Rice planting type identification method and device and electronic equipment

Abstract

The invention discloses a rice planting type identification method and device and electronic equipment, and belongs to the technical field of computers. The method comprises the following steps: acquiring target optical data of a target region in each rice growth stage based on surface reflectance data of the target region in each rice growth stage; the target optical data expresses land characteristics of the target area in each rice growth stage; based on the target optical data of each rice growth stage, obtaining a rice planting type of the target area through land features corresponding to different rice planting types of each rice growth stage; the land features comprise moisture content features and optical features of the target area in each rice growth period. According to the rice planting type identification method disclosed by the invention, the problem that the identification of the rice planting type is difficult to be effectively carried out is solved.

Description

Technical Field

[0001] The present application belongs to the field of computer technology, and in particular relates to a method, device and electronic equipment for identifying rice planting types. Background Art

[0002] As an important staple food crop, rice cultivation area is crucial for ensuring food security. Generally, satellite remote sensing technology can be used to obtain large-scale spatial distribution information on rice cultivation areas. This information can then be used to identify rice planting types using trained machine learning models.

[0003] However, since a large number of training samples are required in the process of training machine learning models, it is difficult to effectively identify rice planting types when the training samples do not cover rice planting types (such as direct-seeded rice and transplanted rice) or the sample size is insufficient. Summary of the Invention

[0004] The present application aims to solve at least one of the technical problems existing in the prior art. To this end, the present application proposes a rice planting type identification method, device and electronic device to solve the problem of difficulty in effectively carrying out rice planting type identification.

[0005] In a first aspect, the present application provides a method for identifying rice planting types, the method comprising: Based on the surface reflectance data of the target area at each rice growth stage, target optical data of the target area at each rice growth stage is obtained; the target optical data expresses the land characteristics of the target area at each rice growth stage; Based on the target optical data of each rice growth stage, the rice planting type of the target area is obtained through the land characteristics corresponding to different rice planting types in each rice growth stage; the land characteristics include the moisture content characteristics and optical characteristics of the target area in each rice growth period.

[0006] According to the rice planting type identification method of the present application, the target optical data of the target area at each rice growth stage is obtained based on the surface reflectance data of the target area at each rice growth stage. Based on the target optical data at each rice growth stage, the rice planting type of the target area is obtained by using the land characteristics corresponding to different rice planting types at each rice growth stage. In this way, the rice planting type of the target area can be obtained without collecting samples for training the machine learning model, thereby solving the problem of difficulty in effectively carrying out rice planting type identification.

[0007] According to one embodiment of the present application, each rice growth stage includes a transplanting period, a growing period, and a ripening period. Based on target optical data of each rice growth stage, the rice planting type of the target area is obtained by using land features corresponding to different rice planting types in each rice growth stage, including: Obtaining a first judgment result based on the target optical data during the transplanting period and the growing period; the first judgment result indicates whether transplanted rice exists in the target area; Obtaining a second judgment result based on the optical data of the target during the growth period and the maturity period; the second judgment result indicates whether the crop planted in the target area is rice; Based on the first judgment result and the second judgment result, the rice planting type of the target area is obtained.

[0008] According to one embodiment of the present application, the target optical data includes land surface moisture index data and enhanced vegetation index data; and obtaining a second judgment result based on the target optical data during the growth period and the maturity period includes: A second judgment result is obtained based on the enhanced vegetation index data of the growing period and the land surface moisture index data and the enhanced vegetation index data of the mature period.

[0009] According to one embodiment of the present application, obtaining a second judgment result based on the enhanced vegetation index data of the growing period and the land surface moisture index data and enhanced vegetation index data of the mature period includes: When the enhanced vegetation index data in the growth period reaches the first indicator, the land surface moisture index data in the maturity period reaches the second indicator, and the enhanced vegetation index data in the maturity period reaches the second indicator, a second judgment result of planting rice in the target area is determined.

[0010] According to one embodiment of the present application, obtaining the rice planting type of the target area based on the first judgment result and the second judgment result includes: When the first judgment result indicates that there is no transplanted rice in the target area, and the second judgment result indicates that rice is planted in the target area, it is determined that the rice planting type in the target area is direct seeding.

[0011] According to one embodiment of the present application, obtaining a first judgment result based on target optical data during the transplanting period and the growth period includes: Based on the target optical data during the transplanting period, a flooding signal of the target area is obtained; the flooding signal indicates whether the water level in the target area is higher than the soil; A first judgment result is obtained based on the target optical data during the growth period and the flooding signal.

[0012] According to one embodiment of the present application, after obtaining the rice planting type of the target area based on the target optical data of each rice growth stage and using the land features corresponding to different rice planting types at each rice growth stage, the method further includes: Obtain the backscatter coefficient of the target area at each rice growth stage; Based on the backscatter coefficients of each rice growth stage, a target enhancement operation is performed on the rice planting type in the target area; the target enhancement operation is used to eliminate interference caused by environmental factors around the target area.

[0013] According to one embodiment of the present application, a target enhancement operation is performed on the rice planting type in the target area based on the backscatter coefficient of each rice growth stage, including: When the rice planting type in the target area is direct seeding and the backscatter coefficient in the maturity period is smaller than the backscatter coefficient in the growth period, a target enhancement operation is performed on the rice planting type in the target area.

[0014] In a second aspect, the present application provides a rice planting type identification device, comprising: The first acquisition module is used to acquire target optical data of the target area at each rice growth stage; The second acquisition module is used to obtain the rice planting type of the target area based on the target optical data of each rice growth stage through the rice planting type analysis model; the rice planting type analysis model is used to obtain the rice planting type based on the moisture content characteristics of the target area at each rice growth period.

[0015] According to the rice planting type identification device of the present application, target optical data of the target area at each rice growth stage is obtained based on the surface reflectivity data of the target area at each rice growth stage. Based on the target optical data at each rice growth stage, the rice planting type of the target area is obtained through the land characteristics corresponding to different rice planting types at each rice growth stage. In this way, the rice planting type of the target area can be obtained without collecting samples for training the machine learning model, thereby solving the problem of difficulty in effectively carrying out rice planting type identification.

[0016] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the rice planting type identification method of the first aspect.

[0017] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the rice planting type identification method according to the first aspect.

[0018] In a fifth aspect, the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of the rice planting type identification method in the first aspect.

[0019] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become obvious from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which: Figure 1 This is one of the flow charts of the rice planting type identification method provided in the embodiment of the present application; Figure 2 This is the second flow chart of the rice planting type identification method provided in the embodiment of the present application; Figure 3 Schematic diagram of the structure of the rice planting type identification device provided in an embodiment of the present application; Figure 4 It is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] The following will be combined with the accompanying drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the embodiments described are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of this application.

[0022] The terms "first," "second," and the like in the specification and claims of this application are used to distinguish similar objects, and are not used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of this application can be implemented in an order other than that illustrated or described herein, and that the objects distinguished by "first," "second," and the like are generally of the same type, and do not limit the number of objects; for example, the first object can be one or more. In addition, the term "and / or" in the specification and claims refers to at least one of the connected objects, and the character " / " generally indicates that the objects connected are in an "or" relationship.

[0023] The rice planting type identification method, device and electronic device provided in the embodiments of the present application are described in detail below with reference to the accompanying drawings through specific embodiments and their application scenarios.

[0024] The rice planting type identification method may be applied to a terminal, and may be specifically executed by hardware or software in the terminal.

[0025] The terminal includes, but is not limited to, a portable communication device such as a mobile phone or tablet computer having a touch-sensitive surface (e.g., a touch screen display and / or a touch pad). It should also be understood that in some embodiments, the terminal may not be a portable communication device, but rather a desktop computer having a touch-sensitive surface (e.g., a touch screen display and / or a touch pad).

[0026] In the following embodiments, a terminal including a display and a touch-sensitive surface is described. However, it should be understood that the terminal may include one or more other physical user interface devices such as a physical keyboard, a mouse, and a joystick.

[0027] The rice planting type identification method provided in the embodiment of the present application can be executed by an electronic device or a functional module or functional entity in the electronic device that can implement the rice planting type identification method. The electronic devices mentioned in the embodiment of the present application include but are not limited to mobile phones, tablets, computers, cameras and wearable devices, etc. The rice planting type identification method provided in the embodiment of the present application is explained below using an electronic device as an example of the execution subject.

[0028] like Figure 1 As shown, the rice planting type identification method includes: step 110 and step 120.

[0029] Step 110: Based on the surface reflectance data of the target area at each rice growth stage, target optical data of the target area at each rice growth stage is obtained; the target optical data expresses the land characteristics of the target area at each rice growth stage.

[0030] In actual implementation, the target area may be land belonging to any region, and the target area may also be land where crops of any rice planting type are planted.

[0031] In actual implementation, surface reflectance data of the target area at each rice growth stage can be obtained based on target remote sensing technology. In some embodiments, images of the target area at each rice growth stage can be obtained based on a target satellite, and the surface reflectance data of the target area at each rice growth stage can be obtained based on the images of the target area at each rice growth stage.

[0032] In actual implementation, the surface reflectance data may include red band reflectance, near infrared reflectance, blue band reflectance, shortwave infrared reflectance, etc.

[0033] In actual implementation, the growth stages of rice may include the planting period, the growing period and the ripening period.

[0034] In actual implementation, the target optical data may include at least one of land surface wetness index data and enhanced vegetation index data.

[0035] In some embodiments, the enhanced vegetation index data for each rice growth stage may include at least one of a mean value or a maximum value of the enhanced vegetation index for the rice growth stage.

[0036] In actual implementation, the enhanced vegetation index for each rice growth stage can be obtained based on the following formula:

[0037] in, represents the reflectivity of the red band, represents the near-infrared reflectivity, It represents the reflectance of the blue band, and EVI represents the Enhanced Vegetation Index.

[0038] In some embodiments, the land surface moisture index data for each rice growth stage may include at least one of a mean value or a maximum value of the land surface moisture index for that rice growth stage.

[0039] In actual implementation, the land surface moisture index at each rice growth stage can be obtained based on the following formula:

[0040] in, represents the reflectivity of short-wave infrared, represents the near-infrared reflectance, and LSWI represents the land surface wetness index.

[0041] Step 120: Based on the target optical data at each rice growth stage, the rice planting type of the target area is obtained through land characteristics corresponding to different rice planting types at each rice growth stage; the land characteristics include moisture content characteristics and optical characteristics of the target area at each rice growth period.

[0042] In actual implementation, the land characteristics may be characteristics representing land water content and vegetation coverage.

[0043] In some embodiments, based on the target optical data of each rice growth stage in the target area, it can be determined whether the target area carries land characteristics corresponding to different rice planting types in each rice growth stage, and then the rice planting type of the target area is obtained.

[0044] In some embodiments, after obtaining the rice planting type of the target area, an enhancement operation may be performed on the target rice planting type to eliminate interference and influence of the surrounding environment of the target area.

[0045] According to the rice planting type identification method of the present application, the target optical data of the target area at each rice growth stage is obtained based on the surface reflectance data of the target area at each rice growth stage. Based on the target optical data at each rice growth stage, the rice planting type of the target area is obtained by using the land characteristics corresponding to different rice planting types at each rice growth stage. In this way, the rice planting type of the target area can be obtained without collecting samples for training the machine learning model, thereby solving the problem of difficulty in effectively carrying out rice planting type identification.

[0046] In some embodiments, the various rice growth stages include the transplanting period, the growing period, and the maturity period; a first judgment result can be obtained based on the target optical data of the transplanting period and the growing period; the first judgment result indicates whether there is transplanted rice in the target area; a second judgment result is obtained based on the target optical data of the growing period and the maturity period; the second judgment result indicates whether the crop planted in the target area is rice; based on the first judgment result and the second judgment result, the rice planting type in the target area is obtained.

[0047] In actual implementation, the transplanting period refers to the stage when rice planted in transplanting mode grows from seeds into seedlings in flooded land and is transplanted from the flooded land to the target area; the growing period refers to the stage when rice grows from seedlings to the stage when the whole rice turns yellow; and the maturity period refers to the stage when rice undergoes grain filling to maturity and harvest.

[0048] In actual implementation, a first judgment can be made on the target area based on the target optical data during the transplanting period and the growth period. The first judgment is used to determine whether there is transplanted rice in the target area.

[0049] In actual implementation, a second judgment can be made on the target area based on the target optical data at the growth stage and the maturity stage, and the second judgment is used to determine whether the planted crop is rice.

[0050] In some embodiments, a second judgment of the target area may be made based on the land surface moisture index data and enhanced vegetation index data during the growing period and the mature period.

[0051] In some embodiments, the first judgment result indicates that transplanted rice is present in the target area as a first state, and the first judgment result indicates that transplanted rice is not present in the target area as a second state. The second judgment result indicates that the crop planted in the target area is rice as a first state, and the second judgment result indicates that the crop planted in the target area is a non-rice crop, such as corn, potatoes, or peanuts as a second state. Both the first judgment result and the second judgment result can be represented by numerical values, symbols, or any other theoretically feasible method.

[0052] According to the rice planting type identification method of the present application, a first judgment result is obtained based on the target optical data during the transplanting period and the growing period; the first judgment result indicates whether there is transplanted rice in the target area; a second judgment result is obtained based on the target optical data during the growing period and the ripening period; the second judgment result indicates whether the crop planted in the target area is rice; based on the first judgment result and the second judgment result, the rice planting type of the target area is obtained. In this way, the rice planting type of the target area can be obtained without collecting samples for training the machine learning model, thereby solving the problem of difficulty in effectively carrying out the identification of rice planting types.

[0053] In some embodiments, the target optical data includes land surface moisture index data and enhanced vegetation index data; the second judgment result can be obtained based on the enhanced vegetation index data in the growing period and the land surface moisture index data and enhanced vegetation index data in the mature period.

[0054] In some embodiments, a second judgment can be made on the target area based on the enhanced vegetation index data during the growth period and the land surface moisture index data and enhanced vegetation index data during the maturity period to obtain a second judgment result to determine whether the planted crop is rice.

[0055] In some embodiments, based on the enhanced vegetation index data of the growing period and the land surface moisture index data and enhanced vegetation index data of the maturing period, it can be determined whether the growth period of the target area includes the land characteristics of the rice growing period, and whether the maturing period of the target area includes the land characteristics of the rice maturing period, so as to obtain a second judgment result to determine whether the planted crop is rice.

[0056] According to the rice planting type identification method of the present application, a first judgment result is obtained based on the target optical data during the transplanting period and the growing period; the first judgment result indicates whether there is transplanted rice in the target area; a second judgment result is obtained based on the enhanced vegetation index data during the growing period and the land surface moisture index data and enhanced vegetation index data during the maturity period; based on the first judgment result and the second judgment result, the rice planting type of the target area is obtained. In this way, the rice planting type of the target area can be obtained without collecting samples for training the machine learning model, thereby solving the problem of difficulty in effectively carrying out the identification of rice planting types.

[0057] In some embodiments, when the enhanced vegetation index data in the growth period reaches a first indicator, the land surface moisture index data in the maturity period reaches a second indicator, and the enhanced vegetation index data in the maturity period reaches a second indicator, a second judgment result of planting rice in the target area is determined.

[0058] In practice, non-rice crops often reach extreme dryness during their maturation phase, while rice-grown fields often maintain a relatively moist canopy. This can be used as a basis for determining whether a target area is suitable for rice planting. A Land Surface Moisture Index reaching the second indicator during maturity indicates that the target area's field canopy remains relatively moist.

[0059] In actual implementation, when the field canopy is still in a relatively high moist state during the maturity period, the crop growth is generally poor due to the influence of water bodies. The influence of water bodies during the maturity period can be removed based on the enhanced vegetation index data reaching the second indicator during the maturity period.

[0060] In actual implementation, due to the phenomenon of temporary water level reduction in land flooded during the non-mature period, the enhanced vegetation index data during the growing period reaching the first indicator can be used as one of the bases to determine whether the target area is covered by vegetation to remove the above interference.

[0061] In actual implementation, the enhanced vegetation index data during the growing season may include the maximum value of the enhanced vegetation index during each time period during the growing season. The land surface moisture index data during the maturity period can include the average value of the land surface moisture index in each time period during the maturity period. The enhanced vegetation index data of the mature period can include the average value of the enhanced vegetation index of each time period of the mature period. .

[0062] In some embodiments, the second judgment result can be obtained by the following formula:

[0063] in, Indicates the second judgment result, =1 indicates the second judgment result indicating that rice is planted in the target area, =0 represents the second judgment result indicating that non-rice crops are planted in the target area, A represents the second indicator, and B represents the first indicator.

[0064] In some embodiments, the maximum value of the enhanced vegetation index at each time period during the growing season can be used. Greater than 0.5, the average value of the land surface moisture index in each time period of the maturity period The average value of the enhanced vegetation index greater than or equal to 0.4 in each time period of maturity When the value is greater than or equal to 0.4, the target area is determined to be planted with rice.

[0065] According to the rice planting type identification method of the present application, a first judgment result is obtained based on the target optical data during the transplanting period and the growing period; the first judgment result indicates whether there is transplanted rice in the target area; a second judgment result is obtained based on the enhanced vegetation index data during the growing period and the land surface moisture index data and enhanced vegetation index data during the maturity period; based on the first judgment result and the second judgment result, the rice planting type of the target area is obtained. In this way, the rice planting type of the target area can be obtained without collecting samples for training the machine learning model, thereby solving the problem of difficulty in effectively carrying out the identification of rice planting types.

[0066] In some embodiments, when the first judgment result indicates that there is no transplanted rice in the target area and the second judgment result indicates that rice is planted in the target area, the rice planting type in the target area may be determined to be direct seeding.

[0067] In actual implementation, rice planting types may include transplanting or direct seeding.

[0068] In actual implementation, when the first judgment result is the second state (i.e., there is no transplanted rice in the target area) and the second judgment result is the first state (i.e., the crop planted in the target area is rice), it can be determined that the rice planting type in the target area is direct seeding.

[0069] In some embodiments, the rice planting type of the target area can be determined based on the following formula: ; in, Indicates the rice planting type in the target area, =1 indicates the second judgment result indicating that rice is planted in the target area, =0 indicates that there is no transplanted rice in the target area. =0 means the target area is planted with non-direct-seeded rice. Indicates that the rice planting type in the target area is direct seeding.

[0070] According to the rice planting type identification method of the present application, a first judgment result is obtained based on the target optical data during the transplanting period and the growing period; the first judgment result indicates whether there is transplanted rice in the target area; a second judgment result is obtained based on the enhanced vegetation index data during the growing period and the land surface moisture index data and enhanced vegetation index data during the maturity period; based on the first judgment result and the second judgment result, the rice planting type of the target area is obtained. In this way, the rice planting type of the target area can be obtained without collecting samples for training the machine learning model, thereby solving the problem of difficulty in effectively carrying out the identification of rice planting types.

[0071] In some embodiments, a flooding signal of the target area can be obtained based on the target optical data during the transplanting period; the flooding signal indicates whether the water level line of the target area is higher than the soil; and a first judgment result is obtained based on the target optical data and the flooding signal during the growing period.

[0072] In actual implementation, the target optical data during the transplanting period may include an enhanced vegetation index EVIi (i is a positive integer greater than 1) and a land surface wetness index LSWIi of each of several periods during the transplanting period.

[0073] In some embodiments, the flooding signal of each of several cycles during the transplanting period of the target area can be obtained based on the following formula: ; in, i represents the flooding signal of the i-th cycle, i=1 means that there is transplantation in the i-th cycle, i = 0 means that there is no transplantation phenomenon in the i-th cycle.

[0074] In some embodiments, when transplanting occurs in any period of the transplanting period of the target area, it is determined that the water level of the target area is higher than the soil, ie, F=1.

[0075] In some embodiments, the target optical data during the growing period may further include an EVImean of the enhanced vegetation index in each period of the growing period.

[0076] In some embodiments, when the water level of the target area during the transplanting period is higher than the soil and the target optical data during the growth period meets the third standard, the first judgment result is determined to be the first state, that is, transplanted rice exists in the target area.

[0077] In some embodiments, the first judgment result may be obtained based on the following formula: ; in, Indicates the first judgment result, =0 means there is no transplanted rice in the target area; =1 means there is transplanted rice in the target area, It means that the water level in the target area during the transplanting period is higher than the soil level. It means that the water level in the target area is lower than the soil during the transplanting period.

[0078] According to the rice planting type identification method of the present application, a flooding signal of the target area is obtained based on the target optical data during the transplanting period; the flooding signal indicates whether the water level line of the target area is higher than the soil; a first judgment result is obtained based on the target optical data and the flooding signal during the growth period; the first judgment result indicates whether there is transplanted rice in the target area; a second judgment result is obtained based on the enhanced vegetation index data during the growth period and the land surface moisture index data and enhanced vegetation index data during the maturity period; based on the first judgment result and the second judgment result, the rice planting type of the target area is obtained. In this way, the rice planting type of the target area can be obtained without collecting samples for training the machine learning model, thereby solving the problem of difficulty in effectively carrying out the identification of rice planting types.

[0079] In some embodiments, after obtaining the rice planting type of the target area based on the target optical data of each rice growth stage through the land characteristics corresponding to different rice planting types in each rice growth stage, the backscattering coefficient of the target area in each rice growth stage can be obtained; based on the backscattering coefficient of each rice growth stage, the rice planting type of the target area is subjected to a target enhancement operation; the target enhancement operation is used to eliminate interference caused by environmental factors surrounding the target area.

[0080] In actual implementation, the backscatter coefficients of each rice growth stage can include the mean backscatter coefficient of the maturity stage. and the mean backscatter coefficient during the growth period .

[0081] In some embodiments, the target procedure can be used to determine the mean of the backscatter coefficients during the mature period. , the mean backscatter coefficient during the growth period The geographical location is matched with the rice planting type of the target area, and the target enhancement operation is performed based on the matched rice planting type of the target area.

[0082] In some embodiments, since the moisture content of the rice field of direct-seeded rice in the mature stage is still relatively low, the backscatter coefficient in the mature stage is lower than that in the growth stage. Based on this, target enhancement operations can be performed on the rice planting type in the target area.

[0083] According to the rice planting type identification method of the present application, after obtaining the rice planting type of the target area, the backscatter coefficient of the target area at each rice growth stage is obtained; based on the backscatter coefficient of each rice growth stage, a target enhancement operation is performed on the rice planting type of the target area to eliminate the interference of environmental factors around the target area on the obtained rice planting type of the target area, so as to improve the accuracy of obtaining the rice planting type of the target area.

[0084] In some embodiments, when the rice planting type in the target area is direct seeding and the backscatter coefficient in the maturity period is less than the backscatter coefficient in the growth period, a target enhancement operation is performed on the rice planting type in the target area.

[0085] In actual implementation, the target enhancement operation can be performed on the rice planting type in the target area based on the following formula: ; in, represents the rice planting type in the target area after the target enhancement operation, =1 means the rice planting type in the target area after the target enhancement operation is direct seeding, =0 means that the rice planting type in the target area after the target enhancement operation is non-direct seeding, It indicates that the backscatter coefficient in the mature stage is smaller than that in the growth stage.

[0086] According to the rice planting type identification method of the present application, after obtaining the rice planting type of the target area, the backscatter coefficient of the target area at each rice growth stage is obtained; based on the backscatter coefficient of each rice growth stage, a target enhancement operation is performed on the rice planting type of the target area to eliminate the interference of environmental factors around the target area on the obtained rice planting type of the target area, so as to improve the accuracy of obtaining the rice planting type of the target area.

[0087] In order to better understand the rice planting type identification method provided in the embodiment of the present application, further explanation is given below. It should be understood that the following discussion is only exemplary.

[0088] This application provides a method for identifying rice planting types, and the specific steps can be as follows: Figure 2 As shown: Step 210: Based on the surface reflectance data of the target area at each rice growth stage, target optical data of the target area at each rice growth stage is obtained; the target optical data expresses the land characteristics of the target area at each rice growth stage.

[0089] In actual implementation, the target area may be land belonging to any region, and the target area may also be land where crops of any rice planting type are planted.

[0090] In actual implementation, surface reflectance data of the target area at each rice growth stage can be obtained based on target remote sensing technology. In some embodiments, images of the target area at each rice growth stage can be obtained based on a target satellite, and the surface reflectance data of the target area at each rice growth stage can be obtained based on the images of the target area at each rice growth stage.

[0091] In actual implementation, the surface reflectance data may include red band reflectance, near infrared reflectance, blue band reflectance, shortwave infrared reflectance, etc.

[0092] In actual implementation, the growth stages of rice may include the planting period, the growing period and the ripening period.

[0093] In actual implementation, the target optical data may include at least one of land surface wetness index data and enhanced vegetation index data.

[0094] In some embodiments, the enhanced vegetation index data for each rice growth stage may include at least one of a mean value or a maximum value of the enhanced vegetation index for the rice growth stage.

[0095] In actual implementation, the enhanced vegetation index for each rice growth stage can be obtained based on the following formula:

[0096] in, represents the reflectivity of the red band, represents the near-infrared reflectivity, represents the reflectance of the blue band, and EVI represents the Enhanced Vegetation Index.

[0097] In some embodiments, the land surface moisture index data for each rice growth stage may include at least one of a mean value or a maximum value of the land surface moisture index for that rice growth stage.

[0098] In actual implementation, the land surface moisture index at each rice growth stage can be obtained based on the following formula:

[0099] in, represents the reflectivity of short-wave infrared, represents the near-infrared reflectance, and LSWI represents the land surface wetness index.

[0100] Step 220: Based on the target optical data during the transplanting period, a flooding signal of the target area is obtained; the flooding signal indicates whether the water level in the target area is higher than the soil; based on the target optical data during the growing period and the flooding signal, a first judgment result is obtained.

[0101] In actual implementation, the target optical data during the transplanting period may include an enhanced vegetation index EVIi (i is a positive integer greater than 1) and a land surface wetness index LSWIi of each of several periods during the transplanting period.

[0102] In some embodiments, the flooding signal of each of several cycles during the transplanting period of the target area can be obtained based on the following formula: ; in, i represents the flooding signal of the i-th cycle, i=1 means that there is transplantation in the i-th cycle, i = 0 means that there is no transplantation phenomenon in the i-th cycle.

[0103] In some embodiments, when transplanting occurs in any period of the transplanting period of the target area, it is determined that the water level of the target area is higher than the soil, ie, F=1.

[0104] In some embodiments, the target optical data during the growing period may further include an EVImean of the enhanced vegetation index in each period of the growing period.

[0105] In some embodiments, when the water level of the target area during the transplanting period is higher than the soil and the target optical data during the growth period meets the third standard, the first judgment result is determined to be the first state, that is, transplanted rice exists in the target area.

[0106] In some embodiments, the first judgment result may be obtained based on the following formula: ; in, Indicates the first judgment result, =0 means there is no transplanted rice in the target area; =1 means there is transplanted rice in the target area, It means that the water level in the target area during the transplanting period is higher than the soil level. It means that the water level in the target area is lower than the soil during the transplanting period.

[0107] Step 230: Obtain a second judgment result based on the enhanced vegetation index data of the growing period and the land surface moisture index data and enhanced vegetation index data of the mature period.

[0108] In some embodiments, a second judgment can be made on the target area based on the enhanced vegetation index data during the growth period and the land surface moisture index data and enhanced vegetation index data during the maturity period to obtain a second judgment result to determine whether the planted crop is rice.

[0109] In some embodiments, based on the enhanced vegetation index data of the growing period and the land surface moisture index data and enhanced vegetation index data of the maturing period, it can be determined whether the growth period of the target area includes the land characteristics of the rice growing period, and whether the maturing period of the target area includes the land characteristics of the rice maturing period, so as to obtain a second judgment result to determine whether the planted crop is rice.

[0110] In some embodiments, when the enhanced vegetation index data in the growth period reaches a first indicator, the land surface moisture index data in the maturity period reaches a second indicator, and the enhanced vegetation index data in the maturity period reaches a second indicator, a second judgment result of planting rice in the target area is determined.

[0111] In practice, non-rice crops often reach extreme dryness during their maturation phase, while rice-grown fields often maintain a relatively moist canopy. This can be used as a basis for determining whether a target area is suitable for rice planting. A Land Surface Moisture Index reaching the second indicator during maturity indicates that the target area's field canopy remains relatively moist.

[0112] In actual implementation, when the field canopy is still in a relatively high moist state during the maturity period, the crop growth is generally poor due to the influence of water bodies. The influence of water bodies during the maturity period can be removed based on the enhanced vegetation index data reaching the second indicator during the maturity period.

[0113] In actual implementation, due to the phenomenon of temporary water level reduction in land flooded during the non-mature period, the enhanced vegetation index data during the growing period reaching the first indicator can be used as one of the bases to determine whether the target area is covered by vegetation to remove the above interference.

[0114] In actual implementation, the enhanced vegetation index data during the growing season may include the maximum value of the enhanced vegetation index during each time period during the growing season. The land surface moisture index data during the maturity period can include the average value of the land surface moisture index in each time period during the maturity period. The enhanced vegetation index data of the mature period can include the average value of the enhanced vegetation index of each time period of the mature period. .

[0115] In some embodiments, the second judgment result can be obtained by the following formula:

[0116] in, Indicates the second judgment result, =1 indicates the second judgment result indicating that rice is planted in the target area, =0 represents the second judgment result indicating that non-rice crops are planted in the target area, A represents the second indicator, and B represents the first indicator.

[0117] In some embodiments, the maximum value of the enhanced vegetation index at each time period during the growing season can be used. Greater than 0.5, the average value of the land surface moisture index in each time period of the maturity period The average value of the enhanced vegetation index greater than or equal to 0.4 in each time period of maturity When the value is greater than or equal to 0.4, the target area is determined to be planted with rice.

[0118] Step 240: When the first judgment result indicates that there is no transplanted rice in the target area and the second judgment result indicates that rice is planted in the target area, determine that the rice planting type in the target area is direct seeding.

[0119] In actual implementation, rice planting types may include transplanting or direct seeding.

[0120] In actual implementation, when the first judgment result is the second state (i.e., there is no transplanted rice in the target area) and the second judgment result is the first state (i.e., the crop planted in the target area is rice), it can be determined that the rice planting type in the target area is direct seeding.

[0121] In some embodiments, the rice planting type of the target area can be determined based on the following formula: ; in, Indicates the rice planting type in the target area, =1 indicates the second judgment result indicating that rice is planted in the target area, =0 indicates that there is no transplanted rice in the target area. =0 means the target area is planted with non-direct-seeded rice. Indicates that the rice planting type in the target area is direct seeding.

[0122] Step 250: Obtain backscatter coefficients of the target area at each rice growth stage; perform target enhancement on the rice planting type in the target area based on the backscatter coefficients at each rice growth stage; the target enhancement is used to eliminate interference caused by environmental factors surrounding the target area.

[0123] In actual implementation, the backscatter coefficients of each rice growth stage can include the mean backscatter coefficient of the maturity stage. and the mean backscatter coefficient during the growth period .

[0124] In some embodiments, the target procedure can be used to determine the mean of the backscatter coefficients during the mature period. , the mean backscatter coefficient during the growth period The geographical location is matched with the rice planting type of the target area, and the target enhancement operation is performed based on the matched rice planting type of the target area.

[0125] In some embodiments, since the moisture content of the rice field of direct-seeded rice in the mature stage is still relatively low, the backscatter coefficient in the mature stage is lower than that in the growth stage. Based on this, target enhancement operations can be performed on the rice planting type in the target area.

[0126] In some embodiments, when the rice planting type in the target area is direct seeding and the backscatter coefficient in the maturity period is less than the backscatter coefficient in the growth period, a target enhancement operation is performed on the rice planting type in the target area.

[0127] In actual implementation, the target enhancement operation can be performed on the rice planting type in the target area based on the following formula: ; in, represents the rice planting type in the target area after the target enhancement operation, =1 means the rice planting type in the target area after the target enhancement operation is direct seeding, =0 means that the rice planting type in the target area after the target enhancement operation is non-direct seeding, It indicates that the backscatter coefficient in the mature stage is smaller than that in the growth stage.

[0128] According to the rice planting type identification method of the embodiment of the present application, the target optical data of the target area at each rice growth stage is obtained based on the surface reflectance data of the target area at each rice growth stage. Based on the target optical data at each rice growth stage, the rice planting type of the target area is obtained by using the land characteristics corresponding to different rice planting types at each rice growth stage. In this way, the rice planting type of the target area can be obtained without collecting samples for training the machine learning model, thereby solving the problem that the non-data-driven method of the related technology is difficult to identify direct-seeded rice, resulting in significant missed detections, and effectively improving the detection rate of direct-seeded rice.

[0129] An embodiment of the present application also provides a device for identifying rice planting types.

[0130] like Figure 3 As shown, the rice planting type identification device 300 includes a first acquisition module 310 and a second acquisition module 320 .

[0131] The first acquisition module 310 is used to acquire target optical data of the target area at each rice growth stage; The second acquisition module 320 is used to obtain the rice planting type of the target area based on the target optical data of each rice growth stage through a rice planting type analysis model; the rice planting type analysis model is used to obtain the rice planting type based on the moisture content characteristics of the target area at each rice growth period.

[0132] According to the rice planting type identification device of the present application, target optical data of the target area at each rice growth stage is obtained based on the surface reflectivity data of the target area at each rice growth stage. Based on the target optical data at each rice growth stage, the rice planting type of the target area is obtained through the land characteristics corresponding to different rice planting types at each rice growth stage. In this way, the rice planting type of the target area can be obtained without collecting samples for training the machine learning model, thereby solving the problem of difficulty in effectively carrying out rice planting type identification.

[0133] In some embodiments, each rice growth stage includes a transplanting stage, a growing stage, and a ripening stage; the second acquisition module 320 includes: A first obtaining unit is configured to obtain a first judgment result based on the target optical data during the transplanting period and the growing period; the first judgment result indicates whether transplanted rice exists in the target area; a second acquiring unit, configured to acquire a second judgment result based on the optical data of the target during the growth period and the maturity period; the second judgment result indicating whether the crop planted in the target area is rice; The third obtaining unit is configured to obtain the rice planting type of the target area based on the first judgment result and the second judgment result.

[0134] In some embodiments, the target optical data includes land surface moisture index data and enhanced vegetation index data; the second acquisition unit is used to obtain a second judgment result based on the enhanced vegetation index data of the growing period and the land surface moisture index data and enhanced vegetation index data of the mature period.

[0135] The second acquisition unit is used to determine a second judgment result of planting rice in the target area when the enhanced vegetation index data in the growth period reaches a first indicator, the land surface moisture index data in the maturity period reaches a second indicator, and the enhanced vegetation index data in the maturity period reaches a second indicator.

[0136] In some embodiments, the third acquisition unit is configured to determine that the rice planting type in the target area is direct seeding when the first judgment result indicates that there is no transplanted rice in the target area and the second judgment result indicates that rice is planted in the target area.

[0137] In some embodiments, the first acquisition unit is configured to acquire a flooding signal of the target area based on the target optical data during the transplanting period; the flooding signal indicates whether the water level of the target area is higher than the soil; A first judgment result is obtained based on the target optical data during the growth period and the flooding signal.

[0138] In some embodiments, the rice planting type identification device 300 further includes a third acquisition module and an enhancement module: The third acquisition module is used to obtain the backscatter coefficient of the target area at each rice growth stage; The enhancement module is used to perform target enhancement operations on the rice planting types in the target area based on the backscatter coefficients of each rice growth stage; the target enhancement operation is used to eliminate interference caused by environmental factors around the target area.

[0139] In some embodiments, the enhancement module is used to perform a target enhancement operation on the rice planting type in the target area when the rice planting type in the target area is direct seeding and the backscatter coefficient in the maturity period is less than the backscatter coefficient in the growth period.

[0140] The rice planting type identification device in the embodiments of the present application can be an electronic device or a component of an electronic device, such as an integrated circuit or chip. The electronic device can be a terminal or other device other than a terminal. For example, the electronic device can be a mobile phone, a tablet computer, a laptop computer, a PDA, an in-vehicle electronic device, a mobile internet device (MID), an augmented reality (AR) / virtual reality (VR) device, a robot, a wearable device, an ultra-mobile personal computer (UMPC), a netbook, or a personal digital assistant (PDA). It can also be a server, a network attached storage (NAS), a personal computer (PC), a television (TV), a teller machine (ATM), or an automated machine, etc., and the embodiments of the present application are not specifically limited thereto.

[0141] The rice planting type identification device in the embodiments of the present application can be a device having an operating system. The operating system can be a Microsoft (Windows) operating system, an Android operating system, an iOS operating system, or other possible operating systems, which are not specifically limited in the embodiments of the present application.

[0142] The rice planting type identification device 300 provided in the embodiment of the present application can realize Figures 1 to 2 To avoid repetition, the various processes implemented in the method embodiment are not described here.

[0143] In some embodiments, as Figure 4 As shown, the embodiment of the present application also provides a computer device 400, including a processor 401, a memory 402, and a computer program stored in the memory 402 and executable on the processor 401. When the program is executed by the processor 401, each process of the above-mentioned rice planting type identification method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0144] It should be noted that the computer devices in the embodiments of the present application include the mobile electronic devices and non-mobile electronic devices mentioned above.

[0145] An embodiment of the present application also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the various processes of the above-mentioned rice planting type identification method embodiment are implemented, and the same technical effects can be achieved. To avoid repetition, they are not described here.

[0146] The processor is the processor in the electronic device described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0147] An embodiment of the present application also provides a computer program product, including a computer program, which implements the above-mentioned rice planting type identification method when executed by a processor.

[0148] The processor is the processor in the electronic device described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0149] An embodiment of the present application further provides a chip, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned rice planting type identification method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0150] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0151] It should be noted that, in this article, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the statement "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element. In addition, it should be noted that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in the opposite order according to the functions involved. For example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0152] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better embodiment. Based on this understanding, the technical solution of this application, or the part that contributes to the existing technology, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of this application.

[0153] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of this application, ordinary technicians in this field can also make many forms without departing from the purpose of this application and the scope of protection of the claims, all of which are within the protection of this application.

[0154] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0155] Although the embodiments of the present application have been shown and described, those skilled in the art will appreciate that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and intent of the present application, and that the scope of the present application is defined by the claims and their equivalents.

Claims

1. A method for identifying rice planting types, characterized in that: include: Based on the surface reflectance data of the target area at each rice growth stage, target optical data of the target area at each rice growth stage is obtained; The target optical data expresses land characteristics of the target area at various rice growth stages; Based on the target optical data at each rice growth stage, the rice planting type of the target area is obtained through the land characteristics corresponding to different rice planting types at each rice growth stage; the land characteristics include the moisture content characteristics and optical characteristics of the target area at each rice growth period.

2. The rice planting type identification method according to claim 1, characterized in that: The rice growth stages include a transplanting stage, a growing stage, and a ripening stage. The rice planting type of the target area is obtained based on the target optical data of each rice growth stage and land features corresponding to different rice planting types in each rice growth stage, including: Obtaining a first judgment result based on the target optical data during the transplanting period and the growing period; the first judgment result indicates whether transplanted rice exists in the target area; obtaining a second judgment result based on the optical data of the target during the growth period and the maturity period; the second judgment result indicates whether the crop planted in the target area is rice; Based on the first judgment result and the second judgment result, the rice planting type of the target area is obtained.

3. The rice planting type identification method according to claim 2, characterized in that: The target optical data includes land surface moisture index data and enhanced vegetation index data; and obtaining a second judgment result based on the target optical data during the growth period and the maturity period includes: A second judgment result is obtained based on the enhanced vegetation index data of the growing period and the land surface moisture index data and the enhanced vegetation index data of the mature period.

4. The rice planting type identification method according to claim 3, characterized in that: The obtaining of the second judgment result based on the enhanced vegetation index data of the growing period and the land surface moisture index data and enhanced vegetation index data of the mature period includes: When the enhanced vegetation index data in the growth period reaches the first indicator, the land surface moisture index data in the maturity period reaches the second indicator, and the enhanced vegetation index data in the maturity period reaches the second indicator, a second judgment result of planting rice in the target area is determined.

5. The rice planting type identification method according to claim 2, characterized in that: The obtaining of the rice planting type of the target area based on the first judgment result and the second judgment result includes: When the first judgment result indicates that there is no transplanted rice in the target area, and the second judgment result indicates that rice is planted in the target area, it is determined that the rice planting type in the target area is direct seeding.

6. The rice planting type identification method according to claim 2, characterized in that: The obtaining of a first judgment result based on the target optical data during the transplanting period and the growing period includes: Based on the target optical data during the transplanting period, a flooding signal of the target area is obtained; the flooding signal indicates whether the water level of the target area is higher than the soil; A first judgment result is obtained based on the target optical data during the growth period and the flooding signal.

7. The rice planting type identification method according to any one of claims 1 to 6, characterized in that: After obtaining the rice planting type of the target area based on the target optical data at each rice growth stage and using land features corresponding to different rice planting types at each rice growth stage, the method further includes: Obtaining the backscatter coefficient of the target area at each rice growth stage; Based on the backscatter coefficients of the various rice growth stages, a target enhancement operation is performed on the rice planting type in the target area; the target enhancement operation is used to eliminate interference caused by environmental factors surrounding the target area.

8. The rice planting type identification method according to claim 7, characterized in that: The target enhancement operation is performed on the rice planting type in the target area based on the backscatter coefficients of the various rice growth stages, including: When the rice planting type in the target area is direct seeding and the backscatter coefficient in the maturity period is smaller than the backscatter coefficient in the growth period, a target enhancement operation is performed on the rice planting type in the target area.

9. A rice planting type identification device, characterized in that: include: The first acquisition module is used to acquire target optical data of the target area at each rice growth stage; The second acquisition module is used to obtain the rice planting type of the target area based on the target optical data at each rice growth stage through a rice planting type analysis model; the rice planting type analysis model is used to obtain the rice planting type based on the moisture content characteristics of the target area at each rice growth period.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the rice planting type identification method according to any one of claims 1 to 8 is implemented.

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