CN-116636518-B - Unmanned aerial vehicle pesticide application control method and system

CN116636518BCN 116636518 BCN116636518 BCN 116636518BCN-116636518-B

Abstract

The invention provides a method and a system for controlling pesticide application of an unmanned aerial vehicle, which belong to the technical field of agriculture and comprise the steps of obtaining position information of the unmanned aerial vehicle and operating parameters of spraying operation of the unmanned aerial vehicle on a target land block; the method comprises the steps of inputting operation parameters into a mist deposition prediction model of the unmanned aerial vehicle, obtaining predicted mist density output by the mist deposition prediction model, and comparing and analyzing the predicted mist density with an application prescription map of a target land block based on position information to determine parameter adjustment quantity of the unmanned aerial vehicle, wherein the parameter adjustment quantity is used for adjusting the operation parameters. According to the unmanned aerial vehicle pesticide application control method and system, the unmanned aerial vehicle operation parameters are utilized to predict the fog drop density in the pesticide application process, and the predicted quantity is compared with the prescription chart, so that the operation parameters are adjusted in real time according to the pesticide application quality in the unmanned aerial vehicle pesticide application process, the utilization rate of the pesticide is improved, the pesticide application quality is guaranteed, and accurate pesticide application is realized.

Inventors

ZHANG RUIRUI
CHEN LIPING
LI LONGLONG
DING CHENCHEN
ZHANG LINHUAN
CHENG WU

Assignees

北京市农林科学院智能装备技术研究中心

Dates

Publication Date: 20260512
Application Date: 20230515

Claims (8)

1. An unmanned aerial vehicle administration control method, characterized by comprising: acquiring position information of an unmanned aerial vehicle and operation parameters of spraying operation of the unmanned aerial vehicle on a target land, wherein the operation parameters comprise flight speed and pesticide application flow; inputting the operation parameters into a mist deposition prediction model of the unmanned aerial vehicle, and obtaining predicted mist density output by the mist deposition prediction model, wherein the predicted mist density is obtained by calculating the mist density application amount of the operation parameters by the mist deposition prediction model; Determining a pesticide application requirement corresponding to the position information in a pesticide application prescription map of the target land block, wherein the pesticide application prescription map of the target land block is acquired based on the steps of acquiring a hyperspectral image of the target land block, determining a pest and disease extent and a pesticide application prescription map of the target land block based on the hyperspectral image, and determining the pesticide application prescription map of the target land block according to the pest and disease extent and the pesticide application prescription map, wherein the pesticide application prescription map comprises distribution of pesticide application requirement in the target land block; comparing and analyzing the predicted fog drop density with the drug application amount requirement to generate a drug amount error; and determining a parameter adjustment amount of the unmanned aerial vehicle according to the drug quantity error, wherein the parameter adjustment amount is used for adjusting the operation parameter.
2. The unmanned aerial vehicle dosing control method of claim 1, wherein before inputting the operating parameters to the unmanned aerial vehicle's droplet deposition prediction model, obtaining the predicted droplet density output by the droplet deposition prediction model, further comprises: Acquiring sample fog point cloud quantity and sample fog drop deposition quantity of a plurality of sample areas of the unmanned aerial vehicle under different sample operation parameters, wherein the sample operation parameters comprise sample flight speed and sample application flow; And constructing the mist drop deposition prediction model based on the sample mist drop point cloud quantity and the sample mist drop deposition quantity of each sample area of each sample operation parameter.
3. The unmanned aerial vehicle dosing control method of claim 2, wherein the constructing the droplet deposition prediction model based on the sample droplet point cloud quantity and the sample droplet deposition quantity for each sample region for each sample operating parameter comprises: Based on the number of sample fog point clouds and sample fog drop deposition amount of the unmanned aerial vehicle in each sample area under a plurality of sample operation parameters, determining a linear relation between the number of the fog point clouds and the fog drop deposition amount of the unmanned aerial vehicle operation and a deposition relation between the fog drop deposition amount, the flying speed and the application flow; Determining a quantitative relationship between the flight speed, the application flow rate and the number of droplet point clouds based on the linear relationship and the deposition relationship; determining a density relationship between the flight speed, the application flow rate and the fog drop density based on the number relationship and the area of the sample area; and constructing the mist deposition prediction model based on the density relation.
4. The unmanned aerial vehicle pesticide application control system is characterized by comprising an unmanned aerial vehicle, an unmanned aerial vehicle real-time tracking platform, a digital radio station and an industrial personal computer; the unmanned aerial vehicle is used for spraying operation; The unmanned aerial vehicle real-time tracking platform is provided with a plurality of laser radars; the laser radar is used for collecting fog drop point cloud data in the unmanned aerial vehicle spraying operation process, and the fog drop point cloud data are used for constructing a fog drop deposition prediction model; the unmanned aerial vehicle is provided with a GPS antenna, and the GPS antenna is used for collecting the position information of the unmanned aerial vehicle and sending the position information to the industrial personal computer through the digital radio station; the plurality of laser radars send the fog drop point cloud data to the industrial personal computer through the digital radio station; The unmanned aerial vehicle dispensing control system comprises an industrial personal computer, a processor, a memory, and a program or instructions stored in the memory and capable of running on the processor, wherein the program or instructions execute the unmanned aerial vehicle dispensing control method according to any one of claims 1-3 when executed by the processor.
5. An industrial personal computer, characterized by comprising: The acquisition module is used for acquiring the position information of the unmanned aerial vehicle and the operation parameters of the unmanned aerial vehicle spraying operation on the target land, wherein the operation parameters comprise the flying speed and the pesticide spraying flow; the input module is used for inputting the operation parameters into a fog drop deposition prediction model of the unmanned aerial vehicle and obtaining predicted fog drop density output by the fog drop deposition prediction model, wherein the predicted fog drop density is obtained by calculating the fog drop density application amount of the operation parameters by the fog drop deposition prediction model; The system comprises an analysis module, a pesticide application prescription graph and a pesticide application prescription graph, wherein the analysis module is used for determining the pesticide application prescription graph corresponding to the position information in the pesticide application prescription graph of the target land, the pesticide application prescription graph of the target land is acquired based on the steps of acquiring a hyperspectral image of the target land, determining the range of the plant diseases and insect pests and the degree of the plant diseases and insect pests of the target land based on the hyperspectral image, and determining the pesticide application prescription graph of the target land according to the range of the plant diseases and the degree of the plant diseases and insect pests, wherein the pesticide application prescription graph comprises the distribution of the pesticide application prescription graph in the target land; The analysis module is also used for comparing and analyzing the predicted fogdrop density with the drug application requirement to generate a drug application error; and the analysis module is also used for determining the parameter adjustment quantity of the unmanned aerial vehicle according to the drug quantity error, wherein the parameter adjustment quantity is used for adjusting the operation parameters.
6. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the unmanned aerial vehicle administration control method of any one of claims 1-3 when the program is executed.
7. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the unmanned aerial vehicle administration control method of any one of claims 1-3.
8. A computer program product comprising a computer program which, when executed by a processor, implements the unmanned aerial vehicle administration control method of any one of claims 1 to 3.

Description

Unmanned aerial vehicle pesticide application control method and system Technical Field The invention relates to the technical field of agriculture, in particular to an unmanned aerial vehicle pesticide application control method and system. Background Unmanned aerial vehicle pesticide application operation is one of the most commonly used modes in current plant protection machinery operation by virtue of the advantages of high operation efficiency, low operation cost, wide terrain adaptability and the like. At present, whether the unmanned aerial vehicle is applied medicine and accurately sprays the problem directly influences the pesticide utilization ratio, and the most important factor that influences unmanned aerial vehicle and accurately apply medicine is the decision-making implementation method of applying medicine. In recent years, one research direction of drug delivery decision making is to use image information based on deep learning and a spectrum information fusion technology, so as to design a drug delivery auxiliary decision making system to provide data support for a drug delivery prescription of an unmanned aerial vehicle. However, unmanned aerial vehicle dispensing in the above scheme cannot guarantee dispensing quality. Disclosure of Invention The unmanned aerial vehicle drug delivery control method and system provided by the invention are used for solving the defect that the drug delivery quality cannot be ensured in the prior art, realizing the real-time adjustment of the operation parameters according to the drug delivery quality in the unmanned aerial vehicle drug delivery process, improving the utilization rate of the drug, ensuring the drug delivery quality and realizing the accurate drug delivery. The invention provides an unmanned aerial vehicle drug delivery control method, which comprises the following steps: acquiring position information of an unmanned aerial vehicle and operation parameters of spraying operation of the unmanned aerial vehicle on a target land, wherein the operation parameters comprise flight speed and pesticide application flow; inputting the operation parameters into a mist deposition prediction model of the unmanned aerial vehicle, and obtaining predicted mist density output by the mist deposition prediction model, wherein the predicted mist density is obtained by calculating the mist density application amount of the operation parameters by the mist deposition prediction model; And comparing and analyzing the predicted fog drop density with the pesticide application prescription map of the target land block based on the position information to determine the parameter adjustment quantity of the unmanned aerial vehicle, wherein the parameter adjustment quantity is used for adjusting the operation parameters. According to the unmanned aerial vehicle pesticide application control method provided by the invention, before the operation parameters are input into the unmanned aerial vehicle droplet deposition prediction model, the method further comprises the following steps of: Acquiring sample fog point cloud quantity and sample fog drop deposition quantity of a plurality of sample areas of the unmanned aerial vehicle under different sample operation parameters, wherein the sample operation parameters comprise sample flight speed and sample application flow; And constructing the mist drop deposition prediction model based on the sample mist drop point cloud quantity and the sample mist drop deposition quantity of each sample area of each sample operation parameter. According to the unmanned aerial vehicle application control method provided by the invention, the construction of the droplet deposition prediction model based on the sample droplet point cloud quantity and the sample droplet deposition quantity of each sample area of each sample operation parameter comprises the following steps: Based on the number of sample fog point clouds and sample fog drop deposition amount of the unmanned aerial vehicle in each sample area under a plurality of sample operation parameters, determining a linear relation between the number of the fog point clouds and the fog drop deposition amount of the unmanned aerial vehicle operation and a deposition relation between the fog drop deposition amount, the flying speed and the application flow; Determining a quantitative relationship between the flight speed, the application flow rate and the number of droplet point clouds based on the linear relationship and the deposition relationship; determining a density relationship between the flight speed, the application flow rate and the fog drop density based on the number relationship and the area of the sample area; and constructing the mist deposition prediction model based on the density relation. According to the unmanned aerial vehicle drug delivery control method provided by the invention, the drug delivery prescription diagram is obtained based on the following steps: acquirin