中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 地理科学与资源研究所
  3. 中国科学院地理科学与资源研究所
A New Method for Crop Row Detection Using Unmanned Aerial Vehicle Images

文献类型：期刊论文

作者Chen, Pengfei3,4,5; Ma, Xiao2,3; Wang, Fangyong1; Li, Jing3
刊名REMOTE SENSING
出版日期2021-09-01
卷号13期号:17页码:16
关键词row detection wheat cotton unmanned aerial vehicle image
DOI10.3390/rs13173526
通讯作者Li, Jing(Jingli@igsnrr.ac.cn)
英文摘要Crop row detection using unmanned aerial vehicle (UAV) images is very helpful for precision agriculture, enabling one to delineate site-specific management zones and to perform precision weeding. For crop row detection in UAV images, the commonly used Hough transform-based method is not sufficiently accurate. Thus, the purpose of this study is to design a new method for crop row detection in orthomosaic UAV images. For this purpose, nitrogen field experiments involving cotton and nitrogen and water field experiments involving wheat were conducted to create different scenarios for crop rows. During the peak square growth stage of cotton and the jointing growth stage of wheat, multispectral UAV images were acquired. Based on these data, a new crop detection method based on least squares fitting was proposed and compared with a Hough transform-based method that uses the same strategy to preprocess images. The crop row detection accuracy (CRDA) was used to evaluate the performance of the different methods. The results showed that the newly proposed method had CRDA values between 0.99 and 1.00 for different nitrogen levels of cotton and CRDA values between 0.66 and 0.82 for different nitrogen and water levels of wheat. In contrast, the Hough transform method had CRDA values between 0.93 and 0.98 for different nitrogen levels of cotton and CRDA values between 0.31 and 0.53 for different nitrogen and water levels of wheat. Thus, the newly proposed method outperforms the Hough transform method. An effective tool for crop row detection using orthomosaic UAV images is proposed herein.
WOS关键词AUTOMATIC DETECTION ; PRECISION AGRICULTURE ; SYSTEM ; NETWORK ; INDEXES
资助项目National Science and Technology Major Project of China's High Resolution Earth Observation System[21-Y20B01-9001-19/22] ; National Natural Science Foundation of China[41871344] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA23100101]
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000694482100001
出版者MDPI
资助机构National Science and Technology Major Project of China's High Resolution Earth Observation System ; National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences
源URL[http://ir.igsnrr.ac.cn/handle/311030/165433]  
专题中国科学院地理科学与资源研究所
通讯作者Li, Jing
作者单位1.Xinjiang Acad Agr & Redamat Sci, Cotton Inst, Shihezi 832000, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
4.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China
5.Natl Sci & Technol Infrastruct China, Natl Earth Syst Sci Data Ctr, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Chen, Pengfei,Ma, Xiao,Wang, Fangyong,et al. A New Method for Crop Row Detection Using Unmanned Aerial Vehicle Images[J]. REMOTE SENSING,2021,13(17):16.
APA Chen, Pengfei,Ma, Xiao,Wang, Fangyong,&Li, Jing.(2021).A New Method for Crop Row Detection Using Unmanned Aerial Vehicle Images.REMOTE SENSING,13(17),16.
MLA Chen, Pengfei,et al."A New Method for Crop Row Detection Using Unmanned Aerial Vehicle Images".REMOTE SENSING 13.17(2021):16.

入库方式： OAI收割

来源：地理科学与资源研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。