Using Unmanned Aerial Vehicle and Ground-Based RGB Indices to Assess Agronomic Performance of Wheat Landraces and Cultivars in a Mediterranean-Type Environment

Abstract

The adaptability and stability of new bread wheat cultivars that can be successfully grown in rainfed conditions are of paramount importance. Plant improvement can be boosted using effective high-throughput phenotyping tools in dry areas of the Mediterranean basin, where drought and heat stress are expected to increase yield instability. Remote sensing has been of growing interest in breeding programs since it is a cost-effective technology useful for assessing the canopy structure as well as the physiological traits of large genotype collections. The purpose of this study was to evaluate the use of a 4-band multispectral camera on-board an unmanned aerial vehicle (UAV) and ground-based RGB imagery to predict agronomic traits as well as quantify the best estimation of leaf area index (LAI) in rainfed conditions. A collection of 365 bread wheat genotypes, including 181 Mediterranean landraces and 184 modern cultivars, was evaluated during two consecutive growing seasons. Several vegetation indices (VI) derived from multispectral UAV and ground-based RGB images were calculated at different image acquisition dates of the crop cycle. The modified triangular vegetation index (MTVI2) proved to have a good accuracy to estimate LAI (R2 = 0.61). Although the stepwise multiple regression analysis showed that grain yield and number of grains per square meter (NGm2) were the agronomic traits most suitable to be predicted, the R2 were low due to field trials were conducted under rainfed conditions. Moreover, the prediction of agronomic traits was slightly better with ground-based RGB VI rather than with UAV multispectral VIs. NDVI and GNDVI, from multispectral images, were present in most of the prediction equations. Repeated measurements confirmed that the ability of VIs to predict yield depends on the range of phenotypic data. The current study highlights the potential use of VI and RGB images as an efficient tool for high-throughput phenotyping under rainfed Mediterranean conditions.

Identification of QTL hotspots affecting agronomic traits and high-throughput vegetation indices in rainfed wheat

“…Even though water stress affects the growth of wheat, the effects are higher during the grain filling period (Moragues et al 2006). Thus, the LAI and GNDVI values decreased at the end of the growing cycle due to a low chlorophyll content associated with senescence during the grain filling period (Rufo et al 2021). In addition, Gitelson et al (2002) reported that the sensitivity of the green band was higher than that of the red band when the vegetation fraction was more than 60%, so vegetation indices using green wavelengths perform better at high LAI values, which in wheat under Mediterranean conditions are the highest at booting (Aparicio et al 2000; Royo et al 2004; Kyratzis et al 2017; Rufo et al 2021).…”

“…Moreover, Royo et al (2000) found that genotypes with longer GFDs could have greater opportunities to increase grain weight in favourable growing seasons than in warmer and drier seasons. The elevated number of VI-related MTAs found in the driest year (2017) could be explained by the higher variability in traits related to leaf biochemical properties or canopy structural attributes within the set of genotypes grown in environments with water scarcity (Rufo et al 2021). The highest number of MTAs was identified for PH in 2017, when the tallest plants were observed.…”

“…The following traits were measured across the three years according to the protocol described in Rufo et al (2021): grain yield (GY, t ha -1 ), number of spikes per m 2 (NSm 2 ), number of grains per m 2 (NGm 2 ), thousand kernel weight (TKW, g), aboveground biomass at physiological maturity (biomass, t DM ha -1 ), harvest index (HI), plant height (PH, cm) and early vigour (estimated as green area, GA). The HI was calculated as the ratio between grain and plant weights in a 1-m long row sample.…”

“…An increasing frequency and severity of terminal drought stress will reduce grain weight, grain quality, and wheat yield (Araus et al 2002; Slafer et al 2005; Kulkarni et al 2017). Therefore, there is a need to improve the identification of genotypes able to maintain acceptable levels of yield and yield stability in semiarid environments, which have been identified as the most sensitive to the effects of climate change (Rufo et al 2021). The release of improved cultivars with enhanced drought adaptation will be critical for breeding programmes focusing on wheat adaptability and stability under rainfed conditions (Graziani et al 2014; Bhatta et al 2018).…”

“…The recent progress in high-throughput phenotyping (HTP) based on the use of multispectral images acquired from unmanned aerial vehicles (UAVs) has increasingly improved the assessment of agronomic traits (Gracia-Romero et al 2017; Xie and Yang 2020; Gomez-Candon et al 2021; Rufo et al 2021) on large germplasm collections in a rapid, cost-effective, and high spatial resolution way (Duan et al 2017), as it allows the estimation of various plant traits using nonintrusive and nondestructive technology (White et al 2012; Rufo et al 2021). Remote sensing has attracted growing interest in breeding programmes since it can deliver detailed information about biophysical crop traits in many situations to cope with the current phenotyping bottleneck (Araus and Cairns 2014; Juliana et al 2019; Bellvert et al 2021).…”