SUN Na,WANG Yanjun,QIU Quan,et al.Application of LiDAR in Agriculture[J].Northern Horticulture,2019,43(20):150-156.[doi:10.11937/bfyy.20184237]
激光传感器在农业中的应用
- Title:
- Application of LiDAR in Agriculture
- Keywords:
- LiDAR; agriculture; navigation; obstacle avoidance; phenotype
- 文献标志码:
- A
- 摘要:
- 激光传感作为一种新型传感技术,能够通过非接触方式获取目标物形态特征。相比其它类型的传感器,该传感器具有精确度高、扫描速度快、分辨率高以及抗干扰能力强等特点。因此,该传感器已经被广泛用于农业应用研究。该研究从激光传感器的测距原理和在农业中的应用2个方面进行阐述。激光传感器的测距原理分为三角测距法、TOF法和干涉法。后2个方法的精度高,但对硬件设备的要求也相对较高,针对不同的应用场景选用合适的激光传感器可以有效提高作业效率。目前,激光传感器在农业中的应用主要包括农业机械自主导航、农田地形测绘和作物生长状况监测3个方面。农业机械通过安装激光测距仪可以帮助实现自主导航,如障碍物识别和路径规划;使用激光测距仪对农田地形进行测绘,有助于人为改善土地的平整度,从而提高灌溉用水利用率;作物在生长过程中,通过将激光传感器与其它传感器结合使用,能够实现监测作物的生长状态以便及时的人为补充作物生长过程所需要的营养物质。
- Abstract:
- As a new sensing technology,LiDAR can obtain morphological characteristics of the target in a non-contact manner.Compared with other types of sensors,it had many advantages,such as high accuracy,fast scanning speed,high resolution and strong anti-interference ability,and so on.Therefore,the sensor had been widely used in agricultural applications of research.This study introduced the laser sensor from two aspects,which were the principle of measuring distance of LiDAR and its application in agriculture.Principle of LiDAR measurement distance was divided into three types,namely triangulation,time-of-flight and interferometry.Among them,the latter two methods had higher measurement accuracy compared with the first,meanwhile,their requirements for hardware devices were relatively higher.Therefore,selecting the appropriate laser sensor for different application scenarios could effectively improve working efficiency;currently,the application of LiDAR in agriculture mainly included three aspects:autonomous navigation,topographical survey and mapping for field,and crop growth monitoring.Agricultural machinery can achieve autonomous navigation with the help of LiDAR,such as obstacle identification and path planning;based on the topographic mapping of field,we could artificially improve the flatness of the land to increase the utilization rate of irrigation water;by using LiDAR in combination with other sensors,it was possible to monitor the growth of the crop and to artificially supplement the nutrients needed during the crop growing process.
参考文献/References:
[1]赵春江,杨信廷,李斌,等.中国农业信息技术发展回顾及展望[J].中国农业文摘-农业工程,2018,30(4):3-7.[2]胡静涛,高雷,白晓平,等.农业机械自动导航技术研究进展[J].农业工程学报,2015,31(10):1-10.[3]ARAUS J L,CAIRNS J E.Field high-throughput phenotyping:The new crop breeding frontier[J].Trends in Plant Science,2014,19(1):52-61.[4]ARAUS J L,KEFAUVER S C,ZAMAN-ALLAH M,et al.Translating high-throughput phenotyping into genetic gain[J].Trends in Plant Science,2018,23(5):451-466.[5]李晓斌,王玉顺,付丽红.用K-means图像法和主成分分析法监测生菜生长势[J].农业工程学报,2016,32(12):179-186.[6]DIAS P M B,BRUNEL-MUGUET S,DURR C,et al.QTL analysis of seed germination and pre-emergence growth at extreme temperatures in Medicago truncatula[J].Theoretical and Applied Genetics,2011,122(2):429-444.[7]SAKAMOTO T,SHIBAYAMA M,KIMURA A,et al.Assessment of digital camera-derived vegetation indices in quantitative monitoring of seasonal rice growth[J].Isprs Journal of Photogrammetry & Remote Sensing,2011,66(6):872-882.[8]CLARK R T,FAMOSO A N,ZHAO K,et al.High-throughput two-dimensional root system phenotyping platform facilitates genetic analysis of root growth and development[J].Plant Cell and Environment,2013,36(2):454-466.[9]王传宇,郭新宇,杜建军,等.基于时间序列图像的玉米植株干旱胁迫表型检测方法[J].农业工程学报,2016,32(21):189-195.[10]张智韬,边江,韩文霆,等.无人机热红外图像计算冠层温度特征数诊断棉花水分胁迫[J].农业工程学报,2018,34(15):77-84.[11]王传宇,郭新宇,杜建军.基于时间序列红外图像的玉米叶面积指数连续监测[J].农业工程学报,2018,34(6):175-181.[12]贾士伟,李军民,邱权,等.基于激光测距仪的温室机器人道路边缘检测与路径导航[J].农业工程学报,2015,31(13):39-45.[13]郭庆华,杨维才,吴芳芳,等.高通量作物表型监测:育种和精准农业发展的加速器[J].中国科学院院刊,2018,33(9):940-946.[14]穆金虎,陈玉泽,冯慧,等.作物育种学领域新的革命:高通量的表型组学时代[J].植物科学学报,2016,34(6):962-971.[15]郭庆华,吴芳芳,庞树鑫,等.Crop 3D-基于激光雷达技术的作物高通量三维表型测量平台[J].中国科学:生命科学,2016,46(10):1210-1221.[16]LI L,ZHANG Q,HUANG D,et al.A review of imaging techniques for plant phenotyping[J].Sensors,2014,14(11):20078-20111.[17]周俞辰.基于激光三角测距法的激光雷达原理综述[J].电子技术与软件程,2016(19):94-95.[18]李长勇,蔡骏,房爱青,等.多传感器融合的机器人导航算法研究[J].机械设计与制造,2017(5):238-240,244.[19]张智刚,王进,朱金光,等.我国农业机械自动驾驶系统研究进展[J].农业工程技术,2018,38(18):23-27.[20]何勇,蒋浩,方慧,等.车辆智能障碍物检测方法及其农业应用研究进展[J].农业工程学报,2018,34(9):21-32.[21]王潇峰,张礼廉,胡小平,等.基于单目视觉的机器人避障方法研究[J].导航与控制,2018,17(1):56-64.[22]李庆,郑力新,潘书万,等.使用单目视觉的移动机器人导航方法[J].计算机工程与应用,2017,53(4):223-227.[23]王铮,赵晓,佘宏杰,等.基于双目视觉的AGV障碍物检测与避障[J].计算机集成制造系统,2018,24(2):400-409.[24]谷凤伟,金西虎,姜珊.基于双目视觉信息融合的移动机器人避障研究[J].电子世界,2015(18):54-57.[25]邢强,虞凯西,谷玉之.基于测距超声波传感器的间距平衡避障策略[J].现代电子技术,2018,41(20):97-99,103.[26]王玲玲,王宏.基于激光传感器的自主循迹智能车设计[J].电子测量术,2017,40(5):193-196.[27]袁文涛,刘卉,胡书鹏.面向自动导航拖拉机的农田障碍物识别研究[J].农机化研究,2018,40(10):247-251.[28]郭晓波,翟雁,吴丽娜.基于激光雷达信息的机器人障碍物检测[J].激光杂志,2017,38(9):58-60.[29]ASVADI A,PREMEBIDA C,PEIXOTO P,et al.3D Lidar-based static and moving obstacle detection in driving environments:An approach based on voxels and multi-region ground planes[J].Robotics & Autonomous Systems,2016,83(6):299-311.[30]PENG Y,QU D C,ZHONG Y,et al.The obstacle detection and obstacle avoidance algorithm based on 2-D lidar[C]//IEEE International Conference on Information and Automation.IEEE,2015:1648-1653.[31]肖宇峰,黄鹤,郑杰,等.Kinect与二维激光雷达结合的机器人障碍检测[J].电子科技大学学报,2018,47(3):337-342.[32]罗锡文.加快推进国家高标准农田建设[J].农村工作通讯,2015(14):1.[33]杨茂伟.三维激光扫描仪在地质灾害地形测绘中的应用[J].测绘通报,2016(5):145-146.[34]张靖,张爱能,刘国栋.三维激光扫描仪技术在地形测量中的应用[J].西安科技大学学报,2014,34(2):199-203.[35]董康.车载激光雷达农田三维地形测量方法研究与系统开发[D].泰安:山东农业大学,2012.[36]许迪,李益农,刘刚.激光控制农田土地精细平整应用技术体系研究进展[J].农业工程学报,2007(3):267-272.[37]杨青丰,尚业华,刘思雨,等.激光平地机自动调平控制系统的研制[J].安徽农业科学,2017,45(34):218-221.[38]陈国华,丁馨明.一种农田激光平地机及其应用方法[J].农业装备技术,2016,42(6):16-19.[39]KJAER K H,OTTOSEN C O.3D laser triangulation for plant phenotyping in challenging environments[J].Sensors,2015,15(6):13533-13547.[40]PAULUS S,SCHUMANN H,KUHLMANN H,et al.High-precision laser scanning system for capturing 3D plant architecture and analysing growth of cereal plants[J].Biosystems Engineering,2014,121:1-11.[41]MAPHOSA L,THODAY-KENNEDY E,VAKANI J,et al.Phenotyping wheat under salt stress conditions using a 3D laser scanner[J].Israel Journal of Plant Sciences,2016,64(3-4):55-62.[42]苏伟,展郡鸽,张明政,等.基于机载LiDAR数据的农作物叶面积指数估算方法研究[J].农业机械学报,2016,47(3):272-277.[43]刘慧,潘成凯,沈跃,等.基于SICK和Kinect的植株点云超限补偿信息融合[J].农业机械学报,2018,49(10):284-291.[44]张瑜,汪小旵,孙国祥,等.基于激光视觉的温室作物茎叶量测方法[J].农业机械学报,2014,45(9):254-259.[45]GARRIDO M,PARAFOROS D S,REISER D,et al.3D maize plant reconstruction based on georeferenced overlapping LiDAR point clouds[J].Remote Sensing,2015,7(12):17077-17096.[46]郭鹏,武法东,戴建国,等.基于机载LiDAR数据的农田区植被高度估测研究[J].干旱区地理,2017,40(6):1241-1247.[47]BHATTA M,ESKRIDGE K M,ROSE D J,et al.Seeding rate,genotype,and topdressed nitrogen effects on yield and agronomic characteristics of winter wheat[J].Crop Science,2017,57(2):951-963.[48]NAVABI A,IQBAL M,STRENZKE K,et al.The relationship between lodging and plant height in a diverse wheat population[J].Canadian Journal of Plant Science,2006,86(3):723-726.[49]郭新年,周恒瑞,张国良,等.基于激光视觉的农作物株高测量系统[J].农业机械学报,2018,49(2):22-27.[50]YUAN W A,LI J T,BHATTA M,et al.Wheat height estimation using LiDAR in comparison to ultrasonic sensor and UAS[J].Sensors,2018,18(11):3731.[51]UNDERWOOD J,WENDEL A,SCHOFIELD B,et al.Efficient in-field plant phenomics for row-crops with an autonomous ground vehicle[J].Journal of Field Robotics,2017,34(6):1061-1083.[52]VIRLET N,SABERMANESH K,SADEGHI-TEHRAN P,et al.Field Scanalyzer:An automated robotic field phenotyping platform for detailed crop monitoring[J].Functional Plant Biology,2017,44(1):143-153.
相似文献/References:
[1]张淼.浅析农业科研事业单位内部人员收入分配机制的现状及对策[J].北方园艺,2013,37(14):199.
ZHANG Miao.Analysis of Status and Countermeasures of Personnel Income Distribution Mechanism in Agricultural Research Institutions[J].Northern Horticulture,2013,37(20):199.
[2]李汶承,张显,张合旺,等.中国农业博物馆景观环境设计分析[J].北方园艺,2012,36(06):87.
LI Wen-cheng,ZHANG Xian,ZHANG He-wang,et al.The Analysis of the China Agricultural Museum Landscape Environment Design[J].Northern Horticulture,2012,36(20):87.
[3]祁卓麟.基于CNKI的国内外农业文献发文统计分析与评价研究[J].北方园艺,2013,37(18):190.
QIZhuo-lin.StatisticalAnalysisandEvaluationofPapersRelatedtoDomesticand InternationalAgricultureonCNKIfrom2008to2012[J].Northern Horticulture,2013,37(20):190.
[4]韩明臣,梁玉莲,王化儒.中国近三十年持续性农业气象灾害指标时空分布特征[J].北方园艺,2014,38(24):196.
HAN Ming-chen,LIANG Yu-lian,WANG Hua-ru.Temporal and Spatial Distribution Characteristics of Persistence Agrometeorological Disaster Indexes of China in the Latest 30 Years[J].Northern Horticulture,2014,38(20):196.
[5]翁福军,卢树昌.生物炭在农业领域应用的研究进展与前景[J].北方园艺,2015,39(08):199.[doi:10.11937/bfyy.201508052]
WENG Fu-jun,LU Shu-chang.Prospect and Research Advance in Biochar Application in Agricultural Fields[J].Northern Horticulture,2015,39(20):199.[doi:10.11937/bfyy.201508052]
[6]陈琳,宋炳良,曲菲,等.农业主题旅游资源在农业主题休闲园区内的应用研究[J].北方园艺,2015,39(13):202.[doi:10.11937/bfyy.201513056]
CHEN Lin,SONG Bingliang,QU Fei,et al.Research on the Applications of Agricultural Theme Tourism Resources in Agricultural Theme Leisure Parks[J].Northern Horticulture,2015,39(20):202.[doi:10.11937/bfyy.201513056]
[7]王亚秋,王弋,王煦,等.基于知识产权创造的河北省农业科技自主创新能力分析[J].北方园艺,2015,39(19):205.[doi:10.11937/bfyy.201519051]
WANG Yaqiu,WANG Yi,WANG Xu,et al.Analysis of the Independent Innovation Capability of Agricultural Technology Based on the Creation of Intellectual Property in Hebei Province[J].Northern Horticulture,2015,39(20):205.[doi:10.11937/bfyy.201519051]
[8]毕洪文,李金霞,宋丽娟.基于文献的我国农业物联网研究发展态势分析[J].北方园艺,2015,39(24):200.[doi:10.11937/bfyy.201524052]
BI Hongwen,LI Jinxia,SONG Lijuan.Status and Trends of Agricultural and Internet of Things Research in China Based on Bibliometrics[J].Northern Horticulture,2015,39(20):200.[doi:10.11937/bfyy.201524052]
[9]韩瑞,芮雪琴.人力资本、物质资本对农业经济增长影响的对比研究[J].北方园艺,2016,40(16):204.[doi:10.11937/bfyy.201616051]
HAN Rui,RUI Xueqin.Comparative Study on Human Capital,Physical Capital of Impact of Agriculture Economic Growth[J].Northern Horticulture,2016,40(20):204.[doi:10.11937/bfyy.201616051]
[10]阎世江,李照全,张治家.生物菌肥的研究现状与应用[J].北方园艺,2017,41(05):189.[doi:10.11937/bfyy.201705044]
YAN Shijiang,LI Zhaoquan,ZHANG Zhijia.Research Status and Application of Bacterial Manure[J].Northern Horticulture,2017,41(20):189.[doi:10.11937/bfyy.201705044]
备注/Memo
第一作者简介:孙娜(1991-),女,硕士研究生,研究方向为农业移动机器人。E-mail:785781566@qq.com.责任作者:王艳君(1964-),女,博士,教授,研究方向为电力系统分析与控制、智能电网。E-mail:764895320@qq.com.基金项目:国家重点研发计划资助项目(2017YFD0700303);国家自然科学基金资助项目(61305105);国家自然科学基金面上资助项目(31571564)。收稿日期:2019-03-13