IB-CAS  > 植被与环境变化国家重点实验室
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 PublicationIEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN0196-2892
Volume58Issue:4Pages:2644-2658
AbstractSeparating 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.
KeywordClassification deep learning LiDAR phenotype segmentation structural components
Subject AreaGeochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
DOI10.1109/TGRS.2019.2953092
Indexed BySCI
Language英语
WOS KeywordRECONSTRUCTION ; DENSITY ; CANOPY ; GROWTH ; FOREST
WOS Research AreaGeochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000538748900029
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
SubtypeArticle
Publication PlacePISCATAWAY
EISSN1558-0644
Funding OrganizationNational 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 Emailjinshichao@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
Cited Times:24[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ibcas.ac.cn/handle/2S10CLM1/21728
Collection植被与环境变化国家重点实验室
Affiliation1.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.
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