Wheat Yield Monitoring System and Method Based on Remote Sensing Technology

Abstract

The present application provides a wheat yield monitoring system and method based on remote sensing technology. Remote sensing images and meteorological data are acquired; the remote sensing images are preprocessed to generate preprocessed images, and the preprocessed images are segmented to generate segmented images; band parameters are extracted from the segmented images, and yield indices corresponding to the segmented images are determined based on the band parameters and meteorological data; linear processing is performed according to the yield indices corresponding to the segmented images in the preprocessed images to determine the yield index corresponding to the remote sensing image; and the wheat yield of the current region is predicted according to the yield index corresponding to the remote sensing image. By integrating remote sensing images and meteorology for preprocessing and analysis, band parameters are accurately extracted to calculate the yield index, thereby realizing the efficient prediction of wheat yield. The prediction accuracy is improved, the efficiency is enhanced by automated processing, scientific support is provided for agricultural decision-making, and it has good scalability and dynamic monitoring capabilities.

Description

Technical Field

[0001] This application relates to the field of computer technology. Specifically, it relates to a wheat yield monitoring system and method based on remote sensing technology. Background Art

[0002] Remote sensing technology refers to the use of sensors to collect surface information and analyze and interpret surface features. Through the acquired spectral, thermal infrared, and microwave data, researchers can evaluate factors such as vegetation growth, soil moisture, and climate change. The growth cycle of wheat includes stages such as sowing, germination, jointing, heading, filling, and maturity. Different growth stages are sensitive to environmental conditions, and remote sensing data can help monitor these changes in real time. Many countries and regions have used remote sensing technology for wheat yield monitoring and prediction. For example, through the analysis of remote sensing data, the impacts of drought and pests on wheat yield can be identified, providing a basis for decision-making. The spatial and temporal resolutions of existing remote sensing data may not be sufficient to capture the subtle changes in wheat growth, easily causing inaccurate wheat yield monitoring. Summary of the Invention

[0003] Embodiments of this application provide a wheat yield monitoring system and method based on remote sensing technology, which can at least to some extent solve the problem of inaccurate wheat yield monitoring.

[0004] Other features and advantages of this application will become apparent through the following detailed description, or be learned in part through the practice of this application.

[0005] According to one aspect of this application, a wheat yield monitoring method based on remote sensing technology is provided, including: acquiring remote sensing images and meteorological data; preprocessing the remote sensing images to generate preprocessed images, and segmenting the preprocessed images to generate segmented images; extracting band parameters from the segmented images, and determining the yield index corresponding to the segmented images based on the band parameters and the meteorological data; performing linear processing according to the yield indices corresponding to the segmented images in the preprocessed images to determine the yield index corresponding to the remote sensing images; and predicting the wheat yield in the current area according to the yield index corresponding to the remote sensing images.

[0006] In this application, based on the foregoing solution, the acquiring of remote sensing images and meteorological data includes: acquiring satellite images from a satellite server, detecting the image content of the satellite images, and screening the images containing wheat planting areas as the remote sensing images according to the image content; acquiring meteorological data through a meteorological data interface; the meteorological data includes temperature data and precipitation.

[0007] In this application, based on the foregoing solution, the preprocessing of the remote sensing image to generate a preprocessed image includes: geometrically correcting the remote sensing image based on a geographic coordinate system to generate a corrected image; performing cloud detection on the corrected image to remove the areas covered by clouds and generate a preprocessed image.

[0008] In this application, based on the foregoing solution, the segmentation of the preprocessed image to generate a segmented image includes: segmenting the preprocessed image based on a set segmentation scale to generate a segmented image.

[0009] In this application, based on the foregoing solution, the extraction of band parameters from the segmented image and the determination of the yield index corresponding to the segmented image based on the band parameters and the meteorological data include: extracting the band parameters corresponding to a set band from the segmented image based on a raster processing interface; determining the growth factors of wheat based on the temperature data and precipitation data in the meteorological data; and determining the yield index corresponding to the segmented image based on the band parameters and the growth factors.

[0010] In this application, based on the foregoing solution, the linear processing of the yield indices corresponding to the segmented images in the preprocessed image to determine the yield index corresponding to the remote sensing image includes: linearly processing the yield indices corresponding to the segmented images based on the proportion of the wheat area in each segmented image in the preprocessed image to determine the yield index corresponding to the remote sensing image.

[0011] In this application, based on the foregoing solution, it further includes: obtaining historical wheat yield data; and displaying the historical wheat yield data and the predicted wheat yield in the form of a map or chart.

[0012] According to one aspect of this application, a wheat yield monitoring system based on remote sensing technology is provided, including:

[0013] An acquisition unit for acquiring remote sensing images and meteorological data;

[0014] A segmentation unit for preprocessing the remote sensing image to generate a preprocessed image and segmenting the preprocessed image to generate a segmented image;

[0015] An index unit for extracting band parameters from the segmented image and determining the yield index corresponding to the segmented image based on the band parameters and the meteorological data;

[0016] A processing unit for linearly processing the yield indices corresponding to the segmented images in the preprocessed image to determine the yield index corresponding to the remote sensing image;

[0017] A prediction unit for predicting the wheat yield of the current area based on the yield index corresponding to the remote sensing image.

[0018] In this application, based on the foregoing solution, the obtaining of the remote sensing image and meteorological data includes: obtaining a satellite image from a satellite server, detecting the image content of the satellite image, and screening the image containing the wheat planting area as the remote sensing image according to the image content; obtaining meteorological data through a meteorological data interface; the meteorological data includes temperature data and precipitation.

[0019] In this application, based on the foregoing solution, the preprocessing of the remote sensing image to generate a preprocessed image includes: performing geometric correction on the remote sensing image based on a geographic coordinate system to generate a corrected image; performing cloud detection on the corrected image to remove the area covered by clouds and generate a preprocessed image.

[0020] In this application, based on the foregoing solution, the segmentation of the preprocessed image to generate a segmented image includes: segmenting the preprocessed image based on a set segmentation scale to generate a segmented image.

[0021] In this application, based on the foregoing solution, the extraction of band parameters from the segmented image and the determination of the yield index corresponding to the segmented image based on the band parameters and the meteorological data include: extracting the band parameters corresponding to a set band from the segmented image based on a raster processing interface; determining the growth factors of wheat based on the temperature data and precipitation data in the meteorological data; determining the yield index corresponding to the segmented image based on the band parameters and the growth factors.

[0022] In this application, based on the foregoing solution, the linear processing of the yield indices corresponding to the segmented images in the preprocessed image to determine the yield index corresponding to the remote sensing image includes: performing linear processing on the yield indices corresponding to the segmented images based on the proportion of the wheat area in each segmented image in the preprocessed image to determine the yield index corresponding to the remote sensing image.

[0023] In this application, based on the foregoing solution, it further includes: obtaining historical wheat yield data; displaying the historical wheat yield data and the predicted wheat yield in the form of a map or chart.

[0024] According to one aspect of this application, a computer-readable medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, it implements the wheat yield monitoring method based on remote sensing technology as described in the above embodiments.

[0025] According to one aspect of the present application, there is provided an electronic device, including: one or more processors; a storage device for storing one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement the wheat yield monitoring method based on remote sensing technology as described in the above embodiments.

[0026] According to one aspect of the present application, there is provided a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, causing the computer device to execute the wheat yield monitoring method based on remote sensing technology provided in the above various optional implementation manners.

[0027] In the technical solution of the present application, remote sensing images and meteorological data are acquired; the remote sensing images are preprocessed to generate preprocessed images, and the preprocessed images are segmented to generate segmented images; band parameters are extracted from the segmented images, and based on the band parameters and the meteorological data, the yield index corresponding to the segmented images is determined; linear processing is performed according to the yield indices corresponding to the segmented images in the preprocessed images to determine the yield index corresponding to the remote sensing images; according to the yield index corresponding to the remote sensing images, the wheat yield of the current region is predicted. By integrating remote sensing images and meteorological data for preprocessing and analysis, band parameters are accurately extracted to calculate the yield index, thereby realizing the efficient prediction of wheat yield. The prediction accuracy is improved, the efficiency is enhanced by automated processing, and scientific support is provided for agricultural decision-making, with good scalability and dynamic monitoring capabilities.

[0028] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application. Obviously, the drawings in the following description are only some embodiments of the present application, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0030] Figure 1 Schematically shows a flowchart of a wheat yield monitoring method based on remote sensing technology in an embodiment of the present application.

[0031] Figure 2 Schematically shows a flowchart of determining a yield index in an embodiment of the present application.

[0032] Figure 3 Schematically shows a schematic diagram of a wheat yield monitoring system based on remote sensing technology in an embodiment of the present application.

[0033] Figure 4 Shows a schematic structural diagram of a computer system of an electronic device suitable for implementing the embodiments of the present application. Detailed implementation manners

[0034] Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more complete and comprehensive, and will fully convey the concept of the example embodiments to those skilled in the art.

[0035] In addition, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of the embodiments of the present application. However, those skilled in the art will realize that the technical solutions of the present application can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the present application.

[0036] The block diagrams shown in the drawings are only functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0037] The flowcharts shown in the drawings are only illustrative and do not necessarily include all the contents and operations / steps, nor do they necessarily have to be executed in the described order. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to the actual situation.

[0038] The implementation details of the technical solutions of the present application are elaborated in detail below:

[0039] Figure 1 Shows a flowchart of a wheat yield monitoring method based on remote sensing technology according to an embodiment of the present application. Referring to Figure 1 As shown, the wheat yield monitoring method based on remote sensing technology at least includes steps S110 to S150, which are introduced in detail as follows:

[0040] In step S110, remote sensing images and meteorological data are acquired.

[0041] In one embodiment of the present application, obtaining remote sensing images and meteorological data includes: obtaining satellite images from a satellite server, detecting the image content of the satellite images, and screening the images containing wheat planting areas as the remote sensing images according to the image content; obtaining meteorological data through a meteorological data interface; the meteorological data includes temperature data and precipitation.

[0042] In one embodiment of the present application, satellite data services are used to obtain real-time or historical satellite images. Then, satellite images in different formats are processed. Computer vision and deep learning models are used to train the satellite images to automatically detect and identify wheat planting areas. And the images containing wheat planting areas are screened as the remote sensing images.

[0043] Real-time or historical temperature and precipitation data are obtained through a meteorological data interface. The obtained meteorological data is parsed into a structured format and stored in a database.

[0044] In step S120, the remote sensing image is preprocessed to generate a preprocessed image, and the preprocessed image is segmented to generate segmented images.

[0045] In one embodiment of the present application, preprocessing the remote sensing image to generate a preprocessed image includes:

[0046] Based on the geographic coordinate system, geometric correction is performed on the remote sensing image to generate a corrected image;

[0047] Cloud detection is performed on the corrected image to remove the areas covered by clouds and generate a preprocessed image.

[0048] In one embodiment of the present application, in one embodiment of the present application, during geometric correction, multiple feature points are selected in the satellite image and the corresponding geographic coordinate system. These feature points have clear positions in the image and known coordinates in the geographic coordinate system. The original coordinates of the feature points in the remote sensing image in this embodiment are , calculating the transformed coordinates as:

[0049]

[0050] where represents the coefficient in the transformation matrix, represents the linear combination of x coordinates, affecting scaling and rotation. represents the linear combination of y coordinates; , represent the translation parameters, indicating translation in x axis and yTranslation amount in the axis direction.

[0051] Calculate the transformation matrix parameters based on the transformed coordinates and set the objective function as:

[0052]

[0053] where f and g are transformation model functions, n is the number of feature points, i represents the identifier of the feature point. In this embodiment, the objective function is used to represent the difference between the transformed coordinates and the output coordinates of the transformation model. By calculating the minimum value of the objective function, the transformed coordinates corresponding to the minimum value are obtained, and adjustment is made according to the transformed coordinates to obtain the corrected image.

[0054] After obtaining the corrected image, cloud detection is performed on the corrected image to remove the areas covered by clouds and generate a preprocessed image. Specifically, the geometrically corrected image is read using an image processing library, and noise reduction processing is performed on the corrected image to improve the accuracy of subsequent cloud detection. The brightness value of the corrected image is detected, and by selecting an appropriate threshold, the cloud area and the non-cloud area are distinguished. The detected cloud area is generated into a binary mask image, and the binary mask image is applied to the corrected image to remove the cloud area and generate a preprocessed image. For the areas blocked by clouds, neighborhood pixels are used for interpolation to fill in the missing data to ensure the coherence of the image.

[0055] Through the above steps, the areas covered by clouds can be effectively detected and removed, and a high-quality preprocessed image can be generated, providing reliable data for subsequent remote sensing analysis.

[0056] In an embodiment of the present application, the preprocessed image is segmented to generate a segmented image, including: segmenting the preprocessed image based on a set segmentation scale to generate a segmented image.

[0057] In an embodiment of the present application, the segmentation scale is set according to the analysis requirements, and the preprocessed image is sliced to generate segmented images of the same size. Through the above steps, the preprocessed image can be effectively segmented based on the set segmentation scale, and high-quality segmented images can be generated, providing basic data for subsequent analysis and applications.

[0058] In step S130, band parameters are extracted from the segmented image, and the yield index corresponding to the segmented image is determined based on the band parameters and the meteorological data.

[0059] Such as Figure 2As shown, in an embodiment of the present application, extracting band parameters from the segmented image and determining the yield index corresponding to the segmented image based on the band parameters and the meteorological data includes:

[0060] S210, extracting the band parameters corresponding to the set band from the segmented image based on the raster processing interface;

[0061] S220, determining the growth factors of wheat based on the temperature data and precipitation data in the meteorological data;

[0062] S230, determining the yield index corresponding to the segmented image based on the band parameters and the growth factors.

[0063] In practical applications, each satellite image usually contains multiple bands. Identify and select the bands related to the calculation of band parameters. For example, the red band is usually about 640 - 670 nm in wavelength; the near-infrared band is usually about 850 - 880 nm in wavelength.

[0064] In an embodiment of the present application, based on the raster processing interface and the wavelength of each band, the band parameters corresponding to the set band, that is, the band reflectance, are extracted from the segmented image. By extracting the reflectance of the red band and the near-infrared band in the satellite image, it is convenient for subsequent prediction of wheat yield.

[0065] In an embodiment of the present application, determining the growth factors of wheat based on the temperature data and precipitation data in the meteorological data includes:

[0066] Based on the temperature data in the meteorological data T and the suitable temperature range , determining the first growth factor of wheat as:

[0067]

[0068] where represents the temperature factor; when the temperature is within the suitable range , calculate The value of is the first growth factor of the temperature relative to the suitable range, indicating the impact of temperature on growth.

[0069] In addition, when the temperature is lower than the lowest suitable temperature , wheat cannot grow normally. When the first growth factor is 0 or close to 0, it means that the temperature exceeds the suitable range, and the growth of wheat will be inhibited, which may lead to delayed development, weakened roots, and even frost damage. Through the above method, it is possible to help evaluate the impact of temperature on wheat growth and provide a basis for further growth models.

[0070] In one embodiment of the present application, based on the precipitation data in the meteorological data and the suitable precipitation range , the second growth factor of wheat is determined as:

[0071]

[0072] wherein, represents the precipitation factor; when the precipitation is within the suitable range , calculate The value of is the second growth factor of the precipitation relative to the suitable range, indicating the impact of precipitation on growth.

[0073] When the precipitation is lower than the minimum suitable precipitation or higher than the maximum suitable precipitation , the growth of wheat is inhibited. Excessive precipitation may lead to poor drainage and root suffocation, so the impact of moisture is no longer positive. When the precipitation is within the suitable range , calculate the second growth factor of the precipitation relative to the suitable range, reflecting the impact of moisture on the growth of wheat. Through the above method, the impact of precipitation on the growth of wheat can be effectively evaluated, providing a basis for the subsequent growth model.

[0074] In one embodiment of the present application, based on the band parameters and the growth factors, the yield index corresponding to the segmented image is determined, including:

[0075] Based on the reflectance of the red light band and the reflectance of the near-infrared band in the band parameters, as well as the first growth factor and the second growth factor, the yield index corresponding to the segmented image is determined as:

[0076]

[0077] Through the above process, by combining the influences of different factors, the wheat yield index corresponding to the segmented image can be more comprehensively evaluated, providing a reference basis for agricultural decision-making.

[0078] In step S140, according to the yield index corresponding to each segmented image in the preprocessed image, linear processing is performed to determine the yield index corresponding to the remote sensing image.

[0079] In one embodiment of the present application, based on the proportion of the wheat area in each segmented image in the preprocessed image, linear processing is performed on the yield index corresponding to each segmented image to determine the yield index corresponding to the remote sensing image.

[0080] In an embodiment of the present application, the wheat regions of each segmented image in the preprocessed image are detected, the proportion of the wheat regions is determined, and based on the proportion of the wheat regions , a linear processing is performed on the yield index corresponding to each segmented image to determine the yield index corresponding to the remote sensing image as:

[0081]

[0082] wherein, k represents the identifier of the segmented image, m and represents the total number of segmented images. Through the above method, the yield indexes of each segmented image can be effectively integrated, so as to calculate the overall yield index of the preprocessed image, providing a basis for further agricultural decision-making.

[0083] In step S150, based on the yield index corresponding to the remote sensing image, the wheat yield of the current region is predicted.

[0084] In an embodiment of the present application, after calculating the yield index corresponding to the remote sensing image, the wheat yield of the current region is predicted. It can be based on the corresponding relationship between the historical yield index and the wheat yield, and according to the yield index and this corresponding relationship, the wheat yield of the current field is predicted.

[0085] In an embodiment of the present application, it further includes: obtaining the wheat yield data of previous years; displaying the wheat yield data of previous years and the predicted wheat yield in the form of a map or a chart.

[0086] In an embodiment of the present application, the wheat yield data of previous years and the prediction data are merged into a unified data frame to ensure that the same fields are included. Using a map visualization tool, the wheat yield data is displayed in a geographic information system. Mark the wheat yields of different regions, and use color gradients or symbol sizes to represent the yield levels. Use forms such as bar charts and line charts to display the comparison between the wheat yields of previous years and the predicted yields. A dual-axis chart can be drawn to show the change trends of the actual yield and the predicted yield. Analyze the trends, changes and their meanings shown in the charts and maps. Identify the influencing factors of the yield changes. Through these steps, the wheat yield data of previous years and the prediction results can be effectively displayed, providing intuitive decision-making support.

[0087] In the technical solution of this application, remote sensing images and meteorological data are acquired; the remote sensing images are preprocessed to generate preprocessed images, and the preprocessed images are segmented to generate segmented images; band parameters are extracted from the segmented images, and based on the band parameters and the meteorological data, the yield index corresponding to the segmented images is determined; linear processing is performed according to the yield indices corresponding to the segmented images in the preprocessed images to determine the yield index corresponding to the remote sensing images; according to the yield index corresponding to the remote sensing images, the wheat yield of the current area is predicted. By integrating remote sensing images and meteorological data for preprocessing and analysis, band parameters are accurately extracted to calculate the yield index, thereby realizing the efficient prediction of wheat yield. The prediction accuracy is improved, the efficiency is enhanced by automated processing, and scientific support is provided for agricultural decision-making, with good scalability and dynamic monitoring capabilities.

[0088] The following introduces the device embodiments of this application, which can be used to execute the wheat yield monitoring method based on remote sensing technology in the above embodiments of this application. It can be understood that the device can be a computer program (including program code) running on a computer device, for example, the device is an application software; the device can be used to execute the corresponding steps in the method provided by the embodiments of this application. For the details not disclosed in the device embodiments of this application, please refer to the embodiments of the wheat yield monitoring method based on remote sensing technology above in this application.

[0089] Figure 3 The block diagram of a wheat yield monitoring system based on remote sensing technology according to an embodiment of this application is shown.

[0090] Refer to Figure 3 As shown, a wheat yield monitoring system based on remote sensing technology according to an embodiment of this application includes:

[0091] An acquisition unit 310, configured to acquire remote sensing images and meteorological data;

[0092] A segmentation unit 320, configured to preprocess the remote sensing images to generate preprocessed images, and segment the preprocessed images to generate segmented images;

[0093] An index unit 330, configured to extract band parameters from the segmented images, and determine the yield index corresponding to the segmented images based on the band parameters and the meteorological data;

[0094] A processing unit 340, configured to perform linear processing according to the yield indices corresponding to the segmented images in the preprocessed images to determine the yield index corresponding to the remote sensing images;

[0095] A prediction unit 350, configured to predict the wheat yield of the current area according to the yield index corresponding to the remote sensing images.

[0096] In this application, based on the foregoing solution, the obtaining of remote sensing images and meteorological data includes: obtaining satellite images from a satellite server, detecting the image content of the satellite images, and screening out the images containing wheat planting areas as the remote sensing images according to the image content; obtaining meteorological data through a meteorological data interface; the meteorological data includes temperature data and precipitation.

[0097] In this application, based on the foregoing solution, the preprocessing of the remote sensing images to generate preprocessed images includes: geometrically correcting the remote sensing images based on a geographic coordinate system to generate corrected images; performing cloud detection on the corrected images to remove the areas covered by clouds and generate preprocessed images.

[0098] In this application, based on the foregoing solution, the segmentation of the preprocessed images to generate segmented images includes: segmenting the preprocessed images based on a set segmentation scale to generate segmented images.

[0099] In this application, based on the foregoing solution, the extraction of band parameters from the segmented images and the determination of the yield index corresponding to the segmented images based on the band parameters and the meteorological data includes: extracting the band parameters corresponding to a set band from the segmented images based on a raster processing interface; determining the growth factors of wheat based on the temperature data and precipitation data in the meteorological data; determining the yield index corresponding to the segmented images based on the band parameters and the growth factors.

[0100] In this application, based on the foregoing solution, the linear processing of the yield indices corresponding to the segmented images in the preprocessed images to determine the yield index corresponding to the remote sensing images includes: performing linear processing on the yield indices corresponding to the segmented images based on the proportion of the wheat areas in the segmented images in the preprocessed images to determine the yield index corresponding to the remote sensing images.

[0101] In this application, based on the foregoing solution, it further includes: obtaining historical wheat yield data; displaying the historical wheat yield data and the predicted wheat yield in the form of a map or a chart.

[0102] In the technical solution of the present application, remote sensing images and meteorological data are acquired; the remote sensing images are preprocessed to generate preprocessed images, and the preprocessed images are segmented to generate segmented images; band parameters are extracted from the segmented images, and the yield index corresponding to the segmented images is determined based on the band parameters and the meteorological data; linear processing is performed according to the yield indices corresponding to the segmented images in the preprocessed images to determine the yield index corresponding to the remote sensing images; and the wheat yield of the current region is predicted according to the yield index corresponding to the remote sensing images. By integrating remote sensing images and meteorological data for preprocessing and analysis, band parameters are accurately extracted to calculate the yield index, thereby realizing the efficient prediction of wheat yield. The prediction accuracy is improved, the efficiency is enhanced by automated processing, scientific support is provided for agricultural decision-making, and good scalability and dynamic monitoring capabilities are possessed.

[0103] Figure 4 FIG. shows a schematic structural diagram of a computer system of an electronic device suitable for implementing the embodiments of the present application.

[0104] It should be noted that the computer system 400 of the electronic device shown in the figure is only an example and should not bring any limitation to the functions and usage scopes of the embodiments of the present application.

[0105] Among them, the computer system 400 includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 402 or the program loaded from the storage section 408 into the random access memory (RAM) 403, such as executing the method described in the above embodiments. In the RAM 403, various programs and data required for system operation are also stored. The CPU 401, ROM 402, and RAM 403 are connected to each other through a bus 404. The input / output (I / O) interface 405 is also connected to the bus 404.

[0106] The following components are connected to the I / O interface 405: an input section 406 including a keyboard, a mouse, etc.; an output section 407 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. A removable medium 411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is installed on the drive 410 as needed so that a computer program read therefrom is installed into the storage section 408 as needed.

[0107] Specifically, according to an embodiment of the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, an embodiment of the present application includes a computer program product that includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for performing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 409, and / or installed from the removable medium 411. When the computer program is executed by a central processing unit (CPU) 401, various functions defined in the system of the present application are executed.

[0108] It should be noted that the computer-readable medium shown in the embodiments of the present application can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium can include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device. In the present application, the computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable computer program is carried. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, and this computer-readable medium can send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted by any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0109] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present application. Among them, each block in the flowchart or block diagram can represent a module, a program segment, or a part of code, and the above module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, and the combination of blocks in the block diagram or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0110] The units involved in the embodiments described in this application can be implemented in software or in hardware, and the described units can also be provided in a processor. Among them, the names of these units do not constitute a limitation to the unit itself in certain cases.

[0111] According to one aspect of the present application, there is provided a computer program product or a computer program, the computer program product or the computer program including computer instructions, the computer instructions being stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the methods provided in the above various alternative implementation manners.

[0112] As another aspect, the present application further provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or may exist alone without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by an electronic device, the electronic device implements the methods described in the above embodiments.

[0113] It should be noted that although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of the two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0114] Through the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described here can be implemented by software or by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which may be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which may be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the methods according to the embodiments of the present application.

[0115] After considering the specification and practicing the disclosed embodiments here, those skilled in the art will readily conceive of other embodiments of the present application. The present application is intended to cover any variations, uses, or adaptations of the present application, which follow the general principles of the present application and include common general knowledge or conventional technical means in the technical field not disclosed in the present application.

[0116] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present application is only limited by the appended claims.

Claims

1. A wheat yield monitoring method based on remote sensing technology, characterized in that: include: Obtain remote sensing images and meteorological data; Preprocessing the remote sensing image to generate a preprocessed image, and segmenting the preprocessed image based on a set segmentation scale to generate a segmented image; Extracting band parameters from the segmented image, and determining a yield index corresponding to the segmented image based on the band parameters and the meteorological data; Performing linear processing according to the yield index corresponding to each segmented image in the pre-processed image to determine the yield index corresponding to the remote sensing image; Predicting the wheat yield in the current area according to the yield index corresponding to the remote sensing image; The step of extracting band parameters from the segmented image and determining a yield index corresponding to the segmented image based on the band parameters and the meteorological data includes: Based on the raster processing interface, extracting the band parameters corresponding to the set band from the segmented image; Based on the temperature data and precipitation data in the meteorological data, determining the growth factor of wheat; including: based on the temperature data T in the meteorological data and the suitable temperature range [T min , T max ], and the first growth factor of wheat, Par_T, was determined to be: Based on the precipitation data W and the suitable precipitation range [W in the meteorological data min , W max ], and the second growth factor of wheat, Par_W, was determined to be: Based on the reflectivity Fle_re of the red light band and the reflectivity Fle_ne of the near infrared band in the band parameters, as well as the first growth factor and the second growth factor, the yield index Ine_pro corresponding to the segmented image is determined as: Among them, α represents the temperature factor and β represents the precipitation factor.

2. The method according to claim 1, characterized in that Access remote sensing images and meteorological data, including: Acquire satellite images from a satellite server, detect image content of the satellite images, and select images containing wheat-growing areas as the remote sensing images according to the image content; The meteorological data is obtained through a meteorological data interface; the meteorological data includes temperature data and precipitation.

3. The method according to claim 1, characterized in that Preprocessing the remote sensing image to generate a preprocessed image includes: Based on the geographic coordinate system, geometrically correct the remote sensing image to generate a corrected image; Perform cloud detection on the corrected image, remove the area covered by clouds, and generate a pre-processed image.

4. The method according to claim 1, characterized in that: Performing linear processing according to the yield index corresponding to each segmented image in the pre-processed image to determine the yield index corresponding to the remote sensing image includes: Based on the proportion of wheat areas in each segmented image in the preprocessed image, linear processing is performed on the yield index corresponding to each segmented image to determine the yield index corresponding to the remote sensing image.

5. The method according to claim 1, characterized in that Also includes: Obtain wheat production data over the years; The wheat production data of previous years and the predicted wheat production are presented in the form of a map or a chart.

6. A wheat yield monitoring system based on remote sensing technology, characterized in that: include: An acquisition unit, used to acquire remote sensing images and meteorological data; A segmentation unit, used to pre-process the remote sensing image to generate a pre-processed image, and segment the pre-processed image based on a set segmentation scale to generate a segmented image; An index unit, used for extracting band parameters from the segmented image, and determining a yield index corresponding to the segmented image based on the band parameters and the meteorological data; A processing unit, configured to perform linear processing according to the yield index corresponding to each segmented image in the pre-processed image, to determine the yield index corresponding to the remote sensing image; A prediction unit, used to predict the wheat yield in the current area according to the yield index corresponding to the remote sensing image; The step of extracting band parameters from the segmented image and determining a yield index corresponding to the segmented image based on the band parameters and the meteorological data includes: Based on the raster processing interface, extracting the band parameters corresponding to the set band from the segmented image; Based on the temperature data and precipitation data in the meteorological data, determining the growth factor of wheat; including: based on the temperature data T in the meteorological data and the suitable temperature range [T min , T max ], and the first growth factor of wheat, Par_T, was determined to be: Based on the precipitation data W and the suitable precipitation range [W min , W max ], and the second growth factor of wheat, Par_W, was determined to be: Based on the reflectivity Fle_re of the red light band and the reflectivity Fle_ne of the near infrared band in the band parameters, as well as the first growth factor and the second growth factor, the yield index Ine_pro corresponding to the segmented image is determined as: Among them, α represents the temperature factor and β represents the precipitation factor.

7. A computer readable medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the wheat yield monitoring method based on remote sensing technology as described in any one of claims 1 to 5 is implemented.

8. An electronic device, characterized in that: include: one or more processors; A storage device for storing one or more programs, which, when executed by the one or more processors, enables the one or more processors to implement the wheat yield monitoring method based on remote sensing technology as described in any one of claims 1 to 5.

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