000			01807nab|a22003377a|4500
999			_c63325 _d63317
001			63325
003			MX-TxCIM
005			20211006073557.0
008			190911s2020||||xxu|||p|op||||00||0|eng|d
022			_a1559-128X
022			_a2155-3165 (Online)
024	8		_ahttps://doi.org/10.1364/AO.397844
040			_aMX-TxCIM
041			_aeng
100	0		_918444 _aQiong Zheng
245	1	0	_aUsing continous wavelet analysis for monitoring wheat yellow rust in different infestation stages based on unmanned aerial vehicle hyperspectral images
260			_aUSA : _bOSA Publishing, _c2020.
500			_aPeer review
520			_aYellow rust is the most extensive disease in wheat cultivation, seriously affecting crop quality and yield. This study proposes sensitive wavelet features (WFs) for wheat yellow rust monitoring based on unmanned aerial vehicle hyperspectral imagery of different infestation stages [26 days after inoculation (26 DAI) and 42 DAI]. Furthermore, we evaluated the monitoring ability of WFs and vegetation indices on wheat yellow rust through linear discriminant analysis and support vector machine (SVM) classification frameworks in different infestation stages, respectively. The results show that WFs-SVM have promising potential for wheat yellow rust monitoring in both the 26 DAI and 42 DAI stages.
546			_aText in English
650		7	_2AGROVOC _96664 _aMonitoring
650		7	_aPlant diseases _gAGROVOC _2 _91206
650		7	_2AGROVOC _911401 _aUnmanned aerial vehicles
700	0		_918428 _aWenjiang Huang
700	0		_918445 _aHuichun Ye
700	0		_918446 _aYingying Dong
700	0		_918447 _aYue Shi
700	0		_918448 _aShuisen Chen
773	0		_gv. 59, no. 26, p. 8003-8013 _dUSA : OSA Publishing, 2020. _x1559-128X _tApplied Optics
942			_cJA _n0 _2ddc