution histogram of the to-be-monitored sub-area according to remote sensing data of the to-be-monitored sub-area; determining a growth condition level of crops planted in the to-be-monitored sub-area according to a kurtosis and skewness value of the frequency distribution histogram; determining a target sub-area in the plurality of sub-areas according to the growth condition level; and determining a growth condition of the to-be-monitored sub-area according to remote sensing data of the target sub-area. According to the technical scheme, different target sub-areas are determined according to the growth condition level of the to-be-monitored sub-area, so that the growth condition of crops in the to-be-monitored area can be determined only by remote sensing data, the amount of data processing is reduced, and the determination accuracy is improved.

Description

Technical Field

[0001] This application relates to the field of agricultural data, and more specifically, to a method, processor, and storage medium for monitoring crop growth based on remote sensing data. Background Technology

[0002] In crop growth management, farmers need to frequently inspect the fields to obtain relevant information and take necessary agricultural measures to monitor crop growth. Remote sensing data has been increasingly used in recent years, and vegetation indices such as NDVI can be applied over large areas for crop growth monitoring.

[0003] Currently, when monitoring crop growth using remote sensing data, a large amount of historical data, remote sensing data of the surrounding area of ​​the farm, and information such as the planting period and precise growth period of each field on the farm are usually required to conduct quantitative assessment and monitoring of crop growth. The complexity and large amount of data make it impossible to accurately determine the current stage of the crop and the corresponding growth condition, thus making it impossible to achieve automated growth monitoring. Summary of the Invention

[0004] The purpose of this application is to provide a method, processor, and storage medium for accurately monitoring crop growth using remote sensing data.

[0005] To achieve the above objectives, this application provides a method for monitoring crop growth based on remote sensing data, the method comprising:

[0006] Determine remote sensing data for agricultural regions, which include multiple sub-regions;

[0007] For any given sub-region to be monitored, a frequency distribution histogram of the sub-region is generated based on the remote sensing data of the sub-region.

[0008] The growth level of crops planted in the sub-region to be monitored is determined based on the kurtosis value of the frequency distribution histogram.

[0009] Target sub-regions are determined within multiple sub-regions based on growth level;

[0010] The growth status of the sub-region to be monitored is determined based on remote sensing data of the target sub-region.

[0011] In this embodiment of the application, determining the target sub-region among multiple sub-regions based on the growth level includes: when the growth level is a first preset level, determining the sub-region within a preset range surrounding the sub-region to be monitored that has the same planting plan as the sub-region to be monitored as the target sub-region, wherein the planting plan includes planting time and planting type.

[0012] In this embodiment of the application, determining the growth status of the sub-region to be monitored based on the remote sensing data of the target sub-region includes: determining a first average value of the remote sensing data in the sub-region to be monitored and a first value range of the remote sensing data in the target sub-region; if the first average value is lower than the lower limit of the first value range by a first preset proportion, determining the growth status of the crops planted in the sub-region to be monitored as a first level; if the first average value is within the first preset range of the first value range, determining the growth status of the crops planted in the sub-region to be monitored as a second level; if the first average value is higher than the upper limit of the first value range by a first preset proportion, determining the growth status of the crops planted in the sub-region to be monitored as a third level.

[0013] In this embodiment of the application, determining the target sub-regions in multiple sub-regions based on the growth level includes: when the growth level is a second preset level, determining each remote sensing grid cell in the sub-region to be monitored as multiple target sub-regions.

[0014] In this embodiment of the application, determining the growth status of the monitored sub-region based on the remote sensing data of the target sub-region includes: determining a second numerical range of the remote sensing data within the monitored sub-region; for each target sub-region, determining a first remote sensing value for the target sub-region; for each target sub-region, if the first remote sensing value is lower than the lower limit of the second numerical range by a second preset proportion, determining the growth status of the crops planted in the target sub-region as a first level; for each target sub-region, if the first remote sensing value is higher than the upper limit of the second numerical range by a second preset proportion, determining the growth status of the crops planted in the target sub-region as a third level; and for all target sub-regions, determining the growth status of the crops planted in the other target sub-regions, excluding the target sub-regions of the first level and the target sub-regions of the third level, as a second level.

[0015] In this embodiment of the application, determining the target sub-region among multiple sub-regions based on the growth level includes: when the growth level is the third preset level, determining all sub-regions within the agricultural area that have the same planting plan as the sub-region to be monitored as the target sub-region, wherein the planting plan includes planting time and planting type.

[0016] In this embodiment of the application, determining the growth status of the sub-region to be monitored based on the remote sensing data of the target sub-region includes: generating a frequency distribution histogram of the target sub-region based on the remote sensing data in the target sub-region, and determining a third numerical interval of the remote sensing data in the target sub-region; determining a second remote sensing value for each remote sensing grid cell in the sub-region to be monitored; and determining the growth status of crops planted in each remote sensing grid cell in the sub-region to be monitored based on the skewness of the frequency distribution histogram, the third numerical interval, and the second remote sensing value.

[0017] In this embodiment of the application, determining the growth status of crops planted in each remote sensing grid cell within the monitored sub-region based on the skewness of the frequency distribution histogram, the third numerical interval, and the second remote sensing value includes: when the skewness is within the first skewness range, for each remote sensing grid cell, when the second remote sensing value is lower than the lower limit of the third numerical interval by a third preset proportion, determining the growth status of crops in the remote sensing grid cell as a first level; for each remote sensing grid cell, when the second remote sensing value is within the second preset range of the third numerical interval, determining the growth status of crops in the remote sensing grid cell as a third level; and for all remote sensing grid cells, determining the growth status of crops in remote sensing grid cells other than those at the first level and the third level as a second level.

[0018] In this embodiment of the application, determining the growth status of crops planted in each remote sensing grid cell within the monitored sub-region based on the skewness of the frequency distribution histogram, the third numerical interval, and the second remote sensing value includes: when the skewness is within the second skewness range, for each remote sensing grid cell, if a second preset proportion is determined to be higher than the upper limit of the third numerical interval, the growth status of crops in the remote sensing grid cell is determined to be abnormal; for each remote sensing grid cell, if the second remote sensing value is determined to be within the third preset range of the third numerical interval, the growth status of crops in the remote sensing grid cell is determined to be at the first level; for each remote sensing grid cell, if the second remote sensing value is determined to be within the fourth preset range of the third numerical interval, the growth status of crops in the remote sensing grid cell is determined to be at the third level; for all remote sensing grid cells, the growth status of crops in remote sensing grid cells other than those at the first level, the third level, and the abnormal remote sensing grid cells is determined to be at the second level.

[0019] In this embodiment of the application, determining the growth status of crops planted in each remote sensing grid cell within the monitored sub-region based on the skewness of the frequency distribution histogram, the third numerical interval, and the second remote sensing value includes: when the skewness is within the third skewness range, for each remote sensing grid cell, if the second remote sensing value is determined to be higher than the upper limit of the third numerical interval by a first preset proportion, the growth status of crops in the remote sensing grid cell is determined to be abnormal; for each remote sensing grid cell, if the second remote sensing value is determined to be within the fifth preset range of the third numerical interval, the growth status of crops in the remote sensing grid cell is determined to be at the first level; for each remote sensing grid cell, if the second remote sensing value is determined to be within the sixth preset range of the third numerical interval, the growth status of crops in the remote sensing grid cell is determined to be at the third level; for all remote sensing grid cells, the growth status of crops in remote sensing grid cells other than those at the first level, the second level, and the abnormal grid cells is determined to be at the second level.

[0020] In this embodiment of the application, determining the growth status of crops planted in each remote sensing grid cell within the monitored sub-region based on the skewness of the frequency distribution histogram, the third numerical interval, and the second remote sensing value includes: when the skewness is within the fourth skewness range, for each remote sensing grid cell, if the second remote sensing value is determined to be below the lower limit of the third numerical interval by a second preset proportion, the growth status of crops in the remote sensing grid cell is determined to be abnormal; for each remote sensing grid cell, if the second remote sensing value is determined to be within the seventh preset range of the third numerical interval, the growth status of crops in the remote sensing grid cell is determined to be at the first level; for each remote sensing grid cell, if the second remote sensing value is determined to be within the eighth preset range of the third numerical interval, the growth status of crops in the remote sensing grid cell is determined to be at the third level; for all remote sensing grid cells, the growth status of crops in remote sensing grid cells other than those at the first level, the second level, and the abnormal remote sensing grid cells is determined to be at the second level.

[0021] In this embodiment of the application, determining the growth status of crops planted in each remote sensing grid cell within the monitored sub-region based on the skewness of the frequency distribution histogram, the third numerical interval, and the second remote sensing value includes: when the skewness is within the fifth skewness range, for each remote sensing grid cell, if the second remote sensing value is determined to be below the lower limit of the third numerical interval by a first preset proportion, the growth status of crops in the remote sensing grid cell is determined to be abnormal; for each remote sensing grid cell, if the second remote sensing value is determined to be within the ninth preset range of the third numerical interval, the growth status of crops in the remote sensing grid cell is determined to be at the first level; for each remote sensing grid cell, if the second remote sensing value is determined to be within the tenth preset range of the third numerical interval, the growth status of crops in the remote sensing grid cell is determined to be at the third level; for all remote sensing grid cells, the growth status of crops in remote sensing grid cells other than those at the first level, the second level, and the abnormal remote sensing grid cells is determined to be at the second level.

[0022] A second aspect of this application provides a processor configured to perform a method for monitoring crop growth based on remote sensing data according to any one of the above.

[0023] A third aspect of this application provides an apparatus for monitoring crop growth based on remote sensing data, including the processor described above.

[0024] 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 any of the above-described methods for monitoring growth based on remote sensing data.

[0025] The above technical solution involves determining the frequency distribution histogram of remote sensing data within the monitored sub-region, identifying different growth levels of the monitored sub-region based on the frequency distribution histogram, and then determining different target sub-regions according to the different growth levels. This allows for the determination of crop growth status within the monitored sub-region based solely on remote sensing data, reducing data processing workload and improving accuracy.

[0026] Other features and advantages of this application will be described in detail in the following detailed description section. Attached Figure Description

[0027] The accompanying drawings are provided to further illustrate the present application and form part of the specification. They are used together with the following detailed description to explain the present application, but do not constitute a limitation thereof. In the drawings:

[0028] Figure 1 The schematic diagram illustrates a flow chart of a method for monitoring crop growth based on remote sensing data according to an embodiment of this application;

[0029] Figure 2 The diagram illustrates the internal structure of a computer device according to an embodiment of this application. Detailed Implementation

[0030] The specific embodiments of this application will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit this application.

[0031] It should be noted that if the embodiments of this application involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0032] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0033] Figure 1 A schematic flowchart illustrating a method for monitoring crop growth based on remote sensing data according to an embodiment of this application is shown. Figure 1 As shown in one embodiment of this application, a method for monitoring crop growth based on remote sensing data is provided, comprising the following steps:

[0034] Step 101: Determine the remote sensing data of the agricultural region, which includes multiple sub-regions;

[0035] Step 102: For any sub-region to be monitored, generate a frequency distribution histogram of the sub-region based on the remote sensing data of the sub-region to be monitored;

[0036] Step 103: Determine the growth level of crops planted in the sub-region to be monitored based on the kurtosis value of the frequency distribution histogram;

[0037] Step 104: Determine the target sub-regions among multiple sub-regions based on the growth level;

[0038] Step 105: Determine the growth status of the sub-region to be monitored based on the remote sensing data of the target sub-region.

[0039] The processor can acquire satellite or UAV remote sensing imagery of agricultural areas and determine remote sensing data for these areas based on the imagery. An agricultural area can include multiple sub-regions. For example, the agricultural area could be a farm selected by the user, and the sub-regions could be multiple plots within the farm. The processor can use image recognition technology to identify the boundaries of the plots, thereby determining the remote sensing data for each plot. The remote sensing data can be the Normalized Difference Vegetation Index (NDVI), the Normalized Red Edge Index (NDI), and the Normalized Moisture Index (NMI). Users can choose the type of remote sensing data according to their needs. For example, if a user needs to determine the nitrogen content of crops, they can choose the NDI; if a user needs to determine the canopy moisture content, they can choose the NMI; if a user needs to determine the overall growth of crops within the agricultural area, they can choose the NDI. For any given sub-region to be monitored, the processor can generate a distribution histogram of the sub-region based on the remote sensing data of the sub-region. After generating the frequency distribution histogram of the sub-region, the processor can determine the kurtosis value of the frequency distribution histogram to determine the growth level of the crops planted in the sub-region. Based on the growth level determined by the frequency distribution histogram, the processor can determine the target sub-region among multiple sub-regions of the agricultural area, and determine the growth status of the sub-region to be monitored based on the remote sensing data of the target sub-region.

[0040] In one embodiment, determining the target sub-region among multiple sub-regions based on the growth level includes: when the growth level is a first preset level, determining the sub-region within a preset range surrounding the sub-region to be monitored that has the same planting plan as the sub-region to be monitored as the target sub-region, wherein the planting plan includes planting time and planting type.

[0041] The processor can determine the growth level of crops planted in a monitored sub-region based on the kurtosis of the frequency distribution histogram. For example, the processor can determine the kurtosis of the frequency distribution histogram. When the kurtosis is greater than 3.5, the processor can determine that the vegetation dispersion in the monitored sub-region is small and the overall growth is uniform. The processor can then identify the monitored sub-region corresponding to the frequency distribution histogram with a kurtosis greater than 3.5 as the first preset level. The processor can determine different target sub-regions based on different growth levels. If the processor determines that the growth level of the monitored sub-region is the first preset level, the processor can identify sub-regions within a preset range surrounding the monitored sub-region that have the same planting plan as the monitored sub-region as target sub-regions. The planting plan includes planting time and planting type. In other words, the processor can identify sub-regions within a preset range surrounding the monitored sub-region that have the same planting type and planting time as the monitored sub-region as target sub-regions.

[0042] In one embodiment, a first average value of remote sensing data in the sub-region to be monitored and a first value range of remote sensing data in the target sub-region are determined; the first value range of remote sensing data in the target sub-region is obtained; if the first average value is lower than the lower limit of the first value range by a first preset proportion, the growth status of crops planted in the sub-region to be monitored is determined to be a first level; if the first average value is within the first preset range of the first value range, the growth status of crops planted in the sub-region to be monitored is determined to be a second level; if the first average value is higher than the upper limit of the first value range by a first preset proportion, the growth status of crops planted in the sub-region to be monitored is determined to be a third level.

[0043] After determining the growth level of the monitored area to be a first preset level, and identifying the target sub-region based on the growth level, the processor can determine the first average value of the remote sensing data within the monitored sub-region. The processor can also acquire the remote sensing data within the target sub-region and determine the first numerical range of the remote sensing data within the target sub-region. The processor can classify the growth status into three levels: poor, moderate, and good, corresponding to the first, second, and third levels, respectively. The processor can compare the first average value with the first numerical range. If the first average value is lower than the lower limit of the first numerical range by a first preset proportion, the processor can determine that the growth status of the crops planted in the monitored sub-region is at the first level (i.e., poor growth status). For example, assuming the processor sets the first preset proportion to 0.5, that is, if the first average value is lower than 0.5 of the lower limit of the first numerical range, the processor can determine that the growth status of the crops in the monitored sub-region is poor. If the first average value is within a first preset range of the first value interval, the processor can determine the growth status of the crops planted in the monitored sub-region as the second level (i.e., medium). If the first average value is higher than the upper limit of the first value interval by a first preset proportion, the processor can determine the growth status of the crops planted in the monitored sub-region as the third level (i.e., good).

[0044] In one embodiment, determining the target sub-regions within multiple sub-regions based on the growth level includes: when the growth level is a second preset level, determining each remote sensing grid cell within the sub-region to be monitored as multiple target sub-regions.

[0045] The processor can determine the growth level of crops planted in a monitored sub-region based on the kurtosis of the frequency distribution histogram. For example, the processor can determine the kurtosis of the frequency distribution histogram. When the kurtosis is less than 2.5, the processor can determine that the vegetation dispersion in the monitored sub-region is relatively large and the overall growth is uneven. The processor can then identify the monitored sub-region corresponding to the frequency distribution histogram with a kurtosis less than 2.5 as the second preset level. The processor can determine different target sub-regions based on different growth levels. When the processor determines that the growth level of the monitored sub-region is the second preset level, the processor can extract the remote sensing grid cells within the monitored sub-region and identify each remote sensing grid cell within the monitored region as multiple target sub-regions.

[0046] In one embodiment, determining the growth status of a monitored sub-region based on remote sensing data of a target sub-region includes: determining a second numerical range of remote sensing data within the monitored sub-region; determining a first remote sensing value for each target sub-region; determining the growth status of crops planted in the target sub-region as a first level if the first remote sensing value is lower than the lower limit of the second numerical range by a second preset proportion; determining the growth status of crops planted in the target sub-region as a third level if the first remote sensing value is higher than the upper limit of the second numerical range by a second preset proportion; and determining the growth status of crops planted in all target sub-regions except for the target sub-regions at the first level and the target sub-regions at the third level as the second level.

[0047] After determining that the growth level of the monitored area is a second preset level, and after identifying multiple target sub-regions based on the growth level, the processor can acquire remote sensing data within the monitored sub-regions and determine a second numerical range for each sub-region. The processor can categorize growth status into three levels: poor, moderate, and good, corresponding to the first, second, and third levels, respectively. For each target sub-region, the processor can determine the first remote sensing data. When the processor determines that the first remote sensing data for the target sub-region is lower than a second preset proportion of the lower limit of the second numerical range, the processor can determine that the growth status of the crops planted in that target sub-region is at the first level (i.e., poor). For example, assuming the user sets the second preset proportion to 0.25, when the processor determines that the first remote sensing value of the target sub-region is lower than 0.25 of the lower limit of the second numerical range, the processor can determine that the growth of the crops in that target sub-region is poor. For each target sub-region, under a second preset ratio where the first remote sensing value is higher than the upper limit of the second value range, the growth status of crops planted in the target sub-region is determined to be level three (i.e., good). For all target sub-regions, the processor can determine the growth status of crops planted in other target sub-regions besides the level one and level three target sub-regions to be level two (i.e., medium). In other words, after the processor determines the level one and level three remote sensing grid cells in the sub-region to be monitored, the processor can determine the crops planted in other remote sensing grid cells in the sub-region to be monitored besides the level one and level three to be level two (i.e., medium).

[0048] In one embodiment, determining the target sub-region among multiple sub-regions based on the growth level includes: when the growth level is the third preset level, identifying all sub-regions within the agricultural area that have the same planting plan as the sub-region to be monitored as the target sub-region, wherein the planting plan includes planting time and planting type.

[0049] The processor can determine the growth level of crops planted in a monitored sub-region based on the kurtosis of its frequency distribution histogram. For example, the processor can determine the kurtosis of the frequency distribution histogram. When the kurtosis is greater than or equal to 2.5 and less than or equal to 3.5, the processor can determine that the vegetation dispersion in the monitored sub-region is moderate. The processor can then identify the monitored sub-region corresponding to the frequency distribution histogram with a kurtosis greater than or equal to 2.5 and less than or equal to 3.5 as the third preset level. The processor can determine different target sub-regions based on different growth levels. If the processor determines that the growth level of the monitored sub-region is the third preset level, the processor can identify all sub-regions within the agricultural area that have the same planting plan as the monitored sub-region, that is, the same planting species and planting time, as target sub-regions.

[0050] In one embodiment, determining the growth status of a sub-region to be monitored based on remote sensing data of a target sub-region includes: generating a frequency distribution histogram of the target sub-region based on remote sensing data within the target sub-region, and determining a third numerical interval of the remote sensing data within the target sub-region; determining a second remote sensing value for each remote sensing grid cell within the sub-region to be monitored; and determining the growth status of crops planted in each remote sensing grid cell within the sub-region to be monitored based on the skewness of the frequency distribution histogram, the third numerical interval, and the second remote sensing value.

[0051] After determining that the growth level of the area to be monitored is the third preset level, and after identifying all sub-areas within the farm area with the same planting plan as the sub-area to be monitored as target sub-areas based on the growth level of the area to be monitored, the processor can generate a frequency distribution histogram of the target sub-area based on the remote sensing data in the target sub-area, and determine the third numerical range of the remote sensing data in the target sub-area. The processor can determine the second remote sensing value of each remote sensing grid cell in the sub-area to be monitored. The processor can determine the growth status of the crops planted in each remote sensing grid cell in the sub-area to be monitored based on the skewness of the frequency distribution histogram of the target sub-area, the third numerical range of the remote sensing data in the target sub-area, and the second remote sensing value.

[0052] In one embodiment, determining the growth status of crops planted in each remote sensing grid cell within the monitored sub-region based on the skewness of the frequency distribution histogram, a third numerical interval, and a second remote sensing value includes: when the skewness is within a first skewness range, for each remote sensing grid cell, if the second remote sensing value is lower than the lower limit of the third numerical interval by a third preset proportion, determining the growth status of crops in the remote sensing grid cell as a first level; for each remote sensing grid cell, if the second remote sensing value is within a second preset range of the third numerical interval, determining the growth status of crops in the remote sensing grid cell as a third level; and for all remote sensing grid cells, determining the growth status of crops in remote sensing grid cells other than those at the first level and the third level as a second level.

[0053] The processor can determine the skewness range of the target sub-region based on the skewness of its frequency distribution histogram. For example, the processor can set a skewness range greater than or equal to -0.5 and greater than or equal to 0.5 as the first skewness range. The processor can classify crop growth status into first, second, and third levels according to poor, moderate, and good growth status, respectively. When the processor determines that the skewness of the frequency distribution histogram of the target sub-region is within the first skewness range, for each remote sensing grid cell in the sub-region to be monitored, if the second remote sensing value corresponding to that grid cell is lower than the lower limit of the third value interval of the remote sensing values ​​of the target sub-region by a third preset proportion, the processor determines the crop growth status of that grid cell to be at the first level (i.e., poor). For example, assuming the processor sets the third preset proportion to 0.1, when the second remote sensing value corresponding to a grid cell in the sub-region to be monitored is lower than the lower limit of the third value interval of 0.1, the processor can determine that the crop growth status of that grid cell is poor. Similarly, for each remote sensing grid cell within the sub-region to be monitored, if the second remote sensing value corresponding to that grid cell falls within a second preset range of the third value interval of the remote sensing values ​​in the target sub-region, the processor determines the crop growth status of that grid cell to be at level three (i.e., good). The processor can set the second preset range based on user-input data. For all remote sensing grid cells within the sub-region to be monitored, the processor can determine the crop growth status of all grid cells except for those at level one and level three to be at level two (i.e., moderate).

[0054] In one embodiment, determining the growth status of crops planted in each remote sensing grid cell within the monitored sub-region based on the skewness of the frequency distribution histogram, a third numerical interval, and a second remote sensing value includes: when the skewness is within the second skewness range, for each remote sensing grid cell, if a second preset proportion is determined to be higher than the upper limit of the third numerical interval, determining the growth status of crops in the remote sensing grid cell as abnormal; for each remote sensing grid cell, if the second remote sensing value is determined to be within the third preset range of the third numerical interval, determining the growth status of crops in the remote sensing grid cell as first level; for each remote sensing grid cell, if the second remote sensing value is determined to be within the fourth preset range of the third numerical interval, determining the growth status of crops in the remote sensing grid cell as third level; and for all remote sensing grid cells, determining the growth status of crops in remote sensing grid cells other than those at the first level, the third level, and the abnormal remote sensing grid cells as second level.

[0055] The processor can determine the skewness range of the target sub-region based on the skewness of its frequency distribution histogram. For example, the processor can set a second skewness range for skewness greater than 0.5. The processor can classify crop growth status into three levels: poor, moderate, and good, corresponding to first, second, and third levels, respectively. When the processor determines that the skewness of the target sub-region's frequency distribution histogram is within the second skewness range, for each remote sensing grid cell within the monitored sub-region, if the second remote sensing value corresponding to that grid cell is higher than the upper limit of the third value interval of the target sub-region's remote sensing values ​​by a second preset proportion, the processor determines that the crop growth status within that grid cell is abnormal. For example, assuming the processor sets the second preset proportion to 0.25, when the second remote sensing value corresponding to a grid cell within the monitored sub-region is higher than the upper limit of the third value interval (0.25), the processor can determine that the crop growth status within that grid cell is abnormal, such as weed infestation. For each remote sensing grid cell within the sub-region to be monitored, if the second remote sensing value corresponding to the grid cell falls within a third preset range of the third value interval of the remote sensing values ​​in the target sub-region, the processor determines the crop growth status of that grid cell to be at level one (i.e., poor). For each remote sensing grid cell within the sub-region to be monitored, if the second remote sensing value corresponding to the grid cell falls within a fourth preset range of the third value interval of the remote sensing values ​​in the target sub-region, the processor determines the crop growth status of that grid cell to be at level three (i.e., good). The processor can set the third and fourth preset ranges based on user-input data. For all remote sensing grid cells within the sub-region to be monitored, the processor can determine the crop growth status of all grid cells except those at level one, level three, and those exhibiting anomalies to be at level two (i.e., moderate).

[0056] In one embodiment, determining the growth status of crops planted in each remote sensing grid cell within the monitored sub-region based on the skewness of the frequency distribution histogram, a third numerical interval, and a second remote sensing value includes: when the skewness is within the third skewness range, for each remote sensing grid cell, if a first preset proportion is determined to be higher than the upper limit of the third numerical interval, determining the growth status of crops in the remote sensing grid cell as abnormal; for each remote sensing grid cell, if a fifth preset range is determined to be within the third numerical interval, determining the growth status of crops in the remote sensing grid cell as first level; for each remote sensing grid cell, if a sixth preset range is determined to be within the third numerical interval, determining the growth status of crops in the remote sensing grid cell as third level; and for all remote sensing grid cells, determining the growth status of crops in remote sensing grid cells other than those at first level, second level, and abnormal grid cells as second level.

[0057] The processor can determine the skewness range of the target sub-region based on the skewness of its frequency distribution histogram. For example, the processor can set a third skewness range for skewness ranges greater than 1. The processor can classify crop growth status into first, second, and third levels, respectively, based on poor, moderate, and good growth status. When the processor determines that the skewness of the frequency distribution histogram of the target sub-region is within the third skewness range, for each remote sensing grid cell within the monitored sub-region, if the second remote sensing value corresponding to that grid cell is higher than the upper limit of the third value interval of the remote sensing values ​​of the target sub-region by a first preset proportion, the processor determines that the crop growth status within that grid cell is abnormal. For example, assuming the processor sets the first preset proportion to 0.5, when the second remote sensing value corresponding to a grid cell within the monitored sub-region is higher than 0.5 of the upper limit of the third value interval, the processor can determine that the crop growth status within that grid cell is abnormal, such as weed infestation occurring within that grid cell. For each remote sensing grid cell within the sub-region to be monitored, if the second remote sensing value corresponding to that grid cell falls within the fifth preset range of the third value interval of the remote sensing values ​​in the target sub-region, the processor determines the crop growth status of that grid cell to be at level one (i.e., poor). For each remote sensing grid cell within the sub-region to be monitored, if the second remote sensing value corresponding to that grid cell falls within the sixth preset range of the third value interval of the remote sensing values ​​in the target sub-region, the processor determines the crop growth status of that grid cell to be at level three (i.e., good). The processor can set the fifth and sixth preset ranges based on user-input data. For all remote sensing grid cells within the sub-region to be monitored, the processor can determine the crop growth status of all grid cells except those at level one, level three, and those exhibiting anomalies to be at level two (i.e., moderate).

[0058] In one embodiment, determining the growth status of crops planted in each remote sensing grid cell within the monitored sub-region based on the skewness of the frequency distribution histogram, the third numerical interval, and the second remote sensing value includes: when the skewness is within the fourth skewness range, for each remote sensing grid cell, if a second preset proportion is determined to be lower than the lower limit of the third numerical interval, the growth status of crops in the remote sensing grid cell is determined to be abnormal; for each remote sensing grid cell, if the second remote sensing value is determined to be within the seventh preset range of the third numerical interval, the growth status of crops in the remote sensing grid cell is determined to be at the first level; for each remote sensing grid cell, if the second remote sensing value is determined to be within the eighth preset range of the third numerical interval, the growth status of crops in the remote sensing grid cell is determined to be at the third level; for all remote sensing grid cells, the growth status of crops in remote sensing grid cells other than those at the first level, the second level, and the abnormal remote sensing grid cells is determined to be at the second level.

[0059] The processor can determine the skewness range of the target sub-region based on the skewness of its frequency distribution histogram. For example, the processor can set a skewness range less than -0.5 as the fourth skewness range. The processor can classify crop growth status into three levels: poor, moderate, and good, corresponding to the first, second, and third levels, respectively. When the processor determines that the skewness of the target sub-region's frequency distribution histogram is within the fourth skewness range, for each remote sensing grid cell within the monitored sub-region, if the processor determines that the crop growth status within that grid cell is abnormal when the second remote sensing value corresponding to that grid cell is lower than the lower limit of the third value interval of the target sub-region's remote sensing values ​​by a second preset proportion, the processor can determine that the crop growth status within that grid cell is abnormal, such as the crop possibly being affected by pests or diseases. For each remote sensing grid cell within the sub-region to be monitored, if the second remote sensing value corresponding to the grid cell falls within the seventh preset range of the third value interval of the remote sensing values ​​in the target sub-region, the processor determines the crop growth status of that grid cell to be at level one (i.e., poor). For each remote sensing grid cell within the sub-region to be monitored, if the second remote sensing value corresponding to the grid cell falls within the eighth preset range of the third value interval of the remote sensing values ​​in the target sub-region, the processor determines the crop growth status of that grid cell to be at level three (i.e., good). The processor can set the seventh and eighth preset ranges based on user-input data. For all remote sensing grid cells within the sub-region to be monitored, the processor can determine the crop growth status of all grid cells except those at level one, level three, and those exhibiting anomalies to be at level two (i.e., moderate).

[0060] In one embodiment, determining the growth status of crops planted in each remote sensing grid cell within the monitored sub-region based on the skewness of the frequency distribution histogram, the third numerical interval, and the second remote sensing value includes: when the skewness is within the fifth skewness range, for each remote sensing grid cell, if the second remote sensing value is determined to be below the lower limit of the third numerical interval by a first preset proportion, the growth status of crops in the remote sensing grid cell is determined to be abnormal; for each remote sensing grid cell, if the second remote sensing value is determined to be within the ninth preset range of the third numerical interval, the growth status of crops in the remote sensing grid cell is determined to be at the first level; for each remote sensing grid cell, if the second remote sensing value is determined to be within the tenth preset range of the third numerical interval, the growth status of crops in the remote sensing grid cell is determined to be at the third level; for all remote sensing grid cells, the growth status of crops in remote sensing grid cells other than those at the first level, the second level, and the abnormal remote sensing grid cells is determined to be at the second level.

[0061] The processor can determine the skewness range of the target sub-region based on the skewness of its frequency distribution histogram. For example, the processor can set a skewness range less than -1 as the fifth skewness range. The processor can classify crop growth status into three levels: poor, moderate, and good, corresponding to the first, second, and third levels, respectively. When the processor determines that the skewness of the target sub-region's frequency distribution histogram is within the fifth skewness range, for each remote sensing grid cell within the monitored sub-region, if the processor determines that the crop growth status within that grid cell is abnormal when the second remote sensing value corresponding to that grid cell is lower than the lower limit of the third value interval of the target sub-region's remote sensing values ​​by a first preset proportion, the processor can determine that the crop growth status within that grid cell is abnormal, such as the possibility of crop infection by pests or diseases. For each remote sensing grid cell within the sub-region to be monitored, if the second remote sensing value corresponding to the grid cell falls within the ninth preset range of the third value interval of the remote sensing values ​​in the target sub-region, the processor determines the crop growth status of that grid cell to be at level one (i.e., poor). For each remote sensing grid cell within the sub-region to be monitored, if the second remote sensing value corresponding to the grid cell falls within the tenth preset range of the third value interval of the remote sensing values ​​in the target sub-region, the processor determines the crop growth status of that grid cell to be at level three (i.e., good). The processor can set the ninth and tenth preset ranges based on user-input data. For all remote sensing grid cells within the sub-region to be monitored, the processor can determine the crop growth status of all grid cells except those at level one, level three, and those exhibiting anomalies to be at level two (i.e., moderate).

[0062] In one embodiment, a processor is provided, configured to perform any of the above-described methods for monitoring crop growth based on remote sensing data.

[0063] The processor can acquire satellite or UAV remote sensing imagery of agricultural areas and determine the remote sensing data of these areas. Using image recognition technology, the processor can identify multiple sub-regions within the agricultural area. For any given sub-region to be monitored, the processor can generate a frequency distribution histogram based on the remote sensing data and determine the kurtosis value of this histogram. Based on user-input data and different ranges of kurtosis values, the processor can set different preset growth levels for crops. Then, based on the frequency distribution histogram of the remote sensing data for the sub-region, the processor determines the growth level of the crops planted within that sub-region. For sub-regions with different growth levels determined by kurtosis values, the processor can identify the target sub-region corresponding to each growth level. By acquiring the remote sensing data of the target sub-region, the processor determines the growth status of the sub-region to be monitored. For example, when the kurtosis value of the frequency distribution histogram determines that the growth level of crops planted in the monitored sub-region is a first preset level, the processor can identify target sub-regions within a preset range surrounding the monitored sub-region that have the same planting plan as the monitored sub-region. Then, by comparing the remote sensing data in the target sub-region with the remote sensing data in the monitored sub-region, the processor can determine whether the growth level of crops in the monitored sub-region is good, moderate, or poor. When the kurtosis value of the frequency distribution histogram determines that the growth level of crops planted in the monitored sub-region is a second preset level, the processor can identify each remote sensing grid cell in the monitored sub-region as multiple target sub-regions. Then, by comparing the remote sensing data in the target sub-region with the remote sensing data in the monitored sub-region, the processor can determine whether the growth level of crops in the monitored sub-region is good, moderate, or poor.

[0064] When the kurtosis value of the frequency distribution histogram determines that the growth level of the crops planted in the sub-region to be monitored is the third preset level, the processor can identify all sub-regions in the agricultural area with the same planting plan as the sub-region to be monitored as target sub-regions. Further, it determines the frequency distribution histogram of the target sub-region generated from the remote sensing data in the target sub-region, the third numerical interval of the remote sensing data of the target sub-region, and the second remote sensing value of each remote sensing grid cell in the sub-region to be monitored. Based on the skewness of the frequency distribution histogram, the third numerical interval, and the second remote sensing value, it determines whether the growth status of the crops planted in each remote sensing grid cell in the sub-region to be monitored is good, medium, or poor.

[0065] The above technical solution determines the crop growth level of the monitored sub-region by generating a frequency distribution histogram from the remote sensing data within the monitored sub-region, and then determines the corresponding target sub-region according to different growth levels. This allows the determination of the crop growth status of the monitored sub-region based on the remote sensing data of the target sub-region. By setting different target sub-regions for different levels, the accuracy of crop growth information within the monitored sub-region is improved. Furthermore, only remote sensing data is needed to determine the crop growth status within the monitored region, without relying on crop growth period or crop type information, thus reducing data processing workload.

[0066] In one embodiment, a crop growth monitoring device based on remote sensing data is provided, including the processor as described above.

[0067] In one embodiment, a machine-readable storage medium is provided, on which instructions are stored, which, when executed by a processor, cause the processor to be configured to perform a method for monitoring growth based on remote sensing data according to any one of the foregoing.

[0068] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0069] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 2 As shown. The computer device includes a processor A01, a network interface A02, a 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 relevant data for agricultural areas. 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 monitoring crop growth based on remote sensing data.

[0070] Figure 1 This is a flowchart illustrating a method for monitoring crop growth based on remote sensing data in one embodiment. It should be understood that, although... Figure 1The 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 explicitly stated herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Furthermore, 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.

[0071] 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: determining remote sensing data of an agricultural region, which includes multiple sub-regions; for any sub-region to be monitored, generating a frequency distribution histogram of the sub-region based on the remote sensing data of the sub-region; determining the growth level of crops planted in the sub-region based on the kurtosis value of the frequency distribution histogram; determining a target sub-region among the multiple sub-regions based on the growth level; and determining the growth status of the sub-region to be monitored based on the remote sensing data of the target sub-region.

[0072] In one embodiment, determining the target sub-region among multiple sub-regions based on the growth level includes: when the growth level is a first preset level, determining the sub-region within a preset range surrounding the sub-region to be monitored that has the same planting plan as the sub-region to be monitored as the target sub-region, wherein the planting plan includes planting time and planting type.

[0073] In one embodiment, determining the growth status of the sub-region to be monitored based on remote sensing data of the target sub-region includes: determining a first average value of the remote sensing data in the sub-region to be monitored and a first value range of the remote sensing data in the target sub-region; determining the growth status of the crops planted in the sub-region to be monitored as a first level if the first average value is lower than the lower limit of the first value range by a first preset proportion; determining the growth status of the crops planted in the sub-region to be monitored as a second level if the first average value is within the first preset range of the first value range; and determining the growth status of the crops planted in the sub-region to be monitored as a third level if the first average value is higher than the upper limit of the first value range by a first preset proportion.

[0074] In one embodiment, determining the target sub-regions within multiple sub-regions based on the growth level includes: when the growth level is a second preset level, determining each remote sensing grid cell within the sub-region to be monitored as multiple target sub-regions.

[0075] In one embodiment, determining the growth status of a monitored sub-region based on remote sensing data of a target sub-region includes: determining a second numerical range of remote sensing data within the monitored sub-region; determining a first remote sensing value for each target sub-region; determining the growth status of crops planted in the target sub-region as a first level if the first remote sensing value is lower than the lower limit of the second numerical range by a second preset proportion; determining the growth status of crops planted in the target sub-region as a third level if the first remote sensing value is higher than the upper limit of the second numerical range by a second preset proportion; and determining the growth status of crops planted in all target sub-regions except for the target sub-regions at the first level and the target sub-regions at the third level as the second level.

[0076] In one embodiment, determining the target sub-region among multiple sub-regions based on the growth level includes: when the growth level is the third preset level, identifying all sub-regions within the agricultural area that have the same planting plan as the sub-region to be monitored as the target sub-region, wherein the planting plan includes planting time and planting type.

[0077] In one embodiment, determining the growth status of a sub-region to be monitored based on remote sensing data of a target sub-region includes: generating a frequency distribution histogram of the target sub-region based on remote sensing data within the target sub-region, and determining a third numerical interval of the remote sensing data within the target sub-region; determining a second remote sensing value for each remote sensing grid cell within the sub-region to be monitored; and determining the growth status of crops planted in each remote sensing grid cell within the sub-region to be monitored based on the skewness of the frequency distribution histogram, the third numerical interval, and the second remote sensing value.

[0078] In one embodiment, determining the growth status of crops planted in each remote sensing grid cell within the monitored sub-region based on the skewness of the frequency distribution histogram, a third numerical interval, and a second remote sensing value includes: when the skewness is within a first skewness range, for each remote sensing grid cell, if the second remote sensing value is lower than the lower limit of the third numerical interval by a third preset proportion, determining the growth status of crops in the remote sensing grid cell as a first level; for each remote sensing grid cell, if the second remote sensing value is within a second preset range of the third numerical interval, determining the growth status of crops in the remote sensing grid cell as a third level; and for all remote sensing grid cells, determining the growth status of crops in remote sensing grid cells other than those at the first level and the third level as a second level.

[0079] In one embodiment, determining the growth status of crops planted in each remote sensing grid cell within the monitored sub-region based on the skewness of the frequency distribution histogram, a third numerical interval, and a second remote sensing value includes: when the skewness is within the second skewness range, for each remote sensing grid cell, if a second preset proportion is determined to be higher than the upper limit of the third numerical interval, determining the growth status of crops in the remote sensing grid cell as abnormal; for each remote sensing grid cell, if the second remote sensing value is determined to be within the third preset range of the third numerical interval, determining the growth status of crops in the remote sensing grid cell as first level; for each remote sensing grid cell, if the second remote sensing value is determined to be within the fourth preset range of the third numerical interval, determining the growth status of crops in the remote sensing grid cell as third level; and for all remote sensing grid cells, determining the growth status of crops in remote sensing grid cells other than those at the first level, the third level, and the abnormal remote sensing grid cells as second level.

[0080] In one embodiment, determining the growth status of crops planted in each remote sensing grid cell within the monitored sub-region based on the skewness of the frequency distribution histogram, a third numerical interval, and a second remote sensing value includes: when the skewness is within the third skewness range, for each remote sensing grid cell, if a first preset proportion is determined to be higher than the upper limit of the third numerical interval, determining the growth status of crops in the remote sensing grid cell as abnormal; for each remote sensing grid cell, if a fifth preset range is determined to be within the third numerical interval, determining the growth status of crops in the remote sensing grid cell as first level; for each remote sensing grid cell, if a sixth preset range is determined to be within the third numerical interval, determining the growth status of crops in the remote sensing grid cell as third level; and for all remote sensing grid cells, determining the growth status of crops in remote sensing grid cells other than those at first level, second level, and abnormal grid cells as second level.

[0081] In one embodiment, determining the growth status of crops planted in each remote sensing grid cell within the monitored sub-region based on the skewness of the frequency distribution histogram, the third numerical interval, and the second remote sensing value includes: when the skewness is within the fourth skewness range, for each remote sensing grid cell, if a second preset proportion is determined to be lower than the lower limit of the third numerical interval, the growth status of crops in the remote sensing grid cell is determined to be abnormal; for each remote sensing grid cell, if the second remote sensing value is determined to be within the seventh preset range of the third numerical interval, the growth status of crops in the remote sensing grid cell is determined to be at the first level; for each remote sensing grid cell, if the second remote sensing value is determined to be within the eighth preset range of the third numerical interval, the growth status of crops in the remote sensing grid cell is determined to be at the third level; for all remote sensing grid cells, the growth status of crops in remote sensing grid cells other than those at the first level, the second level, and the abnormal remote sensing grid cells is determined to be at the second level.

[0082] In one embodiment, determining the growth status of crops planted in each remote sensing grid cell within the monitored sub-region based on the skewness of the frequency distribution histogram, the third numerical interval, and the second remote sensing value includes: when the skewness is within the fifth skewness range, for each remote sensing grid cell, if the second remote sensing value is determined to be below the lower limit of the third numerical interval by a first preset proportion, the growth status of crops in the remote sensing grid cell is determined to be abnormal; for each remote sensing grid cell, if the second remote sensing value is determined to be within the ninth preset range of the third numerical interval, the growth status of crops in the remote sensing grid cell is determined to be at the first level; for each remote sensing grid cell, if the second remote sensing value is determined to be within the tenth preset range of the third numerical interval, the growth status of crops in the remote sensing grid cell is determined to be at the third level; for all remote sensing grid cells, the growth status of crops in remote sensing grid cells other than those at the first level, the second level, and the abnormal remote sensing grid cells is determined to be at the second level.

[0083] 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.

[0084] 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, as well as 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, create a machine for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0085] 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.

[0086] 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.

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

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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 monitoring crop growth based on remote sensing data, characterized in that, The method includes: Remote sensing data of an agricultural region, which includes multiple sub-regions, are used to determine the agricultural region. For any sub-region to be monitored, a frequency distribution histogram of the sub-region to be monitored is generated based on the remote sensing data of the sub-region to be monitored. The growth level of crops planted in the sub-region to be monitored is determined based on the kurtosis value of the frequency distribution histogram, where the growth level refers to the vegetation dispersion in the sub-region to be monitored. Determine the target sub-region among the multiple sub-regions based on the growth level; The growth status of the sub-region to be monitored is determined based on the remote sensing data of the target sub-region. The step of determining the target sub-region among the plurality of sub-regions based on the growth level includes: When the growth level is the first preset level, the sub-regions within a preset range around the sub-region to be monitored that have the same planting plan as the sub-region to be monitored are identified as target sub-regions, wherein the planting plan includes planting time and planting type; When the growth level is the second preset level, each remote sensing grid unit in the sub-region to be monitored is determined as multiple target sub-regions; When the growth level is the third preset level, all sub-regions within the agricultural area that have the same planting plan as the sub-region to be monitored are identified as target sub-regions, wherein the planting plan includes planting time and planting type.

2. The method for monitoring crop growth based on remote sensing data according to claim 1, characterized in that, Determining the growth status of the monitored sub-region based on the remote sensing data of the target sub-region includes: Determine the first average value of the remote sensing data in the sub-region to be monitored and the first value range of the remote sensing data in the target sub-region; If the first average value is lower than the lower limit of the first value range by a first preset proportion, the growth status of the crops planted in the sub-region to be monitored is determined to be the first level. If the first average value is within a first preset range of the first value interval, the growth status of the crops planted in the sub-region to be monitored is determined to be at the second level. When the first average value is higher than the upper limit of the first value range by the first preset proportion, the growth status of the crops planted in the sub-region to be monitored is determined to be level three.

3. The method for monitoring crop growth based on remote sensing data according to claim 1, characterized in that, Determining the growth status of the monitored sub-region based on the remote sensing data of the target sub-region includes: Determine the second numerical range of remote sensing data within the sub-region to be monitored; For each target sub-region, determine the first remote sensing value of the target sub-region; For each target sub-region, if the first remote sensing value is lower than the lower limit of the second value range by a second preset ratio, the growth status of the crops planted in the target sub-region is determined to be at the first level. For each target sub-region, if the first remote sensing value is higher than the upper limit of the second value range by the second preset ratio, the growth status of the crops planted in the target sub-region is determined to be the third level. For all target sub-regions, the growth status of crops planted in the other target sub-regions, excluding the first-level target sub-regions and the third-level target sub-regions, is classified as the second level.

4. The method for monitoring crop growth based on remote sensing data according to claim 1, characterized in that, Determining the growth status of the monitored sub-region based on the remote sensing data of the target sub-region includes: Generate a frequency distribution histogram of the target sub-region based on the remote sensing data within the target sub-region, and determine the third numerical interval of the remote sensing data within the target sub-region; Determine the second remote sensing value of each remote sensing grid cell within the sub-region to be monitored; The growth status of crops planted in each remote sensing grid cell within the monitored sub-region is determined based on the skewness of the frequency distribution histogram, the third numerical interval, and the second remote sensing value.

5. The method for monitoring crop growth based on remote sensing data according to claim 4, characterized in that, The step of determining the growth status of crops planted in each remote sensing grid cell within the monitored sub-region based on the skewness of the frequency distribution histogram, the third numerical interval, and the second remote sensing value includes: When the skewness is within the first skewness range, for each remote sensing grid cell, when the second remote sensing value is lower than the lower limit of the third value range by a third preset ratio, the growth status of the crops in the remote sensing grid cell is determined to be at the first level. For each remote sensing grid cell, if the second remote sensing value is within a second preset range of the third value interval, the growth status of the crops in the remote sensing grid cell is determined to be at the third level. For all remote sensing grid cells, the growth status of crops in remote sensing grid cells other than those in the first level and the third level is determined as the second level.

6. The method for monitoring crop growth based on remote sensing data according to claim 4, characterized in that, The step of determining the growth status of crops planted in each remote sensing grid cell within the monitored sub-region based on the skewness of the frequency distribution histogram, the third numerical interval, and the second remote sensing value includes: When the skewness is within the second skewness range, for each remote sensing grid cell, if the second remote sensing value is determined to be higher than the upper limit of the third value range by a second preset proportion, the growth status of the crops in the remote sensing grid cell is determined to be abnormal. For each remote sensing grid cell, if the second remote sensing value is determined to be within a third preset range of the third value interval, the growth status of the crops in the remote sensing grid cell is determined to be at the first level. For each remote sensing grid cell, if the second remote sensing value is determined to be within a fourth preset range of the third value interval, the growth status of the crops in the remote sensing grid cell is determined to be at the third level. For all remote sensing grid cells, the crop growth status in remote sensing grid cells other than the first-level remote sensing grid cells, the third-level remote sensing grid cells, and the abnormal remote sensing grid cells is determined as the second level.

7. The method for monitoring crop growth based on remote sensing data according to claim 4, characterized in that, The step of determining the growth status of crops planted in each remote sensing grid cell within the monitored sub-region based on the skewness of the frequency distribution histogram, the third numerical interval, and the second remote sensing value includes: When the skewness is within the third skewness range, for each remote sensing grid cell, if the second remote sensing value is determined to be higher than the upper limit of the third value range by a first preset proportion, the growth status of the crops in the remote sensing grid cell is determined to be abnormal. For each remote sensing grid cell, if the second remote sensing value is determined to be within the fifth preset range of the third value interval, the growth status of the crops in the remote sensing grid cell is determined to be the first level. For each remote sensing grid cell, if the second remote sensing value is determined to be within the sixth preset range of the third value interval, the growth status of the crops in the remote sensing grid cell is determined to be the third level. For all remote sensing grid cells, the crop growth status in remote sensing grid cells other than the first-level grid cells, the third-level remote sensing grid cells, and the abnormal grid cells is determined as the second level.

8. The method for monitoring crop growth based on remote sensing data according to claim 4, characterized in that, The step of determining the growth status of crops planted in each remote sensing grid cell within the monitored sub-region based on the skewness of the frequency distribution histogram, the third numerical interval, and the second remote sensing value includes: When the skewness is within the fourth skewness range, for each remote sensing grid cell, if the second remote sensing value is determined to be lower than the lower limit of the third value range by a second preset proportion, the growth status of the crops in the remote sensing grid cell is determined to be abnormal. For each remote sensing grid cell, if the second remote sensing value is determined to be within the seventh preset range of the third value interval, the growth status of the crops in the remote sensing grid cell is determined to be the first level. For each remote sensing grid cell, if the second remote sensing value is determined to be within the eighth preset range of the third value interval, the growth status of the crops in the remote sensing grid cell is determined to be the third level. For all remote sensing grid cells, the crop growth status in remote sensing grid cells other than the first-level remote sensing grid cells, the third-level remote sensing grid cells, and the abnormal remote sensing grid cells is determined as the second level.

9. The method for monitoring crop growth based on remote sensing data according to claim 4, characterized in that, The step of determining the growth status of crops planted in each remote sensing grid cell within the monitored sub-region based on the skewness of the frequency distribution histogram, the third numerical interval, and the second remote sensing value includes: When the skewness is within the fifth skewness range, for each remote sensing grid cell, if the second remote sensing value is determined to be lower than the lower limit of the third value range by a first preset proportion, the growth status of the crops in the remote sensing grid cell is determined to be abnormal. For each remote sensing grid cell, if the second remote sensing value is determined to be within the ninth preset range of the third value interval, the growth status of the crops in the remote sensing grid cell is determined to be the first level. For each remote sensing grid cell, if the second remote sensing value is determined to be within the tenth preset range of the third value interval, the growth status of the crops in the remote sensing grid cell is determined to be the third level. For all remote sensing grid cells, the crop growth status in remote sensing grid cells other than the first-level grid cells, the third-level grid cells, and the abnormal remote sensing grid cells is determined as the second level.

10. A processor, characterized in that, It is configured to perform a method for monitoring crop growth based on remote sensing data as described in any one of claims 1 to 9.

11. A device for monitoring crop growth based on remote sensing data, characterized in that, Includes the processor as described in claim 10.

12. A machine-readable storage medium, characterized in that, The machine-readable storage medium stores instructions that, when executed by a processor, configure the processor to perform a method for monitoring growth based on remote sensing data according to any one of claims 1 to 9.

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