Next Article in Journal
SBAS-Aided GPS Positioning with an Extended Ionosphere Map at the Boundaries of WAAS Service Area
Next Article in Special Issue
Improved Estimation of Winter Wheat Aboveground Biomass Using Multiscale Textures Extracted from UAV-Based Digital Images and Hyperspectral Feature Analysis
Previous Article in Journal
Mapping Frozen Ground in the Qilian Mountains in 2004–2019 Using Google Earth Engine Cloud Computing
Previous Article in Special Issue
Wheat Yellow Rust Detection Using UAV-Based Hyperspectral Technology
Article

Applying RGB- and Thermal-Based Vegetation Indices from UAVs for High-Throughput Field Phenotyping of Drought Tolerance in Forage Grasses

1
Plant Sciences Unit, Research Institute for Agriculture, Fisheries and Food (ILVO), 9090 Melle, Belgium
2
UAV Research Centre (URC), Department of Plants and Crops, Ghent University, 9000 Ghent, Belgium
3
Sustainable Crop Production, Department of Plants and Crops, Ghent University, 9000 Ghent, Belgium
4
Laboratory of Plant Ecology, Department of Plants and Crops, Ghent University, 9000 Ghent, Belgium
5
Department of Plant Biotechnology and Bioinformatics, Ghent University, 9000 Ghent, Belgium
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Remote Sens. 2021, 13(1), 147; https://doi.org/10.3390/rs13010147
Received: 17 November 2020 / Revised: 15 December 2020 / Accepted: 29 December 2020 / Published: 5 January 2021
(This article belongs to the Special Issue UAVs for Vegetation Monitoring)
The persistence and productivity of forage grasses, important sources for feed production, are threatened by climate change-induced drought. Breeding programs are in search of new drought tolerant forage grass varieties, but those programs still rely on time-consuming and less consistent visual scoring by breeders. In this study, we evaluate whether Unmanned Aerial Vehicle (UAV) based remote sensing can complement or replace this visual breeder score. A field experiment was set up to test the drought tolerance of genotypes from three common forage types of two different species: Festuca arundinacea, diploid Lolium perenne and tetraploid Lolium perenne. Drought stress was imposed by using mobile rainout shelters. UAV flights with RGB and thermal sensors were conducted at five time points during the experiment. Visual-based indices from different colour spaces were selected that were closely correlated to the breeder score. Furthermore, several indices, in particular H and NDLab, from the HSV (Hue Saturation Value) and CIELab (Commission Internationale de l’éclairage) colour space, respectively, displayed a broad-sense heritability that was as high or higher than the visual breeder score, making these indices highly suited for high-throughput field phenotyping applications that can complement or even replace the breeder score. The thermal-based Crop Water Stress Index CWSI provided complementary information to visual-based indices, enabling the analysis of differences in ecophysiological mechanisms for coping with reduced water availability between species and ploidy levels. All species/types displayed variation in drought stress tolerance, which confirms that there is sufficient variation for selection within these groups of grasses. Our results confirmed the better drought tolerance potential of Festuca arundinacea, but also showed which Lolium perenne genotypes are more tolerant. View Full-Text
Keywords: UAV; RGB camera; thermal camera; drought tolerance; forage grass; HSV; CIELab; broad-sense heritability; phenotyping gap; high throughput field phenotyping UAV; RGB camera; thermal camera; drought tolerance; forage grass; HSV; CIELab; broad-sense heritability; phenotyping gap; high throughput field phenotyping
Show Figures

Graphical abstract

MDPI and ACS Style

De Swaef, T.; Maes, W.H.; Aper, J.; Baert, J.; Cougnon, M.; Reheul, D.; Steppe, K.; Roldán-Ruiz, I.; Lootens, P. Applying RGB- and Thermal-Based Vegetation Indices from UAVs for High-Throughput Field Phenotyping of Drought Tolerance in Forage Grasses. Remote Sens. 2021, 13, 147. https://doi.org/10.3390/rs13010147

AMA Style

De Swaef T, Maes WH, Aper J, Baert J, Cougnon M, Reheul D, Steppe K, Roldán-Ruiz I, Lootens P. Applying RGB- and Thermal-Based Vegetation Indices from UAVs for High-Throughput Field Phenotyping of Drought Tolerance in Forage Grasses. Remote Sensing. 2021; 13(1):147. https://doi.org/10.3390/rs13010147

Chicago/Turabian Style

De Swaef, Tom, Wouter H. Maes, Jonas Aper, Joost Baert, Mathias Cougnon, Dirk Reheul, Kathy Steppe, Isabel Roldán-Ruiz, and Peter Lootens. 2021. "Applying RGB- and Thermal-Based Vegetation Indices from UAVs for High-Throughput Field Phenotyping of Drought Tolerance in Forage Grasses" Remote Sensing 13, no. 1: 147. https://doi.org/10.3390/rs13010147

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop