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Deep Learning: Individual Maize Segmentation From Terrestrial Lidar Data Using Faster R-CNN and Regional Growth Algorithms

文献类型：期刊论文

作者Jin, Shichao5; Su, Yanjun; Gao, Shang5; Wu, Fangfang5; Hu, Tianyu; Liu, Jin; Li, Wankai4; Wang, Dingchang3; Chen, Shaojiang3; Jiang, Yuanxi2
刊名FRONTIERS IN PLANT SCIENCE
出版日期2018
卷号9
关键词deep learning detection classification segmentation phenotype Lidar (light detection and ranging)
ISSN号1664-462X
DOI10.3389/fpls.2018.00866
文献子类Article
英文摘要The rapid development of light detection and ranging (Lidar) provides a promising way to obtain three-dimensional (3D) phenotype traits with its high ability of recording accurate 3D laser points. Recently, Lidar has been widely used to obtain phenotype data in the greenhouse and field with along other sensors. Individual maize segmentation is the prerequisite for high throughput phenotype data extraction at individual crop or leaf level, which is still a huge challenge. Deep learning, a state-of-the-art machine learning method, has shown high performance in object detection, classification, and segmentation. In this study, we proposed a method to combine deep leaning and regional growth algorithms to segment individual maize from terrestrial Lidar data. The scanned 3D points of the training site were sliced row and row with a fixed 3D window. Points within the window were compressed into deep images, which were used to train the Faster R-CNN (region-based convolutional neural network) model to learn the ability of detecting maize stem. Three sites of different planting densities were used to test the method. Each site was also sliced into many 3D windows, and the testing deep images were generated. The detected stem in the testing images can be mapped into 3D points, which were used as seed points for the regional growth algorithm to grow individual maize from bottom to up. The results showed that the method combing deep leaning and regional growth algorithms was promising in individual maize segmentation, and the values of r, p, and F of the three testing sites with different planting density were all over 0.9. Moreover, the height of the truly segmented maize was highly correlated to the manually measured height (R-2 > 0.9). This work shows the possibility of using deep leaning to solve the individual maize segmentation problem from Lidar data.
学科主题Plant Sciences
出版地LAUSANNE
WOS关键词TREE CROWNS ; FOOD SECURITY ; PHENOTYPING TECHNOLOGY ; STEM VOLUME ; DENSITY ; SYSTEM
语种英语
WOS记录号WOS:000436081200001
出版者FRONTIERS MEDIA SA
资助机构National Key R&D Program of China [2017YFC0503905, 2016YFC0500202] ; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [41471363, 31741016] ; Strategic Priority Research Program of the Chinese Academy of SciencesChinese Academy of Sciences [XDA08040107] ; Beijing City University ; CAS Pioneer Hundred Talents Program ; US National Science FoundationNational Science Foundation (NSF) [EAR 0922307]
源URL[http://ir.ibcas.ac.cn/handle/2S10CLM1/20372]  
专题植被与环境变化国家重点实验室
作者单位1.Beijing City Univ, Urban Construct Sch, Beijing, Peoples R China
2.China Agr Univ, Natl Maize Improvement Ctr China, Beijing, Peoples R China
3.Sun Yat Sen Univ, Sch Geog & Planning, Guangdong Prov Key Lab Urbanizat & Geosimulat, Guangzhou, Guangdong, Peoples R China
4.Univ Chinese Acad Sci, Beijing, Peoples R China
5.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Jin, Shichao,Su, Yanjun,Gao, Shang,et al. Deep Learning: Individual Maize Segmentation From Terrestrial Lidar Data Using Faster R-CNN and Regional Growth Algorithms[J]. FRONTIERS IN PLANT SCIENCE,2018,9.
APA Jin, Shichao.,Su, Yanjun.,Gao, Shang.,Wu, Fangfang.,Hu, Tianyu.,...&Guo, Qinghua.(2018).Deep Learning: Individual Maize Segmentation From Terrestrial Lidar Data Using Faster R-CNN and Regional Growth Algorithms.FRONTIERS IN PLANT SCIENCE,9.
MLA Jin, Shichao,et al."Deep Learning: Individual Maize Segmentation From Terrestrial Lidar Data Using Faster R-CNN and Regional Growth Algorithms".FRONTIERS IN PLANT SCIENCE 9(2018).

入库方式： OAI收割

来源：植物研究所

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