Prevention and control method for adult leafbee pests

Abstract

The invention relates to the technical field of leaf bee prevention and control, and particularly discloses a leaf bee pest adult prevention and control method, which comprises the steps of S1, data collection, S2, climate prediction model establishment, S3, precise monitoring, S4, precise prevention and control, S5, dynamic adjustment and S6, monitoring optimization. By using the unmanned aerial vehicle and the remote sensing technology, a gathering area of the adult leafbees can be accurately identified, a high-risk area can be intensively prevented and treated, blind large-area pesticide spraying is avoided, the prevention and treatment effect is remarkably improved, the climate prediction model can predict the activity peak period of the adult leafbees in advance, farmers are helped to take prevention and treatment measures at the best opportunity, and the prevention and treatment efficiency is improved. Through real-time analysis of a climate prediction model and remote sensing data, a farmer can formulate a scientific prevention and control strategy according to the data, dependence on experience and subjective judgment is reduced, the decision accuracy is improved, and according to feedback of real-time monitoring data and the prediction model, changes of pests are coped with in time, so that the prevention and control effect is improved. The continuous optimization of the control effect is ensured.

Description

Technical Field

[0001] The invention belongs to the technical field of sawfly control, and in particular relates to a method for controlling adult sawfly pests. Background Art

[0002] At present, sawfly control mainly relies on traditional chemical pesticide spraying methods, which often use large-scale, indiscriminate spraying methods, resulting in excessive use of pesticides, which not only increases production costs, but also causes serious environmental pollution, destroys the ecological balance, and affects the quality and safety of agricultural products. At the same time, traditional methods rely on farmers' experience and intuition to control sawfly pests, making it difficult to accurately grasp the timing and area of ​​control, resulting in unstable control effects.

[0003] In this regard, the inventor proposes a method for controlling adult sawfly pests to solve the above-mentioned problem. Summary of the invention

[0004] The object of the present invention is to provide a method for controlling adult sawfly pests to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for preventing and controlling adult sawfly pests comprises the following steps:

[0007] S1. Data collection: Use the meteorological platform to collect real-time regional temperature, humidity, rainfall, and wind speed climate data. Use drones to regularly conduct remote sensing monitoring of farmland to identify the activity areas and densities of sawfly adults and obtain distribution data. Deploy sensor networks in farmland to obtain sensor data and monitor soil moisture and vegetation health data in real time to assist in determining the impact of sawfly activities on crops.

[0008] S2. Establish a climate prediction model, combine the climate data, distribution data and sensor data collected in S1, analyze the relationship between the activity patterns of sawfly adults and climate conditions, establish a climate prediction model, and predict the peak hatching, activity and reproduction periods of sawfly adults;

[0009] S3. Precise monitoring. Based on the climate prediction model, an early warning system is established. When the climate conditions are suitable for the activities of sawfly adults, the system will issue an early warning and notify farmers to take corresponding prevention and control measures. During the predicted peak period of sawfly activities, drones are used to cruise and photograph farmland. Combined with remote sensing data and image analysis technology, the aggregation areas of sawfly adults are accurately identified to obtain the spatial density distribution of sawfly adults.

[0010] S4. Precision control: Based on the results of drone inspections, drones or smart sprayers are used to carry out targeted spraying in hotspots where sawfly adults gather, using low-toxic biological pesticides to minimize the impact on the environment and non-target organisms;

[0011] S5. Dynamic adjustment: Combine climate prediction models with data collected by drones to analyze the activity changes of sawfly adults in real time, dynamically adjust prevention and control strategies, divide risk levels in different areas, flexibly adjust the amount of pesticides used and spraying time, and optimize spraying strategies;

[0012] S6. Monitoring and optimization: After each growing season, summarize the climate forecast and prevention and control effects, optimize the climate model and precision agriculture technology, and adjust the prevention and control plan for the next season based on the actual results.

[0013] Preferably, the meteorological platform includes a weather station, a meteorological satellite or an agricultural meteorological network.

[0014] Preferably, the sensor network includes a temperature sensor, a humidity sensor, a light sensor, a vegetation index sensor, a remote sensing imaging sensor and a multispectral sensor.

[0015] Preferably, the climate prediction model is specifically a climate-adult activity model, and the relationship between the activity intensity A(t) of sawfly adults and the climate factors of temperature, humidity and light is:

[0016]

[0017] Where A(t): the activity intensity of sawfly adults at time t;

[0018] T(t): ambient temperature at time t;

[0019] Topt: optimal temperature for the activity of sawfly adults;

[0020] σT: standard deviation of temperature change, reflecting the sensitivity of temperature to activity;

[0021] H(t): relative humidity at time t;

[0022] Hopt: optimal humidity for the activity of sawfly adults;

[0023] α: coefficient of influence of humidity on the activity of sawfly adults;

[0024] L(t): light intensity at time t;

[0025] Lopt: optimal light intensity for sawfly adult activity;

[0026] β: the effect coefficient of light on the activity of sawfly adults;

[0027] The above formula is used to predict the activity intensity of sawfly adults under comprehensive climatic factors, thereby helping agricultural practitioners identify high-risk periods and take timely prevention and control measures.

[0028] Preferably, the spatial density distribution of the sawfly adults is estimated using a spatial interpolation method, and the calculation expression is:

[0029]

[0030] Where D(x,y): the estimated density of sawfly adults at the spatial point (x,y);

[0031] di: Density of sawfly adults at known sampling point i;

[0032] λi(x,y): weight coefficient, which depends on the distance and spatial correlation between (x,y) and sampling point i;

[0033] N: the number of sampling points involved in the interpolation calculation;

[0034] The above formula can be used to estimate the spatial density distribution of sawfly adults in the entire farmland, identify high-density areas, and provide a basis for precise spraying.

[0035] Preferably, based on the spatial density distribution D(x, y) of sawfly adults, the spraying amount Q(x, y) of the agent is accurately controlled, and its expression is:

[0036] Q(x,y)=k·D(x,y)

[0037] Where Q(x,y): the amount of the spray at the spatial point (x,y);

[0038] D(x,y): estimated density of sawfly adults at the spatial point (x,y);

[0039] k: pesticide spraying coefficient, which depends on the efficacy of the pesticide, crop type and economic cost factors;

[0040] According to the estimated pest density distribution map, the amount of pesticide spraying at each point is calculated by the above formula to achieve accurate and efficient pest control.

[0041] Preferably, the spraying strategy is optimized in step S5, and its objective function J is expressed as:

[0042]

[0043] J Total cost function, including economic cost and environmental cost;

[0044] m: number of different areas in the field;

[0045] Qi: the amount of pesticide sprayed in area i;

[0046] C(Qi): economic cost of drug use in region i;

[0047] E(Qi): the environmental impact cost of the use of the agent in region i;

[0048] Constraints:

[0049] D i≤Dmax: Pest density must be controlled within a safe range;

[0050] Q i≥Qmin: The spraying amount of the pesticide must reach the minimum effective amount;

[0051] Through the above objective function, the economic cost and environmental impact of the pesticide can be minimized while controlling the adult sawflies, thus achieving the optimization of the prevention and control strategy.

[0052] Preferably, in step S3, the remote sensing data is analyzed by spectral analysis to generate a vegetation index NDVI to assess the health of vegetation and further identify the aggregation area of ​​sawfly adults, which is expressed as:

[0053]

[0054] NDVI: Normalized Difference Vegetation Index, used to assess vegetation health;

[0055] NIR: reflectance in the near-infrared band;

[0056] RED: reflectivity of red light band;

[0057] NDVI is used to identify areas of abnormal vegetation health in agricultural fields, which are areas where sawfly adults gather. Lower NDVI values ​​generally indicate vegetation damage or stress, presumably as a result of sawfly adult activity.

[0058] Preferably, the image analysis technology in step S3 divides the image into different regions and detects the target object, which is expressed as:

[0059]

[0060] Where Canny(x,y): the edge strength of the image at position (x,y);

[0061] I(x,y): pixel value of the image at position (x,y);

[0062] The gradient of the image in the x direction;

[0063] The gradient of the image in the y direction;

[0064] Through edge detection, the outlines and aggregation areas of sawfly adults can be identified, which helps to generate heat maps for further density analysis and precise prevention and control.

[0065] Preferably, the prevention and control strategy in step S5 is specifically: spraying pesticides in high-risk areas, and introducing natural enemies and using insect-proof nets for prevention and control in medium- and low-risk areas.

[0066] Compared with the prior art, the present invention has the following beneficial effects:

[0067] (1) The present invention uses drones and remote sensing technology to accurately identify the gathering areas of sawfly adults, concentrate on controlling high-risk areas, avoid blindly spraying pesticides over large areas, and significantly improve the control effect. The climate prediction model can predict the peak activity period of sawfly adults in advance, helping farmers take control measures at the best time, thereby effectively curbing the reproduction and spread of pests.

[0068] (2) The present invention uses precision agriculture technology to spray pesticides only in required areas and at required times, significantly reducing the total amount of pesticides used, reducing pollution to the environment, and reducing harmful substances remaining in agricultural products. The use of biological pesticides helps reduce damage to non-target organisms, maintain the ecological balance of farmland, and promote the protection of biodiversity.

[0069] (3) Through real-time analysis of climate prediction models and remote sensing data, the present invention allows farmers to formulate scientific prevention and control strategies based on data, reducing reliance on experience and subjective judgment and improving the accuracy of decision-making. Based on the feedback from real-time monitoring data and prediction models, farmers can dynamically adjust prevention and control measures and respond to changes in pests in a timely manner, ensuring continuous optimization of prevention and control effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 A flow chart of a method for controlling adult sawfly pests of the present invention;

[0071] Figure 2 It is a block diagram of the meteorological platform of the present invention;

[0072] Figure 3 A block diagram of the sensor network composition of the present invention;

[0073] Figure 4 A block diagram of the climate prediction model of the present invention;

[0074] Figure 5 It is a frame diagram of the prevention and control strategy of the present invention;

[0075] Figure 6 It is a frame diagram of the climate factor composition of the present invention. DETAILED DESCRIPTION

[0076] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0077] Embodiment 1:

[0078] See also Figures 1 to 6 As shown, a method for controlling adult sawfly pests comprises the following steps:

[0079] S1. Data collection: using meteorological platforms such as weather stations, meteorological satellites or agricultural meteorological networks to collect real-time regional temperature, humidity, rainfall, and wind speed climate data, using drones to regularly conduct remote sensing monitoring of farmland, identify the activity areas and densities of sawfly adults, obtain distribution data, deploy sensor networks in farmland, obtain sensor data, and monitor soil moisture and vegetation health data in real time to assist in determining the impact of sawfly activities on crops;

[0080] The sensor network includes temperature sensors, humidity sensors, light sensors, vegetation index sensors, remote sensing imaging sensors, and multispectral sensors;

[0081] Temperature sensors, such as DS18B20, monitor the air and soil temperatures in and around farmland and are placed at different heights and soil depths in the field to capture the temperature difference between day and night and the temperature fluctuations on the soil surface, helping to predict the peak activity period of sawflies. These data are used to analyze the activity patterns of sawfly adults and provide input data for climate prediction models.

[0082] Humidity sensors: such as HDC1080, which are installed in fields and greenhouses. The data is used to adjust the prediction model, measure the relative humidity of the air and the moisture of the soil, and evaluate the impact of the environment on the activity of sawfly adults.

[0083] Light sensors, such as the TSL2591, are installed in the field or on drones to measure light intensity and sunshine duration. They are used to monitor changes in light throughout the day and help predict peak periods of sawfly activity.

[0084] Vegetation index sensors: such as Sentera NDVI, which are installed on drones or fixed in the field. They capture near-infrared and visible light reflected by plants to calculate the vegetation index (NDVI) for assessing crop health. They can monitor crop growth in real time, detect plant stress responses caused by sawfly adult activity, and identify sawfly damage to crops at an early stage.

[0085] Remote sensing imaging sensors: such as FLIR Vue Pro, installed on drones or fixed monitoring systems, use thermal and multispectral imaging to detect the activity and distribution of sawfly adults in farmland. They are used to scan large areas of farmland, generate pest distribution maps, and assist in precision spraying.

[0086] Multispectral sensors: such as Parrot Sequoia, installed on drones, capture multispectral images of farmland to generate vegetation indices such as NDVI and pest heat maps, conduct periodic inspections of farmland, identify the activity areas of sawfly adults and crop health, and provide a basis for precise prevention and control;

[0087] S2. Establish a climate prediction model, combine the climate data, distribution data and sensor data collected in S1, analyze the relationship between the activity patterns of sawfly adults and climate conditions, establish a climate prediction model, and predict the peak hatching, activity and reproduction periods of sawfly adults;

[0088] S3. Precise monitoring. Based on the climate prediction model, an early warning system is established. When the climate conditions are suitable for the activities of sawfly adults, the system will issue an early warning and notify farmers to take corresponding prevention and control measures. During the predicted peak period of sawfly activities, drones are used to cruise and photograph farmland. Combined with remote sensing data and image analysis technology, the aggregation areas of sawfly adults are accurately identified to obtain the spatial density distribution of sawfly adults.

[0089] S4. Precision control: Based on the results of drone inspections, drones or smart sprayers are used to carry out targeted spraying in hotspots where sawfly adults gather, using low-toxic biological pesticides to minimize the impact on the environment and non-target organisms;

[0090] S5. Dynamic adjustment: Combine climate prediction models with data collected by drones to analyze the activity changes of sawfly adults in real time, dynamically adjust prevention and control strategies, divide risk levels in different areas, flexibly adjust the amount of pesticides used and spraying time, optimize spraying strategies, use pesticides in high-risk areas, and introduce natural enemies and insect-proof nets for prevention and control in medium- and low-risk areas;

[0091] S6. Monitoring and optimization: After each growing season, summarize the climate forecast and prevention and control effects, optimize the climate model and precision agriculture technology, and adjust the prevention and control plan for the next season based on the actual results.

[0092] As can be seen from the above, the use of drones and remote sensing technology can accurately identify the gathering areas of sawfly adults, concentrate on preventing and controlling high-risk areas, avoid blindly spraying pesticides over large areas, and significantly improve the prevention and control effect. The climate prediction model can predict the peak activity period of sawfly adults in advance, helping farmers take prevention and control measures at the best time, thereby effectively curbing the reproduction and spread of pests;

[0093] Through precision agriculture technology, pesticides are sprayed only in required areas and at required times, which significantly reduces the total use of pesticides, reduces pollution to the environment, and reduces harmful substances remaining in agricultural products. The use of biological pesticides helps reduce damage to non-target organisms, maintains the ecological balance of farmland, and promotes the protection of biodiversity.

[0094] Embodiment 2:

[0095] refer to Figures 1 to 6 As shown in Figure 1, the climate prediction model is specifically a climate-adult activity model, and the relationship between the activity intensity A(t) of sawfly adults and the climate factors of temperature, humidity and light is:

[0096]

[0097] Where A(t): the activity intensity of sawfly adults at time t;

[0098] T(t): ambient temperature at time t;

[0099] Topt: optimal temperature for the activity of sawfly adults;

[0100] σT: standard deviation of temperature change, reflecting the sensitivity of temperature to activity;

[0101] H(t): relative humidity at time t;

[0102] Hopt: optimal humidity for the activity of sawfly adults;

[0103] α: coefficient of influence of humidity on the activity of sawfly adults;

[0104] L(t): light intensity at time t;

[0105] Lopt: optimal light intensity for sawfly adult activity;

[0106] β: the effect coefficient of light on the activity of sawfly adults;

[0107] The above formula is used to predict the activity intensity of sawfly adults under comprehensive climatic factors, thereby helping agricultural practitioners identify high-risk periods and take timely prevention and control measures.

[0108] Specifically, the spatial density distribution of sawfly adults is estimated using the spatial interpolation method, and the calculation expression is:

[0109]

[0110] Where D(x,y): the estimated density of sawfly adults at the spatial point (x,y);

[0111] di: Density of sawfly adults at known sampling point i;

[0112] λi(x,y): weight coefficient, which depends on the distance and spatial correlation between (x,y) and sampling point i;

[0113] N: the number of sampling points involved in the interpolation calculation;

[0114] The above formula can be used to estimate the spatial density distribution of sawfly adults in the entire farmland, identify high-density areas, and provide a basis for precise spraying.

[0115] Specifically, based on the spatial density distribution D(x, y) of sawfly adults, the spraying amount Q(x, y) of the pesticide is accurately controlled, and its expression is:

[0116] Q(x,y)=k·D(x,y)

[0117] Where Q(x,y): the amount of the spray at the spatial point (x,y);

[0118] D(x,y): estimated density of sawfly adults at the spatial point (x,y);

[0119] k: pesticide spraying coefficient, which depends on the efficacy of the pesticide, crop type and economic cost factors;

[0120] According to the estimated pest density distribution map, the amount of pesticide spraying at each point is calculated by the above formula to achieve accurate and efficient pest control.

[0121] Specifically, in step S5, the spraying strategy is optimized, and its objective function J is expressed as:

[0122]

[0123] J Total cost function, including economic cost and environmental cost;

[0124] m: number of different areas in the field;

[0125] Qi: the amount of pesticide sprayed in area i;

[0126] C(Qi): economic cost of drug use in region i;

[0127] E(Qi): the environmental impact cost of the use of the agent in region i;

[0128] Constraints:

[0129] D i≤Dmax: Pest density must be controlled within a safe range;

[0130] Q i≥Qmin: The spraying amount of the pesticide must reach the minimum effective amount;

[0131] Through the above objective function, the economic cost and environmental impact of the pesticide can be minimized while controlling the adult sawflies, thus achieving the optimization of the prevention and control strategy.

[0132] Specifically, in step S3, the remote sensing data is used to generate a vegetation index NDVI through spectral analysis to assess the health of vegetation and further identify the aggregation area of ​​sawfly adults, which is expressed as:

[0133]

[0134] NDVI: Normalized Difference Vegetation Index, used to assess vegetation health;

[0135] NIR: reflectance in the near-infrared band;

[0136] RED: reflectivity of red light band;

[0137] NDVI is used to identify areas of abnormal vegetation health in agricultural fields, which are areas where sawfly adults gather. Lower NDVI values ​​generally indicate vegetation damage or stress, presumably as a result of sawfly adult activity.

[0138] Specifically, the image analysis technology in step S3 divides the image into different regions and detects the target object, which is expressed as:

[0139]

[0140] Where Canny(x,y): the edge strength of the image at position (x,y);

[0141] I(x,y): pixel value of the image at position (x,y);

[0142] The gradient of the image in the x direction;

[0143] The gradient of the image in the y direction;

[0144] Through edge detection, the outlines and aggregation areas of sawfly adults can be identified, which helps to generate heat maps for further density analysis and precise prevention and control.

[0145] From the above, we can see that through real-time analysis of climate forecasts and remote sensing data, farmers can formulate scientific prevention and control strategies based on the data, reduce reliance on experience and subjective judgment, and improve the accuracy of decision-making. According to the feedback from real-time monitoring data and prediction models, farmers can dynamically adjust prevention and control measures, respond to changes in pests in a timely manner, and ensure continuous optimization of prevention and control effects.

[0146] Embodiment three:

[0147] This design is applied to various crop planting areas, especially tea gardens, forest areas and field crop areas that are susceptible to sawfly infestation. In the vast tea garden, drones are equipped with high-resolution cameras and multispectral sensors, and fly regularly to collect crop growth status and the activity of sawfly adults. These data are combined with the climate prediction model through the cloud analysis platform to accurately predict the peak activity period and gathering area of ​​sawfly adults. Tea farmers use drones or intelligent spraying equipment to accurately apply pesticides at specific times and areas based on these prediction results, which not only effectively controls the reproduction and spread of pests, but also greatly reduces the use of pesticides, protects the ecological environment and quality of tea gardens, and thus avoids the problem that traditional methods rely on farmers' experience and intuition to control sawfly pests, making it difficult to accurately grasp the timing and area of ​​control, resulting in unstable control effects;

[0148] Furthermore, the following is a detailed implementation case of using climate prediction models and precision agriculture technology for sawfly adult control:

[0149] Location: A 50-hectare tea plantation in Sichuan Province.

[0150] Crop: Tea trees (mainly attacked by adult sawflies).

[0151] The main activity season of sawfly adults: May to August.

[0152] 1. Data Collection

[0153] 1.1 Climate data collection:

[0154] Data sources: ground meteorological stations and satellite remote sensing.

[0155] Monitoring frequency: every hour.

[0156] Key climate parameters:

[0157] Temperature: average daily temperature T = 25°C;

[0158] Humidity: average relative humidity H = 70%;

[0159] Light: average light intensity L = 60000l ux;

[0160] 1.2 Farmland data collection:

[0161] Equipment: UAV (equipped with multispectral camera), soil moisture sensor, NDVI sensor.

[0162] Monitoring frequency: Cruise once a week.

[0163] Parameters and indicators:

[0164] NDVI range: 0.3-0.6 (sawfly activity area);

[0165] Soil moisture: 30%-40% (suitable humidity range for the growth of sawfly larvae);

[0166] Temperature sensor: set up a measuring point every 0.5 meters to record data for analysis of sawfly activity patterns.

[0167] 2. Build a climate prediction model

[0168] 2.1 Prediction of sawfly adult activity:

[0169] Model type: Climate-adult activity model;

[0170] Input variables: T, H, L;

[0171] Prediction output: probability P of peak adult activity period;

[0172] Current prediction results: The peak activity period of sawfly adults is predicted to be from mid-June to early July, with a probability of P = 85%;

[0173] 3. Accurate monitoring

[0174] 3.1 Early Warning System:

[0175] System response time: 48 hours;

[0176] Early warning measures: 48 hours before the predicted peak, farmers will be notified via SMS and apps to prepare for prevention and control measures;

[0177] 3.2 Drone cruise shooting:

[0178] Area covered: 50 hectares of tea garden;

[0179] Cruising altitude: 120 meters;

[0180] Cruising speed: 5m / s;

[0181] Data collection time: Each cruise takes 1.5 hours to cover the entire farmland;

[0182] The NDVI range obtained: 0.4 (healthy) to 0.6 (abnormal);

[0183] Real-time data feedback:

[0184] Processing time: The heat map of sawfly adult distribution is generated within 30 minutes after the drone cruise ends.

[0185] 4. Precision prevention

[0186] Precision spraying:

[0187] Agent type: low-toxic biological pesticide (Bt bacteria);

[0188] Spraying area: Based on the heat map, the high-risk areas are targeted, which account for about 20% of the tea garden area;

[0189] Spraying volume: 500ml per hectare;

[0190] Spraying tools: drone spraying system;

[0191] Covering protection net:

[0192] Mesh size: 1mm;

[0193] Coverage area: Deployed around high-risk areas, covering an area of ​​5 hectares.

[0194] 5. Dynamic adjustment:

[0195] Basis for real-time adjustment: Comparison and analysis results of each drone cruise data and climate data;

[0196] Dynamic adjustment of drug usage:

[0197] Adjustment frequency: once every two weeks;

[0198] Adjustment basis: Changes in adult density and weather forecast (reduce spraying frequency when humidity increases).

[0199] 6. Monitoring optimization:

[0200] Seasonal summary:

[0201] Summary time: September after the activity season ends;

[0202] Optimization content: According to the actual results, adjust the parameter weights in the climate prediction model and optimize the path and frequency of drone cruises;

[0203] To summarize:

[0204] Environmental benefits:

[0205] Reduced pesticide use: Through precision spraying, pesticide use is reduced by 40%;

[0206] Protect biodiversity: Biopesticides are harmless to natural enemies and improve the ecological balance in the field;

[0207] Economic benefits:

[0208] Increased crop yields: Control measures effectively reduced the damage of sawflies to tea trees, increasing tea yields by 15%;

[0209] Reduced control costs: Precision spraying and optimized path planning save 30% of labor and pesticide costs;

[0210] Number of drone cruises: 8 (every two weeks from early May to early August);

[0211] Prediction accuracy: 80% (the peak activity period of sawfly adults is highly consistent with the model prediction);

[0212] Total amount of pesticide used: 20 liters (40% less than traditional methods);

[0213] Total investment and return:

[0214] Investment: 12,000 yuan (including the cost of sensors, drone cruise, pesticides, etc.);

[0215] Increased income: The increase in production directly resulted in an increase in income of approximately RMB 30,000.

[0216] Through the above implementation plan, farmers have successfully achieved precise control of sawfly adults, effectively improved the yield and quality of tea, and significantly reduced environmental pollution and control costs.

[0217] From the above, we can see that the sawfly adult control method combining climate prediction models and precision agriculture technology not only performs well in improving control efficiency and reducing environmental impact, but also can bring significant economic and social benefits, and has broad application prospects and sustainable development potential.

[0218] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for controlling adult sawfly pests, characterized in that: The following steps are involved: S1. Data collection: Use the meteorological platform to collect real-time regional temperature, humidity, rainfall, and wind speed climate data. Use drones to regularly conduct remote sensing monitoring of farmland to identify the activity areas and densities of sawfly adults and obtain distribution data. Deploy sensor networks in farmland to obtain sensor data and monitor soil moisture and vegetation health data in real time to assist in determining the impact of sawfly activities on crops. S2. Establish a climate prediction model, combine the climate data, distribution data and sensor data collected in S1, analyze the relationship between the activity patterns of sawfly adults and climate conditions, establish a climate prediction model, and predict the peak hatching, activity and reproduction periods of sawfly adults; S3. Precise monitoring. Based on the climate prediction model, an early warning system is established. When the climate conditions are suitable for the activities of sawfly adults, the system will issue an early warning and notify farmers to take corresponding prevention and control measures. During the predicted peak period of sawfly activities, drones are used to cruise and photograph farmland. Combined with remote sensing data and image analysis technology, the aggregation areas of sawfly adults are accurately identified to obtain the spatial density distribution of sawfly adults. S4. Precision control: Based on the results of drone inspections, drones or smart sprayers are used to carry out targeted spraying in hotspots where sawfly adults gather, using low-toxic biological pesticides to minimize the impact on the environment and non-target organisms; S5. Dynamic adjustment: Combine climate prediction models with data collected by drones to analyze the activity changes of sawfly adults in real time, dynamically adjust prevention and control strategies, divide risk levels in different areas, flexibly adjust the amount of pesticides used and spraying time, and optimize spraying strategies; S6. Monitoring and optimization: After each growing season, summarize the climate forecast and prevention and control effects, optimize the climate model and precision agriculture technology, and adjust the prevention and control plan for the next season based on the actual results.

2. The method for controlling adult sawfly pests according to claim 1, characterized in that: The meteorological platform includes a meteorological station, a meteorological satellite or an agricultural meteorological network.

3. The method for controlling adult sawfly pests according to claim 1, characterized in that: The sensor network includes a temperature sensor, a humidity sensor, a light sensor, a vegetation index sensor, a remote sensing imaging sensor and a multi-spectral sensor.

4. The method for controlling adult sawfly pests according to claim 1, characterized in that: The climate prediction model is specifically a climate-adult activity model, in which the relationship between the activity intensity A(t) of sawfly adults and the climate factors of temperature, humidity and light is: Where A(t): the activity intensity of sawfly adults at time t; T(t): ambient temperature at time t; Topt: optimal temperature for the activity of sawfly adults; σT: standard deviation of temperature change, reflecting the sensitivity of temperature to activity; H(t): relative humidity at time t; Hopt: optimal humidity for the activity of sawfly adults; α: coefficient of influence of humidity on the activity of sawfly adults; L(t): light intensity at time t; Lopt: optimal light intensity for sawfly adult activity; β: the effect coefficient of light on the activity of sawfly adults; The above formula is used to predict the activity intensity of sawfly adults under comprehensive climatic factors, thereby helping agricultural practitioners identify high-risk periods and take timely prevention and control measures.

5. The method for controlling adult sawfly pests according to claim 1, characterized in that: The spatial density distribution of the sawfly adults is estimated using the spatial interpolation method, and the calculation expression is: Where D(x,y): the estimated density of sawfly adults at the spatial point (x,y); di: Density of sawfly adults at known sampling point i; λi(x,y): weight coefficient, which depends on the distance and spatial correlation between (x,y) and sampling point i; N: the number of sampling points involved in the interpolation calculation; Through the above formula, the spatial density distribution of sawfly adults in the entire farmland can be estimated, high-density areas can be identified, and a basis can be provided for precise spraying.

6. The method for controlling adult sawfly pests according to claim 5, characterized in that: Based on the spatial density distribution D(x,y) of sawfly adults, the spraying amount Q(x,y) of the pesticide can be accurately controlled. The expression is: Q(x,y)=k·D(x,y) Where Q(x,y): the amount of the spray at the spatial point (x,y); D(x,y): estimated density of sawfly adults at the spatial point (x,y); k: pesticide spraying coefficient, which depends on the efficacy of the pesticide, crop type and economic cost factors; According to the estimated pest density distribution map, the amount of pesticide spraying at each point is calculated by the above formula to achieve accurate and efficient pest control.

7. The method for controlling adult sawfly pests according to claim 1, characterized in that: In step S5, the spraying strategy is optimized, and its objective function J is expressed as: J Total cost function, including economic cost and environmental cost; m: number of different areas in the field; Qi: the amount of pesticide sprayed in area i; C(Qi): economic cost of drug use in region i; E(Qi): the environmental impact cost of the use of the agent in region i; Constraints: Di≤Dmax: Pest density must be controlled within a safe range; Qi≥Qmin: The spraying amount of the pesticide must reach the minimum effective amount; Through the above objective function, the economic cost and environmental impact of the pesticide can be minimized while controlling the adult sawflies, thus achieving the optimization of the prevention and control strategy.

8. The method for controlling adult sawfly pests according to claim 1, characterized in that: In step S3, the remote sensing data is used to generate the vegetation index NDVI through spectral analysis to assess the health of vegetation and further identify the aggregation areas of sawfly adults, which is expressed as: NDVI: Normalized Difference Vegetation Index, used to assess vegetation health; NIR: reflectance in the near-infrared band; RED: reflectivity of red light band; NDVI is used to identify areas of abnormal vegetation health in agricultural fields, which are areas where sawfly adults gather. Lower NDVI values ​​generally indicate vegetation damage or stress, presumably as a result of sawfly adult activity.

9. The method for controlling adult sawfly pests according to claim 1, characterized in that: The image analysis technology in step S3 divides the image into different regions and detects the target object, which is expressed as: Where Canny(x,y): the edge strength of the image at position (x,y); I(x,y): pixel value of the image at position (x,y); The gradient of the image in the x direction; The gradient of the image in the y direction; Through edge detection, the outlines and aggregation areas of sawfly adults can be identified, which helps to generate heat maps for further density analysis and precise prevention and control.

10. The method for controlling adult sawfly pests according to claim 1, characterized in that: The specific control strategy in step S5 is: spraying pesticides in high-risk areas, and introducing natural enemies and using insect-proof nets to control in medium- and low-risk areas.

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