000			03332nab|a22003977a|4500
001			64498
003			MX-TxCIM
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008			200910s2021||||xxk|||p|op||||00||0|eng|d
022			_a1746-4811
024	8		_ahttps://doi.org/10.1186/s13007-021-00750-5
040			_aMX-TxCIM
041			_aeng
100	0		_aJuanjuan Zhang _924663
245	1	0	_aLeaf area index estimation model for UAV image hyperspectral data based on wavelength variable selection and machine learning methods
260			_aLondon (United Kingdom) : _bBioMed Central, _c2021.
500			_aPeer review
500			_aOpen Access
520			_aBackground: To accurately estimate winter wheat leaf area index (LAI) using unmanned aerial vehicle (UAV) hyperspectral imagery is crucial for crop growth monitoring, fertilization management, and development of precision agriculture. Methods: The UAV hyperspectral imaging data, Analytical Spectral Devices (ASD) data, and LAI were simultaneously obtained at main growth stages (jointing stage, booting stage, and filling stage) of various winter wheat varieties under various nitrogen fertilizer treatments. The characteristic bands related to LAI were extracted from UAV hyperspectral data with different algorithms including first derivative (FD), successive projections algorithm (SPA), competitive adaptive reweighed sampling (CARS), and competitive adaptive reweighed sampling combined with successive projections algorithm (CARS_SPA). Furthermore, three modeling machine learning methods including partial least squares regression (PLSR), support vector machine regression (SVR), and extreme gradient boosting (Xgboost) were used to build LAI estimation models. Results: The results show that the correlation coefficient between UAV and ASD hyperspectral data is greater than 0.99, indicating the UAV data can be used for estimation of wheat growth information. The LAI bands selected by using different algorithms were slightly different among the 15 models built in this study. The Xgboost model using nine consecutive characteristic bands selected by CARS_SPA algorithm as input was proved to have the best performance. This model yielded identical results of coefficient of determination (0.89) for both calibration set and validation set, indicating a high accuracy of this model. Conclusions: The Xgboost modeling method in combine with CARS_SPA algorithm can reduce input variables and improve the efficiency of model operation. The results provide reference and technical support for nondestructive and rapid estimation of winter wheat LAI by using UAV.
546			_aText in English
650		7	_2AGROVOC _92104 _aWinter wheat
650		0	_aLeaf area index _gAGROVOC _98953
650		7	_2AGROVOC _911401 _aUnmanned aerial vehicles
650		7	_2AGROVOC _910231 _aImagery
650		7	_2AGROVOC _911127 _aMachine learning
650		7	_2AGROVOC _94859 _aModels
700	0		_924668 _aTao Cheng
700	0		_98202 _aWei Guo
700	0		_921876 _aXin Xu
700	0		_924669 _aHongbo Qiao
700	0		_924670 _aYimin Xie
700	0		_914278 _aXinming Ma
773	0		_tPlant Methods _gv. 17, art. 49 _dLondon (United Kingdom) : BioMed Central, 2021. _x1471-2229 _w57210
856	4		_yClick here to access online _uhttps://doi.org/10.1186/s13007-021-00750-5
942			_cJA _n0 _2ddc
999			_c64498 _d64490