Knowledge Management System Of Institute Of Botany,CAS
| Deep Learning: Individual Maize Segmentation From Terrestrial Lidar Data Using Faster R-CNN and Regional Growth Algorithms | |
Jin, Shichao1; Su, Yanjun; Gao, Shang1; Wu, Fangfang1; Hu, Tianyu; Liu, Jin; Li, Wankai2; Wang, Dingchang3; Chen, Shaojiang3; Jiang, Yuanxi4; Pang, Shuxin; Guo, Qinghua1
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| 2018 | |
| 发表期刊 | FRONTIERS IN PLANT SCIENCE
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| ISSN | 1664-462X |
| 卷号 | 9 |
| 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. |
| 关键词 | deep learning detection classification segmentation phenotype Lidar (light detection and ranging) |
| 学科领域 | Plant Sciences |
| DOI | 10.3389/fpls.2018.00866 |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS关键词 | TREE CROWNS ; FOOD SECURITY ; PHENOTYPING TECHNOLOGY ; STEM VOLUME ; DENSITY ; SYSTEM |
| WOS记录号 | WOS:000436081200001 |
| 出版者 | FRONTIERS MEDIA SA |
| 文献子类 | Article |
| 出版地 | LAUSANNE |
| 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] |
| 作者邮箱 | suyanjun1987@gmail.com ; guo.qinghua@gmail.com |
| 作品OA属性 | gold, Green Published |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | 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.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Sun Yat Sen Univ, Sch Geog & Planning, Guangdong Prov Key Lab Urbanizat & Geosimulat, Guangzhou, Guangdong, Peoples R China 4.China Agr Univ, Natl Maize Improvement Ctr China, Beijing, Peoples R China 5.Beijing City Univ, Urban Construct Sch, 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). |
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| S1664462X_S1664462X1（2278KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | 浏览 请求全文 | |
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