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dc.contributor.authorSong, Zen_NZ
dc.contributor.authorTomasetto, Fen_NZ
dc.contributor.authorNiu, Xen_NZ
dc.contributor.authorYan, WQen_NZ
dc.contributor.authorJiang, Jen_NZ
dc.contributor.authorLi, Yen_NZ
dc.date.accessioned2022-05-12T03:24:58Z
dc.date.available2022-05-12T03:24:58Z
dc.date.copyright2022-04-22en_NZ
dc.identifier.citationPlant Phenomics, vol. 2022, Article ID 9783785, 14 pages, 2022. https://doi.org/10.34133/2022/9783785
dc.identifier.issn2643-6515en_NZ
dc.identifier.urihttp://hdl.handle.net/10292/15128
dc.description.abstractTraditional methods used to monitor the aboveground biomass (AGB) and belowground biomass (BGB) of slash pine (<jats:italic>Pinus elliottii</jats:italic>) rely on on-ground measurements, which are time- and cost-consuming and suited only for small spatial scales. In this paper, we successfully applied unmanned aerial vehicle (UAV) integrated with structure from motion (UAV-SfM) data to estimate the tree height, crown area (CA), AGB, and BGB of slash pine for in slash pine breeding plantations sites. The CA of each tree was segmented by using marker-controlled watershed segmentation with a treetop and a set of minimum three meters heights. Moreover, the genetic variation of these traits has been analyzed and employed to estimate heritability (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:msup><mml:mrow><mml:mi>h</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math>). The results showed a promising correlation between UAV and ground truth data with a range of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:msup><mml:mrow><mml:mi>R</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math> from 0.58 to 0.85 at 70 m flying heights and a moderate estimate of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:msup><mml:mrow><mml:mi>h</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math> for all traits ranges from 0.13 to 0.47, where site influenced the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M4"><mml:msup><mml:mrow><mml:mi>h</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math> value of slash pine trees, where <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M5"><mml:msup><mml:mrow><mml:mi>h</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math> in site 1 ranged from 0.13~0.25 lower than that in site 2 (range: 0.38~0.47). Similar genetic gains were obtained with both UAV and ground truth data; thus, breeding selection is still possible. The method described in this paper provides faster, more high-throughput, and more cost-effective UAV-SfM surveys to monitor a larger area of breeding plantations than traditional ground surveys while maintaining data accuracy.en_NZ
dc.languageenen_NZ
dc.publisherAmerican Association for the Advancement of Science (AAAS)en_NZ
dc.relation.urihttps://spj.sciencemag.org/journals/plantphenomics/2022/9783785/
dc.rightsCopyright © 2022 Zhaoying Song et al. Exclusive Licensee Nanjing Agricultural University. Distributed under a Creative Commons Attribution License (CC BY 4.0).
dc.titleEnabling Breeding Selection for Biomass in Slash Pine Using UAV-Based Imagingen_NZ
dc.typeJournal Article
dc.rights.accessrightsOpenAccessen_NZ
dc.identifier.doi10.34133/2022/9783785en_NZ
aut.relation.endpage14
aut.relation.startpage1
aut.relation.volume2022en_NZ
pubs.elements-id454167
aut.relation.journalPlant Phenomicsen_NZ


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