Tea lesser leafhopper ecological prevention and control method based on tea garden landscape habitat optimized layout

Abstract

The invention discloses a tea lesser leafhopper ecological prevention and control method based on tea garden landscape habitat optimized layout, and relates to the technical field of tea garden planting, the method comprises the following steps: collecting terrain and vegetation canopy data of a target tea garden, and generating an air flow path diagram in the tea garden in combination with regional climate wind data; planting repellent plants and arranging a repellent release device in an upwind area of the air flow path diagram to form a dynamic smell barrier; planting trapping plants and arranging traps in a downwind area and a quiet wind area of the air flow path diagram to form trapping prevention points; analyzing the number and distribution condition of tea lesser leafhoppers in the tea garden, selecting key nodes in the tea garden, and constructing natural enemy conservation facilities in a targeted manner; evaluating the ecological prevention and control effect of the tea lesser leafhoppers in the tea garden by taking seasons as periods, and adjusting ecological prevention and control measures according to evaluation results. According to the method, the ecological prevention and control of the pests are realized by formulating an air circulation path diagram and taking ecological prevention and control measures.

Description

Technical Field

[0001] This invention relates to the field of tea garden planting technology, and in particular to an ecological control method for the tea green leafhopper based on the optimized layout of tea garden landscape habitat. Background Technology

[0002] The tea green leafhopper is a major piercing-sucking pest that damages the tender shoots of tea trees in tea gardens. Both adults and nymphs feed on the sap of buds and leaves, directly causing the veins of the tea leaves to turn red and the leaf edges to scorch. In severe cases, the entire tea bush appears "burnt," resulting not only in significant yield reduction but also in a bitter taste, increased fragmentation, and severely deteriorated quality of the finished tea. For a long time, control has relied mainly on chemical pesticides, but this method has inherent drawbacks: first, the control effect is limited because the insect is active in the lower and middle parts of the tea bush, making it difficult to achieve complete coverage; second, it leads to pesticide resistance problems, with long-term use of a single pesticide rapidly increasing resistance, resulting in decreased efficacy and increased pesticide costs; third, it disrupts the ecological balance, as broad-spectrum pesticides kill not only the pest but also a large number of its natural enemies (such as spiders and parasitic wasps), disrupting the ecological constraints of the tea garden and potentially leading to a more rampant outbreak of the pest.

[0003] Therefore, current prevention and control practices are urgently shifting towards green integrated pest management centered on ecological regulation. By restoring and strengthening the natural biological constraints and physical barriers within the system, this approach can not only directly replace chemical pesticides, effectively avoiding the ecological balance collapse and pest resurgence caused by excessive pesticide residues, soaring pest resistance, and accidental killing of beneficial natural enemies, but also proactively build a long-term defense line. By scientifically planting nectar-producing plants or functional plants between rows in tea gardens, natural enemies of the tea green leafhopper can be continuously provided with habitats, breeding grounds, and overwintering food, thereby significantly increasing the abundance and stability of the natural enemy population and naturally suppressing the number of tea green leafhoppers below the level of economic damage. At the same time, reasonable vegetation cover can also regulate the microclimate of the tea garden, retain moisture and warmth, and enhance soil fertility, thereby improving the overall health and resilience of tea trees.

[0004] However, existing technologies require precise planning of density, location, and timing based on the specific conditions of tea gardens, which places high demands on the knowledge and management skills of users. The current promotion model, which relies on project demonstrations, often lacks a long-term mechanism, which can easily lead to significant results at demonstration sites but difficulty in widespread adoption. Therefore, there is an urgent need to propose an ecological control method for the tea green leafhopper based on the optimized layout of tea garden landscape habitats. Summary of the Invention

[0005] This invention provides an ecological control method for the tea green leafhopper based on the optimized layout of tea garden landscape habitat. According to the topographic data and local climate data of the tea garden, an air circulation path map of the tea garden is formulated, and targeted ecological control measures are taken to solve the technical problem that the existing technology relies on the user's own knowledge and management level, which makes it unable to meet the needs of large areas.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: This invention provides an ecological control method for the tea green leafhopper based on optimized layout of tea garden landscape habitat, comprising: S1, collect topographic and canopy data of the target tea garden, and generate an airflow path map inside the tea garden by combining regional climate and wind data; S2, plant repellent plants and install repellent release devices in the upwind area of ​​the air flow path map to form a dynamic odor barrier. S3, plant attracting plants and set up traps in the downwind and calm areas of the air flow path map to form attracting and control points; S4. Analyze the number and distribution of tea green leafhoppers in the tea garden, and select key nodes in the tea garden to construct natural enemy conservation facilities. S5 assesses the ecological control effect of the tea green leafhopper in tea gardens on a seasonal basis and adjusts the ecological control measures based on the assessment results.

[0007] The beneficial effects of the technical solution provided by this invention include at least the following: The method of this invention facilitates the precise deployment of subsequent prevention and control measures by constructing a three-dimensional digital model and simulating airflow. By forming an integrated ecological prevention and control model at key inlets, channels and convergence points of airflow, it enables the concentrated use of prevention and control resources and significantly improves prevention and control efficiency.

[0008] The method of this invention forms a synergistic ecosystem of repellent plants, repellent agents, attractant plants, and natural enemy conservation. It increases biodiversity by planting functional plants and rapidly enhances local natural enemy populations by remotely sensing targeted deployment of facilities, thus promoting the transformation of tea gardens from relying on pesticide management to relying on internal ecological management. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a flowchart of an ecological control method for the tea green leafhopper based on the optimized layout of tea garden landscape habitat provided in an embodiment of the present invention. Detailed Implementation

[0011] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0012] Example An ecological control method for the tea green leafhopper based on the optimized layout of tea garden landscape habitat.

[0013] Please refer to Figure 1 This is a flowchart of an ecological control method for the tea green leafhopper based on the optimized layout of tea garden landscape habitat provided in an embodiment of the present invention.

[0014] S1, collect topographic and canopy data of the target tea garden, and generate an airflow path map inside the tea garden by combining regional climate and wind data; S101, based on the topographic data and vegetation canopy data of the tea garden, constructs a three-dimensional digital model of the airflow path within the tea garden; It should be noted that tea garden topographic data and vegetation canopy data can be obtained through aerial surveys using drones equipped with lidar.

[0015] The specific construction process of the 3D tea garden digital model is as follows: the acquired tea garden point cloud data is separated into ground point cloud and vegetation point cloud data, and a digital elevation model (DEM) and a digital surface model (DSM) are generated based on the ground point cloud and vegetation point cloud data, respectively. The canopy height model (CHM) representing the vegetation height is obtained by performing a rasterization step of subtracting the DEM from the DSM. The terrain surface is defined according to the DEM and the geometric features of the vegetation area are defined according to the canopy height model to construct an initial 3D geometric model. The computational domain of the initial 3D geometric model is meshed, and the corresponding surface roughness attributes are assigned to different areas based on the canopy height model to generate a 3D tea garden digital model that can be used for computational fluid dynamics calculations.

[0016] S102 uses climate wind data as input to perform fluid dynamics simulation calculations on a three-dimensional digital model of a tea garden, and solves the airflow field at the height of the tea garden canopy. It should be noted that the specific process of the simulation calculation is as follows: the climate wind data is defined as the inlet boundary condition of the computational domain of the three-dimensional tea garden digital model, and the model surface is defined as a wall boundary with specific roughness properties. By spatially discretizing the computational domain of the three-dimensional tea garden digital model, a computational grid for fluid dynamics solution is generated. The k-ε turbulence model and SIMPLE solution algorithm are used to iteratively solve the Navier-Stokes equations on the computational grid until a stable airflow field solution is obtained. The wind speed vector and streamline data of the horizontal profile at a height of 1.5 meters to 2.0 meters above the ground are extracted as the airflow field at the height of the tea garden canopy.

[0017] Climate wind data includes prevailing wind direction and average wind speed information, with spring and summer weather data from the tea garden area being preferred. It should be noted that climate wind data can be obtained from local meteorological bureaus, and the data should include wind direction, wind speed, and frequency data for at least the past 3-5 years. Taking Fujian Province as an example, spring and summer are the peak seasons for the number of tea green leafhoppers, and selecting climate wind data from this season can improve the efficiency of ecological control.

[0018] S103, extract the calculation results of the airflow field and generate an internal airflow path map of the tea garden that includes the airflow velocity distribution and the main path.

[0019] It should be noted that the airflow path diagram includes characteristic areas such as high-speed airflow channels, calm airflow zones, and airflow vortex zones.

[0020] S2, plant repellent plants and install repellent release devices in the upwind area of ​​the air flow path map to form a dynamic odor barrier. S201, determine the barrier deployment points in the upwind main wind tunnel inlet area of ​​the air flow path map, and plant repellent plants at the points to form a plant substrate zone; Repellent plants are planted in strips, with the angle between the extension direction of the repellent plant strip and the prevailing wind direction of the airflow channel being less than 45 degrees. The row spacing of the repellent plant strip is 15-25cm, the plant spacing is 10-20cm, and the width of the repellent plant strip is 0.8m-1.5m. It should be noted that repellent plants can include marigolds, pyrethrum, lavender, basil, and artemisia, and can be planted in multiple strips, with a spacing of 3-8 meters between adjacent planting strips.

[0021] S202, a repellent release device is installed in the plant substrate zone, and the release parameters of the repellent are controlled according to a preset regulation strategy; The repellent release devices are installed at intervals of 15-25m in the plant substrate zone, and the release port of the repellent release device is installed at the same height as the tea tree canopy.

[0022] It should be noted that the repellent release device includes a storage tank, a control valve, an IoT communication module, and a power module.

[0023] The preset control strategies include time-based periodic release strategies and data-based trigger release strategies; The triggering strategy for the periodic release strategy is to set the release mode of the avoidant to be periodically started and stopped during two periods each day, in the morning and evening. The triggering conditions for the release strategy are: when the climate and wind data are suitable for pest migration, the repellent is continuously released. The specific triggering conditions for the release strategy include wind direction, wind speed, and temperature.

[0024] It should be noted that, taking Fujian Province as an example, the periodic release strategy can be set to two time periods each day: 05:00-08:00 and 17:00-20:00.

[0025] When two of the wind direction, wind speed, and temperature data reach the trigger threshold, the single release time is extended or the number of releases per unit time is increased, for example, by increasing the release time and release range; when all three data points reach the trigger threshold of the release strategy, the release device enters continuous release mode. S3, plant attracting plants and set up traps in the downwind and calm areas of the air flow path map to form attracting and control points; S301, determine multiple trapping and prevention deployment points in the downwind and calm areas of the airflow path map; The locations for trapping and prevention are determined based on the intersection of the airflow settling zone and the main airflow path in the airflow path diagram. It should be noted that the specific process for determining the locations of the trapping and prevention deployment points is as follows: wind speed field and turbulence field data of the tea garden are obtained through computational fluid dynamics simulation. Based on a preset wind speed ratio threshold (the average wind speed in the area is not higher than 30% of the wind speed at the upwind reference point), continuous low-speed areas are identified from the wind speed field and marked as airflow settling zones. Based on the divergence value and turbulence kinetic energy value of the wind speed vector, areas where airflow converges and mixes strongly are identified from the wind field data and marked as wind channel intersection areas. The spatial geographic information of the airflow settling zones and wind channel intersection areas is output to generate multiple prevention and control deployment points.

[0026] S302, Planting attracting plants at the designated locations to form attracting patches, and setting up trapping devices within the patches; The attracting plants are planted in small patches, with each patch measuring 2-4 square meters. 2 .

[0027] It should be noted that the attracting plants can be large-leaved privet or rosemary; the trapping devices can be one or more of the following: yellow sticky insect boards, attractant traps, or light traps, which can be adjusted according to seasonal changes or the population density of tea green leafhoppers in the tea garden.

[0028] The locations for trapping and prevention are dynamically optimized based on the prevailing wind direction data of the target season and the target prevention and control strategy; The dynamic optimization process is as follows: For the target season, calculate the comprehensive point attraction efficiency value for each prevention and control deployment point according to the prevailing wind direction and prevention and control strategy of that season, and sort and classify all prevention and control deployment points according to the point attraction efficiency value. The point attraction efficiency value is obtained by the ratio of wind direction matching coefficient, target strategy matching degree, and point feature coefficient; It should be noted that the specific calculation process is as follows: Efficiency value = wind direction matching coefficient × strategic target matching degree × location characteristic coefficient; the wind direction matching coefficient is determined based on the relative position of the location in the prevailing wind field of the current season. The location is divided into upwind key entry point (1.2), internal diffusion path point (1.0), and leeward or irrelevant point (0.6); the target strategy matching degree is determined based on the degree of matching between the spatial location of the location and the tea green leafhopper control strategy of the current season, and is divided into complete matching (1.0), partial matching (0.7), and non-matching (0.4); the location characteristic coefficient is set differently depending on whether the location is located in the airflow settling zone or wind channel intersection area. When a location is classified as an airflow settling zone or wind channel intersection area, its location characteristic coefficient is 1.2. When a location is classified as a general area, its location characteristic coefficient is 1.0.

[0029] Taking Fujian province in summer as an example, assuming that point A is located on the northwest boundary of a tea garden, and the airflow path map determines that this point is in an airflow settling zone, its main design function is to intercept externally migrating insect sources. The trapping efficiency value of this point is calculated as follows: Since it is located on the northwest leeward boundary, it is a leeward-side point, and the wind direction matching coefficient is taken as 0.6; since its main function is peripheral interception, it partially matches the summer control strategy of the tea garden, and the target strategy matching degree is taken as 0.7; since this point is in an airflow settling zone, the point characteristic coefficient is taken as 1.2; therefore, the theoretical interception efficiency value E (summer) of point A in summer is... A =0.6×0.7×1.2=0.504. Assume another point B located in the core area of ​​the tea garden, at the intersection of wind channels, with a wind direction matching coefficient of 1.0 (airflow path point), a target strategy matching degree of 1.0 (core internal control point), and a point characteristic coefficient of 1.2. Then its efficiency value E (Summer) B =1.0×1.0×1.2=1.2. The trapping efficiency value of point B is much higher than that of point A. Therefore, in summer optimization, point B will have a higher priority than point A.

[0030] Prevention and control deployment points are generated based on the attraction efficiency value, and are divided into core deployment points, backup deployment points, and recommended closure points according to the season.

[0031] It should be noted that, based on the point attraction efficiency, the top 30% of points with the highest efficiency values ​​are identified as core defense points, the bottom 30% of points are identified as recommended points to be closed, and the rest are backup defense points.

[0032] Targeted prevention and control strategies include peripheral interception strategies, internal control point strategies, and end-point governance strategies; It should be noted that the peripheral interception strategy is mainly applicable to the early spring season, and the upwind boundary of the tea garden is determined based on the prevailing wind direction in spring; the internal control point strategy is mainly applicable to the high-temperature breeding period from late spring to summer; and the end-of-pipe treatment strategy is mainly applicable to autumn, and the downwind boundary of the tea garden is determined based on the prevailing wind direction in autumn.

[0033] The perimeter interception strategy is designed to prevent overwintering adults from migrating into tea gardens from external hosts. It is suitable for the upwind boundary areas of the tea garden's internal airflow. Internal control point strategies are used to suppress the reproduction and spread of introduced populations within tea gardens, and are applicable to key points in the aggregation and spread of pests within tea gardens. End-of-pipe management strategies are used to reduce the number of late-stage adult insects migrating to overwintering hosts and are suitable for downwind edge areas of tea gardens with good airflow.

[0034] S4. Analyze the number and distribution of tea green leafhoppers in the tea garden, and select key nodes in the tea garden to construct natural enemy conservation facilities. S401 uses a drone equipped with a hyperspectral imaging device to collect hyperspectral image data of the tea green leafhopper in the tea garden. The collected hyperspectral image data stream is radiometrically corrected in real time, and a spectral feature map for pest stress is calculated and generated. The spectral feature map is input into a convolutional neural network model to perform pixel-level inference on the hyperspectral image data to obtain the pest stress probability value at each pixel location. It should be noted that the specific process of generating the spectral feature map is as follows: the system controls a hyperspectral imager to perform a flight scan of the tea garden along a preset route, simultaneously collecting geocoded hyperspectral image data streams; during the flight, real-time radiometric correction is performed on the collected hyperspectral image data streams, and a spectral index feature map for the tea green leafhopper pest stress is calculated and generated; the spectral index feature map is input into a convolutional neural network model for pixel-level inference, and the pest stress probability value for each pixel location is output; the pest stress probability value is combined with the geographic coordinates of the corresponding pixel and transmitted to the ground station via a data link.

[0035] The spectral feature map includes the plant senescence reflectance index, which indicates leaf senescence and pigment changes, and the normalized water index, which indicates changes in leaf water and cell structure. It should be noted that the spectral feature map extraction process is as follows: real-time radiometric correction is performed on the raw hyperspectral image data stream collected by the UAV, and the surface reflectance data is output to extract multiple predetermined key band reflectance values ​​in parallel (the preset bands include one or more combinations of 430nm, 500nm, 550nm, 670nm, 680nm, 750nm, 800nm, and 860nm to cover the characteristic spectral bands from visible light to near-infrared that are sensitive to vegetation stress); based on the extracted key band reflectance values, the spectral indices of two different types of tea green leafhopper damage are calculated, and corresponding spectral index layers are generated; the two spectral index layers are stacked to synthesize a multi-channel spectral feature map.

[0036] S402, based on the pest stress probability value of the tea green leafhopper distribution, calculate the coordinates of key target points in the tea garden where natural enemy conservation facilities need to be deployed. It should be noted that the selection rules for key target coordinates are as follows: the center of a continuous area where the stress level is higher than the first threshold in the pest stress probability value is set as the first-level target; and the area where the stress level is higher than the second threshold and is located downwind of the tea garden's prevailing wind is set as the second-level target.

[0037] S403, construct natural enemy conservation facilities for the tea green leafhopper at key target coordinates for ecological control.

[0038] It should be noted that the specific process of the natural enemy conservation facility is as follows: the pre-determined primary target point coordinates and secondary target point coordinates are imported into the mobile terminal and matched with the terrain of the tea garden. The first type of modular conservation facility is deployed at the primary target point coordinates of the tea garden, and the second type of modular conservation facility is deployed at the secondary target point coordinates.

[0039] The first type of modular conservation facility is an active intervention nest, a porous structure substrate used by the tea green leafhopper to provide habitats for its natural enemies. This substrate can release attractants that attract the tea green leafhopper's predators. The second type of modular conservation facility is an interception and propagation type ecological substrate, which includes: a composite porous structure for various insects to inhabit and a water-retaining cultivation substrate pre-embedded with repellent plant propagules. The repellent plant propagules can be pre-embedded with flowering plants such as marigolds and pyrethrum, which can both repel the tea green leafhopper and promote the conservation of its natural enemies.

[0040] The construction process of the convolutional neural network model is as follows: a convolutional neural network with an encoder-decoder structure is constructed, multi-channel feature images from the sample set are input into the network, deep features are extracted by the encoder, spatial details are restored by the decoder upsampling, the difference between the network output and the ground truth is supervised by a hybrid loss function, and the network parameters are iteratively optimized. It should be noted that the training data preparation process is as follows: Select the registered hyperspectral images of tea gardens and their corresponding ground truth maps of the tea green leafhopper damage and stress areas, perform radiometric correction and feature calculation on the hyperspectral images, and construct a sample set composed of multi-channel feature images and ground truth maps.

[0041] The hybrid loss function is a weighted sum of the binary cross-entropy loss and the Dice coefficient loss, and its expression is:

[0042] In the formula, Represents the loss function; The table shows the weighting coefficients, which are used to simultaneously optimize pixel-level classification accuracy and the overall shape integrity of the stressed region during training. This represents the binary cross-entropy loss; Dice coefficient loss.

[0043] The specific process of pixel-level inference is as follows: the convolutional neural network propagates the input data forward through a sliding window, and outputs a probability value representing the point as belonging to the tea green leafhopper damage stress area for each pixel, forming an initial probability map. By performing spatial stitching and smoothing filtering based on geographic coordinates on the initial probability map, the final tea garden pest stress probability distribution map is generated.

[0044] S5. The ecological control effect of the tea green leafhopper in the tea garden is evaluated on a seasonal basis, and the ecological control measures are adjusted according to the evaluation results. S501: Collect multidimensional data of tea gardens within a single seasonal cycle, and quantitatively evaluate the control effect of tea green leafhopper based on the multidimensional data; Quantitative assessment refers to comparing and analyzing multidimensional data with preset thresholds and historical data from the same period. It should be noted that the multidimensional data includes the distribution and number of tea green leafhopper populations, the species composition and number of natural enemies, the number of insects attracted per unit time at the trapping and control sites, the difference in insect population between the dynamic odor barrier area and the control area, environmental climate data, and the triggering frequency of the repellent release device.

[0045] S502, Based on the results of the quantitative assessment, generate an optimized adjustment plan for ecological control measures in the next cycle; It should be noted that the specific optimization process is as follows: based on the assessment results, locate the tea garden areas where the prevention and control effect has not met expectations and the specific ecological prevention and control facilities where the efficiency has not reached the target, and select adjustment plans to optimize the prevention and control shortcomings.

[0046] The adjustment plan includes: optimizing the configuration and varieties of functional plants, adjusting the deployment locations or release strategies of repellent release devices, and increasing, decreasing, or modifying the types and quantities of natural enemy conservation facilities.

[0047] S503, based on the optimization and adjustment plan, updates the air flow path map, ecological facility layout, and equipment operation strategy of the tea garden, and applies the updated parameters to the prevention and control practices of the next seasonal cycle.

[0048] It should be noted that if the assessment finds that the odor barrier is ineffective, the layout of the repellent plant strip should be optimized and the repellent release strategy should be adjusted; if the assessment finds that the trapping point is inefficient, the location should be re-optimized and the trapping plants or traps should be replaced; if the assessment finds that the natural enemy's pest control effect is insufficient, the density or type of conservation facilities should be increased; if the assessment finds that the airflow path has changed significantly, the airflow path map needs to be updated.

[0049] Updating the airflow path map refers to calibrating the input parameters of the computational fluid dynamics simulation based on the actual climate data and vegetation growth changes recorded in the current cycle, and generating a more accurate airflow path prediction map for the next cycle.

[0050] Furthermore, it should be noted that the present invention can be provided as a method, apparatus, or computer program product. Therefore, embodiments of the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, embodiments of the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code.

[0051] The embodiments of the present invention are described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0052] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing terminal equipment to cause a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0053] It should also be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0054] Finally, it should be noted that the above description represents a preferred embodiment of the present invention. It should be pointed out that although preferred embodiments have been described, those skilled in the art, once they understand the basic inventive concept of the present invention, can make various improvements and modifications without departing from the principles described herein. These improvements and modifications should also be considered within the scope of protection of the present invention. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the embodiments of the present invention.

Claims

1. A method for the ecological control of the tea green leafhopper based on the optimized layout of tea garden landscape habitat, characterized in that, include: S1, collect topographic and canopy data of the target tea garden, and generate an airflow path map inside the tea garden by combining regional climate and wind data; S2, plant repellent plants and install repellent release devices in the upwind area of ​​the air flow path map to form a dynamic odor barrier. S3, plant attracting plants and set up traps in the downwind and calm areas of the air flow path map to form attracting and control points; S4. Analyze the number and distribution of tea green leafhoppers in the tea garden, and select key nodes in the tea garden to construct natural enemy conservation facilities. S5 assesses the ecological control effect of the tea green leafhopper in tea gardens on a seasonal basis and adjusts the ecological control measures based on the assessment results.

2. The ecological control method for the tea green leafhopper based on the optimized layout of tea garden landscape habitat as described in claim 1, characterized in that, S1 collects topographic and canopy data of the target tea garden and combines it with regional climate and wind data to generate an airflow path map within the tea garden, wherein: S101, based on the topographic data and vegetation canopy data of the tea garden, constructs a three-dimensional digital model of the airflow path within the tea garden; S102 uses climate wind data as input to perform fluid dynamics simulation calculations on a three-dimensional digital model of a tea garden, and solves the airflow field at the height of the tea garden canopy. The climate wind data includes prevailing wind direction and average wind speed information, and the climate wind data is preferably taken from the spring and summer meteorological data of the tea garden area. S103, extract the calculation results of the airflow field and generate an internal airflow path map of the tea garden that includes the airflow velocity distribution and the main path.

3. The ecological control method for the tea green leafhopper based on the optimized layout of tea garden landscape habitat as described in claim 1, characterized in that, S2 involves planting repellent plants and deploying repellent release devices in the upwind area of ​​the airflow path map to form a dynamic odor barrier, wherein: S201, determine the barrier deployment points in the upwind main wind tunnel inlet area of ​​the air flow path map, and plant repellent plants at the points to form a plant substrate zone; The repellent plants are planted in strips, with the angle between the extension direction of the repellent plant strip and the prevailing wind direction of the airflow channel being less than 45 degrees. The row spacing of the repellent plant strip is 15-25cm, the plant spacing is 10-20cm, and the width of the repellent plant strip is 0.8m-1.5m. S202, a repellent release device is installed in the plant substrate zone, and the release parameters of the repellent are controlled according to a preset regulation strategy; The repellent release device is installed at a spacing of 15-25m in the plant substrate zone, and the installation height of the release port of the repellent release device is the same as the height of the tea tree canopy.

4. The ecological control method for the tea green leafhopper based on the optimized layout of tea garden landscape habitat as described in claim 3, characterized in that, The release parameters of the repellent are controlled according to a preset regulation strategy, wherein: The preset control strategies include a time-based periodic release strategy and a data-based trigger release strategy. The triggering strategy for the periodic release strategy is to set a periodic start-stop release mode for the repellent during two time periods each day, in the morning and evening. The triggering conditions for the release strategy are: when the climate and wind data are suitable for pest migration, the repellent is continuously released. The specific triggering conditions for the release strategy include wind direction, wind speed, and temperature.

5. The ecological control method for the tea green leafhopper based on the optimized layout of tea garden landscape habitat as described in claim 1, characterized in that, S3 involves planting attracting plants and setting traps in the downwind and calm areas of the airflow path map to form attracting and control points, wherein: S301, determine multiple trapping and prevention deployment points in the downwind and calm areas of the airflow path map; The locations for the trapping and prevention deployment points are determined based on the intersection area of ​​the airflow settling zone and the main wind channel in the airflow path diagram. S302, Planting attracting plants at the designated locations to form attracting patches, and setting up trapping devices within the patches; The attracting plants are planted in small patches, with each patch measuring 2-4 square meters. 2 .

6. The ecological control method for the tea green leafhopper based on the optimized layout of tea garden landscape habitat as described in claim 5, characterized in that, Multiple trapping and control deployment points are determined in the downwind and calm wind areas of the airflow path map, among which: The locations for trapping and prevention are dynamically optimized based on the prevailing wind direction data of the target season and the target prevention and control strategy. The dynamic optimization process is as follows: For the target season, calculate the comprehensive point attraction efficiency value for each prevention and control deployment point according to the prevailing wind direction and prevention and control strategy of the season, and sort and classify all prevention and control deployment points according to the point attraction efficiency value. The point attraction efficiency value is obtained by the ratio of wind direction matching coefficient, target strategy matching degree, and point feature coefficient; The prevention and control deployment points are generated based on the attraction efficiency value and are divided into core deployment points, backup deployment points, and recommended closure points according to the season.

7. The ecological control method for the tea green leafhopper based on the optimized layout of tea garden landscape habitat as described in claim 6, characterized in that, The locations of the trapping and prevention deployment points are dynamically optimized based on the prevailing wind direction data of the target season and the target prevention and control strategy, wherein: The target prevention and control strategy includes peripheral interception strategy, internal control point strategy, and end-point governance strategy; The peripheral interception strategy is designed to prevent overwintering adult insects from migrating into the tea garden from external hosts, and is applicable to the upwind boundary area of ​​the airflow inside the tea garden. The internal control point strategy is used to suppress the reproduction and spread of the introduced population within the tea garden, and is applicable to key points of pest aggregation and spread within the tea garden. The aforementioned end-of-life management strategy is used to reduce the number of late-stage adult insects migrating to overwintering hosts and is applicable to the downwind edge areas of tea gardens with good airflow.

8. The ecological control method for the tea green leafhopper based on the optimized layout of tea garden landscape habitat as described in claim 1, characterized in that, S4 analyzes the number and distribution of tea green leafhoppers in the tea garden, and selects key nodes in the tea garden to construct natural enemy conservation facilities, including: S401 uses a drone equipped with a hyperspectral imaging device to collect hyperspectral image data of the tea green leafhopper in the tea garden. The collected hyperspectral image data stream is radiometrically corrected in real time, and a spectral feature map for pest stress is calculated and generated. The spectral feature map is input into a convolutional neural network model to perform pixel-level inference on the hyperspectral image data to obtain the pest stress probability value at each pixel location. The spectral feature map includes the plant senescence reflectance index, which indicates leaf senescence and pigment changes, and the normalized water index, which indicates changes in leaf moisture and cell structure. S402, based on the pest stress probability value of the tea green leafhopper distribution, calculate the coordinates of key target points in the tea garden where natural enemy conservation facilities need to be deployed. S403, construct natural enemy conservation facilities for the tea green leafhopper at key target coordinates for ecological control.

9. The ecological control method for the tea green leafhopper based on the optimized layout of tea garden landscape habitat as described in claim 8, characterized in that, The process involves inputting spectral feature maps into a convolutional neural network model to perform pixel-level inference on hyperspectral image data, wherein: The construction process of the convolutional neural network model is as follows: a convolutional neural network with an encoder-decoder structure is constructed, multi-channel feature images from the sample set are input into the network, deep features are extracted by the encoder, spatial details are restored by the decoder upsampling, a hybrid loss function is used to supervise the difference between the network output and the ground truth map, and the network parameters are iteratively optimized. The specific process of pixel-level inference is as follows: the convolutional neural network is propagated forward through the input data using a sliding window method, and a probability value representing that point belongs to the tea green leafhopper damage and stress area is output pixel by pixel to form an initial probability map. The initial probability map is then spatially stitched and smoothed based on geographic coordinates to generate the final tea garden pest stress probability distribution map.

10. The ecological control method for the tea green leafhopper based on the optimized layout of tea garden landscape habitat as described in claim 1, characterized in that, S5 assesses the ecological control effect of the tea green leafhopper in tea gardens on a seasonal basis, and adjusts the ecological control measures based on the assessment results, including: S501: Collect multidimensional data of tea gardens within a single seasonal cycle, and quantitatively evaluate the control effect of tea green leafhopper based on the multidimensional data; The quantitative assessment refers to comparing and analyzing multidimensional data with preset thresholds and historical data from the same period. S502, Based on the results of the quantitative assessment, generate an optimized adjustment plan for ecological control measures in the next cycle; S503, based on the optimization and adjustment plan, updates the air flow path map, ecological facility layout, and equipment operation strategy of the tea garden, and applies the updated parameters to the prevention and control practices of the next seasonal cycle.

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