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

文献类型：期刊论文

作者Jin, Shichao1; Su, Yanjun1; Wu, Fangfang1; Pang, Shuxin1; Gao, Shang1; Hu, Tianyu1; Liu, Jin1; Guo, Qinghua1
刊名IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
出版日期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
DOI10.1109/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
出版地PISCATAWAY
电子版国际标准刊号1558-0644
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
WOS记录号WOS:000460321300009
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
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
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.

入库方式： OAI收割

来源：植物研究所

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