000			03626nab a22003617a 4500
999			_c60013 _d60005
001			60013
003			MX-TxCIM
005			20231009164119.0
008			190124s2019 ne |||po|p||| 00| 0 eng d
022			_a0168-9452
024	8		_ahttps://doi.org/10.1016/j.plantsci.2018.10.022
040			_aMX-TxCIM
041			_aeng
100	1		_97723 _aHassan, M.A.
245	1	3	_aA rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform
260			_aNetherlands : _bElsevier, _c2019.
500			_aPeer review
520			_aWheat improvement programs require rapid assessment of large numbers of individual plots across multiple environments. Vegetation indices (VIs) that are mainly associated with yield and yield-related physiological traits, and rapid evaluation of canopy normalized difference vegetation index (NDVI) can assist in-season selection. Multi-spectral imagery using unmanned aerial vehicles (UAV) can readily assess the VIs traits at various crop growth stages. Thirty-two wheat cultivars and breeding lines grown in limited irrigation and full irrigation treatments were investigated to monitor NDVI across the growth cycle using a Sequoia sensor mounted on a UAV. Significant correlations ranging from R2 = 0.38 to 0.90 were observed between NDVI detected from UAV and Greenseeker (GS) during stem elongation (SE) to late grain gilling (LGF) across the treatments. UAV-NDVI also had high heritabilities at SE (h2 = 0.91), flowering (F)(h2 = 0.95), EGF (h2 = 0.79) and mid grain filling (MGF) (h2 = 0.71) under the full irrigation treatment, and at booting (B) (h2 = 0.89), EGF (h2 = 0.75) in the limited irrigation treatment. UAV-NDVI explained significant variation in grain yield (GY) at EGF (R2 = 0.86), MGF (R2 = 0.83) and LGF (R2 = 0.89) stages, and results were consistent with GS-NDVI. Higher correlations between UAV-NDVI and GY were observed under full irrigation at three different grain-filling stages (R2 = 0.40, 0.49 and 0.45) than the limited irrigation treatment (R2 = 0.08, 0.12 and 0.14) and GY was calculated to be 24.4% lower under limited irrigation conditions. Pearson correlations between UAV-NDVI and GY were also low ranging from r = 0.29 to 0.37 during grain-filling under limited irrigation but higher than GS-NDVI data. A similar pattern was observed for normalized difference red-edge (NDRE) and normalized green red difference index (NGRDI) when correlated with GY. Fresh biomass estimated at late flowering stage had significant correlations of r = 0.30 to 0.51 with UAV-NDVI at EGF. Some genotypes Nongda 211, Nongda 5181, Zhongmai 175 and Zhongmai 12 were identified as high yielding genotypes using NDVI during grain-filling. In conclusion, a multispectral sensor mounted on a UAV is a reliable high-throughput platform for NDVI measurement to predict biomass and GY and grain-filling stage seems the best period for selection.
526			_aWC _cFP2
546			_aText in English
650		7	_aWheat _gAGROVOC _2 _91310
650		7	_2AGROVOC _93634 _aPhenotypes
650		7	_91138 _aGrain _2AGROVOC
700	0		_97724 _aMengjiao Yang
700	1		_aAwais Rasheed _gGlobal Wheat Program _8I1706474 _91938
700	0		_98337 _aGuijun Yang
700	1		_aReynolds, M.P. _gGlobal Wheat Program _8INT1511 _9831
700	0		_9377 _aXianchun Xia
700	0		_91687 _aYonggui Xiao
700	1		_aHe Zhonghu _gGlobal Wheat Program _8INT2411 _9838
773	0		_gv. 282, p. 95-103 _tPlant Science _wu444702 _x0168-9452 _dNetherlands : Elsevier, 2019.
942			_2ddc _cJA _n0