High-Throughput Extraction of the Distributions of Leaf Base and Inclination Angles of Maize in the Field

被引:3
作者
Lei, Lei [1 ,2 ]
Li, Zhenhong [3 ,4 ]
Yang, Guijun [5 ,6 ,7 ]
Yang, Hao [5 ]
机构
[1] Changan Univ, Coll Geol Engn & Geomat, Key Lab Loess, Xian 710054, Peoples R China
[2] Changan Univ, Big Data Ctr Geosci & Satellites BDCGS, Xian 710054, Peoples R China
[3] Changan Univ, Coll Geol Engn & Geomat, Big Data Ctr Geosci & Satellites BDCGS, Key Lab Loess,Minist Educ,Key Lab Western Chinas M, Xian 710054, Peoples R China
[4] Changan Univ, Key Lab Ecol Geol & Disaster Prevent, Minist Nat Resources, Xian 710054, Peoples R China
[5] Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China
[6] Key Lab Quantitat Remote Sensing Agr Minist Agr &, Beijing 100097, Peoples R China
[7] Changan Univ, Coll Geol Engn & Geomat, Xian 710054, Peoples R China
来源
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | 2023年 / 61卷
关键词
Data mining; Crops; Skeleton; Point cloud compression; Geoengineering; Forestry; Image reconstruction; Different cultivars; different growth stages; different planting densities; distributions of leaf base and inclination angles; light interception capacity; terrestrial laser {scanning (TLS)}; TERRESTRIAL LIDAR; PHOTOSYNTHESIS; DENSITY; MODEL;
D O I
10.1109/TGRS.2023.3332869
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
Distributions of leaf base and inclination angles are important crop phenotypic traits, influencing light interception and productivity. Light detection and ranging (LiDAR), and especially terrestrial laser scanning (TLS), provides unprecedented detail of the 3-D structure of the crop canopy. Recent research mainly focuses on the leaf base and inclination angles of maize at the individual level or lower planting density. It is difficult to extract the distributions of leaf base and inclination angles of maize in the field due to the interlocked and overlapped nature of leaves. In this study, we have proposed a high-throughput method to extract the distributions of leaf base and inclination angles of maize in the field. Following the separation of the leaf and stem of maize, hollow cylinders with different thicknesses were used to extract the local leaf points from the separated leaf points based on each stem fit line, and the density-based spatial clustering of applications with noise (DBSCAN) algorithm and singular value decomposition were used to calculate the leaf base and inclination angles. The distributions of leaf base and inclination angles of maize in the field with different cultivars [Jingjiuqingchu 16 (A1), Tianci 19 (A2), Jingnuo 2008 (A3), Nongkenuo 336 (A4), and Zhengdan 958 (A5)], planting densities (3.32, 4.65, 6.64, and 8.63 plants/m(2)), and growth stages (jointing, silking, and filling stages) were extracted and analyzed, and these performed well against the validation data. In addition to TLS data, the extraction of the distributions of leaf base and inclination angles based on LiBackpack and unmanned aerial vehicle (UAV)-LiDAR data was also discussed. This further validated the potential of the method proposed in this study for the extraction of the distributions of leaf base and inclination angles of maize in the field. Furthermore, the relationship between maize with different leaf base angle distributions and the daily cumulative absorbed photosynthetically active radiation (APAR) was analyzed, which demonstrated that compact maize cultivars exhibited higher light interception capabilities than scattered ones under high planting densities. The distributions of leaf base and inclination angles exert a substantial influence on the light interception capacity of maize, thereby exerting a consequential effect on maize yield. The high-throughput extraction of these distributions in maize fields holds significant importance for studying the optimal maize cultivar in conjunction with radiative transfer models.
引用
收藏
页码:1 / 28
页数:28
相关论文
共 49 条
[1]   Using terrestrial laser scanning for characterizing tree structural parameters and their changes under different management in a Mediterranean open woodland [J].
Bogdanovich, Ekaterina ;
Perez-Priego, Oscar ;
El-Madany, Tarek S. ;
Guderle, Marcus ;
Pacheco-Labrador, Javier ;
Levick, Shaun R. ;
Moreno, Gerardo ;
Carrara, Arnaud ;
Martin, M. Pilar ;
Migliavacca, Mirco .
FOREST ECOLOGY AND MANAGEMENT, 2021, 486
[2]   3D terrestrial lidar data classification of complex natural scenes using a multi-scale dimensionality criterion: Applications in geomorphology [J].
Brodu, N. ;
Lague, D. .
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2012, 68 :121-134
[3]   Estimation of maize plant height and leaf area index dynamics using an unmanned aerial vehicle with oblique and nadir photography [J].
Che, Yingpu ;
Wang, Qing ;
Xie, Ziwen ;
Zhou, Long ;
Li, Shuangwei ;
Hui, Fang ;
Wang, Xiqing ;
Li, Baoguo ;
Ma, Yuntao .
ANNALS OF BOTANY, 2020, 126 (04) :765-773
[4]   A MODEL FOR SIMULATING PHOTOSYNTHESIS IN PLANT COMMUNITIES [J].
DUNCAN, WG ;
LOOMIS, RS ;
WILLIAMS, WA ;
HANAU, R .
HILGARDIA, 1967, 38 (04) :181-&
[5]   An automated approach for wood-leaf separation from terrestrial LIDAR point clouds using the density based clustering algorithm DBSCAN [J].
Ferrara, Roberto ;
Virdis, Salvatore G. P. ;
Ventura, Andrea ;
Ghisu, Tiziano ;
Duce, Pierpaolo ;
Pellizzaro, Grazia .
AGRICULTURAL AND FOREST METEOROLOGY, 2018, 262 :434-444
[6]   Discrete Anisotropic Radiative Transfer (DART 5) for Modeling Airborne and Satellite Spectroradiometer and LIDAR Acquisitions of Natural and Urban Landscapes [J].
Gastellu-Etchegorry, Jean-Philippe ;
Yin, Tiangang ;
Lauret, Nicolas ;
Cajgfinger, Thomas ;
Gregoire, Tristan ;
Grau, Eloi ;
Feret, Jean-Baptiste ;
Lopes, Mailys ;
Guilleux, Jordan ;
Dedieu, Gerard ;
Malenovsky, Zbynek ;
Cook, Bruce Douglas ;
Morton, Douglas ;
Rubio, Jeremy ;
Durrieu, Sylvie ;
Cazanave, Gregory ;
Martin, Emmanuel ;
Ristorcelli, Thomas .
REMOTE SENSING, 2015, 7 (02) :1667-1701
[7]   Effects of Topographic Variability and Lidar Sampling Density on Several DEM Interpolation Methods [J].
Guo, Qinghua ;
Li, Wenkai ;
Yu, Hong ;
Alvarez, Otto .
PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING, 2010, 76 (06) :701-712
[8]   The rule-based language XL and the modelling environment GroIMP illustrated with simulated tree competition [J].
Hemmerling, Reinhard ;
Kniemeyer, Ole ;
Lanwert, Dirk ;
Kurth, Winfried ;
Buck-Sorlin, Gerhard .
FUNCTIONAL PLANT BIOLOGY, 2008, 35 (9-10) :739-750
[9]  
Jackson D, 1999, DEVELOPMENT, V126, P315
[10]   Simulation on Different Patterns of Mobile Laser Scanning with Extended Application on Solar Beam Illumination for Forest Plot [J].
Jiang, Kang ;
Chen, Liang ;
Wang, Xiangjun ;
An, Feng ;
Zhang, Huaiqing ;
Yun, Ting .
FORESTS, 2022, 13 (12)