000			02819nab a22003737a 4500
001			69545
003			MX-TxCIM
005			20251124113536.0
008			251120s2025 sz |||p|op||| 00| 0 eng d
022			_a2073-4441 (Online)
024	8		_ahttps://doi.org/10.3390/w17192796
040			_aMX-TxCIM
041			_aeng
100	1		_aMasemola, R. _940562
245	1	0	_aAssessing the potential of drone remotely sensed data in detecting the soil moisture content and taro leaf chlorophyll content across different phenological stages
260			_aBasel (Switzerland) : _bMDPI, _c2025.
500			_aPeer review
500			_aOpen Access
520			_aSoil moisture content is an important determinant of crop productivity, especially in agricultural systems that are dependent on rainfall. Climate variability has introduced water management challenges for smallholder farmers in Southern Africa. The emergence of unmanned aerial vehicle (UAV)-borne remote sensing offers modern solutions for monitoring soil moisture, plant health and overall crop productivity in real-time. This study evaluated the utility of UAV-acquired data in conjunction with random forest regression in predicting soil moisture content and chlorophyll across different growth stages of taro. The estimation models achieved R2 values up to 0.90 with rRMSE as low as 1.25%, demonstrating the robust performance of random forest in concert with different spectral datasets in estimating soil moisture and chlorophyll. Correlation analysis confirmed the association between these two variables, with the strongest correlation observed during the vegetative stage (r = 0.81, p < 0.05) and the weakest during the late vegetative stage (r = 0.78, p < 0.05). The results showed that UAV bands were crucial in predicting soil moisture and chlorophyll across all stages. These results demonstrate the utility of remote sensing, particularly UAV-borne sensors, in monitoring crop productivity in smallholder farms. By employing UAV-borne sensors, farmers can improve on-farm water management and make better and more informed decisions.
546			_aText in English
650		7	_aSoil Water Content _2AGROVOC _99061
650		7	_aChlorophylls _2AGROVOC _97635
650		7	_aSmallholders _2AGROVOC _91763
650		7	_aUnmanned aerial vehicles _2AGROVOC _911401
650		7	_aRemote sensing _2AGROVOC _91986
700	1		_aSibanda, M. _923029
700	1		_aMutanga, O. _919859
700	1		_aKunz, R. _919181
700	1		_aChimonyo, V.G.P. _8001712688 _gSustainable Agrifood Systems _919177
700	1		_aMabhaudhi, T. _918478
773	0		_dBasel (Switzerland) : MDPI, 2025. _gv. 17, no. 19, art. 2796 _tWater _x2073-4441
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/36124
942			_2ddc _cJA _n0
999			_c69545 _d69537