Author:
Zang Jingrong,Jin Shichao,Zhang Songyin,Li Qing,Mu Yue,Li Ziyu,Li Shaochen,Wang Xiao,Su Yanjun,Jiang Dong
Abstract
AbstractCanopy height (CH) is an important trait for crop breeding and production. The rapid development of 3D sensing technologies shed new light on high-throughput height measurement. However, a systematic comparison of the accuracy and heritability of different 3D sensing technologies is seriously lacking. Moreover, it is questionable whether the field-measured height is as reliable as believed. This study uncovered these issues by comparing traditional height measurement with four advanced 3D sensing technologies, including terrestrial laser scanning (TLS), backpack laser scanning (BLS), gantry laser scanning (GLS), and digital aerial photogrammetry (DAP). A total of 1920 plots covering 120 varieties were selected for comparison. Cross-comparisons of different data sources were performed to evaluate their performances in CH estimation concerning different CH, leaf area index (LAI), and growth stage (GS) groups. Results showed that 1) All 3D sensing data sources had high correlations with field measurement (r > 0.82), while the correlations between different 3D sensing data sources were even better (r > 0.87). 2) The prediction accuracy between different data sources decreased in subgroups of CH, LAI, and GS. 3) Canopy height showed high heritability from all datasets, and 3D sensing datasets had even higher heritability (H2 = 0.79–0.89) than FM (field measurement) (H2 = 0.77). Finally, outliers of different datasets are analyzed. The results provide novel insights into different methods for canopy height measurement that may ensure the high-quality application of this important trait.
Funder
National Natural Science Foundation of China
Jiangsu Agricultural Science and Technology Innovation Fund
JBGS Project of Seed Industry Revitalization in Jiangsu Province
High-Level Personnel Project of Jiangsu Province
Strategic Priority Research Program of the Chinese Academy of Sciences
Publisher
Springer Science and Business Media LLC
Subject
Plant Science,Genetics,Biotechnology
Reference78 articles.
1. Aasen H, Burkart A, Bolten A, Bareth G. Generating 3d hyperspectral information with lightweight uav snapshot cameras for vegetation monitoring: from camera calibration to quality assurance. ISPRS J Photogramm Remote Sens. 2015;108:245–59.
2. Ali-Sisto D, Gopalakrishnan R, Kukkonen M, Savolainen P, Packalen P. A method for vertical adjustment of digital aerial photogrammetry data by using a high-quality digital terrain model. Int J Appl Earth Obs Geoinf. 2020;84:101954–63.
3. Bai D, Li D, Zhao C, Wang Z, Shao M, Guo B, Liu Y, Wang Q, Li J, Guo S, Wang R, Li YH, Qiu LJ, Jin X. Estimation of soybean yield parameters under lodging conditions using rgb information from unmanned aerial vehicles. Front Plant Sci. 2022;13:1012293–312.
4. Busemeyer L, Mentrup D, Moller K, Wunder E, Alheit K, Hahn V, Maurer HP, Reif JC, Wurschum T, Muller J, Rahe F, Ruckelshausen A. Breedvision–a multi-sensor platform for non-destructive field-based phenotyping in plant breeding. Sensors. 2013;13(3):2830–47.
5. Chandana BS, Mahto RK, Singh RK, Ford R, Vaghefi N, Gupta SK, Yadav HK, Manohar M, Kumar R. Epigenomics as potential tools for enhancing magnitude of breeding approaches for developing climate resilient chickpea. Front Genet. 2022;13:900253–78.
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