Accuracy verification method and device for agricultural data report, and electronic equipment

Abstract

The embodiment of the invention discloses an accuracy verification method and device for an agricultural data report and electronic equipment. The method comprises the steps of determining a planting enterprise, and determining an area coordinate interval of an agricultural planting area; acquiring first video data, and performing image recognition on the first video data to obtain first recognition data; after the growth waiting duration, obtaining second video data shared by the planting enterprise, and performing image recognition on the second video data to obtain second recognition data; and respectively determining a first matching result of the first identification data and the second identification data and a second matching result of the first identification data and the agricultural data report, and generating a verification result based on the first matching result and the second matching result. According to the embodiment of the invention, cross validation can be carried out on the agricultural data report according to the first identification data and the second identification data, the accuracy of the agricultural data report can be effectively verified, and the authenticity of agricultural field data is guaranteed.

Description

Technical Field

[0001] One or more embodiments of the present specification relate to data processing technology, and more particularly, to a method, device, and electronic device for verifying the accuracy of agricultural data reports. Background Art

[0002] Every year, the agricultural management department needs to determine the number of agricultural plantings for the year based on the agricultural data reports submitted by various agricultural production enterprises, so as to estimate the agricultural planting output for the year. Since enterprises are scattered in different places, the report data are collected by subordinate departments and reported layer by layer. In the case of poor data flow between departments and possible concealment of data reported by enterprises, the report data collected by the agricultural management department may be inaccurate, making it difficult to ensure the authenticity of agricultural data, which in turn leads to inaccurate estimates of agricultural planting output for the year, affecting the formulation of strategies such as agricultural operation support. Summary of the invention

[0003] In order to solve the above problems, one or more embodiments of this specification describe a method, device and electronic device for verifying the accuracy of agricultural data reports.

[0004] According to a first aspect, a method for verifying the accuracy of an agricultural data report is provided, the method comprising: Determine the planting enterprise corresponding to the reported agricultural data report, and determine the regional coordinate interval of the agricultural planting area based on the agricultural data report; Acquire first video data shared by the planting enterprise, perform image recognition on the first video data, and obtain first identification data, wherein the first video data is video data taken by an unmanned aerial vehicle for spraying pesticides when performing spraying work in the coordinate interval of the area, and the first identification data includes crop type, crop growth stage, crop quantity, and crop coordinates; Setting a growth waiting time based on the crop type, acquiring second video data shared by the planting enterprise after the growth waiting time has elapsed, and performing image recognition on the second video data to obtain second recognition data; A first matching result between the first identification data and the second identification data and a second matching result between the first identification data and the agricultural data report are determined respectively, and a verification result is generated based on the first matching result and the second matching result.

[0005] Preferably, the setting of the growth waiting time based on the crop type includes: The growth time of the crop in the current crop growth stage is determined based on the crop type, and the growth waiting time is set according to the growth time.

[0006] Preferably, the determining of the first matching result between the first identification data and the second identification data and the second matching result between the first identification data and the agricultural data report comprises: Comparing the first identification data and the second identification data to obtain a first matching result; An estimated yield is determined based on the first identification data, and the estimated yield is compared with an agricultural data report to obtain a second matching result.

[0007] Preferably, the verification result includes a first verification result and a second verification result; Generating a verification result based on the first matching result and the second matching result includes: When both the first matching result and the second matching result are characterized as successful matches, generating a first verification result, wherein the first verification result is used to indicate successful verification; When at least one of the first matching result and the second matching result is characterized as a matching failure, a second verification result is generated, and the second verification result is used to characterize the verification failure.

[0008] Preferably, the method further comprises: Recording first crop coordinates of the dead crop based on the first identification data, and recording second crop coordinates of the dead crop based on the second identification data; Determine a third matching result between the first crop coordinates and the second crop coordinates; Generating a verification result based on the first matching result and the second matching result includes: A verification result is generated based on the first matching result, the second matching result, and the third matching result.

[0009] Preferably, after performing image recognition on the second video data to obtain second recognition data, the method further includes: When the working route change information of the drone is detected, the coordinate adjustment value of each frame image in the video data is determined based on the working route change information, and the coordinates of each crop in the second identification data are updated according to each coordinate adjustment value.

[0010] Preferably, the method further comprises: The historical planting information of the planting enterprise is obtained, and the crop data of the currently planted crops that have not yet been harvested is determined based on the historical planting information, and the crop data is eliminated from the first identification data and the second identification data.

[0011] According to a second aspect, a device for verifying the accuracy of an agricultural data report is provided, the device comprising: A determination module, used to determine the planting enterprise corresponding to the reported agricultural data report, and determine the regional coordinate interval of the agricultural planting area based on the agricultural data report; A first recognition module is used to obtain first video data shared by the planting enterprise, perform image recognition on the first video data, and obtain first recognition data, wherein the first video data is video data taken by an unmanned aerial vehicle for spraying pesticides when performing spraying work in the coordinate interval of the area, and the first recognition data includes crop types, crop growth stages, crop quantities, and crop coordinates; A second recognition module is used to set a growth waiting time based on the crop type, obtain second video data shared by the planting enterprise after the growth waiting time has passed, and perform image recognition on the second video data to obtain second recognition data; The verification module is used to respectively determine a first matching result between the first identification data and the second identification data and a second matching result between the first identification data and the agricultural data report, and to generate a verification result based on the first matching result and the second matching result.

[0012] According to a third aspect, there is provided an electronic device, comprising a processor and a memory; The processor is connected to the memory; The memory is used to store executable program code; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the steps of the method provided in the first aspect or any possible implementation manner of the first aspect.

[0013] According to a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and the computer-readable storage medium stores instructions, which, when the instructions are executed on a computer or a processor, cause the computer or the processor to execute the method provided in the first aspect or any possible implementation of the first aspect.

[0014] The method and device provided in the embodiments of this specification can perform image recognition based on the first video data collected by the drone when spraying medicine, so as to determine the first identification data of the crops actually planted and maintained by the planting enterprise, and determine the second identification data based on the second video data of the next growth stage after the growth waiting period, and finally cross-verify the agricultural data report based on the first identification data and the second identification data. Since the cross-verification is performed based on the video data collected during the normal operation of the planting enterprise, it will not increase the data collection burden of the planting enterprise, but also eliminates the space for data falsification operations of the planting enterprise, and can effectively verify the accuracy of the agricultural data report to ensure the authenticity of the data in the agricultural field. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 It is a flowchart of a method for verifying the accuracy of an agricultural data report in one embodiment of the present specification.

[0017] Figure 2 It is a structural schematic diagram of an accuracy verification device for an agricultural data report in one embodiment of the present specification.

[0018] Figure 3 It is a structural diagram of an electronic device in one embodiment of this specification. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0020] In the following introduction, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance. The following introduction provides multiple embodiments of the present application, and different embodiments can be replaced or combined, so the present application can also be considered to include all possible combinations of the same and / or different embodiments recorded. Therefore, if one embodiment includes features A, B, and C, and another embodiment includes features B and D, then the present application should also be considered to include embodiments containing one or more of all other possible combinations of A, B, C, and D, although the embodiment may not be clearly recorded in the following text.

[0021] The following description provides examples and does not limit the scope, applicability or examples set forth in the claims. Changes may be made to the functions and arrangements of the elements described without departing from the scope of the present application. Various processes or components may be appropriately omitted, substituted or added to each example. For example, the described method may be performed in an order different from the order described, and various steps may be added, omitted or combined. In addition, features described in some examples may be combined in other examples.

[0022] See also Figure 1 , Figure 1 : is a flow chart of a method for verifying the accuracy of an agricultural data report provided in an embodiment of the present application. In the embodiment of the present application, the method includes: S101, determining the planting enterprise corresponding to the reported agricultural data report, and determining the regional coordinate interval of the agricultural planting area based on the agricultural data report.

[0023] The execution entity of this application can be a cloud server.

[0024] In the embodiment of this specification, the planting enterprise will report the agricultural data report to the agricultural management department at a designated time point each year to inform the crop planting plan of the year, so that the agricultural management department can estimate the yield of various varieties of crops of the year based on the summarized agricultural data report, so as to plan the decision-making of resource scheduling, agricultural support, etc. in advance. However, the planting enterprise may report false data in order to meet the corresponding data requirements, and the report is reported by various departments layer by layer. In the reporting process, the reported data may also be biased due to the non-circulation of the data system, and it is ultimately difficult to ensure the authenticity of the agricultural data report collected by the agricultural management department. Therefore, in order to verify the accuracy of the collected agricultural data report, the cloud server first performs an associated information query on the reported agricultural data report to determine the planting enterprise of the reported data and the agricultural planting area planned by the enterprise in the agricultural data report. Then, the cloud server can construct a coordinate system in advance according to the total area under the jurisdiction of the agricultural management department, and determine the regional coordinate interval corresponding to the agricultural planting area in the coordinate system.

[0025] S102: Acquire first video data shared by the planting enterprise, perform image recognition on the first video data, and obtain first recognition data.

[0026] The first video data is aerial video data taken when a drone used for spraying medicine performs spraying work in the coordinate range of the area, and the first identification data includes crop types, crop growth stages, crop quantities, and crop coordinates.

[0027] In the embodiment of this specification, during normal production activities, the planting enterprise will spray the planted crops with medicine by means of drone cruising to reduce insect pests. In order to ensure the effect of spraying medicine, the drone will generally continue to collect images of the crops passing by during the cruising process to identify the insect pests of the crops. The planting enterprise can share the first video data taken by the drone when spraying the regional coordinate interval to the designated storage space, and the cloud server will retrieve the first video data from the storage space, and analyze the first video data by means of image recognition to obtain the first identification data to determine the actual crop type, crop growth stage, crop quantity, crop coordinates and other data in the regional coordinate interval. This method can have the following advantages. On the one hand, the planting enterprise itself needs to obtain the corresponding video data during the normal spraying process. This application only uses the video data shared by the planting enterprise for accuracy verification, which does not cause additional data collection responsibilities to the planting enterprise, that is, there is no need for the planting enterprise to specially spend manpower and material resources to collect video data in order to allow the agricultural management department to verify. On the other hand, this application obtains video data collected by drones when spraying medicine in the regional coordinate interval corresponding to the agricultural planting area in the agricultural data report. If the planting company falsifies the data, then the planting company will either find it difficult to provide matching video data, or it will need a special piece of land to shoot the corresponding video. The actual cost and effort to be paid are not much different from the actual planting. Therefore, obtaining the first identification data through the first video data can more accurately characterize the actual planting situation of the planting company, so as to facilitate subsequent accuracy verification.

[0028] Among them, the video data is actually composed of frames of image data. By performing image recognition on the image data, the crop image in the image can be identified, and then the type and growth stage of the crop can be determined based on the comparison of the crop image with the database. The position coordinates of the drone can be determined by GPS and other methods. The direction of the drone can be determined by combining with a gyroscope, and then the coordinate range corresponding to the image captured by the drone at a certain position can also be determined. After image recognition, the crop coordinates can be determined based on the position of the crop in the image. The specific process of image recognition is common knowledge well known to those skilled in the art, so it will not be described in detail here.

[0029] S103: setting a growth waiting time based on the crop type, obtaining second video data shared by the planting enterprise after the growth waiting time has elapsed, performing image recognition on the second video data, and obtaining second recognition data.

[0030] In the embodiments of the present specification, although it is difficult for a planting enterprise to falsely report data when sharing is required, it is still possible for it to specifically plant a batch of crop seedlings to shoot a video. Therefore, the cloud server will also determine the growth cycle of the crop according to the type of crop, and then set the growth waiting time accordingly. After the growth waiting time, it is considered that the crop has undergone a certain growth and entered a new growth stage, which is already quite different from the previous seedling morphology. At this point, the cloud server will obtain the second video data collected at this time point shared by the planting enterprise, and also analyze the second video data to obtain the second identification data. In this way, in the subsequent process, the authenticity of the data can be mutually verified based on the first identification data and the second identification data.

[0031] In one possible implementation, setting the growth waiting time based on the crop type includes: The growth time of the crop in the current crop growth stage is determined based on the crop type, and the growth waiting time is set according to the growth time.

[0032] In the embodiment of the present specification, the cloud server will determine the growth duration of the crop in the current growth stage according to the crop type, that is, determine how long it will take for the crop to grow to the next growth stage. Then, the growth duration is set as the growth waiting duration to ensure that the growth stage of the crop corresponding to the second video data has changed, which is convenient for subsequent mutual verification of the authenticity of the data.

[0033] In one possible implementation manner, after performing image recognition on the second video data to obtain second recognition data, the method further includes: When the working route change information of the drone is detected, the coordinate adjustment value of each frame image in the video data is determined based on the working route change information, and the coordinates of each crop in the second identification data are updated according to each coordinate adjustment value.

[0034] In the embodiments of this specification, because the planting enterprises have different planting arrangements or spraying arrangements at different time points, the working route of the drone may change. The cloud server assumes by default that the working route of the drone has not changed, which will cause the coordinates of the crops identified later to be wrong. Therefore, when the planting enterprise changes the working route of the drone, it will send the working route change information to the cloud server. As long as the same coordinate system is referenced, the coordinate changes corresponding to each frame of the image after the route changes can be determined by simulation software such as PTV-VISSIM, FakeLocation, SUMO (Simulation of Urban MObility), so that the coordinate adjustment values ​​corresponding to each frame of the image can be obtained. Through the coordinate adjustment value, the cloud server will update the coordinates of the crops in the second identification data to ensure its accuracy.

[0035] S104, respectively determine a first matching result between the first identification data and the second identification data and a second matching result between the first identification data and the agricultural data report, and generate a verification result based on the first matching result and the second matching result.

[0036] In the embodiment of the present specification, the cloud server matches the first identification data with the second identification data to determine whether the number of crops, the type of crops, the coordinates of crops, etc. can be matched, and generates a first matching result. At the same time, the first identification data is also matched with the agricultural data report to generate a second matching result. If the first matching result indicates that the two match, it means that the crops corresponding to the first identification data are indeed planted and grown normally, proving the authenticity and validity of the first identification data, and if the second matching result indicates that the two match, it means that the crop data determined in the actual collected video matches the crop data reported by the enterprise, proving that the enterprise has not falsely reported data. Finally, a verification result can be generated based on the first matching result and the second matching result, and the accuracy of the agricultural data report can be determined based on the verification result. Among them, taking into account the error of image recognition and the fact that crops may be necrotic in small amounts due to climate, pests, etc., a matching ratio (for example, 95%) can be pre-set. As long as the matching ratio is reached, the two are considered to match, and the two do not need to be exactly the same.

[0037] In one possible implementation, the determining of the first matching result between the first identification data and the second identification data and the second matching result between the first identification data and the agricultural data report respectively includes: Comparing the first identification data and the second identification data to obtain a first matching result; An estimated yield is determined based on the first identification data, and the estimated yield is compared with an agricultural data report to obtain a second matching result.

[0038] In the embodiment of the present specification, based on the crop types in the first identification data, the unit output of each crop after maturity can be determined. Combined with the number of crops, the yield of the crops for the year can be estimated. Finally, the estimated yield is matched and compared with the planned yield in the agricultural data report to generate a second matching result.

[0039] In one possible implementation, the verification result includes a first verification result and a second verification result; Generating a verification result based on the first matching result and the second matching result includes: When both the first matching result and the second matching result are characterized as successful matches, generating a first verification result, wherein the first verification result is used to indicate successful verification; When at least one of the first matching result and the second matching result is characterized as a matching failure, a second verification result is generated, and the second verification result is used to characterize the verification failure.

[0040] In the embodiment of the present specification, only when the first matching result and the second matching result are both characterized as successful matching, the cloud server will consider that the verification of the agricultural data report is successful, that is, the agricultural data report is accurate. As long as at least one of the two matching results is unmatched, the reliability of the agricultural data report will be considered to be questionable and may be inaccurate. The agricultural management department can conduct corresponding inspections on the planting enterprises based on the verification results to verify the agricultural data report.

[0041] In one embodiment, the method further comprises: Recording first crop coordinates of the dead crop based on the first identification data, and recording second crop coordinates of the dead crop based on the second identification data; Determine a third matching result between the first crop coordinates and the second crop coordinates; Generating a verification result based on the first matching result and the second matching result includes: A verification result is generated based on the first matching result, the second matching result, and the third matching result.

[0042] In the embodiments of the present specification, in order to prevent the planting enterprise from using video data or historical video data collected by other means, when performing image recognition on the first video data and obtaining the first identification data, the dead crops can also be specifically identified, and the first crop coordinates of the dead crops can be recorded separately. Similarly, when generating the second identification data, the second crop coordinates of the dead crops will also be recorded. If the planting enterprise uses other video data for sharing, although the crop types, crop quantities, etc. in the first identification data and the second identification data can be matched, the location of the dead crops in the video will be different because the same piece of land is not actually photographed. Therefore, the cloud server will also determine the third matching result of the first crop coordinates and the second crop coordinates. When generating the verification result, the third matching result will also be considered. Only when all three matching results are characterized as successful matching, the agricultural data report is considered to be accurate.

[0043] In one embodiment, the method further comprises: The historical planting information of the planting enterprise is obtained, and the crop data of the currently planted crops that have not yet been harvested is determined based on the historical planting information, and the crop data is eliminated from the first identification data and the second identification data.

[0044] In the embodiments of this specification, within the regional coordinate interval, there may be a small number of crops that have been planted before and have not been harvested yet, and these crops should be excluded when verifying the agricultural data report. Therefore, the cloud server will obtain the historical planting information of the planting enterprise to determine the corresponding crop data, determine the crop coordinates of these crops based on the crop data, and eliminate the crop data corresponding to these coordinates in the first identification data and the second identification data.

[0045] The following will be combined with the attached Figure 2 , the accuracy verification device of the agricultural data report provided by the embodiment of the present application is introduced in detail. It should be noted that the attached Figure 2 The accuracy verification device of the agricultural data report shown is used to implement the present application Figure 1 For the convenience of explanation, only the part related to the embodiment of the present application is shown. For the specific technical details not disclosed, please refer to the present application. Figure 1 The embodiment shown.

[0046] See also Figure 2 , Figure 2 Schematic diagram of the structure of an accuracy verification device for agricultural data reports provided in an embodiment of the present application. Figure 2 As shown, the device comprises: The determination module 201 is used to determine the planting enterprise corresponding to the reported agricultural data report, and determine the regional coordinate interval of the agricultural planting area based on the agricultural data report; The first recognition module 202 is used to obtain the first video data shared by the planting enterprise, perform image recognition on the first video data, and obtain first recognition data, wherein the first video data is the video data taken by the drone used for spraying the pesticide when performing the spraying work in the coordinate interval of the area, and the first recognition data includes the crop type, crop growth stage, crop quantity, and crop coordinates; The second recognition module 203 is used to set a growth waiting time based on the crop type, obtain the second video data shared by the planting enterprise after the growth waiting time has passed, and perform image recognition on the second video data to obtain second recognition data; The verification module 204 is used to respectively determine a first matching result between the first identification data and the second identification data and a second matching result between the first identification data and the agricultural data report, and generate a verification result based on the first matching result and the second matching result.

[0047] In one possible implementation, the second identification module 203 is specifically used for: The growth time of the crop in the current crop growth stage is determined based on the crop type, and the growth waiting time is set according to the growth time.

[0048] In one possible implementation, the verification module 204 is specifically used to: Comparing the first identification data and the second identification data to obtain a first matching result; An estimated yield is determined based on the first identification data, and the estimated yield is compared with an agricultural data report to obtain a second matching result.

[0049] In one possible implementation, the verification result includes a first verification result and a second verification result; The verification module 204 is also specifically used for: When both the first matching result and the second matching result are characterized as successful matches, generating a first verification result, wherein the first verification result is used to indicate successful verification; When at least one of the first matching result and the second matching result is characterized as a matching failure, a second verification result is generated, and the second verification result is used to characterize the verification failure.

[0050] In one possible implementation, the verification module 204 is further configured to: Recording first crop coordinates of the dead crop based on the first identification data, and recording second crop coordinates of the dead crop based on the second identification data; Determine a third matching result between the first crop coordinates and the second crop coordinates; A verification result is generated based on the first matching result, the second matching result, and the third matching result.

[0051] In one possible implementation, the second identification module 203 is further configured to: When the working route change information of the drone is detected, the coordinate adjustment value of each frame image in the video data is determined based on the working route change information, and the coordinates of each crop in the second identification data are updated according to each coordinate adjustment value.

[0052] In one embodiment, the device further comprises: The elimination module is used to obtain the historical planting information of the planting enterprise, determine the crop data of the currently planted crops that have not been harvested based on the historical planting information, and eliminate the crop data from the first identification data and the second identification data.

[0053] Those skilled in the art can clearly understand that the technical solutions of the embodiments of the present application can be implemented with the help of software and / or hardware. The "unit" and "module" in this specification refer to software and / or hardware that can independently complete or cooperate with other components to complete specific functions, where the hardware can be, for example, a field programmable gate array (FPGA), an integrated circuit (IC), etc.

[0054] Each processing unit and / or module of the embodiments of the present application may be implemented by an analog circuit that implements the functions described in the embodiments of the present application, or may be implemented by software that executes the functions described in the embodiments of the present application.

[0055] See also Figure 3 , which shows a schematic diagram of the structure of an electronic device involved in an embodiment of the present application, the electronic device can be used to implement Figure 1 The method in the embodiment shown. Figure 3 As shown, the electronic device 300 may include: at least one processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 , and at least one communication bus 302 .

[0056] The communication bus 302 is used to realize the connection and communication between these components.

[0057] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0058] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0059] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts of the entire electronic device 300, and executes various functions and processes data of the electronic device 300 by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics central processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0060] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally be at least one storage device located away from the aforementioned processor 301. As Figure 3 As shown, the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module, and program instructions.

[0061] exist Figure 3 In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the accuracy verification application of the agricultural data report stored in the memory 305, and specifically perform the following operations: Determine the planting enterprise corresponding to the reported agricultural data report, and determine the regional coordinate interval of the agricultural planting area based on the agricultural data report; Acquire first video data shared by the planting enterprise, perform image recognition on the first video data, and obtain first identification data, wherein the first video data is video data taken by an unmanned aerial vehicle for spraying pesticides when performing spraying work in the coordinate interval of the area, and the first identification data includes crop type, crop growth stage, crop quantity, and crop coordinates; Setting a growth waiting time based on the crop type, acquiring second video data shared by the planting enterprise after the growth waiting time has elapsed, and performing image recognition on the second video data to obtain second recognition data; A first matching result between the first identification data and the second identification data and a second matching result between the first identification data and the agricultural data report are determined respectively, and a verification result is generated based on the first matching result and the second matching result.

[0062] The present application also provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the steps of the above method are implemented. The computer-readable storage medium may include, but is not limited to, any type of disk, including a floppy disk, an optical disk, a DVD, a CD-ROM, a micro drive, and a magneto-optical disk, a ROM, a RAM, an EPROM, an EEPROM, a DRAM, a VRAM, a flash memory device, a magnetic card or an optical card, a nanosystem (including a molecular memory IC), or any type of medium or device suitable for storing instructions and / or data.

[0063] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0064] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0065] In the several embodiments provided in the present application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0066] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0067] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0068] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM), random access memory (RAM), mobile hard disk, disk or optical disk and other media that can store program codes.

[0069] A person skilled in the art may understand that all or part of the steps in the various methods of the above embodiments may be completed by entering a program to instruct related hardware, and the program may be stored in a computer-readable memory, which may include a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0070] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure here, those skilled in the art will easily think of the implementation scheme of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field not recorded in the present disclosure. The description and examples are regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for verifying the accuracy of agricultural data reports, characterized in that: The method comprises: Determine the planting enterprise corresponding to the reported agricultural data report, and determine the regional coordinate interval of the agricultural planting area based on the agricultural data report; Acquire first video data shared by the planting enterprise, perform image recognition on the first video data, and obtain first identification data, wherein the first video data is video data taken by an unmanned aerial vehicle for spraying pesticides when performing spraying work in the coordinate interval of the area, and the first identification data includes crop type, crop growth stage, crop quantity, and crop coordinates; Setting a growth waiting time based on the crop type, acquiring second video data shared by the planting enterprise after the growth waiting time has elapsed, and performing image recognition on the second video data to obtain second recognition data; A first matching result between the first identification data and the second identification data and a second matching result between the first identification data and the agricultural data report are determined respectively, and a verification result is generated based on the first matching result and the second matching result.

2. The method according to claim 1, characterized in that The step of setting the growth waiting time based on the crop type includes: The growth time of the crop in the current crop growth stage is determined based on the crop type, and the growth waiting time is set according to the growth time.

3. The method according to claim 1, characterized in that: The determining of the first matching result between the first identification data and the second identification data and the second matching result between the first identification data and the agricultural data report comprises: Comparing the first identification data and the second identification data to obtain a first matching result; An estimated yield is determined based on the first identification data, and the estimated yield is compared with an agricultural data report to obtain a second matching result.

4. The method according to claim 1, characterized in that: The verification result includes a first verification result and a second verification result; Generating a verification result based on the first matching result and the second matching result includes: When both the first matching result and the second matching result are characterized as successful matches, generating a first verification result, wherein the first verification result is used to indicate successful verification; When at least one of the first matching result and the second matching result is characterized as a matching failure, a second verification result is generated, and the second verification result is used to characterize the verification failure.

5. The method according to claim 1, characterized in that The method further comprises: Recording first crop coordinates of the dead crop based on the first identification data, and recording second crop coordinates of the dead crop based on the second identification data; Determine a third matching result between the first crop coordinates and the second crop coordinates; Generating a verification result based on the first matching result and the second matching result includes: A verification result is generated based on the first matching result, the second matching result, and the third matching result.

6. The method according to claim 1, characterized in that After performing image recognition on the second video data to obtain second recognition data, the method further includes: When the working route change information of the drone is detected, the coordinate adjustment value of each frame image in the video data is determined based on the working route change information, and the coordinates of each crop in the second identification data are updated according to each coordinate adjustment value.

7. The method according to claim 1, characterized in that The method further comprises: The historical planting information of the planting enterprise is obtained, and the crop data of the currently planted crops that have not yet been harvested is determined based on the historical planting information, and the crop data is eliminated from the first identification data and the second identification data.

8. An accuracy verification device for agricultural data reports, characterized in that: The device comprises: A determination module, used to determine the planting enterprise corresponding to the reported agricultural data report, and determine the regional coordinate interval of the agricultural planting area based on the agricultural data report; A first recognition module is used to obtain first video data shared by the planting enterprise, perform image recognition on the first video data, and obtain first recognition data, wherein the first video data is video data taken by an unmanned aerial vehicle for spraying pesticides when performing spraying work in the coordinate interval of the area, and the first recognition data includes crop types, crop growth stages, crop quantities, and crop coordinates; A second recognition module is used to set a growth waiting time based on the crop type, obtain second video data shared by the planting enterprise after the growth waiting time has passed, and perform image recognition on the second video data to obtain second recognition data; The verification module is used to respectively determine a first matching result between the first identification data and the second identification data and a second matching result between the first identification data and the agricultural data report, and to generate a verification result based on the first matching result and the second matching result.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, wherein the computer-readable storage medium has instructions stored therein, and when the instructions are executed on a computer or a processor, the computer or the processor executes the steps of the method according to any one of claims 1 to 7.

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