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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
发表期刊IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN0196-2892
卷号58期号:4页码:2644-2658
摘要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.
关键词Classification deep learning LiDAR phenotype segmentation structural components
学科领域Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
DOI10.1109/TGRS.2019.2953092
收录类别SCI
语种英语
WOS关键词RECONSTRUCTION ; DENSITY ; CANOPY ; GROWTH ; FOREST
WOS研究方向Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号WOS:000538748900029
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
文献子类Article
出版地PISCATAWAY
EISSN1558-0644
资助机构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
作者邮箱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
引用统计
被引频次:55[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ibcas.ac.cn/handle/2S10CLM1/21728
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
3.Mississippi State Univ, Dept Forestry, Starkville, MS 39762 USA
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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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