Knowledge Management System Of Institute Of Botany,CAS
| Separating the Structural Components of Maize for Field Phenotyping Using Terrestrial LiDAR Data and Deep Convolutional Neural Networks | |
Jin, Shichao1; Su, Yanjun1; Gao, Shang1; Wu, Fangfang1; Ma, Qin1,2; Xu, Kexin1; Hu, Tianyu1; Liu, Jin1; Pang, Shuxin1; Guan, Hongcan1; Zhang, Jing1; Guo, Qinghua1
| |
| 2020 | |
| Source Publication | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| ISSN | 0196-2892 |
| Volume | 58Issue:4Pages:2644-2658 |
| Abstract | Separating structural components is important but also challenging for plant phenotyping and precision agriculture. Light detection and ranging (LiDAR) technology can potentially overcome these difficulties by providing high quality data. However, there are difficulties in automatically classifying and segmenting components of interest. Deep learning can extract complex features, but it is mostly used with images. Here, we propose a voxel-based convolutional neural network (VCNN) for maize stem and leaf classification and segmentation. Maize plants at three different growth stages were scanned with a terrestrial LiDAR and the voxelized LiDAR data were used as inputs. A total of 3000 individual plants (22 004 leaves and 3000 stems) were prepared for training through data augmentation, and 103 maize plants were used to evaluate the accuracy of classification and segmentation at both instance and point levels. The VCNN was compared with traditional clustering methods K-means and density-based spatial clustering of applications with noise), a geometry-based segmentation method, and state-of-the-art deep learning methods (PointNet and PointNet++). The results showed that: 1) at the instance level, the mean accuracy of classification and segmentation (F-score) were 1.00 and 0.96, respectively; 2) at the point level, the mean accuracy of classification and segmentation (F-score) were 0.91 and 0.89, respectively; 3) the VCNN method outperformed traditional clustering methods; and 4) the VCNN was on par with PointNet and PointNet++ in classification, and performed the best in segmentation. The proposed method demonstrated LiDAR's ability to separate structural components for crop phenotyping using deep learning, which can be useful for other fields. |
| Keyword | Classification deep learning LiDAR phenotype segmentation structural components |
| Subject Area | Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology |
| DOI | 10.1109/TGRS.2019.2953092 |
| Indexed By | SCI |
| Language | 英语 |
| WOS Keyword | RECONSTRUCTION ; DENSITY ; CANOPY ; GROWTH ; FOREST |
| WOS Research Area | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS ID | WOS:000538748900029 |
| Publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| Subtype | Article |
| Publication Place | PISCATAWAY |
| EISSN | 1558-0644 |
| Funding Organization | National Key Research and Development Program of China [2016YFC0500202] ; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [31741016, 41871332] ; Strategic Priority Research Program of the Chinese Academy of SciencesChinese Academy of Sciences [XDA08040107] ; CAS Pioneer Hundred Talents Program |
| Corresponding Author Email | jinshichao@ibcas.ac.cn ; suyanjun1987@gmail.com ; gaos931024@gmail.com ; wufangfang@ibcas.ac.cn ; maqin@ibcas.ac.cn ; kexinxu@ibcas.ac.cn ; tianyuhu@ibcas.ac.cn ; liujing1030@ibcas.ac.cn ; pangshuxin@ibcas.ac.cn ; guanhongcan@gmail.com ; eve.zhangj@gmail.com ; guo.qinghua@gmail.com |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ibcas.ac.cn/handle/2S10CLM1/21728 |
| Collection | 植被与环境变化国家重点实验室 |
| Affiliation | 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 3.Mississippi State Univ, Dept Forestry, Starkville, MS 39762 USA |
| Recommended Citation GB/T 7714 | Jin, Shichao,Su, Yanjun,Gao, Shang,et al. Separating the Structural Components of Maize for Field Phenotyping Using Terrestrial LiDAR Data and Deep Convolutional Neural Networks[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2020,58(4):2644-2658. |
| APA | Jin, Shichao.,Su, Yanjun.,Gao, Shang.,Wu, Fangfang.,Ma, Qin.,...&Guo, Qinghua.(2020).Separating the Structural Components of Maize for Field Phenotyping Using Terrestrial LiDAR Data and Deep Convolutional Neural Networks.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,58(4),2644-2658. |
| MLA | Jin, Shichao,et al."Separating the Structural Components of Maize for Field Phenotyping Using Terrestrial LiDAR Data and Deep Convolutional Neural Networks".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 58.4(2020):2644-2658. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| 175923.pdf（14928KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Application Full Text | |
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment