Precise herbicide application process

Abstract

The invention relates to the technical field of agricultural engineering, and discloses a precise herbicide application process which comprises the following process steps: acquiring data of weed types, distribution positions, density and corresponding herbicide types through multi-source data collection, uploading the data to a data processing center, and preparing targeted herbicide application equipment according to different herbicide application scenes; the data processing center combines real-time weather, crop growth cycle and topographic features to generate differentiated pesticide application schemes, monitors the pesticide application process in the whole process, rechecks the weed death condition 72 hours after pesticide application through the multispectral unmanned aerial vehicle, and when it is found that the expected effect is not achieved, data of the current non-pesticide application position and the surrounding area are collected again, and the pesticide application process is completed. And uploading to the data processing center, re-applying the pesticide to form a closed loop mode, and integrating the pesticide application effect, the environment variable and the equipment state by the data processing center, updating the weed data in real time, regularly detecting the equipment wear condition, and replacing the aged nozzle or sensor.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural engineering, and in particular to a process for precisely applying herbicides. Background Art

[0002] Traditional herbicide application methods rely on manual experience, resulting in inaccurate weed identification, poor application uniformity, and high costs. Manual surveys make it difficult to accurately locate weed distribution and species. Applications can easily lead to wasted and uneven distribution due to poor path planning or outdated equipment. Real-time monitoring of application effectiveness is also impossible, making closed-loop optimization difficult. Furthermore, manual labor is inefficient and the cost per unit area is high, especially in complex environments such as orchards and paddy fields. Application is even more challenging, and resource allocation lacks a scientific basis. Therefore, a precision application process that integrates multi-source data, intelligent devices, and dynamic feedback is urgently needed to improve weed control effectiveness, ensure application uniformity, and reduce costs. Summary of the Invention

[0003] (1) Technical problems solved In response to the shortcomings of the existing technology, the present invention provides a precise herbicide application process with the advantages of multi-source data fusion, intelligent scenario-based decision-making and closed-loop dynamic optimization, which solves the problems of inaccurate weed identification, poor application uniformity, high cost and lack of scientific feedback mechanism in traditional herbicide application.

[0004] (2) Technical solution To achieve the above object, the present invention provides the following technical solution: a process for precise herbicide application, characterized in that it comprises the following process steps: Step 1: Data integration: Collect weed species, distribution locations, density, and corresponding herbicide types through multi-source data collection and upload to the data processing center; Step 2: Prepare spraying equipment: Prepare targeted spraying equipment according to the three different spraying scenarios of dry fields, orchards, and paddy fields, and debug and perform performance tests on the equipment before use; Step 3: Scenario-based precision pesticide application: The data processing center combines real-time weather, crop growth cycles, and terrain characteristics to generate differentiated pesticide application plans. It also receives real-time feedback from equipment during the application process and automatically adjusts parameters. Step 4: Monitoring and feedback on pesticide application results: The entire application process is supervised, with real-time monitoring of application results using sensors and image recognition technology. A multispectral drone is used to recheck weed mortality 72 hours after application. If the expected effect is not achieved, data on the current unapplication location and surrounding areas is collected again and uploaded to the data processing center for reapplication, forming a closed-loop model. Step 5. Data and equipment update and maintenance: The data processing center integrates the results of each spraying, environmental variables and equipment status, updates weed data in real time, regularly checks equipment wear and tear, and replaces aging nozzles or sensors.

[0005] Preferably, the data integration process in step 1 includes obtaining weed distribution and density information through satellite remote sensing, drone aerial photography, and ground sensors, while simultaneously accessing a weed species database through the Internet and retrieving herbicide usage data corresponding to different weed species in historical pesticide application records. Finally, a data processing center conducts comprehensive analysis to establish a precision pesticide application database that matches weed distribution, species, and herbicides.

[0006] Preferably, the pesticide application equipment in step 2 includes: a drone equipped with a variable spraying system, a drone with a float device, an intelligent spraying vehicle, a self-propelled sprayer, a handheld sprayer, an intelligent sprayer and a variable spraying system.

[0007] Preferably, the dryland weed control process in step 3 is: S1.1.1. UAV aerial photography: Use high-resolution cameras or hyperspectral cameras carried by drones to obtain distribution images and density information of weeds in dry fields and the types of surrounding crops; S1.1.2. Beidou Positioning System: Utilize the high-precision positioning capabilities of the Beidou satellite navigation system, combined with the longitude and latitude coordinates in the weed identification data, to accurately mark the specific locations of weeds on electronic maps; S1.1.3. Networked weed species identification: Identify weed species in aerial images using a networked weed species database to supplement weed species information. S1.1.4, Pesticide application route design: Plan the flight path of the drone based on the weed areas marked on the electronic map; S1.1.5. Design of spraying parameters: Based on the crop type and growth cycle, weed type and density, and combined with the analysis results of the data processing center, design appropriate spraying parameters such as spray type, spray rate, nozzle pressure, and liquid concentration; S1.1.6. Selection of pesticide application equipment: Use drones equipped with variable spraying systems that can dynamically adjust the amount of pesticide applied based on real-time data; S1.1.7. Spraying operation: The drone will carry out targeted spraying of weed areas according to the preset spraying path and spraying amount parameters. During the spraying process, the drone's variable spraying system will adjust the spraying amount in real time according to the changing weed density and type.

[0008] Preferably, the orchard pesticide application process in step 3: S1.2.1. 3D Modeling: Use drones to obtain information on the types, distribution, height, and canopy width of fruit trees in the orchard. Combined with the spatial structure of the greenhouse, use 3D modeling technology to construct a spatial model of the orchard. S1.2.2, Path planning: Plan the application path based on the three-dimensional model; S1.2.3. Selection of pesticide application equipment: Choose an intelligent sprayer equipped with a high-precision positioning system; S1.2.4. Dynamic adjustment of pesticide application: During the application process, the intelligent sprayer dynamically adjusts the nozzle angle and application amount based on real-time feedback of positioning information and weed distribution data; S1.2.5, Pesticide Application Feedback: After the application is completed, the intelligent sprayer will upload the temporary equipment adjustment data during the application process to the data processing center; S1.2.6. Database update: The data processing center updates the precision pesticide application database based on the collected data, analyzes problems and deficiencies in the pesticide application process, and provides optimization references for subsequent pesticide application.

[0009] Preferably, the paddy field application process in step 3: S1.3.1 Topographic mapping data: Use topographic mapping technology to obtain the topographic slope information of paddy fields and analyze the waterlogging areas and drainage conditions of paddy fields; S1.3.2. Soil moisture data collection: Soil moisture sensors and monitoring equipment installed in paddy fields monitor soil moisture, water depth, and weed data in real time. This data is uploaded to the data processing center. Based on the terrain slope, soil texture, and water depth, the difficult areas and key areas for pesticide application in the paddy fields are analyzed to develop a pesticide application plan. S1.3.3. Selection of spraying equipment and methods: Choose a drone with a float device and use a fan-shaped spray nozzle for spraying; S1.3.4, Parameter adjustment: Combined with the spraying parameter instructions and spraying path of the data processing center, the spraying equipment's spraying amount, spraying height, spraying pressure and flight speed parameters are automatically adjusted; S1.3.5, Application Feedback: After application is completed, upload the data of temporary equipment adjustments during the application process to the data processing center; S1.3.6. Database update: The data processing center updates the precision pesticide application database based on the collected data, analyzes problems and deficiencies in the pesticide application process, and provides optimization references for subsequent pesticide application.

[0010] Preferably, the data processing center includes a data module and a calculation module. The monitoring data module includes multispectral drone review data, monitoring equipment data, pesticide application cost data and pesticide application feedback data. The calculation module calculates the weed control effect through the multispectral drone review data. The calculation module calculates the uniformity deviation of the application by monitoring the feedback data. The calculation module calculates the cost saving per unit area of ​​pesticide application through the pesticide application cost data. .

[0011] Preferably, the weed control effect The calculation formula is:

[0012] In the formula, Indicates weed control effect, Indicates the area of ​​weeds in the target area before application. It indicates the area occupied by weeds that are still alive when checked 72 hours after application.

[0013] Preferably, the uniformity deviation of the application The calculation formula is:

[0014] In the formula, Indicates the deviation of uniformity of application of pesticides. represents the actual flow rate of the i-th nozzle, represents the theoretical target flow rate of the i-th nozzle, Indicates the number of sprinklers or sampling points.

[0015] Preferably, the application unit area saves cost The calculation formula is:

[0016] In the formula, represents the total cost of traditional pesticide application, represents the total cost of precision pesticide application, Indicates the area of ​​pesticide application.

[0017] Compared with the prior art, the present invention provides a precise herbicide application process with the following beneficial effects: 1. This invention collects multi-source data, links it with a weed species database, and combines it with real-time analysis from a data processing center to accurately locate weed areas, identify weed species, and match them with corresponding herbicides, significantly improving weed control effectiveness and far exceeding the level of traditional manual application.

[0018] 2. The present invention achieves the beneficial effect of ensuring the uniformity of pesticide application by selecting special pesticide application equipment for different scenarios, combining positioning technology with calibration and debugging of flow controller equipment, and automatically correcting parameters according to equipment feedback during the pesticide application process. The deviation of pesticide application uniformity after 72 hours of application is controlled within the standard range, thereby avoiding the problem of uneven traditional manual pesticide application.

[0019] 3. The present invention reduces the amount of pesticides used through a variable spraying system, improves work efficiency by using drones and intelligent equipment, and dynamically allocates equipment resources in combination with a data processing center, thereby achieving the beneficial effect of effectively controlling the cost of pesticide application. Compared with traditional manual pesticide application, the cost per unit area can be saved and increased, thereby achieving cost reduction and efficiency improvement. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a process flow chart of the present invention. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0022] See also Figure 1 , a herbicide precision application process, comprising the following process steps: Step 1: Data integration: Collect data on weed species, distribution locations, density, and corresponding herbicide types through multi-source data collection and upload to the data processing center. This data must be updated in real time to support subsequent accurate decision-making; Step 2: Prepare spraying equipment: Prepare targeted spraying equipment according to the three different spraying scenarios of dry fields, orchards, and paddy fields. Debug and perform performance tests on the equipment before use (calibrate GPS positioning, flow controller, and infrared obstacle avoidance system) to ensure spraying accuracy. Step 3: Scenario-based precision pesticide application: The data processing center combines real-time weather (wind speed, humidity), crop growth cycle, and terrain characteristics to generate differentiated pesticide application plans. It also receives real-time feedback from equipment during the application process (such as flow deviation and positioning offset) to automatically correct parameters. Targeted control of dryland weeds: Utilize high-precision positioning systems and weed identification data to accurately locate weed areas and control pesticide application equipment for targeted application.

[0023] Precision pesticide application in orchards, greenhouses, and other scenarios: Plan the pesticide application route based on the distribution of fruit trees in the orchard and the spatial characteristics of the greenhouse to ensure that herbicides act precisely on weeds and avoid affecting crops.

[0024] Adaptive technology for different terrains, such as paddy fields and dry fields: In view of the characteristics of paddy fields prone to waterlogging and dry fields with complex terrain, the parameters of pesticide application equipment and the method of application are adjusted to ensure effective coverage of herbicides; Step 4: Monitoring and feedback on pesticide application results: The entire application process is supervised, with real-time monitoring of application results using sensors and image recognition technology. A multispectral drone is used to recheck weed mortality 72 hours after application. If the expected effect is not achieved, data on the current unapplication location and surrounding areas is collected again and uploaded to the data processing center for reapplication, forming a closed-loop model. Step 5. Data and equipment update and maintenance: Integrate the results of each spraying, environmental variables (such as rainfall), and equipment status, update weed data in real time, regularly check equipment wear, replace aging nozzles or sensors, and dynamically deploy equipment to high-priority areas (such as fields with high incidence of pests and diseases) based on seasonal needs.

[0025] The advantages are: by integrating multi-source data from satellite remote sensing, drone aerial photography and ground sensors, combined with real-time analysis of the weed species database and data processing center, accurate matching of weed distribution, species and herbicides can be achieved; through high-precision positioning equipment and variable spraying systems, spraying parameters can be dynamically adjusted to ensure uniformity in complex scenarios; through multi-spectral drone review and data closed-loop feedback mechanism, the spraying effect can be optimized and the cost per unit area can be reduced, promoting the development of agricultural spraying towards intelligence and refinement.

[0026] Specifically, the data integration process in step one involves obtaining weed distribution and density information through satellite remote sensing, drone aerial photography, and ground sensors (integrating multi-source data). This process is coupled with network access to a weed species database (to supplement species identification) and access to herbicide usage data corresponding to different weed species from historical spraying records (associated with pesticide selection). Finally, through comprehensive analysis by the data processing center, a precision spraying database is established that matches weed distribution, species, and herbicides (forming a closed-loop logic).

[0027] Specifically, the pesticide application equipment in step 2 includes: drones equipped with variable spraying systems, drones with float devices, intelligent spraying vehicles, self-propelled sprayers, handheld sprayers, intelligent sprayers and variable spraying systems.

[0028] The advantages are: through the linkage of multi-source data collection (satellite remote sensing, drone aerial photography, ground sensors) and the weed species database, combined with real-time analysis by the data processing center, weed areas can be accurately located, weed species can be identified and matched with corresponding herbicides, which greatly improves the weed control effect, far exceeding the level of traditional manual spraying. By selecting special spraying equipment (drones and smart sprayers equipped with variable spraying systems) for different scenarios (dry fields, orchards, and paddy fields), combining positioning technology with the calibration and debugging of flow controller equipment, and automatically correcting parameters according to equipment feedback during the spraying process, the beneficial effect of ensuring the uniformity of spraying is achieved, so that the deviation of spraying uniformity after 72 hours of spraying is controlled within the standard range, thereby avoiding the problem of uneven spraying in traditional manual spraying.

[0029] Specifically, the data processing center includes a data module and a calculation module. The monitoring data module includes multispectral drone review data, monitoring equipment data, pesticide application cost data, and pesticide application feedback data. The calculation module is used to quantitatively evaluate the weed control effect after pesticide application. This module collects weed distribution data before and after pesticide application, image recognition results and other information to objectively and scientifically evaluate the weed control effect. The calculation module calculates the weed control effect based on the multispectral drone review data. To verify the effect of pesticide application, the calculation module calculates the uniformity deviation of pesticide application through monitoring feedback data , to verify the deviation between the actual application amount and the target application amount, the calculation module calculates the cost saving per unit area of ​​application through the application cost data , to verify the cost-reducing and efficiency-increasing advantages of precision pesticide application.

[0030] Specifically, weed control effect The calculation formula is:

[0031] In the formula, Indicates the weed-occupied area of ​​the target area before application (via image recognition or manual counting), Indicates the area occupied by weeds that have not died after 72 hours of spraying. The data is determined based on multispectral image analysis. ≥95%, it is judged that the application is effective; when the weed control effect ≤80%, triggering the closed-loop re-spray mechanism.

[0032] Specifically, the deviation of uniformity of application The calculation formula is:

[0033] In the formula, represents the actual flow rate of the i-th nozzle (monitored in real time by the flow sensor), represents the theoretical target flow rate of the i-th sprinkler (dynamically calculated according to weed density), Indicates the number of nozzles or sampling points. ≤5% is qualified; when the uniformity of application is >10%, the flow controller needs to be calibrated or the nozzle needs to be checked for blockage.

[0034] Specifically, the cost per unit area of ​​pesticide application is saved The calculation formula is:

[0035] In the formula, Represents the total cost of traditional pesticide application (including pesticides, labor, and equipment depreciation), represents the total cost of precision pesticide application (based on reduced pesticide usage due to variable rate spraying, improved drone operation efficiency, etc.), Indicates the area of ​​pesticide application, and verifies the cost-reducing and efficiency-increasing advantages of precision pesticide application through comparison.

[0036] The advantages are: reducing the amount of pesticides through the variable spraying system, improving work efficiency by using drones and intelligent equipment, and dynamically allocating equipment resources in combination with the data processing center, thereby achieving the beneficial effect of effectively controlling the cost of pesticide application. Compared with traditional manual pesticide application, the cost per unit area can be saved and increased to achieve cost reduction and efficiency improvement.

[0037] Example 1 Dryland weed control process: S1.1.1. UAV aerial photography: Use high-resolution cameras or hyperspectral cameras carried by drones to obtain distribution images and density information of weeds in dry fields and the types of surrounding crops; S1.1.2. Beidou Positioning System: Utilize the high-precision positioning capabilities of the Beidou satellite navigation system, combined with the longitude and latitude coordinates in the weed identification data, to accurately mark the specific locations of weeds on electronic maps; S1.1.3. Networked weed species identification: Identify weed species in aerial images using a networked weed species database to supplement weed species information. S1.1.4. Design of spraying routes: Plan the flight routes of the drone based on the weed areas marked on the electronic map to ensure that the spraying process covers all weed distribution areas while avoiding unnecessary flights and spraying. S1.1.5. Spraying parameter design: Based on the crop type and growth cycle, weed type and density, and combined with the analysis results of the data processing center, design appropriate spray type, spray volume, nozzle pressure, and liquid concentration parameters to achieve precise spraying; S1.1.6. Selection of pesticide application equipment: Use drones equipped with variable spray systems, which can dynamically adjust the amount of pesticide applied based on real-time data to ensure accurate and uniform application. S1.1.7. Spraying operation: The drone applies pesticides to the weed area in a targeted manner according to the preset spraying path and spraying amount parameters. During the spraying process, the drone's variable spraying system will adjust the spraying amount in real time according to the changing weed density and type, ensuring that the herbicide acts accurately on the weeds and reducing the impact on non-target areas.

[0038] Comparative Example 1 (using a similar weed area as in Example 1 and using manual methods to remove weeds) S2.1.1. Manual survey: Workers record the approximate distribution of dryland weeds through field observation. However, they cannot accurately locate every weed and can only make a rough estimate of weed density. S2.1.2 Weed Identification: Staff members identify weed species based on their experience, which can lead to errors and prevent rapid and comprehensive acquisition of weed species information. S2.1.3. Preparation for spraying: Manually using a handheld sprayer makes it difficult to adjust spraying parameters according to the actual weed situation. The spraying volume, nozzle pressure, and liquid concentration can only be set based on conventional experience. S2.1.4. Pesticide application: Workers use handheld sprayers to apply pesticides in weedy areas. They rely entirely on their personal experience to control their walking speed and spraying range, making it difficult to ensure uniformity and accuracy in application, and easily affecting non-target areas. S2.1.5 Data Recording: During the application process, it is not possible to obtain equipment-related data in real time. Only the approximate time, area, and amount of herbicide used can be recorded.

[0039] Example 2 The process of applying pesticides to orchard weeds: S1.2.1. 3D Modeling: Use drones to obtain information on the types, distribution, height, and canopy width of fruit trees in the orchard. Combined with the spatial structure of the greenhouse, use 3D modeling technology to construct a spatial model of the orchard. S1.2.2. Path Planning: Plan the spraying path based on the 3D model to ensure that the sprayer avoids trees and facilities when moving within the orchard, achieving collision-free operation. At the same time, ensure that the spraying path covers all weed distribution areas to avoid omissions. S1.2.3. Selection of pesticide application equipment: Choose an intelligent sprayer equipped with a high-precision positioning system; S1.2.4. Dynamic adjustment of pesticide application: During the application process, the intelligent sprayer dynamically adjusts the nozzle angle and application amount based on real-time feedback of positioning information and weed distribution data; S1.2.5, Application Feedback: After application is completed, the intelligent sprayer will upload the temporary equipment adjustment data (such as temporary path deviation, application amount and nozzle angle) during the application process to the data processing center; S1.2.6. Database update: The data processing center updates the precision pesticide application database based on the collected data, analyzes problems and deficiencies in the pesticide application process, provides optimization references for subsequent pesticide application, and forms a closed-loop optimization mechanism.

[0040] Comparative Example 2 (using a similar weed area as in Example 2 and manually removing weeds) S2.2.1. Manual survey: Workers enter the orchard and roughly measure and record the distribution, height, and crown width of the fruit trees. This makes it impossible to construct an accurate three-dimensional spatial model and to fully understand the distribution of weeds. S2.2.2. Route planning: Workers rely on experience to plan their spraying routes. This can lead to errors in judgment and collisions with fruit trees during spraying, and it is difficult to ensure complete coverage of weed-infested areas. S2.2.3. Selection of pesticide application equipment: Handheld sprayers cannot dynamically adjust the nozzle angle and application rate according to the orchard environment and actual weed conditions; S2.2.4, Pesticide application: Workers walk around the orchard with handheld sprayers applying pesticides. During the application process, they are unable to obtain real-time positioning information and weed distribution data, making it difficult to accurately control the application of pesticides. S2.2.5. Data Recording: After spraying is completed, only the type of herbicide used, the amount used, and the approximate spraying area are recorded. Equipment adjustment data cannot be obtained.

[0041] Example 3 The process of applying pesticides to paddy field weeds: S1.3.1 Topographic mapping data: Use topographic mapping technology to obtain the topographic slope information of paddy fields and analyze the waterlogging areas and drainage conditions of paddy fields; S1.3.2. Soil moisture data collection: Soil moisture sensors and monitoring equipment installed in paddy fields monitor soil moisture, water depth, and weed counts in real time. This data is uploaded to the data processing center to provide a basis for pesticide application plans. Based on the terrain slope, soil texture, and water depth, the difficulties and key areas of pesticide application in paddy fields are analyzed to develop a pesticide application plan, providing data support for selecting appropriate pesticide application equipment and methods. S1.3.3. Selection of spraying equipment and methods: Choose drones with float devices and use fan-shaped spray nozzles to avoid waste and uneven distribution of spray liquid. Ensure that the spraying equipment can operate normally in waterlogged environments to avoid equipment damage. S1.3.4, Parameter adjustment: Combined with the spraying parameter instructions and spraying path of the data processing center, the spraying equipment's spraying amount, spraying height, spraying pressure and flight speed parameters are automatically adjusted to ensure the accuracy and uniformity of spraying; S1.3.5, Application Feedback: After application is completed, upload the data of temporary equipment adjustments during the application process (such as spraying height, pressure and flight speed) to the data processing center; S1.3.6. Database update: The data processing center updates the precision pesticide application database based on the collected data, analyzes problems and deficiencies in the pesticide application process, provides optimization references for subsequent pesticide application, and forms a closed-loop optimization mechanism.

[0042] Comparative Example 3 (using a similar weed area as in Example 3 and manually removing weeds) S2.3.1. Manual survey: Workers conduct limited topographic surveys in paddy fields, making it difficult to obtain comprehensive and accurate data on terrain slope, waterlogged areas, and drainage conditions. S2.3.2 Data Collection: Workers use simple tools to measure soil moisture and water depth. The data is inaccurate and cannot be uploaded to a unified data processing center in real time, making it difficult to comprehensively analyze the application plan. S2.3.3. Selection of application equipment and methods: Using a handheld sprayer to apply pesticides in a waterlogged paddy field can easily damage the equipment and cause waste and uneven distribution of the pesticide solution. S2.3.4, Pesticide application operation: Workers have difficulty walking in the paddy fields to apply pesticides, which makes it difficult to control the speed and range of application, and the application parameters cannot be adjusted according to the actual situation; S2.3.5. Data recording: After spraying is completed, only the spraying area and herbicide dosage are recorded, and equipment adjustment data cannot be obtained.

[0043] According to the comparison between the above embodiment 123 and the comparative example 123, the calculation module calculates the weed control effect by reviewing the data of the multi-spectral drone. =How much, to verify the effect of pesticide application, the calculation module calculates the uniformity deviation of pesticide application through monitoring feedback data = how much, to verify the deviation between the actual application amount and the target application amount, the calculation module calculates the cost saving per unit area of ​​application through the application cost data =How much, the calculated data is as follows Table 1: Table 1

[0044] The comparative verification data in Table 1 show that the precision pesticide application process of the present invention is superior to traditional manual methods in terms of weed control effect, application uniformity, and cost control. The advantages are particularly obvious in complex scenarios (orchards and paddy fields).

[0045] While 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 these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A herbicide precision application process, characterized in that: The process steps include: Step 1: Data integration: Collect weed species, distribution locations, density, and corresponding herbicide types through multi-source data collection and upload to the data processing center; Step 2: Prepare spraying equipment: Prepare targeted spraying equipment according to the three different spraying scenarios of dry fields, orchards, and paddy fields, and debug and perform performance tests on the equipment before use; Step 3: Scenario-based precision pesticide application: The data processing center combines real-time weather, crop growth cycles, and terrain characteristics to generate differentiated pesticide application plans. It also receives real-time feedback from equipment during the application process and automatically adjusts parameters. Step 4: Monitoring and feedback on pesticide application results: The entire application process is supervised, with real-time monitoring of application results using sensors and image recognition technology. A multispectral drone is used to recheck weed mortality 72 hours after application. If the expected effect is not achieved, data on the current unapplication location and surrounding areas is collected again and uploaded to the data processing center for reapplication, forming a closed-loop model. Step 5. Data and equipment update and maintenance: The data processing center integrates the results of each spraying, environmental variables and equipment status, updates weed data in real time, regularly checks equipment wear and tear, and replaces aging nozzles or sensors.

2. The herbicide precision application process according to claim 1, characterized in that: The data integration process in step 1 includes obtaining weed distribution and density information through satellite remote sensing, drone aerial photography, and ground sensors, while simultaneously connecting to a weed species database online and retrieving herbicide usage data corresponding to different weed species from historical spraying records. Finally, through comprehensive analysis by a data processing center, a precision spraying database is established that matches weed distribution, species, and herbicides.

3. The herbicide precision application process according to claim 1, characterized in that: The pesticide application equipment in step 2 includes: a drone equipped with a variable spraying system, a drone with a float device, an intelligent sprayer, a self-propelled sprayer, a handheld sprayer, an intelligent sprayer and a variable spraying system.

4. The herbicide precision application process according to claim 1, characterized in that: The process of controlling weeds in dry fields in step 3: S1.1.

1. UAV aerial photography: Use high-resolution cameras or hyperspectral cameras carried by drones to obtain distribution images and density information of weeds in dry fields and the types of surrounding crops; S1.1.

2. Beidou Positioning System: Utilize the high-precision positioning capabilities of the Beidou satellite navigation system, combined with the longitude and latitude coordinates in the weed identification data, to accurately mark the specific locations of weeds on electronic maps; S1.1.

3. Networked weed species identification: Identify weed species in aerial images using a networked weed species database to supplement weed species information. S1.1.4, Pesticide application route design: Plan the flight path of the drone based on the weed areas marked on the electronic map; S1.1.

5. Design of spraying parameters: Based on the crop type and growth cycle, weed type and density, and combined with the analysis results of the data processing center, design appropriate spraying parameters such as spray type, spray rate, nozzle pressure, and liquid concentration; S1.1.

6. Selection of pesticide application equipment: Use drones equipped with variable spraying systems that can dynamically adjust the amount of pesticide applied based on real-time data; S1.1.

7. Spraying operation: The drone will carry out targeted spraying of weed areas according to the preset spraying path and spraying amount parameters. During the spraying process, the drone's variable spraying system will adjust the spraying amount in real time according to the changing weed density and type.

5. The herbicide precision application process according to claim 1, characterized in that: The orchard pesticide application process in step 3: S1.2.

1. 3D Modeling: Use drones to obtain information on the types, distribution, height, and canopy width of fruit trees in the orchard. Combined with the spatial structure of the greenhouse, use 3D modeling technology to construct a spatial model of the orchard. S1.2.2, Path planning: Plan the application path based on the three-dimensional model; S1.2.

3. Selection of pesticide application equipment: Choose an intelligent sprayer equipped with a high-precision positioning system; S1.2.

4. Dynamic adjustment of pesticide application: During the application process, the intelligent sprayer dynamically adjusts the nozzle angle and application amount based on real-time feedback of positioning information and weed distribution data; S1.2.5, Pesticide Application Feedback: After the application is completed, the intelligent sprayer will upload the temporary equipment adjustment data during the application process to the data processing center; S1.2.

6. Database update: The data processing center updates the precision pesticide application database based on the collected data, analyzes problems and deficiencies in the pesticide application process, and provides optimization references for subsequent pesticide application.

6. The herbicide precision application process according to claim 1, characterized in that: The paddy field application process in step 3: S1.3.1 Topographic mapping data: Use topographic mapping technology to obtain the topographic slope information of paddy fields and analyze the waterlogging areas and drainage conditions of paddy fields; S1.3.

2. Soil moisture data collection: Soil moisture sensors and monitoring equipment installed in paddy fields monitor soil moisture, water depth, and weed data in real time. This data is uploaded to the data processing center. Based on the terrain slope, soil texture, and water depth, the difficult areas and key areas for pesticide application in the paddy fields are analyzed to develop a pesticide application plan. S1.3.

3. Selection of spraying equipment and methods: Choose a drone with a float device and use a fan-shaped spray nozzle for spraying; S1.3.4, Parameter adjustment: Combined with the spraying parameter instructions and spraying path of the data processing center, the spraying equipment's spraying amount, spraying height, spraying pressure and flight speed parameters are automatically adjusted; S1.3.5, Application Feedback: After application is completed, upload the data of temporary equipment adjustments during the application process to the data processing center; S1.3.

6. Database update: The data processing center updates the precision pesticide application database based on the collected data, analyzes problems and deficiencies in the pesticide application process, and provides optimization references for subsequent pesticide application.

7. The herbicide precision application process according to claim 1, characterized in that: The data processing center includes a data module and a calculation module. The monitoring data module includes multispectral drone review data, monitoring equipment data, pesticide application cost data and pesticide application feedback data. The calculation module calculates the weed control effect through the multispectral drone review data. The calculation module calculates the uniformity deviation of the application by monitoring the feedback data. The calculation module calculates the cost saving per unit area of ​​pesticide application through the pesticide application cost data. .

8. The herbicide precision application process according to claim 7, characterized in that: The weed control effect The calculation formula is: ； In the formula, Indicates weed control effect, Indicates the area of ​​weeds in the target area before application. It indicates the area occupied by weeds that are still alive when checked 72 hours after application.

9. The herbicide precision application process according to claim 7, characterized in that: The uniformity deviation of the application The calculation formula is: ； In the formula, Indicates the deviation of uniformity of application of pesticides. represents the actual flow rate of the i-th nozzle, represents the theoretical target flow rate of the i-th nozzle, Indicates the number of sprinklers or sampling points.

10. The herbicide precision application process according to claim 7, characterized in that: The unit area cost saving of the pesticide application The calculation formula is: ； In the formula, represents the total cost of traditional pesticide application, represents the total cost of precision pesticide application, Indicates the area of ​​pesticide application.

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