IGSNRR OpenIR
Optimization of multi-source UAV RS agro-monitoring schemes designed for field-scale crop phenotyping
Zhu, Wanxue1,2; Sun, Zhigang1,2,3,4; Huang, Yaohuan2,5; Yang, Ting4; Li, Jing1; Zhu, Kangying1,2; Zhang, Junqiang6,7; Yang, Bin7; Shao, Changxiu1; Peng, Jinbang1,2; Li, Shiji1,2; Hu, Hualang8; Liao, Xiaohan2,5,9,10,11
2021-05-03
Source PublicationPRECISION AGRICULTURE
ISSN1385-2256
Pages35
Corresponding AuthorSun, Zhigang(sun.zhigang@igsnrr.ac.cn)
AbstractUnmanned 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.
KeywordUnmanned aerial vehicle (UAV) Multispectral Hyperspectral Thermal LiDAR Phenotyping
DOI10.1007/s11119-021-09811-0
WOS KeywordLEAF-AREA INDEX ; SNAPSHOT HYPERSPECTRAL SENSOR ; CANOPY CHLOROPHYLL CONTENT ; VEGETATION INDEXES ; ABOVEGROUND BIOMASS ; WINTER-WHEAT ; MULTISPECTRAL IMAGES ; SURFACE MODELS ; GRAIN-YIELD ; MAIZE
Indexed BySCI
Language英语
Funding ProjectStrategic 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)
Funding OrganizationStrategic 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)
WOS Research AreaAgriculture
WOS SubjectAgriculture, Multidisciplinary
WOS IDWOS:000646491600001
PublisherSPRINGER
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/161619
Collection中国科学院地理科学与资源研究所
Corresponding AuthorSun, Zhigang
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
3.Shandong Dongying Inst Geog Sci, Dongying 257000, Peoples R China
4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, CAS Engn Lab Yellow River Delta Modern Agr, Beijing 100101, Peoples R China
5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
6.Chinese Acad Sci, Changchun Inst Opt, Fine Mech & Phys, Changchun 130033, Peoples R China
7.Yusense Informat Technol & Equipment Qingdao Ltd, Qingdao 266000, Peoples R China
8.Acad Agr Planning & Engn, Minist Agr & Rural Affairs, Key Lab Cultivated Land Use, Beijing 100125, Peoples R China
9.Chinese Acad Sci, Res Ctr UAV Applicat & Regulat, Beijing 100101, Peoples R China
10.Inst UAV Applicat Res, Tianjin, Peoples R China
11.Chinese Acad Sci, Tianjin 301800, Peoples R China
Recommended Citation
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.
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