Assessing the effect of drought on winter wheat growth using unmanned aerial system (UAS) -based phenotyping

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dc.contributor.authorBhandari, Mahendra
dc.contributor.authorBaker, Shannon
dc.contributor.authorRudd, Jackie C.
dc.contributor.authorIbrahim, Amir M. H.
dc.contributor.authorChang, Anjin
dc.contributor.authorXue, Qingwu
dc.contributor.authorJung, Jinha
dc.contributor.authorLandivar, Juan
dc.contributor.authorAuvermann, Brent
dc.creator.orcidhttps://orcid.org/0000-0001-9186-3386en_US
dc.creator.orcidhttps://orcid.org/0000-0003-1176-3540en_US
dc.creator.orcidhttps://orcid.org/0000-0001-9186-3386
dc.creator.orcidhttps://orcid.org/0000-0003-1176-3540
dc.creator.orcidhttps://orcid.org/0000-0001-9186-3386
dc.creator.orcidhttps://orcid.org/0000-0003-1176-3540https://orcid.org/0000-0001-9186-3386
dc.creator.orcidhttps://orcid.org/0000-0003-1176-3540
dc.date.accessioned2021-10-29T17:10:57Z
dc.date.available2021-10-29T17:10:57Z
dc.date.issued2021-03-17
dc.description.abstractDrought significantly limits wheat productivity across the temporal and spatial domains. Unmanned Aerial Systems (UAS) has become an indispensable tool to collect refined spatial and high temporal resolution imagery data. A 2-year field study was conducted in 2018 and 2019 to determine the temporal effects of drought on canopy growth of winter wheat. Weekly UAS data were collected using red, green, and blue (RGB) and multispectral (MS) sensors over a yield trial consisting of 22 winter wheat cultivars in both irrigated and dryland environments. Raw-images were processed to compute canopy features such as canopy cover (CC) and canopy height (CH), and vegetation indices (VIs) such as Normalized Difference Vegetation Index (NDVI), Excess Green Index (ExG), and Normalized Difference Red-edge Index (NDRE). The drought was more severe in 2018 than in 2019 and the effects of growth differences across years and irrigation levels were visible in the UAS measurements. CC, CH, and VIs, measured during grain filling, were positively correlated with grain yield (r = 0.4–0.7, p < 0.05) in the dryland in both years. Yield was positively correlated with VIs in 2018 (r = 0.45–0.55, p < 0.05) in the irrigated environment, but the correlations were non-significant in 2019 (r = 0.1 to −0.4), except for CH. The study shows that high-throughput UAS data can be used to monitor the drought effects on wheat growth and productivity across the temporal and spatial domains.en_US
dc.description.abstractDrought significantly limits wheat productivity across the temporal and spatial domains. Unmanned Aerial Systems (UAS) has become an indispensable tool to collect refined spatial and high temporal resolution imagery data. A 2-year field study was conducted in 2018 and 2019 to determine the temporal effects of drought on canopy growth of winter wheat. Weekly UAS data were collected using red, green, and blue (RGB) and multispectral (MS) sensors over a yield trial consisting of 22 winter wheat cultivars in both irrigated and dryland environments. Raw-images were processed to compute canopy features such as canopy cover (CC) and canopy height (CH), and vegetation indices (VIs) such as Normalized Difference Vegetation Index (NDVI), Excess Green Index (ExG), and Normalized Difference Red-edge Index (NDRE). The drought was more severe in 2018 than in 2019 and the effects of growth differences across years and irrigation levels were visible in the UAS measurements. CC, CH, and VIs, measured during grain filling, were positively correlated with grain yield (r = 0.4–0.7, p < 0.05) in the dryland in both years. Yield was positively correlated with VIs in 2018 (r = 0.45–0.55, p < 0.05) in the irrigated environment, but the correlations were non-significant in 2019 (r = 0.1 to −0.4), except for CH. The study shows that high-throughput UAS data can be used to monitor the drought effects on wheat growth and productivity across the temporal and spatial domains.
dc.identifier.citationBhandari, M., Baker, S., Rudd, J.C., Ibrahim, A.M., Chang, A., Xue, Q., Jung, J., Landivar, J. and Auvermann, B., 2021. Assessing the Effect of Drought on Winter Wheat Growth Using Unmanned Aerial System (UAS)-Based Phenotyping. Remote Sensing, 13(6), p.1144.en_US
dc.identifier.citationBhandari, M., Baker, S., Rudd, J.C., Ibrahim, A.M., Chang, A., Xue, Q., Jung, J., Landivar, J. and Auvermann, B., 2021. Assessing the Effect of Drought on Winter Wheat Growth Using Unmanned Aerial System (UAS)-Based Phenotyping. Remote Sensing, 13(6), p.1144.
dc.identifier.doihttps://doi.org/10.3390/rs13061144
dc.identifier.urihttps://hdl.handle.net/1969.6/89932
dc.language.isoen_USen_US
dc.language.isoen_US
dc.publisherMDPIen_US
dc.publisherMDPI
dc.rightsAttribution 4.0 International*
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectunmanned aircraft system (uas)en_US
dc.subjectunmanned aircraft systemsen_US
dc.subjectdrought monitoringen_US
dc.subjectwheat breedingen_US
dc.subjectphenotypingen_US
dc.subjectcanopy featuresen_US
dc.subjectwinter wheat phenologyen_US
dc.subjectunmanned aircraft system (uas)
dc.subjectunmanned aircraft systems
dc.subjectdrought monitoring
dc.subjectwheat breeding
dc.subjectphenotyping
dc.subjectcanopy features
dc.subjectwinter wheat phenology
dc.titleAssessing the effect of drought on winter wheat growth using unmanned aerial system (UAS) -based phenotypingen_US
dc.titleAssessing the effect of drought on winter wheat growth using unmanned aerial system (UAS) -based phenotyping
dc.typeArticleen_US
dc.typeArticle

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