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

文献类型：期刊论文

作者Jin, Shichao1; Su, Yanjun1; Gao, Shang1; Wu, Fangfang1; Ma, Qin1,2; Xu, Kexin1; Hu, Tianyu1; Liu, Jin1; Pang, Shuxin1; Guan, Hongcan1
刊名IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
出版日期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
DOI10.1109/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
出版地PISCATAWAY
电子版国际标准刊号1558-0644
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
WOS记录号WOS:000538748900029
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
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
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.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
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.

入库方式： OAI收割

来源：植物研究所

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