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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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
MDPI
Abstract
Climatic 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.
Description
Keywords
Drones, Maize phenotyping, Agriculture, Farming, Climate change
Citation
Brewer, 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/drones6070169