Accuracy assessment of plant height using an unmanned aerial vehicle for quantitative genomic analysis in bread wheat
Plant height is an important selection target since it is associated with yield potential, stability and particularly with lodging resistance in various environments. Rapid and cost-effective estimation of plant height from airborne devices using a digital surface model can be integrated with academic research and practical wheat breeding programs. A bi-parental wheat population consisting of 198 doubled haploid lines was used for time-series assessments of progress in reaching final plant height and its accuracy was assessed by quantitative genomic analysis. UAV-based data were collected at the booting and mid-grain fill stages from two experimental sites and compared with conventional measurements to identify quantitative trait loci (QTL) underlying plant height.
A significantly high correlation of R2 = 0.96 with a 5.75 cm root mean square error was obtained between UAV-based plant height estimates and ground truth observations at mid-grain fill across both sites. Correlations for UAV and ground-based plant height data were also very high (R2 = 0.84–0.85, and 0.80–0.83) between plant height at the booting and mid-grain fill stages, respectively. Broad sense heritabilities were 0.92 at booting and 0.90–0.91 at mid-grain fill across sites for both data sets. Two major QTL corresponding to Rht-B1 on chromosome 4B and Rht-D1 on chromosome 4D explained 61.3% and 64.5% of the total phenotypic variations for UAV and ground truth data, respectively. Two new and stable QTL on chromosome 6D seemingly associated with accelerated plant growth was identified at the booting stage using UAV-based data. Genomic prediction accuracy for UAV and ground-based data sets was significantly high, ranging from r = 0.47–0.55 using genome-wide and QTL markers for plant height. However, prediction accuracy declined to r = 0.20–0.31 after excluding markers linked to plant height QTL.
This study provides a fast way to obtain time-series estimates of plant height in understanding growth dynamics in bread wheat. UAV-enabled phenotyping is an effective, high-throughput and cost-effective approach to understand the genetic basis of plant height in genetic studies and practical breeding.
KeywordsAerial surveillance Genomic prediction Quantitative trait loci Triticum aestivum
Plant height is an important agronomic trait and it was reduction in plant height that enabled the Green Revolution . Although plant height has been reduced to around 75–80 cm for irrigated wheat with high yield potential, its control remains a very important aspect in breeding programs. Two major genes, Rht1 (or Rht-B1b) and Rht2 (or Rht-D1b) confer reduced plant height without detrimental effects on grain yield potential in varying environments . Rht genes also have confounding effects on anther extrusion: a major trait for hybrid wheat production [3, 4], resistance to Fusarium head blight (FHB) [5, 6], and resistance to at least one insect pest . Therefore, fine-tuning of plant height for a target environment is not only important for pure-line breeding but can also be important in hybrid wheat breeding where tallness of the male parent is required for efficient production of hybrids . However, the association of Rht-B1 and Rht-D1 with undesirable traits, for example shortened coleoptile length, has caused wheat researcher to seek alternate dwarfing genes with less adverse effects. Recently, Rht24 was reported as new gene for reduced plant height but affecting floral architecture and response to FHB [8, 9]. It was also reported to increase kernel weight . Reports of some other reduced height genes, such as Rht4, Rht5, Rht7, Rht8, Rht9, Rht12, Rht13, Rht14, Rht16, Rht18, Rht21, Rht23, and Rht25, also offer other possibilities for wheat improvement .
Marker-assisted selection based on quantitative trait loci (QTL) or functional genes can enhance the selection accuracy and ultimately increase genetic gain in each breeding cycle [12, 13]. Wheat has determinate growth habit thus plant height progressively increases during vegetative growth until the reproductive stage. Conventionally, plant height is measured once, after anthesis when full height potential has been reached. Therefore, temporal characterization of plant height could provide a better understanding about the mechanism of plant growth and underlying genetics . Quantitative methods, such as QTL analysis and association mapping, can give an insight about the genetic loci and genomic prediction analysis help in selection of genotypes with strong genetic basis for trait of interest [15, 16].
Multi-location characterization of wheat germplasm is essential to evaluate adaptability of genotypes and patterns of G × E interaction for trait stability . Field-based phenotyping tends to be laborious, with high likelihood of error and represents a major bottleneck for genome-to-phenome knowledge . High throughput phenotyping platforms have higher capability for high precision, non-destructive characterization of quantitative traits . Recent advances in proximal remote sensing using unmanned aerial vehicles (UAV) with RGB (red, green, blue) and multi-spectral imaging have made it possible to create high throughput, cost-effective and accurate quantitative phenotyping datasets [12, 20]. UAV platforms can easily acquire multi-point data for complex traits such as biomass, normalized difference vegetation index, plant density, early emergence, rate of senescence rate, and plant height [20, 21, 22, 23, 24, 25]. These platforms are low cost compared to traditional and recently advanced ground-based phenotyping platforms .
UAV-based plant height has been estimated using digital surface models (DSM). High correlations with ground-based reference measurements have been made for barley , wheat , poppy  and sorghum . DSM gives information of altitude in the form of raster values. The drawbacks of previous approaches were that estimations were made of the average heights of whole canopies, including not only the heights of ears, but also the heights of lower leaves and even the elevation of bare ground patches within canopy gaps . Furthermore, accurate assessment of the ground surface elevation imposes a major restriction factor data acquisition for UAV-based phenotyping of plant height in crops such as wheat with dense canopies. These limitations have made UAV-based platforms more complex and time-consuming by increasing the workload such as flights before planting and post-imaging quality control analysis . This kind of data noise can adversely affect genetic analyses and genome-based selection. Previously, DSM-derived plant height data had been applied for genomic prediction in sorghum . Therefore, there is a need to standardize UAV-based data for accurate and error-free characterization of plant height for quantitative genetic studies and selection of advanced lines in breeding program. To date, there is no report on the use of UAV-derived plant height data for quantitative loci analysis in wheat.
The major objectives of the present study were to (1) standardize a rapid method for plant height estimation using a UAV platform, (2) identify quantitative trait loci for plant height using UAV and ground-based measurements, and (3) assess genomic prediction accuracy for plant height in wheat.
Materials and methods
Germplasm and experimental design
A set of 198 doubled haploid (DH) lines derived from the cross Yangmai 16/Zhongmai 895 were used to evaluate a UAV-based platform for measuring plant height and its application in QTL analysis and genomic prediction. Yangmai 16 and Zhongmai 895 are elite varieties that have been widely cultivated in Yangtze River, and Yellow and Huai Valleys regions, respectively. Experiments were conducted during 2016–2017 and 2017–2018 at Xinxiang (35°18′0″N, 113°52′0″E) and Luohe (33°34′0″N, 114°2′0″E), both in Henan province. The DH lines and two parents were planted in randomized complete blocks with three replications (200 genotype × 3 replications) at each site. The size of each plot was 3.9 m2 (1.3 m × 3 m) with six rows at 0.30 cm spacing and the plant density was maintained at 270 plants/m2. Both sites were irrigated at same developmental stages according to local agricultural practices.
Remote sensing campaign, mosaicking and DSM generation
An auto-operational DJI Inspires 1 model T600 (SZ DJI Technology Co., Shenzhen) carrying a Sequoia 4.0 camera (https://www.micasense.com/parrotsequoia/) was used for aerial imagery. Sequoia has a 16-megapixel RGB camera and 4 monochrome sensors (NIR, Red, Green and Red-edge). Flight missions over the targeted field were controlled by flight planning software Altizure DJI version 3.6.0 (https://www.altizure.com). Images were acquired in sunny conditions from 30 m altitude while maintaining 85% forward and 85% side overlapping between images to ensure enough ground sampling distance. Pix4D Mapper (PIX4d, Lausanne, Switzerland) (https://pix4d.com/) was used for orthomosaic and DSM generation using world geographic coordinates of GCPs as previously reported by Hassan et al. . Pix4D has the advantage of auto-processing in feature point matching and point cloud generation. All correspondence between overlapping images estimated from their geographical coordinates and pixels were used to detect the accuracy of matching points to minimize spaces between point clouds. The image resolution or ground sampling distance at 30 m was 2.5 cm/pixel.
DSM evaluation and plant height model (PHM) development
Estimation and validation of UAV-based plant heights
H is plant height estimated from PHM, where U is the highest point and L is the lowest point of the upper boundary of the canopy at specific location.
UAV-based plant height was validated through ground-based measurements using a ruler at mid-grain fill. Plant height was averaged from 10 plants of each plot representing a DH line. A total 600 of plots were measured in 2 days at each experimental site. Average height error was calculated as the difference between ground measurements and plant height estimated from the UAV platform. The root means square error (RMSE) was also calculated along with the regression fit for validation of UAV platform measurements.
SNP genotyping, QTL analysis and genomic prediction
The Yangmai 16/Zhongmai 895 DH population and parents were genotyped at Capital Bio Corporation (Beijing, China; http://www.capitalbio.com) using the commercially available Affymetrix wheat 660 K SNP array.Previously, This array was used for genome-wide QTL mapping studies [30, 31, 32]. IciMapping 4.0 was used for linkage map construction using Kosambi mapping approach. Inclusive composite interval mapping-additive (ICIM-ADD) method was used for the QTL analysis at LOD threshold of 2.5 . To assess the accuracy of identification of QTL from UAV-based remote sensing, we cross-validated our results with ground truth data obtained at mid-grain fill. For this, the averaged data from 2 years (2016–2017 and 2017–2018) at both experimental sites was used for quantitative genomic analysis. For temporal assessment of genomic variation, plant height was phenotyped at booting and mid-grain fill. QTL with overlapping confidence intervals were considered to be the same. Differences between the phenotypic variances explained by QTL from both data sets were detected as validation for UAV-based QTL.
We cross-validated UAV-based data through estimating predication accuracy by removing markers and chromosomes linked with major plant height reducing genes.
Accuracy assessment of UAV-based plant height
Summary of statistics for both plant height data sets and developmental stages
Yangmai 16 and Zhongmai 895
G × E (F.value)
Identification of QTL and their impact on phenotype
Validation of UAV-based QTL results
Genomic prediction accuracy of UAV-based data set
Accuracy and phenotypic variations in UAV-based plant height
UAV is a promising platform to predict time-series development of crop canopies, and further use this data to understand the genetic basis of phenotypic variation . Previously some studies have been reported different workflows for the estimation of plant height using UAV platform [21, 26, 28] The UAV-platform requires far fewer images and less computing capacity to construct the digital elevation model compared to ground-based imaging platforms . Ground-based LiDAR technology has been reported more accurate , but it has some limitations such as in coving large and multilocational trials. Aerial estimation of plant height could be error-prone due to low efficiency in pre- and post-imagery processing methods such as altitude of imaging platform, accuracy in DTM construction, and height extraction strategy from images [21, 28, 29]. High altitude of the UAV flight is likely to generate low pixel resolution of images casing increased data noise. UAV flights were taken at low altitude (30 m) to minimise probability of error due to low pixel numbers. DTM gives information about the elevation of the ground surface. DTM accuracy is an important factor, and low accuracy in DTM can lead to high over- or under-estimations of canopy elevation [21, 27]. The precision in estimating depends on number and distribution of bare ground patches across experimental sites if the terrain is to geographically variable. In crops with dense canopies like wheat, it is difficult to generate accurate DTM from DSM images at later growth stages acquiring time-points to develop PHM from single flights. Terrain and distribution of bare ground can be handled through better experimental design and management. Our trial field was well managed with enough spacing between and along the plots to be used to estimate ground elevations across the field. DTM generated from both experimental sites at booting and mid-grain fill had low errors varying from ± 3.5 to 4.5 cm, similar to a previous report on a poppy crop  (Fig. 3b). It also reduced the computing load and time required for pre-planting flights to generate DTM of bare fields as done in other reports [24, 27, 28]. Our method also overcame the problem of data noise in height extraction from PHM due to the detection of lower parts of the canopy such as elevation of leaf from gaps between plants. Using this method, height of a single plant from a particular position of the experimental plot can be measured even in the case of a thin canopy. Higher correlations (R2= 0.96; 5.75 cm RMSE) were estimated between ground and UAV data sets at mid-grain fill. Our results were better than previous reports where correlations were slightly lower between UAV-derived plant height and reference observations (0.85–0.90) in wheat and barley [26, 29] (Fig. 2). This was due to the better strategy of measuring pixel values from the highest points of the imaging to be the upper boundary of the canopy rather than mean values from the whole canopy as previously done in wheat, barley and sorghum [21, 24, 26]. Both data sets showed transgressive segregation among DH lines relative to the parents with significant phenotypic variation and high heritability. Moreover, high heritability and no significant G × E allowed detection of stable quantitative loci for plant height.
UAV-based QTLs and their effects on phenotype
Height reducing homoeoalleles Rht-B1 and Rht-D1 on the short arms of chromosomes 4B and 4D are GA-insensitive and major plummeting factor for wheat height by reduced GA response mechanism [39, 40]. Plant height in wheat is a developmental trait and the genetic basis underlying for its development over time is still being unmasked from a number of potential quantitative loci . Rht-B1b and Rht-D1b were already reported in parent cultivars Yangmai16 and Zhongmai895, respectively . UAV-based plant height accuracy was confirmed by identification of QTL corresponding to these Rht genes, high correlations between ground truth data and UAV-based data sets, and consistent identification of the same QTL in both UAV-based and ground-based datasets (Fig. 4). UAV-based phenotype data successfully verified the dynamic presence of these two major genes as previously reported by Zhang et al. . Two new QTL with minor phenotypic effect of 1.50–1.97% was identified on chromosome 6D using UAV-based booting data from both sites. QTL were also identified 6D at under heat and drought condition which help plant for adaptation without confounding agronomic effects . While in our study, these QTL might be involved in seedling vigour, but further validation is required. The QTL on chromosome 6D at booting is likely to affect the plant growth. The phenotypic validation of Rht-B1 and Rht-D1 on plant height measured by UAV confirmed the accuracy of this platform and proved that UAV has potential for genetic studies.
Accuracy of UAV-based QTL
In quantitative genetics, erroneous phenotypic data is a major bottleneck . Probability of error in UAV-based data can influence the QTL analysis and other genomics studies. In our study, accuracy of QTL detected from both data sets was also validated from multi-location trials. The identification of chromosome 4B and 4D QTL underpinning plant height was consistent across sites (Fig. 4b and Additional file 1: Table S1). Similarly, QTL with less phenotypic variation ranging 1.50–1.97% at booting was also consistent at both sites confirming the accuracy of the UAV-based platform for reliable quantitative genomic analysis. The new QTL on chromosome 6D identified using UAV-based data indicated that the UAV platform was effective in detecting genetic variation. These results indicated the potential high efficiency of UAV-based remote sensing for major QTL identification as well as temporal genetic dissection of traits.
Accuracy of UAV-based data for genomic prediction
Genomic prediction is regarded as a relatively new breeding strategy to better exploit quantitative variation in crop breeding and in increasing selection accuracy by optimization of resource allocation in breeding programs [13, 43]. In revolutionizing phenotyping platforms for capture of data at lower cost, accuracy for true genomic selection cannot be compromised . Therefore, UAV platforms have potential to contribute in enhancement of genomic prediction accuracy cos-effectively. Rutkoski et al.  used UAV-based multispectral secondary traits and reported their high prediction accuracy (r = 0.41–0.56) for traits related to grain yield in wheat. Here we demonstrate the use of plant height data captured by a UAV-based aerial platform for high accuracy genomic selection. Similar trends in prediction ability were obtained with and without consideration of the QTL across the data sets. The prediction accuracy declined as markers linked with the QTL were excluded in both data sets. However, remaining genome-wide SNPs predicted accuracy ranged from r = 0.20–0.31 (Fig. 6). Our results indicated the presence of an additional gene with minor effect that was not detected in earlier QTL mapping. Our findings also indicate that the use of UAV platforms for genomic selection of quantitative traits could improve prediction ability by continuous capture of cost-effective phenotypic data from multiple environments.
This study describes a UAV-based method for plant height estimation in wheat and its application in quantitative genomic analysis and functional gene characterization. Traditionally, plant height is measured only once, despite the fact that progression to final plant height may differ among genotypes. Our UAV-based approach facilitates rapid, cost-effective, high-throughput capture of plant height data at different growth stages. High R2 between UAV and ground-based data sets indicated that UAV-platforms could be used for quantitative genomic analysis. This technique can also be applied in practical breeding after adjustment of UAV data according to the average difference (in this case, 14.03 cm) calculated between UAV and ground reference observations. The potential of UAV-based high throughput plant height phenotyping not only reduces the labour costs but is also capable of providing time-lapse reproducible data from large breeding trials to identify the underlying genetics and permit genomic selection for complex traits such as plant height.
They managed the UAV flights for aerial imagery, analysed the data and wrote paper under supervision of ZH; MY and LF conducted ground-based field measurements, YX managed and directed the trial; YX, AR, XX and BZ gave comments and suggestions during preparation of the manuscript. All authors read and approved the final manuscript.
We are grateful to Prof. R. A. McIntosh, Plant Breeding Institute, University of Sydney, for reviewing this manuscript. This work was funded by the National Key Project (2016YFD0101804), the Fundamental Research Funds for the Institute Planning in Chinese Academy of Agricultural Sciences (S2018QY02), and the National Natural Science Foundation of China (31671691, 3171101265).
The authors declare that they have no competing interests.
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- 4.Würschum T, Langer SM, Longin CFH, Tucker MR, Leiser WL. A three-component system incorporating Ppd-D1, copy number variation at Ppd-B1, and numerous small-effect quantitative trait loci facilitates adaptation of heading time in winter wheat cultivars of worldwide origin. Plant Cell Environ. 2018;41(6):1407–16.CrossRefGoogle Scholar
- 11.McIntosh RA, Dubcovsky J, Rogers WJ, Morris C, Xia XC. Catalogue of gene symbols for wheat. 2017. supplement. Downloaded from http://shigen.nig.ac.jp/wheat/komugi/genes/symbolClassList.jsp. Accessed 02 Feb 2017.
- 36.R Core Team. R. A Language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2016.Google Scholar
- 44.Rutkoski J, Poland J, Mondal S, Autrique E, Pérez LG, Crossa J, et al. Canopy temperature and vegetation indices from high-throughput phenotyping improve accuracy of pedigree and genomic selection for grain yield in wheat. G3 Genes Genomes Genet. 2016;6(9):2799–808.Google Scholar
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