Wheat yield monitoring system and method based on remote sensing technology

Abstract

The invention provides a wheat yield monitoring system and method based on a remote sensing technology. Acquiring a remote sensing image and meteorological data; preprocessing the remote sensing image to generate a preprocessed image, and segmenting the preprocessed image to generate a segmented image; extracting wave band parameters from the segmented images, and determining yield indexes corresponding to the segmented images based on the wave band parameters and the meteorological data; performing linear processing according to the yield index corresponding to each segmented image in the preprocessed image, and determining the yield index corresponding to the remote sensing image; and predicting the wheat yield of the current area according to the yield index corresponding to the remote sensing image. Preprocessing and analysis are carried out by integrating remote sensing images and weather, waveband parameters are accurately extracted, yield indexes are calculated, and therefore efficient prediction of the wheat yield is achieved. The method improves the prediction accuracy, improves the efficiency through automatic processing, provides scientific support for agricultural decision making, and has good expandability and dynamic monitoring capability.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular 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, analyze and interpret surface features. By acquiring spectral, thermal infrared and microwave data, researchers can assess factors such as vegetation growth, soil moisture and climate change. The wheat growth cycle 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 to monitor and predict wheat yields. For example, through the analysis of remote sensing data, the impact of drought and pests and diseases on wheat yields can be identified, providing a basis for decision-making. The spatial and temporal resolution of existing remote sensing data may not be sufficient to capture subtle changes in wheat growth, which can easily lead to inaccurate wheat yield monitoring. Summary of the invention

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

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

[0005] According to one aspect of the present 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 index corresponding to each segmented image in the preprocessed image to determine the yield index corresponding to the remote sensing image; and predicting the wheat yield in the current area according to the yield index corresponding to the remote sensing image.

[0006] In the present application, based on the aforementioned scheme, the acquisition 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 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 the present application, based on the aforementioned scheme, the remote sensing image is preprocessed to generate a preprocessed image, including: based on a geographic coordinate system, geometrically correcting the remote sensing image to generate a corrected image; performing cloud detection on the corrected image to remove areas covered by clouds to generate a preprocessed image.

[0008] In the present application, based on the above-mentioned solution, the segmenting of the pre-processed image to generate a segmented image includes: segmenting the pre-processed image based on a set segmentation scale to generate a segmented image.

[0009] In the present application, based on the aforementioned scheme, the 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, including: based on a raster processing interface, extracting the band parameters corresponding to the set band from the segmented image; determining the growth factor 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 factor.

[0010] In the present application, based on the aforementioned scheme, linear processing is performed on the yield index corresponding to each segmented image in the preprocessed image to determine the yield index corresponding to the remote sensing image, including: based on the proportion of 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.

[0011] In the present application, based on the above scheme, it also includes: obtaining wheat production data of previous years; and displaying the wheat production data of previous years and the predicted wheat production in the form of a map or chart.

[0012] According to one aspect of the present application, a wheat yield monitoring system based on remote sensing technology is provided, comprising: An acquisition unit, used to acquire remote sensing images and meteorological data; A segmentation unit, used for preprocessing the remote sensing image to generate a preprocessed image, and segmenting the preprocessed image 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; The prediction unit is used to predict the wheat yield in the current area according to the yield index corresponding to the remote sensing image.

[0013] In the present application, based on the aforementioned scheme, the acquisition 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 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.

[0014] In the present application, based on the aforementioned scheme, the remote sensing image is preprocessed to generate a preprocessed image, including: based on a geographic coordinate system, geometrically correcting the remote sensing image to generate a corrected image; performing cloud detection on the corrected image to remove areas covered by clouds to generate a preprocessed image.

[0015] In the present application, based on the above-mentioned solution, the segmenting of the pre-processed image to generate a segmented image includes: segmenting the pre-processed image based on a set segmentation scale to generate a segmented image.

[0016] In the present application, based on the aforementioned scheme, the 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, including: based on a raster processing interface, extracting the band parameters corresponding to the set band from the segmented image; determining the growth factor 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 factor.

[0017] In the present application, based on the aforementioned scheme, linear processing is performed on the yield index corresponding to each segmented image in the preprocessed image to determine the yield index corresponding to the remote sensing image, including: based on the proportion of 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.

[0018] In the present application, based on the above scheme, it also includes: obtaining wheat production data of previous years; and displaying the wheat production data of previous years and the predicted wheat production in the form of a map or chart.

[0019] According to one aspect of the present application, a computer-readable medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method for monitoring wheat yield based on remote sensing technology as described in the above embodiment is implemented.

[0020] According to one aspect of the present application, an electronic device is provided, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the wheat yield monitoring method based on remote sensing technology as described in the above embodiments.

[0021] According to one aspect of the present application, a computer program product or computer program is provided, the computer program product or computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. A processor of a 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 wheat yield monitoring method based on remote sensing technology provided in the above various optional implementations.

[0022] In the technical solution of the present application, remote sensing images and meteorological data are obtained; 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 index corresponding to each segmented image in the preprocessed image to determine the yield index corresponding to the remote sensing image; and the wheat yield in the current area is predicted according to the yield index corresponding to the remote sensing image. By integrating remote sensing images and meteorological data for preprocessing and analysis, band parameters are accurately extracted to calculate the yield index, thereby achieving efficient prediction of wheat yield. The prediction accuracy is improved, the automated processing improves efficiency, and scientific support is provided for agricultural decision-making, with good scalability and dynamic monitoring capabilities.

[0023] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0025] Figure 1 A flowchart of a wheat yield monitoring method based on remote sensing technology in an embodiment of the present application is schematically shown.

[0026] Figure 2 A flowchart for determining a yield index in one embodiment of the present application is schematically shown.

[0027] Figure 3 A schematic diagram of a wheat yield monitoring system based on remote sensing technology in an embodiment of the present application is schematically shown.

[0028] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown. DETAILED DESCRIPTION

[0029] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of 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 comprehensive and complete and fully convey the concept of the example embodiments to those skilled in the art.

[0030] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, known methods, devices, realizations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0031] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may 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.

[0032] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. 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 actual conditions.

[0033] The implementation details of the technical solution of this application are described in detail below: Figure 1 FIG. 1 is a flowchart of a wheat yield monitoring method based on remote sensing technology according to an embodiment of the present application. Figure 1 As shown, the wheat yield monitoring method based on remote sensing technology includes at least steps S110 to S150, which are described in detail as follows: In step S110, remote sensing images and meteorological data are acquired.

[0034] In one embodiment of the present application, remote sensing images and meteorological data are obtained, including: obtaining satellite images from a satellite server, detecting image content of the satellite images, and filtering images containing wheat-growing areas as the remote sensing images based on the image content; obtaining meteorological data through a meteorological data interface; the meteorological data includes temperature data and precipitation.

[0035] In one embodiment of the present application, a satellite data service is used to obtain real-time or historical satellite images. Satellite images of different formats are then processed. Satellite images are trained using computer vision and deep learning models to automatically detect and identify wheat planting areas. Images containing wheat planting areas are selected as the remote sensing images.

[0036] Obtain real-time or historical temperature and precipitation data through the meteorological data interface. Parse the acquired meteorological data into a structured format and store it in the database.

[0037] In step S120, the remote sensing image is preprocessed to generate a preprocessed image, and the preprocessed image is segmented to generate a segmented image.

[0038] In one embodiment of the present application, 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.

[0039] In one embodiment of the present application, in the process of 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 also have known coordinates in the geographic coordinate system. In this embodiment, the original coordinates of the feature points in the remote sensing image are , calculate the transformed coordinates after transformation for:

[0040] in, represents the coefficients in the transformation matrix, Express x A linear combination of coordinates, affecting both scaling and rotation. Express y Linear combinations of coordinates; , Represents the translation parameter, which means x Axis and y The amount of translation along the axis.

[0041] Calculate the transformation matrix parameters based on the transformed coordinates and set the objective function for:

[0042] in, f and g is the transformation model function, n is the number of feature points, i 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 adjustments are made according to the transformed coordinates to obtain the corrected image.

[0043] After obtaining the corrected image, perform cloud detection on the corrected image, remove the cloud-covered area, and generate a preprocessed image. Specifically, use the image processing library to read the geometrically corrected image, and perform denoising on the corrected image to improve the accuracy of subsequent cloud detection. Detect the brightness value of the corrected image, distinguish the cloud area from the non-cloud area by selecting an appropriate threshold, generate a binary mask map for the detected cloud area, apply the binary mask map to the corrected image, remove the cloud area, and generate a preprocessed image. For areas blocked by clouds, use neighboring pixels to interpolate and fill in missing data to ensure image continuity.

[0044] Through the above steps, cloud-covered areas can be effectively detected and removed, and high-quality preprocessed images can be generated to provide reliable data for subsequent remote sensing analysis.

[0045] In one embodiment of the present application, segmenting the preprocessed image to generate a segmented image includes: segmenting the preprocessed image based on a set segmentation scale to generate a segmented image.

[0046] In one embodiment of the present application, the segmentation scale is set according to the analysis requirements, the pre-processed image is segmented, and segmented images of the same size are generated. Through the above steps, the pre-processed image can be effectively segmented based on the set segmentation scale to generate high-quality segmented images, providing basic data for subsequent analysis and application.

[0047] In step S130, band parameters are extracted from the segmented image, and a yield index corresponding to the segmented image is determined based on the band parameters and the meteorological data.

[0048] like Figure 2 As shown, in one 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: S210, extracting band parameters corresponding to a set band from the segmented image based on a grid processing interface; S220, determining a growth factor of wheat based on the temperature data and precipitation data in the meteorological data; S230: Determine a yield index corresponding to the segmented image based on the band parameter and the growth factor.

[0049] In practical applications, each satellite image usually contains multiple bands. The bands related to the band parameter calculation are identified and selected. 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.

[0050] In one embodiment of the present application, based on the wavelength of each band, the raster processing interface extracts the band parameters corresponding to the set band from the segmented image, that is, the band reflectivity. The reflectivity of the red light band and the near infrared band in the satellite image is extracted to facilitate the subsequent prediction of wheat yield.

[0051] In one embodiment of the present application, determining the growth factor of wheat based on the temperature data and precipitation data in the meteorological data includes: Based on the temperature data in the meteorological data T and suitable temperature range , identifying the first growth factor in wheat for:

[0052] in, Indicates the temperature factor; when the temperature is within the appropriate range ,calculate The value of is the first growth factor of air temperature relative to the suitable range, indicating the influence of air temperature on growth.

[0053] In addition, the temperature is below the minimum suitable temperature. , wheat cannot grow normally. When the first growth factor is 0 or close to 0, it means that the temperature is beyond the suitable range, and the growth of wheat will be inhibited, which may lead to delayed development, weakened root system, and even frost damage. The above method can help evaluate the impact of temperature on wheat growth and provide a basis for further growth models.

[0054] In one embodiment of the present application, based on the precipitation data in the meteorological data and suitable precipitation range , identifying the second growth factor in wheat for:

[0055] in, Represents the precipitation factor; when the precipitation is within the appropriate range ,calculate The value of is the second growth factor of precipitation relative to the suitable range, indicating the effect of precipitation on growth.

[0056] When the precipitation is lower than the minimum suitable precipitation , or above the maximum suitable precipitation When the precipitation is within the appropriate range, wheat growth is inhibited and excessive precipitation may lead to poor drainage and root suffocation, so the effect of water is no longer positive. , calculate the second growth factor of precipitation relative to the suitable range, reflecting the effect of water on wheat growth. The above method can effectively evaluate the effect of precipitation on wheat growth and provide a basis for subsequent growth models.

[0057] In an embodiment of the present application, determining the yield index corresponding to the segmented image based on the band parameter and the growth factor includes: Based on the reflectivity of the red light band in the band parameters and near-infrared reflectivity , and the first growth factor and the second growth factor, determine the yield index corresponding to the segmented image for:

[0058] Through the above process, the influence of different factors is combined together, which can more comprehensively evaluate the wheat yield index corresponding to the segmented image and provide a reference for agricultural decision-making.

[0059] In step S140, linear processing is performed 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.

[0060] In one embodiment of the present application, based on the proportion of 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.

[0061] In one embodiment of the present application, the wheat area of ​​each segmented image in the pre-processed image is detected, the wheat area ratio is determined, and the wheat area ratio is determined according to the wheat area ratio. , the yield index corresponding to each segmented image Perform linear processing to determine the yield index corresponding to the remote sensing image for:

[0062] in, k The identifier of the segmented image, mRepresents the total number of segmented images. The above method can effectively integrate the yield index of each segmented image, thereby calculating the overall yield index of the preprocessed image, providing a basis for further agricultural decision-making.

[0063] In step S150, the wheat yield in the current area is predicted according to the yield index corresponding to the remote sensing image.

[0064] In one embodiment of the present application, after calculating the yield index corresponding to the remote sensing image, the wheat yield of the current area is predicted. The wheat yield of the current area can be predicted based on the correspondence between the historical yield index and the wheat yield, and based on the yield index and the correspondence, the wheat yield of the current area is predicted.

[0065] In one embodiment of the present application, the method further includes: obtaining wheat production data of previous years; and displaying the wheat production data of previous years and the predicted wheat production in the form of a map or a chart.

[0066] In one embodiment of the present application, the wheat yield data and the predicted data of previous years are merged into a unified data frame to ensure that the same fields are contained. The wheat yield data is displayed in a geographic information system using a map visualization tool. The wheat yield in different regions is marked, and the yield level is indicated by color gradient or symbol size. The comparison between the wheat yield of previous years and the predicted yield is displayed in the form of a bar chart, a line chart, etc. A dual-axis graph can be drawn to show the changing trend of the actual yield and the predicted yield. The trends, changes and their significance shown in the analysis charts and maps are identified. The influencing factors of yield changes are identified. Through these steps, the wheat yield data and prediction results of previous years can be effectively displayed, providing intuitive decision support.

[0067] In the technical solution of the present application, remote sensing images and meteorological data are obtained; 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 index corresponding to each segmented image in the preprocessed image to determine the yield index corresponding to the remote sensing image; and the wheat yield in the current area is predicted according to the yield index corresponding to the remote sensing image. By integrating remote sensing images and meteorological data for preprocessing and analysis, band parameters are accurately extracted to calculate the yield index, thereby achieving efficient prediction of wheat yield. The prediction accuracy is improved, the automated processing improves efficiency, and scientific support is provided for agricultural decision-making, with good scalability and dynamic monitoring capabilities.

[0068] The following describes an embodiment of the device of the present application, which can be used to execute the wheat yield monitoring method based on remote sensing technology in the above-mentioned embodiment of the present application. It can be understood that the device can be a computer program (including program code) running in 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 in the embodiment of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the wheat yield monitoring method based on remote sensing technology mentioned above in the present application.

[0069] Figure 3 A block diagram of a wheat yield monitoring system based on remote sensing technology according to an embodiment of the present application is shown.

[0070] Reference Figure 3 As shown, a wheat yield monitoring system based on remote sensing technology according to an embodiment of the present application includes: An acquisition unit 310 is used to acquire remote sensing images and meteorological data; The segmentation unit 320 is used to preprocess the remote sensing image to generate a preprocessed image, and segment the preprocessed image to generate a segmented image; An index unit 330, configured to extract band parameters from the segmented image, and determine a yield index corresponding to the segmented image based on the band parameters and the meteorological data; A processing unit 340 is used 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; The prediction unit 350 is used to predict the wheat yield in the current area according to the yield index corresponding to the remote sensing image.

[0071] In the present application, based on the aforementioned scheme, the acquisition 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 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.

[0072] In the present application, based on the aforementioned scheme, the remote sensing image is preprocessed to generate a preprocessed image, including: based on a geographic coordinate system, geometrically correcting the remote sensing image to generate a corrected image; performing cloud detection on the corrected image to remove areas covered by clouds to generate a preprocessed image.

[0073] In the present application, based on the above-mentioned solution, the segmenting of the pre-processed image to generate a segmented image includes: segmenting the pre-processed image based on a set segmentation scale to generate a segmented image.

[0074] In the present application, based on the aforementioned scheme, the 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, including: based on a raster processing interface, extracting the band parameters corresponding to the set band from the segmented image; determining the growth factor 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 factor.

[0075] In the present application, based on the aforementioned scheme, linear processing is performed on the yield index corresponding to each segmented image in the preprocessed image to determine the yield index corresponding to the remote sensing image, including: based on the proportion of 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.

[0076] In the present application, based on the above scheme, it also includes: obtaining wheat production data of previous years; and displaying the wheat production data of previous years and the predicted wheat production in the form of a map or chart.

[0077] In the technical solution of the present application, remote sensing images and meteorological data are obtained; 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 index corresponding to each segmented image in the preprocessed image to determine the yield index corresponding to the remote sensing image; and the wheat yield in the current area is predicted according to the yield index corresponding to the remote sensing image. By integrating remote sensing images and meteorological data for preprocessing and analysis, band parameters are accurately extracted to calculate the yield index, thereby achieving efficient prediction of wheat yield. The prediction accuracy is improved, the automated processing improves efficiency, and scientific support is provided for agricultural decision-making, with good scalability and dynamic monitoring capabilities.

[0078] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown.

[0079] 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 scope of use of the embodiments of the present application.

[0080] 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 part 408 to the random access memory (RAM) 403, such as executing the method described in the above embodiment. Various programs and data required for system operation are also stored in the RAM 403. The CPU 401, the ROM 402 and the RAM 403 are connected to each other through the bus 404. The input / output (I / O) interface 405 is also connected to the bus 404.

[0081] 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.

[0082] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 409, and / or installed from a removable medium 411. When the computer program is executed by the central processing unit (CPU) 401, various functions defined in the system of the present application are executed.

[0083] It should be noted that the computer-readable medium shown in the embodiment of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable computer program. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate, or transmit programs for use by or in conjunction with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0084] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0085] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0086] According to one aspect of the present application, a computer program product or a computer program is provided, the computer program product or the computer program comprising computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a 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-mentioned various optional implementations.

[0087] As another aspect, the present application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiment; or may exist independently 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 method described in the above embodiment.

[0088] It should be noted that, although several modules or units of the equipment 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 two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0089] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software, or by combining software with necessary hardware. Therefore, the technical solution according to the implementation method 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 can 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 can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the implementation method of the present application.

[0090] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. 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 knowledge or customary technical means in the art that are not disclosed in the present application.

[0091] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only 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 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; The wheat yield in the current area is predicted based on the yield index corresponding to the remote sensing image.

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: Segmenting the preprocessed image to generate a segmented image includes: Based on the set segmentation scale, the pre-processed image is segmented to generate a segmented image.

5. The method according to claim 4, characterized in that 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, including: Based on the raster processing interface, extracting the band parameters corresponding to the set band from the segmented image; Determining the growth factor of wheat based on the temperature data and precipitation data in the meteorological data; Based on the band parameters and the growth factor, a yield index corresponding to the segmented image is determined.

6. 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.

7. 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.

8. 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 for preprocessing the remote sensing image to generate a preprocessed image, and segmenting the preprocessed image 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; The prediction unit is used to predict the wheat yield in the current area according to the yield index corresponding to the remote sensing image.

9. 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 7 is implemented.

10. 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 7.

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