Stem-Leaf Segmentation and Phenotypic Trait Extraction of Individual Maize Using Terrestrial LiDAR Data

doi:10.1109/TGRS.2018.2866056

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

	Stem-Leaf Segmentation and Phenotypic Trait Extraction of Individual Maize Using Terrestrial LiDAR Data
	Jin, Shichao 1; Su, Yanjun 1; Wu, Fangfang 1; Pang, Shuxin 1; Gao, Shang 1; Hu, Tianyu 1; Liu, Jin 1; Guo, Qinghua 1
刊名	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
	2019
卷号	57 期号:3 页码:1336-1346
关键词	Light detection and ranging (LiDAR) phenotypic traits regional growth segmentation skeleton
ISSN号	0196-2892
DOI	10.1109/TGRS.2018.2866056
文献子类	Article
英文摘要	Accurate and high throughput extraction of crop phenotypic traits, as a crucial step of molecular breeding, is of great importance for yield increasing. However, automatic stem-leaf segmentation as a prerequisite of many precise phenotypic trait extractions is still a big challenge. Current works focus on the study of the 2-D image-based segmentation, which are sensitive to illumination and occlusion. Light detection and ranging (LiDAR) can obtain accurate 3-D information with its active laser scanning and strong penetration ability, which breaks through phenotyping from 2-D to 3-D. However, few researches have addressed the problem of the LiDAR-based stem-leaf segmentation. In this paper, we proposed a median normalized-vector growth (MNVG) algorithm, which can segment stem and leaf with four steps, i.e., preprocessing, stem growth, leaf growth, and postprocessing. The MNVG method was tested by 30 maize samples with different heights, compactness, leaf numbers, and densities from three growing stages. Moreover, phenotypic traits at leaf, stem, and individual levels were extracted with the truly segmented instances. The mean accuracy of segmentation at point level in terms of the recall, precision, F-score, and overall accuracy were 0.92, 0.93, 0.92, and 0.93, respectively. The accuracy of phenotypic trait extraction in leaf, stem, and individual levels ranged from 0.81 to 0.95, 0.64 to 0.97, and 0.96 to 1, respectively. To our knowledge, this paper proposed the first LiDAR-based stem-leaf segmentation and phenotypic trait extraction method in agriculture field, which may contribute to the study of LiDAR-based plant phonemics and precise agriculture.
学科主题	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
电子版国际标准刊号	1558-0644
出版地	PISCATAWAY
WOS关键词	F-SCORE ; PLANT ; RECONSTRUCTION ; IDENTIFICATION ; PHENOMICS ; RESPONSES ; PLATFORM ; DENSITY ; GROWTH ; TREES
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000460321300009
资助机构	National Key R&D Program of China [2017YFC0503905, 2016YFC0500202] ; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [41471363, 31741016] ; Strategic Priority Research Program of the Chinese Academy of SciencesChinese Academy of Sciences [XDA08040107] ; Annual Graduation Practice Training Program of Beijing City University ; CAS Pioneer Hundred Talents Program
内容类型	期刊论文
源URL	[http://ir.ibcas.ac.cn/handle/2S10CLM1/19492]
专题	植被与环境变化国家重点实验室
作者单位	1.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Jin, Shichao,Su, Yanjun,Wu, Fangfang,et al. Stem-Leaf Segmentation and Phenotypic Trait Extraction of Individual Maize Using Terrestrial LiDAR Data[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2019,57(3):1336-1346.
APA	Jin, Shichao.,Su, Yanjun.,Wu, Fangfang.,Pang, Shuxin.,Gao, Shang.,...&Guo, Qinghua.(2019).Stem-Leaf Segmentation and Phenotypic Trait Extraction of Individual Maize Using Terrestrial LiDAR Data.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,57(3),1336-1346.
MLA	Jin, Shichao,et al."Stem-Leaf Segmentation and Phenotypic Trait Extraction of Individual Maize Using Terrestrial LiDAR Data".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 57.3(2019):1336-1346.