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中国农机化学报

中国农机化学报 ›› 2022, Vol. 43 ›› Issue (11): 155-164.DOI: 10.13733/j.jcam.issn.2095-5553.2022.11.022

• 农业信息化工程 • 上一篇    下一篇

LiDAR传感器及技术在农业场景的应用进展综述

王潇1,张美娜1, 2,Zhou Jianfeng3,孙传亮2,吴茜2,曹静2   

  1. 1. 南京农业大学工学院,南京市,210031; 2. 江苏省农业科学院农业信息研究所/农业数字孪生联合实验室、

    种质资源创新与信息化利用联合实验室,南京市,210014; 3. 美国密苏里大学,密苏里州哥伦比亚市,MO 65211
  • 出版日期:2022-11-15 发布日期:2022-10-25
  • 基金资助:
    国家自然科学基金资助项目(61803187、31871522);江苏省自然科学基金项目(BK20200277);江苏省农业科技自主创新项目(CX(20)1005);江苏省重点研发计划(BE2020409);江苏省农科院探索性颠覆性创新计划项目(ZX(21)1210)

A review on the application of LiDAR sensors and technologies in agricultural scenarios

Wang Xiao, Zhang Meina, Zhou Jianfeng, Sun Chuanliang, Wu Qian, Cao Jing.   

  • Online:2022-11-15 Published:2022-10-25

摘要: 农业传感器是实现农业现代化发展的关键支撑技术,先进成熟的工业传感器向农业领域拓展应用有效补充了农业传感器的体量。LiDAR传感器由于其较强的抗干扰能力,在复杂多变的农业场景中应用越来越广泛、深入。首先,介绍LiDAR传感器的性能特点,工作原理与分类,市场应用与新技术;然后,基于国内外大量相关研究,系统介绍LiDAR传感器及技术在森林参数测量、果树靶标几何特征探测、作物几何表型特征检测、农业车辆自主导航定位以及农药雾滴飘移检测这5个农业场景的应用进展;同时,针对农业场景中探测对象的特殊性,讨论分析LiDAR传感器及技术在上述5类农业场景应用中的发展趋势;最后,展望LiDAR新技术在农业场景应用中的发展方向,即通过集成自动化采集系统装备与数据智能分析方法进一步提升LiDAR数据精准性、全面性、丰富性和实时性。

关键词: LiDAR传感器, 作物表型检测, 自主导航定位, 精准变量施药

Abstract: Agricultural sensor is the key supporting technology to realize agricultural modernization. In order to supplement the volume of agricultural sensors, more and more advanced and mature industrial sensors have been used in agricultural field effectively. There are some outstanding advantages of LiDAR system for complicated and changeable agricultural scenario since LiDAR system is not affected easily by light environment, and has the ability to build threedimensional model, higher resolution ratio, strong antiinterference capability, mature technology and products. This paper briefly introduces the performance characteristics, working principle, classification, market applications and new technologies of LiDAR system. Based on a large number of relevant studies at home and abroad, systematic presentations about LiDAR system applied in the following five agricultural scenarios were given including detection of forest parameters, geometric characteristics of fruit tree, geometric characteristics of crop phenotypes, autonomous navigation and positioning of agricultural vehicles, and sprayer drift. Then the requirements and challenges of LiDAR technology and data processing methods for agricultural scenario were discussed. Finally, new LiDAR product and technology combined with automatic acquisition system and intelligent data analysis method would greatly improve the application requirements of different levels of agricultural scenes in terms of multiscale, precision, comprehensiveness, richness and realtime data.

Key words: LiDARsensor, crop phenotypic detection, autonomous navigation and positioning, precise variable application

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