Application of unmanned aerial systems for high throughput phenotyping of large wheat breeding nurseries

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dc.contributor.author Haghighattalab, A.
dc.contributor.author Perez, L. G.
dc.contributor.author Mondal, S.
dc.contributor.author Singh, D.
dc.contributor.author Schinstock, Dale
dc.contributor.author Rutkoski, J.
dc.contributor.author Ortiz-Monasterio, I.
dc.contributor.author Singh, R. P.
dc.contributor.author Goodin, Douglas G.
dc.contributor.author Poland, Jesse A.
dc.date.accessioned 2017-02-15T15:29:01Z
dc.date.available 2017-02-15T15:29:01Z
dc.identifier.uri http://hdl.handle.net/2097/35201
dc.description Citation: Haghighattalab, A., Perez, L. G., Mondal, S., Singh, D., Schinstock, D., Rutkoski, J., . . . Poland, J. (2016). Application of unmanned aerial systems for high throughput phenotyping of large wheat breeding nurseries. Plant Methods, 12, 15. doi:10.1186/s13007-016-0134-6
dc.description.abstract Background: Low cost unmanned aerial systems (UAS) have great potential for rapid proximal measurements of plants in agriculture. In the context of plant breeding and genetics, current approaches for phenotyping a large number of breeding lines under field conditions require substantial investments in time, cost, and labor. For field-based high-throughput phenotyping (HTP), UAS platforms can provide high-resolution measurements for small plot research, while enabling the rapid assessment of tens-of-thousands of field plots. The objective of this study was to complete a baseline assessment of the utility of UAS in assessment field trials as commonly implemented in wheat breeding programs. We developed a semi-automated image-processing pipeline to extract plot level data from UAS imagery. The image dataset was processed using a photogrammetric pipeline based on image orientation and radiometric calibration to produce orthomosaic images. We also examined the relationships between vegetation indices (VIs) extracted from high spatial resolution multispectral imagery collected with two different UAS systems (eBee Ag carrying MultiSpec 4C camera, and IRIS+ quadcopter carrying modified NIR Canon S100) and ground truth spectral data from hand-held spectroradiometer. Results: We found good correlation between the VIs obtained from UAS platforms and ground-truth measurements and observed high broad-sense heritability for VIs. We determined radiometric calibration methods developed for satellite imagery significantly improved the precision of VIs from the UAS. We observed VIs extracted from calibrated images of Canon S100 had a significantly higher correlation to the spectroradiometer (r = 0.76) than VIs from the MultiSpec 4C camera (r = 0.64). Their correlation to spectroradiometer readings was as high as or higher than repeated measurements with the spectroradiometer per se. Conclusion: The approaches described here for UAS imaging and extraction of proximal sensing data enable collection of HTP measurements on the scale and with the precision needed for powerful selection tools in plant breeding. Low-cost UAS platforms have great potential for use as a selection tool in plant breeding programs. In the scope of tools development, the pipeline developed in this study can be effectively employed for other UAS and also other crops planted in breeding nurseries.
dc.relation.uri https://doi.org/10.1186/s13007-016-0134-6
dc.rights Attribution 4.0 International (CC BY 4.0)
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.subject Unmanned Aerial Vehicles/Systems (Uav/Uas)
dc.subject Wheat
dc.subject High-Throughput
dc.subject Phenotyping
dc.subject Remote Sensing
dc.subject Gndvi
dc.title Application of unmanned aerial systems for high throughput phenotyping of large wheat breeding nurseries
dc.type Article
dc.date.published 2016
dc.citation.doi 10.1186/s13007-016-0134-6
dc.citation.issn 1746-4811
dc.citation.jtitle Plant Methods
dc.citation.spage 15
dc.citation.volume 12
dc.contributor.authoreid jpoland
dc.contributor.authoreid dales
dc.contributor.authoreid dgoodin
dc.contributor.kstate Poland, Jesse A.
dc.contributor.kstate Schinstock, Dale
dc.contributor.kstate Goodin, Douglas G.


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