Knowledge Management System Of Institute Of Botany,CAS
| Stem-Leaf Segmentation and Phenotypic Trait Extraction of Individual Maize Using Terrestrial LiDAR Data | |
Jin, Shichao1; Su, Yanjun1; Wu, Fangfang1; Pang, Shuxin1; Gao, Shang1; Hu, Tianyu1; Liu, Jin1; Guo, Qinghua1
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| 2019 | |
| 发表期刊 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
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| ISSN | 0196-2892 |
| 卷号 | 57期号:3页码:1336-1346 |
| 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. |
| 关键词 | Light detection and ranging (LiDAR) phenotypic traits regional growth segmentation skeleton |
| 学科领域 | Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology |
| DOI | 10.1109/TGRS.2018.2866056 |
| 收录类别 | SCI |
| 语种 | 英语 |
| 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 |
| WOS记录号 | WOS:000460321300009 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 文献子类 | Article |
| 出版地 | PISCATAWAY |
| EISSN | 1558-0644 |
| 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 |
| 作者邮箱 | jinshichao@ibcas.ac.cn ; suyanjun1987@gmail.com ; wufangfang@ibcas.ac.cn ; pangshuxin@ibcas.ac.cn ; gaoshang@ibcas.ac.cn ; tianyuhu@ibcas.ac.cn ; liujing1030@ibcas.ac.cn ; guo.qinghua@gmail.com |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | 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. |
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