A reinterpretation of the gap fraction of tree crowns from the perspectives of computer graphics and porous media theory
文献类型: 外文期刊
作者: Zhu, Yunfeng 1 ; Li, Dongni 1 ; Fan, Jiangchuan 2 ; Zhang, Huaiqing 3 ; Eichhorn, Markus P. 4 ; Wang, Xiangjun 6 ; Yun, Ting 1 ;
作者机构: 1.Nanjing Forestry Univ, Sch Informat Sci & Technol, Nanjing, Peoples R China
2.Natl Engn Res Ctr Informat Technol Agr, Beijing, Peoples R China
3.Chinese Acad Forestry, Res Inst Forestry Resource Informat Tech, Beijing, Peoples R China
4.Univ Coll Cork, Sch Biol Earth & Environm Sci, Cork, Ireland
5.Univ Coll Cork, Environm Res Inst, Cork, Ireland
6.Chinese Acad Trop Agr Sci, Rubber Res Inst, Haikou, Peoples R China
7.Nanjing Forestry Univ, Forestry Coll, Nanjing, Peoples R China
关键词: volume-based gap fraction; porous media theory; fine geometric characterization; equivalent leaf thickness; computer graphics
期刊名称:FRONTIERS IN PLANT SCIENCE ( 影响因子:5.6; 五年影响因子:6.8 )
ISSN: 1664-462X
年卷期: 2023 年 14 卷
页码:
收录情况: SCI
摘要: The gap fraction (GF) of vegetative canopies is an important property related to the contained bulk of reproductive elements and woody facets within the tree crown volume. This work was developed from the perspectives of porous media theory and computer graphics techniques, considering the vegetative elements in the canopy as a solid matrix and treating the gaps between them as pores to guide volume-based GF(vol) calculations. Woody components and individual leaves were extracted from terrestrial laser scanning data. The concept of equivalent leaf thickness describing the degrees of leaf curling and drooping was proposed to construct hexagonal prisms properly enclosing the scanned points of each leaf, and cylinder models were adopted to fit each branch segment, enabling the calculation of the equivalent leaf and branch volumes within the crown. Finally, the volume-based GF(vol) of the tree crown following the definition of the void fraction in porous media theory was calculated as one minus the ratio of the total plant leaf and branch volume to the canopy volume. This approach was tested on five tree species and a forest plot with variable canopy architecture, yielding an estimated maximum volume-based GF(vol) of 0.985 for a small crepe myrtle and a minimal volume-based GF(vol) of 0.953 for a sakura tree. The 3D morphology of each compositional element in the tree canopy was geometrically defined and the canopy was considered a porous structure to conduct GF(vol) calculations based on multidisciplinary theory.
- 相关文献
作者其他论文 更多>>
-
Method for the real-time detection of tomato ripeness using a phenotype robot and RP-YolactEdge
作者:Wang, Yuanqiao;Zhao, Chunjiang;Wang, Yuanqiao;Gou, Wenbo;Wang, Chuanyu;Fan, Jiangchuan;Wen, Weiliang;Lu, Xianju;Zhao, Chunjiang;Wang, Yuanqiao;Gou, Wenbo;Wang, Chuanyu;Wen, Weiliang;Lu, Xianju;Guo, Xinyu;Fan, Jiangchuan
关键词:instance segmentation; phenotype robot; tomato; greenhouse-based plant phenotyping; ripeness detection
-
Detection and Identification of Tassel States at Different Maize Tasseling Stages Using UAV Imagery and Deep Learning
作者:Du, Jianjun;Li, Jinrui;Fan, Jiangchuan;Gu, Shenghao;Guo, Xinyu;Zhao, Chunjiang;Du, Jianjun;Li, Jinrui;Fan, Jiangchuan;Gu, Shenghao;Guo, Xinyu;Li, Jinrui;Zhao, Chunjiang
关键词:
-
An improved YOLOv5 method for clam seedlings biological feature detection under the microscope
作者:Zhao, Yue;Fan, Jiangchuan;Xu, Wenkai;Zhao, Chunjiang;Chen, Liping;Zhao, Yue;Fan, Jiangchuan;Guo, Xinyu;Gou, Wenbo;Wen, Weiliang;Lu, Xianju;Zhao, Chunjiang;Zhao, Yue;Fan, Jiangchuan;Guo, Xinyu;Gou, Wenbo;Wen, Weiliang;Lu, Xianju;Zhao, Chunjiang;Chen, Liping;Xu, Wenkai;Jiang, Yongcheng
关键词:Deep learning; Object detection; YOLOv5; Clam; Attention mechanism
-
Research advance in phenotype detection robots for agriculture and forestry
作者:Wang, Yuanqiao;Fan, Jiangchuan;Yu, Shuan;Cai, Shuangze;Zhao, Chunjiang;Wang, Yuanqiao;Fan, Jiangchuan;Guo, Xinyu;Zhao, Chunjiang
关键词:computer vision; plant phenotype detection robot; phenotyping analysis; sensor; evaluation system; device clustering
-
Application of Internet of Things to Agriculture - the LQ-FieldPheno Platform: a High-throughput Platform for Obtaining Crop Phenotypes in Field
作者:Fan, Jiangchuan;Li, Yinglun;Yu, Shuan;Gou, Wenbo;Guo, Xinyu;Zhao, Chunjiang;Fan, Jiangchuan;Li, Yinglun;Yu, Shuan;Gou, Wenbo;Guo, Xinyu;Zhao, Chunjiang;Fan, Jiangchuan;Li, Yinglun;Yu, Shuan;Gou, Wenbo;Guo, Xinyu;Zhao, Chunjiang
关键词:plant phenotype; high-throughput phenotyping; Internet of Things; maize; point cloud
-
Deep learning models based on hyperspectral data and time-series phenotypes for predicting quality attributes in lettuces under water stress
作者:Yu, Shuan;Shao, Song;Liang, Dong;Yang, Xiaozeng;Guo, Xinyu;Zhao, Chunjiang;Yu, Shuan;Fan, Jiangchuan;Lu, Xianju;Wen, Weilian;Shao, Song;Guo, Xinyu;Zhao, Chunjiang;Yang, Xiaozeng
关键词:Hyperspectral data; Time-series phenotypes; Quality attributes; Water stress; Deep learning
-
Multi-Source Data Fusion Improves Time-Series Phenotype Accuracy in Maize under a Field High-Throughput Phenotyping Platform
作者:Li, Yinglun;Wen, Weiliang;Fan, Jiangchuan;Gou, Wenbo;Gu, Shenghao;Lu, Xianju;Guo, Xinyu;Li, Yinglun;Wen, Weiliang;Fan, Jiangchuan;Gou, Wenbo;Gu, Shenghao;Lu, Xianju;Yu, Zetao;Wang, Xiaodong;Guo, Xinyu
关键词: