智能化无人机植保作业关键技术及研究进展

doi:10.12133/j.smartag.2019.1.2.201812-SA025

Smart Agriculture ›› 2019, Vol. 1 ›› Issue (2): 20-33.doi: 10.12133/j.smartag.2019.1.2.201812-SA025

智能化无人机植保作业关键技术及研究进展

徐旻^1,², 张瑞瑞^1,², 陈立平^1,^2,^*(), 唐青^1,², 徐刚^1,²

^1. 国家农业智能装备工程技术研究中心,北京 100097
^2. 北京农业智能装备技术研究中心,北京 100097

收稿日期:2018-12-31 修回日期:2019-04-02 出版日期:2019-04-30
基金资助:
国家自然科学基金项目(31771674);北京市农林科学院2018创新能力建设专项(KJCX20180424);北京市农林科学院2016青年科研基金(QNJJ201632)
作者简介:徐旻（1979-）,男,博士,副研究员,研究方向：精准农业技术,飞行器导航控制与智能控制,Email：xum@nercita.org.cn。
通信作者: 陈立平（1973-）, 女, 博士, 研究员, 研究方向：精准农业技术,传感器与智能控制, 电话：010-51503425, Email： chenlp@nercita.org.cn

Key technology analysis and research progress of UAV intelligent plant protection

Xu Min^1,², Zhang Ruirui^1,², Chen Liping^1,^2,^*(), Tang Qing^1,², Xu Gang^1,²

^1. National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China
^2. Beijing Research Center for Intelligent Agricultural Equipment, Beijing 100097, China

Received:2018-12-31 Revised:2019-04-02 Online:2019-04-30
corresponding author: Chen Liping, Email： chenlp@nercita.org.cn
About author:Xu Min,Email：xum@nercita.org.cn
Supported by:
National Natural Science Foundation of China(31771674);Beijing Academy of Agriculture and Forestry Sciences 2018 Innovation Capacity Building Special Project (KJCX20180424); Beijing Academy of Agriculture and Forestry Sciences 2016 Youth Research Fund (QNJJ201632)

摘要/Abstract

摘要：

搭载高性能传感器和施药装备的农业植保无人机系统是精准农业领域具有代表性的智能装备之一。本研究首先从前端田间作业环境动态感知技术出发,阐述了无人机光谱成像遥感、多传感器融合的SLAM实时环境建模等技术在无人机植保作业方面的应用情况;然后对精准施药过程建模与优化控制有关的前沿技术进行了分析,包括旋翼下方风场结构演化及雾滴沉积过程仿真建模、多区域全覆盖条件下的智能作业路径规划、精准变量施药控制等;最后论述了作业效果评估与过程监管相关技术的发展现状,包括施药作业质量评价方法、基于云平台数据管理的全过程可视化监管等。在总结现有技术发展现状基础上,对未来智能化无人机植保关键技术发展趋势进行了预测,阐明了光谱图像获取与计算智能的深度学习识别聚类、基于高精度雾滴谱和风场模型预测的精准变量施药作业路径规划、基于传感器实时数据的作业质量评估和作业监管等新技术手段,将在遥感信息反演、药液飘移抑制、作业效率优化、施药过程管控等方面带来革命性的进步,使植保作业数据化、透明化,全过程可观化可控制,推动农业生产管理从机械化向智能化和智慧化迈进。

关键词: 无人机, 植保, 智能, 感知, 施药

Abstract:

UAV plant protection operation faces very complicated environmental conditions. On one hand, its ultra low altitude operations are vulnerable to ground structures and basic hydropower facilities; on the other hand, the effectiveness of plant protection operation is strong, and it is necessary to spray the pesticides to the specific parts of crops at the prescribed time so as to ensure good pesticide application effect. At present, UAV plant protection technology mainly refers to the existing mature technology and flight platform in general aviation field to basically "fly and spray". However, the lack of penetrating research and theoretical guidance on environmental perception in farmland operation, the movement mechanism of droplets under the rotor airflow, and the penetrability of the droplet to different crops canopy lead to low penetration rate of the UAV plant protection operation, easy drifting, frequent accidents, large damage probability and low comprehensive operational efficiency. Benefiting from the breakthroughs in artificial intelligence, parallel computing technology and intelligent hardware, the UAV plant protection technology is developing in the direction of intellectualization, systematization and precision. The real-time perception of the environment under non established conditions, intelligent job decision method based on intelligent recognition of crop diseases and pests, the control of the toward-target pesticide spraying control based on the variable of wind field droplet deposition model and the data based job evaluation system have gradually become the key technology of the UAV intelligent plant protection. The manuscript analyzed and summarized the research status and technical achievements in the field of UAV intelligent plant protection from the field information perception, the modeling and optimization control of accurate pesticide application, the evaluation and monitoring of the operation effect. Based on the existing research, the research also predicted the development trend of the key technologies of intelligent UAV plant protection in the future. The clustering method of hyper-spectral image acquisition and computational intelligence based deep learning recognition will become the key technology for real-time and efficient acquisition of crop target information in plant protection work, which greatly improves the accuracy of remote sensing information inversion recognition; machine vision and multi machine cooperative sensing technology can acquire dynamic information of field operation at multiple levels and time; the high precision droplet spectrum control technology independently controlled by nozzle design and the precision variable spraying control technology based on the wind field model can further improve the droplet deposition effect and reduce the liquid drifting; the breakthrough of high accuracy mesh solution technology will change the prediction mode of droplet drift from artificial experience judgment to computer simulation and numerical deduction; the job path planning technology will greatly improve the efficiency of multi machine and multi area operation and reduce the distance of invalid operation; the job quality evaluation based on the real-time data of the sensor and the operation supervision system of large data technology will replace people to effectively control the process of the UAV plant protection operation, achieve data and transparency of plant protection, and ensure the process is observable and controllable.

Key words: UAV, plant protection, intelligence, sensing, spraying

中图分类号:

S251

徐旻, 张瑞瑞, 陈立平, 唐青, 徐刚. 智能化无人机植保作业关键技术及研究进展[J]. 智慧农业(中英文), 2019, 1(2): 20-33.

Xu Min, Zhang Ruirui, Chen Liping, Tang Qing, Xu Gang. Key technology analysis and research progress of UAV intelligent plant protection[J]. Smart Agriculture, 2019, 1(2): 20-33.

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智能化无人机植保作业关键技术及研究进展

Key technology analysis and research progress of UAV intelligent plant protection

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