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- W2735537098 abstract "In the context of climate change, general temperature increase and summer drought periods are expected, notably in the Mediterranean region. Research of tolerant varieties to water stress is therefore relevant regarding future breeding scope. This research aimed at phenotyping the apple tree response to water stress in a field trial where a segregating population (123 hybrids) was submitted to contrasting water regimes. Our assumption was that differences in stomatal closure among progenies can be efficiently estimated through high-resolution airborne imaging, including thermal signature, since reduction in transpiration rate and subsequent loss of latent heat flux is traduced by an elevation of canopy surface temperature.By using Unmanned Aerial Vehicle (UAV) as vector, and multispectral and thermal image acquisition, a series of flights were planned on sunny days with high evaporative demand, at 40m elevation, making it possible to characterize the tree response at individual scale, simultaneously across the trial. Zenithal images were pretreated, i.e. orthorectified and normalized, then georeferenced and mosaicked. Retrieval of spectral values from orthomosaics was performed on tree individuals within a central 60cm radius buffer zone. This allowed computing individual vegetation and water stress indices. Since indices computation relied on composite images, we tested the contribution of supervised classification of thermal images for unravelling well-illuminated leaves from shadowed vegetation or soil pixels. Comparison between different image analysis methods revealed usefulness of classification to improve the interpretation of thermal images, in relation to water stress and genotype effects. It opens the perspective of high-throughput pipelines for image analyses." @default.
- W2735537098 created "2017-07-21" @default.
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- W2735537098 date "2015-11-11" @default.
- W2735537098 modified "2023-10-12" @default.
- W2735537098 title "High-throughput field phenotyping of apple tree response to drought by UAV thermal imaging: Supervised image classification for unravelling intra-canopy temperature variation" @default.
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