Deciphering the contributions of spectral and structural data to wheat yield estimation from proximal sensing

doi:10.1016/j.cj.2022.06.005

CORC > 植物研究所 > 中国科学院植物研究所 > 植被与环境变化国家重点实验室

	Deciphering the contributions of spectral and structural data to wheat yield estimation from proximal sensing
	Li, Qing ; Jin, Shichao 1; Zang, Jingrong ; Wang, Xiao ; Sun, Zhuangzhuang ; Li, Ziyu ; Xu, Shan ; Ma, Qin 5; Su, Yanjun 6; Guo, Qinghua 3,4
刊名	CROP JOURNAL
	2022
卷号	10 期号:5 页码:1334-1345
关键词	LiDAR Multispectral Yield Phenotype Hyper-temporal
ISSN号	2095-5421
DOI	10.1016/j.cj.2022.06.005
文献子类	Article
英文摘要	Accurate, efficient, and timely yield estimation is critical for crop variety breeding and management optimization. However, the contributions of proximal sensing data characteristics (spectral, temporal, and spatial) to yield estimation have not been systematically evaluated. We collected long-term, hyper temporal,and large-volume light detection and ranging (LiDAR) and multispectral data to (i) identify the best machine learning method and prediction stage for wheat yield estimation, (ii) characterize the contribution of multisource data fusion and the dynamic importance of structural and spectral traits to yield estimation, and (iii) elucidate the contribution of time-series data fusion and 3D spatial information to yield estimation. Wheat yield could be accurately (R-2 = 0.891) and timely (approximately-two months before harvest) estimated from fused LiDAR and multispectral data. The artificial neural network model and the flowering stage were always the best method and prediction stage, respectively. Spectral traits (such as CIgreen) dominated yield estimation, especially in the early stage, whereas the contribution of structural traits (such as height) was more stable in the late stage. Fusing spectral and structural traits increased estimation accuracy at all growth stages. Better yield estimation was realized from traits derived from complete 3D points than from canopy surface points and from integrated multi-stage (especially from jointing to heading and flowering stages) data than from single-stage data. We suggest that this study offers a novel perspective on deciphering the contributions of spectral, structural, and time-series information to wheat yield estimation and can guide accurate, efficient, and timely estimation of wheat yield.(C) 2022 Crop Science Society of China and Institute of Crop Science, CAAS. Production and hosting by Elsevier B.V.
学科主题	Agronomy ; Plant Sciences
电子版国际标准刊号	2214-5141
出版地	BEIJING
WOS关键词	PREDICTING GRAIN-YIELD ; PROTEIN-CONTENT ; RANDOM FOREST ; BIOMASS ; LEAF ; NITROGEN ; MODELS
WOS研究方向	Science Citation Index Expanded (SCI-EXPANDED)
语种	英语
出版者	KEAI PUBLISHING LTD
WOS记录号	WOS:000895382300004
资助机构	Jiangsu Agricultural Science and Technology Independent Innovation Fund Project [CX(21) 3107] ; National Natural Science Foundation of China [32030076] ; High Level Personnel Project of Jiangsu Province [JSSCBS20210271] ; China Postdoctoral Science Foundation [2021 M691490] ; Jiangsu Planned Projects for Postdoctoral Research Funds [2021K520C] ; Strategic Priority Research Program of the Chinese Academy of Sciences [XDA24020202] ; Jiangsu 333 Program
内容类型	期刊论文
源URL	[http://ir.ibcas.ac.cn/handle/2S10CLM1/28496]
专题	植被与环境变化国家重点实验室
作者单位	1.Nanjing Agr Univ, Plant Phen Res Ctr,Acad Adv Interdisciplinary Stu, Reg Tech Innovat Ctr Wheat Prod,Minist Agr,Collab, Key Lab Crop Physiol & Ecol Southern China,Jiangs, Nanjing 210095, Jiangsu, Peoples R China 2.Peking Univ, Minist Educ, Key Lab Earth Surface Proc, Beijing 100871, Peoples R China 3.Peking Univ, Dept Ecol, Coll Environm Sci, Beijing 100871, Peoples R China 4.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China 5.Nanjing Univ, Int Inst Earth Syst Sci, Jiangsu Prov Key Lab Geog Informat Sci & Technol, Nanjing 210023, Jiangsu, Peoples R China 6.Mississippi State Univ, Dept Forestry, Mississippi State, MS 39759 USA
推荐引用方式 GB/T 7714	Li, Qing,Jin, Shichao,Zang, Jingrong,et al. Deciphering the contributions of spectral and structural data to wheat yield estimation from proximal sensing[J]. CROP JOURNAL,2022,10(5):1334-1345.
APA	Li, Qing.,Jin, Shichao.,Zang, Jingrong.,Wang, Xiao.,Sun, Zhuangzhuang.,...&Jiang, Dong.(2022).Deciphering the contributions of spectral and structural data to wheat yield estimation from proximal sensing.CROP JOURNAL,10(5),1334-1345.
MLA	Li, Qing,et al."Deciphering the contributions of spectral and structural data to wheat yield estimation from proximal sensing".CROP JOURNAL 10.5(2022):1334-1345.