Estimation of maize foliar temperature and stomatal conductance as indicators of water stress based on optical and thermal imagery acquired using an unmanned aerial vehicle (uav) platform

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dc.contributor.authorBrewer, Kiara
dc.contributor.authorClulow, Alistair
dc.contributor.authorSibanda, Mbulisi
dc.date.accessioned2022-10-05T10:22:14Z
dc.date.available2022-10-05T10:22:14Z
dc.date.issued2022
dc.description.abstractClimatic variability and extreme weather events impact agricultural production, especially in sub-Saharan smallholder cropping systems, which are commonly rainfed. Hence, the development of early warning systems regarding moisture availability can facilitate planning, mitigate losses and optimise yields through moisture augmentation. Precision agricultural practices, facilitated by unmanned aerial vehicles (UAVs) with very high-resolution cameras, are useful for monitoring farm-scale dynamics at near-real-time and have become an important agricultural management tool. Considering these developments, we evaluated the utility of optical and thermal infrared UAV imagery, in combination with a random forest machine-learning algorithm, to estimate the maize foliar temperature and stomatal conductance as indicators of potential crop water stress and moisture content over the entire phenological cycle.en_US
dc.identifier.citationBrewer, K. et al. (2022). Estimation of maize foliar temperature and stomatal conductance as indicators of water stress based on optical and thermal imagery acquired using an unmanned aerial vehicle (uav) platform. Drones, 6(7), 169. https://doi.org/10.3390/drones6070169en_US
dc.identifier.issn2504-446X
dc.identifier.urihttps://doi.org/10.3390/drones6070169
dc.identifier.urihttps://hdl.handle.net/10566/8020
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.subjectDronesen_US
dc.subjectMaize phenotypingen_US
dc.subjectAgricultureen_US
dc.subjectFarmingen_US
dc.subjectClimate changeen_US
dc.titleEstimation of maize foliar temperature and stomatal conductance as indicators of water stress based on optical and thermal imagery acquired using an unmanned aerial vehicle (uav) platformen_US
dc.typeArticleen_US

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