Forest pest and disease damage accurate spraying system based on unmanned aerial vehicle

Abstract

The invention provides an unmanned aerial vehicle-based accurate spraying system for forest diseases and insect pests, and relates to the technical field of disease and insect pest treatment, and the system comprises a real-time monitoring module which is used for carrying out real-time monitoring on a forest region; the division and identification module is used for carrying out detection and classification, generating a data classification result, carrying out division and identification on the forest region and determining grid parameters of the forest region; the diffusion prediction module is used for performing disease and pest diffusion prediction to obtain a disease and pest diffusion coefficient; the synchronous updating module is used for constructing a pest and disease prediction spreading trend; and the precise spraying module is used for formulating a spraying task, executing and carrying out effect evaluation, carrying out dynamic feedback optimization, generating a spraying optimization task and carrying out precise spraying of diseases and insect pests. The technical problems that in the prior art, small differences between plant diseases and insect pests and healthy plants are often difficult to distinguish accurately, especially when the plant diseases and insect pests are distributed dispersedly or hidden, spraying leakage or spraying non-uniformity exists, and then the spraying effect is insufficient are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of pest control, and in particular to a precision spraying system for forest pests based on an unmanned aerial vehicle. Background Art

[0002] The use of drones for real-time monitoring of crops, identification of pests and diseases, and precise spraying can greatly improve the efficiency and effectiveness of pest control. However, in terms of forest pest control, although drone technology has also been gradually introduced, due to the particularities of forest areas, such as more complex terrain, sparse vegetation, and different types of trees, existing technologies still have certain limitations.

[0003] On the one hand, in forest areas, due to the large variety and density of trees and vegetation, the monitoring and identification of pests and diseases is more complicated than that of agricultural crops. Existing monitoring technologies often find it difficult to accurately distinguish the subtle differences between pests and diseases and healthy plants, especially when the distribution of pests and diseases is more dispersed or hidden, which may lead to spraying omissions or uneven spraying. On the other hand, traditional spraying mainly relies on regular and fixed doses of pesticides, which is difficult to adjust according to the spread dynamics of pests and diseases. In a complex forest environment, the spread pattern of pests and diseases is not fixed and is affected by many factors (such as climate change, forest type, terrain, etc.), and the spread path is difficult to accurately predict. Summary of the invention

[0004] The present application provides a precision spraying system for forest pests and diseases based on drones, aiming to solve the technical problem that the existing technology often has difficulty in accurately distinguishing the slight differences between pests and diseases and healthy plants, especially when the pests and diseases are distributed more dispersedly or hidden, there are spraying omissions or uneven spraying, which leads to insufficient spraying effect.

[0005] The present application discloses a precision spraying system for forest pests based on unmanned aerial vehicles, the system comprising: a real-time monitoring module, used for real-time monitoring of forest areas through an integrated sensor device group of the unmanned aerial vehicle, and obtaining a forest real-time monitoring sensor data set; a division and identification module, used for detection and classification based on the forest real-time monitoring sensor data set, generating a data classification result, dividing and identifying the forest area according to the data classification result, and determining the forest area grid parameters; a diffusion prediction module, used for traversing the forest area grid parameters to predict the diffusion of pests and diseases, and obtaining a pest and disease diffusion coefficient; a synchronous update module, used for synchronously updating the forest area grid parameters according to the pest and disease diffusion coefficient, and constructing a predicted spread trend of pests and diseases; a precision spraying module, used for formulating a spraying task based on the predicted spread trend of pests and diseases combined with the forest area, performing an effect evaluation on the spraying task by executing the spraying task through the unmanned aerial vehicle, dynamically feedback optimizing the spraying task according to the evaluation result, generating a spraying optimization task, and performing precision spraying of pests and diseases on the forest area according to the spraying optimization task.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: Real-time monitoring of forest areas through the integrated sensor equipment group carried by drones can quickly and accurately obtain forest monitoring data sets, provide timely and accurate data support, and provide a basis for subsequent spraying decisions; use real-time monitoring sensor data for detection and classification, and divide the forest area based on the classification results. In this way, the forest area is divided into multiple grids, and the pest and disease density, health status and other characteristics of each grid are identified. This fine regional division enables spraying operations to more accurately locate high-risk areas and improve spraying efficiency and accuracy; by traversing the forest area grid parameters, predicting the spread of pests and diseases, and calculating the pest and disease diffusion coefficient, it provides spatial and temporal warnings for subsequent spraying tasks, allowing spraying tasks to be covered first Cover areas that may spread faster or have higher risks; based on the synchronous update of the pest and disease diffusion coefficient, a predicted pest and disease spread trend is constructed. This trend can reflect the spread of pests and diseases in different areas in real time, so that the spraying task can flexibly respond to changes in pests and diseases in forest areas and improve prevention and control efficiency; through comprehensive analysis of the predicted spread trend of pests and diseases and forest areas, precise spraying tasks are formulated, and spraying tasks are performed by drones. The spraying effect is dynamically evaluated, and the spraying tasks are optimized and adjusted based on the evaluation results to ensure that the spraying dosage matches the actual pest and disease density, thereby improving the spraying efficiency and pest and disease control effect. This dynamic feedback optimization ensures that the spraying task can be flexibly adjusted according to the real-time monitoring and evaluation results, so that the spraying operation is continuously improved to adapt to changes in pests and diseases.

[0007] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A schematic structural diagram of a drone-based precision spraying system for forest pests and diseases provided in an embodiment of the present application.

[0009] Figure 2 A schematic diagram of the structure of a division and identification module in a drone-based precision spraying system for forest pests and diseases provided in an embodiment of the present application.

[0010] Explanation of the accompanying drawings: real-time monitoring module 10, division and recognition module 20, diffusion prediction module 30, synchronous update module 40, precision spraying module 50, semantic segmentation unit 21, ratio calculation unit 22, density classification unit 23, feature extraction unit 24, feature adding unit 25. DETAILED DESCRIPTION

[0011] The embodiment of the present application provides a precision spraying system for forest pests and diseases based on a drone, which solves the technical problem that the prior art often finds it difficult to accurately distinguish the slight differences between pests and diseases and healthy plants, especially when the pests and diseases are distributed more dispersedly or hidden, resulting in missed spraying or uneven spraying, which in turn leads to insufficient spraying effect.

[0012] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically introduced below in conjunction with the accompanying drawings of the specification. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0013] like Figure 1 As shown, the embodiment of the present application provides a precision spraying system for forest pests and diseases based on a drone, the system comprising: The real-time monitoring module 10 is used to perform real-time monitoring of the forest area through the integrated sensor equipment group of the drone to obtain a forest real-time monitoring sensor data set.

[0014] The drone's integrated sensing equipment group is equipped with a variety of sensors, including but not limited to visual sensors, infrared sensors, lidar sensors, meteorological sensors, positioning systems, etc. These sensors can cover different types of monitoring needs, such as pest detection, forest health monitoring, environmental change tracking, etc. The drone flies regularly and monitors the forest area in real time. The data collected by all sensors are centrally processed to form a forest real-time monitoring sensor data set, which is a multi-dimensional, large data set containing outputs from different types of sensors. This data set is used for subsequent analysis and decision-making processes.

[0015] The division and identification module 20 is used to perform detection and classification based on the forest real-time monitoring sensor data set, generate data classification results, divide and identify the forest area according to the data classification results, and determine the forest area grid parameters.

[0016] Analyze the forest real-time monitoring sensor data set, detect and classify the data through machine learning or deep learning algorithms. This process can use different technical means according to the specific task. Specifically, for image data, semantic segmentation is performed through deep learning algorithms to identify different objects in the image and mark the pest and disease areas, healthy vegetation areas and disease-free areas; extract features from sensor data, such as pest and disease characteristics, color changes of trees, etc., and classify them through data mining technology to generate different forest area states. Through these processes, a data classification result is finally formed, which divides the forest area into different categories, such as healthy plant areas, pest and disease areas, and areas that need to be monitored more closely.

[0017] According to the classification results, spatial data, such as GPS positioning information, are used to spatially divide the forest area so that the status of each area can be visualized on the map. After spatial division, the forest area is divided into multiple small grid cells, each of which represents a specific area, such as a small forest block. By analyzing the data classification results, the status of each grid cell can be identified and the corresponding treatment measures can be assigned. For example, if the density of pests and diseases in a grid cell is too high, drone spraying operations can be prioritized in subsequent steps.

[0018] For each grid cell, its corresponding grid parameters are calculated and determined. The grid parameters include the area of ​​the grid, the surrounding environment, the density of pests and diseases, the forest vegetation type, etc. These grid parameters are the basic data for subsequent pest and disease prediction, spraying tasks, monitoring and evaluation, etc.

[0019] The diffusion prediction module 30 is used to traverse the forest area grid parameters to predict the spread of pests and diseases and obtain the pest and disease diffusion coefficient.

[0020] The characteristic data of each grid is analyzed one by one to predict the spread of pests and diseases, so as to predict how pests and diseases will spread from known infected areas to the surrounding areas. For example, if some grid cells have been infected by pests and diseases, by analyzing the characteristics of the cells, it is predicted how the pests and diseases will spread to adjacent cells. The prediction process takes into account multiple factors that affect the spread, such as meteorological conditions, geographical features, vegetation types, types of pests and diseases, etc. These factors determine the speed and range of the spread of pests and diseases in different areas.

[0021] Through diffusion prediction, the pest and disease diffusion coefficient of each grid unit is calculated. This coefficient reflects the degree of pest and disease diffusion around the grid unit. The larger the value, the stronger the potential for pest and disease spread in the area.

[0022] The synchronous updating module 40 is used to synchronously update the forest area grid parameters according to the pest diffusion coefficient to construct a predicted pest spread trend.

[0023] Using the obtained pest and disease diffusion coefficient, each grid unit is updated synchronously. This means that the original forest area grid parameters will be adjusted according to the spread potential of pests and diseases. For example, some areas have a larger diffusion coefficient, indicating that the possibility of pest and disease spread is higher. Therefore, the grid parameters of these areas will be updated to a higher risk level. The updated grid parameters will include the diffusion coefficient to support subsequent spraying tasks, monitoring of key areas and other decisions.

[0024] Based on the updated results of the pest and disease diffusion coefficient, the predicted spread trend of pests and diseases is constructed. This trend can be displayed in the form of a spatial distribution map, showing the spread trajectory of pests and diseases in the future time period. Furthermore, the predicted trend can be displayed through color coding (such as red for high-risk areas and green for low-risk areas) or numerical values, which helps to determine the dynamic situation of pest and disease spread and provide a basis for subsequent precision spraying, monitoring and other control measures.

[0025] The precision spraying module 50 is used to formulate spraying tasks based on the predicted spread trend of the pests and diseases and in combination with the forest area, evaluate the effect of the spraying tasks by executing the spraying tasks through drones, perform dynamic feedback optimization on the spraying tasks according to the evaluation results, generate spraying optimization tasks, and perform precision spraying of pests and diseases in the forest area according to the spraying optimization tasks.

[0026] In forest areas, based on the predicted spread trend of pests and diseases, we identify which areas have the most serious pests and diseases and which areas have a higher risk of transmission. Based on this information, we preliminarily formulate spraying tasks, including planning the spraying area and spraying amount, as well as planning the flight path, flight altitude, spraying altitude, etc. in combination with the forest area, to ensure that the pests and diseases in each area are reasonably controlled.

[0027] The drones fly according to the established spraying missions and spray pests. After the spraying is completed, the effects are evaluated. Specifically, the pest density is evaluated. Through subsequent monitoring data analysis, the changes in pest density after spraying are evaluated to determine whether the spraying has achieved the expected effect, for example, whether the pest density has significantly decreased after spraying; the coverage effect is evaluated, the spraying coverage area is monitored, and whether the spraying has fully covered the target area. If some areas are not effectively covered, subsequent spraying may be required.

[0028] According to the evaluation results of the spraying effect, the spraying task is optimized by feedback, including adjusting the spraying dosage, such as increasing or decreasing the spraying volume; adjusting the spraying area and path, focusing on covering the missed areas; adjusting the flight altitude and spraying height to ensure the uniformity and effectiveness of the distribution of the agent. Based on the optimized spraying task, the drone performs precision spraying again. Precision spraying ensures that each area can obtain enough agents through the optimized spraying dosage, spraying path and flight altitude, while avoiding waste of resources and negative impacts on the environment. This not only improves the spraying efficiency, but also ensures the effectiveness and environmental friendliness of pest control.

[0029] Furthermore, if Figure 2 As shown, the partition identification module 20 includes: A semantic segmentation unit 21 is used to obtain a forest pest and disease monitoring data set and a forest healthy plant monitoring data set by performing semantic segmentation on the forest real-time monitoring sensor data set; a ratio calculation unit 22 is used to perform ratio calculation based on the forest pest and disease monitoring data set and the forest healthy plant monitoring data set to obtain a pest and disease density value; a density classification unit 23 is used to perform density classification on forest areas according to the pest and disease density values ​​and determine multiple density levels; a feature extraction unit 24 is used to perform feature extraction on the forest pest and disease monitoring data set based on the multiple density levels and determine multiple pest and disease area features; a feature addition unit 25 is used to add the multiple pest and disease area features to the data classification result.

[0030] Semantic segmentation is a computer vision technology that aims to classify each pixel in an image. Through semantic segmentation, different areas in an image can be identified as different categories, for example, separating pest and disease areas from healthy plant areas in an image. In this step, the forest real-time monitoring sensor dataset is processed using a semantic segmentation algorithm, such as using a convolutional neural network to perform pixel-level classification of images. By training the model, it is possible to identify pest and disease areas and healthy plant areas in the image. Through semantic segmentation, the forest real-time monitoring sensor dataset is divided into a forest pest and disease monitoring dataset and a forest healthy plant monitoring dataset. The forest pest and disease monitoring dataset contains areas infected by pests and diseases, which may appear as abnormalities in color, shape or other features in images or sensor data; the forest healthy plant monitoring dataset contains healthy vegetation areas, which are free of pests and diseases or damage.

[0031] Ratio calculations based on pixel numbers, areas, etc. are performed on the forest pest and disease monitoring dataset and the forest health plant monitoring dataset. For example, the pest and disease density value is equal to the area or number of pixels of the pest and disease area divided by the total area or total number of pixels of the forest area. Through this ratio calculation, the density value of pests and diseases in the forest area is obtained. This density value measures the distribution and severity of pests and diseases in the forest area. The higher the density value, the more serious the infection level of pests and diseases in the area, and stronger prevention and control measures may be needed.

[0032] Based on the density values ​​of pests and diseases, forest areas are divided into different density levels according to preset thresholds. For example, areas with density values ​​below a certain threshold can be classified as low-density levels. In low-density areas, pests and diseases are relatively light and the infected area is small; areas with density values ​​within a certain range can be classified as medium-density levels. In medium-density areas, pests and diseases have a certain spread and may require routine monitoring or prevention and control; areas with higher density values ​​are classified as high-density levels. In high-density areas, pests and diseases are serious and immediate measures need to be taken, such as spraying pesticides or other control measures.

[0033] Based on regions with different density levels, the forest pest monitoring data set was further analyzed to extract the pest regional characteristics of each density level region, including the spatial distribution characteristics of pests, including the morphology, boundaries, and distribution patterns of pest regions, to help identify the distribution trend of pest regions; the temporal characteristics of pests, such as the changing trends of pests at different time points, reflecting the speed and direction of pest spread; environmental characteristics, such as temperature, humidity, soil type, etc., which may be closely related to the occurrence and spread of pests; pest species characteristics. These extracted features support a better understanding of the causes and transmission paths of pests, and provide a basis for pest control.

[0034] The extracted multiple pest and disease regional characteristics are added to the data classification results to provide data support and decision-making basis for subsequent pest and disease control measures.

[0035] Furthermore, the partition identification module 20 includes: An identification unit is used to traverse the forest area to construct a forest geographic coordinate system, synchronize the characteristics of the multiple pest and disease areas to the forest geographic coordinate system for identification, and set multiple area boundaries; a fuzzy segmentation unit is used to fuzzy segment the forest area according to the multiple area boundaries to obtain multiple pest and disease areas; a screening unit is used to screen the forest area based on the multiple pest and disease areas to determine multiple healthy plant areas; a spatial aggregation unit is used to spatially aggregate the multiple pest and disease areas with the multiple healthy plant areas to construct the forest area grid parameters.

[0036] The forest area is spatially traversed to construct a forest geographic coordinate system. A geographic coordinate system refers to a reference system used to represent locations on the earth, usually defined by longitude and latitude. In this step, the constructed forest geographic coordinate system is used to map the data of the forest area into a unified spatial framework.

[0037] The extracted multiple pest and disease regional features are synchronized into the forest geographic coordinate system, which ensures that each pest and disease regional feature has a clear geographical location, which is convenient for spatial analysis. These pest and disease regional features will be identified and correspond to the geographic information of the forest area. According to the synchronized pest and disease regional features, multiple regional boundaries are set in the forest area. These boundaries are used to distinguish different pest and disease areas and serve as the basis for subsequent regional segmentation and management. The boundary setting can be based on factors such as the density, type, and diffusion trend of pests and diseases to help identify which areas need to be focused on or sprayed.

[0038] Fuzzy segmentation is a spatial data processing technology used to divide an area into multiple sub-areas, and this division does not require strict boundaries. Unlike traditional precise division, fuzzy segmentation allows a certain overlap or fuzzy area between areas. The forest area is fuzzy segmented using the set area boundaries, that is, fuzzy area division is performed according to the characteristics of the pest and disease area. Each pest and disease area may partially overlap with adjacent areas, or the degree of pest and disease in some areas is low. After fuzzy segmentation, the forest area is divided into multiple pest and disease areas, which correspond to different pest and disease densities or different types of pests and diseases. The divided pest and disease areas provide detailed spatial information for subsequent management and operations.

[0039] Based on the identified pest and disease areas, screening operations are carried out, which means removing these pest and disease areas from the entire forest area. The goal is to find healthy plant areas that are not affected by pests and diseases. Healthy plant areas refer to those areas without pests and diseases or with very mild pests and diseases. The vegetation in these areas is relatively healthy and does not require special intervention, or requires lighter monitoring and management.

[0040] Spatial aggregation is the process of combining information from different areas according to their spatial locations. Multiple pest and disease areas are spatially aggregated with multiple healthy plant areas to form a comprehensive forest area grid. Corresponding parameters are assigned to each grid cell, including the area of ​​the grid, the surrounding environment, the density of pests and diseases, the type of forest vegetation, etc. These grid parameters are the basic data for subsequent pest and disease prediction, spraying tasks, monitoring and evaluation and other processes.

[0041] Furthermore, the diffusion prediction module 30 includes: A geographically weighted regression unit is used to perform geographically weighted regression on the multiple pest and disease areas to generate pest and disease distribution data; an impact analysis unit is used to perform impact analysis on the multiple healthy plant areas based on the pest and disease distribution data to obtain multiple impact factors; a diffusion analysis unit is used to perform diffusion analysis according to the multiple impact factors in combination with the pest and disease distribution data to determine multiple diffusion patterns; a diffusion path determination unit is used to traverse the multiple pest and disease areas based on the multiple diffusion patterns to determine multiple diffusion paths; a diffusion calculation unit is used to match the multiple diffusion paths with the multiple diffusion patterns, perform diffusion calculation on the forest area grid parameters, and obtain the pest and disease diffusion coefficient.

[0042] Geographically weighted regression is a spatial statistical method used to analyze spatial heterogeneity in geographic data. It analyzes spatial location differences and studies the relationship between independent variables and dependent variables, and can calculate different regression parameters for each geographic location. In this step, multiple pest and disease areas are subjected to geographically weighted regression, that is, the distribution of pests and diseases is modeled and analyzed through changes in spatial location. By considering the influencing factors at different locations, the distribution patterns of pests and diseases, as well as the spatial relationship between pests and diseases and environmental factors are identified. Through geographically weighted regression, pest and disease distribution data are generated to reflect the spatial distribution of pests and diseases in forest areas. These data include the intensity, diffusion path and potential impact area of ​​pests and diseases in different regions.

[0043] An impact analysis of healthy plant areas is conducted based on pest and disease distribution data. This involves assessing whether the spread of pests and diseases in forest areas will have an impact on healthy plants, and the extent to which these healthy plants are affected by the spread of pests and diseases. During the analysis, multiple influencing factors are determined. Influencing factors refer to factors that affect the spread of pests and diseases or plant health, including but not limited to climate factors, geographical factors, vegetation factors, and the types and transmission routes of pests and diseases. These influencing factors help quantify the risk of healthy plant areas being affected by pests and diseases.

[0044] Combining influencing factors and pest distribution data, a pest diffusion analysis is conducted to determine how pests and diseases spread within forest areas, especially the extent of their impact in healthy plant areas. The goal of the diffusion analysis is to identify the paths, speeds, and modes of pest spread in different areas. Through diffusion analysis, multiple diffusion patterns are determined, which reflect the different paths and characteristics of pest spread. For example, in the radial diffusion pattern, pests and diseases spread outward from a central area, similar to a circular diffusion pattern; in the linear diffusion pattern, pests and diseases spread along a certain path or in a certain direction, such as along forest trails or along the banks of a river; in the group diffusion pattern, pests and diseases spread in local clusters, which is usually closely related to vegetation types, terrain, etc.

[0045] Based on different diffusion patterns, multiple pest and disease areas are traversed and specific diffusion paths are determined for each area. Each pest and disease area will have one or more possible diffusion paths, which help describe how the pests and diseases spread.

[0046] The obtained diffusion path is matched with the diffusion pattern. The matching process is based on the propagation characteristics of pests and diseases and the type of diffusion pattern. Different types of diffusion patterns may have different effects on the corresponding diffusion paths. The goal of matching is to ensure that each diffusion path corresponds to the most suitable diffusion pattern. According to the matched diffusion path and diffusion pattern, the grid parameters of the forest area are diffused. That is, by calculating the impact range, diffusion speed and degree of the spread of pests and diseases, a pest and disease diffusion coefficient is assigned to each grid unit. The pest and disease diffusion coefficient represents the diffusion intensity of pests and diseases in a certain area. The larger the coefficient, the faster the pest and disease spreads and the wider the impact range; the smaller the coefficient, the slower the pest and disease spreads and the smaller the impact range.

[0047] Furthermore, the precision spraying module 50 includes: A flight simulation unit is used to perform flight simulation on a UAV based on a forest area and determine multiple flight simulation parameters, wherein the multiple flight simulation parameters include a flight simulation altitude and a flight simulation speed; a matching unit is used to match the multiple pest and disease areas according to the multiple density levels and generate pest and disease matching results; a spraying calculation unit is used to perform spraying calculation according to the flight simulation altitude and the pest and disease matching results and determine spraying concentration information; a descending processing unit is used to perform descending processing on the pest and disease matching results and generate a sequence of areas to be sprayed; an integration unit is used to perform integration according to the sequence of areas to be sprayed combined with the spraying concentration information by executing the flight simulation speed through a UAV and determine spraying dosage data; a first data adding unit is used to add the spraying dosage data to the spraying task.

[0048] The flight of UAV is simulated in the forest area so as to determine the flight simulation parameters according to the specific conditions of the forest area and the mission requirements, including the flight simulation altitude and the flight simulation speed. The flight simulation altitude refers to the flight altitude of the UAV when performing the spraying mission. The choice of flight altitude will be optimized according to the specific characteristics of the forest (such as the height of trees, the density of vegetation, etc.). Too low a flight altitude may result in the failure of the pesticide to cover a large enough area, and too high a flight altitude may result in uneven distribution of the pesticide. The flight simulation speed refers to the flight speed of the UAV. The flight speed determines the time it takes for the UAV to complete the spraying mission and the accuracy of the pesticide distribution. Pest and disease areas with different density levels may require different flight speeds to ensure the efficiency and effectiveness of the spraying mission.

[0049] According to different density levels, the pest and disease areas in the forest area are matched with the corresponding density levels. For high-density areas, higher spraying doses need to be matched because the spread of pests and diseases in these areas is more serious. For medium-density and low-density areas, the spraying dose can be reduced accordingly. The generated pest and disease matching results include the density level corresponding to each pest and disease area, thereby determining the spraying needs for different areas. Through the pest and disease matching results, specific area division and spraying dose information can be provided for the spraying task.

[0050] According to the flight simulation altitude and the results of pest and disease matching, spraying calculations are performed to determine the appropriate spraying concentration. The spraying concentration directly affects the coverage effect of the pesticide and the prevention and control effect of pests and diseases. Specifically, pest and disease areas with different densities require different concentrations of spraying doses. For example, high-density areas require higher spraying concentrations to ensure effective control of pests and diseases in these areas; low-density areas require lower spraying concentrations, which can avoid excessive use of pesticides and achieve the effect of controlling pests and diseases; the flight simulation altitude determines the distribution range and uniformity of the pesticide. A low flight altitude may cause the pesticide to be concentrated in a smaller area, while a high flight altitude may cause the pesticide to be distributed too widely and the concentration to be thin. By combining the flight simulation altitude and the results of pest and disease matching, the spraying concentration is determined to ensure the efficiency and accuracy of the spraying task in areas of different densities.

[0051] Processing the pest and disease matching results in descending order means placing areas with higher pest and disease density in front, and converting the pest and disease matching results after descending order into a sequence of areas to be sprayed. Each area in the sequence will be processed in turn according to its pest and disease density and spraying requirements. This sequence provides a clear execution order for subsequent spraying tasks.

[0052] The spraying task is performed using the flight simulation speed of the drone and the sequence of areas to be sprayed. The spraying efficiency of each area is calculated based on the priority of the areas to be sprayed and the flight simulation speed. Areas with higher flight speeds can complete the spraying faster, while areas with lower flight speeds may require more time to complete the spraying task. The actual spraying dose of each area is calculated based on the density level of different areas and the flight simulation speed. The determination of the spraying dose not only needs to consider the concentration of the agent, but also needs to be comprehensively calculated based on the size of the area, the density of pests and diseases, and the flight speed. Combining the above factors, the spraying dose data for each area is generated, that is, the specific amount of medicine required for each area to be sprayed.

[0053] The calculated spraying dosage data is added to the drone's spraying mission, and the drone performs operations according to the spraying dosage data provided in the mission to ensure that pests and diseases are effectively controlled.

[0054] Furthermore, the precision spraying module 50 includes: An identification unit is used to identify the predicted spread trend of the pests and diseases in combination with the multiple diffusion paths, and generate multiple identification data to be sprayed; a flight analysis unit is used to traverse the forest geographic coordinate system, perform flight analysis according to the multiple identification data to be sprayed in combination with the multiple flight simulation parameters, and determine the flight attitude parameters of the UAV; a dynamic analysis unit is used to retrieve the historical environment record data set of the forest area, perform dynamic analysis based on the historical environment record data set, and construct a forest environment change curve map; a path planning unit is used to perform path planning based on the forest environment change curve map in combination with the flight attitude parameters of the UAV, and generate spraying path data; a second data adding unit is used to add the spraying path data to the spraying task.

[0055] Combined with the predicted spread trend and diffusion path of pests and diseases, the areas to be sprayed are marked in the forest area. These areas are where pests and diseases spread faster, or are high-risk areas predicted to spread in the future. Spraying identification data is generated in these areas, indicating that these areas need to be sprayed. These identification data can provide a basis for subsequent spraying tasks.

[0056] Traversing the forest geographic coordinate system, the coordinate system provides precise coordinates for each area, ensuring that the UAV flight path and target area can be accurately calibrated when planning the flight mission. The identification data to be sprayed is combined with the forest geographic coordinate system to accurately calibrate the area that needs to be sprayed. Each area to be sprayed will have a clear geographical location and pest and disease density information, providing data support for subsequent flight analysis.

[0057] The flight simulation parameters include flight altitude and flight speed. Combined with the identification data to be sprayed and the flight simulation parameters, flight analysis is performed to determine the flight attitude parameters of the UAV when performing the spraying task to ensure that the spraying operation can accurately cover the area to be sprayed. The flight attitude parameters include the flight angle, pitch angle, roll angle, etc. of the UAV, which determine the heading and altitude of the UAV. The flight attitude parameters are calculated based on the location of the area to be sprayed, the flight simulation parameters and the needs of the target area to ensure that the UAV can perform the spraying task with a suitable attitude, maintain a stable flight trajectory, and avoid uneven distribution of the agent.

[0058] Retrieve historical environmental record data sets of forest areas. These data contain environmental data such as climate, temperature, humidity, wind speed, precipitation, sunshine, etc. in the forest areas over the past period of time. Based on the historical environmental data sets, evaluate the environmental change trends over the past period of time through dynamic analysis methods. For example, certain weather conditions (such as high temperature or high humidity) may promote the spread of pests and diseases. Draw forest environmental change curves based on the results of dynamic analysis to show the environmental change trends of forest areas over a period of time. These curves usually show how factors such as temperature, humidity, and precipitation change over time.

[0059] The forest environment change curve provides basic data on changes in environmental conditions, and the UAV flight attitude parameters provide requirements for the flight trajectory. The spraying path is planned based on the environmental change trend and flight attitude parameters. The environmental change curve indicates that pests and diseases in certain areas may spread rapidly in the future, so these areas need to be sprayed first, and the flight attitude parameters ensure that the UAV can cover these areas with an appropriate flight attitude. The spraying path is generated based on the aforementioned analysis results. These paths guide the UAV on how to cover the pest and disease areas when performing spraying tasks and how to adjust the flight trajectory under different environmental conditions to ensure that the agent can be evenly sprayed to the target area. For example, high-density pest and disease areas require lower flight altitudes and slower flight speeds, while low-density areas can use higher flight altitudes and faster speeds.

[0060] Add the spraying path data to the drone's spraying mission, and the drone will start spraying according to this mission. Through a reasonably planned path, it can ensure that pests and diseases in each area are effectively controlled while avoiding waste of pesticides.

[0061] Furthermore, the precision spraying module 50 includes: A density assessment unit is used to perform pest density assessment on the spraying dosage data by performing the spraying task through a drone, and generate a pest reduction ratio value; a coverage assessment unit is used to perform pest coverage assessment on the spraying path data by performing the spraying task through a drone, and generate a pest coverage ratio value; an effect assessment unit is used to perform effect assessment based on the pest reduction ratio value combined with the pest coverage ratio value, and generate a spraying effect score; a dynamic feedback optimization unit is used to add the spraying effect score to the evaluation result, perform dynamic feedback optimization on the spraying task, and generate the spraying optimization task.

[0062] The drone's sensors are used to obtain pest and disease distribution data before and after spraying. The data before spraying reflects the original distribution status of pests and diseases, while the data after spraying reflects the changes in pests and diseases. By comparing the pest and disease distribution before and after spraying, the pest and disease change rate is calculated. Based on the above calculation results, a pest and disease reduction ratio value is generated. This ratio value represents the effect of the spraying task. The higher the value, the better the spraying effect and the greater the reduction ratio of pests and diseases.

[0063] The area after spraying is monitored by the sensors carried by the drones, and pest coverage is evaluated. The coverage effect of the spraying is evaluated. The evaluation indicators include the coverage range, uniformity and utilization rate of the drug. Specifically, check whether the drug covers the predetermined pest area. The coverage range should be consistent with the area to be sprayed to ensure that there is no omission; evaluate the uniformity of the drug during the spraying process to ensure that each pest area can receive the drug evenly. If the spraying concentration is uneven or the coverage is uneven, it may lead to poor pest control effects in some areas; evaluate whether the drug is effectively used for pest control, rather than wasted or drifting to irrelevant areas. High utilization rate means that the spraying dose is effectively used, reducing drug waste and environmental burden. Based on the above evaluation content, the pest coverage ratio value is calculated. The ratio value represents the coverage effect of the spraying. The higher the value, the wider the spray coverage range, the better the uniformity, and the higher the utilization rate of the drug.

[0064] Combining the pest reduction ratio value and the pest coverage ratio value can comprehensively reflect the spraying effect. Among them, a higher pest reduction ratio value indicates that the spraying is effective in reducing pests and diseases, and a higher pest coverage ratio value indicates that the spraying coverage is wide, the pesticide is evenly distributed, and the control effect is good. Combining the two generates a spraying effect score, which can be a weighted average or a comprehensive score, reflecting the overall effect of the spraying task.

[0065] Dynamic feedback optimization of spraying tasks is carried out according to the spraying effect score. For example, the spraying amount or concentration of the pesticide is adjusted according to the effect score, especially in areas with high density of certain pests and diseases. Through this feedback mechanism, the execution effect of the spraying task can be continuously improved, and the spraying efficiency and accuracy can be improved.

[0066] Furthermore, the dynamic feedback optimization unit includes: A scoring judgment channel is used to set an expected spraying threshold and judge whether the spraying effect score meets the expected spraying threshold; a tracking and monitoring channel is used to continuously track and monitor the forest area based on the spraying task if the spraying effect score meets the expected spraying threshold, generate dynamic tracking and monitoring data, and continuously feed back the dynamic tracking and monitoring data to the spraying task; an abnormality analysis channel is used to generate a feedback instruction if the spraying effect score does not meet the expected spraying threshold, and perform abnormal analysis on the spraying task through the feedback instruction to determine the spraying dosage abnormality data and / or the spraying path abnormality data; a dynamic adjustment channel is used to use the spraying dosage abnormality data and / or the spraying path abnormality data as indexes, feed them back to the spraying task for dynamic adjustment, and generate the spraying optimization task.

[0067] The expected spraying threshold refers to the target value of the spraying effect score set according to actual needs, task objectives and predetermined standards. This threshold reflects the minimum requirements of the spraying task. Only when this threshold is reached can the spraying task be judged as successful and achieve the expected control effect. If the spraying effect score reaches or exceeds the expected spraying threshold, it means that the spraying task is effective and the pest control effect has reached the expected effect; conversely, if the spraying effect score is lower than the expected spraying threshold, it means that the effect of the spraying task is insufficient, and there may be problems in the spraying dosage, coverage, uniformity, etc., which need to be adjusted and optimized.

[0068] If the spraying effect score meets the expected spraying threshold, it means that the spraying task has achieved the expected effect. Next, continuous tracking and monitoring will be carried out to ensure that pests and diseases will not recur or spread in the subsequent period of time. Dynamic tracking and monitoring data is collected through sensors to continuously monitor the forest area after the spraying task is completed. Real-time data such as the distribution of pests and diseases and environmental changes. The dynamic tracking and monitoring data is continuously fed back to the spraying task to ensure that the spraying task can continue to meet the actual needs of the forest area and ensure that pests and diseases are effectively controlled for a long time. Through dynamic feedback, not only the immediate effect of the spraying task is improved, but also the resource allocation and prevention and control strategies of subsequent spraying are optimized.

[0069] If the spraying effect score does not reach the expected spraying threshold, it means that the effect of the spraying task has not met expectations, which may be due to problems with the spraying amount, coverage or uniformity of the agent. For spraying effects that do not meet expectations, feedback instructions are automatically generated to identify anomalies in the spraying task and guide subsequent adjustments and optimizations. The feedback instructions will contain a specific description of the problem, such as insufficient spraying dosage, inaccurate spraying path, etc., in order to help analyze the cause and propose solutions.

[0070] According to the feedback instructions, the actual data of the spraying task is reviewed and analyzed to find out the problems. The abnormal analysis includes abnormal spraying dosage data and abnormal spraying path data. Abnormal spraying dosage data includes insufficient or excessive amount of spraying in certain areas, which fails to meet the predetermined spraying volume standard; abnormal spraying path data includes the spraying path not covering all target areas, or deviations in path planning, resulting in missed spraying or uneven spraying in certain areas.

[0071] Abnormal spraying dose data and / or abnormal spraying path data are used as indexes and fed back to the spraying task, which means that these abnormal data will be used as key basis to trigger the adjustment of the spraying task. For example, if the spraying dose in some areas is too low, the spraying dose in these areas will be increased; if there is a deviation in the path, the spraying path will be replanned to ensure that all areas that need to be sprayed are fully covered. The purpose of dynamic adjustment is to ensure that the spraying task can be flexibly adjusted based on real-time monitoring and feedback to adapt to various environmental changes and spraying effects. Through dynamic adjustment, a spraying optimization task is generated to guide subsequent spraying operations, making the spraying task more accurate and efficient, while ensuring the maximum effect of pest control.

[0072] In summary, the forest pest precision spraying system based on drone provided in the embodiment of the present application has the following technical effects: Real-time monitoring of forest areas through the integrated sensor equipment group carried by drones can quickly and accurately obtain forest monitoring data sets, provide timely and accurate data support, and provide a basis for subsequent spraying decisions; use real-time monitoring sensor data for detection and classification, and divide the forest area based on the classification results. In this way, the forest area is divided into multiple grids, and the pest and disease density, health status and other characteristics of each grid are identified. This fine regional division enables spraying operations to more accurately locate high-risk areas and improve spraying efficiency and accuracy; by traversing the forest area grid parameters, predicting the spread of pests and diseases, and calculating the pest and disease diffusion coefficient, it provides spatial and temporal warnings for subsequent spraying tasks, allowing spraying tasks to be covered first Cover areas that may spread faster or have higher risks; based on the synchronous update of the pest and disease diffusion coefficient, a predicted pest and disease spread trend is constructed. This trend can reflect the spread of pests and diseases in different areas in real time, so that the spraying task can flexibly respond to changes in pests and diseases in forest areas and improve prevention and control efficiency; through comprehensive analysis of the predicted spread trend of pests and diseases and forest areas, precise spraying tasks are formulated, and spraying tasks are performed by drones. The spraying effect is dynamically evaluated, and the spraying tasks are optimized and adjusted based on the evaluation results to ensure that the spraying dosage matches the actual pest and disease density, thereby improving the spraying efficiency and pest and disease control effect. This dynamic feedback optimization ensures that the spraying task can be flexibly adjusted according to the real-time monitoring and evaluation results, so that the spraying operation is continuously improved to adapt to changes in pests and diseases.

[0073] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A precision spraying system for forest pests and diseases based on drones, characterized in that: The system comprises: A real-time monitoring module is used to monitor the forest area in real time through the integrated sensor equipment group of the drone to obtain a forest real-time monitoring sensor data set; A division and identification module is used to perform detection and classification based on the forest real-time monitoring sensor data set, generate data classification results, divide and identify the forest area according to the data classification results, and determine the forest area grid parameters; A diffusion prediction module, used to traverse the grid parameters of the forest area to predict the spread of pests and diseases and obtain the pest and disease diffusion coefficient; A synchronous updating module, used for synchronously updating the grid parameters of the forest area according to the pest diffusion coefficient, and constructing a predicted spread trend of pests and diseases; The precision spraying module is used to formulate spraying tasks based on the predicted spread trend of the pests and diseases and in combination with the forest area, evaluate the effect of the spraying tasks by executing the spraying tasks through drones, perform dynamic feedback optimization on the spraying tasks according to the evaluation results, generate spraying optimization tasks, and perform precision spraying of pests and diseases in the forest area according to the spraying optimization tasks.

2. The forest pest precision spraying system based on drone as claimed in claim 1, characterized in that: The partition identification module comprises: A semantic segmentation unit, configured to obtain a forest pest and disease monitoring dataset and a forest health plant monitoring dataset by performing semantic segmentation on the forest real-time monitoring sensor dataset; A ratio calculation unit, used for performing ratio calculation based on the forest pest and disease monitoring data set and the forest healthy plant monitoring data set to obtain a pest and disease density value; A density classification unit, used to classify the forest area according to the pest density value and determine multiple density levels; A feature extraction unit, configured to extract features from the forest pest monitoring data set based on the multiple density levels, and determine multiple pest area features; A feature adding unit is used to add the plurality of pest and disease area features to the data classification result.

3. The forest pest precision spraying system based on drone as claimed in claim 2, characterized in that: The partition identification module comprises: An identification unit, used for traversing the forest area to construct a forest geographic coordinate system, synchronizing the plurality of pest and disease area features to the forest geographic coordinate system for identification, and setting a plurality of area boundaries; A fuzzy segmentation unit, used for fuzzily segmenting the forest area according to the multiple area boundaries to obtain multiple pest and disease areas; A screening unit, used for screening the forest area based on the multiple pest and disease areas to determine multiple healthy plant areas; The spatial aggregation unit is used to spatially aggregate the multiple pest and disease areas with the multiple healthy plant areas to construct the forest area grid parameters.

4. The forest pest precision spraying system based on drone as claimed in claim 3, characterized in that: The diffusion prediction module comprises: A geographically weighted regression unit, used for performing geographically weighted regression on the plurality of pest and disease areas to generate pest and disease distribution data; An impact analysis unit, used to perform impact analysis on the plurality of healthy plant areas according to the pest and disease distribution data to obtain a plurality of impact factors; A diffusion analysis unit, used to perform diffusion analysis according to the multiple influencing factors combined with the pest distribution data to determine multiple diffusion patterns; A diffusion path determination unit, configured to determine a plurality of diffusion paths by traversing the plurality of pest and disease areas based on the plurality of diffusion patterns; The diffusion calculation unit is used to match the multiple diffusion paths with the multiple diffusion patterns, perform diffusion calculation on the forest area grid parameters, and obtain the pest diffusion coefficient.

5. The forest pest precision spraying system based on drone as claimed in claim 4, characterized in that: The precision spraying module comprises: A flight simulation unit, used to perform flight simulation on the UAV based on the forest area, and determine a plurality of flight simulation parameters, wherein the plurality of flight simulation parameters include a flight simulation altitude and a flight simulation speed; A matching unit, used for matching the plurality of pest and disease areas according to the plurality of density levels to generate a pest and disease matching result; A spraying calculation unit, used to perform spraying calculation according to the flight simulation altitude and the pest matching result, and determine spraying concentration information; A descending order processing unit, used for processing the pest and disease matching results in descending order to generate a sequence of areas to be sprayed; An integration unit is used to integrate the flight simulation speed by the drone according to the sequence of the areas to be sprayed and the spraying concentration information to determine the spraying dosage data; The first data adding unit is used to add the spraying dosage data to the spraying task.

6. The forest pest precision spraying system based on drone as claimed in claim 5, characterized in that: The precision spraying module comprises: An identification unit, used for identifying the predicted spread trend of the pests and diseases in combination with the multiple diffusion paths, and generating multiple identification data to be sprayed; A flight analysis unit, configured to traverse the forest geographic coordinate system, perform flight analysis according to the plurality of identification data to be sprayed and the plurality of flight simulation parameters, and determine the flight attitude parameters of the UAV; A dynamic analysis unit, used to retrieve a historical environmental record data set of a forest area, perform dynamic analysis based on the historical environmental record data set, and construct a forest environment change curve diagram; A path planning unit, used to perform path planning based on the forest environment change curve diagram in combination with the UAV flight attitude parameters to generate spraying path data; The second data adding unit is used to add the spraying path data to the spraying task.

7. The forest pest precision spraying system based on drone as claimed in claim 6, characterized in that: The precision spraying module comprises: A density assessment unit, used to perform pest density assessment on the spraying dosage data by using a drone to perform the spraying task, and generate a pest reduction ratio value; A coverage assessment unit, used to perform pest and disease coverage assessment on the spraying path data by executing the spraying task through a drone, and generate a pest and disease coverage ratio value; An effect evaluation unit, used to perform effect evaluation based on the pest reduction ratio value combined with the pest coverage ratio value, and generate a spraying effect score; A dynamic feedback optimization unit is used to add the spraying effect score to the evaluation result to perform dynamic feedback optimization on the spraying task to generate the spraying optimization task.

8. The forest pest precision spraying system based on drone as claimed in claim 7, characterized in that: The dynamic feedback optimization unit comprises: A scoring judgment channel is used to set an expected spraying threshold and judge whether the spraying effect score meets the expected spraying threshold; A tracking and monitoring channel, for continuously tracking and monitoring the forest area based on the spraying task, generating dynamic tracking and monitoring data, and continuously feeding back the dynamic tracking and monitoring data to the spraying task if the spraying effect score meets the expected spraying threshold; An abnormality analysis channel, used for generating a feedback instruction if the spraying effect score does not meet the expected spraying threshold, and performing an abnormality analysis on the spraying task through the feedback instruction to determine abnormal spraying dosage data and / or abnormal spraying path data; The dynamic adjustment channel is used to use the abnormal spraying dosage data and / or the abnormal spraying path data as indexes, feed them back to the spraying task for dynamic adjustment, and generate the spraying optimization task.

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