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Optimization of multi-source UAV RS agro-monitoring schemes designed for field-scale crop phenotyping

文献类型：期刊论文

作者Zhu, Wanxue1,6; Sun, Zhigang1,5,6,11; Huang, Yaohuan3,6; Yang, Ting11; Li, Jing1; Zhu, Kangying1,6; Zhang, Junqiang9,10; Yang, Bin10; Shao, Changxiu1; Peng, Jinbang1,6
刊名PRECISION AGRICULTURE
出版日期2021-05-03
页码35
ISSN号1385-2256
关键词Unmanned aerial vehicle (UAV) Multispectral Hyperspectral Thermal LiDAR Phenotyping
DOI10.1007/s11119-021-09811-0
通讯作者Sun, Zhigang(sun.zhigang@igsnrr.ac.cn)
英文摘要Unmanned aerial vehicle (UAV) system is an emerging remote sensing tool for profiling crop phenotypic characteristics, as it distinctly captures crop real-time information on field scales. For optimizing UAV agro-monitoring schemes, this study investigated the performance of single-source and multi-source UAV data on maize phenotyping (leaf area index, above-ground biomass, crop height, leaf chlorophyll concentration, and plant moisture content). Four UAV systems [i.e., hyperspectral, thermal, RGB, and Light Detection and Ranging (LiDAR)] were used to conduct flight missions above two long-term experimental fields involving multi-level treatments of fertilization and irrigation. For reducing the effects of algorithm characteristics on maize parameter estimation and ensuring the reliability of estimates, multi-variable linear regression, backpropagation neural network, random forest, and support vector machine were used for modeling. Highly correlated UAV variables were filtered, and optimal UAV inputs were determined using a recursive feature elimination procedure. Major conclusions are (1) for single-source UAV data, LiDAR and RGB texture were suitable for leaf area index, above-ground biomass, and crop height estimation; hyperspectral outperformed on leaf chlorophyll concentration estimation; thermal worked for plant moisture content estimation; (2) model performance was slightly boosted via the fusion of multi-source UAV datasets regarding leaf area index, above-ground biomass, and crop height estimation, while single-source thermal and hyperspectral data outperformed multi-source data for the estimation of plant moisture and leaf chlorophyll concentration, respectively; (3) the optimal UAV scheme for leaf area index, above-ground biomass, and crop height estimation was LiDAR + RGB + hyperspectral, while considering practical agro-applications, optical Structure from Motion + customer-defined multispectral system was recommended owing to its cost-effectiveness. This study contributes to the optimization of UAV agro-monitoring schemes designed for field-scale crop phenotyping and further extends the applications of UAV technologies in precision agriculture.
WOS关键词LEAF-AREA INDEX ; SNAPSHOT HYPERSPECTRAL SENSOR ; CANOPY CHLOROPHYLL CONTENT ; VEGETATION INDEXES ; ABOVEGROUND BIOMASS ; WINTER-WHEAT ; MULTISPECTRAL IMAGES ; SURFACE MODELS ; GRAIN-YIELD ; MAIZE
资助项目Strategic Priority Research Program of the Chinese Academy of Sciences[XDA23050102] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19040303] ; Chinese Academy of Sciences Key Project[KFZD-SW-113] ; Chinese Academy of Sciences Key Project[KJZD-EW-G20] ; National Key Research and Development Program of China[2017YFC0503805] ; National Natural Science Foundation of China[31870421] ; National Natural Science Foundation of China[41771388] ; Tianjin Intelligent Manufacturing Project: Technology of Intelligent Networking by Autonomous Control UAVs for Observation and Application[Tianjin-IMP-2] ; Yellow River Delta Scholars Program (2020-2024)
WOS研究方向Agriculture
语种英语
出版者SPRINGER
WOS记录号WOS:000646491600001
资助机构Strategic Priority Research Program of the Chinese Academy of Sciences ; Chinese Academy of Sciences Key Project ; National Key Research and Development Program of China ; National Natural Science Foundation of China ; Tianjin Intelligent Manufacturing Project: Technology of Intelligent Networking by Autonomous Control UAVs for Observation and Application ; Yellow River Delta Scholars Program (2020-2024)
源URL[http://ir.igsnrr.ac.cn/handle/311030/161618]  
专题中国科学院地理科学与资源研究所
通讯作者Sun, Zhigang
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China
2.Acad Agr Planning & Engn, Minist Agr & Rural Affairs, Key Lab Cultivated Land Use, Beijing 100125, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
4.Chinese Acad Sci, Res Ctr UAV Applicat & Regulat, Beijing 100101, Peoples R China
5.Shandong Dongying Inst Geog Sci, Dongying 257000, Peoples R China
6.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
7.Chinese Acad Sci, Tianjin 301800, Peoples R China
8.Inst UAV Applicat Res, Tianjin, Peoples R China
9.Chinese Acad Sci, Changchun Inst Opt, Fine Mech & Phys, Changchun 130033, Peoples R China
10.Yusense Informat Technol & Equipment Qingdao Ltd, Qingdao 266000, Peoples R China
推荐引用方式
GB/T 7714		 Zhu, Wanxue,Sun, Zhigang,Huang, Yaohuan,et al. Optimization of multi-source UAV RS agro-monitoring schemes designed for field-scale crop phenotyping[J]. PRECISION AGRICULTURE,2021:35.
APA Zhu, Wanxue.,Sun, Zhigang.,Huang, Yaohuan.,Yang, Ting.,Li, Jing.,...&Liao, Xiaohan.(2021).Optimization of multi-source UAV RS agro-monitoring schemes designed for field-scale crop phenotyping.PRECISION AGRICULTURE,35.
MLA Zhu, Wanxue,et al."Optimization of multi-source UAV RS agro-monitoring schemes designed for field-scale crop phenotyping".PRECISION AGRICULTURE (2021):35.

入库方式： OAI收割

来源：地理科学与资源研究所

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