Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/176423
Title: UAV and Ground Image-Based Phenotyping: A Proof of Concept with Durum Wheat
Author: Gracia Romero, Adrián
Kefauver, Shawn Carlisle
Fernandez Gallego, José Armando
Vergara Díaz, Omar
Nieto Taladriz, María Teresa
Araus Ortega, José Luis
Keywords: Canvi climàtic
Rendiment
Conreu
Cereals
Climatic change
Performance
Crops
Cereals
Issue Date: 25-May-2019
Publisher: MDPI
Abstract: Climate change is one of the primary culprits behind the restraint in the increase of cereal crop yields. In order to address its effects, effort has been focused on understanding the interaction between genotypic performance and the environment. Recent advances in unmanned aerial vehicles (UAV) have enabled the assembly of imaging sensors into precision aerial phenotyping platforms, so that a large number of plots can be screened effectively and rapidly. However, ground evaluations may still be an alternative in terms of cost and resolution. We compared the performance of red-green-blue (RGB), multispectral, and thermal data of individual plots captured from the ground and taken from a UAV, to assess genotypic differences in yield. Our results showed that crop vigor, together with the quantity and duration of green biomass that contributed to grain filling, were critical phenotypic traits for the selection of germplasm that is better adapted to present and future Mediterranean conditions. In this sense, the use of RGB images is presented as a powerful and low-cost approach for assessing crop performance. For example, broad sense heritability for some RGB indices was clearly higher than that of grain yield in the support irrigation (four times), rainfed (by 50%), and late planting (10%). Moreover, there wasn't any significant effect from platform proximity (distance between the sensor and crop canopy) on the vegetation indexes, and both ground and aerial measurements performed similarly in assessing yield.
Note: Reproducció del document publicat a: https://doi.org/10.3390/rs11101244
It is part of: Remote Sensing, 2019, vol. 11, num. 10
URI: http://hdl.handle.net/2445/176423
Related resource: https://doi.org/10.3390/rs11101244
ISSN: 2072-4292
Appears in Collections:Articles publicats en revistes (Biologia Evolutiva, Ecologia i Ciències Ambientals)

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