Advancing High-Throughput Phenotyping of Wheat in Early Selection Cycles

Abstract

Enhancing plant breeding to ensure global food security requires new technologies. For wheat phenotyping, only limited seeds and resources are available in early selection cycles. This forces breeders to use small plots with single or multiple row plots in order to include the maximum number of genotypes/lines for their assessment. High-throughput phenotyping through remote sensing may meet the requirements for the phenotyping of thousands of genotypes grown in small plots in early selection cycles. Therefore, the aim of this study was to compare the performance of an unmanned aerial vehicle (UAV) for assessing the grain yield of wheat genotypes in different row numbers per plot in the early selection cycles with ground-based spectral sensing. A field experiment consisting of 32 wheat genotypes with four plot designs (1, 2, 3, and 12 rows per plot) was conducted. Near infrared (NIR)-based spectral indices showed significant correlations (p < 0.01) with the grain yield at flowering to grain filling, regardless of row numbers, indicating the potential of spectral indices as indirect selection traits for the wheat grain yield. Compared with terrestrial sensing, aerial-based sensing from UAV showed consistently higher levels of association with the grain yield, indicating that an increased precision may be obtained and is expected to increase the efficiency of high-throughput phenotyping in large-scale plant breeding programs. Our results suggest that high-throughput sensing from UAV may become a convenient and efficient tool for breeders to promote a more efficient selection of improved genotypes in early selection cycles. Such new information may support the calibration of genomic information by providing additional information on other complex traits, which can be ascertained by spectral sensing.

Development and Application of Image-Based High-Throughput Phenotyping Methodology for Salt Tolerance in Lentils

“…In recent years, high-throughput phenotyping has been used to complement or replace conventional methods. For many crops, including cotton (Gossypium hirsutum L.) [13], perennial ryegrass (Lolium perenne L.) [14][15][16][17], cereals [18][19][20][21][22][23][24], and pulses [25][26][27][28][29], high-throughput phenomics has facilitated the rapid measurement of complex traits including plant growth, yield, and resistance to biotic and abiotic stress factors under field and greenhouse conditions [30,31]. Several types of digital imaging sensors, including visible/red-green-blue (RGB), fluorescence, hyperspectral and other 3D imaging techniques (e.g., light detection and ranging (LiDAR)) have been used depending on the type of application [32][33][34].…”

Near-infrared spectroscopy outperforms genomics for predicting sugarcane feedstock quality traits

“…Nevertheless, examples that make use of NIR spectroscopy for breeding purposes are also available and include the classification of sugarcane clones based on quality parameters [ 93 ], resistance to diseases [ 94 ], and pests [ 17 ]. Moreover, plant breeders have been benefiting from NIR spectroscopy using spectrometer sensors coupled with UAV and ground-based platforms [ 11 , 95 , 96 ]. Rincent et al, (2018) [ 97 ] proposed an approach in which relationship matrices are derived from NIR spectra data and compared the efficiency of predictions with standard GP models considering markers.…”

High-Throughput Plant Phenotyping Platform (HT3P) as a Novel Tool for Estimating Agronomic Traits From the Lab to the Field

“…The eBee UAV (senseFly, http://www.geosense.gr/en/ebee/ ) is becoming a commonly used fixed-wing platform. A multispectral camera mounted on eBee was used to evaluate the yield of early wheat genotypes (Hu et al, 2020 ) and thermal camera performance (Sagan et al, 2019a ), as well as for identifying, positioning, and mapping weedy patches of Silybum marianum (Tamouridou et al, 2017 ). Research on this platform shows that using its highest resolution fails to provide the highest accuracy for weed classification.…”