Grassland livestock capacity evaluation method and system based on remote sensing technology

Abstract

The invention discloses a grassland livestock capacity evaluation method and system based on a remote sensing technology. The method comprises the following steps: S1, acquiring a satellite remote sensing image of a to-be-detected area by using a satellite remote sensing technology; s2, inputting the satellite remote sensing image into a spectral feature extraction model to obtain the grass yield of the to-be-detected area; s3, calculating an edible coefficient based on the edible quantity of the grassland, and correcting the edible quantity of the grassland based on the edible coefficient; and S4, collecting unit feed intake of the livestock, and evaluating the grassland livestock carrying capacity based on the corrected grassland edible quantity and the unit feed intake to obtain an evaluation result.

Description

Technical Field

[0001] The present invention relates to the technical field of livestock carrying capacity assessment, and in particular to a grassland livestock carrying capacity assessment method and system based on remote sensing technology. Background Art

[0002] In traditional grassland management and assessment, grass yield calculations typically rely solely on area measurements. However, this approach has significant limitations, particularly when faced with short, dense grasses. Area calculations alone cannot accurately reflect the true grass yield. This method fails to account for factors such as grass growth conditions, such as height, density, health, and overlap between grasses. This leads to significant errors in grass yield estimates, making it difficult to meet the needs of practical applications such as precision livestock management.

[0003] Furthermore, when it comes to grassland resource utilization planning, traditional methods for assessing the edible amount of grassland are inaccurate. They fail to fully consider the edible coefficient of grassland, specifically the impact of factors such as grassland type, the area ratio of edible to inedible grass, and differences in quality on the actual amount of available grassland resources. Furthermore, when assessing grassland carrying capacity, previous methods fail to fully incorporate the edible amount of grassland and the differences in feed intake among livestock of different species and at different growth stages. This can lead to biased assessments of grassland carrying capacity and fail to provide a reliable basis for rational grassland use planning and scientific livestock breeding. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides a grassland carrying capacity assessment method based on remote sensing technology, the method comprising:

[0005] Step S1: using satellite remote sensing technology to obtain satellite remote sensing images of the area to be detected and perform preprocessing;

[0006] Step S2: inputting the pre-processed satellite remote sensing image into the spectral feature extraction model to obtain the grass yield of the area to be detected;

[0007] Step S3, calculating an edible coefficient based on the edible amount of the grass, and correcting the edible amount of the grass based on the edible coefficient;

[0008] Step S4: collecting the unit feed intake of livestock, and evaluating the grassland carrying capacity based on the corrected edible amount of grassland and the unit feed intake to obtain an evaluation result.

[0009] Optionally, in step S1, the satellite remote sensing image preprocessing process specifically includes:

[0010] The satellite remote sensing image is regionally enhanced by using guided filtering to obtain a regional enhanced remote sensing image.

[0011] extracting detail information of spectral scale and spatial difference from the regional enhanced remote sensing image, and obtaining a dual-scale detail image based on the detail information;

[0012] A detail injection image is obtained based on the spectral correlation between the original multispectral image and the brightness component and the edge information of the regional enhanced remote sensing image as constraints of the dual-scale detail image;

[0013] The satellite remote sensing image and the detail injection image are added together to obtain a fused high-resolution multispectral image, thereby completing the satellite remote sensing image preprocessing.

[0014] Optionally, the content of the regional enhanced remote sensing image may include:

[0015]

[0016] Among them, GF(·) is the guided filter function, corr(X,Y) is the correlation coefficient between the two matrices, is the multispectral image after guided filtering enhancement, where n=bands.

[0017] Optionally, in step S2, the process of obtaining the grass yield in the area to be detected specifically includes:

[0018] Extract the local grayscale maximum points from the pre-processed satellite remote sensing image of the area to be detected and perform binarization calculation;

[0019] The S-curve transformation is used to enhance the contrast of binary images, and the contrast-enhanced images are simulated using Matlab.

[0020] Using the mesh function to calculate the gray value peak point of the simulation image;

[0021] The grassland area is calculated based on the contrast-enhanced image, and the repeated area is added to the grassland area based on the gray value peak point to obtain the grass yield of the area to be detected.

[0022] The present invention also provides a grassland carrying capacity assessment system based on remote sensing technology, the system comprising:

[0023] A data acquisition module is used to obtain satellite remote sensing images of the area to be inspected using satellite remote sensing technology and perform preprocessing;

[0024] The yield calculation module is used to input the pre-processed satellite remote sensing image into the spectral feature extraction model to obtain the grass yield of the area to be detected;

[0025] an edible grass correction module, configured to calculate an edible coefficient based on the edible amount of grass, and correct the edible amount of grass based on the edible coefficient;

[0026] The stocking capacity assessment module is used to collect the unit feed intake of livestock, and to assess the grassland stocking capacity based on the corrected edible amount of grassland and the unit feed intake to obtain an assessment result.

[0027] Optionally, the satellite remote sensing image preprocessing process specifically includes:

[0028] The satellite remote sensing image is regionally enhanced by using guided filtering to obtain a regional enhanced remote sensing image.

[0029] extracting detail information of spectral scale and spatial difference from the regional enhanced remote sensing image, and obtaining a dual-scale detail image based on the detail information;

[0030] A detail injection image is obtained based on the spectral correlation between the original multispectral image and the brightness component and the edge information of the regional enhanced remote sensing image as constraints of the dual-scale detail image;

[0031] The satellite remote sensing image and the detail injection image are added together to obtain a fused high-resolution multispectral image, thereby completing the satellite remote sensing image preprocessing.

[0032] Optionally, the content of the regional enhanced remote sensing image may include:

[0033]

[0034] Among them, GF(·) is the guided filter function, corr(X,Y) is the correlation coefficient between the two matrices, is the multispectral image after guided filtering enhancement, where n=bands.

[0035] Optionally, the process of obtaining the grass yield in the area to be detected specifically includes:

[0036] Extract the local grayscale maximum points from the pre-processed satellite remote sensing image of the area to be detected and perform binarization calculation;

[0037] The S-curve transformation is used to enhance the contrast of binary images, and the contrast-enhanced images are simulated using Matlab.

[0038] Using the mesh function to calculate the gray value peak point of the simulation image;

[0039] The grassland area is calculated based on the contrast-enhanced image, and the repeated area is added to the grassland area based on the gray value peak point to obtain the grass yield of the area to be detected.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] This method uses satellite remote sensing technology to acquire images and performs a series of complex preprocessing operations, including guided filter region enhancement and extraction of dual-scale detail information, to produce clearer and more detailed images. Furthermore, a method for calculating overlapping areas, which integrates area calculation, grass growth status, and grayscale peak value calculations, fully considers grass growth characteristics, such as grass stature and density, to accurately calculate grass yield, effectively addressing the inaccuracy of traditional area-based grass yield calculations.

[0042] This paper introduces an edible coefficient calculation, which takes into account factors such as grassland type coefficient, the area ratio of edible and inedible grasses within the grassland, and quality differences, to correct the edible amount of grassland. This makes the assessment of grassland edible resources more realistic and provides a more accurate data foundation for subsequent grassland management and utilization.

[0043] This method fully accounts for differences in livestock feed intake per unit of land when collecting data on livestock, ensuring more detailed and accurate data collection. This method, based on the corrected edible amount of grassland and the precise unit feed intake, assesses grassland carrying capacity. This method fully considers the actual carrying capacity of grassland resources and the feed needs of livestock, avoiding overgrazing and waste of grass resources. It provides a reliable reference for the rational planning and use of grasslands and the scientific breeding of livestock, contributing to the sustainable use of grassland resources and the efficient development of animal husbandry. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0045] Figure 1 This is a step diagram of a method for assessing grassland carrying capacity based on remote sensing technology according to an embodiment of the present invention;

[0046] Figure 2 is a schematic structural diagram of an electronic device according to an embodiment of the present invention;

[0047] Description of reference numerals:

[0048] 1010 , processor; 1020 , memory; 1030 , input / output interface; 1040 , communication interface; 1050 , bus. DETAILED DESCRIPTION

[0049] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0050] Example 1

[0051] A method for assessing grassland stocking capacity based on remote sensing technology, such as Figure 1 As shown, the method includes:

[0052] Step S1: Use satellite remote sensing technology to obtain satellite remote sensing images of the area to be detected and perform preprocessing. The satellite remote sensing image preprocessing process specifically includes:

[0053] The obtained satellite remote sensing image is regionally enhanced by using guided filtering to obtain a regional enhanced remote sensing image. The contents of the regional enhanced remote sensing image specifically include:

[0054]

[0055] Among them, GF(·) is the guided filter function, corr(X,Y) is the correlation coefficient between the two matrices, is the multispectral image after guided filtering enhancement, where n=bands.

[0056] extracting detail information of spectral scale and spatial difference from the regional enhanced remote sensing image, and obtaining a dual-scale detail image based on the detail information;

[0057] The method for extracting the detail information of spatial differences is:

[0058] D spa =P-PL

[0059] Among them, P is the full color image, P L is the degraded image.

[0060] A detail injection image is obtained based on the spectral correlation between the original multispectral image and the brightness component and the edge information of the regional enhanced remote sensing image as constraints of the dual-scale detail image;

[0061]

[0062] Wherein, is the spectral correlation coefficient of the nth band. The larger the correlation coefficient, the smaller the spectral distortion. It can also be used to adjust the proportion of injected spatial information. L For multispectral images and constant brightness component images, the calculation method is:

[0063]

[0064] The satellite remote sensing image and the detail injection image are added together to obtain a fused high-resolution multispectral image, thereby completing the satellite remote sensing image preprocessing.

[0065] Step S2: input the pre-processed satellite remote sensing image into the spectral feature extraction model to obtain the grass yield of the area to be detected.

[0066] In step S2, the process of obtaining the grass yield of the area to be detected specifically includes:

[0067] Extract the local grayscale maximum points from the pre-processed satellite remote sensing image of the area to be detected and perform binarization calculation;

[0068] The S-curve transformation is used to enhance the contrast of binary images, and the contrast-enhanced images are simulated using Matlab.

[0069] Using the mesh function to calculate the gray value peak point of the simulation image;

[0070] The grassland area is calculated based on the contrast-enhanced image, and the repeated area is added to the grassland area based on the gray value peak point to obtain the grass yield of the area to be detected.

[0071] In the calculation of grass yield in ordinary grasslands, the area is generally used to calculate the grass yield. However, in reality, the grass is relatively short and dense, and the use of simple area calculation cannot meet the requirements of accurate calculation of grass yield. The present invention integrates the area calculation, grass growth status and the overlapping area obtained by grayscale value peak point calculation to obtain the accurate grass yield of the area to be detected.

[0072] According to the scale and terrain characteristics of the grassland, the entire grassland should be divided into several representative sampling units. For example, for grasslands with a larger area, a grid division method can be adopted, and the size of each grid can be 10m×10m or other appropriate sizes. Make sure that these sampling units can cover areas with different growth conditions in the grassland, such as fertile areas close to water sources, relatively barren areas with higher terrain, etc. Use a high-resolution digital camera or an imaging system mounted on a drone. The resolution of the camera should be high enough to clearly capture the microscopic features of the grass, such as the outline of a single grass plant, the details of the leaves, etc. For ordinary grasslands, the camera pixel is recommended to be no less than 12 million pixels, and it should be equipped with a suitable wide-angle lens so that a larger area of grassland image can be obtained in one shot.

[0073] To obtain a grayscale image for subsequent grayscale peak value calculation, the camera must be able to output uncompressed raw grayscale image data or be set to grayscale capture mode. If a color camera is used, it can be converted to a grayscale image using image processing software.

[0074] Convert the captured color image to a grayscale image (if it is not already a grayscale image). You can use the grayscale conversion function in image processing software (such as MATLAB or Photoshop) to generate a grayscale image by combining the values of the RGB channels of the color image according to certain weights. This example uses the following formula: Grayscale value = 0.299 × R + 0.587 × G + 0.114 × B.

[0075] Because actual image capture may be affected by environmental noise (such as dust in the air and camera sensor noise), grayscale images need to be denoised. Methods such as median filtering and Gaussian filtering can be used. Median filtering is effective for removing salt and pepper noise. It replaces the grayscale value of each pixel with the median of the grayscale values in its neighborhood. Gaussian filtering filters the image in the frequency domain and effectively suppresses Gaussian noise. It uses a two-dimensional Gaussian function to perform a weighted average of the image.

[0076] Generate a grayscale histogram for the grayscale image, with the horizontal axis representing the grayscale value level (0-255) and the vertical axis representing the number of pixels with that grayscale value. Observe the shape of the grayscale histogram and find the peak point. This example uses Matlab's mesh function to calculate the grayscale peak point of the simulated image.

[0077] The method for calculating the overlapping area is to first determine the boundaries of two grayscale value regions, and then calculate the area of the region between the two boundaries to obtain the overlapping area. The boundaries can be determined using image segmentation algorithms, such as threshold-based segmentation, edge detection segmentation, etc. This embodiment uses the Otsu algorithm to automatically determine an optimal threshold to segment the image into foreground (grass) and background (soil, etc.). The boundaries and overlap of regions of different grayscale values in the foreground area are then further analyzed.

[0078] Step S3: Calculate an edible coefficient based on the edible amount of the grass, and correct the edible amount of the grass based on the edible coefficient.

[0079] Edible coefficient:

[0080]

[0081] Among them, K is the grassland type coefficient, S e is the area of edible grass in the grassland, S t is the total area of grassland, Q c is the average mass of edible grass in the grassland, Q t It is the average overall quality level of grass in the grassland.

[0082] The edible amount of the grass is revised to:

[0083] E'=E×C

[0084] Where E is the initial estimated edible amount of grassland.

[0085] Step S4: collecting the unit feed intake of livestock, and evaluating the grassland carrying capacity based on the corrected edible amount of grassland and the unit feed intake to obtain an evaluation result.

[0086] Data collection is carried out for livestock of different types and growth stages, for example, by monitoring the daily diet of livestock or consulting relevant animal husbandry literature to obtain accurate numerical information. Specifically, for livestock of different breeds such as cattle and sheep under the same feeding conditions in the same region, as well as livestock in different stages such as fattening and early growth, their feed intake varies greatly. Therefore, it is necessary to ensure the accuracy and pertinence of the collected data to ensure the accuracy of subsequent calculations. Subsequently, the grassland carrying capacity is evaluated based on the edible amount of grassland corrected in the previous step and the collected livestock unit feed intake. The assessment of carrying capacity is a key link, which involves the rational use of grassland resources and the maximization of livestock breeding benefits. Through the calculation formula, the grassland carrying capacity (measured in animal units) is equal to the corrected edible amount of grassland divided by the livestock unit feed intake, and multiplied by the corresponding grazing cycle, so as to obtain a more scientific and reasonable grassland carrying capacity assessment result. However, to ensure the reliability of the results, it is necessary to consider multiple factors, such as grassland area, topography, seasonal changes in grass growth, and livestock nutritional needs, and conduct comprehensive analysis and adjustments. The final assessment results can provide an important reference for the rational planning and use of grasslands and scientific livestock breeding. For example, if the grassland area is fixed and the grass growth is stable, if there is sufficient edible grass and livestock feed intake is moderate, then a higher stocking rate can be maintained. Otherwise, the stocking rate should be reduced to avoid overgrazing.

[0087] Example 2

[0088] A grassland carrying capacity assessment system based on remote sensing technology, the system includes:

[0089] The data acquisition module is used to use satellite remote sensing technology to obtain satellite remote sensing images of the area to be detected and perform preprocessing. The satellite remote sensing image preprocessing process specifically includes:

[0090] The satellite remote sensing image preprocessing process specifically includes:

[0091] The obtained satellite remote sensing image is regionally enhanced by using guided filtering to obtain a regional enhanced remote sensing image. The contents of the regional enhanced remote sensing image specifically include:

[0092]

[0093] Among them, GF(·) is the guided filter function, corr(X,Y) is the correlation coefficient between the two matrices, is the multispectral image after guided filtering enhancement, where n=bands.

[0094] extracting detail information of spectral scale and spatial difference from the regional enhanced remote sensing image, and obtaining a dual-scale detail image based on the detail information;

[0095] The method for extracting the detail information of spatial differences is:

[0096] D spa =PP L

[0097] Among them, P is the full color image, P L is the degraded image.

[0098] A detail injection image is obtained based on the spectral correlation between the original multispectral image and the brightness component and the edge information of the regional enhanced remote sensing image as constraints of the dual-scale detail image;

[0099]

[0100] Wherein, is the spectral correlation coefficient of the nth band. The larger the correlation coefficient, the smaller the spectral distortion. It can also be used to adjust the proportion of injected spatial information. L For multispectral images and constant brightness component images, the calculation method is:

[0101]

[0102] The satellite remote sensing image and the detail injection image are added together to obtain a fused high-resolution multispectral image, thereby completing the satellite remote sensing image preprocessing.

[0103] The yield calculation module is used to input the pre-processed satellite remote sensing image into the spectral feature extraction model to obtain the grass yield of the area to be detected. The process of obtaining the grass yield of the area to be detected specifically includes:

[0104] Extract the local grayscale maximum points from the pre-processed satellite remote sensing image of the area to be detected and perform binarization calculation;

[0105] The S-curve transformation is used to enhance the contrast of binary images, and the contrast-enhanced images are simulated using Matlab.

[0106] Using the mesh function to calculate the gray value peak point of the simulation image;

[0107] The grassland area is calculated based on the contrast-enhanced image, and the repeated area is added to the grassland area based on the gray value peak point to obtain the grass yield of the area to be detected.

[0108] In the calculation of grass yield in ordinary grasslands, the area is generally used to calculate the grass yield. However, in reality, the grass is relatively short and dense, and the use of simple area calculation cannot meet the requirements of accurate calculation of grass yield. The present invention integrates the area calculation, grass growth status and the overlapping area obtained by grayscale value peak point calculation to obtain the accurate grass yield of the area to be detected.

[0109] According to the scale and terrain characteristics of the grassland, the entire grassland should be divided into several representative sampling units. For example, for grasslands with a larger area, a grid division method can be adopted, and the size of each grid can be 10m×10m or other appropriate sizes. Make sure that these sampling units can cover areas with different growth conditions in the grassland, such as fertile areas close to water sources, relatively barren areas with higher terrain, etc. Use a high-resolution digital camera or an imaging system mounted on a drone. The resolution of the camera should be high enough to clearly capture the microscopic features of the grass, such as the outline of a single grass plant, the details of the leaves, etc. For ordinary grasslands, the camera pixel is recommended to be no less than 12 million pixels, and it should be equipped with a suitable wide-angle lens so that a larger area of grassland image can be obtained in one shot.

[0110] To obtain a grayscale image for subsequent grayscale peak value calculation, the camera must be able to output uncompressed raw grayscale image data or be set to grayscale capture mode. If a color camera is used, it can be converted to a grayscale image using image processing software.

[0111] Convert the captured color image to a grayscale image (if it is not already a grayscale image). You can use the grayscale conversion function in image processing software (such as MATLAB or Photoshop) to generate a grayscale image by combining the values of the RGB channels of the color image according to certain weights. This example uses the following formula: Grayscale value = 0.299 × R + 0.587 × G + 0.114 × B.

[0112] Because actual image capture may be affected by environmental noise (such as dust in the air and camera sensor noise), grayscale images need to be denoised. Methods such as median filtering and Gaussian filtering can be used. Median filtering is effective for removing salt and pepper noise. It replaces the grayscale value of each pixel with the median of the grayscale values in its neighborhood. Gaussian filtering filters the image in the frequency domain and effectively suppresses Gaussian noise. It uses a two-dimensional Gaussian function to perform a weighted average of the image.

[0113] Generate a grayscale histogram for the grayscale image, with the horizontal axis representing the grayscale value level (0-255) and the vertical axis representing the number of pixels with that grayscale value. Observe the shape of the grayscale histogram and find the peak point. This example uses Matlab's mesh function to calculate the grayscale peak point of the simulated image.

[0114] The method for calculating the overlapping area is to first determine the boundaries of two grayscale value regions, and then calculate the area of the region between the two boundaries to obtain the overlapping area. The boundaries can be determined using image segmentation algorithms, such as threshold-based segmentation, edge detection segmentation, etc. This embodiment uses the Otsu algorithm to automatically determine an optimal threshold to segment the image into foreground (grass) and background (soil, etc.). The boundaries and overlap of regions of different grayscale values in the foreground area are then further analyzed.

[0115] The edible grass correction module is used to calculate an edible coefficient based on the edible amount of grass, and to correct the edible amount of grass based on the edible coefficient.

[0116] Edible coefficient:

[0117]

[0118] Among them, K is the grassland type coefficient, S e is the area of edible grass in the grassland, S t is the total area of grassland, Q c is the average mass of edible grass in the grassland, Q t It is the average overall quality level of grass in the grassland.

[0119] The edible amount of the grass is revised to:

[0120] E'=E×C

[0121] Where E is the initial estimated edible amount of grassland.

[0122] The stocking capacity assessment module is used to collect the unit feed intake of livestock, and to assess the grassland stocking capacity based on the corrected edible amount of grassland and the unit feed intake to obtain an assessment result.

[0123] Data collection is carried out for livestock of different types and growth stages, for example, by monitoring the daily diet of livestock or consulting relevant animal husbandry literature to obtain accurate numerical information. Specifically, for livestock of different breeds such as cattle and sheep under the same feeding conditions in the same region, as well as livestock in different stages such as fattening and early growth, their feed intake varies greatly. Therefore, it is necessary to ensure the accuracy and pertinence of the collected data to ensure the accuracy of subsequent calculations. Subsequently, the grassland carrying capacity is evaluated based on the edible amount of grassland corrected in the previous step and the collected livestock unit feed intake. The assessment of carrying capacity is a key link, which involves the rational use of grassland resources and the maximization of livestock breeding benefits. Through the calculation formula, the grassland carrying capacity (measured in animal units) is equal to the corrected edible amount of grassland divided by the livestock unit feed intake, and multiplied by the corresponding grazing cycle, so as to obtain a more scientific and reasonable grassland carrying capacity assessment result. However, to ensure the reliability of the results, it is necessary to consider multiple factors, such as grassland area, topography, seasonal changes in grass growth, and livestock nutritional needs, and conduct comprehensive analysis and adjustments. The final assessment results can provide an important reference for the rational planning and use of grasslands and scientific livestock breeding. For example, if the grassland area is fixed and the grass growth is stable, if there is sufficient edible grass and livestock feed intake is moderate, then a higher stocking rate can be maintained. Otherwise, the stocking rate should be reduced to avoid overgrazing.

[0124] Example 3

[0125] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present disclosure also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor, wherein when the processor executes the program, the grassland carrying capacity assessment method based on remote sensing technology described in any of the above embodiments is implemented.

[0126] Figure 2 10 is a schematic diagram showing a more specific hardware structure of an electronic device provided in this embodiment. The device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are communicatively connected to each other within the device via the bus 1050.

[0127] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0128] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage devices, dynamic storage devices, etc. The memory 1020 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0129] The input / output interface 1030 is used to connect input / output modules to implement information input and output. The input / output modules can be configured as components within the device (not shown in the figure) or can be externally connected to the device to provide corresponding functions. Input devices may include a keyboard, mouse, touch screen, microphone, various sensors, etc., and output devices may include a display, speaker, vibrator, indicator light, etc.

[0130] The communication interface 1040 is used to connect to a communication module (not shown) to enable communication between the device and other devices. The communication module can communicate via a wired method (such as USB (Universal Serial Bus), network cable, etc.) or a wireless method (such as mobile network, WIFI (Wireless Fidelity), Bluetooth, etc.).

[0131] The bus 1050 comprises a pathway for transmitting information between the various components of the device (eg, the processor 1010 , the memory 1020 , the input / output interface 1030 , and the communication interface 1040 ).

[0132] It should be noted that although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040, and the bus 1050, in a specific implementation, the device may also include other components necessary for normal operation. In addition, it will be understood by those skilled in the art that the above device may only include the components necessary to implement the embodiments of this specification, and does not necessarily include all the components shown in the figure.

[0133] The system of the above embodiment is used to implement the corresponding grassland carrying capacity assessment method based on remote sensing technology in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0134] Example 4

[0135] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present disclosure also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the grassland carrying capacity assessment method based on remote sensing technology as described in any of the above embodiments.

[0136] The computer-readable media of this embodiment include permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0137] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the grassland carrying capacity assessment method based on remote sensing technology as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0138] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present disclosure (including the claims) is limited to these examples. Within the scope of the present disclosure, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present disclosure as described above, which are not provided in detail for the sake of simplicity.

[0139] In addition, to simplify the description and discussion, and so as not to obscure the embodiments of the present disclosure, known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided figures. In addition, devices may be shown in the form of block diagrams to avoid obscuring the embodiments of the present disclosure, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform on which the embodiments of the present disclosure are to be implemented (i.e., these details should be fully within the purview of those skilled in the art). Where specific details (e.g., circuits) are set forth to describe exemplary embodiments of the present disclosure, it will be apparent to those skilled in the art that the embodiments of the present disclosure may be implemented without these specific details or with variations in these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0140] The embodiments described above are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by persons skilled in the art should fall within the scope of protection defined by the claims of the present invention.

Claims

1. A grassland carrying capacity assessment method based on remote sensing technology, characterized in that: Methods include: Step S1: using satellite remote sensing technology to obtain satellite remote sensing images of the area to be detected and perform preprocessing; Step S2: inputting the pre-processed satellite remote sensing image into the spectral feature extraction model to obtain the grass yield of the area to be detected; Step S3, calculating an edible coefficient based on the edible amount of the grass, and correcting the edible amount of the grass based on the edible coefficient; Step S4: collecting the unit feed intake of livestock, and evaluating the grassland carrying capacity based on the corrected edible amount of grassland and the unit feed intake to obtain an evaluation result.

2. The method for assessing grassland carrying capacity based on remote sensing technology according to claim 1, characterized in that: In step S1, the satellite remote sensing image preprocessing process specifically includes: The satellite remote sensing image is regionally enhanced by using guided filtering to obtain a regional enhanced remote sensing image. extracting detail information of spectral scale and spatial difference from the regional enhanced remote sensing image, and obtaining a dual-scale detail image based on the detail information; A detail injection image is obtained based on the spectral correlation between the original multispectral image and the brightness component and the edge information of the regional enhanced remote sensing image as constraints of the dual-scale detail image; The satellite remote sensing image and the detail injection image are added together to obtain a fused high-resolution multispectral image, thereby completing the satellite remote sensing image preprocessing.

3. The method for assessing grassland carrying capacity based on remote sensing technology according to claim 2, characterized in that: The contents of the regional enhanced remote sensing image include: Among them, GF(·) is the guided filter function, corr(X,Y) is the correlation coefficient between the two matrices, is the multispectral image after guided filtering enhancement, where n=bands.

4. The method for assessing grassland carrying capacity based on remote sensing technology according to claim 1, characterized in that: In step S2, the process of obtaining the grass yield in the area to be detected specifically includes: Extract the local grayscale maximum points from the pre-processed satellite remote sensing image of the area to be detected and perform binarization calculation; The S-curve transformation is used to enhance the contrast of binary images, and the contrast-enhanced images are simulated using Matlab. Using the mesh function to calculate the gray value peak point of the simulation image; The grassland area is calculated based on the contrast-enhanced image, and the repeated area is added to the grassland area based on the gray value peak point to obtain the grass yield of the area to be detected.

5. A grassland carrying capacity assessment system based on remote sensing technology, the system being used to implement the grassland carrying capacity assessment method according to any one of claims 1 to 4, characterized in that: The system includes: A data acquisition module is used to obtain satellite remote sensing images of the area to be inspected using satellite remote sensing technology and perform preprocessing; The yield calculation module is used to input the pre-processed satellite remote sensing image into the spectral feature extraction model to obtain the grass yield of the area to be detected; an edible grass correction module, configured to calculate an edible coefficient based on the edible amount of grass, and correct the edible amount of grass based on the edible coefficient; The stocking capacity assessment module is used to collect the unit feed intake of livestock, and to assess the grassland stocking capacity based on the corrected edible amount of grassland and the unit feed intake to obtain an assessment result.

6. The grassland carrying capacity assessment system based on remote sensing technology according to claim 5, characterized in that: The satellite remote sensing image preprocessing process specifically includes: The satellite remote sensing image is regionally enhanced by using guided filtering to obtain a regional enhanced remote sensing image. extracting detail information of spectral scale and spatial difference from the regional enhanced remote sensing image, and obtaining a dual-scale detail image based on the detail information; A detail injection image is obtained based on the spectral correlation between the original multispectral image and the brightness component and the edge information of the regional enhanced remote sensing image as constraints of the dual-scale detail image; The satellite remote sensing image and the detail injection image are added together to obtain a fused high-resolution multispectral image, thereby completing the satellite remote sensing image preprocessing.

7. The grassland carrying capacity assessment system based on remote sensing technology according to claim 1, characterized in that: The contents of the regional enhanced remote sensing image include: Among them, GF(·) is the guided filter function, corr(X,Y) is the correlation coefficient between the two matrices, is the multispectral image after guided filtering enhancement, where n=bands.

8. The grassland carrying capacity assessment system based on remote sensing technology according to claim 1, characterized in that: The process of obtaining the grass yield of the area to be detected specifically includes: Extract the local grayscale maximum points from the pre-processed satellite remote sensing image of the area to be detected and perform binarization calculation; The S-curve transformation is used to enhance the contrast of binary images, and the contrast-enhanced images are simulated using Matlab. Using the mesh function to calculate the gray value peak point of the simulation image; The grassland area is calculated based on the contrast-enhanced image, and the repeated area is added to the grassland area based on the gray value peak point to obtain the grass yield of the area to be detected.

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