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Article

UAV and Ground Image-Based Phenotyping: A Proof of Concept with Durum Wheat

1
Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028 Barcelona, Spain
2
AGROTECNIO (Center for Research in Agrotechnology), Av. Rovira Roure 191, 25198 Lleida, Spain
3
Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Ctra. de la Coruña Km. 7.5, 28040 Madrid, Spain
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(10), 1244; https://doi.org/10.3390/rs11101244
Received: 14 March 2019 / Revised: 20 May 2019 / Accepted: 22 May 2019 / Published: 25 May 2019
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. View Full-Text
Keywords: wheat; grain yield; High-Throughput Plant Phenotyping; UAV; RGB; multispectral; canopy temperature wheat; grain yield; High-Throughput Plant Phenotyping; UAV; RGB; multispectral; canopy temperature
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MDPI and ACS Style

Gracia-Romero, A.; Kefauver, S.C.; Fernandez-Gallego, J.A.; Vergara-Díaz, O.; Nieto-Taladriz, M.T.; Araus, J.L. UAV and Ground Image-Based Phenotyping: A Proof of Concept with Durum Wheat. Remote Sens. 2019, 11, 1244. https://doi.org/10.3390/rs11101244

AMA Style

Gracia-Romero A, Kefauver SC, Fernandez-Gallego JA, Vergara-Díaz O, Nieto-Taladriz MT, Araus JL. UAV and Ground Image-Based Phenotyping: A Proof of Concept with Durum Wheat. Remote Sensing. 2019; 11(10):1244. https://doi.org/10.3390/rs11101244

Chicago/Turabian Style

Gracia-Romero, Adrian, Shawn C. Kefauver, Jose A. Fernandez-Gallego, Omar Vergara-Díaz, María T. Nieto-Taladriz, and José L. Araus 2019. "UAV and Ground Image-Based Phenotyping: A Proof of Concept with Durum Wheat" Remote Sensing 11, no. 10: 1244. https://doi.org/10.3390/rs11101244

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