000			02997nab|a22003857a|4500
001			64434
003			MX-TxCIM
005			20230630193512.0
008			202110s2021||||sz |||p|op||||00||0|eng|d
022			_a2072-4292 (Online)
024	8		_ahttps://doi.org/10.3390/rs13204091
040			_aMX-TxCIM
041			_aeng
100	1		_aNdlovu, H.S. _924381
245	1	2	_aA comparative estimation of maize leaf water content using machine learning techniques and unmanned aerial vehicle (UAV)-based proximal and remotely sensed data
260			_aBasel (Switzerland) : _bMDPI, _c2021.
500			_aPeer review
500			_aOpen Access
520			_aDetermining maize water content variability is necessary for crop monitoring and in developing early warning systems to optimise agricultural production in smallholder farms. However, spatially explicit information on maize water content, particularly in Southern Africa, remains elementary due to the shortage of efficient and affordable primary sources of suitable spatial data at a local scale. Unmanned Aerial Vehicles (UAVs), equipped with light-weight multispectral sensors, provide spatially explicit, near-real-time information for determining the maize crop water status at farm scale. Therefore, this study evaluated the utility of UAV-derived multispectral imagery and machine learning techniques in estimating maize leaf water indicators: equivalent water thickness (EWT), fuel moisture content (FMC), and specific leaf area (SLA). The results illustrated that both NIR and red-edge derived spectral variables were critical in characterising the maize water indicators on smallholder farms. Furthermore, the best models for estimating EWT, FMC, and SLA were derived from the random forest regression (RFR) algorithm with an rRMSE of 3.13%, 1%, and 3.48%, respectively. Additionally, EWT and FMC yielded the highest predictive performance and were the most optimal indicators of maize leaf water content. The findings are critical towards developing a robust and spatially explicit monitoring framework of maize water status and serve as a proxy of crop health and the overall productivity of smallholder maize farms.
546			_aText in English
650		0	_aPrecision agriculture _94619 _2AGROVOC
650		0	_aCrop monitoring _911128 _2AGROVOC
650		0	_aUnmanned aerial vehicles _911401 _2AGROVOC
650		7	_aSmallholders _91763 _2AGROVOC
650		0	_aMachine learning _911127 _2AGROVOC
700	1		_aOdindi, J. _924382
700	1		_aSibanda, M. _923029
700	1		_aMutanga, O. _919859
700	1		_aClulow, A.D. _923030
700	1		_aChimonyo, V.G.P. _8001712688 _gSustainable Intensification Program _gSustainable Agrifood Systems _919177
700	1		_aMabhaudhi, T. _918478
773	0		_tRemote Sensing _gv. 13, no. 20, art. 4091 _dBasel (Switzerland) : MDPI, 2021. _x2072-4292 _w57403
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/21718
942			_cJA _n0 _2ddc
999			_c64434 _d64426