Multi-technology cooperation-based green prevention and control method and system for poppy moth

Abstract

The invention, which relates to the technical field of forestry pest control, discloses a multi-technology cooperation-based green prevention and control method and system for Penicilidae, and the method comprises the steps: obtaining high-definition image data of a forest region through aerial photography of an unmanned aerial vehicle, carrying out spatial analysis in combination with an ArcGIS technology, and generating a pest distribution thematic map; on the basis of pest development duration observation data and meteorological data, an occurrence period and occurrence amount prediction model is constructed, and the pest occurrence trend is predicted; performing prevention and control operation planning and design by applying an ArcGIS and an Ovoucher map, wherein the prevention and control operation planning and design comprises prevention and control area division, operation route planning and agent dosage calculation; according to the prediction result and the operation design, implementing grading prevention and control strategies including forestry prevention and control, biological prevention and control and chemical emergency prevention and control; and carrying out prevention and control effect dynamic evaluation by adopting an Otwei map and a today watermark camera, wherein the evaluation comprises insect decline rate and prevention and control cost calculation. By constructing a set of multi-technology collaborative closed-loop prevention and control system, early recognition and accurate intervention of insect pests can be realized.

Description

Technical Field

[0001] This invention belongs to the field of forestry pest control technology, and in particular relates to a green control method and system for poplar leafminer moth based on multi-technology synergy. Background Technology

[0002] The poplar small boat moth (Micromelalopha troglodyta) is a major leaf-eating pest of poplar trees in my country, characterized by overlapping generations and explosive outbreaks. A long-standing core problem in traditional control practices for the poplar small boat moth lies in the limitations and lag of monitoring methods. For a long time, understanding the pest's range and severity has relied primarily on manual ground surveys. This method is not only inefficient, making simultaneous surveys of large forest areas difficult, but also results in insufficient timeliness and coverage of the data. Due to the inability to quickly and comprehensively obtain information on the pest's spatial distribution, control decisions often lack timely and sufficient basis, leading to delayed control actions and difficulty in implementing effective interventions in the early stages of outbreaks, ultimately resulting in a reactive approach. Therefore, the following solutions are proposed to address these problems. Summary of the Invention

[0003] The purpose of this invention is to provide a green control method and system for poplar leafminer moth based on multi-technology synergy. By constructing a closed-loop control system based on multi-technology synergy, early identification and precise intervention of pests can be achieved, solving the problem of delayed control caused by outdated monitoring methods in existing technologies.

[0004] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution:

[0005] This invention relates to a green control method for the poplar leafminer moth based on multi-technology synergy, the control method comprising the following steps:

[0006] Step S1, Data Acquisition: High-resolution image data of the forest area is acquired through drone aerial photography, and spatial analysis is performed using ArcGIS technology to generate a thematic map of pest distribution;

[0007] Step S2, Forecasting and Prediction: Based on the observation data of the pest's development period and meteorological data, construct a prediction model for the occurrence period and the amount of pests to predict the trend of pest occurrence;

[0008] Step S3, Scientific Design: Use ArcGIS and Aowei Map to plan and design prevention and control operations, including the division of prevention and control areas, operation route planning, and calculation of pesticide dosage;

[0009] Step S4, Tiered Control: Based on the forecast results and operational design, implement tiered control strategies, including forestry control, biological control and chemical emergency control;

[0010] Step S5, Efficacy Evaluation: Dynamic evaluation of control effectiveness is conducted using Aowei Map and Today's Watermark Camera, including insect population reduction rate and control cost calculation.

[0011] Furthermore, in step S1, the drone aerial photography during data acquisition uses a multispectral camera, with a flight altitude of 50-100 meters and a ground resolution of 5cm.

[0012] Furthermore, in step S2, the prediction model in the forecasting uses time series analysis or LSTM neural network model, and the input parameters include meteorological factors such as temperature, humidity, rainfall, light intensity, and developmental history data of each insect stage.

[0013] Furthermore, in step S3, the scientific design includes dividing the prevention and control priority areas based on ArcGIS spatial analysis, planning the optimal operation route and personnel configuration using Ovi Map, and calculating the precise dosage of pesticides based on the insect population density.

[0014] Furthermore, in step S4, the graded control system is divided into three levels based on insect population density:

[0015] Level 1 control: When there are 5-10 pupae per plant or 2-5 larvae per 50cm branch, forestry measures should be adopted, including planting alfalfa, pruning, and tilling to kill pupae.

[0016] Secondary control: When there are 11-20 pupae per plant or 6-10 larvae per 50cm branch, biological control should be adopted, including spraying Bt preparations, applying Beauveria bassiana, and releasing Trichogramma or Trichoderma julibrissin.

[0017] Level 3 control: When there are more than 21 pupae per plant or more than 11 larvae per 50cm branch, emergency control measures should be taken, including spraying biological pesticides or pollution-free pesticides.

[0018] Furthermore, in step S5, the efficacy evaluation includes using image recognition technology to automatically calculate the insect population reduction rate and leaf survival rate, and using a mobile APP to achieve real-time data upload and automatic generation of evaluation reports.

[0019] The green control system for the poplar leafminer moth based on multi-technology collaboration includes the following modules:

[0020] The data acquisition module is used to acquire forest area image data through drone aerial photography and to perform spatial analysis using ArcGIS technology to generate thematic maps of pest distribution.

[0021] The forecasting module is used to construct a prediction model for the occurrence period and the amount of pests based on observation data of the pest's development period and meteorological data, and to predict the trend of pest occurrence.

[0022] The scientific design module is used to plan and design prevention and control operations using ArcGIS and Aowei Map, including the division of prevention and control areas, operation route planning, and calculation of pesticide dosage.

[0023] The tiered prevention and control module is used to implement tiered prevention and control strategies based on forecast results and operational design, including forestry control, biological control and chemical emergency control.

[0024] The efficacy evaluation module is used to dynamically assess the control effect using Aowei Map and Today's Watermark Camera, including the insect population reduction rate and control cost calculation.

[0025] Furthermore, in the data acquisition module, the UAV is equipped with a multispectral camera, with a flight altitude of 50-100 meters and a ground resolution of 5cm;

[0026] The prediction and forecasting module uses time series analysis or LSTM neural network model. The input parameters include meteorological factors such as temperature, humidity, rainfall, light intensity, and developmental data of each insect stage.

[0027] The scientific design module includes dividing priority areas for prevention and control based on ArcGIS spatial analysis, planning the optimal operation route and personnel configuration using Aowei Map, and calculating the precise dosage of pesticides based on insect population density;

[0028] Furthermore, the tiered control module is divided into three levels based on insect population density:

[0029] Level 1 control: When there are 5-10 pupae per plant or 2-5 larvae per 50cm branch, forestry measures should be adopted;

[0030] Secondary control: When there are 11-20 pupae per plant or 6-10 larvae per 50cm branch, use biological control.

[0031] Level 3 control: When there are more than 21 pupae per plant or more than 11 larvae per 50cm branch, emergency control measures should be adopted.

[0032] Furthermore, the control efficacy evaluation module includes the use of image recognition technology to automatically calculate the insect population reduction rate and leaf survival rate, and enables real-time data upload and automatic generation of evaluation reports through a mobile APP.

[0033] The present invention has the following beneficial effects:

[0034] This invention achieves intelligent and refined management of forest pests by constructing a closed-loop management system covering the entire process from monitoring, prediction, design, control, and evaluation. The method integrates various technologies such as UAV remote sensing, geographic information systems, predictive models, and mobile terminals, improving the comprehensiveness and timeliness of monitoring and enhancing the foresight of early warning capabilities. Through a tiered control strategy, it achieves the rational allocation of control measures, improves resource utilization efficiency, reduces reliance on chemical pesticides, and minimizes the impact on the ecological environment. Simultaneously, the dynamic and visualized evaluation of control effectiveness provides a basis for continuous optimization of control strategies, promoting the development of forest pest control towards a green, efficient, and sustainable direction.

[0035] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0036] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the 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.

[0037] Figure 1 This is a flowchart illustrating the green control method for poplar leafminer moth based on multi-technology synergy according to the present invention.

[0038] Figure 2 This is a flowchart of the drone monitoring process of the present invention;

[0039] Figure 3 This is a schematic diagram of the early warning monitoring process of the present invention;

[0040] Figure 4 This is a schematic diagram of the prevention and control operation design process of the present invention;

[0041] Figure 5 This is a schematic diagram of the hierarchical prevention and control operation process of the present invention;

[0042] Figure 6 This is a schematic diagram of the prevention and control effect evaluation process of the present invention;

[0043] Figure 7 This is a schematic diagram of the structure of the poplar leafminer moth green control system based on multi-technology synergy of the present invention. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] Please see Figure 1-6 As shown, this invention is a green control method for the poplar leafminer moth based on multi-technology synergy, comprising the following steps:

[0046] Step S1, Data Acquisition: High-resolution image data of the forest area is acquired through drone aerial photography, and spatial analysis is performed using ArcGIS technology to generate a thematic map of pest distribution;

[0047] Step S1, data collection specifically includes the following steps:

[0048] Step S11: Use a DJI M300RTK drone for aerial photography, equipped with a P1 full-frame camera;

[0049] Step S12: Use ArcGIS Pro to process and analyze images to generate thematic maps of pest distribution;

[0050] Step S13: Develop an automatic pest identification algorithm based on deep learning;

[0051] Step S2, Forecasting and Prediction: Based on the observation data of the pest's development period and meteorological data, construct a prediction model for the occurrence period and the amount of pests to predict the trend of pest occurrence;

[0052] Step S2, the forecast specifically includes the following steps:

[0053] Step S21: Establish fixed standard plots and investigate the insect population and pest development progress daily; integrate pest development progress data and construct an outbreak prediction model;

[0054] Step S22: Integrate real-time data from microclimate stations (temperature, humidity, rainfall, and light intensity) to construct a precipitation prediction model;

[0055] Step S3, Scientific Design: Use ArcGIS and Aowei Map to plan and design prevention and control operations, including the division of prevention and control areas, operation route planning, and calculation of pesticide dosage;

[0056] Step S3, scientific design specifically includes the following steps:

[0057] Step S31: Divide the prevention and control priority areas based on ArcGIS spatial analysis functions;

[0058] Step S32: Use the Ovi Map to plan the optimal work route and personnel configuration, and send the vector data to relevant personnel;

[0059] Step S33: Calculate the precise dosage of pesticide based on the insect population density;

[0060] Step S4, Tiered Control: Based on the forecast results and operational design, implement tiered control strategies, including forestry control, biological control and chemical emergency control;

[0061] Step S4, the tiered prevention and control measures specifically include the following steps:

[0062] Step S41: Primary control (5-10 pupae / plant, 2-5 larvae / 50cm branch): Forestry measures (planting alfalfa, pruning, tilling to kill pupae);

[0063] Step S42: Secondary control (11-20 pupae / plant, 6-10 larvae / 50cm branch): Biological control (Bt preparation 2000 IU / μl, dosage 500ml / acre, apply Beauveria bassiana, release Trichogramma or Zhou's parasitic wasp);

[0064] Step S43: Three-level control (pupae 21 or more per plant, larvae 11 or more per 50cm branch): Select fast-acting biopesticides and pollution-free pesticides (25% abamectin·diflubenzuron suspension, 20% diflubenzuron suspension);

[0065] Step S5, Efficacy Evaluation: Dynamic evaluation of control effectiveness is conducted using Aowei Map and Today's Watermark Camera, including insect population reduction rate and control cost calculation.

[0066] Step S5, the efficacy evaluation specifically includes the following steps:

[0067] Step S51: Use the Today Watermark camera to record before-and-after comparison images of epidemic prevention and control;

[0068] Step S52: Develop a mobile app to enable real-time data uploading and analysis;

[0069] Step S53: Automatically generate an assessment report containing indicators such as insect population reduction rate and control costs.

[0070] Please see Figure 7 As shown, this invention is a green control system for the poplar leafminer moth based on multi-technology synergy, comprising the following modules:

[0071] The data acquisition module is used to acquire forest area image data through drone aerial photography and to perform spatial analysis using ArcGIS technology to generate thematic maps of pest distribution.

[0072] The forecasting module is used to construct a prediction model for the occurrence period and the amount of pests based on observation data of the pest's development period and meteorological data, and to predict the trend of pest occurrence.

[0073] The scientific design module is used to plan and design prevention and control operations using ArcGIS and Aowei Map, including the division of prevention and control areas, operation route planning, and calculation of pesticide dosage.

[0074] The tiered prevention and control module is used to implement tiered prevention and control strategies based on forecast results and operational design, including forestry control, biological control and chemical emergency control.

[0075] The efficacy evaluation module is used to dynamically assess the control effect using Aowei Map and Today's Watermark Camera, including the insect population reduction rate and control cost calculation.

[0076] The specific application of this embodiment is as follows:

[0077] Example 1: Implementation of Monitoring and Early Warning for Poplar Small Boat Moth

[0078] Step S1: Start drone aerial monitoring every April:

[0079] Step S11, Equipment Configuration: Use a DJI M300RTK drone equipped with a P1 full-frame camera, set the flight altitude to 100 meters, and set the forward overlap rate and lateral overlap rate to 80% and 70% respectively to ensure that there are no blind spots in the image coverage;

[0080] Step S12, Aerial Photography Planning: The boundaries of the monitoring area are marked in advance using the Ovi Map, and an automatic flight route is planned. The aerial photography coverage area is about 200 hectares, and the flight time is controlled within 30 minutes.

[0081] Step S13, Image Acquisition Standard: Select a clear, windless day (wind speed < level 3) between 9 and 11 am for aerial photography to avoid interference from strong light and shadows. Store the raw data in RAW format and record GPS coordinates and flight logs simultaneously.

[0082] Step S2: Image data is transmitted back to the cloud platform in real time via the 5G network.

[0083] Step S21, Data Transmission: Using the drone's built-in 5G module, aerial images are uploaded to the ArcGIS Enterprise server in the cloud in real time. Each image is compressed into JPEG 2000 format (compression ratio 1:10), with an average transmission latency of <200ms.

[0084] Step S22, AI preprocessing: The deep learning model (based on ResNet50 architecture) deployed on the cloud platform automatically completes image stitching, radiometric correction and initial pest screening, and identifies suspicious areas as red warning zones (confidence level > 85%).

[0085] Step S23, Manual Verification: Forestry experts use a mobile app (such as "Forest Protection App") to conduct a secondary verification of the AI-marked areas, ensuring that the marking error rate is controlled within 5%.

[0086] Step S3: Update meteorological data hourly and input it into the prediction model:

[0087] Step S31, Data source integration: Connect to the small climate station network to obtain real-time data on temperature (accuracy ±0.5℃), humidity (±3%RH), rainfall (±0.2mm), and light intensity (±5%lux) of the monitoring area;

[0088] Step S32, Model Calculation: Compare meteorological data with historical pest outbreak database, use LSTM neural network to predict the peak hatching period of larvae in the next 7 days, and output the risk level (low / medium / high);

[0089] Step S33, Threshold setting: When the average temperature for 3 consecutive days is >18℃ and the humidity is >65%, the model automatically raises the warning level to yellow; if the predicted insect population density is >11 insects / 50cm branch, it is upgraded to a red warning.

[0090] Step S4: When the predicted insect population density exceeds the threshold, an early warning will be automatically triggered.

[0091] Step S41, Yellow Alert (Insect population density 5-10 insects / 50cm branch): The system sends a text message notification to the county-level forest protection station, requiring manual verification to be completed within 48 hours;

[0092] Step S42, Red Alert (>11 insects / 50cm branch): Simultaneously trigger a pop-up alert on the municipal platform and generate a PDF report containing coordinates and insect photos, which is then directly reported to the provincial competent department;

[0093] Step S43, Emergency Response: Automatically plan the optimal prevention and control route through the AVC map and push the task list (including the type of medicine, the list of equipment and the area to be operated) to the nearest prevention and control team.

[0094] Example 2: Graded Implementation of Green Control for Poplar Small Boat Moth

[0095] Step S1: Allocate appropriate prevention and control resources according to the warning level:

[0096] Step S11, Resource Management: Establish a dynamic material database to display the status of Bt preparations (inventory accurate to liters), Trichogramma bee cards (unit: 10,000 heads), and drone batteries (remaining power) in each warehouse in real time;

[0097] Step S12, Intelligent Allocation Algorithm: Based on the area of ​​the warning zone and the insect population density, automatically calculate the required resources (e.g., for red warning zones, allocate 500ml Bt preparation / acre + 300,000 Trichogramma wasps / hectare) and generate an electronic feed requisition form with a QR code;

[0098] Step S2: Navigate to the target area using Ovi Maps:

[0099] Step S21, Navigation Optimization: The prevention and control team uses a customized version of the AVC Map APP to load pre-divided work blocks (KML format) and automatically avoids steep slopes (slope > 25°) and sensitive areas (such as bee farms);

[0100] Step S22, Real-time monitoring: The team leader uploads location information every 5 minutes using a handheld terminal. The background screen displays the trajectory of all team members. The system will automatically vibrate to remind them when they deviate from the preset route by more than 50 meters.

[0101] Step S3: Implement precise prevention and control according to the preset plan:

[0102] Step S31, Primary control (larval population density 2-5 larvae / 50cm branch): Manually remove egg masses and webs, and simultaneously intercrop alfalfa in the forest (row spacing 2m×3m), and apply 1.5kg of Beauveria bassiana powder per acre; use a backpack electric sprayer to spray 0.3% matrine aqueous solution (30L per acre), and control the droplet size to 150-200μm;

[0103] Step S32, Secondary control (larval population density 6-10 larvae / 50cm branch): Drone aerial spraying (flight altitude 5m, speed 4m / s) spraying 2000IU / μl Bt preparation, adding 5% organosilicon adjuvant to improve adhesion rate; hang 20 Trichogramma wasp cards per hectare (5000 wasps / card), with a release point spacing of 50m;

[0104] Step S33, Level 3 control (11 larvae / branch over 50cm): Deploy a cluster of plant protection drones (3 drones operating simultaneously) and spray 25% abamectin·diflubenzuron suspension (diluted 1000 times, 15L of solution per acre); immediately erect warning signs after application (prohibit humans and animals from entering for 72 hours).

[0105] Step S4: Conduct a follow-up assessment of the effectiveness within 24 hours after treatment.

[0106] Step S41, Re-inspection technique: Use a handheld device equipped with a microscope camera (such as Huawei P70 Pro) to photograph the insect residue on a standard branch (50cm long), and automatically count and calculate the insect population reduction rate through the APP (Formula: Reduction rate = Number of insects before treatment - Number of insects after treatment Number of insects before treatment × 100% Reduction rate = Number of insects before treatment Number of insects before treatment - Number of insects after treatment × 100%);

[0107] Step S42, Report Generation: The system integrates review data, cost details (including labor, pesticides, and fuel consumption) and image comparison charts to generate a prevention and control effect evaluation report that conforms to the GB / T 15781-2015 standard, which can be exported to the regulatory platform with one click.

[0108] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0109] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A green control method for poplar leafminer moth based on multi-technology synergy, characterized in that, The prevention and control method includes the following steps: Step S1, Data Acquisition: High-resolution image data of the forest area is acquired through drone aerial photography, and spatial analysis is performed using ArcGIS technology to generate a thematic map of pest distribution; Step S2, Forecasting and Prediction: Based on the observation data of the pest's development period and meteorological data, construct a prediction model for the occurrence period and the amount of pests to predict the trend of pest occurrence; Step S3, Scientific Design: Use ArcGIS and Aowei Map to plan and design prevention and control operations, including the division of prevention and control areas, operation route planning, and calculation of pesticide dosage; Step S4, Tiered Control: Based on the forecast results and operational design, implement tiered control strategies, including forestry control, biological control and chemical emergency control; Step S5, Efficacy Evaluation: Dynamic evaluation of control effectiveness is conducted using Aowei Map and Today's Watermark Camera, including insect population reduction rate and control cost calculation.

2. The green control method for poplar leafminer moth based on multi-technology synergy according to claim 1, characterized in that, In step S1, the data acquisition process involves using a multispectral camera for drone aerial photography at a flight altitude of 50-100 meters, achieving a ground resolution of 5cm.

3. The green control method for poplar leafminer moth based on multi-technology synergy according to claim 1, characterized in that, In step S2, the prediction model in the forecasting process adopts time series analysis or LSTM neural network model, and the input parameters include meteorological factors such as temperature, humidity, rainfall, light intensity, and developmental history data of each insect stage.

4. The green control method for poplar leafminer moth based on multi-technology synergy according to claim 1, characterized in that, Step S3, the scientific design includes dividing the prevention and control priority areas based on ArcGIS spatial analysis, planning the optimal operation route and personnel configuration using Aowei Map, and calculating the precise amount of pesticide based on the insect population density.

5. The green control method for poplar leafminer moth based on multi-technology synergy according to claim 1, characterized in that, In step S4, the graded control system is divided into three levels based on insect population density: Level 1 control: When there are 5-10 pupae per plant or 2-5 larvae per 50cm branch, forestry measures should be adopted, including planting alfalfa, pruning, and tilling to kill pupae; Secondary control: When there are 11-20 pupae per plant or 6-10 larvae per 50cm branch, biological control should be adopted, including spraying Bt preparations, applying Beauveria bassiana, and releasing Trichogramma or Trichoderma julibrissin. Level 3 control: When there are more than 21 pupae per plant or more than 11 larvae per 50cm branch, emergency control measures should be taken, including spraying biological pesticides or pollution-free pesticides.

6. The green control method for poplar leafminer moth based on multi-technology synergy according to claim 1, characterized in that, In step S5, the efficacy evaluation includes using image recognition technology to automatically calculate the insect population reduction rate and leaf survival rate, and using a mobile APP to upload data in real time and automatically generate an evaluation report.

7. A green control system for the poplar leafminer moth based on multi-technology synergy, characterized in that, Includes the following modules: The data acquisition module is used to acquire forest area image data through drone aerial photography and to perform spatial analysis using ArcGIS technology to generate thematic maps of pest distribution. The forecasting module is used to construct a prediction model for the occurrence period and the amount of pests based on the observation data of the pest development period and meteorological data, and to predict the trend of pest occurrence. The scientific design module is used to plan and design prevention and control operations using ArcGIS and Aowei Map, including the division of prevention and control areas, operation route planning, and calculation of pesticide dosage. The tiered prevention and control module is used to implement tiered prevention and control strategies based on forecast results and operational design, including forestry control, biological control and chemical emergency control. The efficacy evaluation module is used to dynamically assess the control effect using Aowei Map and Today's Watermark Camera, including the insect population reduction rate and control cost calculation.

8. The poplar leafminer moth green control system based on multi-technology synergy according to claim 7, characterized in that, In the data acquisition module, the UAV is equipped with a multispectral camera, with a flight altitude of 50-100 meters and a ground resolution of 5cm. The prediction and forecasting module uses time series analysis or LSTM neural network model. The input parameters include meteorological factors such as temperature, humidity, rainfall, light intensity, and developmental data of each insect stage. The scientific design module includes dividing priority areas for prevention and control based on ArcGIS spatial analysis, planning the optimal operation route and personnel configuration using Aowei Map, and calculating the precise dosage of pesticides based on insect population density.

9. The poplar leafminer moth green control system based on multi-technology synergy according to claim 7, characterized in that, The graded prevention and control module is divided into three levels based on insect population density: Level 1 control: When there are 5-10 pupae per plant or 2-5 larvae per 50cm branch, forestry measures should be adopted; Secondary control: When there are 11-20 pupae per plant or 6-10 larvae per 50cm branch, use biological control. Level 3 control: When there are more than 21 pupae per plant or more than 11 larvae per 50cm branch, emergency control measures should be adopted.

10. The poplar leafminer moth green control system based on multi-technology synergy according to claim 7, characterized in that, The control efficacy evaluation module includes the use of image recognition technology to automatically calculate the insect population reduction rate and leaf survival rate, and enables real-time data upload and automatic generation of evaluation reports through a mobile APP.

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