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

Abstract

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.

Peanut Leaf Wilting Estimation From RGB Color Indices and Logistic Models

“…Vegetation indices can be derived from RGB color space indices, which represent international standards for color perception by the human eye and were adopted by the Commission Internationale de l'Eclairage (CIE) in 1976 (Yam and Papadakis, 2004;Trussell et al, 2005;Casadesús et al, 2007;Liu et al, 2011;Kipp et al, 2014;Zhou et al, 2015). Several studies show that RGB color space indices, such as hue, a * , u * , and other derived indices, such as green area (GA) and the normalized difference CIELab index (NDlab), outperformed spectral indices such as NGRDI, NDVI, and gNDVI, in predicting yield of wheat and maize more accurately and had higher broad sense heritability for drought tolerance in forage grasses (Kefauver et al, 2015;Vergara-Díaz et al, 2015Zhou et al, 2015;Gracia-Romero et al, 2017Buchaillot et al, 2018Buchaillot et al, , 2019Fernandez-Gallego et al, 2019;De Swaef et al, 2021).…”

A European perspective on opportunities and demands for field-based crop phenotyping

“…Some highly equipped field platforms also allow a degree of environmental control and manipulation, for example using rain-out shelters (e.g De Swaef et al, 2021) and irrigation to control rainfall and soil water content, respectively, or Free Air CO 2 Enrichment (FACE) to manipulate CO 2 levels to simulate future climatic conditions (Kimball, 2016). Soil conditions (temperature, nutrient treatment, management schemes) can also be monitored and manipulated.…”

UAVs for Vegetation Monitoring: Overview and Recent Scientific Contributions

“…Similar approaches were followed for water deficiencies causing effects on chlorophyll content and organization in the mesophyll and leaf structure. It was possible to address this goal using RGB and thermal-based VIs [21] or multispectral sensors and convolutional neural networks (CNNs) [22]. It is also common to use thermal sensors since water stress causes stomatal closure that reduces transpiration and evaporative cooling and increases temperature [23].…”

“…It is also common to use thermal sensors since water stress causes stomatal closure that reduces transpiration and evaporative cooling and increases temperature [23]. In this way, data from thermal sensors were combined with RGB [21] or multispectral sensors [24,25] to take advantage of the information provided in different areas of the spectrum and the higher spatial resolution of such sensors.…”

“…De Swaef et al (2021) [21] evaluated the drought tolerance of three common types of forage species (Festuca arundinacea, diploid, and tetraploid Lolium perenne) with VIs from RGB and thermal imaging. The methodology was tested in five UAV flight dates throughout the summer, which overlapped with visual breeder score-taking.…”