Phytoseiulus mite targeting feature analysis method and system based on global mite damage monitoring data

Abstract

The invention discloses a phytoseiulus mite targeting feature analysis method and system based on global mite damage monitoring data, and relates to the technical field of data processing. The method comprises the following steps: carrying out global mite damage monitoring on a crop protection area to obtain multi-source mite damage monitoring data; performing mite damage space distribution analysis according to the multi-source mite damage monitoring data, and dividing the crop protection area into three levels of protection areas through a space clustering algorithm; carrying out a phytoseiulus mite release test on the third-level protection area, carrying out target feature analysis according to a test result, and outputting a third-level phytoseiulus mite release strategy; and applying a three-level phytoseiulus mite putting strategy, and mapping to carry out dynamic mite damage prevention and control of a three-level protection area. The technical problems that in the prior art, mite damage monitoring data is single, phytoseiulus mite putting lacks pertinence, and the mite damage prevention and control efficiency is low are solved, and the technical effects that mite damage partition management is achieved based on comprehensive monitoring data, a targeted phytoseiulus mite putting strategy is formulated, and the mite damage prevention and control efficiency and effect are improved are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and system for analyzing target characteristics of phytoseiid mites based on global mite pest monitoring data. Background Art

[0002] In the process of modern agricultural production, mite pests have become an important factor restricting the stable production and increase of crop income. With the in-depth promotion of green agriculture and sustainable development concepts, biological control methods such as phytoseiid mites have been widely used in mite pest control due to their environmental protection and high efficiency. However, the current mite pest monitoring methods are relatively simple, mainly relying on manual inspections or a single type of monitoring equipment, which makes it difficult to fully grasp the spatial distribution characteristics of mite pests, resulting in the lack of targeted release of phytoseiid mites, low resource utilization efficiency, and unstable prevention and control effects. Summary of the invention

[0003] The present application provides a method and system for analyzing the target characteristics of phytoseiid mites based on global mite pest monitoring data, which solves the technical problems in the prior art of single mite pest monitoring data and lack of targetedness in the release of phytoseiid mites, resulting in low mite pest control efficiency.

[0004] In a first aspect of the present application, a method for analyzing the target characteristics of phytoseiid mites based on global mite pest monitoring data is provided, the method comprising: Conduct global mite pest monitoring on the crop protection area to obtain multi-source mite pest monitoring data, wherein the multi-source mite pest monitoring data includes satellite remote sensing data, drone image data and ground sensor data; conduct spatial distribution analysis of mite pests based on the multi-source mite pest monitoring data, and divide the crop protection area into three-level protection areas through a spatial clustering algorithm, wherein the three-level protection areas include severe mite pest areas, mite pest edge areas and green safety areas; conduct phytoseiid mite release tests on the three-level protection areas, and conduct target feature analysis based on the test results to output a three-level phytoseiid mite release strategy; apply the three-level phytoseiid mite release strategy to map and conduct dynamic mite pest control in the three-level protection areas.

[0005] The second aspect of the present application provides a phytoseiid mite target feature analysis system based on global mite pest monitoring data, the system comprising: A monitoring module is used to conduct full-region mite damage monitoring on a crop protection area to obtain multi-source mite damage monitoring data. Among them, the multi-source mite damage monitoring data includes satellite remote sensing data, unmanned aerial vehicle image data, and ground sensor data. A spatial division module is used to analyze the spatial distribution of mite damage based on the multi-source mite damage monitoring data and divide the crop protection area into three-level protection areas through a spatial clustering algorithm. Among them, the three-level protection areas include severely damaged areas, marginal damaged areas, and green safety areas. An analysis module is used to conduct a test on the release of phytoseiid mites in the three-level protection areas and perform target-specific feature analysis based on the test results, and output a three-level phytoseiid mite release strategy. A prevention and control module is used to apply the three-level phytoseiid mite release strategy to map and conduct dynamic mite damage prevention and control in the three-level protection areas.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: First, conduct full-region mite damage monitoring on the crop protection area to obtain multi-source mite damage monitoring data. Among them, the multi-source mite damage monitoring data includes satellite remote sensing data, unmanned aerial vehicle image data, and ground sensor data. Then, analyze the spatial distribution of mite damage based on the multi-source mite damage monitoring data and divide the crop protection area into three-level protection areas through a spatial clustering algorithm. Among them, the three-level protection areas include severely damaged areas, marginal damaged areas, and green safety areas. Then, conduct a test on the release of phytoseiid mites in the three-level protection areas and perform target-specific feature analysis based on the test results, and output a three-level phytoseiid mite release strategy. Finally, apply the three-level phytoseiid mite release strategy to map and conduct dynamic mite damage prevention and control in the three-level protection areas. This solves the technical problem in the prior art that the mite damage monitoring data is single and the release of phytoseiid mites lacks pertinence, resulting in low mite damage prevention and control efficiency, and achieves the technical effect of realizing mite damage zoning management based on comprehensive monitoring data and formulating a targeted phytoseiid mite release strategy, thereby improving the efficiency and effect of mite damage prevention and control. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for description in the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0008] Figure 1 It is a schematic flowchart of a method for analyzing the target-specific characteristics of phytoseiid mites based on full-region mite damage monitoring data provided by an embodiment of this application; Figure 2 It is a schematic structural diagram of a system for analyzing the target-specific characteristics of phytoseiid mites based on full-region mite damage monitoring data provided by an embodiment of this application.

[0009] Description of the attached drawing reference numerals: Monitoring module 11, spatial division module 12, analysis module 13, prevention and control module 14. Detailed implementation manners

[0010] By providing a method and a system for analyzing the target characteristics of phytoseiid mites based on the whole-region mite damage monitoring data, the present application solves the technical problems in the prior art that the mite damage monitoring data is single and the release of phytoseiid mites lacks pertinence, resulting in low efficiency of mite damage prevention and control.

[0011] Next, the technical solutions in the embodiments of the present application will be clearly and completely described with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0012] It should be noted that the terms "include" and "have" are intended to cover non-exclusive inclusion. For example, a process, method, system, product or server including a series of steps or units does not necessarily have to be limited to those clearly listed steps or units, but may include other steps or modules not clearly listed or inherent to these processes, methods, products or devices.

[0013] Embodiment 1, as Figure 1 shown, the present application provides a method for analyzing the target characteristics of phytoseiid mites based on the whole-region mite damage monitoring data. Among them, the method includes: Conduct whole-region mite damage monitoring on the crop protection area to obtain multi-source mite damage monitoring data. Among them, the multi-source mite damage monitoring data includes satellite remote sensing data, unmanned aerial vehicle image data, and ground sensor data.

[0014] By conducting multi-source whole-region mite damage monitoring on the crop protection area, multi-source mite damage monitoring data is obtained, including satellite remote sensing data, unmanned aerial vehicle image data, and ground sensor data.

[0015] At the satellite remote sensing level, a remote sensing satellite with multi-spectral imaging capabilities is used to obtain remote sensing image data covering the crop protection area. The image resolution meets the requirements for preliminary identification of vegetation conditions and mite damage signs (such as leaf discoloration, canopy sparseness, etc.). The satellite remote sensing data includes indicators such as NDVI (Normalized Difference Vegetation Index), red edge index, and temperature field data.

[0016] In terms of obtaining UAV image data, based on the boundaries of the crop protection area and the requirements for target monitoring accuracy, multi-rotor or fixed-wing UAVs are configured and equipped with multi-spectral imaging devices. According to the monitoring resolution and flight altitude parameters, the monitoring flight routes are planned, and the spiral or zonal grid flight mode is adopted to collect images during the high-incidence or active period of mite damage. The data collected by the UAV includes visible light images, near-infrared images, thermal infrared maps, etc. Based on these image data, the locations and degrees of influence of pest damage can be identified, thereby realizing the identification of ground mite damage.

[0017] In terms of obtaining ground sensor data, a ground sensor array is deployed within the crop protection area, including mite damage traps, optical recognition devices, temperature and humidity sensors, soil monitors, etc. The distribution of the sensors is optimized according to the crop planting density and historical mite damage data. Each sensor node collects data in an intermittent manner to obtain ground sensor data, including the frequency of mite damage occurrence, environmental temperature, humidity, etc.

[0018] Furthermore, for the whole-region mite damage monitoring of the crop protection area, multi-source mite damage monitoring data is obtained. The methods include: Taking the crop protection area as the coverage constraint, remote sensing data is collected to obtain the satellite remote sensing data; according to the imaging range of the multi-spectral camera carried by the UAV, the flight trajectory is planned within the crop protection area, and the monitoring flight trajectory is output; the active period of mite damage is preset, and during the active period of mite damage, the UAV is controlled to perform image coverage collection of the crop protection area along the monitoring flight trajectory to obtain the UAV image data; a ground sensor array is pre-deployed within the crop protection area to intermittently collect ground data to obtain the ground sensor data.

[0019] Preferably, using the crop protection area as the coverage of the monitoring area, a high-resolution remote sensing satellite is used to collect remote sensing data of the crop protection area to obtain satellite remote sensing image data, which includes multi-spectral images, infrared images, etc., and can effectively reflect the crop growth status, mite damage distribution, and vegetation health status. Then, according to the imaging range of the multi-spectral camera carried by the unmanned aerial vehicle (UAV), the flight trajectory of the crop protection area is planned to ensure that the UAV can cover the entire crop protection area and conduct comprehensive monitoring. According to the preset active period of mite damage and the crop growth situation, the UAV is controlled to collect images along the planned flight trajectory during the active period of mite damage to obtain high-resolution data including visible light images and near-infrared images. These data can accurately capture the signs of mite damage to the crops and provide detailed spatial distribution information of mite damage, further supporting the precise positioning and analysis of mite damage. In addition, a ground sensor array is pre-deployed in the crop protection area. The ground sensor array includes mite traps, environmental temperature and humidity sensors, soil humidity sensors, etc., and can monitor the activity of mite damage and environmental changes in real time. The sensor array periodically collects data at set time intervals to obtain ground sensor data including information such as the activity frequency of mite damage, environmental temperature, and humidity.

[0020] Furthermore, according to the imaging range of the multi-spectral camera carried by the UAV, a flight trajectory is planned in the crop protection area to output a monitoring flight trajectory. The method includes: Presetting a monitoring flight altitude and a monitoring resolution; locally calling the imaging performance information of the multi-spectral camera; calculating an effective monitoring range according to the monitoring flight altitude, the monitoring resolution, and the imaging performance information, and outputting the imaging range; using the imaging range as a constraint, planning a spiral flight trajectory in the crop protection area, and outputting the monitoring flight trajectory.

[0021] Specifically, first, preset the monitoring flight altitude and monitoring resolution. The monitoring flight altitude refers to the flight altitude of the UAV when performing tasks, and the monitoring resolution refers to the image clarity that the imaging device can obtain. Set appropriate flight altitude and resolution according to the area of the crop protection area and the target monitoring accuracy to ensure that the entire monitoring area can be covered and the accuracy requirements can be met. Secondly, locally call the imaging performance information of the multispectral camera, including the field of view angle, sensor sensitivity, band response characteristics, etc. of the camera to determine the effective imaging ability of the camera at a specific flight altitude and resolution. Through this imaging performance information, combined with the set monitoring flight altitude and monitoring resolution, calculate the effective monitoring range of the UAV at this flight altitude, that is, the size and range of the area where the camera can clearly image. Among them, the effective monitoring range = 2×(tan(FOV / 2)×flight altitude), and FOV is the field of view angle, that is, the maximum angle range of the camera imaging. Then, according to the calculated imaging range, combined with the specific area and shape of the crop protection area, plan the flight trajectory. Through the spiral flight trajectory planning method, it can be ensured that the UAV can cover every corner of the crop protection area within a limited flight time. Finally, according to the obtained imaging range and flight path planning, output the final monitoring flight trajectory, which will be used as the guiding path for the UAV to conduct efficient and comprehensive monitoring, ensuring that the UAV can complete the whole-region mite damage monitoring task of the crop protection area according to the predetermined route.

[0022] Analyze the spatial distribution of mite damage based on the multi-source mite damage monitoring data, and divide the crop protection area into three-level protection areas through a spatial clustering algorithm. Among them, the three-level protection areas include severe mite damage areas, mite damage edge areas, and green safety areas.

[0023] Integrate the multi-source mite damage monitoring data (including satellite remote sensing data, UAV image data, and ground sensor data). The geographic information system (GIS) platform can be used to perform spatial registration and time alignment on these data, enabling data from different sources to be analyzed under a unified coordinate system.

[0024] First, input satellite remote sensing data, UAV image data, and ground sensor data into the GIS platform, perform spatial alignment and data overlay based on a preset grid scale to obtain the fused monitoring spatio-temporal data. Then, extract multi-dimensional features from the fused monitoring data to obtain grid clustering features of multiple spatial grids, including mite damage density, vegetation density, patch proportion, average environmental temperature, and average environmental humidity, etc. Then, perform hierarchical clustering analysis on the multiple grid clustering features through a spatial clustering algorithm. Based on factors such as mite damage density, environmental parameters, and vegetation distribution, divide the crop protection area into three levels of protection areas, including the severe mite damage area, the marginal mite damage area, and the green safety area. Among them, the severe mite damage area is the area with a high mite damage density and a high risk of spread, the marginal mite damage area is the area with a low mite damage density but with the potential for spread, and the green safety area is the area with a low mite damage density and environmental conditions conducive to crop growth.

[0025] Furthermore, perform mite damage spatial distribution analysis based on the multi-source mite damage monitoring data, and divide the crop protection area into three levels of protection areas through a spatial clustering algorithm. The method includes: Input the satellite remote sensing data, UAV image data, and ground sensor data into the GIS platform, perform spatial alignment and overlay based on a preset grid scale to obtain fused monitoring spatio-temporal data; extract multi-dimensional features from the fused monitoring spatio-temporal data to obtain multiple grid clustering features of multiple spatial grids, where the grid clustering features include mite damage density, vegetation density, patch proportion, average environmental temperature, and average environmental humidity; perform hierarchical clustering on the multiple grid clustering features through a spatial clustering algorithm to divide the crop protection area into the three levels of protection areas.

[0026] First, input satellite remote sensing data, UAV image data, and ground sensor data into the GIS platform, and perform spatial alignment and overlay on the data based on a preset grid scale. Through this processing, the monitoring information from different data sources is fused to form unified monitoring spatio-temporal data, providing a data basis for subsequent analysis. Next, perform multi-dimensional feature extraction on the fused monitoring spatio-temporal data to obtain multiple grid clustering features of multiple spatial grids. The grid clustering features include mite damage density, vegetation density, patch proportion, average environmental temperature, and average environmental humidity, etc., which reflect the comprehensive information of mite damage distribution and environmental changes in the crop protection area. Finally, use a spatial clustering algorithm to perform hierarchical clustering analysis on the multiple grid clustering features; through the hierarchical clustering algorithm, based on multi-dimensional features such as mite damage density, vegetation density, patch proportion, and environmental temperature and humidity, the crop protection area is divided into three levels of protection areas, including severely damaged areas, marginal damaged areas, and green safe areas. Among them, the severely damaged area is the area with a relatively high mite damage density and environmental conditions suitable for mite damage diffusion; the marginal damaged area is the area with a relatively low mite damage density but with a risk of diffusion; the green safe area is the area with a low mite damage density and environmental conditions not conducive to mite damage occurrence.

[0027] Furthermore, to perform multi-dimensional feature extraction on the fused monitoring spatio-temporal data to obtain multiple grid clustering features of multiple spatial grids, the method includes: Extract the first grid monitoring spatio-temporal data of the first spatial grid from the fused monitoring spatio-temporal data; standardize the NDVI value of the first grid monitoring spatio-temporal data to obtain the first vegetation density; according to the layout position characteristics of the first local sensing array in the first spatial grid, perform interpolation solution on the first grid monitoring spatio-temporal data, and output the first average environmental temperature, the first average environmental humidity, and the first mite damage density; extract the first UAV raster data from the first grid monitoring spatio-temporal data to solve the patch proportion and obtain the first patch proportion; among them, the first mite damage density, the first vegetation density, the first patch proportion, the first average environmental temperature, and the first average environmental humidity constitute the first grid clustering feature of the first spatial grid; and so on, through multi-dimensional feature extraction and interpolation calculation, obtain the multiple grid clustering features.

[0028] First, extract the first grid monitoring spatio-temporal data from the fusion monitoring spatio-temporal data extraction. The first grid monitoring spatio-temporal data contains various information such as mite damage, vegetation, and environment within the first spatial grid. Next, for the extracted first grid monitoring spatio-temporal data, perform standardization processing on the NDVI (Normalized Difference Vegetation Index) value to obtain the first vegetation density of the grid. Then, based on the layout position characteristics of the first local sensing array in the first spatial grid, perform interpolation calculation on the monitoring data to output the first environmental temperature mean, the first environmental humidity mean, and the first mite damage density of the first spatial grid. Interpolation calculation can compensate for the missing data caused by uneven sensor position distribution, ensuring accurate estimation of environmental parameters and mite damage density throughout the grid, thereby reflecting the environmental conditions and mite damage distribution in this area. Next, extract the first UAV raster data from the monitoring spatio-temporal data of the first spatial grid, and calculate the first patch proportion based on this data. The patch proportion refers to the relative proportion of different types of land cover areas within the crop protection area, which can reveal the spatial distribution characteristics of mite damage occurrence and spread within the crop area. Finally, the obtained first mite damage density, first vegetation density, first patch proportion, first environmental temperature mean, and first environmental humidity mean constitute the first grid clustering characteristics of the first spatial grid. By performing multi-dimensional feature extraction and interpolation calculation on multiple grids, and so on, the grid clustering characteristics of all spatial grids are obtained.

[0029] Furthermore, based on the layout position characteristics of the first local sensing array in the first spatial grid, perform interpolation solution on the first grid monitoring spatio-temporal data to output the first environmental temperature mean, the first environmental humidity mean, and the first mite damage density. The method includes: Extract the first local temperature array, the first local humidity array, and the first local mite damage array corresponding to the first local sensing array from the first grid monitoring spatio-temporal data; perform interpolation expansion on the first local temperature array, the first local humidity array, and the first local mite damage array according to the layout position characteristics of the first local sensing array to obtain the first local temperature distribution, the first local humidity distribution, and the first local mite damage distribution; perform mean calculation on the first local temperature distribution, the first local humidity distribution, and the first local mite damage distribution respectively to output the first environmental temperature mean, the first environmental humidity mean, and the first mite damage density.

[0030] Specifically, the first local temperature array, the first local humidity array, and the first local mite damage array corresponding to the first local sensing array are extracted from the first grid monitoring spatio-temporal data, and these data respectively reflect the temperature, humidity, and mite damage density conditions at different positions within the first spatial grid. Next, based on the layout position characteristics of the first local sensing array, the extracted first local temperature array, the first local humidity array, and the first local mite damage array are expanded through an interpolation method; the interpolation process is based on local sensor data and fills in the data of surrounding positions through estimation to generate the first local temperature distribution, the first local humidity distribution, and the first local mite damage distribution. By calculating the mean values of the first local temperature distribution, the first local humidity distribution, and the first local mite damage distribution respectively, the first environmental temperature mean value, the first environmental humidity mean value, and the first mite damage density are obtained, and these mean values represent the comprehensive characteristics of the environmental temperature, humidity, and mite damage density within the first spatial grid, providing key environmental parameters and mite damage distribution information for subsequent mite damage prevention and control analysis.

[0031] Perform a phytoseiid mite release test on the third-level protected area, and conduct a target-feature analysis based on the test results to output a third-level phytoseiid mite release strategy.

[0032] In different regions of the third-level protected area, phytoseiid mite release test points are respectively arranged to test the prevention and control effects of phytoseiid mites under different environmental and mite damage distribution conditions. By releasing phytoseiid mites at each test point, the time-series data of the predation behavior of phytoseiid mites, the time-series data of mite damage density, and the time-series data of environmental changes are collected; according to the mite damage density data, a mite damage density decay curve is fitted, an environmental parameter fluctuation curve is fitted based on the environmental data, and a predation efficiency fluctuation curve is constructed based on the predation behavior data, and then the predation intervention window is determined; by coupling and analyzing the mite damage density decay curve and the predation efficiency fluctuation curve, the intermittent release amount of phytoseiid mites is inversely calculated, and an adaptive phytoseiid mite release strategy is output according to the mite damage conditions in different regions to ensure the accuracy and efficiency of mite damage prevention and control.

[0033] Furthermore, performing a phytoseiid mite release test on the third-level protected area, and conducting a target-feature analysis based on the test results to output a third-level phytoseiid mite release strategy, the method includes: In the severely mite-infested area, dynamic monitoring nodes are deployed for the release test of phytoseiid mites, and the time-series data of the predation behavior of phytoseiid mites, the time-series data of mite infestation density, and the time-series data of environmental changes are collected. Based on the time-series data of mite infestation density, a mite infestation density decay curve is fitted. Based on the time-series data of environmental changes, an environmental parameter fluctuation curve is fitted. Based on the time-series data of predation behavior, a predation efficiency fluctuation curve is constructed, and the predation intervention window is located according to the fluctuation characteristics of the predation efficiency fluctuation curve. The mite infestation density decay curve and the predation efficiency fluctuation curve are coupled and analyzed to invert the threshold of the target control ability of phytoseiid mites, and the intermittent release amount of phytoseiid mites is output. The intermittent release amount and the predation intervention window constitute the first-level phytoseiid mite release strategy. By analogy testing, the third-level phytoseiid mite release strategy is output.

[0034] Dynamic monitoring nodes are deployed in the severely mite-infested area for the release test of phytoseiid mites. Through these dynamic monitoring nodes, the time-series data of the predation behavior of phytoseiid mites, the time-series data of mite infestation density, and the time-series data of environmental changes are continuously collected and recorded. The time-series data of predation behavior reflects the change trend of the predation activities of phytoseiid mites over time. The time-series data of mite infestation density reflects the change of mite infestation quantity over time. The time-series data of environmental changes records the time-series changes of environmental conditions (such as temperature, humidity, etc.), and these factors may affect the predation efficiency of phytoseiid mites and the control effect of mite infestation.

[0035] Based on the collected time-series data of mite infestation density, a mite infestation density decay curve is fitted. The mite infestation density decay curve represents the change trend of mite infestation quantity after the release of phytoseiid mites, and is used to analyze the actual control effect of phytoseiid mites on mite infestation and its control speed. Based on the collected time-series data of environmental changes, an environmental parameter fluctuation curve is fitted. This curve can reflect the change of environmental conditions (such as temperature, humidity, etc.) over time, and provides the influencing factors of the environment on the control effect of phytoseiid mites for subsequent analysis. Based on the time-series data of predation behavior, a predation efficiency fluctuation curve is constructed. The predation efficiency fluctuation curve describes the change law of the predation efficiency of phytoseiid mites, especially the fluctuation characteristics of its predation ability under different environmental conditions. According to the fluctuation characteristics of the predation efficiency fluctuation curve, the predation intervention window is located, that is, the optimal release time and frequency of phytoseiid mites are determined.

[0036] Through the coupled analysis of the mite damage density decay curve and the predation efficiency fluctuation curve, the threshold of the target control ability of phytoseiid mites is inverted, so as to output the intermittent release amount of phytoseiid mites. The intermittent release amount refers to the optimal release amount determined according to the changes in mite damage density and the predation efficiency of phytoseiid mites within a specific period. Through the adaptability test of different strategies in different regions (severe mite damage area, marginal mite damage area, green safety area), the first-level phytoseiid mite release strategy is finally output. Based on the analog test, the three-level phytoseiid mite release strategies applicable to different protected areas are output. Specifically, the release strategy in the severe mite damage area has a higher release amount and a more frequent release frequency; the marginal mite damage area adopts a strategy with medium frequency and release amount; while the green safety area adopts a strategy with lower frequency and less release amount. Through the implementation of these strategies, accurate mite damage prevention and control can be achieved in different regions, thereby improving the effect of phytoseiid mite prevention and control and the effective utilization of resources.

[0037] Apply the three-level phytoseiid mite release strategy to map and carry out dynamic mite damage prevention and control in the three-level protected area.

[0038] Apply the three-level phytoseiid mite release strategy to implement precise mite damage prevention and control in the crop protected area by dynamically adjusting the release amount and frequency of phytoseiid mites. Specifically, in the severe mite damage area, phytoseiid mites are released according to a higher release amount and a more frequent release frequency to quickly reduce the mite damage density; in the marginal mite damage area, a strategy with medium frequency and release amount is adopted to effectively contain the further spread of mite damage; in the green safety area, a strategy with lower frequency and release amount is adopted to maintain the existing mite damage control effect and reduce resource waste. Through the dynamic adjustment and implementation of these strategies, precise mite damage prevention and control in the crop protected area is achieved, and the release strategy is optimized in real time according to the changes in mite damage, so as to ensure the high efficiency and sustainability of mite damage prevention and control.

[0039] In summary, the embodiments of the present application have at least the following technical effects: First, conduct a comprehensive mite damage monitoring of the crop protection area to obtain multi-source mite damage monitoring data. Among them, the multi-source mite damage monitoring data includes satellite remote sensing data, UAV image data, and ground sensor data. Then, based on the multi-source mite damage monitoring data, conduct an analysis of the spatial distribution of mite damage, and divide the crop protection area into three levels of protection areas through a spatial clustering algorithm. Among them, the three-level protection areas include severely damaged areas, marginal damaged areas, and green safety areas. Then, conduct a release test of phytoseiid mites in the three-level protection areas, and conduct a target-specific feature analysis based on the test results to output a three-level phytoseiid mite release strategy. Finally, apply the three-level phytoseiid mite release strategy to map and conduct dynamic mite damage prevention and control in the three-level protection areas. This solves the technical problem in the prior art that the mite damage monitoring data is single and the release of phytoseiid mites lacks pertinence, resulting in low efficiency of mite damage prevention and control, and achieves the technical effect of realizing the zonal management of mite damage based on comprehensive monitoring data and formulating a targeted phytoseiid mite release strategy, thereby improving the efficiency and effect of mite damage prevention and control.

[0040] Embodiment 2, based on the same inventive concept as the method for analyzing the target-specific characteristics of phytoseiid mites based on comprehensive mite damage monitoring data in the foregoing embodiment, as Figure 2 shown, this application provides a system for analyzing the target-specific characteristics of phytoseiid mites based on comprehensive mite damage monitoring data. Among them, the system includes: A monitoring module 11 for conducting a comprehensive mite damage monitoring of the crop protection area to obtain multi-source mite damage monitoring data. Among them, the multi-source mite damage monitoring data includes satellite remote sensing data, UAV image data, and ground sensor data; a spatial division module 12 for conducting an analysis of the spatial distribution of mite damage based on the multi-source mite damage monitoring data and dividing the crop protection area into three levels of protection areas through a spatial clustering algorithm. Among them, the three-level protection areas include severely damaged areas, marginal damaged areas, and green safety areas; an analysis module 13 for conducting a release test of phytoseiid mites in the three-level protection areas and conducting a target-specific feature analysis based on the test results to output a three-level phytoseiid mite release strategy; a prevention and control module 14 for applying the three-level phytoseiid mite release strategy to map and conduct dynamic mite damage prevention and control in the three-level protection areas.

[0041] Furthermore, the analysis module 13 is used to execute the following method: In the severely mite-damaged area, dynamic monitoring nodes are arranged to conduct the release test of phytoseiid mites, and the time-series data of the predation behavior of phytoseiid mites, the time-series data of mite damage density, and the time-series data of environmental changes are collected; a mite damage density decay curve is fitted based on the time-series data of mite damage density; an environmental parameter fluctuation curve is fitted based on the time-series data of environmental changes; a predation efficiency fluctuation curve is constructed based on the time-series data of predation behavior, and the predation intervention window is located according to the fluctuation characteristics of the predation efficiency fluctuation curve; the mite damage density decay curve and the predation efficiency fluctuation curve are coupled and analyzed to invert the threshold of the target control ability of phytoseiid mites, and the intermittent release amount of phytoseiid mites is output; the intermittent release amount and the predation intervention window constitute the first-level phytoseiid mite release strategy; by analogy testing, the three-level phytoseiid mite release strategy is output.

[0042] Further, the monitoring module 11 is used to execute the following method: With the crop protection area as the coverage constraint, remote sensing data is collected to obtain the satellite remote sensing data; according to the imaging range of the multispectral camera carried by the unmanned aerial vehicle, the flight trajectory is planned in the crop protection area, and the monitoring flight trajectory is output; the active period of mite damage is preset, and during the active period of mite damage, the unmanned aerial vehicle is controlled to perform image coverage collection of the crop protection area along the monitoring flight trajectory to obtain the unmanned aerial vehicle image data; a ground sensor array is pre-deployed in the crop protection area to intermittently collect ground data to obtain the ground sensor data.

[0043] Further, the space division module 12 is used to execute the following method: The satellite remote sensing data, the unmanned aerial vehicle image data, and the ground sensor data are input into the GIS platform, and spatial alignment and superposition are performed based on a preset grid scale to obtain the fused monitoring spatio-temporal data; multi-dimensional feature extraction is performed on the fused monitoring spatio-temporal data to obtain multiple grid clustering features of multiple spatial grids, where the grid clustering features include mite damage density, vegetation density, patch ratio, average environmental temperature, and average environmental humidity; hierarchical clustering is performed on the multiple grid clustering features through a spatial clustering algorithm to divide the crop protection area into the three-level protection areas.

[0044] Further, the space division module 12 is used to execute the following method: Extract the first grid monitoring spatio-temporal data of the first spatial grid from the fused monitoring spatio-temporal data; standardize the NDVI value of the first grid monitoring spatio-temporal data to obtain the first vegetation density; based on the layout position characteristics of the first local sensing array in the first spatial grid, perform interpolation solution on the first grid monitoring spatio-temporal data, and output the first average environmental temperature, the first average environmental humidity, and the first mite damage density; extract the first UAV raster data from the first grid monitoring spatio-temporal data to solve the patch proportion, and obtain the first patch proportion; wherein, the first mite damage density, the first vegetation density, the first patch proportion, the first average environmental temperature, and the first average environmental humidity constitute the first grid clustering characteristics of the first spatial grid; and so on, through multi-dimensional feature extraction and interpolation calculation, obtain the multiple grid clustering characteristics.

[0045] Further, the space division module 12 is used to execute the following method: Extract the first local temperature array, the first local humidity array, and the first local mite damage array corresponding to the first local sensing array from the first grid monitoring spatio-temporal data; perform interpolation expansion on the first local temperature array, the first local humidity array, and the first local mite damage array according to the layout position characteristics of the first local sensing array to obtain the first local temperature distribution, the first local humidity distribution, and the first local mite damage distribution; calculate the average value of the first local temperature distribution, the first local humidity distribution, and the first local mite damage distribution respectively, and output the first average environmental temperature, the first average environmental humidity, and the first mite damage density.

[0046] Further, the monitoring module 11 is used to execute the following method: Preset the monitoring flight altitude and monitoring resolution; locally call the imaging performance information of the multispectral camera; calculate the effective monitoring range according to the monitoring flight altitude, the monitoring resolution, and the imaging performance information, and output the imaging range; with the imaging range as a constraint, perform spiral flight trajectory planning in the crop protection area, and output the monitoring flight trajectory.

[0047] It should be noted that the above sequence of embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above describes specific embodiments of this specification. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0048] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0049] This specification and the drawings are merely exemplary descriptions of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications therein.

Claims

1. A method for analyzing the target characteristics of Phytoseiidae based on global mite damage monitoring data, characterized in that, The method includes: Conducting whole-region mite damage monitoring on the crop protection area to obtain multi-source mite damage monitoring data, where the multi-source mite damage monitoring data includes satellite remote sensing data, UAV image data, and ground sensor data; Performing mite damage spatial distribution analysis based on the multi-source mite damage monitoring data, and dividing the crop protection area into three levels of protection areas through a spatial clustering algorithm, where the three levels of protection areas include severe mite damage areas, mite damage marginal areas, and green safety areas; Conducting phytoseiid mite release tests on the three levels of protection areas, and performing target feature analysis based on the test results to output a three-level phytoseiid mite release strategy; Applying the three-level phytoseiid mite release strategy to map and conduct dynamic mite damage prevention and control in the three levels of protection areas.

2. The method for analyzing the target characteristics of Phytoseiidae based on the global mite damage monitoring data according to claim 1, wherein, Conducting phytoseiid mite release tests on the three levels of protection areas, and performing target feature analysis based on the test results to output a three-level phytoseiid mite release strategy, the method includes: Deploying dynamic monitoring nodes in the severe mite damage areas to conduct phytoseiid mite release tests, and collecting sequential data on the predation behavior of phytoseiid mites, sequential data on mite damage density, and sequential data on environmental changes; Fitting a mite damage density decay curve based on the sequential data on mite damage density; Fitting an environmental parameter fluctuation curve based on the sequential data on environmental changes; Constructing a predation efficiency fluctuation curve based on the sequential data on predation behavior, and locating a predation intervention window according to the fluctuation characteristics of the predation efficiency fluctuation curve; Conducting coupled analysis on the mite damage density decay curve and the predation efficiency fluctuation curve, inversely inferring the threshold of the target control ability of phytoseiid mites, and outputting the intermittent release amount of phytoseiid mites; The intermittent release amount and the predation intervention window constitute the first-level phytoseiid mite release strategy; Performing analog tests to output the three-level phytoseiid mite release strategy.

3. The method for analyzing the target characteristics of Phytoseiidae based on the global mite damage monitoring data according to claim 1, wherein Conducting whole-region mite damage monitoring on the crop protection area to obtain multi-source mite damage monitoring data, the method includes: Taking the crop protection area as the coverage constraint, collecting remote sensing data to obtain the satellite remote sensing data; According to the imaging range of the multispectral camera carried by the UAV, planning a flight trajectory in the crop protection area to output a monitoring flight trajectory; Presetting the active period of mite damage, and controlling the UAV to perform image coverage collection of the crop protection area along the monitoring flight trajectory during the active period of mite damage to obtain the UAV image data; Pre-deploying a ground sensing array in the crop protection area to intermittently collect ground data to obtain the ground sensor data.

4. The method for analyzing the target characteristics of Phytoseiidae based on the global mite damage monitoring data according to claim 3, wherein Performing mite damage spatial distribution analysis based on the multi-source mite damage monitoring data, and dividing the crop protection area into three levels of protection areas through a spatial clustering algorithm, the method includes: Inputting the satellite remote sensing data, UAV image data, and ground sensor data into a GIS platform, and performing spatial alignment and overlay based on a preset grid scale to obtain fused monitoring spatio-temporal data; Performing multi-dimensional feature extraction on the fused monitoring spatio-temporal data to obtain multiple grid clustering features of multiple spatial grids, where the grid clustering features include mite damage density, vegetation density, patch proportion, average environmental temperature, and average environmental humidity; Performing hierarchical clustering on the multiple grid clustering features through a spatial clustering algorithm to divide the crop protection area into the three levels of protection areas.

5. The method for analyzing the target characteristics of Phytoseiidae based on the global mite damage monitoring data according to claim 4, wherein Perform multi-dimensional feature extraction on the fused monitoring spatio-temporal data to obtain grid clustering features of multiple spatial grids. The method includes: Extract the first grid monitoring spatio-temporal data of the first spatial grid from the fused monitoring spatio-temporal data; Normalize the NDVI value of the first grid monitoring spatio-temporal data to obtain the first vegetation density; According to the layout position characteristics of the first local sensing array in the first spatial grid, perform interpolation solution on the first grid monitoring spatio-temporal data, and output the first average environmental temperature, the first average environmental humidity, and the first mite damage density; Extract the first UAV raster data from the first grid monitoring spatio-temporal data to solve the patch proportion, and obtain the first patch proportion; Among them, the first mite damage density, the first vegetation density, the first patch proportion, the first average environmental temperature, and the first average environmental humidity constitute the first grid clustering feature of the first spatial grid; And so on, through multi-dimensional feature extraction and interpolation calculation, obtain the multiple grid clustering features.

6. The method for analyzing the target characteristics of Phytoseiidae based on the global mite damage monitoring data according to claim 5, wherein According to the layout position characteristics of the first local sensing array in the first spatial grid, perform interpolation solution on the first grid monitoring spatio-temporal data, and output the first average environmental temperature, the first average environmental humidity, and the first mite damage density. The method includes: Extract the first local temperature array, the first local humidity array, and the first local mite damage array corresponding to the first local sensing array from the first grid monitoring spatio-temporal data; Perform interpolation expansion on the first local temperature array, the first local humidity array, and the first local mite damage array according to the layout position characteristics of the first local sensing array, and obtain the first local temperature distribution, the first local humidity distribution, and the first local mite damage distribution; By performing mean calculation on the first local temperature distribution, the first local humidity distribution, and the first local mite damage distribution respectively, output the first average environmental temperature, the first average environmental humidity, and the first mite damage density.

7. The method for analyzing the target characteristics of Phytoseiidae based on the global mite damage monitoring data according to claim 3, wherein According to the imaging range of the multispectral camera carried by the UAV, perform flight trajectory planning in the crop protection area, and output the monitoring flight trajectory. The method includes: Preset the monitoring flight altitude and monitoring resolution; Locally call the imaging performance information of the multispectral camera; Calculate the effective monitoring range according to the monitoring flight altitude, monitoring resolution, and imaging performance information, and output the imaging range; With the imaging range as a constraint, perform spiral flight trajectory planning in the crop protection area, and output the monitoring flight trajectory.

8. A Phytoseiulus persimilis target feature analysis system based on global mite damage monitoring data, characterized in that For implementing the Phytoseiulus persimilis target feature analysis method based on the whole-region mite damage monitoring data according to any one of claims 1-7, the system includes: A monitoring module, configured to perform whole-region mite damage monitoring on the crop protection area to obtain multi-source mite damage monitoring data, where the multi-source mite damage monitoring data includes satellite remote sensing data, UAV image data, and ground sensor data; A space division module, configured to perform mite damage spatial distribution analysis based on the multi-source mite damage monitoring data, and divide the crop protection area into three-level protection areas through a spatial clustering algorithm, where the three-level protection areas include severe mite damage areas, mite damage marginal areas, and green safety areas; An analysis module is used to conduct a phytoseiid mite release test on the third-level protection area, perform target feature analysis based on the test results, and output a third-level phytoseiid mite release strategy. A prevention and control module is used to apply the third-level phytoseiid mite release strategy to map and conduct dynamic mite damage prevention and control in the third-level protection area.

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