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.
Highlights
The current study examines the performance of vegetation indices (VI) obtained at different dates from a 4-band multispectral camera (Parrot Sequoia) on-board unmanned aerial vehicle (UAV) and those obtained from ground-based RGB images to assess agronomic traits of large panels of bread wheat landraces and modern cultivars adapted to Mediterranean conditions
This study demonstrated the potential of a 4-band multispectral camera (Parrot Sequoia) and RGB images for assessing agronomic traits— yield and grain number per unit area—in bread wheat grown in a Mediterranean-type environment
The suitability of the models proved to be specific, as their consistency depended on the canopy structure, leaf dimensions and orientation, and the environmental conditions during vegetative growth, which poses a difficulty for their general use in a random crop season
Summary
Wheat is the main crop around the world and provides 18% of the global human intake of calories and 20% of protein Since global wheat demand is predicted to increase by 60% by the year 2050, there is an urgent need to raise wheat production by 1.7% per year until [1]. The rate of genetic improvement required in the decades is higher than that achieved so far [2]. Given the limitations imposed by the soil availability for agricultural uses, most increases rely on the release of improved cultivars with enhanced yield potential and stability under variable environmental conditions. Drought stress during the grain filling period, originating from a combination of water deficit and high temperatures, is the main
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