A method, device and electronic device for verifying the accuracy of agricultural data reports

Abstract

An embodiment of this specification discloses a method, device, and electronic device for verifying the accuracy of agricultural data reports. The method includes determining a planting enterprise and determining the regional coordinate range of an agricultural planting area; obtaining first video data, performing image recognition on the first video data to obtain first recognition data; after a growth waiting duration, obtaining second video data shared by the planting enterprise, performing image recognition on the second video data to obtain second recognition data; respectively determining a first matching result between the first recognition data and the second recognition data and a second matching result between the first recognition data and the agricultural data report, and generating a verification result based on the first matching result and the second matching result. The embodiment of this specification can perform cross-verification on the agricultural data report according to the first recognition data and the second recognition data, can effectively verify the accuracy of the agricultural data report, and ensure the authenticity of data in the agricultural field.

Description

Technical Field

[0001] One or more embodiments of this specification relate to data processing technologies, and in particular, to a method, apparatus, and electronic device for verifying the accuracy of agricultural data reports. Background Art

[0002] Agricultural management departments need to determine the annual agricultural planting quantity based on the agricultural data reports submitted by each agricultural production enterprise every year, so as to estimate the annual agricultural planting output. Since the enterprises are scattered in different places, the report data are collected by the subordinate departments and reported layer by layer. When the data circulation between departments is not smooth and the data reported by the enterprises may be underreported, the report data finally collected by the agricultural management department may be inaccurate, making it difficult to guarantee the authenticity of the data in the agricultural field, and further leading to inaccurate estimation of the annual agricultural planting output and affecting the formulation of strategies such as agricultural operation support. Summary of the Invention

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

[0004] According to a first aspect, there is provided a method for verifying the accuracy of an agricultural data report, the method including:

[0005] Determine the planting enterprises corresponding to the reported agricultural data reports, and based on the agricultural data reports, determine the regional coordinate range of the agricultural planting area;

[0006] Obtain the first video data shared by the planting enterprises, perform image recognition on the first video data to obtain first recognition data, where the first video data is video data taken by a drone for spraying pesticides during spraying work in the regional coordinate range, and the first recognition data includes the types of crops, the growth stages of crops, the quantity of crops, and the coordinates of crops;

[0007] Set a growth waiting duration based on the types of crops. After the growth waiting duration has passed, obtain the second video data shared by the planting enterprises, and perform image recognition on the second video data to obtain second recognition data;

[0008] Respectively determine the first matching result between the first recognition data and the second recognition data, and the second matching result between the first recognition data and the agricultural data reports, and generate a verification result based on the first matching result and the second matching result.

[0009] Preferably, the setting of the growth waiting duration based on the types of crops includes:

[0010] Determine the growth duration of the crops at the current crop growth stage based on the type of crops, and set the growth waiting duration according to the growth duration.

[0011] Preferably, the steps of separately determining 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 include:

[0012] Compare the first identification data and the second identification data to obtain a first matching result;

[0013] Based on the first identification data, determine the estimated yield, and compare the estimated yield with the agricultural data report to obtain a second matching result.

[0014] Preferably, the verification result includes a first verification result and a second verification result;

[0015] The step of generating a verification result based on the first matching result and the second matching result includes:

[0016] When both the first matching result and the second matching result indicate successful matching, generate a first verification result, and the first verification result is used to indicate successful verification;

[0017] When at least one of the first matching result and the second matching result indicates failed matching, generate a second verification result, and the second verification result is used to indicate failed verification.

[0018] Preferably, the method further includes:

[0019] Based on the first identification data, record the first crop coordinates of the necrotic crops, and based on the second identification data, record the second crop coordinates of the necrotic crops;

[0020] Determine the third matching result between the first crop coordinates and the second crop coordinates;

[0021] The step of generating a verification result based on the first matching result and the second matching result includes:

[0022] Generate a verification result based on the first matching result, the second matching result and the third matching result.

[0023] Preferably, after performing image recognition on the second video data to obtain the second identification data, the method further includes:

[0024] When detecting the work route change information of the drone, determine the coordinate adjustment value of each frame of image in the video data based on the work route change information, and update the crop coordinates in the second identification data according to the respective coordinate adjustment values.

[0025] Preferably, the method further includes:

[0026] Obtaining historical planting information of the planting enterprise, determining crop data of planted crops that have not been harvested yet based on the historical planting information, and removing the crop data from the first identification data and the second identification data.

[0027] According to a second aspect, there is provided an accuracy verification device for an agricultural data report, the device including:

[0028] A determination module, configured to determine the planting enterprise corresponding to the reported agricultural data report, and determine the regional coordinate range of the agricultural planting area based on the agricultural data report;

[0029] A first identification module, configured to obtain first video data shared by the planting enterprise, perform image recognition on the first video data to obtain first identification data, where the first video data is video data taken by a drone for spraying pesticides during spraying work in the regional coordinate range, and the first identification data includes crop types, crop growth stages, crop quantities, and crop coordinates;

[0030] A second identification module, configured to set a growth waiting duration based on the crop types, and after the growth waiting duration, obtain second video data shared by the planting enterprise, perform image recognition on the second video data to obtain second identification data;

[0031] A verification module, configured 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.

[0032] According to a third aspect, there is provided an electronic device, including a processor and a memory;

[0033] The processor is connected to the memory;

[0034] The memory is used to store executable program code;

[0035] 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 one of the possible implementation manners of the first aspect.

[0036] According to a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored. Instructions are stored in the computer-readable storage medium. When the instructions are run on a computer or a processor, the computer or the processor is caused to execute the method provided in the first aspect or any possible implementation manner of the first aspect.

[0037] The method and device provided in the embodiments of this specification can perform image recognition on the first video data collected by the drone itself during the medicine spraying operation to determine the first recognition data of the crops actually planted and maintained by the planting enterprise. After a growth waiting period, the second recognition data is determined based on the second video data of the next growth stage. Finally, the agricultural data report is cross-validated based on the first recognition data and the second recognition data. Since the cross-validation is performed based on the video data collected during the normal operation of the planting enterprise, it neither adds an additional data collection burden to the planting enterprise nor leaves room for data fraud by the planting enterprise, and can effectively verify the accuracy of the agricultural data report and ensure the authenticity of the data in the agricultural field. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0039] Figure 1 It is a flowchart of a method for verifying the accuracy of an agricultural data report in an embodiment of this specification.

[0040] Figure 2 It is a structural schematic diagram of a device for verifying the accuracy of an agricultural data report in an embodiment of this specification.

[0041] Figure 3 It is a structural schematic diagram of an electronic device in an embodiment of this specification. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application.

[0043] In the following description, the terms "first" and "second" are for descriptive purposes only and should not be construed as indicating or implying relative importance. The following description provides multiple embodiments of the present application, and different embodiments can be replaced or combined. Therefore, the present application can also be considered to include all possible combinations of the same and / or different embodiments described. Thus, 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, even though such embodiments may not be explicitly described in the following content.

[0044] The following description provides examples and does not limit the scope, applicability, or examples set forth in the claims. Changes can be made to the functions and arrangements of the described elements without departing from the scope of the present application. Various processes or components can be appropriately omitted, substituted, or added to each example. For example, the described method can be executed in a different order than the described order, and various steps can be added, omitted, or combined. In addition, the features described for some examples can be combined into other examples.

[0045] See Figure 1 , Figure 1 is a schematic flowchart of a method for verifying the accuracy of an agricultural data report provided by an embodiment of the present application. In an embodiment of the present application, the method includes:

[0046] S101. Determine the planting enterprise corresponding to the reported agricultural data report, and determine the regional coordinate range of the agricultural planting area based on the agricultural data report.

[0047] The execution subject of the present application can be a cloud server.

[0048] 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.

[0049] S102: Acquire first video data shared by the planting enterprise, perform image recognition on the first video data, and obtain first recognition data.

[0050] 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.

[0051] In the embodiments of this specification, during the normal production activities of a planting enterprise, it will spray pesticides on the planted crops by means of drone cruising to reduce pests. To ensure the effect of pesticide spraying, the drone generally continuously collects images of the passing crops during cruising to identify the pest situation of the crops. The planting enterprise can share the first video data captured by the drone during the spraying work for a specific regional coordinate range to a specified storage space. The cloud server will retrieve the first video data from the storage space and parse the first video data through image recognition to obtain the first recognition data, so as to determine data such as the actual crop types, crop growth stages, crop quantities, and crop coordinates within the regional coordinate range. This method can have the following advantages. On the one hand, during the normal pesticide spraying process of the planting enterprise, it is necessary to obtain corresponding video data. This application only uses the video data shared by the planting enterprise for accuracy verification, which will not cause additional data collection burden on the planting enterprise. That is, the planting enterprise does not need to specially expend manpower and material resources to collect video data for the agricultural management department to verify. On the other hand, the video data obtained in this application is the video data collected by the drone during the pesticide spraying work for the regional coordinate range corresponding to the agricultural planting area in the agricultural data report. If the planting enterprise falsifies data, then the planting enterprise will either have difficulty providing matching video data or need a dedicated plot of land to specifically shoot the corresponding video, and the actual costs and efforts involved are not much different from the actual planting. Therefore, obtaining the first recognition data through the first video data can more accurately represent the actual planting situation of the planting enterprise for subsequent accuracy verification.

[0052] Among them, video data is actually composed of frames of image data. By performing image recognition on the image data, the crop images in the images can be recognized, and then through comparison with the database based on the crop images, the types, growth stages, etc. of the crops can be determined. The position coordinates of the drone can be determined by means such as GPS. Combining with a gyroscope, the direction of the drone can be determined, and then the coordinate range corresponding to the image captured by the drone at a certain position can also be determined. Furthermore, after image recognition, the crop coordinates can be determined according to the position of the crops in the image. The specific process of image recognition is common general knowledge well-known to those skilled in the art, so it will not be elaborated here.

[0053] S103. Set a growth waiting duration based on the crop types. After the growth waiting duration has passed, obtain the second video data shared by the planting enterprise, and perform image recognition on the second video data to obtain the second recognition data.

[0054] In the embodiments of this specification, although it has become difficult for planting enterprises to misreport data when sharing is required, it is still possible for them to specifically plant a batch of crop seedlings for video shooting. Therefore, the cloud server will also determine the growth cycle of the crops based on the crop types, and then set the growth waiting duration accordingly. After the growth waiting duration has passed, it is considered that the crops have undergone a certain amount of growth and entered a new growth stage, which is significantly different from the previous seedling form. At this time, the cloud server will obtain the second video data shared by the planting enterprise at this time node and analyze the second video data in the same way to obtain the second recognition data. In this way, in the subsequent process, the authenticity of the data can be mutually verified based on the first recognition data and the second recognition data.

[0055] In one implementable manner, setting the growth waiting duration based on the crop types includes:

[0056] Determining the growth duration of the crops in the current crop growth stage based on the crop types, and setting the growth waiting duration according to the growth duration.

[0057] In the embodiments of this specification, the cloud server will determine the growth duration of the crops in the current growth stage based on the crop types, that is, determine how long it takes for the crops to grow to the next growth stage. Then, set this growth duration as the growth waiting duration to ensure that the growth stage of the crops corresponding to the second video data has changed, facilitating the subsequent mutual verification of data authenticity.

[0058] In one implementable manner, after performing image recognition on the second video data to obtain the second recognition data, it further includes:

[0059] When detecting the work route change information of the drone, determining the coordinate adjustment value of each frame image in the video data based on the work route change information, and updating the crop coordinates in the second recognition data according to each coordinate adjustment value.

[0060] In the embodiments of this specification, since the planting arrangements or pesticide spraying arrangements of planting enterprises are different at different time nodes, the working route of the drone may change. By default, the cloud server assumes that the working route of the drone has not changed, which may cause errors in the coordinates of each identified crop. Therefore, when a planting enterprise changes the working route of the drone, it will send a working route change message to the cloud server. As long as the same coordinate system is referred to, the coordinate changes corresponding to each frame of image after the route changes can be determined by simulation software such as PTV-VISSIM, FakeLocation, SUMO (Simulation of Urban MObility), etc., so that the coordinate adjustment value corresponding to each frame of image can be obtained. Through the coordinate adjustment value, the cloud server will update the crop coordinates in the second identification data to ensure its accuracy.

[0061] S104. Respectively determine the first matching result of the first identification data and the second identification data, and the second matching result of 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.

[0062] In the embodiments of this specification, the cloud server matches the first identification data and the second identification data to determine whether the crop quantity, crop type, crop coordinates, etc. can be matched, and generates a first matching result. At the same time, the first identification data will also be 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 growing normally, proving the authenticity and validity of the first identification data. If the second matching result indicates that the two match, it means that the crop data determined from the actually collected video matches the crop data reported by the enterprise, proving that the enterprise has not reported false 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 according to the verification result. Among them, considering the errors in image recognition and the possible small amount of necrosis of crops due to reasons such as climate and pests, a matching ratio (for example, 95%) can be preset in advance. As long as the matching ratio is reached, it is considered that the two match, without the need for them to be exactly the same.

[0063] In an implementable manner, the respectively determining the first matching result of the first identification data and the second identification data, and the second matching result of the first identification data and the agricultural data report includes:

[0064] Compare the first identification data and the second identification data to obtain a first matching result;

[0065] Determine the estimated output based on the first recognition data, compare the estimated output with the agricultural data report, and obtain the second matching result.

[0066] In the embodiments of this specification, according to the types of crops in the first recognition data, the unit output after each crop matures can be determined. Combining the quantity of crops, the output of the crops in the current year can be estimated. Finally, the estimated output is matched and compared with the planned output in the agricultural data report to generate the second matching result.

[0067] In an implementable manner, the verification result includes a first verification result and a second verification result;

[0068] Generating the verification result based on the first matching result and the second matching result includes:

[0069] When both the first matching result and the second matching result are characterized as successful matches, generate a first verification result, and the first verification result is used to characterize successful verification;

[0070] When at least one of the first matching result and the second matching result is characterized as a failed match, generate a second verification result, and the second verification result is used to characterize failed verification.

[0071] In the embodiments of this specification, only when both the first matching result and the second matching result are characterized as successful matches, will the cloud server 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 a mismatch, it will be considered that the reliability of the agricultural data report is in doubt and may be inaccurate. The agricultural management department can conduct corresponding inspections on the planting enterprises according to the verification results to verify the agricultural data report.

[0072] In an implementable manner, the method further includes:

[0073] Record the first crop coordinates of the necrotic crops based on the first recognition data, and record the second crop coordinates of the necrotic crops based on the second recognition data;

[0074] Determine the third matching result of the first crop coordinates and the second crop coordinates;

[0075] Generating the verification result based on the first matching result and the second matching result includes:

[0076] Generate the verification result based on the first matching result, the second matching result, and the third matching result.

[0077] In the embodiments of this specification, in order to prevent planting enterprises from using video data collected through other channels or historical video data, when performing image recognition on the first video data to obtain the first recognition data, necrosis of crops can also be specifically identified, and the first crop coordinates of the necrotic crops are separately recorded. Similarly, when generating the second recognition data, the second crop coordinates of the necrotic crops are also recorded. If a planting enterprise uses other video data for sharing, although the types and quantities of crops in the first recognition data and the second recognition data can be matched, the positions of the necrotic crops in the video will be different because the actual shooting is not of the same plot of land. Therefore, the cloud server will also determine the third matching result between the first crop coordinates and the second crop coordinates, and the third matching result will also be considered when generating the verification result. Only when all three matching results indicate successful matching is the agricultural data report considered accurate.

[0078] In an implementable manner, the method further includes:

[0079] Obtain the historical planting information of the planting enterprise, determine the crop data of the planted crops that have not been harvested yet based on the historical planting information, and exclude the crop data from the first recognition data and the second recognition data.

[0080] In the embodiments of this specification, within the regional coordinate range, there may be a small number of crops that were planted previously and have not been harvested yet. 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 exclude the crop data corresponding to these coordinates from the first recognition data and the second recognition data.

[0081] Next, in conjunction with the attached Figure 2 drawings, the accuracy verification device for the agricultural data report provided in the embodiments of this application will be introduced in detail. It should be noted that the accuracy verification device for the agricultural data report shown in the attached Figure 2 drawings is used to execute the method of the embodiments of this application. For the sake of convenience of description, only the parts related to the embodiments of this application are shown. For the specific technical details not disclosed, please refer to the embodiments shown in Figure 1 this application. Figure 1 shown.

[0082] Please refer to Figure 2 the Figure 2 figure, which is a schematic structural diagram of an accuracy verification device for an agricultural data report provided in the embodiments of this application. As Figure 2 shown, the device includes:

[0083] A determination module 201, configured to determine the planting enterprises corresponding to the reported agricultural data reports, and determine the regional coordinate range of the agricultural planting area based on the agricultural data reports;

[0084] A first recognition module 202, configured to obtain first video data shared by the planting enterprise, perform image recognition on the first video data to obtain first recognition data, where the first video data is video data taken by a drone for spraying pesticides during the spraying operation in the regional coordinate range, and the first recognition data includes crop types, crop growth stages, crop quantities, and crop coordinates;

[0085] A second recognition module 203, configured to set a growth waiting duration based on the crop types, and after the growth waiting duration has passed, obtain second video data shared by the planting enterprise, perform image recognition on the second video data to obtain second recognition data;

[0086] A verification module 204, configured to respectively determine a first matching result between the first recognition data and the second recognition data and a second matching result between the first recognition data and the agricultural data reports, and generate a verification result based on the first matching result and the second matching result.

[0087] In an implementable manner, the second recognition module 203 is specifically configured to:

[0088] Determine the growth duration of the crops at the current crop growth stage based on the crop types, and set the growth waiting duration according to the growth duration.

[0089] In an implementable manner, the verification module 204 is specifically configured to:

[0090] Compare the first recognition data and the second recognition data to obtain a first matching result;

[0091] Determine an estimated yield based on the first recognition data, and compare the estimated yield with the agricultural data reports to obtain a second matching result.

[0092] In an implementable manner, the verification result includes a first verification result and a second verification result;

[0093] The verification module 204 is further specifically configured to:

[0094] When both the first matching result and the second matching result indicate successful matching, generate a first verification result, where the first verification result is used to indicate successful verification;

[0095] When at least one of the first matching result and the second matching result indicates failed matching, generate a second verification result, where the second verification result is used to indicate failed verification.

[0096] In an implementable manner, the verification module 204 is further specifically configured to:

[0097] Based on the first recognition data, record the first crop coordinates of the necrotic crops, and based on the second recognition data, record the second crop coordinates of the necrotic crops;

[0098] Determine a third matching result between the first crop coordinates and the second crop coordinates;

[0099] Generate a verification result based on the first matching result, the second matching result, and the third matching result.

[0100] In an implementable manner, the second recognition module 203 is further specifically configured to:

[0101] When detecting the work route change information of the drone, determine the coordinate adjustment value of each frame of image in the video data based on the work route change information, and update the crop coordinates in the second recognition data according to the respective coordinate adjustment values.

[0102] In an implementable manner, the device further includes:

[0103] A rejection module, configured to obtain the historical planting information of the planting enterprise, determine the crop data of the planted crops that have not been harvested yet based on the historical planting information, and reject the crop data in the first recognition data and the second recognition data.

[0104] Those skilled in the art can clearly understand that the technical solutions of the embodiments of the present application can be implemented by means of software and / or hardware. The "units" and "modules" 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.

[0105] Each processing unit and / or module of the embodiments of the present application can be implemented by an analog circuit that implements the functions described in the embodiments of the present application, or can be implemented by software that executes the functions described in the embodiments of the present application.

[0106] See Figure 3 , which shows a schematic structural diagram of an electronic device related to the embodiments of the present application. This electronic device can be used to implement Figure 1 the method in the embodiments shown. As Figure 3 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.

[0107] Among them, the communication bus 302 is used to implement connection communication between these components.

[0108] Among them, the user interface 303 may include a display screen (Display), a camera (Camera). Optionally, the user interface 303 may further include a standard wired interface and a wireless interface.

[0109] Among them, the network interface 304 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0110] Among them, the processor 301 may include one or more processing cores. The processor 301 connects various parts within the entire electronic device 300 using various interfaces and lines. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by calling data stored in the memory 305, it performs various functions of the electronic device 300 and processes data. Optionally, the processor 301 may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor 301 may integrate a combination of one or several of a central processing unit (CPU), a graphics processing unit (GPU), and a modem, etc. Among them, the CPU mainly processes the operating system, user interface, and application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communication. It can be understood that the above modem may not be integrated into the processor 301 and may be implemented separately by a single chip.

[0111] Among them, the memory 305 may include a Random Access Memory (RAM), or may also include a 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. Among them, 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 method embodiments, etc.; the data storage area may store data involved in the above-mentioned method embodiments. Optionally, the memory 305 may also be at least one storage device located far from the aforementioned processor 301. As Figure 3 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.

[0112] In Figure 3 the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user to obtain user input data; while the processor 301 can be used to call the accuracy verification application program of the agricultural data report stored in the memory 305 and specifically perform the following operations:

[0113] 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;

[0114] Obtain the first video data shared by the planting enterprise, perform image recognition on the first video data to obtain first recognition data. The first video data is the video data taken by a drone for spraying pesticides during the spraying operation in the regional coordinate interval. The first recognition data includes the type of crops, the growth stage of crops, the number of crops, and the coordinates of crops;

[0115] Set a growth waiting duration based on the type of crops. After the growth waiting duration has passed, obtain the second video data shared by the planting enterprise, and perform image recognition on the second video data to obtain second recognition data;

[0116] Respectively determine the first matching result of the first recognition data and the second recognition data, and the second matching result of the first recognition data and the agricultural data report, and generate a verification result based on the first matching result and the second matching result.

[0117] The present application also provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the steps of the above method are implemented. Among them, the computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, microdrives, and magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic cards or optical cards, nano-systems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0118] It should be noted that, for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present application is not limited by the described action sequence, because according to the present application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present application.

[0119] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0120] In several embodiments provided by the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, 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 displayed or discussed coupling or direct coupling or communication connection to each other can be through some service interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical or other form.

[0121] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0122] In addition, in each embodiment of the present application, the functional units can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0123] When 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, in essence, 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 and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned memory includes: USB flash drives, read-only memory (ROM), random access memory (RAM), mobile hard disks, magnetic disks, or optical discs, etc., all of which can store program codes.

[0124] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing relevant hardware through a program. The program can be stored in a computer-readable memory, and the memory can include: flash drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs, etc.

[0125] The above are only exemplary embodiments of the present disclosure, and the scope of the present disclosure cannot be limited thereby. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure still fall within the scope covered by the present disclosure. After considering the specification and practicing the present disclosure herein, those skilled in the art will readily think of other embodiments of the present disclosure. The present application aims to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common general knowledge or conventional technical means in the technical field not described in the present disclosure. The specification and embodiments are only regarded as exemplary, 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; 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; 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.

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 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.

5. 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.

6. 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.

7. 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; A verification module, 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; The verification result includes a first verification result and a second verification result; The verification module is also used to: 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.

8. 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 6 are implemented.

9. 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 6.

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