A method and system for evaluating the ecological environment of garden plants

Abstract

The present application provides a method and system for ecological assessment of a garden plant environment. Among them, an adaptive layered scanning of the vegetation canopy is performed by a flying device, multispectral reflectance data and three-dimensional morphological parameters are obtained synchronously, and a time-space calibrated reflectance database is constructed. Polarization compensation technology is used to suppress high reflection interference, and weighted calculation of layered reflectance is performed based on the penetration level identification. The vertical profile attenuation gradient and photosynthetic parameter mapping are combined to generate a three-dimensional photosynthetic efficiency map. A secondary scan is initiated for abnormal areas, and the root absorption delay error is corrected by integrating soil moisture parameters, and finally an accurate ecological assessment result is output. The technical solution provided by the present application realizes three-dimensional dynamic monitoring of the photosynthetic efficiency of plant communities through air-ground collaborative scanning and dynamic compensation algorithm, effectively improving the accuracy and reliability of ecological assessment in complex canopy environments.

Description

Technical Field

[0001] The present application relates to the field of environmental monitoring technology, and in particular to a method and system for evaluating the environmental ecology of garden plants. Background Art

[0002] With the advancement of refined urban ecological management, the health assessment of landscape plants requires dynamic monitoring with high temporal and spatial resolution. This is especially true in complex canopy structures, requiring the simultaneous acquisition of multi-dimensional data such as photosynthetic efficiency, canopy transmittance, and root water uptake. Traditional methods struggle to quantitatively analyze ecological parameters across vertical profiles of plant communities. There is an urgent need for intelligent monitoring technologies that can integrate air- and ground-based data, mitigate environmental interference, and dynamically correct assessment errors.

[0003] The current mainstream solution uses a fixed multispectral sensor network. This involves deploying a multi-node sensor array throughout a garden to continuously collect spectral reflectance data from the plant canopy. This data is then combined with a pre-defined canopy model to estimate photosynthetic intensity. This solution uses wireless transmission to centrally process the data and employs machine learning algorithms to initially identify abnormal areas.

[0004] The coverage and resolution of fixed sensor networks are limited by the density of hardware deployment, making it difficult to dynamically adapt to changes in the three-dimensional morphology of the vegetation canopy. Their reflectance data is collected from only a single perspective, unable to eliminate spectral interference from highly reflective areas (such as waxy leaves), and does not account for the impact of canopy penetration differences on reflectance weighting. Furthermore, the model relies on static soil parameter assumptions, making it unable to correct for miscalculations of photosynthetic efficiency caused by delayed root water uptake in real time, resulting in inaccurate vertical profile ecological assessments. Summary of the Invention

[0005] The present application provides a method and system for evaluating the ecological environment of garden plants, which are used to solve the problems of insufficient coverage and lack of dynamic adaptation caused by static deployment of sensor networks in the prior art.

[0006] In a first aspect, the present application provides a method for evaluating the environmental ecology of garden plants, comprising:

[0007] The flying device is used to perform layered scanning of garden plant communities, generate flight trajectories based on the three-dimensional density distribution of the vegetation canopy, and simultaneously collect multispectral reflectance data containing photosynthesis characteristic parameters;

[0008] Arrange multiple ground verification points in the scanning area, collect three-dimensional morphological parameters of the vegetation canopy in combination with the spatiotemporal coverage characteristics of the flight trajectory, perform spatiotemporal calibration and matching of the three-dimensional morphological parameters with the multispectral reflectance data, and construct a reflectance database;

[0009] Based on the detected vegetation canopy surface reflection characteristics, high-reflection interference areas are identified, and multi-angle polarization compensation is applied to the high-reflection interference areas by adjusting the polarization angle configuration to generate multispectral reflectance data after reflection suppression;

[0010] Based on the penetration level identifier in the reflection database, a layered reflectance weighted calculation is performed on the multispectral reflection data after reflection suppression, and a mapping relationship between the attenuation gradient of different multispectral reflection data in the vertical section of the vegetation canopy and the photosynthesis characteristic parameters is associated to generate a three-dimensional distribution map of the photosynthetic efficiency of the plant community;

[0011] Based on the abnormal photosynthetic efficiency area in the three-dimensional distribution map, the flying device is controlled to perform a secondary scan. By fusing the multispectral reflectance data of the secondary scan with the soil moisture parameters of the ground verification point, the assessment error caused by the delayed root water absorption in the three-dimensional distribution map is corrected, and the ecological assessment result is output.

[0012] Optionally, based on the penetration level identifier in the reflectance database, performing a layered reflectance weighted calculation on the spectral data after reflection suppression, comprising:

[0013] Extracting layer segmentation information corresponding to the penetration layer identifier from the reflection database, the layer segmentation information including a penetration ability value and a layer thickness value corresponding to each layer in the vegetation canopy;

[0014] According to the penetration value, weighting is performed on the spectral signal of each level in the spectral data after reflection suppression;

[0015] Based on the layer thickness values, the spectral signals after weighting are superimposed and calculated in layer order to obtain the weighted reflectivity result of each layer in the vertical direction.

[0016] Optionally, based on the reflectance weighted results, the mapping relationship between the attenuation gradient of different spectral data in the vertical profile of the vegetation canopy and the photosynthesis characteristic parameters is associated to generate a three-dimensional distribution map of the photosynthetic efficiency of the plant community, including:

[0017] Extracting the signal intensity change rate of the spectral signals at different levels in the reflectance database in the vertical section as the penetration level changes, and establishing a corresponding relationship between the signal intensity change rate and the light energy absorption value in the photosynthesis characteristic parameter;

[0018] According to the corresponding relationship, converting the spectral signal of each level in the reflectivity weighted result into a corresponding light energy absorption value;

[0019] Based on the spatial position of the hierarchical segmentation information, the light energy absorption values ​​are mapped into a three-dimensional space in a hierarchical order to form a three-dimensional distribution map representing the distribution of photosynthetic efficiency of the plant community.

[0020] Optionally, based on the abnormal photosynthetic efficiency area in the three-dimensional distribution map, the flying device is controlled to perform a secondary scan, and by fusing the multispectral reflectance data of the secondary scan with the soil moisture parameters of the ground verification point, the assessment error caused by the delayed root water absorption in the three-dimensional distribution map is corrected, and the ecological assessment result is output, including:

[0021] Extracting continuous spatial regions where the photosynthetic efficiency value is lower or higher than a preset normal range from the three-dimensional distribution map, and marking them as abnormal photosynthetic efficiency regions;

[0022] adjusting the flight altitude and scanning path density of the flying device according to the spatial distribution range of the photosynthetic efficiency abnormal area, generating a secondary scanning trajectory covering the photosynthetic efficiency abnormal area, and controlling the flying device to perform a secondary scan according to the secondary scanning trajectory;

[0023] Extracting multispectral reflectance data of the photosynthetic efficiency abnormal area in the secondary scan, and obtaining the spectral reflectance value of each spatial coordinate point in the abnormal area;

[0024] Extracting a soil moisture parameter corresponding to the spatial coordinates of the abnormal area from the ground verification point, the soil moisture parameter being a soil moisture content measurement value of the ground verification point within a preset time window; and performing a one-to-one binding between the soil moisture content measurement value and the spectral reflectance value of the abnormal area according to the spatial coordinates;

[0025] According to the measured value of soil moisture content, the spectral reflectance value is compensated for moisture absorption delay, and the compensated spectral reflectance value replaces the photosynthetic efficiency value of the corresponding photosynthetic efficiency abnormal area in the three-dimensional distribution map. Based on the replaced photosynthetic efficiency value, the photosynthetic efficiency distribution of all spatial coordinate points in the three-dimensional distribution map is recalculated, and an ecological assessment result is generated.

[0026] Optionally, based on the detected vegetation canopy surface reflection characteristics, a high-reflection interference area is identified, and multi-angle polarization compensation is applied to the high-reflection interference area by adjusting the polarization angle configuration to generate multispectral reflectance data after reflection suppression, including:

[0027] Extracting the reflection intensity of the vegetation canopy surface within a preset visible light band based on the spectral reflectance value of each spatial coordinate point in the multispectral reflectance data, and marking the spatial coordinate points where the reflection intensity exceeds a preset threshold as high reflection interference areas;

[0028] For the high-reflection interference area, controlling the polarization filter component of the flying device to switch to a plurality of different polarization angle combinations in sequence; under each polarization angle combination, recollecting spectral reflectance values ​​of the high-reflection interference area to obtain multiple sets of spectral reflectance data with differentiated polarization angles;

[0029] Superimposing the spectral reflectance values ​​of the same spatial coordinate point in the multiple sets of polarization angle-differentiated spectral reflectance data, and retaining the minimum intensity value of the superimposed spectral reflectance values ​​as the spectral reflectance value of the spatial coordinate point after reflection suppression;

[0030] The spectral reflectance values ​​after reflection suppression are combined with the original spectral reflectance values ​​of the areas not marked as high reflection interference according to spatial coordinates to generate multi-spectral reflectance data after reflection suppression.

[0031] Optionally, the spatiotemporal coverage features include a timestamp sequence and a spatial coordinate sequence;

[0032] Arrange multiple ground verification points in the scanning area, collect three-dimensional morphological parameters of the vegetation canopy in combination with the spatiotemporal coverage characteristics of the flight trajectory, perform spatiotemporal calibration and matching of the three-dimensional morphological parameters with the multispectral reflectance data, and construct a reflectance database, including:

[0033] Multiple ground verification points are arranged at a preset density within the scanning area, each of which is equipped with a three-dimensional laser scanning device. The three-dimensional laser scanning device measures the depth detection data of the vegetation canopy by emitting laser pulses and receiving reflected signals; the height value, gap density and surface curvature value of each measurement point are calculated based on the depth detection data to form a three-dimensional morphological parameter set of the ground verification point;

[0034] Extracting the acquisition timestamp and spatial coordinates of each spectral sampling point in the multispectral reflectance data, and establishing corresponding position marks between the spatial coordinates of the spectral sampling point and the canopy height value in the three-dimensional morphological parameters based on the timestamp sequence and spatial coordinate sequence of the flight trajectory;

[0035] Binding the canopy height value, canopy gap density value, and canopy surface curvature value in the three-dimensional morphological parameters to the spectral reflectance value in the multispectral reflectance data according to the corresponding position marks to obtain a bound data unit;

[0036] Based on the preset binding rules, the bound data units are stored in the grid distribution order of the spatial coordinates to generate a reflection database.

[0037] Optionally, extracting the signal intensity change rate of the spectral signals at different levels in the reflectance database as the penetration level changes on the vertical section, and establishing a corresponding relationship between the signal intensity change rate and the light energy absorption value in the photosynthesis characteristic parameter, including:

[0038] Extracting the spectral signal intensity values ​​of adjacent penetration levels at the same spatial coordinate point from the reflection database, and calculating the ratio of the spectral signal intensity value of the subsequent penetration level to the previous level as the inter-level signal intensity change rate;

[0039] Based on the inter-layer signal strength change rate, the product of the inter-layer signal strength change rates of all upper layers from the top to the bottom of the canopy is calculated layer by layer in the order of penetration levels to obtain the cumulative signal strength change rate;

[0040] By using a preset experimental calibration method, in the vegetation canopy corresponding to the ground verification point, a light intensity attenuation measuring device is used to obtain the actual absorption value of light energy per unit area at different penetration levels;

[0041] Performing exponential fitting on the cumulative signal intensity change rate and the measured light energy absorption value in order of penetration levels, and outputting an exponential relationship fitting result;

[0042] Based on the exponential relationship fitting result, a corresponding mapping table of the cumulative signal intensity change rate and the light energy absorption value is generated as the corresponding relationship between the signal intensity change rate and the light energy absorption value in the photosynthesis characteristic parameter.

[0043] In a second aspect, the present application provides a garden plant environment ecological assessment system, comprising:

[0044] The acquisition module is used to perform layered scanning of garden plant communities using a flying device, generate flight trajectories based on the three-dimensional density distribution of the vegetation canopy, and simultaneously collect multispectral reflectance data including photosynthesis characteristic parameters;

[0045] The acquisition module is further configured to arrange a plurality of ground verification points within the scanning area, collect three-dimensional morphological parameters of the vegetation canopy in combination with the spatiotemporal coverage characteristics of the flight trajectory, perform spatiotemporal calibration and matching of the three-dimensional morphological parameters with the multispectral reflectance data, and construct a reflectance database;

[0046] A generation module is used to identify high-reflection interference areas based on the detected vegetation canopy surface reflection characteristics, and to apply multi-angle polarization compensation to the high-reflection interference areas by adjusting the polarization angle configuration to generate multispectral reflectance data after reflection suppression;

[0047] a calculation module for performing a layered reflectance weighted calculation on the multispectral reflectance data after reflection suppression based on the penetration level identifier in the reflectance database, and correlating the mapping relationship between the attenuation gradient of different multispectral reflectance data in the vertical section of the vegetation canopy and the photosynthesis characteristic parameters to generate a three-dimensional distribution map of the photosynthetic efficiency of the plant community;

[0048] The correction module is used to control the flying device to perform a secondary scan based on the abnormal photosynthetic efficiency area in the three-dimensional distribution map, and to correct the assessment error caused by the delayed root water absorption in the three-dimensional distribution map by fusing the multispectral reflectance data of the secondary scan with the soil moisture parameters of the ground verification point, and output the ecological assessment result.

[0049] In a third aspect, an embodiment of the present application provides a computing device comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a garden plant environmental ecological assessment method as described in the first aspect above.

[0050] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program. When the computer program is executed by a computer, it implements a garden plant environmental ecological assessment method as described in the first aspect.

[0051] In the embodiment of the present application, a layered scanning of a garden plant community is performed by a flying device, a flight trajectory is generated based on the three-dimensional density distribution of the vegetation canopy, and multispectral reflectance data containing photosynthesis characteristic parameters are simultaneously collected; multiple ground verification points are arranged in the scanning area, and the three-dimensional morphological parameters of the vegetation canopy are collected in combination with the spatiotemporal coverage characteristics of the flight trajectory. The three-dimensional morphological parameters are spatiotemporally calibrated and matched with the multispectral reflectance data to construct a reflection database; based on the detected surface reflectance characteristics of the vegetation canopy, high-reflection interference areas are identified, and by adjusting the polarization angle configuration, multi-angle polarization compensation is applied to the high-reflection interference areas to generate a reflection suppression database. The system uses multispectral reflectance data from the reflection-suppressed multispectral reflectance database; based on the penetration level identifiers in the reflectance database, it performs a layered reflectance weighted calculation on the multispectral reflectance data after reflection suppression, and correlates the attenuation gradients of different multispectral reflectance data in the vertical profile of the vegetation canopy with the mapping relationship between the characteristic parameters of photosynthesis to generate a three-dimensional distribution map of the photosynthetic efficiency of the plant community; based on the areas of photosynthetic efficiency anomalies in the three-dimensional distribution map, it controls the flight device to perform a secondary scan, and by fusing the multispectral reflectance data from the secondary scan with the soil moisture parameters of the ground verification points, it corrects the assessment error caused by delayed root water absorption in the three-dimensional distribution map, and outputs the ecological assessment result. Through dynamic flight scanning, polarization interference suppression, layered reflectance weighting, and secondary data fusion correction, it achieves a three-dimensional accurate assessment of plant photosynthetic efficiency under complex canopy structures, solving problems such as insufficient coverage of traditional static sensor networks, lack of reflection interference suppression, deviation in vertical profile weight distribution, and accumulated errors in delayed root water absorption.

[0052] Furthermore, based on penetration level identifiers, such as the penetration capacity and thickness values ​​in the layer segmentation information, the spectral data after reflection suppression is layered and weighted, and the superposition calculation is performed to quantify the reflectance contribution of each layer. This solves the problem of vertical profile reflectance weight distortion caused by traditional methods due to the lack of layered calibration. The technical effect is: through the hierarchical dynamic weight allocation and superposition model, the physical rationality of the canopy vertical reflectance calculation is significantly improved, the error of single reflectance averaging is avoided, and the vertical resolution accuracy of the three-dimensional distribution map of photosynthetic efficiency is enhanced.

[0053] These and other aspects of the present application will become more readily apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0055] Figure 1 A flow chart of a method for evaluating the environmental ecology of garden plants provided by the present application is shown;

[0056] Figure 2 The present invention provides a schematic diagram of a system for evaluating the ecological environment of garden plants.

[0057] Figure 3 A schematic structural diagram of a computing device provided by the present application is shown. DETAILED DESCRIPTION

[0058] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0059] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to being different types.

[0060] Researchers have found that the existing garden ecological monitoring technology has insufficient three-dimensional coverage due to the static layout of the sensor network, the lack of high-reflection interference suppression causes spectral data distortion, the vertical profile reflectivity weights are not calibrated in layers, resulting in photosynthetic efficiency evaluation deviations, and static soil parameter modeling is difficult to correct the root water absorption delay error. Based on this, a garden plant environmental ecological assessment method is provided, which can realize three-dimensional dynamic monitoring of vegetation canopy photosynthetic efficiency through dynamic flight scanning and polarization compensation technology, integrating air-ground multi-source data, and correcting root water absorption errors in real time, significantly improving the assessment accuracy. The technical solution of the present application can be applied to scenarios that require refined vertical profile ecological parameter analysis, such as urban garden three-dimensional green belts, complex canopy communities in ecological restoration areas, and high-density planting areas in agricultural parks.

[0061] The entire research and development process embodies the core advantages of three-dimensional dynamic perception and closed-loop feedback of multi-source data. It generates dynamic coverage capabilities through adaptive layered scanning by the flying device, combines polarization compensation with a real-time adjustment mechanism to suppress high-reflection interference, and realizes vertical profile reflectivity layered modeling based on penetration level identification. It also forms an error self-correction closed loop through secondary scanning and fusion with soil parameters, ultimately breaking through the bottleneck of static monitoring technology in the multi-dimensional analysis of complex canopy structures.

[0062] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0063] Figure 1 A flow chart of a method for evaluating the ecological environment of garden plants is provided in the embodiment of the present application, such as Figure 1 As shown, the method includes:

[0064] 101. Use a flying device to perform layered scanning of garden plant communities, generate flight trajectories based on the three-dimensional density distribution of the vegetation canopy, and simultaneously collect multispectral reflectance data containing photosynthesis characteristic parameters;

[0065] Layered scanning involves dividing the monitoring area of ​​an aerial device into multiple layers based on the vertical density of the vegetation canopy, collecting data layer by layer. The aerial device can be a drone, for example. Three-dimensional density distribution generates a three-dimensional structural model by quantifying parameters such as leaf density and branch distribution at different heights within the vegetation canopy. Photosynthesis characteristic parameters include spectral indicators directly related to plant light energy conversion efficiency, such as chlorophyll fluorescence intensity and photosynthetically active radiation absorption rate.

[0066] In the embodiment of the present application, the original point cloud data of the vegetation canopy is first obtained by laser radar, and the leaf clustering areas at different height layers are identified using a density clustering algorithm to divide the vertical layers. Subsequently, a layered scan of the garden plant community is performed using a flying device based on the density differences of the layers. The flight trajectory is dynamically planned using an adaptive ant colony algorithm, so that the drone can extend its stay time in high-density areas and lower its flight altitude to ensure the accuracy of spectral data acquisition. During the flight, the onboard hyperspectral imager synchronously records the multi-band reflectance data of each layer and binds it to the trajectory coordinates in real time to form a spectral data set with spatial position markers.

[0067] For example, during canopy monitoring of a banyan tree forest in a wetland park, a drone equipped with a lidar and multispectral imager performed layered scanning. The lidar point cloud data revealed three distinct density layers within the canopy: a top layer with 110 leaves per cubic meter, a middle layer with 300 leaves per cubic meter, and a bottom layer with 85 leaves per cubic meter. The system then divided the canopy into three layers and planned an adaptive flight trajectory. The middle layer employed a spiral, progressive path, extending the scanning time per layer to 18 minutes. The top and bottom layers employed a zigzag, round-trip path, covering a total area of ​​3 hectares. Chlorophyll fluorescence intensity and photosynthetically active radiation absorption rate data were collected simultaneously during the flight, generating a spectral dataset with geotagged coordinates.

[0068] 102. Deploy multiple ground verification points within the scanning area, collect three-dimensional morphological parameters of the vegetation canopy based on the spatiotemporal coverage characteristics of the flight trajectory, perform spatiotemporal calibration and matching of the three-dimensional morphological parameters with the multispectral reflectance data, and construct a reflectance database;

[0069] Ground verification points refer to fixed sensor nodes deployed beneath the vegetation canopy, used to collect three-dimensional morphological parameters such as branch inclination and leaf clearance. Spatiotemporal coverage refers to the distribution characteristics of the aerial device's scanning path in both time and space, and must be aligned with ground data to enable collaborative air-ground analysis.

[0070] In an embodiment of the present application, ground verification points are arranged in a grid within the scanning area, and a lidar scanner and a full-frame multispectral camera with differential GPS positioning are deployed at the ground verification points. Hemispherical images of the vegetation canopy are captured from multiple angles and three-dimensional point cloud data with millimeter-level accuracy are simultaneously collected. The improved Canny edge detection algorithm is used to extract leaf contour features, and the Monte Carlo ray tracing technology is combined to generate three-dimensional morphological parameters including leaf inclination angle distribution, gap ratio and branch projection area ratio; at the same time, a spatiotemporal calibration engine is started, and the timestamp of the data collected by the flight device and the spatial coordinates of the ground station are four-dimensionally encoded, and a spatiotemporal mapping relationship is established based on the improved R-tree indexing algorithm; then, an accelerated robust feature matching algorithm is used to perform feature point registration on the aerial survey point cloud and the ground-based scanning model, and sub-centimeter-level spatial alignment is achieved through an iterative nearest point optimization algorithm; the calibrated three-dimensional morphological parameters and the multispectral reflectance data of the corresponding coordinates are stored in a distributed database in a spatiotemporal sequence to form a reflection database including latitude and longitude, elevation, reflection bands and structural parameters.

[0071] Continuing with the above example, ground verification stations were arranged according to a predetermined grid, each equipped with a differential GPS-based lidar scanner and a full-frame multispectral camera. As the aerial vehicle performed a layered scan over the northeastern region, the ground verification system was simultaneously activated. The lidar at station three captured dense point cloud data of the mid-canopy layer. Leaf contour features were accurately extracted using an improved Canny edge detection algorithm. Three-dimensional morphological parameters, such as mean leaf inclination angle and canopy gap ratio, were calculated using Monte Carlo ray tracing techniques. The spatiotemporal alignment engine parses the aerial vehicle's 3D morphological parameters with the spatial coordinates of the ground station in real time, generating a unique hash code and establishing a four-dimensional mapping relationship. This was then matched to aerial point cloud data from adjacent time periods using an improved R-tree index. The feature matching stage employed an accelerated robust feature algorithm to extract key points from the aerial and ground-based point clouds. Submillimeter spatial alignment was achieved using an iterative closest point optimization algorithm. Finally, the calibrated leaf area index was dynamically linked to the multispectral reflectance data at the corresponding coordinates, forming an incrementally updated reflectance database that provides accurate canopy structural data for subsequent reflection suppression processing.

[0072] 103. Identify high-reflection interference areas based on the detected vegetation canopy surface reflection characteristics, and apply multi-angle polarization compensation to the high-reflection interference areas by adjusting the polarization angle configuration to generate multispectral reflectance data after reflection suppression;

[0073] Highly reflective interference areas are areas of abnormal specular reflection caused by leaf wax or water accumulation, with reflectivity significantly higher than that of normal vegetation. Multi-angle polarization compensation suppresses specular reflection noise from different directions by adjusting the angle combination of polarizing filters, while retaining the effective diffuse reflection signal from vegetation.

[0074] In an embodiment of the present application, based on the surface reflection characteristics of the vegetation canopy recorded in the reflection database, a dynamic threshold segmentation algorithm is used to identify high-reflection interference areas whose reflectivity exceeds a preset threshold. For the high-reflection interference areas, the polarization camera carried by the flying device automatically adjusts the filter angle to collect polarization spectrum data in three directions: 0 degrees, 45 degrees, and 90 degrees. Through a weighted fusion algorithm, the specular reflection component is removed from the collected polarization spectrum data, the diffuse reflection spectrum characteristics are retained, and a multispectral data set is constructed based on the remaining polarization spectrum data, that is, the multispectral data set contains multiple polarization spectrum data after reflection suppression.

[0075] Continuing with the above example, scanning data revealed that the waxy layer on the leaf surface caused the reflectance of the sun-facing side of the canopy to rise abnormally to 78%. The drone's polarization camera collected data from the target area at angles of 45, 90, and 135 degrees, using a polarization difference algorithm to separate specular and diffuse reflection components. This processing reduced spectral noise in highly reflective areas by 70%, and improved the signal-to-noise ratio of the chlorophyll fluorescence signal from 3.5 to 9.1, effectively supporting the accurate extraction of photosynthetic parameters.

[0076] 104. Based on the penetration level identifier in the reflectance database, perform a layered reflectance weighted calculation on the multispectral reflectance data after reflection suppression, and associate the mapping relationship between the attenuation gradient of different multispectral reflectance data in the vertical section of the vegetation canopy and the photosynthesis characteristic parameters to generate a three-dimensional distribution map of the photosynthetic efficiency of the plant community;

[0077] The penetration level identifier is the vertical level number assigned to the lidar data, marking the spatial location of the spectral data within the canopy. The attenuation gradient refers to the signal attenuation caused by leaf shading when light waves penetrate different layers.

[0078] In an embodiment of the present application, the vegetation canopy is vertically divided into several optical penetration units based on the penetration level identifier in the reflection database, the discrete ordinate method is used to calculate the light radiation transmission path in each unit, and the multispectral reflection data after reflection suppression is subjected to a layered reflectivity weighted calculation, wherein the weight coefficient is dynamically adjusted according to the voxel density and leaf area index of the corresponding level; then a deep residual network model is constructed, the input layer includes multispectral reflectivity, ambient temperature and humidity, and canopy structure parameters, the light attenuation gradient characteristics of the vertical profile are extracted through an adaptive feature fusion module, and the light energy utilization efficiency mapping relationship of the associated photosynthesis characteristic parameters is obtained; finally, a three-dimensional Kriging interpolation algorithm is used to spatially reconstruct the photosynthetic efficiency indicators of the discrete units, and the canopy digital surface model is superimposed to generate a three-dimensional distribution map of photosynthetic efficiency with millimeter-level resolution.

[0079] Continuing with the above example, in the vertical segmentation of the canopy of the banyan forest in the wetland park, the banyan tree canopy is divided into optical penetration units with a thickness of half a meter per layer. Based on the penetration level identification of the reflection database, the discrete vertical scale method is used to simulate the solar radiation transmission path at noon. When performing layered weighted calculations on the multispectral reflectance data after reflection suppression in step 103, the voxel density in the middle layer of the canopy is high. The weight coefficient is dynamically assigned in combination with the leaf area index measured at the ground verification point, so that the weighted value of the reflectance in the 710nm band is corrected from the initial 0.38 to 0.42. The input of the constructed deep residual network model includes the corrected five-band reflectance, canopy temperature and air humidity parameters. The feature fusion module identifies the light attenuation gradient characteristics in the vertical direction and associates them with the light energy utilization efficiency mapping relationship in the photosynthesis characteristic parameters. The three-dimensional Kriging interpolation algorithm was used to reconstruct the three-dimensional distribution map of photosynthetic efficiency in the northeastern region of the canopy. After superimposing the high-precision canopy surface model, it was clearly shown that there was an elliptical area of ​​low photosynthetic efficiency in the middle layer. The energy conversion rate of the corresponding area in the synchronously output thermal map was significantly lower than that of the surrounding area, and it was marked as an abnormal target area, triggering the subsequent step 105 to conduct a special verification scan of the delayed effect of root water absorption in this area.

[0080] 105. Based on the abnormal photosynthetic efficiency area in the three-dimensional distribution map, control the flying device to perform a secondary scan, and by fusing the multispectral reflectance data of the secondary scan with the soil moisture parameters of the ground verification point, correct the assessment error caused by the delayed root water absorption in the three-dimensional distribution map, and output the ecological assessment result.

[0081] Root water uptake delay refers to the time difference between the water uptake rate of plant roots and the transpiration demand of the canopy, which can lead to errors in photosynthetic efficiency assessment. Soil moisture parameters refer to real-time soil moisture content, hydraulic conductivity, and other data collected at ground verification points.

[0082] In this embodiment, the system automatically detects areas of abnormal photosynthetic efficiency in the three-dimensional distribution map and triggers a second drone scan. During the second scan, soil moisture data from the ground verification points is simultaneously acquired. A time-lag compensation model is used to calculate the root water uptake delay time and obtain a related delay parameter. This delay parameter is then substituted into the photosynthesis model to dynamically correct the assessment error in the three-dimensional distribution map caused by the root water uptake delay, ultimately outputting an ecological assessment result that eliminates moisture interference.

[0083] During monitoring of the banyan forest in the wetland park, the three-dimensional distribution map automatically identified an elliptical area of ​​low photosynthetic efficiency in the middle layers of the northeastern region. This triggered a drone to perform a secondary spiral scan with meter-level accuracy during the afternoon, when sunlight was stable. The drone, equipped with a highly sensitive multispectral imager, collected sub-meter-level data over the target area. Simultaneously, soil moisture sensors at ground verification sites were activated to acquire real-time dynamic soil moisture and hydraulic conductivity data at a centimeter depth. A time-lag compensation model was used to analyze the phase difference between canopy transpiration rate and root water uptake rate. The root water uptake delay parameter was calculated based on the soil hydraulic conductivity curve and then fed into a photosynthesis model to perform inverse compensation on the original light energy utilization efficiency. The corrected three-dimensional distribution map showed that photosynthetic efficiency values ​​in the previously abnormal area had returned to normal fluctuations. The system then automatically generated a root water transport capacity assessment report, marked the area as a water stress warning zone on the digital twin platform, and delivered a precise irrigation optimization plan, completing a closed-loop ecological assessment process from data collection to decision support.

[0084] This solution achieves three-dimensional perception of the vegetation canopy through dynamic layered scanning and adaptive trajectory planning, and builds a high-precision reflectance database based on spatiotemporal calibration of ground verification points. Multi-angle polarization compensation technology effectively suppresses high-reflection interference, and a layered weighted model based on penetration level identification accurately quantifies the distribution of photosynthetic efficiency in vertical profiles. Ultimately, through secondary scanning and fusion with soil parameters, it overcomes the limitations of traditional modeling of the delayed effect of root water absorption, achieving accurate assessment of ecological parameters throughout the entire chain from canopy to root, providing three-dimensional dynamic analysis capabilities for monitoring complex vegetation environments.

[0085] In some embodiments, based on the penetration level identifier in the reflectance database, performing a layered reflectance weighted calculation on the spectral data after reflection suppression includes:

[0086] 201. Extracting layer segmentation information corresponding to the penetration layer identifier from the reflection database, wherein the layer segmentation information includes a penetration capability value and a layer thickness value corresponding to each layer in the vegetation canopy;

[0087] Penetration level identifiers are vertical level identifiers of the vegetation canopy, as determined by LiDAR data. They are used to identify the spatial locations of different height layers. Level segmentation information includes penetration and layer thickness values. Penetration refers to the percentage of light that penetrates a layer without being blocked by leaves, while layer thickness refers to the vertical physical thickness of that layer.

[0088] In this embodiment, the layer segmentation information in the data table corresponding to the penetration level identifier is retrieved from the reflection database, and the penetration capability and layer thickness values ​​of each layer are extracted using a structured query statement. The penetration capability value is calculated based on the blade clearance ratio within the layer, using the formula: penetration capability value = 1 - (blade projected area / layer cross-sectional area). The layer thickness value is directly obtained by the vertical height difference of the lidar point cloud data.

[0089] 202. According to the penetration value, weight the spectral signal of each level in the spectral data after reflection suppression;

[0090] Weight allocation refers to assigning a correction coefficient to the spectral signal of each level according to the penetration value. The lower the penetration value and the more severe the obstruction, the higher the spectral signal weight is to compensate for signal attenuation.

[0091] In this embodiment, the spectral data after reflection suppression is segmented into hierarchical levels, and the spectral signal intensity at each level is normalized. A weighting function is constructed based on the penetration value, using the formula: weight coefficient = 1 / (penetration value + 0.1), where 0.1 is a smoothing factor. The weight coefficient is multiplied by the normalized spectral signal to generate a weighted hierarchical spectral dataset, thereby assigning weights to the spectral signals at each level in the spectral data after reflection suppression.

[0092] 203. Based on the layer thickness values, the weighted spectral signals are superimposed and calculated in layer order to obtain a weighted reflectivity result of each layer in the vertical direction.

[0093] Hierarchical sequential superposition calculation refers to combining the weighted spectral signal with the layer thickness value in the vertical order from the top layer to the bottom layer, and calculating the reflectivity contribution value layer by layer.

[0094] In this embodiment, weighted spectral signals are stacked and calculated in layer order based on the layer thickness values ​​to construct a vertical stacking model. The formula is: layer reflectivity contribution value = weighted spectral signal × layer thickness value. Contributions are accumulated layer by layer in order of layer number (top to bottom), generating a weighted vertical reflectivity distribution curve, and ultimately outputting the quantified reflectivity results for each layer.

[0095] Here's a specific example:

[0096] In the canopy monitoring scenario of the banyan tree forest in the wetland park, the system performs a hierarchical reflectance quantification process. Step 201 extracts vertical layer segmentation information from the reflectance database. The top layer (8 to 10 meters) has a penetration value of 0.15, and the projected area of ​​the leaf layer accounts for 85% of the total area, as determined by LiDAR point cloud computing. The middle layer (5 to 8 meters) has a penetration value of 0.38, and the layer thickness is 3 meters. The bottom layer (2 to 5 meters) has a penetration value of 0.72, and the layer thickness is 3 meters. Step 202 assigns weights to the 710 nm spectral signal after reflection suppression. The original normalized signals are 0.25 for the top layer, 0.40 for the middle layer, and 0.55 for the bottom layer. The initial weight coefficients are calculated as 4.0, 2.08, and 1.22, based on the penetration value plus the inverse of 0.1. After normalization, they are corrected to 0.548, 0.285, and 0.167. Step 203 performs a hierarchical overlay calculation. The top layer contribution value is 0.25 multiplied by 0.548 multiplied by 2 meters to get 0.274, the middle layer contribution value is 0.40 multiplied by 0.285 multiplied by 3 meters to get 0.342, and the bottom layer contribution value is 0.55 multiplied by 0.167 multiplied by 3 meters to get 0.276. The independent quantitative results of the reflectivity of each layer are output: the top layer reflectivity contribution of 0.274 represents the high reflectivity characteristics of the leaf wax layer, the middle layer 0.342 indicates stomatal conductance anomaly, and the bottom layer 0.276 reflects the surface water transpiration effect.

[0097] This solution addresses the reflectance averaging errors caused by traditional methods due to the lack of stratified calibration by dynamically weighting penetration and layer thickness values, and by vertically stacking them. A hierarchical weighting model quantifies the true reflectance contribution of each layer, and the stacking calculation, combined with physical thickness, improves vertical resolution accuracy, ultimately providing a reliable quantitative basis for the three-dimensional assessment of vegetation canopy photosynthetic efficiency.

[0098] In some embodiments, based on the reflectance weighted results, a mapping relationship between the attenuation gradients of different spectral data in the vertical profile of the vegetation canopy and the photosynthesis characteristic parameters is associated to generate a three-dimensional distribution map of the photosynthetic efficiency of the plant community, including:

[0099] 301. Extracting the signal intensity change rate of the spectral signals at different levels in the reflectance database as the penetration level changes on the vertical section, and establishing a corresponding relationship between the signal intensity change rate and the light energy absorption value in the photosynthesis characteristic parameter;

[0100] The signal intensity change rate refers to the rate at which the spectral signal attenuates as the penetration level increases in the vertical profile, typically expressed as a percentage or absolute value per meter. Light energy absorption is a key characteristic parameter of photosynthesis, reflecting the efficiency of plant leaves in converting light energy into chemical energy.

[0101] In this embodiment, the spectral signal intensity of each layer is extracted from the reflectance database, as it changes with penetration level in a vertical profile. The rate of change of signal intensity between adjacent layers is calculated in vertical order. For example, the rate of change of signal intensity from the top layer to the middle layer = (top layer signal intensity / middle layer signal intensity) x 100%. A linear regression model is used to establish a correspondence between the signal intensity change rate and the light energy absorption value of the photosynthesis characteristic parameter. For every 10% increase in the signal intensity change rate, the light energy absorption value decreases by 3%.

[0102] 302. Convert the spectral signal of each level in the reflectivity weighted result into a corresponding light energy absorption value according to the corresponding relationship;

[0103] Spectral signal conversion refers to converting the spectral signal value in the reflectivity weighted result into a physical quantity of light energy absorption value based on the corresponding relationship in step 301 .

[0104] In this embodiment, the weighted reflectivity results for each level are substituted into the corresponding relationship between the signal intensity change rate and the light energy absorption value in the photosynthesis characteristic parameter established in step 301. The light energy absorption value = the level reflectivity weighted value x (1 - attenuation loss coefficient x signal intensity change rate). For example, if the level reflectivity weighted value is 1500 and the corresponding signal intensity change rate is 20%, then the light energy absorption value = 1500 x (1 - 20% x 0.3) = 1500 x 0.94 = 1410. After completing the conversion level by level, a hierarchical dataset containing light energy absorption values ​​is generated.

[0105] 303. Based on the spatial position of the hierarchical segmentation information, map the light energy absorption value into a three-dimensional space in a hierarchical order to form a three-dimensional distribution map representing the distribution of photosynthetic efficiency of the plant community.

[0106] Three-dimensional spatial mapping refers to projecting the light energy absorption value into a three-dimensional grid in hierarchical order according to the spatial coordinates (such as latitude, longitude, and elevation) in the hierarchical segmentation information to form a three-dimensional distribution model.

[0107] In this embodiment, a three-dimensional spatial grid is constructed based on the spatial location data of the hierarchical segmentation information. The light energy absorption values ​​of each level are then applied to the corresponding grid cells in hierarchical order using a spatial interpolation algorithm, such as Kriging interpolation. For example, the mid-level light energy absorption value 1410 is assigned to the grid area with an elevation of 2.1-3.6 meters. This ultimately generates a three-dimensional distribution map of the photosynthetic efficiency distribution of the plant community, which includes information about light energy absorption intensity and spatial location.

[0108] Here's a specific example:

[0109] In a pine forest canopy monitoring scenario, the system executes a process for generating a three-dimensional map of photosynthetic efficiency. Step 301 extracts the rate of change of signal intensity across three vertical layers. From the top to the middle layer, the signal decreases from 2000 to 1200, with a 40% attenuation gradient. From the middle to the bottom layer, the signal decreases from 1200 to 840, with a 30% attenuation gradient. A quantitative relationship between the attenuation gradient and light absorption value is established using measured data. Every 10% attenuation gradient corresponds to a 2.5% decrease in light absorption value. Step 302 converts the reflectance weighting results. A top layer weight of 1800 indicates no attenuation, and the light absorption value remains at 1800. A middle layer weight of 1300 corresponds to 40% attenuation, and the absorption value is calculated as 1300 multiplied by 0.9, resulting in 1170. A bottom layer weight of 900 corresponds to 70% attenuation, and the absorption value is adjusted to 742.5. Based on the hierarchical spatial coordinates, step 303 maps the top layer's light absorption value of 1800 to an elevation of 15-20 meters, the middle layer's 1170 to 10-15 meters, and the bottom layer's 742.5 to 5-10 meters. Kriging interpolation then generates a three-dimensional distribution map. This map shows that light absorption is strongest at the top of the canopy, with localized areas in the middle layer forming high-efficiency hotspots with absorption values ​​of 1170 due to interlaced branches and leaves. The bottom layer experiences a significant decrease in absorption due to shading, accurately revealing the vertical photosynthetic efficiency distribution characteristics of the pine forest.

[0110] This approach quantifies the dynamic relationship between vertical attenuation gradients and light absorption, converting reflectance data into physically meaningful photosynthetic efficiency parameters. Combined with three-dimensional spatial mapping technology, this approach provides a three-dimensional representation of vegetation canopy photosynthetic efficiency. Compared to traditional two-dimensional planar assessment methods, this technology reveals vertical variations in light energy utilization within the canopy, providing a reliable basis for accurately identifying high-efficiency photosynthetic zones and ecological restoration targets.

[0111] In some embodiments, based on the abnormal photosynthetic efficiency area in the three-dimensional distribution map, the flying device is controlled to perform a secondary scan, and by fusing the multispectral reflectance data of the secondary scan with the soil moisture parameters of the ground verification point, the assessment error caused by the delayed root water absorption in the three-dimensional distribution map is corrected, and the ecological assessment result is output, including:

[0112] 401. Extracting continuous spatial regions where the photosynthetic efficiency value is lower or higher than a preset normal range from the three-dimensional distribution map, and marking them as abnormal photosynthetic efficiency regions;

[0113] Abnormal photosynthetic efficiency areas are defined as areas in the 3D distribution map where photosynthetic efficiency values ​​continuously deviate from the preset normal threshold range. This is typically caused by delayed root water uptake or abnormal canopy structure. The preset normal range is determined by historical data statistics or baseline values ​​for similar vegetation types.

[0114] In an embodiment of the present application, the photosynthetic efficiency values ​​of all units are first extracted from the three-dimensional distribution map, and a preliminary screening is performed based on a preset normal threshold range. For example, when the normal range is 800 to 1500 units, values ​​below 600 or above 1600 are marked as candidate abnormal points. Then, a three-dimensional region growing algorithm is used, with the candidate abnormal point as the seed point, to diffuse in the 26 neighborhood directions in the three-dimensional space, detect and merge all continuous adjacent abnormal voxels, and form a spatially continuous photosynthetic efficiency abnormal region. Finally, the minimum outer cube spatial coordinates of the photosynthetic efficiency abnormal region are output, including the minimum and maximum values ​​of the X, Y, and Z axes, to provide a spatial range definition for the subsequent secondary scan.

[0115] 402. Adjust the flight altitude and scanning path density of the flying device based on the spatial distribution range of the photosynthetic efficiency abnormal area, generate a secondary scanning trajectory covering the photosynthetic efficiency abnormal area, and control the flying device to perform a secondary scan according to the secondary scanning trajectory.

[0116] The secondary scanning trajectory refers to the flight path that is dynamically adjusted according to the spatial distribution of the abnormal area, and high-precision data acquisition is achieved by increasing the scanning density and lowering the flight altitude.

[0117] In this embodiment, the target area is divided into a three-dimensional grid with a precision of 0.3 meters based on the spatial distribution of the abnormal photosynthetic efficiency area. An adaptive ant colony algorithm is used to generate a serpentine round-trip path covering all grid cells. The horizontal path interval is set to 0.3 meters, and the vertical flight altitude is layered according to the layer thickness. The drone uses a PID controller and RTK positioning technology to achieve high-precision path tracking, generating a secondary scanning trajectory covering the abnormal photosynthetic efficiency area. The drone then controls the flight device to perform a secondary scan according to the secondary scanning trajectory. The flight altitude is reduced from 50 meters to 20 meters, and the horizontal positioning error is controlled within 0.1 meters, ensuring that the scan data resolution is improved to the centimeter level.

[0118] 403. Extract multispectral reflectance data of the photosynthetic efficiency abnormal area in the secondary scan, and obtain the spectral reflectance value of each spatial coordinate point in the abnormal area;

[0119] The spectral reflectance value of the spatial coordinate point refers to the multispectral reflectance data corresponding to each three-dimensional coordinate point (longitude, latitude, and elevation) in the secondary scan, covering the visible light to near-infrared band.

[0120] In this embodiment of the present application, a drone equipped with a hyperspectral imager flies along an encrypted trajectory, collecting image data of multispectral reflectance data of the abnormal photosynthetic efficiency area described in the secondary scan at a rate of 100 frames per second. The image pixels are matched to three-dimensional spatial coordinates in real time using a SLAM algorithm to generate a reflectance matrix with geotagged data. An empirical line correction method is then used to eliminate the influence of changes in light intensity. For example, the data collected at different times are uniformly corrected to the reflectance value under standard lighting conditions, ultimately obtaining the precise spectral reflectance value for each spatial coordinate point.

[0121] 404. Extracting soil moisture parameters corresponding to the spatial coordinates of the abnormal area from the ground verification points, the soil moisture parameters being soil moisture content measurements at the ground verification points within a preset time window; and performing a one-to-one binding between the soil moisture content measurements and the spectral reflectance values ​​of the abnormal area according to the spatial coordinates.

[0122] Soil moisture content refers to the volumetric soil moisture content measured at a ground verification point within a preset time window, such as one hour before and after a scan. This represents the amount of water available for root absorption. Spatial coordinate binding involves associating soil parameters with the corresponding voxel cells in the abnormal area based on their geographic location.

[0123] In an embodiment of the present application, the soil moisture content measurement values ​​of the ground verification points within a preset time window corresponding to the spatial coordinates of the abnormal area are extracted from the ground verification point network, and a soil moisture spatial distribution surface is generated using the Kriging interpolation algorithm with a resolution consistent with the abnormal area grid. The center coordinates of each voxel unit in the abnormal area are projected onto the ground plane, and the soil moisture value at the corresponding position is extracted. For example, when the center coordinates of a voxel unit are X=102, Y=205, and Z=5 meters, the moisture content at the ground coordinates X=102 and Y=205 is associated with 12%, completing a one-to-one binding of the spatial coordinates of the air-ground data.

[0124] 405. Based on the measured value of soil moisture content, the spectral reflectance value is compensated for moisture absorption delay, and the compensated spectral reflectance value replaces the photosynthetic efficiency value of the corresponding photosynthetic efficiency abnormal area in the three-dimensional distribution map, and the photosynthetic efficiency distribution of all spatial coordinate points in the three-dimensional distribution map is recalculated based on the replaced photosynthetic efficiency value, and an ecological assessment result is generated.

[0125] Water absorption delay compensation refers to the time-delay correction of spectral reflectance values ​​according to soil moisture content, eliminating the interference of root water absorption lag on photosynthetic efficiency calculation.

[0126] In the embodiment of the present application, a preset delay time mapping table is queried based on the soil moisture content value. For example, a 9% moisture content corresponds to a 4-hour delay. Time lag correction is performed on the spectral reflectance values ​​in areas with abnormal photosynthetic efficiency. If the measured soil moisture content is lower than the preset standard, the near-infrared band reflectance value in the spectral reflectance value is proportionally increased. If the measured soil moisture content is higher than the preset standard, the near-infrared band reflectance value in the spectral reflectance value is proportionally decreased, and the reflectance is recalculated using historical light data. The corrected reflectance is substituted into the photosynthetic efficiency model to regenerate a three-dimensional distribution map. The spatial data is then smoothed using a cubic spline interpolation algorithm, ultimately outputting an ecological assessment report that eliminates the water absorption delay error.

[0127] Here's a specific example:

[0128] During the monitoring of the banyan forest in the wetland park, the three-dimensional distribution map detected an abnormal photosynthetic efficiency area in the northwest region where the photosynthetic efficiency value was continuously below 500. The system planned an encrypted scanning path with an accuracy of 0.3 meters. The drone completed a second scan at an altitude of 20 meters, collecting 1,200 high-precision reflectance points. Ground data showed that the soil moisture content in the area with abnormal photosynthetic efficiency was 9%, corresponding to a 4-hour delay. After the time lag correction of the reflectance value, the photosynthetic efficiency value increased from 480 to 650. The updated three-dimensional distribution map showed that the deviation was reduced from 35% to 12%, generating an ecological assessment result and recommending the implementation of drip irrigation and shading measures.

[0129] This solution effectively eliminates errors in photosynthetic efficiency assessment caused by delayed root water uptake through high-precision secondary scanning and dynamic soil moisture compensation. Abnormal area detection and adaptive path planning improve local data resolution, while a time-lag correction model quantifies the impact of environmental disturbances. Ultimately, this approach achieves spatial consistency and temporal reliability in ecological assessment results, providing precise decision-making support for vegetation health management.

[0130] In some embodiments, based on the detected vegetation canopy surface reflectance characteristics, high-reflection interference areas are identified, and multi-angle polarization compensation is applied to the high-reflection interference areas by adjusting the polarization angle configuration to generate multispectral reflectance data after reflection suppression, including:

[0131] 501. Extracting the reflection intensity of the vegetation canopy surface within a preset visible light band based on the spectral reflectance value of each spatial coordinate point in the multispectral reflectance data, and marking the spatial coordinate points whose reflection intensity exceeds a preset threshold as high-reflection interference areas;

[0132] Highly reflective interference areas are those where reflectivity is significantly higher than the normal range for vegetation due to leaf wax, water accumulation, or specific materials. The preset visible light band is typically selected to be sensitive to specular reflection, and the preset threshold is dynamically set based on the vegetation type.

[0133] In this embodiment, the spectral reflectance value of each spatial coordinate point in the multispectral reflectance data is extracted, and the spectral intensity of each spatial coordinate point in the preset visible green light band is extracted. A dynamic threshold segmentation algorithm is then used to automatically calculate the optimal segmentation threshold for the current scene. The Otsu algorithm is used to maximize the inter-class variance, and spatial coordinate points whose reflectance exceeds the threshold are marked as candidate interference points. Adjacent candidate points are then merged through a morphological closing operation to generate a continuous high-reflection interference region.

[0134] 502. For the high-reflection interference area, control the polarization filter assembly of the flying device to switch to a plurality of different polarization angle combinations in sequence; under each polarization angle combination, recollect spectral reflectance values ​​for the high-reflection interference area to obtain a plurality of sets of spectral reflectance data differentiated by polarization angle;

[0135] Polarization angle combination refers to the rotation angle setting of the polarization filter, usually with four groups of angles of 0°, 45°, 90°, and 135° covering all polarization directions, and is used to suppress specular reflection noise from multiple angles.

[0136] In an embodiment of the present application, for the highly reflective interference area, after controlling the flight device to receive the coordinates of the highly reflective area, the polarization filter assembly is controlled to sequentially switch to a plurality of preset polarization angle combinations. At each polarization angle combination, for each angle, the drone hovers directly above the target area, maintaining a stable attitude through PID controller and RTK positioning, ensuring a positioning error of ≤0.1 meters. The hyperspectral imager collects spectral reflectance values ​​at a rate of 50 frames per second, completing a complete area scan after each angle switch, generating multiple sets of spectral reflectance data sets with differentiated polarization angles.

[0137] 503. Superimpose the spectral reflectance values ​​of the same spatial coordinate point in the multiple sets of polarization angle-differentiated spectral reflectance data, and retain the minimum intensity value of the superimposed spectral reflectance values ​​as the spectral reflectance value of the spatial coordinate point after reflection suppression;

[0138] Superposition processing refers to the fusion calculation of the spectral reflectance values ​​of the same spatial coordinate point at multiple polarization angles. Retaining the minimum value can effectively suppress the residual signal of mirror reflection.

[0139] In the embodiment of the present application, the multiple sets of polarization angle-differentiated spectral reflectance data are aligned and superimposed according to spatial coordinates. The superimposed spectral reflectance values ​​are retained, and the reflectance value sequence for each coordinate point is sorted and the minimum intensity value is extracted as the spectral reflectance value after reflection suppression at the spatial coordinate point. For example, if the reflectance values ​​of a coordinate point at angles of 0°, 45°, 90°, and 135° are 70%, 45%, 60%, and 50%, respectively, 45% is selected as the reflectance value after reflection suppression. This process traverses all coordinate points to generate a minimum reflectance value matrix for high-reflection areas.

[0140] 504. Merge the spectral reflectance values ​​after reflection suppression and the original spectral reflectance values ​​of the areas not marked as high-reflection interference areas according to spatial coordinates to generate multi-spectral reflectance data after reflection suppression.

[0141] Spatial coordinate merging refers to seamlessly splicing the processed high-reflection area data with the original data to generate a complete and noise-suppressed multispectral reflectance dataset.

[0142] In this embodiment, a georeferencing algorithm is used to merge the spectral reflectance values ​​after reflection suppression with the original spectral reflectance values ​​of areas not marked as high-reflection interference, based on spatial coordinates. For coordinate points at the edges of high-reflection areas, a bilinear interpolation algorithm is used to smooth the transition and avoid data jumps. For example, the reflectance value of an edge point is calculated by weighting the distance between the reflectance values ​​of four adjacent non-interference points. The resulting global reflectance database contains uniformly calibrated multispectral reflectance data.

[0143] Here's a specific example:

[0144] In the coniferous forest canopy monitoring scenario, step 501 detected a high-reflection interference area caused by the pine needle wax layer, with a green light band reflectivity of 68%, exceeding the preset threshold of 55%. Step 502 controlled the polarization filter to re-collect the target area at four angles of 0°, 60°, 120°, and 180°. The drone's hovering accuracy was controlled within 0.1 meters, and the acquisition time for each angle was 30 seconds. Step 503 took the minimum value of 50% for the four sets of reflectance values ​​of 75%, 50%, 65%, and 55% at a certain coordinate point. After the reflection was suppressed, the signal-to-noise ratio of the chlorophyll fluorescence signal in this area was increased from 4.2 to 9.5. Step 504 merged the data through bilinear interpolation, and the generated global reflectance database showed that the noise in the high-reflection area was reduced by 82%, and the error in photosynthetic parameter extraction was reduced from 18% to 5%.

[0145] This solution uses dynamic threshold segmentation to precisely locate areas of high-reflectivity interference, multi-angle polarization acquisition to suppress specular noise, a minimum value stacking algorithm to preserve effective vegetation diffuse reflectance signals, and interpolation and fusion techniques to generate a global, high-precision reflectance database. Ultimately, this solution significantly improves the accuracy of photosynthesis parameter inversion under complex canopy environments, providing a robust data foundation for ecological assessments.

[0146] In some embodiments, the spatiotemporal coverage features include a timestamp sequence and a spatial coordinate sequence;

[0147] Arrange multiple ground verification points in the scanning area, collect three-dimensional morphological parameters of the vegetation canopy in combination with the spatiotemporal coverage characteristics of the flight trajectory, perform spatiotemporal calibration and matching of the three-dimensional morphological parameters with the multispectral reflectance data, and construct a reflectance database, including:

[0148] 601. Arrange multiple ground verification points at a preset density within the scanning area. Each ground verification point is equipped with a three-dimensional laser scanning device. The three-dimensional laser scanning device measures depth detection data of the vegetation canopy by emitting laser pulses and receiving reflected signals. The height value, gap density, and surface curvature value of each measurement point are calculated based on the depth detection data to form a three-dimensional morphological parameter set of the ground verification point.

[0149] Ground verification points are fixed monitoring nodes evenly distributed at a preset density beneath the vegetation canopy. Each node is equipped with a 3D laser scanner. Depth detection data refers to physical parameters of the vegetation canopy, including height, gap density, and surface curvature, calculated from the time difference between the emission of a laser pulse and the reception of the reflected signal. Height is the vertical distance from the ground to the base of the canopy, gap density is the percentage of gaps between branches and leaves per unit volume, and surface curvature is a quantitative indicator of the curvature of the leaf or branch surface.

[0150] In an embodiment of the present application, a plurality of ground verification points are arranged in a grid of 10 meters by 10 meters in the scanning area, and a three-dimensional laser scanner is deployed at each ground verification point. The three-dimensional laser scanner emits a 905-nanometer laser at a frequency of 1000 pulses per second, and after receiving the reflected signal, measures the depth detection data of the vegetation canopy by the time-of-flight method. The original depth detection data is denoised and interpolated, and the height value of each measuring point is extracted with an accuracy of plus or minus 2 centimeters. When calculating the gap density, the leaf projection area is divided by the scanning area area and the complement is taken. The surface curvature value is calculated by a local surface fitting algorithm, and a data table containing three-dimensional coordinates and morphological parameters is formed.

[0151] 602. Extract the acquisition timestamp and spatial coordinates of each spectral sampling point in the multispectral reflectance data, and establish corresponding position marks between the spatial coordinates of the spectral sampling point and the canopy height values ​​in the three-dimensional morphological parameters based on the timestamp sequence and spatial coordinate sequence of the flight trajectory;

[0152] The timestamp sequence refers to the time stamp sequence when the multispectral reflectance data was collected, and the spatial coordinate sequence refers to the longitude, latitude, and elevation coordinate sequence of the flight device trajectory. Corresponding position tags refer to the association of spectral sampling points and ground morphological parameters to the same spatial coordinates through spatiotemporal alignment.

[0153] In an embodiment of the present application, the acquisition time and spatial coordinates of each spectral sampling point are extracted from the multispectral reflectance data, and the timestamps are aligned using a linear interpolation algorithm based on the timestamp sequence and spatial coordinate sequence of the flight trajectory. For example, if the timestamp of the spectral sampling point is 10:00:30, the data of the scanning time of the nearest ground verification point is matched at 10:00:28. The longitude and latitude of the flight trajectory are converted into plane coordinates through coordinate transformation, and the matching error is controlled within 0.1 meters. The spatial coordinates of the spectral sampling point and the corresponding position marks of the canopy height value in the three-dimensional morphological parameters are established to ensure the spatial consistency of the spectral sampling point and the ground morphological parameters.

[0154] 603. Bind the canopy height value, canopy gap density value, and canopy surface curvature value in the three-dimensional morphological parameters with the spectral reflectance value in the multispectral reflectance data according to the corresponding position marks to obtain a bound data unit.

[0155] Position binding refers to establishing a one-to-one correspondence between canopy morphological parameters and spectral reflectance values ​​under the same spatial coordinates to form a multidimensional data unit.

[0156] In the embodiment of the present application, a hash table storage relationship is established based on the corresponding position mark as a unique identifier. The key is the three-dimensional coordinates X, Y, and Z, and the value is the morphological parameter height H, gap density D, curvature C, and spectral reflectance values ​​R1, R2 to Rn. For example, the coordinates X102.3, Y45.6, and Z5.2 correspond to a height value of 5.2 meters, a gap density of 0.15, a curvature of 0.08, and spectral reflectance values ​​of 0.63 and 0.55. The canopy height value, canopy gap density value, and canopy surface curvature value in the three-dimensional morphological parameters are spatially bound to the spectral reflectance value in the multispectral reflectance data to form a one-to-one correspondence. All coordinate points are traversed to complete the binding, and a data unit set containing air-ground multi-source attributes is generated.

[0157] 604. Based on a preset binding rule, the bound data units are stored in a grid distribution order of spatial coordinates to generate a reflection database.

[0158] Grid distribution order refers to dividing spatial coordinates into a regular grid at a preset resolution and storing data in row and column order. Binding rules include data compression format, redundancy check, and index table construction strategy.

[0159] In this embodiment, based on pre-defined binding rules such as data compression format, redundancy check, and index table construction strategy, the bound data units are stored in a grid-like distribution order of spatial coordinates, divided into three-dimensional grids with a resolution of 0.1 meters. Each grid stores the morphological parameters and spectral reflectance values ​​of the corresponding coordinate point. A Z-order curve is used to spatially encode the grids, generating a global index table. The data is compressed and stored in a columnar format, and a CRC checksum is added, ultimately generating a reflectance database that supports fast spatial queries.

[0160] Here is a specific example:

[0161] In a wetland willow forest monitoring scenario, the system deployed 15 ground verification points at 20-meter intervals. A 3D laser scanner at each node scanned the canopy at a pulse rate of 1200 times per second, measuring height values ​​ranging from 2.8 to 6.5 meters, gap density from 0.10 to 0.28, and surface curvature from 0.03 to 0.12. Drones simultaneously collected multispectral data. Through timestamp alignment and UTM coordinate projection, the spatial matching error between the spectral sampling points and the ground morphological parameters was controlled within 0.05 meters. Coordinates X120.5, Y85.3, and Z4.2 were bound to a height of 4.2 meters, a gap density of 0.15, a curvature of 0.08, and spectral reflectance values ​​of 0.62 and 0.58, forming a multidimensional data unit. Finally, a reflectance database was generated with gridded storage at a resolution of 0.05 meters, supporting rapid retrieval of mid-canopy data within an input elevation range of 4.0 to 4.5 meters. It showed that the light energy absorption value in the area with a gap density of 0.15 to 0.20 increased by 25%, accurately locating photosynthetic efficiency hotspots.

[0162] This solution achieves precise binding of canopy physical properties and spectral reflectance through high-precision three-dimensional morphological parameter acquisition at ground-based verification points and spatiotemporal calibration of air-ground data. Gridded storage and index optimization improve data retrieval efficiency, providing a highly consistent multi-source database for ecological analysis of vertical profiles of vegetation canopies and significantly enhancing the reliability of inversion of photosynthesis characteristic parameters in complex environments.

[0163] In some embodiments, extracting the signal intensity change rate of spectral signals at different levels in the reflectance database as the penetration level changes in the vertical section, and establishing a corresponding relationship between the signal intensity change rate and the light energy absorption value in the photosynthesis characteristic parameter, includes:

[0164] 701. Extracting spectral signal intensity values ​​of adjacent penetration levels at the same spatial coordinate point from the reflection database, and calculating a ratio of the spectral signal intensity value of a subsequent penetration level to that of a previous level as the inter-level signal intensity change rate;

[0165] The inter-level signal intensity change rate refers to the ratio of the spectral signal intensities of adjacent penetration levels at the same spatial coordinate point. It is used to quantify the degree of attenuation of light waves during vertical penetration. For example, if the signal intensity in the middle layer is 800 and the top layer is 1000, the change rate is 0.8.

[0166] In an embodiment of the present application, the spectral signal intensity values ​​of adjacent penetration levels are queried from the reflection database according to the spatial coordinates. For example, for a coordinate point, the spectral signal intensity value of the top penetration level is extracted as 1200 units, the middle penetration level is 900 units, and the bottom penetration level is 720 units. The signal intensity ratio of the middle penetration level to the top level is calculated to be 900 / 1200=0.75, and the ratio of the bottom level to the middle level is 720 / 900=0.8. Traverse all spatial coordinate points, calculate the ratio of the spectral signal intensity value of the next penetration level to the previous level, and generate a signal intensity change rate matrix between levels. This process optimizes query efficiency through database indexing to ensure that data of more than 100,000 coordinate points are processed per second.

[0167] 702. Based on the inter-layer signal strength change rate, calculate the product of the inter-layer signal strength change rates of all upper layers from the top to the bottom of the canopy layer in order of penetration layer to obtain a cumulative signal strength change rate;

[0168] The cumulative signal intensity change rate is the product of the signal intensity change rates of all layers from the top to the bottom of the canopy, reflecting the total attenuation of light waves penetrating the entire canopy. For example, if the change rate from the top to the middle layer is 0.8 and from the middle layer to the bottom layer is 0.7, the cumulative change rate is 0.8 × 0.7 = 0.56.

[0169] In an embodiment of the present application, the signal intensity change rate data between layers are read layer by layer from the top layer to the bottom layer in the order of penetration layers. For each spatial coordinate point, the cumulative signal intensity change rate is generated by multiplying the signal intensity change rates of all upper layers from the top to the bottom of the canopy layer in the order of penetration layers. For example, the change rate from the top layer to the middle layer of a certain coordinate point is 0.75, and from the middle layer to the bottom layer is 0.8, then the cumulative change rate is 0.75×0.8=0.6. A parallel computing framework is used to accelerate large-scale data operations. For example, a GPU is used to simultaneously calculate millions of coordinate points to generate a three-dimensional distribution map of the cumulative change rate. This distribution map reflects the overall attenuation characteristics of light waves when they penetrate the canopy.

[0170] 703. Using a preset experimental calibration method, obtain the actual absorption values ​​of light energy per unit area at different penetration levels in the vegetation canopy corresponding to the ground verification point using a light intensity attenuation measurement device;

[0171] The light intensity attenuation measurement device is a device deployed at a ground verification point. It uses a quantum sensor to measure the actual light energy absorption value at different penetration levels in μmol / m² / s.

[0172] In an embodiment of the present application, a light intensity attenuation measuring device, such as a quantum sensor array, is deployed in the vegetation canopy corresponding to the ground verification point through a preset experimental calibration method. The incident light intensity at the top of the canopy, the penetrating light intensity in the middle layer, and the penetrating light intensity in the bottom layer are measured in the calibration area. For example, the incident light intensity at the top layer is 2000μmol / m² / s, the penetrating light intensity in the middle layer is 1200μmol / m² / s, and the penetrating light intensity in the bottom layer is 960μmol / m² / s. The unit area light energy absorption value is calculated based on the actual absorption value of the unit area light energy absorbed by the different penetration levels, which is the difference between the incident and penetrating light intensities. The absorption value of the middle layer is 2000-1200=800μmol / m² / s, and that of the bottom layer is 1200-960=240μmol / m² / s. The statistical significance of the experimental data is ensured by taking the average value through repeated measurements.

[0173] 704. Perform exponential relationship fitting on the cumulative signal intensity change rate and the measured light energy absorption value in order of penetration levels, and output an exponential relationship fitting result;

[0174] Exponential relationship fitting refers to establishing a mathematical relationship between the cumulative change rate X and the measured light energy absorption value Y through a nonlinear regression model, usually in the form of Y=a×e^(bX).

[0175] In an embodiment of the present application, the cumulative signal intensity change rate and the measured light energy absorption value are aligned in the order of penetration level and input into a nonlinear regression model. The Levenberg-Marquardt algorithm is used to fit the exponential function Y=a×e^(bX), where X is the cumulative change rate and Y is the light energy absorption value. For example, the input data pair 0.6,800, 0.48, 1000 is fitted to obtain Y=2500×e^(-1.5X), and the coefficient of determination R²=0.97. The exponential relationship fitting result is output, and the generalization ability of the model is evaluated by the cross-validation method, for example, dividing 80% of the data into 80% for training and 20% for testing to ensure that the prediction error is ≤5%.

[0176] 705. Based on the exponential relationship fitting result, generate a corresponding mapping table of the cumulative signal intensity change rate and the light energy absorption value as the corresponding relationship between the signal intensity change rate and the light energy absorption value in the photosynthesis characteristic parameter.

[0177] The corresponding mapping table is a lookup table that stores the relationship between the cumulative signal intensity change rate and the light energy absorption value, and is used to quickly convert the spectral signal into the physical absorption value.

[0178] In an embodiment of the present application, based on the exponential relationship fitting result, a corresponding mapping table of the cumulative signal intensity change rate and the light energy absorption value is generated. For example, the cumulative change rate is divided into intervals of 0.01, and the corresponding light energy absorption value is calculated. When the cumulative signal intensity change rate is 0.6, the light energy absorption value is 2500×e^(-1.5×0.6)=1015μmol / m² / s. The mapping table is stored in a hash table data structure, with the key being the change rate (precision 0.01) and the value being the absorption value. For change rates that are not exactly matched, a linear interpolation algorithm is used to estimate the absorption value. For example, a change rate of 0.605 is interpolated and calculated as 0.60 corresponding to 1015, 0.61 corresponding to 990, and 0.605 corresponding to 1002.5. Query efficiency is optimized, supporting real-time conversions at the million level per second as the corresponding relationship between the signal intensity change rate and the light energy absorption value in the photosynthesis characteristic parameter.

[0179] Here's a specific example:

[0180] In a tropical rainforest ecological restoration project, the system monitored the banyan tree canopy in a degraded area. Step 701 extracted the signal intensity of three penetration layers at a given coordinate point: 1800 units for the top layer, 1260 units for the middle layer, and 882 units for the bottom layer. The middle / top layer change ratio was calculated to be 0.7, and the bottom / middle layer change ratio was calculated to be 0.7, resulting in a cumulative signal intensity change rate of 0.7 × 0.7 = 0.49. In step 703, the light attenuation measurement device measured the incident light intensity at the canopy top as 2200 μmol / m² / s, the middle layer penetration intensity as 1320 μmol / m² / s, and the bottom layer penetration intensity as 924 μmol / m² / s. These correspond to absorption values ​​of 880 μmol / m² / s for the middle layer and 396 μmol / m² / s for the bottom layer. Step 704 used nonlinear regression fitting to obtain an exponential relationship, Y = 2400 × e^(-1.8X), with a coefficient of determination (R²) of 0.98, indicating a highly reliable model. Step 705 generates a mapping table. A cumulative rate of change of 0.49 corresponds to a light energy absorption value of 2400×e^(-1.8×0.49)=2400×0.402≈965μmol / m² / s. Substituting this value into a three-dimensional map analysis reveals that the actual light energy utilization rate in the middle layers of the degraded area has increased by 28% compared to the traditional linear model, and the assessment error has been reduced from 25% to 4%. Based on this, the project team precisely located three areas of insufficient photosynthetic efficiency and implemented targeted forest gap thinning and soil improvement. Six months later, monitoring showed an average increase of 35% in light energy absorption in the target areas, validating the effectiveness of this approach in complex canopy ecological restoration.

[0181] This solution establishes an exponential mapping relationship for vertical light energy absorption through physical modeling and experimental calibration of inter-layer signal attenuation, breaking through the accuracy limitations of traditional linear models. The continuous multiplication of the cumulative signal intensity change rate quantifies the overall attenuation law of light waves penetrating the canopy. Combined with the exponential relationship fitted by ground-based measured data, it significantly improves the physical consistency between the light energy absorption value and the spectral signal. The generated dynamic mapping table supports real-time and efficient conversion, achieving three-dimensional accurate analysis of the photosynthetic efficiency of the vegetation canopy with a lower error rate than traditional methods. This technology provides a targeted decision-making basis for scenarios such as degraded ecological restoration and high-density agricultural planting. For example, it can accurately identify areas with weak photosynthesis and guide intervention measures such as thinning and irrigation, ultimately promoting the scientific and refined management of ecological and agricultural production, and contributing to the realization of sustainable development goals.

[0182] Figure 2 The present invention provides a schematic diagram of a system for evaluating the ecological environment of garden plants. Figure 2 As shown, the system includes:

[0183] The acquisition module 21 is used to perform layered scanning of the garden plant community using a flying device, generate a flight trajectory based on the three-dimensional density distribution of the vegetation canopy, and simultaneously acquire multispectral reflectance data including photosynthesis characteristic parameters;

[0184] The acquisition module 21 is further configured to arrange a plurality of ground verification points within the scanning area, collect three-dimensional morphological parameters of the vegetation canopy in combination with the spatiotemporal coverage characteristics of the flight trajectory, perform spatiotemporal calibration and matching of the three-dimensional morphological parameters with the multispectral reflectance data, and construct a reflectance database;

[0185] A generation module 22 is configured to identify high-reflection interference areas based on the detected vegetation canopy surface reflectance characteristics, and to apply multi-angle polarization compensation to the high-reflection interference areas by adjusting the polarization angle configuration to generate multispectral reflectance data after reflection suppression;

[0186] A calculation module 23 is configured to perform a layered reflectance weighted calculation on the multispectral reflectance data after reflection suppression based on the penetration level identifier in the reflectance database, and to associate the mapping relationship between the attenuation gradient of different multispectral reflectance data in the vertical section of the vegetation canopy and the photosynthesis characteristic parameters to generate a three-dimensional distribution map of the photosynthetic efficiency of the plant community;

[0187] The correction module 24 is used to control the flying device to perform a secondary scan based on the abnormal photosynthetic efficiency area in the three-dimensional distribution map, and to correct the assessment error caused by the delayed root water absorption in the three-dimensional distribution map by fusing the multispectral reflectance data of the secondary scan with the soil moisture parameters of the ground verification point, and output the ecological assessment result.

[0188] Figure 2The said garden plant environment ecological assessment system can be executed Figure 1 The implementation principle and technical effects of the garden plant environment ecological assessment method described in the illustrated embodiment will not be repeated here. The specific manner in which each module and unit performs operations in the garden plant environment ecological assessment system in the above embodiment has been described in detail in the embodiment of the method and will not be elaborated on here.

[0189] In one possible design, Figure 2 The garden plant environment ecological assessment system of the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0190] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0191] The processing component 32 is used for the above Figure 1 The embodiment provides a method for evaluating the ecological environment of garden plants.

[0192] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above method.

[0193] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0194] Of course, a computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0195] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0196] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0197] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0198] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The embodiment shown is a method for evaluating the ecological environment of garden plants.

[0199] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0200] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0201] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0202] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for evaluating the environmental ecology of garden plants, characterized in that: include: The flying device is used to perform layered scanning of garden plant communities, generate flight trajectories based on the three-dimensional density distribution of the vegetation canopy, and simultaneously collect multispectral reflectance data containing photosynthesis characteristic parameters; Arrange multiple ground verification points in the scanning area, collect three-dimensional morphological parameters of the vegetation canopy in combination with the spatiotemporal coverage characteristics of the flight trajectory, perform spatiotemporal calibration and matching of the three-dimensional morphological parameters with the multispectral reflectance data, and construct a reflectance database; Based on the detected vegetation canopy surface reflection characteristics, high-reflection interference areas are identified, and multi-angle polarization compensation is applied to the high-reflection interference areas by adjusting the polarization angle configuration to generate multispectral reflectance data after reflection suppression; Based on the penetration level identifier in the reflectance database, a layered reflectance weighted calculation is performed on the multispectral reflectance data after reflection suppression, and a mapping relationship between the attenuation gradient of different multispectral reflectance data in the vertical section of the vegetation canopy and the photosynthesis characteristic parameters is associated to generate a three-dimensional distribution map of the photosynthetic efficiency of the plant community; Controlling the flying device to perform a secondary scan based on the abnormal photosynthetic efficiency area in the three-dimensional distribution map, and correcting the assessment error caused by delayed root water absorption in the three-dimensional distribution map by fusing the multispectral reflectance data of the secondary scan with the soil moisture parameters of the ground verification point, and outputting an ecological assessment result; Based on the abnormal photosynthetic efficiency area in the three-dimensional distribution map, the flying device is controlled to perform a secondary scan. By fusing the multispectral reflectance data of the secondary scan with the soil moisture parameters of the ground verification point, the assessment error caused by the delayed root water absorption in the three-dimensional distribution map is corrected, and the ecological assessment results are output, including: Extracting continuous spatial regions where the photosynthetic efficiency value is lower or higher than a preset normal range from the three-dimensional distribution map, and marking them as abnormal photosynthetic efficiency regions; adjusting the flight altitude and scanning path density of the flying device according to the spatial distribution range of the photosynthetic efficiency abnormal area, generating a secondary scanning trajectory covering the photosynthetic efficiency abnormal area, and controlling the flying device to perform a secondary scan according to the secondary scanning trajectory; Extracting multispectral reflectance data of the photosynthetic efficiency abnormal area in the secondary scan, and obtaining the spectral reflectance value of each spatial coordinate point in the abnormal area; Extracting a soil moisture parameter corresponding to the spatial coordinates of the abnormal area from the ground verification point, the soil moisture parameter being a soil moisture content measurement value of the ground verification point within a preset time window; and performing a one-to-one binding between the soil moisture content measurement value and the spectral reflectance value of the abnormal area according to the spatial coordinates; According to the measured value of soil moisture content, the spectral reflectance value is compensated for moisture absorption delay, and the compensated spectral reflectance value replaces the photosynthetic efficiency value of the corresponding photosynthetic efficiency abnormal area in the three-dimensional distribution map. Based on the replaced photosynthetic efficiency value, the photosynthetic efficiency distribution of all spatial coordinate points in the three-dimensional distribution map is recalculated, and an ecological assessment result is generated.

2. The method according to claim 1, characterized in that Based on the penetration level identifier in the reflection database, performing a layered reflectance weighted calculation on the spectral data after reflection suppression, including: Extracting layer segmentation information corresponding to the penetration layer identifier from the reflection database, wherein the layer segmentation information includes a penetration ability value and a layer thickness value corresponding to each layer in the vegetation canopy; According to the penetration value, weighting is performed on the spectral signal of each level in the spectral data after reflection suppression; Based on the layer thickness values, the spectral signals after weighting are superimposed and calculated in layer order to obtain the weighted reflectivity result of each layer in the vertical direction.

3. The method according to claim 2, characterized in that Based on the reflectance weighted results, the mapping relationship between the attenuation gradient of different spectral data in the vertical profile of the vegetation canopy and the photosynthesis characteristic parameters is associated to generate a three-dimensional distribution map of the photosynthetic efficiency of the plant community, including: Extracting the signal intensity change rate of the spectral signals at different levels in the reflectance database in the vertical section as the penetration level changes, and establishing a corresponding relationship between the signal intensity change rate and the light energy absorption value in the photosynthesis characteristic parameter; According to the corresponding relationship, converting the spectral signal of each level in the reflectivity weighted result into a corresponding light energy absorption value; Based on the spatial position of the hierarchical segmentation information, the light energy absorption values ​​are mapped into a three-dimensional space in a hierarchical order to form a three-dimensional distribution map representing the distribution of photosynthetic efficiency of the plant community.

4. The method according to claim 1, wherein Based on the detected vegetation canopy surface reflection characteristics, high-reflection interference areas are identified. By adjusting the polarization angle configuration, multi-angle polarization compensation is applied to the high-reflection interference areas to generate multispectral reflectance data after reflection suppression, including: Extracting the reflection intensity of the vegetation canopy surface within a preset visible light band based on the spectral reflectance value of each spatial coordinate point in the multispectral reflectance data, and marking the spatial coordinate points where the reflection intensity exceeds a preset threshold as high reflection interference areas; For the high-reflection interference area, controlling the polarization filter assembly of the flying device to sequentially switch to a plurality of different polarization angle combinations; under each polarization angle combination, recollecting spectral reflectance values ​​in the high-reflection interference area to obtain multiple sets of spectral reflectance data with differentiated polarization angles; Superimposing the spectral reflectance values ​​of the same spatial coordinate point in the multiple sets of polarization angle-differentiated spectral reflectance data, and retaining the minimum intensity value of the superimposed spectral reflectance values ​​as the spectral reflectance value of the spatial coordinate point after reflection suppression; The spectral reflectance values ​​after reflection suppression are combined with the original spectral reflectance values ​​of the areas not marked as high reflection interference according to spatial coordinates to generate multi-spectral reflectance data after reflection suppression.

5. The method according to claim 1, characterized in that The spatiotemporal coverage features include a timestamp sequence and a spatial coordinate sequence; Arrange multiple ground verification points in the scanning area, collect three-dimensional morphological parameters of the vegetation canopy in combination with the spatiotemporal coverage characteristics of the flight trajectory, perform spatiotemporal calibration and matching of the three-dimensional morphological parameters with the multispectral reflectance data, and construct a reflectance database, including: Multiple ground verification points are arranged at a preset density within the scanning area, each of which is equipped with a three-dimensional laser scanning device. The three-dimensional laser scanning device measures the depth detection data of the vegetation canopy by emitting laser pulses and receiving reflected signals; the height value, gap density and surface curvature value of each measurement point are calculated based on the depth detection data to form a three-dimensional morphological parameter set of the ground verification point; Extracting the acquisition timestamp and spatial coordinates of each spectral sampling point in the multispectral reflectance data, and establishing corresponding position marks between the spatial coordinates of the spectral sampling point and the canopy height value in the three-dimensional morphological parameters based on the timestamp sequence and spatial coordinate sequence of the flight trajectory; Binding the canopy height value, canopy gap density value, and canopy surface curvature value in the three-dimensional morphological parameters to the spectral reflectance value in the multispectral reflectance data according to the corresponding position marks to obtain a bound data unit; Based on the preset binding rules, the bound data units are stored in the grid distribution order of the spatial coordinates to generate a reflection database.

6. The method according to claim 3, characterized in that Extracting the signal intensity change rate of the spectral signals at different levels in the reflectance database in the vertical section as the penetration level changes, and establishing a corresponding relationship between the signal intensity change rate and the light energy absorption value in the photosynthesis characteristic parameter, including: Extracting the spectral signal intensity values ​​of adjacent penetration levels at the same spatial coordinate point from the reflection database, and calculating the ratio of the spectral signal intensity value of the subsequent penetration level to the previous level as the inter-level signal intensity change rate; Based on the inter-layer signal strength change rate, the product of the inter-layer signal strength change rates of all upper layers from the top to the bottom of the canopy is calculated layer by layer in the order of penetration levels to obtain the cumulative signal strength change rate; By using a preset experimental calibration method, in the vegetation canopy corresponding to the ground verification point, a light intensity attenuation measuring device is used to obtain the actual absorption value of light energy per unit area at different penetration levels; Performing exponential fitting on the cumulative signal intensity change rate and the measured light energy absorption value in the order of penetration levels, and outputting the exponential relationship fitting result; Based on the exponential relationship fitting result, a corresponding mapping table of the cumulative signal intensity change rate and the light energy absorption value is generated as the corresponding relationship between the signal intensity change rate and the light energy absorption value in the photosynthesis characteristic parameter.

7. A garden plant environment ecological assessment system for executing a garden plant environment ecological assessment method according to any one of claims 1 to 6, characterized in that: include: The acquisition module is used to perform layered scanning of garden plant communities using a flying device, generate flight trajectories based on the three-dimensional density distribution of the vegetation canopy, and simultaneously collect multispectral reflectance data including photosynthesis characteristic parameters; The acquisition module is further configured to arrange a plurality of ground verification points within the scanning area, collect three-dimensional morphological parameters of the vegetation canopy in combination with the spatiotemporal coverage characteristics of the flight trajectory, perform spatiotemporal calibration and matching of the three-dimensional morphological parameters with the multispectral reflectance data, and construct a reflectance database; A generation module is used to identify high-reflection interference areas based on the detected vegetation canopy surface reflection characteristics, and to apply multi-angle polarization compensation to the high-reflection interference areas by adjusting the polarization angle configuration to generate multispectral reflectance data after reflection suppression; a calculation module for performing a layered reflectance weighted calculation on the multispectral reflectance data after reflection suppression based on the penetration level identifier in the reflectance database, and correlating the mapping relationship between the attenuation gradient of different multispectral reflectance data in the vertical section of the vegetation canopy and the photosynthesis characteristic parameters to generate a three-dimensional distribution map of the photosynthetic efficiency of the plant community; The correction module is used to control the flying device to perform a secondary scan based on the abnormal photosynthetic efficiency area in the three-dimensional distribution map, and to correct the assessment error caused by the delayed root water absorption in the three-dimensional distribution map by fusing the multispectral reflectance data of the secondary scan with the soil moisture parameters of the ground verification point, and output the ecological assessment result.

8. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a garden plant environmental ecological assessment method as described in any one of claims 1 to 6.

9. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, a garden plant environmental ecological assessment method according to any one of claims 1 to 6 is implemented.

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