Estimation of maize plant height and leaf area index dynamics using an unmanned aerial vehicle with oblique and nadir photography

被引:55
作者
Che, Yingpu [1 ]
Wang, Qing [1 ]
Xie, Ziwen [1 ]
Zhou, Long [2 ]
Li, Shuangwei [1 ]
Hui, Fang [1 ]
Wang, Xiqing [2 ]
Li, Baoguo [1 ]
Ma, Yuntao [1 ]
机构
[1] China Agr Univ, Coll Resources & Environm Sci, Key Lab Arable Land Conservat North China, Minist Agr, Beijing 100193, Peoples R China
[2] China Agr Univ, Coll Biol Sci, Ctr Crop Funct Genom & Mol Breeding, Beijing 100193, Peoples R China
基金
美国国家科学基金会;
关键词
UAV; oblique photography; plant height; LAI; 3-D; Zea mays L; LOW-ALTITUDE; PHENOTYPING PLATFORM; VEGETATION INDEXES; FIELD; UAV; DENSITY; SYSTEMS; SORGHUM; VISION; IMAGES;
D O I
10.1093/aob/mcaa097
中图分类号
Q94 [植物学];
学科分类号
071001 ;
摘要
Background and Aims High-throughput phenotyping is a limitation in plant genetics and breeding due to large-scale experiments in the field. Unmanned aerial vehicles (UAVs) can help to extract plant phenotypic traits rapidly and non-destructively with high efficiency. The general aim of this study is to estimate the dynamic plant height and leaf area index (LAI) by nadir and oblique photography with a UAV, and to compare the integrity of the established three-dimensional (3-D) canopy by these two methods. Methods Images were captured by a high-resolution digital RGB camera mounted on a LTAV at five stages with nadir and oblique photography. and processed by Agisoft Metashape to generate point clouds, orthomosaic maps and digital surface models. Individual plots were segmented according to their positions in the experimental design layout. The plant height of each inbred line was calculated automatically by a reference ground method. The LAI was calculated by the 3-D voxel method. The reconstructed canopy was sliced into different layers to compare leaf area density obtained from oblique and nadir photography. Key Results Good agreements were found for plant height between nadir photography, oblique photography and manual measurement during the whole growing season. The estimated LAI by oblique photography correlated better with measured LAI (slope = 0.87, R-2 = 0.67), compared with that of nadir photography (slope = 0.74, R-2 = 0.56). The total number of point clouds obtained by oblique photography was about 2.7-3.1 times than those by nadir photography. Leaf area density calculated by nadir photography was much less than that obtained by oblique photography, especially near the plant base. Conclusions Plant height and LAI can be extracted automatically and efficiently by both photography methods. Oblique photography can provide intensive point clouds and relatively complete canopy information at low cost. The reconstructed 3-D profile of the plant canopy can be easily recognized by oblique photography.
引用
收藏
页码:765 / 773
页数:9
相关论文
共 41 条
  • [1] UAV PHOTOGRAMMETRY WITH OBLIQUE IMAGES: FIRST ANALYSIS ON DATA ACQUISITION AND PROCESSING
    Aicardi, I.
    Chiabrando, F.
    Grasso, N.
    Lingua, A. M.
    Noardo, F.
    Spano, A.
    [J]. XXIII ISPRS CONGRESS, COMMISSION I, 2016, 41 (B1): : 835 - 842
  • [2] [Anonymous], 2017, BIORXIV
  • [3] [Anonymous], 2015, INT ARCH PHOTOGRAMME
  • [4] Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley
    Bendig, Juliane
    Yu, Kang
    Aasen, Helge
    Bolten, Andreas
    Bennertz, Simon
    Broscheit, Janis
    Gnyp, Martin L.
    Bareth, Georg
    [J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2015, 39 : 79 - 87
  • [5] A High-Throughput Model-Assisted Method for Phenotyping Maize Green Leaf Area Index Dynamics Using Unmanned Aerial Vehicle Imagery
    Blancon, Justin
    Dutartre, Dan
    Tixier, Marie-Helene
    Weiss, Marie
    Comar, Alexis
    Praud, Sebastien
    Baret, Frederic
    [J]. FRONTIERS IN PLANT SCIENCE, 2019, 10
  • [6] BreedVision - A Multi-Sensor Platform for Non-Destructive Field-Based Phenotyping in Plant Breeding
    Busemeyer, Lucas
    Mentrup, Daniel
    Moeller, Kim
    Wunder, Erik
    Alheit, Katharina
    Hahn, Volker
    Maurer, Hans Peter
    Reif, Jochen C.
    Wuerschum, Tobias
    Mueller, Joachim
    Rahe, Florian
    Ruckelshausen, Arno
    [J]. SENSORS, 2013, 13 (03) : 2830 - 2847
  • [7] Pheno-Copter: A Low-Altitude, Autonomous Remote-Sensing Robotic Helicopter for High-Throughput Field-Based Phenotyping
    Chapman, Scott C.
    Merz, Torsten
    Chan, Amy
    Jackway, Paul
    Hrabar, Stefan
    Dreccer, M. Fernanda
    Holland, Edward
    Zheng, Bangyou
    Ling, T. Jun
    Jimenez-Berni, Jose
    [J]. AGRONOMY-BASEL, 2014, 4 (02): : 279 - 301
  • [8] Assessing Lodging Severity over an Experimental Maize (Zea mays L.) Field Using UAS Images
    Chu, Tianxing
    Starek, Michael J.
    Brewer, Michael J.
    Murray, Seth C.
    Pruter, Luke S.
    [J]. REMOTE SENSING, 2017, 9 (09)
  • [9] Comparison of ground cover estimates from experiment plots in cotton, sorghum and sugarcane based on images and ortho-mosaics captured by UAV
    Duan, Tao
    Zheng, Bangyou
    Guo, Wei
    Ninomiya, Seishi
    Guo, Yan
    Chapman, Scott C.
    [J]. FUNCTIONAL PLANT BIOLOGY, 2017, 44 (01) : 169 - 183
  • [10] Light interception efficiency explained by two simple variables: a test using a diversity of small- to medium-sized woody plants
    Duursma, R. A.
    Falster, D. S.
    Valladares, F.
    Sterck, F. J.
    Pearcy, R. W.
    Lusk, C. H.
    Sendall, K. M.
    Nordenstahl, M.
    Houter, N. C.
    Atwell, B. J.
    Kelly, N.
    Kelly, J. W. G.
    Liberloo, M.
    Tissue, D. T.
    Medlyn, B. E.
    Ellsworth, D. S.
    [J]. NEW PHYTOLOGIST, 2012, 193 (02) : 397 - 408