000			03776nab|a22003977a|4500
999			_c62820 _d62812
001			62820
003			MX-TxCIM
005			20231017232835.0
008			200624s2020||||sz |||p|op||||00||0|eng|d
022			_a1664-462X
024	8		_ahttps://doi.org/10.3389/fpls.2020.587093
040			_aMX-TxCIM
041			_aeng
100	0		_aXu Wang _99093
245	1	0	_aImproved accuracy of high-throughput phenotyping from unmanned aerial systems by extracting traits directly from orthorectified images
260			_aSwitzerland : _bFrontiers, _c2020.
500			_aPeer review
500			_aOpen Access
520			_aThe development of high-throughput genotyping and phenotyping has provided access to many tools to accelerate plant breeding programs. Unmanned Aerial Systems (UAS)-based remote sensing is being broadly implemented for field-based high-throughput phenotyping due to its low cost and the capacity to rapidly cover large breeding populations. The Structure-from-Motion photogrammetry processes aerial images taken from multiple perspectives over a field to an orthomosaic photo of a complete field experiment, allowing spectral or morphological trait extraction from the canopy surface for each individual field plot. However, some phenotypic information observable in each raw aerial image seems to be lost to the orthomosaic photo, probably due to photogrammetry processes such as pixel merging and blending. To formally assess this, we introduced a set of image processing methods to extract phenotypes from orthorectified raw aerial images and compared them to the negative control of extracting the same traits from processed orthomosaic images. We predict that standard measures of accuracy in terms of the broad-sense heritability of the remote sensing spectral traits will be higher using the orthorectified photos than with the orthomosaic image. Using three case studies, we therefore compared the broad-sense heritability of phenotypes in wheat breeding nurseries including, (1) canopy temperature from thermal imaging, (2) canopy normalized difference vegetation index (NDVI), and (3) early-stage ground cover from multispectral imaging. We evaluated heritability estimates of these phenotypes extracted from multiple orthorectified aerial images via four statistical models and compared the results with heritability estimates of these phenotypes extracted from a single orthomosaic image. Our results indicate that extracting traits directly from multiple orthorectified aerial images yielded increased estimates of heritability for all three phenotypes through proper modeling, compared to estimation using traits extracted from the orthomosaic image. In summary, the image processing methods demonstrated in this study have the potential to improve the quality of the plant trait extracted from high-throughput imaging. This, in turn, can enable breeders to utilize phenomics technologies more effectively for improved selection.
546			_aText in English
650		7	_aUnmanned aerial vehicles _2AGROVOC _911401
650		7	_aCanopy _2AGROVOC _91800
650		7	_aVegetation index _2AGROVOC _95833
650		7	_aWheat _gAGROVOC _2 _91310
700	1		_92283 _aSilva, P.
700	1		_916812 _aBello, N.M.
700	1		_93851 _aSingh, D.
700	1		_916813 _aEvers, B.
700	1		_aMondal, S. _gFormerly Global Wheat Program _8INT3211 _9904
700	1		_aPinto Espinosa, F. _8I1707012 _gFormerly Global Wheat Program _94431
700	1		_aSingh, R.P. _gGlobal Wheat Program _8INT0610 _9825
700	1		_92092 _aPoland, J.A.
773	0		_gv. 11, art. 587093 _dSwitzerland : Frontiers, 2020. _x1664-462X _tFrontiers in Plant Science _wu56875
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/21004
942			_cJA _n0 _2ddc