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ToolsSun, Gang, Lu, Hengyun, Zhao, Yan, Zhou, Jie, Jackson, Robert, Wang, Yongchun, Xu, Ling-Xiang, Wang, Ahong, Colmer, Joshua, Ober, Eric, Zhao, Qiang, Han, Bin and Zhou, Ji (2022) AirMeasurer: open-source software to quantify static and dynamic traits derived from multiseason aerial phenotyping to empower genetic mapping studies in rice. New Phytologist, 236 (4). pp. 1584-1604. ISSN 0028-646X
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Abstract
Low-altitude aerial imaging, an approach that can collect large-scale plant imagery, has grown in popularity recently. Amongst many phenotyping approaches, unmanned aerial vehicles (UAVs) possess unique advantages as a consequence of their mobility, flexibility and affordability. Nevertheless, how to extract biologically relevant information effectively has remained challenging. Here, we present AirMeasurer, an open-source and expandable platform that combines automated image analysis, machine learning and original algorithms to perform trait analysis using 2D/3D aerial imagery acquired by low-cost UAVs in rice (Oryza sativa) trials. We applied the platform to study hundreds of rice landraces and recombinant inbred lines at two sites, from 2019 to 2021. A range of static and dynamic traits were quantified, including crop height, canopy coverage, vegetative indices and their growth rates. After verifying the reliability of AirMeasurer-derived traits, we identified genetic variants associated with selected growth-related traits using genome-wide association study and quantitative trait loci mapping. We found that the AirMeasurer-derived traits had led to reliable loci, some matched with published work, and others helped us to explore new candidate genes. Hence, we believe that our work demonstrates valuable advances in aerial phenotyping and automated 2D/3D trait analysis, providing high-quality phenotypic information to empower genetic mapping for crop improvement.
| Item Type: | Article |
|---|---|
| Additional Information: | Funding Information: HL, YZ, YW, AW, QZ and the rice field experiments were supported by the Chinese Academy of Sciences under BH's supervision (XDA24020205 to QZ). UAV‐based phenotyping was supported by the National Natural Science Foundation of China (32 070 400 to Ji Zhou). RJ and Ji Zhou were partially funded by the United Kingdom Research and Innovation's (UKRI) Biotechnology and Biological Sciences Research Council (BBSRC) Designing Future Wheat Programme (BB/P016855/1). JC was supported by the BBSRC's National Productivity Investment Fund CASE Award, Norwich Research Park's Biosciences Doctoral Training Partnership (BB/M011216/1 to Ji Zhou). GS and Jie Zhou were supported by the Fundamental Research Funds for the Central Universities in China (JCQY201902), as well as by the Jiangsu Collaborative Innovation Center for Modern Crop Production, and the Natural Science Foundation of the Jiangsu Province (BK20191311 to Ji Zhou). Both Ji Zhou and EC were partially supported by a PhenomUK project grant funded by the UKRI (MR/R025746/1 to Ji Zhou). |
| Uncontrolled Keywords: | 3d trait analysis,aerial phenotyping,genetic mapping,predictive modelling,rice,static and dynamic traits,aerial phenotyping,physiology,plant science ,/dk/atira/pure/subjectarea/asjc/1300/1314 |
| Faculty \ School: | Faculty of Science > School of Biological Sciences Faculty of Science > School of Computing Sciences |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 21 Jul 2022 10:30 |
| Last Modified: | 16 May 2024 01:34 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/86781 |
| DOI: | 10.1111/nph.18314 |
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