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| Automatic segmentation of stem and leaf components and individual maize plants in field terrestrial LiDAR data using convolutional neural networks | |
Ao, Zurui; Wu, Fangfang1; Hu, Saihan; Sun, Ying; Guo, Yanjun1; Guo, Qinghua2,3 ; Xin, Qinchuan
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| 2022 | |
| Source Publication | CROP JOURNAL
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| ISSN | 2095-5421 |
| Volume | 10Issue:5Pages:1239-1250 |
| Abstract | High-throughput maize phenotyping at both organ and plant levels plays a key role in molecular breed-ing for increasing crop yields. Although the rapid development of light detection and ranging (LiDAR) pro-vides a new way to characterize three-dimensional (3D) plant structure, there is a need to develop robust algorithms for extracting 3D phenotypic traits from LiDAR data to assist in gene identification and selec-tion. Accurate 3D phenotyping in field environments remains challenging, owing to difficulties in seg-mentation of organs and individual plants in field terrestrial LiDAR data. We describe a two-stage method that combines both convolutional neural networks (CNNs) and morphological characteristics to segment stems and leaves of individual maize plants in field environments. It initially extracts stem points using the PointCNN model and obtains stem instances by fitting 3D cylinders to the points. It then segments the field LiDAR point cloud into individual plants using local point densities and 3D morpho-logical structures of maize plants. The method was tested using 40 samples from field observations and showed high accuracy in the segmentation of both organs (F-score =0.8207) and plants (F -score =0.9909). The effectiveness of terrestrial LiDAR for phenotyping at organ (including leaf area and stem position) and individual plant (including individual height and crown width) levels in field environ-ments was evaluated. The accuracies of derived stem position (position error =0.0141 m), plant height (R2 >0.99), crown width (R2 >0.90), and leaf area (R2 >0.85) allow investigating plant structural and func-tional phenotypes in a high-throughput way. This CNN-based solution overcomes the major challenges in organ-level phenotypic trait extraction associated with the organ segmentation, and potentially con-tributes to studies of plant phenomics and precision agriculture. (c) 2022 2022 Crop Science Society of China and Institute of Crop Science, CAAS. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY-NC -ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
| Keyword | Terrestrial LiDAR Phenotype Organ segmentation Convolutional neural networks |
| Subject Area | Agronomy ; Plant Sciences |
| DOI | 10.1016/j.cj.2021.10.010 |
| Indexed By | SCI |
| Language | 英语 |
| WOS Keyword | RADIOMETRIC CORRECTION ; CLASSIFICATION ; PHENOMICS ; TRAITS ; SYSTEM ; GROWTH |
| WOS Research Area | Science Citation Index Expanded (SCI-EXPANDED) |
| WOS ID | WOS:000888564500001 |
| Publisher | KEAI PUBLISHING LTD |
| Subtype | Article |
| Publication Place | BEIJING |
| EISSN | 2214-5141 |
| Funding Organization | Strategic Priority Research Program of the Chinese Academy of Sciences [XDA24020202] ; National Key Research and Development Program of China [2017YFA0604300] ; Western Talents [2018XBYJRC004] ; Guangdong Top Young Talents [2017TQ04Z359] ; National Natural Science Foundation of China [U1811464, 41875122] |
| Corresponding Author Email | xinqinchuan@gmail.com |
| OA | gold |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ibcas.ac.cn/handle/2S10CLM1/28484 |
| Collection | 植被与环境变化国家重点实验室 |
| Affiliation | 1.Sun Yat Sen Univ, Guangdong Key Lab Urbanizat & Geosimulat, Guangzhou 510275, Guangdong, Peoples R China 2.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China 3.Peking Univ, Coll Urban & Environm Sci, Dept Ecol, Beijing 100871, Peoples R China 4.Peking Univ, Key Lab Earth Surface Proc, Minist Educ, Beijing 100871, Peoples R China |
| Recommended Citation GB/T 7714 | Ao, Zurui,Wu, Fangfang,Hu, Saihan,et al. Automatic segmentation of stem and leaf components and individual maize plants in field terrestrial LiDAR data using convolutional neural networks[J]. CROP JOURNAL,2022,10(5):1239-1250. |
| APA | Ao, Zurui.,Wu, Fangfang.,Hu, Saihan.,Sun, Ying.,Guo, Yanjun.,...&Xin, Qinchuan.(2022).Automatic segmentation of stem and leaf components and individual maize plants in field terrestrial LiDAR data using convolutional neural networks.CROP JOURNAL,10(5),1239-1250. |
| MLA | Ao, Zurui,et al."Automatic segmentation of stem and leaf components and individual maize plants in field terrestrial LiDAR data using convolutional neural networks".CROP JOURNAL 10.5(2022):1239-1250. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| Automatic_segmentati（2926KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Application Full Text | |
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