Agricultural and forestry monitoring integrated system and method based on multi-source remote sensing data

Abstract

The invention provides an agriculture and forestry monitoring integrated system and method based on multi-source remote sensing data. The multiple camera devices acquire multi-source images of a monitoring area; based on the attribute information of the camera device and the feature information of the multi-source images, performing fusion processing on the multi-source images to generate a panoramic image of the monitoring area; detecting image features of the panoramic image to determine a pest area, and determining pest attributes corresponding to the image features of the pest area according to the image features of the pest area; acquiring climate data of a preset period, and comparing the climate data with standard data to determine climate attributes; and predicting the agriculture and forestry yield of the monitoring area according to the insect pest attribute and the climate attribute of each insect pest area. According to the above process, the panorama of the monitoring area is generated by combining the attributes of the camera device and the image features so as to accurately detect the pest and disease damage area and the attributes of the pest and disease damage area, the climate attributes are determined by comparing the current climate with the standard data, the change factors such as pest damage and climate are comprehensively considered, and efficient and accurate yield prediction is achieved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to an integrated agricultural and forestry monitoring system and method based on multi-source remote sensing data. Background Art

[0002] Agricultural and forestry resources are an important foundation for human survival and development. Their quantity, quality and distribution are directly related to national food security, ecological security and sustainable development. With the growth of population and economic development, the demand for agricultural and forestry resources is increasing, and higher requirements are also placed on the management of agricultural and forestry resources. Traditional agricultural and forestry resource management methods have problems such as limited monitoring scope, untimely data updates, and incomplete information, which are difficult to meet the needs of modern agricultural and forestry resource management.

[0003] In the existing technology, changes in the agriculture and forestry industries are generally predicted through monitoring. However, this method ignores the impact of many environmental factors on the agriculture and forestry industries, such as changes in production caused by environmental factors, which in turn causes problems with inaccurate agriculture and forestry monitoring and prediction. Summary of the invention

[0004] The embodiments of the present application provide an integrated system and method for agriculture and forestry monitoring based on multi-source remote sensing data, which can at least to some extent solve the problem of inaccurate agriculture and forestry monitoring and prediction.

[0005] 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.

[0006] According to one aspect of the present application, there is provided an integrated method for agricultural and forestry monitoring based on multi-source remote sensing data, including: acquiring multi-source images of a monitoring area through a variety of camera devices; fusing the multi-source images based on attribute information of the camera devices and feature information of the multi-source images to generate a panoramic image of the monitoring area; detecting image features of the panoramic image to determine pest and disease areas, and determining corresponding pest attributes based on feature parameters of the pest and disease areas; acquiring climate data of a preset period, comparing the climate data with standard data, and determining climate attributes; and predicting agricultural and forestry yields in the monitoring area based on the pest attributes of each pest and disease area and the climate attributes.

[0007] Based on the attribute information of the camera device and the feature information of the multi-source images, the multi-source images are fused to generate a panoramic image of the monitoring area, including: determining the camera parameters of the camera device based on the attribute information of the camera device; extracting the feature information of the multi-source images, wherein the feature information includes feature points, feature lines and feature areas; matching images and positions according to the camera parameters of the camera device and the feature information of the multi-source images to determine the three-dimensional coordinates corresponding to the feature points; constructing a three-dimensional point cloud according to each feature point and its corresponding three-dimensional coordinates, and fusing the feature points based on the three-dimensional point cloud to generate a panoramic image of the monitoring area.

[0008] In the present application, based on the aforementioned scheme, the camera parameters of the camera device are determined based on the attribute information of the camera device, including: determining the shooting parameters of the camera device according to the focal length and optical center of the camera device; determining the posture parameters of the camera device according to the position and posture of the camera device; determining the shooting parameters of the camera device according to the shooting parameters and the posture parameters, as well as the feature points of the captured image.

[0009] In the present application, based on the aforementioned scheme, the image features of the panoramic image are detected to determine the pest and disease area, and the corresponding pest attributes are determined according to the characteristic parameters of the pest and disease area, including: detecting the image features of the panoramic image, and determining whether it is a pest and disease area according to the image features; if it is a pest and disease area, determining the corresponding pest attributes according to the characteristic parameters of the pest and disease area.

[0010] In the present application, based on the aforementioned scheme, the climate data of a preset period is obtained, and the climate data is compared with the standard data to determine the climate attributes, including: obtaining the climate data of a preset period and the standard data; calculating the vector difference between the climate data and the standard data, and determining the climate attributes by comparing the vector difference with a set threshold value.

[0011] In the present application, based on the aforementioned scheme, the agricultural and forestry output of the monitored area is predicted according to the pest attributes and the climate attributes of each pest area, including: combining historical data, linearly programming the pest attributes and climate attributes of each pest area, and predicting the agricultural and forestry output of the monitored area.

[0012] In the present application, based on the aforementioned solution, the camera device includes a satellite, a drone and a fixed camera.

[0013] According to one aspect of the present application, there is provided an integrated agricultural and forestry monitoring system based on multi-source remote sensing data, comprising: An acquisition unit, used for acquiring multi-source images of the monitoring area through a variety of camera devices; a fusion unit, configured to perform fusion processing on the multi-source images based on the attribute information of the camera device and the feature information of the multi-source images to generate a panoramic image of the monitoring area; A detection unit, used to detect image features of the panoramic image to determine a pest area, and determine a corresponding pest attribute according to feature parameters of the pest area; An attribute unit, used to obtain climate data of a preset period, compare the climate data with standard data, and determine climate attributes; The prediction unit is used to predict the agricultural and forestry output of the monitored area according to the pest attributes of each pest area and the climate attributes.

[0014] In the present application, based on the aforementioned scheme, the multi-source images are fused based on the attribute information of the camera device and the feature information of the multi-source images to generate a panoramic image of the monitoring area, including: determining the camera parameters of the camera device based on the attribute information of the camera device; extracting the feature information of the multi-source images, wherein the feature information includes feature points, feature lines and feature areas; matching the image and the position according to the camera parameters of the camera device and the feature information of the multi-source images, and determining the three-dimensional coordinates corresponding to the feature points; constructing a three-dimensional point cloud according to each feature point and its corresponding three-dimensional coordinates, and fusing the feature points based on the three-dimensional point cloud to generate a panoramic image of the monitoring area.

[0015] In the present application, based on the aforementioned scheme, the camera parameters of the camera device are determined based on the attribute information of the camera device, including: determining the shooting parameters of the camera device according to the focal length and optical center of the camera device; determining the posture parameters of the camera device according to the position and posture of the camera device; determining the shooting parameters of the camera device according to the shooting parameters and the posture parameters, as well as the feature points of the captured image.

[0016] In the present application, based on the aforementioned scheme, the image features of the panoramic image are detected to determine the pest and disease area, and the corresponding pest attributes are determined according to the characteristic parameters of the pest and disease area, including: detecting the image features of the panoramic image, and determining whether it is a pest and disease area according to the image features; if it is a pest and disease area, determining the corresponding pest attributes according to the characteristic parameters of the pest and disease area.

[0017] In the present application, based on the aforementioned scheme, the climate data of a preset period is obtained, and the climate data is compared with the standard data to determine the climate attributes, including: obtaining the climate data of a preset period and the standard data; calculating the vector difference between the climate data and the standard data, and determining the climate attributes by comparing the vector difference with a set threshold value.

[0018] In the present application, based on the aforementioned scheme, the agricultural and forestry output of the monitored area is predicted according to the pest attributes and the climate attributes of each pest area, including: combining historical data, linearly programming the pest attributes and climate attributes of each pest area, and predicting the agricultural and forestry output of the monitored area.

[0019] In the present application, based on the aforementioned solution, the camera device includes a satellite, a drone and a fixed camera.

[0020] 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 integrated agricultural and forestry monitoring method based on multi-source remote sensing data as described in the above embodiments is implemented.

[0021] 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 integrated agricultural and forestry monitoring method based on multi-source remote sensing data as described in the above embodiments.

[0022] 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 including 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 agricultural and forestry monitoring integration method based on multi-source remote sensing data provided in the above-mentioned various optional implementations.

[0023] In the technical solution of the present application, a multi-source image of the monitoring area is obtained through a variety of camera devices; based on the attribute information of the camera device and the characteristic information of the multi-source image, the multi-source image is fused to generate a panoramic image of the monitoring area; the image features of the panoramic image are detected to determine the pest area, and the corresponding pest attributes are determined according to the characteristic parameters of the pest area; the climate data of a preset period is obtained, and the climate data is compared with the standard data to determine the climate attributes; according to the pest attributes of each pest area and the climate attributes, the agricultural and forestry output of the monitoring area is predicted. The above process generates a panoramic view of the monitoring area through multi-source image fusion technology, combined with the attributes of the camera device and the image features, so as to accurately detect the pest area and its attributes, and at the same time, the climate attributes are determined by comparing the current climate with the standard data, and the variable factors such as pests and climate are comprehensively considered, so as to realize the accurate prediction and optimized management of the agricultural and forestry output of the monitoring area. It not only helps to improve the efficiency and benefits of agricultural production, but also provides strong technical support for the sustainable development of agriculture.

[0024] 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

[0025] 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.

[0026] Figure 1 A flowchart of an integrated agricultural and forestry monitoring method based on multi-source remote sensing data in one embodiment of the present application is schematically shown.

[0027] Figure 2 The flowchart of generating a panoramic image in one embodiment of the present application is schematically shown.

[0028] Figure 3 A schematic diagram of an integrated agricultural and forestry monitoring system based on multi-source remote sensing data in an embodiment of the present application is schematically shown.

[0029] 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

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] The implementation details of the technical solution of this application are described in detail below: Figure 1 A flowchart of an integrated agricultural and forestry monitoring method based on multi-source remote sensing data according to an embodiment of the present application is shown. Figure 1 As shown, the integrated agricultural and forestry monitoring method based on multi-source remote sensing data includes at least steps S110 to S150, which are described in detail as follows: In step S110, multi-source images of the monitoring area are acquired by using a variety of camera devices.

[0035] In one embodiment of the present application, the camera device includes a satellite, a drone and a fixed camera.

[0036] Specifically, the image data is received through the satellite ground receiving station, and preliminary processing and format conversion are performed for subsequent analysis. The drone collects images in real time during flight and sends the image data to the ground control station via wireless transmission. The ground control station must have strong data processing capabilities to process and analyze image data in real time. The image data collected by the fixed camera must be transmitted to the data center via wired or wireless means for storage and backup.

[0037] In one embodiment of the present application, multiple camera devices are used to comprehensively obtain multi-source image information of the monitored area. These devices each have unique advantages and perspectives, and can capture rich details from macro to micro, providing a solid foundation for subsequent image processing and analysis. Satellite cameras provide a macroscopic view, drones are flexible and maneuverable, and can conduct detailed observations of specific areas, while fixed cameras ensure long-term, continuous monitoring. The three work together to build a comprehensive, multi-level image monitoring system.

[0038] In step S120, based on the attribute information of the camera device and the feature information of the multi-source images, the multi-source images are fused to generate a panoramic image of the monitoring area.

[0039] like Figure 2 As shown, in one embodiment of the present application, based on the attribute information of the camera device and the feature information of the multi-source images, the multi-source images are fused to generate a panoramic image of the monitoring area, including: S210, determining a camera parameter of the camera device based on the attribute information of the camera device; S220, extracting feature information of the multi-source images, the feature information including feature points, feature lines, and feature regions; S230, matching images and positions according to the camera parameters of the camera device and the feature information of the multi-source images, and determining the three-dimensional coordinates corresponding to the feature points; S240, constructing a three-dimensional point cloud according to each feature point and its corresponding three-dimensional coordinates, and performing feature point fusion based on the three-dimensional point cloud to generate a panoramic image of the monitoring area.

[0040] In one embodiment of the present application, determining the camera parameters of the camera device based on the attribute information of the camera device includes: Determining shooting parameters of the camera device according to the focal length and the optical center of the camera device; Determining the position and posture parameters of the camera device according to the position and posture of the camera device; The shooting parameters of the camera device are determined according to the shooting parameters and the posture parameters, as well as the feature points of the captured image.

[0041] In one embodiment of the present application, the shooting parameters of the camera device are determined according to the focal length and the optical center of the camera device. for:

[0042] in, and are the focal lengths on the x-axis and y-axis respectively, represents the coordinates of the optical center; According to the position and posture of the camera device, the posture parameters of the camera device are determined as follows:

[0043] in, R is the rotation matrix, t is the translation vector. The rotation matrix R is an orthogonal matrix that represents the rotation of the camera relative to the world coordinate system; the translation vector t Represents the translation of the camera relative to the world coordinate system.

[0044] After obtaining the shooting parameters and the posture parameters, the equation between the pixel points and the shooting parameters is generated according to the shooting parameters and the posture parameters, as well as the feature points of the captured image:

[0045] in, is the information of the feature points of the captured image, is the coordinate information corresponding to the feature point in the world coordinate system, M Represents the matrix determined according to the shooting parameters and pose parameters.

[0046] After obtaining the above equation, the shooting parameters and posture parameters of the camera device can be determined by calculation based on known quantities, which serve as the shooting parameters of this embodiment.

[0047] Specifically, in the calculation process, we assume n The coordinates of a known three-dimensional point in the world coordinate system are , the projection point on the image is , then the projection error is expressed as:

[0048] in, is a projection function that projects a 3D point onto a 2D image. By minimizing , the shooting parameters and posture parameters of the camera can be solved as the shooting parameters of this embodiment.

[0049] In one embodiment of the present application, in the process of extracting feature information of the multi-source images, the feature information includes feature points, feature lines and feature areas. Feature extraction and matching is an indispensable part of three-dimensional reconstruction, which involves detecting and calculating feature points in two-dimensional images, and matching these feature points between different images.

[0050] Optionally, feature description information can be generated by calculating the histogram of the gradient direction around the key point. Specifically, for each key point, the gradient direction and magnitude are calculated in its scale space, and then the area around the key point is divided into sub-regions, and the gradient histogram of the predetermined direction is calculated for each sub-region, and finally a 128-dimensional feature vector is obtained as feature information.

[0051] The above process can extract rich feature information such as feature points, feature lines and feature regions from multi-source images. These feature information can more comprehensively reflect the local and global characteristics of the image and provide more basis for image matching.

[0052] In one embodiment of the present application, the image and position are matched according to the camera parameters of the camera device and the feature information of the multi-source image. In the process of determining the three-dimensional coordinates corresponding to the feature points, double matching is performed according to the shooting parameters and feature values ​​corresponding to each feature information. This can be achieved by similarity or distance measurement based on the above information, such as Euclidean distance, cosine similarity, etc. According to the results of the matching algorithm, feature point pairs with sufficient similarity are screened out as the same feature point.

[0053] The above process can use the extracted feature information to match images and positions, and can fully consider the similarities and differences between images. Through the accurate matching of feature points, the robustness of matching can be significantly improved and the mismatch rate can be reduced.

[0054] In one embodiment of the present application, when determining the coordinates of feature points, the parallax between the two camera devices (i.e., the pixel difference of the same object in the two cameras) can be calculated first to estimate the depth of the object, and then the three-dimensional coordinates of the feature points can be determined in combination with the camera parameters. The above process uses the matched feature point pairs and camera parameters, and through geometric calculation methods such as triangulation, the coordinates of the feature points in three-dimensional space can be accurately calculated. These three-dimensional coordinate data provide a reliable basis for subsequent three-dimensional reconstruction and panoramic image generation.

[0055] In one embodiment of the present application, a three-dimensional point cloud is constructed based on each feature point and its corresponding three-dimensional coordinates, and feature points are fused based on the three-dimensional point cloud to generate a panoramic image of the monitoring area. A high-quality three-dimensional point cloud is constructed based on each feature point and its corresponding three-dimensional coordinates. Feature point fusion based on the three-dimensional point cloud can further eliminate redundant information and improve the clarity and coherence of the image. A panoramic image of the monitoring area is generated by fusion of feature points, which can provide users with a more intuitive and comprehensive visual experience.

[0056] Optionally, after generating the 3D point cloud, it needs to be optimized to improve its accuracy. The optimization process may include steps such as filtering, denoising, and smoothing. In addition, the 3D point clouds at different perspectives may be aligned to obtain a more complete and accurate 3D model.

[0057] The above process performs fusion processing based on the attribute information of the camera device and the feature information of the multi-source images, which can generate a high-quality panoramic image and improve the resolution and clarity of the image.

[0058] In step S130, the image features of the panoramic image are detected to determine the pest area, and the corresponding pest attributes are determined according to the feature parameters of the pest area.

[0059] In one embodiment of the present application, detecting the image features of the panoramic image to determine the pest area, and determining the corresponding pest attributes according to the feature parameters of the pest area include: Detecting image features of the panoramic image, and determining whether it is a pest and disease area according to the image features; If it is a pest and disease area, the corresponding pest attribute is determined according to the characteristic parameters of the pest and disease area.

[0060] In one embodiment of the present application, potential image features related to pests and diseases are identified by carefully analyzing the panoramic image. These features may include color changes (such as yellowing of leaves, the appearance of lesions), texture abnormalities (such as spots and stripes on the surface of leaves), shape distortions (such as curling and deformation of leaves), etc. Among the identified potential features, target features directly related to pests and diseases are screened out. The features are quantitatively analyzed and evaluated to determine whether they are pest and disease areas. By detecting the image features of the panoramic image, the pest and disease area can be accurately identified, providing strong support for subsequent prevention and control work.

[0061] For each identified pest and disease area, its corresponding characteristic parameters are calculated, which may include: chlorophyll index, contrast, and direction parameters.

[0062] In one embodiment of the present application, according to the red, green and blue information of the target feature ( ), determine the chlorophyll index for:

[0063] Among them, α, β, and γ are coefficients obtained by fitting experimental data, which reflect the different sensitivities of different color channels to chlorophyll content.

[0064] Afterwards, the contrast is quantified by calculating specific texture features in the gray-level co-occurrence matrix for:

[0065] in, L is the grayscale level of the panoramic image, At a given spatial distance d and direction θ Next, grayscale i and j The joint probability density of simultaneous occurrence. The weighted sum of the grayscale differences between all pairs of pixels in the image is calculated in the above way, with the weight being the square of the grayscale difference.

[0066] Directionality can be quantified by calculating the arrangement direction of texture elements in the image, performing Fourier transform on the image, and obtaining a frequency domain representation. Then, the power spectral density of the frequency domain representation is calculated and its main directional parameters are extracted. for:

[0067] in, The frequency of the image in the frequency domain is f and direction is θ The maximum value of the power spectral density in the frequency domain is calculated to reflect the main direction of the texture in the image.

[0068] In order to quantify the extent of pests and diseases in the present application, the characteristic parameters are calculated in the following manner: :

[0069] in, , , They are weight coefficients obtained by fitting experimental data, and they reflect the importance of different characteristic parameters to the assessment of the degree and type of pests and diseases.

[0070] The panoramic image is divided into multiple areas using an image segmentation algorithm. These areas can clearly distinguish between pest and disease areas and normal areas. The pest and disease areas are marked from the panoramic image according to the target features, and the pest attributes are determined according to the feature parameters and the area of ​​the pest and disease area. Specifically, it can be determined by the weighted sum between the feature parameters and the area. According to the image features of the pest and disease area, the corresponding pest attributes can be determined, such as the type of pest and disease, the degree of damage, etc. This helps to formulate targeted prevention and control measures and improve the prevention and control effect.

[0071] Through the above process, target features related to pests and diseases can be extracted from panoramic images, pest and disease areas can be constructed, and pest attributes can be determined based on feature parameters and area. This technology provides strong support for the monitoring, early warning and prevention of pests and diseases.

[0072] In step S140, climate data of a preset period is obtained, and the climate data is compared with standard data to determine climate attributes.

[0073] In one embodiment of the present application, obtaining climate data of a preset period, comparing the climate data with standard data, and determining climate attributes includes: Obtain climate data and standard data for preset periods; The vector difference between the climate data and the standard data is calculated, and the climate attribute is determined by comparing the vector difference with the set threshold.

[0074] In one embodiment of the present application, real-time climate data can be obtained from the National Meteorological Administration, the World Meteorological Organization or other authoritative meteorological agencies. Key meteorological elements such as temperature, humidity, precipitation, wind speed, etc. can be included. In addition, data can also be obtained through the official websites, API interfaces or data sharing platforms of these agencies.

[0075] In this embodiment, the standard data includes the climate data of the most suitable monitoring area. It can also include the average climate conditions of the climate type in the monitoring area over a long period of time. These data can be obtained from the database of meteorological research institutions, universities or government departments. The standard data may include average temperature, average precipitation, average wind speed, etc., as well as the annual, seasonal or monthly variation range of these elements.

[0076] The current climate data and the standard climate data are regarded as two vectors respectively. Each vector contains multiple components, corresponding to different meteorological elements (such as temperature, humidity, precipitation, etc.). For each meteorological element, the difference between the current data and the standard data is calculated. These differences will form a new vector, namely the vector difference. The calculated vector difference is compared with the set threshold. If a component of the vector difference exceeds the corresponding threshold, it means that the current climate deviates from the standard climate condition in this element, which may have a certain impact on agricultural and forestry production. The above process can obtain the climate data of the preset period and compare it with the standard data to grasp the climate conditions of the monitoring area in real time. Through comparative analysis, the changing trends of key meteorological elements such as climate attributes, such as temperature, humidity, and precipitation, can be determined, which is helpful to predict the impact of climate change on agricultural and forestry production.

[0077] The above process analyzes the difference between the current climate and the standard climate based on the various components of the vector difference. Combining the analysis results of the vector difference with the method of determining the climate type, the attributes or type of the current climate are determined. This helps to understand the characteristics and possible impacts of the current climate conditions and provides a scientific basis for predicting agricultural and forestry outputs and formulating response measures.

[0078] In step S150, the agricultural and forestry output of the monitored area is predicted based on the pest attributes of each pest area and the climate attributes.

[0079] In one embodiment of the present application, the pest attributes and climate attributes of each pest area are linearly programmed in combination with historical data to predict the agricultural and forestry output of the monitored area.

[0080] In one embodiment of the present application, historical data includes pest data, climate data, and agricultural and forestry output data. Specifically, pest data includes the type, time of occurrence, location, severity, etc. of pests and diseases, which can be obtained from agricultural departments, research institutions, or related databases; climate data includes historical data of key meteorological elements such as temperature, humidity, precipitation, wind speed, etc., which can be obtained from meteorological institutions or databases; agricultural and forestry output data includes agricultural and forestry output data of historical years, which are used to establish prediction models.

[0081] Use pest attributes and climate attributes as input variables of the model, and agricultural and forestry output as output variables of the model. Use linear regression model or other linear programming methods to establish the linear relationship between pest attributes, climate attributes and agricultural and forestry output. Use historical data to train the model and optimize the model parameters. Input the current pest attributes and climate attributes of the monitoring area into the trained model and output the predicted agricultural and forestry output.

[0082] The above process, combined with the pest attributes and climate attributes of each pest area, can establish a more accurate agricultural and forestry output prediction model. It helps to improve the accuracy of the prediction and provide a more reliable decision-making basis for agricultural production. According to the prediction results, agricultural production plans and management measures can be adjusted in time to optimize resource allocation. It helps to improve the efficiency and benefits of agricultural production and promote sustainable agricultural development. Through the organic combination of multi-source image fusion, pest area detection, pest attribute determination, climate data comparison, and agricultural and forestry output prediction, accurate prediction and optimized management of agricultural and forestry output in the monitored area are achieved. This not only helps to improve the efficiency and benefits of agricultural production, but also provides strong technical support for sustainable agricultural development.

[0083] In the technical solution of the present application, a multi-source image of the monitoring area is obtained through a variety of camera devices; based on the attribute information of the camera device and the characteristic information of the multi-source image, the multi-source image is fused to generate a panoramic image of the monitoring area; the image features of the panoramic image are detected to determine the pest area, and the corresponding pest attributes are determined according to the characteristic parameters of the pest area; the climate data of a preset period is obtained, and the climate data is compared with the standard data to determine the climate attributes; according to the pest attributes of each pest area and the climate attributes, the agricultural and forestry output of the monitoring area is predicted. The above process generates a panoramic view of the monitoring area through multi-source image fusion technology, combined with the camera device attributes and image features, so as to accurately detect the pest area and its attributes, and at the same time, the climate attributes are determined by comparing the current climate with the standard data, and the variable factors such as pests and climate are comprehensively considered, so as to realize the accurate prediction and optimized management of the agricultural and forestry output of the monitoring area. This not only helps to improve the efficiency and benefits of agricultural production, but also provides strong technical support for the sustainable development of agriculture.

[0084] The following introduces an embodiment of the device of the present application, which can be used to execute the integrated method for monitoring agriculture and forestry based on multi-source remote sensing data 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 integrated method for monitoring agriculture and forestry based on multi-source remote sensing data in the above-mentioned embodiment of the present application.

[0085] Figure 3 A block diagram of an integrated agricultural and forestry monitoring system based on multi-source remote sensing data according to an embodiment of the present application is shown.

[0086] Reference Figure 3 As shown, according to an embodiment of the present application, an integrated agricultural and forestry monitoring system based on multi-source remote sensing data includes: An acquisition unit 310 is used to acquire multi-source images of a monitoring area through a variety of camera devices; A fusion unit 320, configured to perform fusion processing on the multi-source images based on the attribute information of the camera device and the feature information of the multi-source images to generate a panoramic image of the monitoring area; A detection unit 330 is used to detect image features of the panoramic image to determine a pest area, and determine a corresponding pest attribute according to feature parameters of the pest area; The attribute unit 340 is used to obtain climate data of a preset period, compare the climate data with standard data, and determine climate attributes; The prediction unit 350 is used to predict the agricultural and forestry output of the monitored area according to the pest attributes of each pest area and the climate attributes.

[0087] In the present application, based on the aforementioned scheme, the multi-source images are fused based on the attribute information of the camera device and the feature information of the multi-source images to generate a panoramic image of the monitoring area, including: determining the camera parameters of the camera device based on the attribute information of the camera device; extracting the feature information of the multi-source images, wherein the feature information includes feature points, feature lines and feature areas; matching the image and the position according to the camera parameters of the camera device and the feature information of the multi-source images, and determining the three-dimensional coordinates corresponding to the feature points; constructing a three-dimensional point cloud according to each feature point and its corresponding three-dimensional coordinates, and fusing the feature points based on the three-dimensional point cloud to generate a panoramic image of the monitoring area.

[0088] In the present application, based on the aforementioned scheme, the camera parameters of the camera device are determined based on the attribute information of the camera device, including: determining the shooting parameters of the camera device according to the focal length and optical center of the camera device; determining the posture parameters of the camera device according to the position and posture of the camera device; determining the shooting parameters of the camera device according to the shooting parameters and the posture parameters, as well as the feature points of the captured image.

[0089] In the present application, based on the aforementioned scheme, the image features of the panoramic image are detected to determine the pest and disease area, and the corresponding pest attributes are determined according to the characteristic parameters of the pest and disease area, including: detecting the image features of the panoramic image, and determining whether it is a pest and disease area according to the image features; if it is a pest and disease area, determining the corresponding pest attributes according to the characteristic parameters of the pest and disease area.

[0090] In the present application, based on the aforementioned scheme, the climate data of a preset period is obtained, and the climate data is compared with the standard data to determine the climate attributes, including: obtaining the climate data of a preset period and the standard data; calculating the vector difference between the climate data and the standard data, and determining the climate attributes by comparing the vector difference with a set threshold value.

[0091] In the present application, based on the aforementioned scheme, the agricultural and forestry output of the monitored area is predicted according to the pest attributes and the climate attributes of each pest area, including: combining historical data, linearly programming the pest attributes and climate attributes of each pest area, and predicting the agricultural and forestry output of the monitored area.

[0092] In the present application, based on the aforementioned solution, the camera device includes a satellite, a drone and a fixed camera.

[0093] In the technical solution of the present application, a multi-source image of the monitoring area is obtained through a variety of camera devices; based on the attribute information of the camera device and the characteristic information of the multi-source image, the multi-source image is fused to generate a panoramic image of the monitoring area; the image features of the panoramic image are detected to determine the pest and disease area, and the corresponding pest attributes are determined according to the characteristic parameters of the pest and disease area; the climate data of a preset period is obtained, and the climate data is compared with the standard data to determine the climate attributes; according to the pest attributes of each pest and disease area and the climate attributes, the agricultural and forestry yields of the monitoring area are predicted. The above process generates a panoramic view of the monitoring area through multi-source image fusion technology, combined with the camera device attributes and image features, so as to accurately detect the pest and disease area and its attributes, and at the same time, the climate attributes are determined by comparing the current climate with the standard data, and the variable factors such as pests and climate are comprehensively considered to achieve efficient and accurate yield prediction.

[0094] Figure 4A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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. An integrated agricultural and forestry monitoring method based on multi-source remote sensing data, characterized in that: include: Acquire multi-source images of the monitoring area through various camera devices; Based on the attribute information of the camera device and the feature information of the multi-source images, the multi-source images are fused to generate a panoramic image of the monitoring area; Detecting image features of the panoramic image to determine a pest area, and determining a corresponding pest attribute according to feature parameters of the pest area; Acquiring climate data of a preset period, comparing the climate data with standard data, and determining climate attributes; The agricultural and forestry outputs in the monitored area are predicted based on the pest attributes and the climate attributes of each pest area.

2. The method according to claim 1, characterized in that Based on the attribute information of the camera device and the feature information of the multi-source images, the multi-source images are fused to generate a panoramic image of the monitoring area, including: Determining the imaging parameters of the imaging device based on the attribute information of the imaging device; Extracting feature information of the multi-source images; Matching images and positions according to the camera parameters of the camera device and the feature information of the multi-source images to determine the three-dimensional coordinates corresponding to the feature points; A three-dimensional point cloud is constructed according to each feature point and its corresponding three-dimensional coordinates, and feature points are fused based on the three-dimensional point cloud to generate a panoramic image of the monitoring area.

3. The method according to claim 2, characterized in that Determining the imaging parameters of the imaging device based on the attribute information of the imaging device includes: Determining shooting parameters of the camera device according to the focal length and the optical center of the camera device; Determining the position and posture parameters of the camera device according to the position and posture of the camera device; The shooting parameters of the camera device are determined according to the shooting parameters and the posture parameters, as well as the feature points of the captured image.

4. The method according to claim 1, characterized in that Detecting the image features of the panoramic image to determine the pest area, and determining the corresponding pest attributes according to the feature parameters of the pest area, including: Detecting image features of the panoramic image, and determining whether it is a pest and disease area according to the image features; If it is a pest and disease area, the corresponding pest attribute is determined according to the characteristic parameters of the pest and disease area.

5. The method according to claim 1, characterized in that Acquiring climate data of a preset period, comparing the climate data with standard data, and determining climate attributes, including: Obtain climate data and standard data for preset periods; The vector difference between the climate data and the standard data is calculated, and the climate attribute is determined by comparing the vector difference with the set threshold.

6. The method according to claim 1, characterized in that According to the pest attributes of each pest area and the climate attributes, the agricultural and forestry output of the monitored area is predicted, including: Combined with historical data, the pest attributes and climate attributes of each pest area are linearly planned to predict the agricultural and forestry output of the monitored area.

7. The method according to claim 1, characterized in that The camera device includes a satellite, a drone and a fixed camera.

8. An integrated agricultural and forestry monitoring system based on multi-source remote sensing data, characterized in that: include: An acquisition unit, used for acquiring multi-source images of the monitoring area through a variety of camera devices; a fusion unit, configured to perform fusion processing on the multi-source images based on the attribute information of the camera device and the feature information of the multi-source images to generate a panoramic image of the monitoring area; A detection unit, used to detect image features of the panoramic image to determine a pest area, and determine a corresponding pest attribute according to feature parameters of the pest area; An attribute unit, used to obtain climate data of a preset period, compare the climate data with standard data, and determine climate attributes; The prediction unit is used to predict the agricultural and forestry output of the monitored area according to the pest attributes of each pest area and the climate attributes.

9. A computer readable medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for integrated agriculture and forestry monitoring based on multi-source remote sensing data 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 integrated agricultural and forestry monitoring method based on multi-source remote sensing data as described in any one of claims 1 to 7.

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