Separating the Structural Components of Maize for Field Phenotyping Using Terrestrial LiDAR Data and Deep Convolutional Neural Networks

doi:10.1109/TGRS.2019.2953092

CORC > 植物研究所 > 中国科学院植物研究所 > 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, Shichao 2; Su, Yanjun 2; Gao, Shang 2; Wu, Fangfang 2; Ma, Qin 1,2; Xu, Kexin 2; Hu, Tianyu 2; Liu, Jin 2; Pang, Shuxin 2; Guan, Hongcan 2
刊名	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
	2020
卷号	58 期号:4 页码:2644-2658
关键词	Classification deep learning LiDAR phenotype segmentation structural components
ISSN号	0196-2892
DOI	10.1109/TGRS.2019.2953092
文献子类	Article
英文摘要	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.
学科主题	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
电子版国际标准刊号	1558-0644
出版地	PISCATAWAY
WOS关键词	RECONSTRUCTION ; DENSITY ; CANOPY ; GROWTH ; FOREST
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000538748900029
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
内容类型	期刊论文
源URL	[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.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China 3.Mississippi State Univ, Dept Forestry, Starkville, MS 39762 USA
推荐引用方式 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.