Agrometeorological disaster dynamic monitoring and yield loss assessment system

Abstract

The invention discloses an agricultural meteorological disaster dynamic monitoring and yield loss assessment system, which relates to the field of agricultural monitoring and comprises a meteorological monitoring module, a preliminary analysis module, a monitoring point layout module, an image acquisition module, an image analysis module and a comprehensive assessment module. Through monitoring and comprehensive analysis of crop types and meteorological information of a target area, monitoring points are intelligently selected, the accuracy and timeliness of yield loss assessment of the target area are effectively improved, and through comprehensive analysis of abnormal crops in the target area, the yield loss assessment accuracy of the target area is improved. The crop yield loss caused by diseases and the meteorological environment is subdivided, and the accuracy of evaluating the crop yield loss of the target area caused by the meteorological environment is improved.

Description

Technical Field

[0001] The present application relates to the field of agricultural monitoring, and in particular to a system for dynamic monitoring of agricultural meteorological disasters and yield loss assessment. Background Art

[0002] In recent years, the suddenness and extremeness of agricultural meteorological disasters have become increasingly prominent, regional droughts have shown a trend of worsening, the frequency of rainstorms and floods has been on the rise, the frequency and intensity of high temperature heat damage have increased significantly, and the impact on agricultural production has become increasingly obvious. Monitoring meteorological disasters and assessing yield losses can help farmers reduce losses and quickly assess the damage to crops.

[0003] Existing technologies usually analyze the impact of meteorological disasters on crop yields through censuses, which is often inefficient in large areas and complex planting environments, prolonging the time for yield loss assessment;

[0004] Existing technologies are rather one-sided when analyzing the impact of meteorological disasters on crop yields, often ignoring the important factor of disease on crop yields, resulting in large deviations in the analysis of the impact of meteorological disasters on crop yields, reducing the accuracy of the assessment of damages suffered by crops. Summary of the invention

[0005] The purpose of the present invention is to provide a system for dynamic monitoring of agricultural meteorological disasters and yield loss assessment to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above-mentioned purpose, the present invention provides the following technical solution: a system for dynamic monitoring of agricultural meteorological disasters and yield loss assessment, comprising:

[0007] Meteorological monitoring module: used to detect the meteorological conditions in the target area and establish an environmental parameter model for the target area;

[0008] Preliminary analysis module: used to collect and analyze images of crops in the target area to obtain the corresponding crop information of the target area;

[0009] Monitoring point layout module: used to analyze the environmental parameter model of the target area and the crop information corresponding to the target area, and obtain the number of monitoring points corresponding to each crop planting area in the target area;

[0010] Image acquisition module: used to monitor the images of each crop planting area in the target area, and screen and analyze the number of monitoring points corresponding to each crop planting area in the target area to obtain the images of each sampling area corresponding to each crop planting area in the target area;

[0011] Image analysis module: used to analyze the images of each sampled area corresponding to each crop planting area in the target area, and obtain the comprehensive environmental yield reduction coefficient corresponding to each crop planting area;

[0012] Comprehensive assessment module: used to conduct yield loss assessment according to the comprehensive environmental yield reduction coefficient corresponding to each crop planting area, and obtain the yield loss assessment results corresponding to each crop planting area.

[0013] In the preferred embodiment of this scheme, the specific implementation method of the meteorological monitoring module is as follows:

[0014] Obtain the weather type of the target area through the cloud weather platform;

[0015] Establish a data extraction relationship between the meteorological monitoring module and the database, extract the meteorological monitoring data set corresponding to each meteorological type stored in the database, where the meteorological monitoring data set refers to various environmental parameters corresponding to each meteorological type and the reference coefficients corresponding to each environmental parameter, and obtain the dedicated equipment corresponding to each environmental parameter through the equipment library screening;

[0016] Environmental monitoring is carried out through special equipment corresponding to various environmental parameters to obtain various environmental parameters corresponding to the target area, and an environmental parameter model corresponding to the target area is established based on the various environmental parameters corresponding to the target area.

[0017] In the preferred embodiment of this scheme, the specific implementation method of the preliminary analysis module is as follows:

[0018] Establish a data extraction relationship between the meteorological monitoring module and the database, and extract the remote sensing satellite feature image set corresponding to various crops in various yield periods stored in the database;

[0019] The target area is photographed by remote sensing satellites to obtain remote sensing satellite images corresponding to the target area, and image recognition and matching is performed between the remote sensing satellite images corresponding to the target area and remote sensing satellite feature image sets corresponding to various crops at various yield periods to obtain various crop planting areas in the target area and the crop types corresponding to each crop planting area;

[0020] The planting area of ​​each crop planting area in the target area is obtained by scaling the remote sensing satellite image corresponding to the target area, and the planting area, crop type and yield period corresponding to each crop planting area in the target area are recorded as the crop information corresponding to the target area.

[0021] In the preferred embodiment of this scheme, the specific implementation method of the monitoring point layout module is as follows:

[0022] Establish a data extraction relationship between the monitoring point layout module and the database, extract the standard environmental parameter model of each crop in each yield period corresponding to each meteorological type stored in the database, where the standard environmental parameter model includes various standard environmental parameters;

[0023] According to the crop types and yield periods corresponding to the crop planting areas in the target area, a standard environmental parameter model corresponding to each crop planting area in the target area is obtained;

[0024] By calculating the formula , calculate the environmental deviation coefficient corresponding to each crop planting area in the target area ,in It is represented by the number of various environmental parameters. Expressed as the number of environmental parameters, Represented as various environmental parameters corresponding to the target area, It is expressed as various standard environmental parameters corresponding to each crop planting area in the target area. It is expressed as the reference coefficient corresponding to various environmental parameters. It is represented by the number of each crop planting area in the target area;

[0025] By calculating the formula , calculate the monitoring point layout coefficient corresponding to each crop planting area in the target area ,in It is expressed as the planting area of ​​each crop planting area in the target region;

[0026] Extract the number of monitoring points corresponding to each monitoring point layout coefficient interval stored in the database,

[0027] The monitoring point layout coefficients corresponding to each crop planting area in the target area are compared and screened with the monitoring point layout coefficient intervals stored in the database to obtain the number of monitoring point layouts corresponding to each crop planting area in the target area.

[0028] In a preferred embodiment of this scheme, the specific implementation method of the image acquisition module is as follows:

[0029] Through patrol drones, image monitoring is performed on each crop planting area in the target area to obtain a full range of regional images corresponding to each crop planting area in the target area;

[0030] Extract the unit sampling area corresponding to each type of crop;

[0031] Irregular sampling is performed based on the number of monitoring points arranged corresponding to each crop planting area in the target area and the unit sampling area corresponding to each crop planting area in the target area, so as to obtain each sampling sub-region image corresponding to each crop planting area in the target area.

[0032] In a preferred embodiment of this solution, the specific implementation method of the image analysis module is as follows:

[0033] Establish a data extraction relationship between the image acquisition module and the database, and extract the normal yield model set corresponding to various types of crops stored in the database;

[0034] Establishing a three-dimensional model of each sampling sub-region image corresponding to each crop planting area, extracting plant models from the three-dimensional model of each sampling sub-region image corresponding to each crop planting area, and obtaining each plant model in each sampling sub-region image corresponding to each crop planting area;

[0035] Compare each plant model in each sampling sub-region image corresponding to each crop planting area with a set of normal crop yield models corresponding to each crop planting area in the target area, obtain each conforming plant model and each non-conforming plant model in each sampling sub-region image corresponding to each crop planting area, and obtain each normal yield plant and each abnormal yield plant in each sampling sub-region image corresponding to each crop planting area;

[0036] Extracting a plant disease characteristic image combination model corresponding to each disease type of each type of crop stored in a database and a yield reduction coefficient corresponding to each disease type, wherein the plant disease characteristic image model includes a plant diseased leaf characteristic image, a plant diseased stem characteristic image, and a plant diseased fruit characteristic image;

[0037] Extract image features of each abnormal yield plant in each sampling sub-region image corresponding to each crop planting area, and obtain a feature image combination model of each abnormal yield plant in each sampling sub-region image corresponding to each crop planting area, wherein the feature image combination model includes plant leaf feature images, plant stem feature images, and plant fruit feature images of the abnormal yield plant;

[0038] Extracting information from the plant fruit feature images of each abnormal yield plant in each sampling sub-region image corresponding to each crop planting area, and obtaining the number of fruits of each abnormal yield plant in each sampling sub-region image corresponding to each crop planting area;

[0039] The model coincidence analysis is performed on the feature image combination model of each abnormal yield plant in each sampling sub-region image corresponding to each crop planting area and the diseased plant feature image combination model of each disease type of each crop, so as to obtain the model matching degree of the feature image combination model of each abnormal yield plant in each sampling sub-region image corresponding to each crop planting area and the diseased plant feature image combination model of each disease type of each crop, which is recorded as the matching degree of each abnormal yield plant and each disease type. The matching degree of the abnormal yield plant and each disease type is compared with the preset matching degree threshold for analysis, and the screening is carried out. Select each disease type greater than the matching degree threshold, record each disease type greater than the matching degree threshold as each preliminary matching disease type, screen the disease type corresponding to the maximum matching degree and record it as the disease type corresponding to the abnormal yield plant, if there is no disease type greater than the matching degree threshold, it means that the abnormal yield plant is not sick, and obtain the corresponding diseased abnormal yield plants and non-diseased abnormal yield plants in each sampling sub-region image corresponding to each crop planting area by statistics, and screen to obtain the number of fruits of each diseased abnormal yield plant and the number of fruits of each non-diseased abnormal yield plant in each sampling sub-region image corresponding to each crop planting area;

[0040] By calculating the formula , calculate the first environmental yield reduction coefficient corresponding to each crop planting area ,in It is expressed as the standard yield of a single plant in each crop planting area. It is represented by the number of fruits of each diseased plant with abnormal yield in each sampled sub-region image corresponding to each crop planting area, It is represented by the yield reduction coefficient of each diseased plant with abnormal yield in each sampling area image corresponding to each crop planting area, It represents the number of each sampled area image corresponding to each crop planting area. It is represented by the number of each diseased plant with abnormal yield in each sampled sub-region image corresponding to each crop planting area, It is represented by the number of sampled area images corresponding to each crop planting area. It is represented by the number of diseased plants with abnormal yield in each sampled sub-region image corresponding to each crop planting area;

[0041] By calculating the formula , calculate the second reduction coefficient corresponding to each crop planting area ,in It is represented by the number of fruits of each non-diseased plant with abnormal yield in each sampled sub-region image corresponding to each crop planting area, It is represented by the number of each non-diseased plant with abnormal yield in each sampled sub-region image corresponding to each crop planting area, It is represented by the number of non-diseased plants with abnormal yield in each sampled area image corresponding to each crop planting area;

[0042] By calculating the formula , calculate the comprehensive environmental yield reduction coefficient corresponding to each crop planting area .

[0043] In the preferred embodiment of this scheme, the specific implementation method of the comprehensive evaluation module is as follows:

[0044] Establish a data extraction relationship between the comprehensive evaluation module and the database, and extract various yield loss assessment models stored in the database, wherein the yield loss assessment model includes crop types, comprehensive environmental yield reduction coefficients, and yield reduction ratios;

[0045] By screening the comprehensive environmental yield reduction coefficient corresponding to each crop planting area and the crop types corresponding to each crop planting area, the yield loss assessment model corresponding to each crop planting area is obtained, the yield reduction ratio corresponding to each crop planting area is obtained, and the yield reduction ratio corresponding to each crop planting area is recorded as the yield loss assessment result corresponding to each crop planting area.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] The present invention monitors and comprehensively analyzes the types of crops and meteorological information in the target area, intelligently selects monitoring points, and effectively improves the accuracy and timeliness of yield loss assessment in the target area.

[0048] The present invention conducts a comprehensive analysis of abnormal crops and subdivides the crop yield losses caused by diseases and meteorological environment, thereby improving the accuracy of assessing the crop yield losses in target areas caused by the meteorological environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0050] Figure 1 This is a schematic diagram of module connection according to an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] See also Figure 1 , the present invention provides a system for dynamic monitoring of agricultural meteorological disasters and yield loss assessment, the system comprising a meteorological monitoring module, a preliminary analysis module, a monitoring point layout module, an image acquisition module, an image analysis module and a comprehensive evaluation module;

[0053] The meteorological monitoring module is connected to the preliminary analysis module, the preliminary analysis module is connected to the monitoring point layout module, the monitoring point layout module is connected to the image acquisition module, the image acquisition module is connected to the image analysis module, and the image analysis module is connected to the comprehensive evaluation module.

[0054] The meteorological monitoring module is used to detect the meteorological conditions in the target area and establish an environmental parameter model for the target area;

[0055] Furthermore, the specific implementation of the meteorological monitoring module is as follows:

[0056] Obtain the weather type of the target area through the cloud weather platform;

[0057] Establish a data extraction relationship between the meteorological monitoring module and the database, extract the meteorological monitoring data set corresponding to each meteorological type stored in the database, where the meteorological monitoring data set refers to various environmental parameters corresponding to each meteorological type and the reference coefficients corresponding to each environmental parameter, and obtain the dedicated equipment corresponding to each environmental parameter through the equipment library screening;

[0058] Environmental monitoring is carried out through special equipment corresponding to various environmental parameters to obtain various environmental parameters corresponding to the target area, and an environmental parameter model corresponding to the target area is established based on the various environmental parameters corresponding to the target area.

[0059] The preliminary analysis module is used to collect and analyze images of crops in the target area to obtain the corresponding crop information of the target area;

[0060] Furthermore, the specific execution method of the preliminary analysis module is as follows:

[0061] Establish a data extraction relationship between the meteorological monitoring module and the database, and extract the remote sensing satellite feature image set corresponding to various crops in various yield periods stored in the database;

[0062] The target area is photographed by remote sensing satellites to obtain remote sensing satellite images corresponding to the target area, and image recognition and matching is performed between the remote sensing satellite images corresponding to the target area and remote sensing satellite feature image sets corresponding to various crops at various yield periods to obtain various crop planting areas in the target area and the crop types corresponding to each crop planting area;

[0063] The planting area of ​​each crop planting area in the target area is obtained by scaling the remote sensing satellite image corresponding to the target area, and the planting area, crop type and yield period corresponding to each crop planting area in the target area are recorded as the crop information corresponding to the target area.

[0064] The monitoring point layout module is used to analyze the environmental parameter model of the target area and the crop information corresponding to the target area, and obtain the number of monitoring points corresponding to each crop planting area in the target area;

[0065] Furthermore, the specific implementation method of the monitoring point layout module is as follows:

[0066] Establish a data extraction relationship between the monitoring point layout module and the database, extract the standard environmental parameter model of each crop in each yield period corresponding to each meteorological type stored in the database, where the standard environmental parameter model includes various standard environmental parameters;

[0067] According to the crop types and yield periods corresponding to the crop planting areas in the target area, a standard environmental parameter model corresponding to each crop planting area in the target area is obtained;

[0068] By calculating the formula , calculate the environmental deviation coefficient corresponding to each crop planting area in the target area ,in It is represented by the number of various environmental parameters. Expressed as the number of environmental parameters, Represented as various environmental parameters corresponding to the target area, It is expressed as various standard environmental parameters corresponding to each crop planting area in the target area. It is expressed as the reference coefficient corresponding to various environmental parameters. It is represented by the number of each crop planting area in the target area;

[0069] By calculating the formula , calculate the monitoring point layout coefficient corresponding to each crop planting area in the target area ,in It is expressed as the planting area of ​​each crop planting area in the target region;

[0070] Extract the number of monitoring points corresponding to each monitoring point layout coefficient interval stored in the database,

[0071] The monitoring point layout coefficients corresponding to each crop planting area in the target area are compared and screened with the monitoring point layout coefficient intervals stored in the database to obtain the number of monitoring point layouts corresponding to each crop planting area in the target area.

[0072] The image acquisition module is used to monitor the images of each crop planting area in the target area, and screen and analyze the number of monitoring points corresponding to each crop planting area in the target area to obtain the images of each sampling area corresponding to each crop planting area in the target area;

[0073] Furthermore, the specific execution mode of the image acquisition module is as follows:

[0074] Through patrol drones, image monitoring is performed on each crop planting area in the target area to obtain a full range of regional images corresponding to each crop planting area in the target area;

[0075] Extract the unit sampling area corresponding to each type of crop;

[0076] Irregular sampling is performed based on the number of monitoring points arranged corresponding to each crop planting area in the target area and the unit sampling area corresponding to each crop planting area in the target area, so as to obtain each sampling sub-region image corresponding to each crop planting area in the target area.

[0077] The image analysis module is used to perform image analysis on each sampled sub-region image corresponding to each crop planting area in the target area to obtain the comprehensive environmental yield reduction coefficient corresponding to each crop planting area;

[0078] Furthermore, the specific execution mode of the image analysis module is as follows:

[0079] Establish a data extraction relationship between the image acquisition module and the database, and extract the normal yield model set corresponding to various types of crops stored in the database;

[0080] Establishing a three-dimensional model of each sampling sub-region image corresponding to each crop planting area, extracting plant models from the three-dimensional model of each sampling sub-region image corresponding to each crop planting area, and obtaining each plant model in each sampling sub-region image corresponding to each crop planting area;

[0081] Compare each plant model in each sampling sub-region image corresponding to each crop planting area with a set of normal crop yield models corresponding to each crop planting area in the target area, obtain each conforming plant model and each non-conforming plant model in each sampling sub-region image corresponding to each crop planting area, and obtain each normal yield plant and each abnormal yield plant in each sampling sub-region image corresponding to each crop planting area;

[0082] Extracting a plant disease characteristic image combination model corresponding to each disease type of each type of crop stored in a database and a yield reduction coefficient corresponding to each disease type, wherein the plant disease characteristic image model includes a plant diseased leaf characteristic image, a plant diseased stem characteristic image, and a plant diseased fruit characteristic image;

[0083] Extract image features of each abnormal yield plant in each sampling sub-region image corresponding to each crop planting area, and obtain a feature image combination model of each abnormal yield plant in each sampling sub-region image corresponding to each crop planting area, wherein the feature image combination model includes plant leaf feature images, plant stem feature images, and plant fruit feature images of the abnormal yield plant;

[0084] Extracting information from the plant fruit feature images of each abnormal yield plant in each sampling sub-region image corresponding to each crop planting area, and obtaining the number of fruits of each abnormal yield plant in each sampling sub-region image corresponding to each crop planting area;

[0085] The model coincidence analysis is performed on the feature image combination model of each abnormal yield plant in each sampling sub-region image corresponding to each crop planting area and the diseased plant feature image combination model of each disease type of each crop, so as to obtain the model matching degree of the feature image combination model of each abnormal yield plant in each sampling sub-region image corresponding to each crop planting area and the diseased plant feature image combination model of each disease type of each crop, which is recorded as the matching degree of each abnormal yield plant and each disease type. The matching degree of the abnormal yield plant and each disease type is compared with the preset matching degree threshold for analysis, and the screening is carried out. Select each disease type greater than the matching degree threshold, record each disease type greater than the matching degree threshold as each preliminary matching disease type, screen the disease type corresponding to the maximum matching degree and record it as the disease type corresponding to the abnormal yield plant, if there is no disease type greater than the matching degree threshold, it means that the abnormal yield plant is not sick, and obtain the corresponding diseased abnormal yield plants and non-diseased abnormal yield plants in each sampling sub-region image corresponding to each crop planting area by statistics, and screen to obtain the number of fruits of each diseased abnormal yield plant and the number of fruits of each non-diseased abnormal yield plant in each sampling sub-region image corresponding to each crop planting area;

[0086] By calculating the formula , calculate the first environmental yield reduction coefficient corresponding to each crop planting area ,in It is expressed as the standard yield of a single plant in each crop planting area. It is represented by the number of fruits of each diseased plant with abnormal yield in each sampled sub-region image corresponding to each crop planting area, It is represented by the yield reduction coefficient of each diseased plant with abnormal yield in each sampling area image corresponding to each crop planting area, It represents the number of each sampled area image corresponding to each crop planting area. It is represented by the number of each diseased plant with abnormal yield in each sampled sub-region image corresponding to each crop planting area, It is represented by the number of sampled area images corresponding to each crop planting area. It is represented by the number of diseased plants with abnormal yield in each sampled sub-region image corresponding to each crop planting area;

[0087] By calculating the formula , calculate the second reduction coefficient corresponding to each crop planting area ,in It is represented by the number of fruits of each non-diseased plant with abnormal yield in each sampled sub-region image corresponding to each crop planting area, It is represented by the number of each non-diseased plant with abnormal yield in each sampled sub-region image corresponding to each crop planting area, It is represented by the number of non-diseased plants with abnormal yield in each sampled area image corresponding to each crop planting area;

[0088] By calculating the formula , calculate the comprehensive environmental yield reduction coefficient corresponding to each crop planting area .

[0089] The comprehensive assessment module is used to conduct yield loss assessment according to the comprehensive environmental yield reduction coefficient corresponding to each crop planting area, and obtain the yield loss assessment results corresponding to each crop planting area.

[0090] Furthermore, the specific implementation of the comprehensive evaluation module is as follows:

[0091] Establish a data extraction relationship between the comprehensive evaluation module and the database, and extract various yield loss assessment models stored in the database, wherein the yield loss assessment model includes crop types, comprehensive environmental yield reduction coefficients, and yield reduction ratios;

[0092] By screening the comprehensive environmental yield reduction coefficient corresponding to each crop planting area and the crop types corresponding to each crop planting area, the yield loss assessment model corresponding to each crop planting area is obtained, the yield reduction ratio corresponding to each crop planting area is obtained, and the yield reduction ratio corresponding to each crop planting area is recorded as the yield loss assessment result corresponding to each crop planting area.

[0093] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A system for dynamic monitoring of agricultural meteorological disasters and yield loss assessment, characterized by: include: Meteorological monitoring module: used to detect the meteorological conditions in the target area and establish an environmental parameter model for the target area; Preliminary analysis module: used to collect and analyze images of crops in the target area to obtain the corresponding crop information of the target area; Monitoring point layout module: used to analyze the environmental parameter model of the target area and the crop information corresponding to the target area, and obtain the number of monitoring points corresponding to each crop planting area in the target area; Image acquisition module: used to monitor the images of each crop planting area in the target area, and screen and analyze the number of monitoring points corresponding to each crop planting area in the target area to obtain the images of each sampling area corresponding to each crop planting area in the target area; Image analysis module: used to analyze the images of each sampled area corresponding to each crop planting area in the target area, and obtain the comprehensive environmental yield reduction coefficient corresponding to each crop planting area; Comprehensive assessment module: used to conduct yield loss assessment according to the comprehensive environmental yield reduction coefficient corresponding to each crop planting area, and obtain the yield loss assessment results corresponding to each crop planting area.

2. The agricultural meteorological disaster dynamic monitoring and yield loss assessment system according to claim 1 is characterized by: The specific implementation method of the meteorological monitoring module is as follows: Obtain the weather type of the target area through the cloud weather platform; Establish a data extraction relationship between the meteorological monitoring module and the database, extract the meteorological monitoring data set corresponding to each meteorological type stored in the database, where the meteorological monitoring data set refers to various environmental parameters corresponding to each meteorological type and the reference coefficients corresponding to each environmental parameter, and obtain the dedicated equipment corresponding to each environmental parameter through the equipment library screening; Environmental monitoring is carried out through special equipment corresponding to various environmental parameters to obtain various environmental parameters corresponding to the target area, and an environmental parameter model corresponding to the target area is established based on the various environmental parameters corresponding to the target area.

3. The agricultural meteorological disaster dynamic monitoring and yield loss assessment system according to claim 1 is characterized by: The specific implementation method of the preliminary analysis module is as follows: Establish a data extraction relationship between the meteorological monitoring module and the database, and extract the remote sensing satellite feature image set corresponding to various crops in various yield periods stored in the database; The target area is photographed by remote sensing satellites to obtain remote sensing satellite images corresponding to the target area, and image recognition and matching is performed between the remote sensing satellite images corresponding to the target area and remote sensing satellite feature image sets corresponding to various crops at various yield periods to obtain various crop planting areas in the target area and the crop types corresponding to each crop planting area; The planting area of ​​each crop planting area in the target area is obtained by scaling the remote sensing satellite image corresponding to the target area, and the planting area, crop type and yield period corresponding to each crop planting area in the target area are recorded as the crop information corresponding to the target area.

4. The agricultural meteorological disaster dynamic monitoring and yield loss assessment system according to claim 3 is characterized by: The specific implementation method of the monitoring point layout module is as follows: Establish a data extraction relationship between the monitoring point layout module and the database, extract the standard environmental parameter model of each crop in each yield period corresponding to each meteorological type stored in the database, where the standard environmental parameter model includes various standard environmental parameters; According to the crop types and yield periods corresponding to the crop planting areas in the target area, a standard environmental parameter model corresponding to each crop planting area in the target area is obtained; By calculating the formula , calculate the environmental deviation coefficient corresponding to each crop planting area in the target area ,in It is represented by the number of various environmental parameters. Expressed as the number of environmental parameters, Represented as various environmental parameters corresponding to the target area, It is expressed as various standard environmental parameters corresponding to each crop planting area in the target area. It is expressed as the reference coefficient corresponding to various environmental parameters. It is represented by the number of each crop planting area in the target area; By calculating the formula , calculate the monitoring point layout coefficient corresponding to each crop planting area in the target area ,in It is expressed as the planting area of ​​each crop planting area in the target region; Extract the number of monitoring points corresponding to each monitoring point layout coefficient interval stored in the database, The monitoring point layout coefficients corresponding to each crop planting area in the target area are compared and screened with the monitoring point layout coefficient intervals stored in the database to obtain the number of monitoring point layouts corresponding to each crop planting area in the target area.

5. The agricultural meteorological disaster dynamic monitoring and yield loss assessment system according to claim 4 is characterized by: The specific implementation of the image acquisition module is as follows: Through patrol drones, image monitoring is performed on each crop planting area in the target area to obtain a full range of regional images corresponding to each crop planting area in the target area; Extract the unit sampling area corresponding to each type of crop; Irregular sampling is performed based on the number of monitoring points arranged corresponding to each crop planting area in the target area and the unit sampling area corresponding to each crop planting area in the target area, so as to obtain each sampling sub-region image corresponding to each crop planting area in the target area.

6. The agricultural meteorological disaster dynamic monitoring and yield loss assessment system according to claim 5 is characterized by: The specific implementation of the image analysis module is as follows: Establish a data extraction relationship between the image acquisition module and the database, and extract the normal yield model set corresponding to various types of crops stored in the database; Establishing a three-dimensional model of each sampling sub-region image corresponding to each crop planting area, extracting plant models from the three-dimensional model of each sampling sub-region image corresponding to each crop planting area, and obtaining each plant model in each sampling sub-region image corresponding to each crop planting area; Compare each plant model in each sampling sub-region image corresponding to each crop planting area with a set of normal crop yield models corresponding to each crop planting area in the target area, obtain each conforming plant model and each non-conforming plant model in each sampling sub-region image corresponding to each crop planting area, and obtain each normal yield plant and each abnormal yield plant in each sampling sub-region image corresponding to each crop planting area; Extracting a plant disease characteristic image combination model corresponding to each disease type of each type of crop stored in a database and a yield reduction coefficient corresponding to each disease type, wherein the plant disease characteristic image model includes a plant diseased leaf characteristic image, a plant diseased stem characteristic image, and a plant diseased fruit characteristic image; Extract image features of each abnormal yield plant in each sampling sub-region image corresponding to each crop planting area, and obtain a feature image combination model of each abnormal yield plant in each sampling sub-region image corresponding to each crop planting area, wherein the feature image combination model includes plant leaf feature images, plant stem feature images, and plant fruit feature images of the abnormal yield plant; Extracting information from the plant fruit feature images of each abnormal yield plant in each sampling sub-region image corresponding to each crop planting area, and obtaining the number of fruits of each abnormal yield plant in each sampling sub-region image corresponding to each crop planting area; The model coincidence analysis is performed on the feature image combination model of each abnormal yield plant in each sampling sub-region image corresponding to each crop planting area and the diseased plant feature image combination model of each disease type of each crop, so as to obtain the model matching degree of the feature image combination model of each abnormal yield plant in each sampling sub-region image corresponding to each crop planting area and the diseased plant feature image combination model of each disease type of each crop, which is recorded as the matching degree of each abnormal yield plant and each disease type. The matching degree of the abnormal yield plant and each disease type is compared with the preset matching degree threshold for analysis, and the screening is carried out. Select each disease type greater than the matching degree threshold, record each disease type greater than the matching degree threshold as each preliminary matching disease type, screen the disease type corresponding to the maximum matching degree and record it as the disease type corresponding to the abnormal yield plant, if there is no disease type greater than the matching degree threshold, it means that the abnormal yield plant is not sick, and obtain the corresponding diseased abnormal yield plants and non-diseased abnormal yield plants in each sampling sub-region image corresponding to each crop planting area by statistics, and screen to obtain the number of fruits of each diseased abnormal yield plant and the number of fruits of each non-diseased abnormal yield plant in each sampling sub-region image corresponding to each crop planting area; By calculating the formula , calculate the first environmental yield reduction coefficient corresponding to each crop planting area ,in It is expressed as the standard yield of a single plant in each crop planting area. It is represented by the number of fruits of each diseased plant with abnormal yield in each sampled sub-region image corresponding to each crop planting area, It is represented by the yield reduction coefficient of each diseased plant with abnormal yield in each sampling area image corresponding to each crop planting area, It represents the number of each sampled area image corresponding to each crop planting area. It is represented by the number of each diseased plant with abnormal yield in each sampled sub-region image corresponding to each crop planting area, It is represented by the number of sampled area images corresponding to each crop planting area. It is represented by the number of diseased plants with abnormal yield in each sampled sub-region image corresponding to each crop planting area; By calculating the formula , calculate the second reduction coefficient corresponding to each crop planting area ,in It is represented by the number of fruits of each non-diseased plant with abnormal yield in each sampled sub-region image corresponding to each crop planting area, It is represented by the number of each non-diseased plant with abnormal yield in each sampled sub-region image corresponding to each crop planting area, It is represented by the number of non-diseased plants with abnormal yield in each sampled area image corresponding to each crop planting area; By calculating the formula , calculate the comprehensive environmental yield reduction coefficient corresponding to each crop planting area .

7. The agricultural meteorological disaster dynamic monitoring and yield loss assessment system according to claim 6 is characterized by: The specific implementation of the comprehensive evaluation module is as follows: Establish a data extraction relationship between the comprehensive evaluation module and the database, and extract various yield loss assessment models stored in the database, wherein the yield loss assessment model includes crop types, comprehensive environmental yield reduction coefficients, and yield reduction ratios; By screening the comprehensive environmental yield reduction coefficient corresponding to each crop planting area and the crop types corresponding to each crop planting area, the yield loss assessment model corresponding to each crop planting area is obtained, the yield reduction ratio corresponding to each crop planting area is obtained, and the yield reduction ratio corresponding to each crop planting area is recorded as the yield loss assessment result corresponding to each crop planting area.

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