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Abstract

A major challenge for crop research in the 21st century is how to predict crop performance as a function of genetic architecture. Advances in “next generation” DNA sequencing have greatly improved genotyping efficiency and reduced genotyping costs. Methods for characterizing plant traits (phenotypes), however, have much progressed more slowly over the past 30 years, and constraints in phenotyping capability limit our ability to dissect the genetics of quantitative traits, especially those related to harvestable yield and stress tolerance. As a case in point, mapping populations for major crops may consist of 20 or more families, each represented by as many as 200 lines, necessitating field trials with over 20,000 plots at a single location. Investing in the resources and labor needed to quantify even a few agronomic traits for linkage with genetic markers in such massive populations is currently impractical for most breeding programs. Herein, we define key criteria, experimental approaches, equipment and data analysis tools required for robust, high-throughput field-based phenotyping (FBP). The focus is on simultaneous proximal sensing for spectral reflectance, canopy temperature, and plant architecture where a vehicle carrying replicated sets of sensors records data on multiple plots, with the potential to record data throughout the crop life cycle. The potential to assess traits, such as adaptations to water deficits or acute heat stress, several times during a single diurnal cycle is especially valuable for quantifying stress recovery. Simulation modeling and related tools can help estimate physiological traits such as canopy conductance and rooting capacity. Many of the underlying techniques and requisite instruments are available and in use for precision crop management. Further innovations are required to better integrate the functions of multiple instruments and to ensure efficient, robust analysis of the large volumes of data that are anticipated. A complement to the core proximal sensing is high-throughput phenotyping of specific traits such as nutrient status, seed composition, and other biochemical characteristics, as well as underground root architecture. The ability to “ground truth” results with conventional measurements is also necessary. The development of new sensors and imaging systems undoubtedly will continue to improve our ability to phenotype very large experiments or breeding nurseries, with the core FBP abilities achievable through strong interdisciplinary efforts that assemble and adapt existing technologies in novel ways.

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... The progress in plant phenotyping has been much slower than the fast development of genotyping technologies, remaining the accurate phenotyping of large-scale multi-location field trials as a significant challenge for the advancement of genetic research (Montes et al. 2007, Furbank 2009, White et al. 2012, Araus and Cairns 2014. However, the latest improvements in image data acquisition and modeling, data mining, aeronautics, as well as robotics, have increased the interest in scientific discipline aiming at the description of phenotypes based on the gathering of high-dimensional phenotypic data (Houle et al. 2010). ...
... The development of molecular techniques has increased the needs of reliable and cost-effective phenotypic information, representing a great challenge for the progress of plant-genetic studies (Araus and Cairns 2014;Montes et al. 2007). High-throughput phenotyping (HTP) has emerged as a suitable strategy for phenotyping thousands of new genotypes effectively and affordably based on reflectance information (Furbank and Tester 2011;White et al. 2012). Unmanned aerial vehicles (UAVs) such as polycopters outperform ground-based HTP platforms regarding working capacity while deriving high-resolution image data (Araus and Cairns 2014). ...
... Examples of the application of HTP in plant breeding are among others, the estimation of above-ground biomass (Babar et al. 2006;Montes et al. 2011;Busemeyer et al. 2013;Fu et al. 2014;Barmeier and Schmidhalter 2017;Yue et al. 2017Yue et al. , 2018 as well as GY, plant responses to biotic and abiotic stress, nitrogen use efficiency, nutrient status, early plant vigor, seeds quality traits, leaf physiology and biochesmistry, vegetation cover fraction, and leaf area index (reviewed by Fahlgren et al. 2015;Yang et al. 2017; Würschum 2019). Therefore, it has been proposed to remotely phenotype large breeding populations in a reliable and costeffective manner (Furbank and Tester 2011;White et al. 2012). HTP platforms, including uncrewed aerial vehicles (UAVs) such as drones mounted with hyperspectral cameras, can simultaneously collect hundreds of high-resolution images, screening the electromagnetic spectrum (from 400 up to 2500 nm) in a continuous mode (Araus and Cairns 2014). ...
Thesis
Currently, the combination of a growing bioenergy demand and the need to diversify the dominant cultivation of energy maize opens a highly attractive scenario for alternative biomass crops. Rye (Secale cereale L.) stands out for its vigorous growth and increased tolerance to abiotic and biotic stressors. In Germany, less than a quarter of the total harvest is used for food production. Consequently, rye arises as a source of renewables with a reduced bioenergy-food tradeoff, emerging biomass as a new breeding objective. However, rye breeding is mainly driven by grain yield while biomass is destructively evaluated in later selection stages by expensive and time-consuming methods. The overall motivation of this research was to investigate the prospects of combining hyperspectral, genomic, and agronomic data for unlocking the potential of hybrid rye as a dual-purpose crop to meet the increasing demand for renewable sources of energy affordably. A specific aim was to predict the biomass yield as precisely as possible at an early selection stage. For this, a panel of 404 elite rye lines was genotyped and evaluated as testcrosses for grain yield and a subset of 274 genotypes additionally for biomass. Field trials were conducted at four locations in Germany in two years (eight environments). Hyperspectral fingerprints consisted of 400 discrete narrow bands (from 410 to 993 nm) and were collected in two points of time after heading for all hybrids in each site by an uncrewed aerial vehicle. In a first study, population parameters were estimated for different agronomic traits and a total of 23 vegetation indices. Dry matter yield showed significant genetic variation and was stronger correlated with plant height (r_g=0.86) than with grain yield (r_g=0.64) and individual vegetation indices (r_g:
... The agronomic yield needs to be in accordance with population demands-the population is estimated to exceed nine billion by 2050, which will exceed yield. This leaves plant scientists and crop specialists with the intensive task of making phenotypic forecasts using genetic organisation of cultivars or lines (White et al. 2012). By linking phenotype to genotype, highly productive, stress-resistant plants can be chosen, both for celerity and proficiency, more than is currently possible. ...
... Remarkable developments in "next generation" DNA sequencing are fast reducing genotyping rates (Jackson et al. 2011). On the other hand, plant phenotyping has become a fashion over the last three decades; nonetheless, attaining appropriate, usable and pertinent phenotypic knowledge, based on a particular plant, remains an exigent task (White et al. 2012). This is apt with regards to composite characters such as from yield perspective and abiotic stress resistance, which are important as regards the improvement of crops and, eventually, viable production (Myles et al. 2009). ...
... This is practiced in order to both assess and evaluate differences in expression patterns of various different genes (i.e. expression of a genotype when it comes in contact with its ambience) , White et al. 2012). ...
... However, this massive phenotyping has rarely been conducted in community ecology (Granier & Vile, 2014), and clear guidelines on how to conduct it in full communities are needed (see Losapio et al., 2018). Approaches to massively measure functional traits in the field and determine molecular contents in the lab can be summarized into three categories (White et al., 2012): (i) direct laboratory analyses, which requires measurements of all the individuals in a plant community, often being too time-consuming (Carmona et al., 2015); (ii) proximal (remote) sensing and imaging, which often has spatial and spectral resolutions that do not allow successfully distinguishing individuals and establishing correlations with functional traits; and (iii) calibration models based on visible-near infrared (Vis-NIR) spectrometry, with measurements taken directly in the plants. Although we acknowledge the extraordinary potential of field-based high-throughput phenotyping platforms (HTPPs; Araus & Cairns, 2014;Araus et al., 2015), they have only been used in monospecific crops, and their extrapolation to natural and complex plant communities seems challenging. ...
... Thus, we encourage the use of available alternatives. Vis-NIR spectroscopy is an inexpensive, regularly used and easy technique to calibrate numerous plant functional traits (Montes et al., 2007;White et al., 2012;Araus & Cairns, 2014). Vis-NIR spectra capture the physical and chemical characteristics of the samples, either vegetative plant tissues, harvested seeds or other plant organs (see Pupeza et al., 2020). ...
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Plant trait-based ecology is a powerful extension of the attempt of community ecolo-gists to unveil assembly mechanisms. However, the two main expected determinants of community assembly, niche and neutral processes, can be confused under this framework. Here, we propose to move from trait-based to phenotype-based community ecology, accounting for the variation between individuals (phenotypes affected by the abiotic and biotic environment, and vice versa), and explicitly considering their ability to compete with or facilitate its neighbours. This would shift our focus from species' niche responses to niche specialization of phenotypes, reducing the space for neutrality at the finest scales. The current assembly framework, based mainly on niche complementarity and using species-average functional traits, has been developed exploring mega-diverse communities, but it fails at describing poor plant communities. Under this framework, monospecificity would be interpreted as an arena where functionally similar individuals compete, consequently leading to regular patterns , which are rarely found in nature. Our niche specialization framework could help explaining coexistence in rich plant communities, where the higher fraction of functional variation is found between species, whereas the intraspecific trait variation dominates in poor species and monospecific communities. We propose a guide to conduct massive phenotyping at the community scale based on the use of visible and near-infrared spectroscopy. We also discuss the need to integrate the so-called plant's eye perspective based on the use of spatial pattern statistics in the current community ecology toolbox. K E Y W O R D S coexistence, neutrality, niche specialization, phenotypes, phenotypic variation, poor plant communities
... Such an approach has been successfully used for crops such as barley and black poplar to assess the stomatal sensitivity to different water regimes in a panel of varieties (Rischbeck et al., 2017;Ludovisi et al., 2017). Proximal measurement of the vegetation surface temperature requires the selection of an appropriate thermal infrared sensor (White et al., 2012). It is also important to consider the influence of the soil underlying the crop (Hackl et al., 2012;Costa et al., 2018) in order to avoid noise in the thermal signal. ...
... It should be noted, however, that it is not possible to compare mature fruiting trees on such platforms due to the adult tree size. The challenges posed by field phenotyping technologies have been addressed in many previous research studies such as White et al. (2012), Araus and Cairns (2014) and Deery et al. (2014). ...
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This research aimed at analyzing the response of apple tree varieties subjected to soil water deficit and atmospheric drought in a field phenotyping platform located in the Mediterranean area. The main assumption of the study was that seasonal and daily stomatal behavior can be monitored by continuous measurement of canopy surface temperature (Ts) as a proxy of stomatal closure. To achieve the study objectives, thermal monitoring of 6 pre-commercial apple varieties was simultaneously carried out throughout one season by nadir-oriented thermo-radiometers placed 1.50 m over the tree top canopy. Two water regimes were applied to each variety during a 4-week summer period: normal irrigation (WW) vs progressive water deficit (WS). The maximum difference in Ts between water regimes was recorded daily between 11:00 and 14:20 GMT, with an earlier closure of stomata in WS trees. During the day, a more negative stem water potential (Ψstem) and a higher diurnal Ts (+1° to +2 °C) were observed on WS trees, resulting in a significant limitation of fruit growth. Tree water stress was caused by both edaphic and atmospheric droughts, in the medium and short terms respectively, with inter-varietal and inter-regime differences highlighting distinct stomatal closure behaviors. Results suggest that some of the varieties studied are well adapted to stressful summer conditions, as long as irrigation needs are met, while other varieties show a particular sensitivity to the mid-day evaporative demand, which may limit their extension. Although these results are not comprehensive enough to predict the optimal performance of varieties under different stress scenarios, the proposed methodology allows to assess the dynamics of tree response to water constraints using non-invasive thermal sensors. It opens up new perspectives for the phenotyping of apple cultivars under abiotic stress, achievable through the quantified study of their transpiration flux in response to stress scenarios. These prospects will require further in planta measurements to dissect varietal differences.
... Such challenges may lead to a need for higher sensor performance (e.g., higher scanning frequency) and careful setup with mobile platforms. Thus, research on the use of LiDAR for cereal crops has, in the main, been focused on the scanning of small plots by dedicated mobile platforms with acquisition designed for high-throughput phenotyping of breeding plots (see reviews of Dworak et al. [16], White et al. [17] and Lin [18] and more recent examples by Yuan et al. [19], Jimenez-Berni et al. [20], Walter et al. [21] and Deery et al. [22]). Examples of such dedicated self-propelled scanning platforms are the Phenomobile [23] and the Field Scanalyzer [24] which enable detailed crop scanning at low travel speeds and with stable sensor positioning and orientation. ...
... First, LiDAR-derived parameters other than crop height could potentially be used for biomass estimation-either as stand-alone variables or in combination with others. Although crop height is amongst the most common parameters for biomass estimation [17], other parameters extracted from the point cloud should be evaluated. For example, volume parameters based on 'voxels'-where each point is attributed with a small cubic volume [20] could be explored. ...
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Crop biomass is an important attribute to consider in relation to site-specific nitrogen (N) management as critical N levels in plants vary depending on crop biomass. Whilst LiDAR technology has been used extensively in small plot-based phenomics studies, large-scale crop scanning has not yet been reported for cereal crops. A LiDAR sensing system was implemented to map a commercial 64-ha wheat paddock to assess the spatial variability of crop biomass. A proximal active reflectance sensor providing spectral indices and estimates of crop height was used as a comparison for the LiDAR system. Plant samples were collected at targeted locations across the field for the assessment of relationships between sensed and measured crop parameters. The correlation between crop biomass and LiDAR-derived crop height was 0.79, which is similar to results reported for plot scanning studies and greatly superior to results obtained for the spectral sensor tested. The LiDAR mapping showed significant crop biomass variability across the field, with estimated values ranging between 460 and 1900 kg ha−1. The results are encouraging for the use of LiDAR technology for large-scale operations to support site-specific management. To promote such an approach, we encourage the development of an automated, on-the-go data processing capability and dedicated commercial LiDAR systems for field operation.
... In order to prevent possible loss of biodiversity linked to the adoption of SHD systems, the selection of local cultivars with the most desirable traits for these planting systems could represent a forward-looking strategy. However, while plant genotyping is nowadays an efficient process thanks to advances in genomics and biotechnology, phenotypic data are mainly collected by manual or visual methods [6,7]. ...
... From the above, the need for time and cost-efficient methods to rapidly identify desirable genotypes is clear. High-throughput phenotyping technologies can play a crucial role in phenotyping trials thanks to their ability to monitor a large number of plants in a short period of time [7,15,16]. Recent studies focused on possible applications of ground and aerial platforms for plant phenotyping under field conditions [16][17][18][19][20][21][22]. ...
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Remote sensing techniques based on images acquired from unmanned aerial vehicles (UAVs) could represent an effective tool to speed up the data acquisition process in phenotyping trials and, consequently, to reduce the time and cost of the field work. In this study, we assessed the ability of a UAV equipped with RGB-NIR cameras in highlighting differences in geometrical and spectral canopy characteristics between eight olive cultivars planted at different planting distances in a hedgerow olive orchard. The relationships between measured and estimated canopy height, projected canopy area and canopy volume were linear regardless of the different cultivars and planting distances (RMSE of 0.12 m, 0.44 m2 and 0.68 m3, respectively). A good relationship (R2 = 0.95) was found between the pruning mass material weighted on the ground and its volume estimated by aerial images. NDVI measured in February 2019 was related to fruit yield per tree measured in November 2018, whereas no relationships were observed with the fruit yield measured in November 2019 due to abiotic and biotic stresses that occurred before harvest. These results confirm the reliability of UAV imagery and structure from motion techniques in estimating the olive geometrical canopy characteristics and suggest further potential applications of UAVs in early discrimination of yield efficiency between different cultivars and in estimating the pruning material volume.
... for many crops are still below what is needed to meet the future demand (Ray et al., 2013). A major challenge for crop improvement is to establish the connection between phenotype and genotype (White et al., 2012). The advances of sequencing and genotyping technologies over the past decade have improved the genotyping efficiency and provided a huge amount of genomic data (Purugganan & Jackson, 2021), but the transition of these data into the identification of desirable traits (i.e., quantitative traits related to harvestable yield and stress tolerance) is constrained by the ability of efficient phenotyping (Cobb et al., 2013;White et al., 2012). ...
... A major challenge for crop improvement is to establish the connection between phenotype and genotype (White et al., 2012). The advances of sequencing and genotyping technologies over the past decade have improved the genotyping efficiency and provided a huge amount of genomic data (Purugganan & Jackson, 2021), but the transition of these data into the identification of desirable traits (i.e., quantitative traits related to harvestable yield and stress tolerance) is constrained by the ability of efficient phenotyping (Cobb et al., 2013;White et al., 2012). Phenotyping under field conditions has become the bottleneck for crop improvement (Cobb et al., 2013). ...
Article
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Plant phenotyping under field conditions plays an important role in agricultural research. Efficient and accurate high-throughput phenotyping strategies enable a better connection between genotype and phenotype. Unmanned aerial vehicle-based high-throughput phenotyping platforms (UAV-HTPPs) provide novel opportunities for large-scale proximal measurement of plant traits with high efficiency, high resolution, and low cost. The objective of this study was to use time series normalized difference vegetation index (NDVI) extracted from UAV-based multispectral imagery to characterize its pattern across development and conduct genetic dissection of NDVI in a large maize population. The time series NDVI data from the multispectral sensor were obtained at five time points across the growing season for 1,752 diverse maize accessions with a UAV-HTPP. Cluster analysis of the acquired measurements classified 1,752 maize accessions into two groups with distinct NDVI developmental trends. To capture the dynamics underlying these static observations, penalized-splines (P-splines) model was used to obtain genotype-specific curve parameters. Genome-wide association study (GWAS) using static NDVI values and curve parameters as phenotypic traits detected signals significantly associated with the traits. Additionally, GWAS using the projected NDVI values from the P-splines models revealed the dynamic change of genetic effects, indicating the role of gene-environment interplay in controlling NDVI across the growing season. Our results demonstrated the utility of ultra-high spatial resolution multispectral imagery, as that acquired using a UAV-based remote sensing, for genetic dissection of NDVI.
... In 2004, Dohm et al. presented a reference genome sequence for sugar beet for the first time [5]. However, the data required for phenotyping still rely on manual measurements, which are time-consuming and expensive [6]. Segmenting individual leaves of individual plants in field canopies of sugar beet is meaningful for acquisition of the phenotype information of field grown sugar beet plants, it would be very helpful to better calculate the information of individual organs of sugar beet breeding materials, such as leaf length, leaf area, leaf spatial layout, and light interception at different angles of single leaf, etc. ...
... The specific calculation methods for leaf length and leaf area are as follows: The leaf length is calculated according to Xiao et al. [6]. The approximate shortest curve between the tip point and the base point of the leaf point cloud is searched. ...
Article
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Accurate segmentation of individual leaves of sugar beet plants is of great significance for obtaining the leaf-related phenotypic data. This paper developed a method to segment the point clouds of sugar beet plants to obtain high-quality segmentation results of individual leaves. Firstly, we used the SFM algorithm to reconstruct the 3D point clouds from multi-view 2D images and obtained the sugar beet plant point clouds after preprocessing. We then segmented them using the multiscale tensor voting method (MSTVM)-based region-growing algorithm, resulting in independent leaves and overlapping leaves. Finally, we used the surface boundary filter (SBF) method to segment overlapping leaves and obtained all leaves of the whole plant. Segmentation results of plants with different complexities of leaf arrangement were evaluated using the manually segmented leaf point clouds as benchmarks. Our results suggested that the proposed method can effectively segment the 3D point cloud of individual leaves for field grown sugar beet plants. The leaf length and leaf area of the segmented leaf point clouds were calculated and compared with observations. The calculated leaf length and leaf area were highly correlated with the observations with R2 (0.80–0.82). It was concluded that the MSTVM-based region-growing algorithm combined with SBF can be used as a basic segmentation step for high-throughput plant phenotypic data extraction of field sugar beet plants.
... HTP methods are capable of improved accounting of the genetic variation across a large set of breeding lines, therefore enhancing selection efficacy and increasing the rate of genetic gain [15,19]. In addition, during the past decade, phenotyping on-board ground-and aerial-based sensing platforms has been used in phenomics and has been shown to be highly successful in deriving specific traits relating to morphological, biochemical, and physiological functions at the canopy level [20][21][22][23][24][25][26][27][28]. ...
Article
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In plant breeding, unmanned aerial vehicles (UAVs) carrying multispectral cameras have demonstrated increasing utility for high-throughput phenotyping (HTP) to aid the interpretation of genotype and environment effects on morphological, biochemical, and physiological traits. A key constraint remains the reduced resolution and quality extracted from “stitched” mosaics generated from UAV missions across large areas. This can be addressed by generating high-quality reflectance data from a single nadir image per plot. In this study, a pipeline was developed to derive reflectance data from raw multispectral UAV images that preserve the original high spatial and spectral resolutions and to use these for phenotyping applications. Sequential steps involved (i) imagery calibration, (ii) spectral band alignment, (iii) backward calculation, (iv) plot segmentation, and (v) application. Each step was designed and optimised to estimate the number of plants and count sorghum heads within each breeding plot. Using a derived nadir image of each plot, the coefficients of determination were 0.90 and 0.86 for estimates of the number of sorghum plants and heads, respectively. Furthermore, the reflectance information acquired from the different spectral bands showed appreciably high discriminative ability for sorghum head colours (i.e., red and white). Deployment of this pipeline allowed accurate segmentation of crop organs at the canopy level across many diverse field plots with minimal training needed from machine learning approaches.
... Design randomization can help control spatial variation to a large degree 5,6 ; however, advanced statistical approaches, such as the separable autoregressive and two-dimensional spline models, can capture local dependence effects between experimental plots 7,8,9 . Aerial imaging can reliably measure high-throughput phenotypes (HTP) across the growing season for all experiment plots in the field, using unoccupied aerial vehicles (UAV) and other systems 10,11,12,13 . A widely studied class of HTP are vegetation indices (VI), particularly the normalized difference vegetation index (NDVI) 14,15 . ...
Preprint
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To accelerate plant breeding genetic gain, spatial heterogeneity must be considered. Previously, design randomizations and spatial corrections have increased understanding of genotypic, spatial, and residual effects in field experiments. This study proposes a two-stage approach for improving agronomic trait genomic prediction (GP) using high-throughput phenotyping (HTP) via unoccupied aerial vehicle (UAV) imagery. The normalized difference vegetation index (NDVI) is measured using a multi-spectral MicaSense camera and ImageBreed. The first stage separates additive genetic effects from local environmental effects (LEE) present in the NDVI throughout the growing season. Considered NDVI LEE (NLEE) are spatial effects from univariate/multivariate two-dimensional splines (2DSpl) and separable autoregressive (AR1) models, as well as permanent environment (PE) effects from random regression models (RR). The second stage leverages the NLEE within genomic best linear unbiased prediction (GBLUP) in two distinct implementations, either modelling an empirical plot-to-plot covariance (L) for random effects or modelling fixed effects (FE). Testing on Genomes-to-Fields (G2F) hybrid maize (Zea mays) field experiments in 2017, 2019, and 2020 for grain yield (GY), grain moisture (GM), and ear height (EH) improves heritability and model fit equally-or-greater than spatial corrections; however, genotypic effect estimation across replicates is not significantly improved. Electrical conductance (EC), elevation, and curvature from a 2019 soil survey significantly improve GP model fit, but less than NLEE. Soil EC and curvature are most correlated to univariate 2DSpl NLEE. Defining L significantly improves genomic heritability and model fit more than setting FE, and RR NLEE can most significantly improve GP for GY and GM.
... Compared to applications on such large scales (hectares to square kilometers), for breeding applications, the objects of interest reside on a submeter scale (Araus and Cairns, 2014). Consequently, the ground sampling distance constrains the suitability of remote sensing products (Hu et al., 2019), and HTFP techniques are almost exclusively based on proximal sensing with unmanned aerial systems (UAS) or ground-based platforms (White et al., 2012). Luckily, affordable and efficient hardware for proximal sensing and data processing are no longer a limit in establishing phenotyping traits , and UAS represent highly scalable tools to capture high-quality data if the flight planning is carried out properly (Roth et al., 2018b). ...
Article
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Soybean is among the most important crops for food and feed production worldwide. Sustainable and local production in regions with marginal climates requires cold-adapted varieties that create high yield and protein content in a short vegetation period. Drone-based high-throughput field phenotyping methods allow monitoring the success and the developmental speed of genotypes in such target environments. This study exemplifies that such frequent and precise analyses of remotely sensed canopy growth traits can be used to derive the optimal genotype, a so-called ideotype, for a given mega-environment. For the case example of Switzerland, a country with a temperate oceanic climate, the results indicate that image-derived traits allow predicting yield and protein content from the dynamics of vegetative growth. Genotypes with early canopy cover produce high yield, whereas genotypes that show a prolonged duration until they have reached their final maximum of leaf area index are characterized by a high protein content. Analyses of early performance trial stage material indicate that there are genotypes that combine both features of growth dynamics. Whether these genotypes are then indeed successful in breeding programs remains to be investigated, since this also depends on disease resistance and other traits of those genotypes. Yet, overall, this study provides strong indications of the high value of high-throughput field phenotyping in the context of physiological and breeding-related analyses of crops.
... NIRS data are routinely collected in breeding programs to estimate the content of seed components such as water content, or protein and oil content (Font et al. 2006;Cen et al. 2007). Moreover, high-throughput phenotyping platforms or unmanned aerial vehicles nowadays enable the breeder to collect spectral data in the field at large scale (Montes et al. 2007;White et al. 2012;Busemeyer et al. 2013;Andrade-Sanchez et al. 2014). Thus, the use of spectral data as predictors could drastically increase the efficiency of selection at greatly reduced costs. ...
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Key message The phenomic predictive ability depends on the genetic architecture of the target trait, being high for complex traits and low for traits with major QTL. Abstract Genomic selection is a powerful tool to assist breeding of complex traits, but a limitation is the costs required for genotyping. Recently, phenomic selection has been suggested, which uses spectral data instead of molecular markers as predictors. It was shown to be competitive with genomic prediction, as it achieved predictive abilities as high or even higher than its genomic counterpart. The objective of this study was to evaluate the performance of phenomic prediction for triticale and the dependency of the predictive ability on the genetic architecture of the target trait. We found that for traits with a complex genetic architecture, like grain yield, phenomic prediction with NIRS data as predictors achieved high predictive abilities and performed better than genomic prediction. By contrast, for mono- or oligogenic traits, for example, yellow rust, marker-based approaches achieved high predictive abilities, while those of phenomic prediction were very low. Compared with molecular markers, the predictive ability obtained using NIRS data was more robust to varying degrees of genetic relatedness between the training and prediction set. Moreover, for grain yield, smaller training sets were required to achieve a similar predictive ability for phenomic prediction than for genomic prediction. In addition, our results illustrate the potential of using field-based spectral data for phenomic prediction. Overall, our result confirmed phenomic prediction as an efficient approach to improve the selection gain for complex traits in plant breeding.
... Moreover, the methods for characterizing genomes and phenomes have not advanced at the same rate (Moore et al., 2013;Yang et al., 2020). While genomic techniques have benefited from new developments in DNA sequencing, including higher resolution and lower costs, plant phenotyping has improved at a slower rate and commercial plant phenotyping platforms are still costly and practically inaccessible for the majority of plant laboratories (Shendure & Ji, 2008;Jackson et al., 2011;White et al., 2012;Reynolds et al., 2019). More importantly, this gap between genomic and phenomic technologies are preventing the full use of resources available in several plant species such as mutant collections or diversity panels to pursue genome-wide studies or the characterization of complex traits at a higher resolution (Xiao et al., 2017). ...
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The first draft of the Arabidopsis genome was released more than 20 years ago and despite intensive molecular research, more than 30% of Arabidopsis genes remained uncharacterized or without an assigned function. This is in part due to gene redundancy within gene families or the essential nature of genes, where their deletion results in lethality ( i.e. , the dark genome ). High-throughput plant phenotyping (HTPP) offers an automated and unbiased approach to characterize subtle or transient phenotypes resulting from gene redundancy or inducible gene silencing; however, commercial HTPP platforms remain unaffordable. Here we describe the design and implementation of OPEN leaf , an open-source HTPP system with cloud connectivity and remote bilateral communication to facilitate data collection, sharing and processing. OPEN leaf , coupled with the SMART imaging processing package was able to consistently document and quantify dynamic morphological changes over time at the whole rosette level and also at leaf-specific resolution when plants experienced changes in nutrient availability. The modular design of OPEN leaf allows for additional sensor integration. Notably, our data demonstrate that VIS sensors remain underutilized and can be used in high-throughput screens to identify characterize previously unidentified phenotypes in a leaf-specific manner.
... To gain insights into complex traits, a study of large numbers of genotyped accessions across multiple environments is required to identify genotype-by-environment (G × E) interactions [31]. This is particularly important in quinoa, due to the large G × E interactions that have been reported [32,33]. ...
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Citation: Stanschewski, C.S.; Rey, E.; Fiene, G.; Craine, E.B.; Wellman, G.; Melino, V.J.; Patiranage, D.S.R.; Johansen, K.; Schmöckel, S.M.; Bertero, D.; et al. Quinoa Abstract: Quinoa is a crop originating in the Andes but grown more widely and with the genetic potential for significant further expansion. Due to the phenotypic plasticity of quinoa, varieties need to be assessed across years and multiple locations. To improve comparability among field trials across the globe and to facilitate collaborations, components of the trials need to be kept consistent, including the type and methods of data collected. Here, an internationally open-access framework for phenotyping a wide range of quinoa features is proposed to facilitate the systematic agronomic, physiological and genetic characterization of quinoa for crop adaptation and improvement. Mature plant phenotyping is a central aspect of this paper, including detailed descriptions and the provision of phenotyping cards to facilitate consistency in data collection. High-throughput methods for multi-temporal phenotyping based on remote sensing technologies are described. Tools for higher-throughput post-harvest phenotyping of seeds are presented. A guideline for approaching quinoa field trials including the collection of environmental data and designing layouts with statistical robustness is suggested. To move towards developing resources for quinoa in line with major cereal crops, a database was created. The Quinoa Germinate Platform will serve as a central repository of data for quinoa researchers globally.
... To gain insights into complex traits, a study of large numbers of genotyped accessions across multiple environments is required to identify genotype-by-environment (G × E) interactions [31]. This is particularly important in quinoa, due to the large G × E interactions that have been reported [32,33]. ...
Article
Full-text available
Quinoa is a crop originating in the Andes but grown more widely and with the genetic potential for significant further expansion. Due to the phenotypic plasticity of quinoa, varieties need to be assessed across years and multiple locations. To improve comparability among field trials across the globe and to facilitate collaborations, components of the trials need to be kept consistent, including the type and methods of data collected. Here, an internationally open‐access framework for phenotyping a wide range of quinoa features is proposed to facilitate the systematic agronomic,physiological and genetic characterization of quinoa for crop adaptation and improvement. Mature plant phenotyping is a central aspect of this paper, including detailed descriptions and the provision of phenotyping cards to facilitate consistency in data collection. High‐throughput methods for multi‐temporal phenotyping based on remote sensing technologies are described. Tools for higher throughput post‐harvest phenotyping of seeds are presented. A guideline for approaching quinoa field trials including the collection of environmental data and designing layouts with statistical robustness is suggested. To move towards developing resources for quinoa in line with major cereal crops, a database was created. The Quinoa Germinate Platform will serve as a central repository of data for quinoa researchers globally.
... Recent technological developments have yielded an alternative phenotyping procedure (i.e., remote sensing). For plant breeding, remote sensing with ground vehicles (White et al., 2012) or aerial platforms such as unmanned aerial vehicles (UAVs) (Yang et al., 2017) were used to evaluate genetic variation in plant growth. From this work, it has been shown that remote sensing could capture the variation in a breeding population (Watanabe et al., 2017). ...
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The application of remote sensing in plant breeding can provide rich information about the growth processes of plants, which leads to better understanding concerning crop yield. It has been shown that traits measured by remote sensing were also beneficial for genomic prediction (GP) because the inclusion of remote sensing data in multitrait models improved prediction accuracies of target traits. However, the present multitrait GP model cannot incorporate high-dimensional remote sensing data due to the difficulty in the estimation of a covariance matrix among the traits, which leads to failure in improving its prediction accuracy. In this study, we focused on growth models to express growth patterns using remote sensing data with a few parameters and investigated whether a multitrait GP model using these parameters could derive better prediction accuracy of soybean [Glycine max (L.) Merr.] biomass. A total of 198 genotypes of soybean germplasm were cultivated in experimental fields, and longitudinal changes of their canopy height and area were measured continuously via remote sensing with an unmanned aerial vehicle. Growth parameters were estimated by applying simple growth models and incorporated into the GP of biomass. By evaluating heritability and correlation, we showed that the estimated growth parameters appropriately represented the observed growth curves. Also, the use of these growth parameters in the multitrait GP model contributed to successful biomass prediction. We conclude that the growth models could describe the genetic variation of soybean growth curves based on several growth parameters. These dimension-reduction growth models will be indispensable for extracting useful information from remote sensing data and using this data in GP and plant breeding.
... Due to the delayed and hidden characteristics of the symptoms of wheat freezing injury, a large number of near field studies are urgently needed. Truss-type phenotypic equipment is widely used for the complex field environment; it has the advantages of high acquisition accuracy, good stability, high imaging quality, and all-day plant monitoring [19,20]. Therefore, the use of near-ground truss type phenotype detection equipment can be a good solution to the above problems. ...
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Background Low temperature freezing stress has adverse effects on wheat seedling growth and final yield. The traditional method to evaluate the wheat injury caused by the freezing stress is by visual observations, which is time-consuming and laborious. Therefore, a more efficient and accurate method for freezing damage identification is urgently needed. Results A high-throughput phenotyping system was developed in this paper, namely, RGB freezing injury system, to effectively and efficiently quantify the wheat freezing injury in the field environments. The system is able to automatically collect, processing, and analyze the wheat images collected using a mobile phenotype cabin in the field conditions. A data management system was also developed to store and manage the original images and the calculated phenotypic data in the system. In this experiment, a total of 128 wheat varieties were planted, three nitrogen concentrations were applied and two biological and technical replicates were performed. And wheat canopy images were collected at the seedling pulling stage and three image features were extracted for each wheat samples, including ExG, ExR and ExV. We compared different test parameters and found that the coverage had a greater impact on freezing injury. Therefore, we preliminarily divided four grades of freezing injury according to the test results to evaluate the freezing injury of different varieties of wheat at the seedling stage. Conclusions The automatic phenotypic analysis method of freezing injury provides an alternative solution for high-throughput freezing damage analysis of field crops and it can be used to quantify freezing stress and has guiding significance for accelerating the selection of wheat excellent frost resistance genotypes.
... The assessment of differences in the response of genotypes to different environmental conditions facilitates the development of genotypes that lead to improved phenotypes in a specific environment [30] and also allow insights into the genetic architecture of a trait. To gain insights into complex traits, a study of large numbers of genotyped accessions across multiple environments is required to identify genotype-by-environment (G × E) interactions [31]. This is particularly important in quinoa, due to the large G × E interactions that have been reported [32,33]. ...
Article
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Quinoa is a crop originating in the Andes but grown more widely and with the genetic potential for significant further expansion. Due to the phenotypic plasticity of quinoa, varieties need to be assessed across years and multiple locations. To improve comparability among field trials across the globe and to facilitate collaborations, components of the trials need to be kept consistent, including the type and methods of data collected. Here, an internationally open-access framework for phenotyping a wide range of quinoa features is proposed to facilitate the systematic agronomic, physiological and genetic characterization of quinoa for crop adaptation and improvement. Mature plant phenotyping is a central aspect of this paper, including detailed descriptions and the provision of phenotyping cards to facilitate consistency in data collection. High-throughput methods for multi-temporal phenotyping based on remote sensing technologies are described. Tools for higher-throughput post-harvest phenotyping of seeds are presented. A guideline for approaching quinoa field trials including the collection of environmental data and designing layouts with statistical robustness is suggested. To move towards developing resources for quinoa in line with major cereal crops, a database was created. The Quinoa Germinate Platform will serve as a central repository of data for quinoa researchers globally.
... The first use of satellites for remote sensing of vegetation goes back to the first National Aeronautics and Space Administration (NASA) Landsat series in the 1970s (Tucker 1979). Since then, the application of remote sensing for vegetation mapping has been constantly developing and several different indices calculated from specific wavelengths have been used to estimate plant growth parameters and crop yield (White et al. 2012). More recently, remote sensing technology has been extended to successfully predict vegetation biophysical attributes like leaf area index (LAI) (Potgieter et al. 2017), biomass (Wang et al. 2016), chlorophyll content (Haboudane et al. 2002), and has also been used for yield prediction of several crops such as soybean (Glycine max L.) (Yu et al. 2016), rice (Oryza sativa L.) (Zhou et al. 2017), and wheat (Triticum aestivum L.) (Du et al. 2017). ...
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Accurate, timely, and non-destructive early crop yield prediction at the field scale is essential in addressing changing crop production challenges and mitigating impacts of climate variability. Unmanned aerial vehicles (UAVs) are increasingly popular in recent years for agricultural remote sensing applications such as crop yield forecasting and precision agriculture (PA). The objective of this study was to evaluate the performance of a low-cost UAV-based remote sensing technology for Bambara groundnut yield prediction. A multirotor UAV equipped with a near-infrared sensitive consumer-grade digital camera was used to collect image data during the 2018 growing season (April to August). Flight missions were carried out six times during critical phenological stages of the life-cycle of the monitored crop. Yield was recorded at harvest. Four vegetation indices (VIs) namely normalized difference vegetation index (NDVI), enhanced vegetation index 2 (EVI2), green normalized difference vegetation index (GNDVI), and simple ratio (SR) generated from the Red-Green- Near Infrared bands were calculated using the georeferenced orthomosaic UAV images. Pearson’s product-moment correlation coefficient (r) and Bland–Altman testing showed a significant agreement between remotely and proximally sensed VIs. Significant and positive correlations were found between the four VIs and yield, with the strongest relationship observed between SR and yield at podfilling stage (r = 0.81, P < 0.01). Multi-temporal accumulative VIs improved yield prediction significantly with the best index being ΣSR and the best interval being from podfilling to maturity (r = 0.88, P < 0.01). The accumulated ΣSR from podfilling to maturity resulted in higher prediction accuracy with a coefficient of determination (R2) of 0.71, root mean square error (RMSE) of 0.20 and mean absolute percentage error (MAPE) of 14.2% than SR spectral index at a single stage (R2 = 0.68, RMSE = 0.24, MAPE = 15.1%). Finally, a yield map was generated using the model developed, to better understand the within-field spatial variations of yield for future site- specific or variable-rate application operations.
... With the rapid development of genotype technology, the traditional phenotype measurement has become the bottleneck of plant breeding (Furbank and Tester, 2011). The traditional phenotyping is usually performed manually which is time-consuming and expensive (White et al., 2012). Therefore, new methods are needed to improve the throughput capability of sugar beet taproot phenotyping. ...
Article
Selecting and breeding crop varieties with high economic benefits is of great significance for social stability and development. The economic benefit of crops is usually reflected by the purchase price. Traditional estimation of economic benefits using purchase price formula based on manual measured traits is time-consuming. Structure-from-Motion in conjunction with multi-view stereo (SFM-MVS) method could extract plant phenotypic traits and has the potential for the efficient and timely estimation of economic benefits for sugar beet. In this study, a framework was developed to obtain phenotypic traits in order to estimate the economic benefits of sugar beet with 207 genotypes based on the calculation of a non-linear formula and the partial least square regression (PLSR) model. The first part of the framework was the designing of a low-cost portable equipment that can be used to obtain multi-view images of taproot in order to facilitate its three-dimensional (3D) reconstruction based on SFM-MVS method. The following part was the development of an automated pipeline for estimating ten traits from the reconstructed 3D taproot. Good agreement was found between measured and estimated traits with R² >0.97. The PLSR model constructed using the data in 2018 was used to predict the data in 2019 with moderate performance (R² = 0.5). A new PLSR model built using 70 % of the data collected in 2018 and 2019 could predict the remaining 30 % of the data with a higher R² of 0.61. The model built with multi-years data had a higher accuracy in estimating phenotypic traits, which suggests that PLSR model can estimate beet economic benefit by using the SFM-MVS method with multi-year data. The current method is more efficient than the manual measurement and may provide a basis for selecting and cultivating sugar beet with high economic benefit.
... Plant phenotyping plays a central role in plant breeding, and the accurate and rapid acquisition of phenotypic data is valuable for exploring the association between genotypes and phenotypes. In the last few decades, remote sensing has been widely used in agriculture (Maes and Steppe, 2019;Galli et al., 2020), particularly for high-throughput phenotyping (HTP) in breeding applications (Furbank and Tester, 2011;White et al., 2012;Araus and Cairns, 2014;Tattaris et al., 2016;Li et al., 2017;Zhao et al., 2019). Remote sensing offers unprecedented spectral, spatial, and temporal resolution, providing detailed vegetation data (Maes and Steppe, 2019). ...
Article
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The application of remote sensing in plant breeding is becoming a routine method for fast and non-destructive high-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) equipped with sensors. Alfalfa (Medicago sativa L.) is a perennial forage legume grown in more than 30 million hectares worldwide. Breeding alfalfa for herbage accumulation (HA) requires frequent and multiple phenotyping efforts, which is laborious and costly. The objective of this study was to assess the efficiency of UAV-based imagery and spatial analysis in the selection of alfalfa for HA. The alfalfa breeding population was composed of 145 full-sib and 34 half-sib families, and the experimental design was a row-column with augmented representation of controls. The experiment was established in November 2017, and HA was harvested four times between August 2018 and January 2019. A UAV equipped with a multispectral camera was used for HTP before each harvest. Four vegetation indices (VIs) were calculated from the UAV-based images: NDVI, NDRE, GNDVI, and GRVI. All VIs showed a high correlation with HA, and VIs predicted HA with moderate accuracy. HA and NDVI were used for further analyses to calculate the genetic parameters using linear mixed models. The spatial analysis had a significant effect in both dimensions (rows and columns) for HA and NDVI, resulting in improvements in the estimation of genetic parameters. Univariate models for NDVI and HA, and bivariate models, were fit to predict family performance for scenarios with various levels of HA data (simulated in silico by assigning missing values to full dataset). The bivariate models provided higher correlation among predicted values, higher coincidence for selection, and higher genetic gain even for scenarios with only 30% of HA data. Hence, HTP is a reliable and efficient method to aid alfalfa phenotyping to improve HA. Additionally, the use of spatial analysis can also improve the accuracy of selection in breeding trials.
... Phenomic selection can also be based on multi-or hyperspectral data obtained from plots in the field. These can be acquired with phenotyping platforms or unmanned aerial vehicles (UAVs; e.g., drones) (e.g., Adak et al., 2021;Andrade-Sanchez et al., 2013;Araus & Cairns, 2014;Busemeyer et al., 2013;Krause et al., 2020;White et al., 2012). As only one sensor for spectral data is required, UAVs may be better suited as they enable a higher throughput. ...
Article
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Abstract The efficiency of breeding programs depends on the ability to screen large numbers of individuals. For complex traits like yield, this can be assisted by genomic selection, which is based on estimating breeding values with genome‐wide marker data. Here, we evaluate phenomic prediction, which, similar to its genomic counterpart, aims to predict the performance of untested individuals but using near‐infrared spectroscopy (NIRS) data. In a large panel of 944 soybean [Glycine max (L.) Merr.] recombinant inbred lines phenotyped for seed yield, thousand‐seed weight, and plant height at three locations, we demonstrate that the phenomic predictive abilities are high and comparable with those obtained by genomic prediction. We found that ridge regression best linear unbiased prediction performs well for phenomic prediction and that the number of wavelengths can be reduced without a decrease in predictive ability. For prediction at different locations, NIRS data from a single location can be used. However, NIRS data from different environments, like years, should be connected by common genotypes in training and prediction sets. Phenomic prediction appears to be less susceptible to relatedness between individuals in training and prediction sets than genomic prediction, as generally half‐sib but also unrelated families achieved high predictive abilities. Moreover, for the same training set sizes phenomic prediction resulted in higher predictive abilities compared to genomic prediction. Phenomic prediction can be applied at different stages in a breeding program, and collectively our results highlight the potential of this approach to increase genetic gain in plant breeding.
... Many times, depending upon the study conducted, other important factors (other than those under consideration) are overlooked. Therefore, phenotypic prediction based on the genetic composition of lines or cultivars must be considered to address all the above-mentioned issues (White et al. 2012). In plant breeding, field experiments conducted at multiple locations are indispensable for evaluating the adaptability of new candidate genotypes in order to examine their pattern of genotype  environment interaction (Chapman et al. 2014). ...
... It is worth noting that Steduto et al. (2009) constructed a crop water response model based on soil water data and crop canopy remote sensing data, which accurately determined the response of crop B to water by calculating WUE. Zhang et al. (2019) combined the FAO crop water response model (AquaCrop) with UAV-based spectral data of crops and achieved precise monitoring of crop growth, B accumulation, and continuous water consumption (White et al., 2012). However, there are currently few studies using this method to monitor agroforestry systems in arid regions. ...
Article
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Tree shelterbelts are crucial for maintaining the ecological environment of oasis, but they may also compete for soil water with adjacent crops, affecting crop yields. To evaluate the impacts of the shelterbelt on water use efficiency (WUE) and normalized water productivity (WP) of adjacent cotton plants, the biomass (B) and WUE of cotton with different distances from the shelterbelt (0.1H, 0.5H, 1H, 2H, and 3H; average tree height = 15 m [H]) were estimated based on unmanned aerial vehicle (UAV) remote sensing data combined with the FAO crop water response model AquaCrop. Besides, the accuracy and universality of the estimation method were also evaluated. The results showed that the method based on UAV remote sensing data and AquaCrop can accurately estimate the impact range and intensity of shelterbelt on WUE, water consumption, and B of adjacent cotton plants. Fierce water competition between shelterbelt and cotton was detected within 0.1H−1H, and the competitiveness of the shelterbelt was weaker in the plots >1H than in the 0.1H−1H. The B, actual evapotranspiration (Tc), and WUE of cotton at 0.1H decreased by 59.3, 48.8, and 23.6%, respectively, compared with those at 3H, but the cotton plants at 2H and 3H were completely unaffected by the shelterbelt. Besides, the B estimated based on WP (root mean square error [RMSE] = 108 g/m2, d = 0.89) was more accurate than that estimated based on WUE (RMSE = 118 g/m2, d = 0.85). This study clarifies the inter-species competition for soil water between crops and shelterbelts under drip irrigation in oases in China.
... A key component of GS is having an adequate training population that has been both genotyped and phenotyped. With the potential to use NGS for applications of marker and gene discovery for both model and nonmodel plants (Bräutigam & Gowik, 2010), phenotyping quickly became a limiting factor for genetic studies and plant breeding (Cobb et al., 2013;Poland, 2015;White et al., 2012). Genomic selection has led to a paradigm shift from phenotyping for plant evaluation and selection to phenotyping for GS model development with a greater emphasis on precision of measurement as well as phenotyping different sets of lines (Cobb et al., 2013;Heffner et al., 2009). ...
Article
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High‐throughput phenotyping (HTP) has the potential to revolutionize plant breeding by providing scientists with exponentially more data than was available through traditional observations. Even though data collection is rapidly increasing, the optimum use of this data and implementation in the breeding program has not been thoroughly explored. In an effort to apply HTP to the earliest stages of a plant breeding program, we extended field‐based HTP pipelines to evaluate and extract data from spaced single plants. Using a panel of 340 winter wheat (Triticum aestivum L.) lines planted in full plots and grid‐spaced single plants for two growing seasons, we evaluated relationships between single plants and full plot yields. Normalized difference vegetation index (NDVI) was collected multiple times through the growing season using an unoccupied aerial vehicle. NDVI measurements during grain filling stage from both single plants and full plots were typically positively associated with their respective grain yield with correlation ranging from ‐0.22 to 0.74. The relationship between single plant NDVI and full plot yield, however, was variable between seasons ranging from ‐0.40 to 0.06. A genome wide association analysis (GWAS) identified the same marker trait associations in both full plots and single plants, but also displayed variability between growing seasons. Strong genotype by environment interactions could impede selection on quantitative traits, yet these methods could provide an effective tool for plant breeding programs to quickly screen early‐generation germplasm. Efficient use of early‐generation, affordable HTP data could improve overall genetic gain in plant breeding. High‐throughput phenotyping pipelines were translated to field‐based single plant analysis. Traits from both single plant and full plots had common associated loci identified by GWAS. Variable correlations were found between single plants and full plots across traits and years. Genotype × environment interactions created challenges to accurately identify superior plants. High‐throughput phenotyping data has potential use for early generation plant breeding.
... However, they are difficult to implement at large scales due to the time to traverse through the entire field, resulting in limited ground space being covered, and often limited temporal resolution. Tractors are further constrained by their large size and low vertical clearance, which hinders their application over taller crops (Busemeyer et al., 2013;Comar et al., 2012;White et al., 2012). Other types of ground vehicles can be unoccupied, such as rovers or ground robots, that semi-automatically navigate the field, acquire data, and upload it without the need for extensive manual labor (Gage et al., 2019;Ruckelshausen et al., 2009;Young et al., 2019). ...
Article
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Developing the resilient crops of the future will require access to a broad set of tools. While advances in sequencing and marker technologies have facilitated marker‐trait associations and the ability to predict the phenotype of an individual from its genotypic information, other tools such as high‐throughput phenotyping are still in their infancy. Advances in sensors, aeronautics, and computing have enabled progress. Here, we review current platforms and sensors available for top‐down field phenotyping with a focus on unoccupied aerial vehicles (UAVs) and red, green, blue sensors. We also review the ability and effectiveness of extracting traits from images captured using combinations of these platforms and sensors. Improvements in trait standardization and extraction software are expected to increase the use of high‐throughput phenotyping in the coming years and further facilitate crop improvement. High throughput phenotyping is an area of active development. Aerial imagery from drones allows for high spatial, temporal, and potentially spectral resolution. Variability in ability and effectiveness of extracting traits from images from different platforms and sensors are reviewed.
... To make genetic progress for drought tolerance, managed stress environments (MSEs) are required, in which the severity and timing of drought stress are controlled in a way that is relevant to target environment conditions. In the absence of rain, precise water management allows stress intensity to be adjusted, maximizing the expression of genetic variability for essential secondary traits and repeating the stress pattern, which is targeted at appropriate growth phases (White et al., 2012). ...
Article
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Drought stress has severely hampered maize production, affecting the livelihood and economics of millions of people worldwide. In the future, as a result of climate change, unpredictable weather events will become more frequent hence the implementation of adaptive strategies will be inevitable. Through utilizing different genetic and breeding approaches, efforts are in progress to develop the drought tolerance in maize. The recent approaches of genomics-assisted breeding, transcriptomics, proteomics, transgenics, and genome editing have fast-tracked enhancement for drought stress tolerance under laboratory and field conditions. Drought stress tolerance in maize could be considerably improved by combining omics technologies with novel breeding methods and high-throughput phenotyping (HTP). This review focuses on maize responses against drought, as well as novel breeding and system biology approaches applied to better understand drought tolerance mechanisms and the development of drought-tolerant maize cultivars. Researchers must disentangle the molecular and physiological bases of drought tolerance features in order to increase maize yield. Therefore, the integrated investments in field-based HTP, system biology, and sophisticated breeding methodologies are expected to help increase and stabilize maize production in the face of climate change.
... Modern imaging methods have high resolution and can visualize multidimensional and multi-parameter data. In controlled environmental systems (in growth chambers or greenhouses) or in the field, imaging techniques are used to quantify complex characteristics under associated growth, yield, and stress applications for plant phenotyping (Arvidsson et al. 2011;White et al. 2012;Berger et al. 2010). It is also possible to use imaging methods to monitor plant growth and dynamic responses under stress in real time. ...
Thesis
The morphological characteristics of fruit, leaf, and endocarp samples from some of the main Lebanon olive cultivars, such as Abou chawkeh, Ayrouni koura, Baladi koura, Baladi kfarchakhna, and Soury koura, were investigated in this study. The samples were collected in three separate areas of Lebanon: Koura region, North Bekka valley and the Kfarchakhna region. The plant materials were analyzed using the ImageJ program, and the results were fed into the MATLAB statistics toolbox. The morphological characterization of the olive cultivars showed considerable variation among the samples examined. This variation was discovered using PCA and clustering analyses. Abou and Ayrouni are the most variable cultivars, with Abou having the highest values for both leaf and fruit parameters with Ayrouni having the lowest. B. koura and B.kfarchakhna have a high degree of similarity between each other, while Soury is the cultivar with the most variability within its organs. The cultivars rated from the lowest to the highest value of endocarp size are as follows: Soury, B. kfarchakhna, Abou, B. koura, and Ayrouni. Moreover, the results demonstrate an important implication for cultivar adaptation to their local environment as well as an agronomic success under particular conditions. Keywords: Lebanon, ImageJ, PCA, dendrogram, olive endocarp, olive leaf, olive fruit, morphological characteristics, Lebanese olives cultivars, olive morphology.
... Although these filtering approaches are very potent, a possible unintended consequence is the elimination of bona fide false-negative variants. In the era of rapid advances in highthroughput phenotyping (phenomics) for both in-field and greenhouse experiments (Araus et al., 2018;Bolger et al., 2019;Furbank & Tester, 2011;Houle et al., 2010;Olsen & Wendel, 2013;Tardieu et al., 2017;Weischenfeldt et al., 2013;White et al., 2012), precisely detecting true variants while filtering false-positive variants and recovering bona fide false-negative variants would greatly improve the precision of mutation detection and greatly enhance phenotypeto-genotype association (Araus et al., 2018). ...
Article
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The precise detection of causal DNA mutations (deoxyribonucleic acid) is very crucial for forward genetic studies. Several sources of errors contribute to false‐positive detections by current variant‐calling algorithms, which impact associating phenotypes with genotypes. To improve the accuracy of mutation detection, we implemented a binning method for the accurate detection of likely ethyl methanesulfonate (EMS)‐induced mutations in a sequenced mutant population. We also implemented a clustering algorithm for detecting likely false negatives with high accuracy. Sorghum bicolor is a very valuable crop species with tremendous potential for uncovering novel gene functions associated with highly desirable agronomical traits. We demonstrate the precision of the described approach in the detection of likely EMS‐induced mutations from the publicly available low‐cost sequencing of the M3 generation from 600 sorghum BTx623 mutants. The approach detected 3,274,606 single nucleotide polymorphisms (SNPs), of which 96% (3,141,908) were G/C to A/T DNA substitutions, as expected by EMS‐mutagenesis mode of action. We demonstrated the general applicability of the described method and showed a high concordance, 94% (3,074,759) SNPs overlap between SAMtools‐based and GATK‐based variant‐calling algorithms. Our clustering algorithm uncovered evidence for an additional 223,048 likely false‐negative shared EMS‐induced mutations. The final 3,497,654 SNPs represent an 87% increase in SNPs detected from the previous analysis of the mutant population, with an average of one SNP per 125 kb in the sorghum genome. Annotation of the final SNPs revealed 10,263 high‐impact and 136,639 moderate‐impact SNPs, including 7217 stop‐gained mutations, which averages 12 stop‐gained mutations per mutant, and four high‐ or medium‐impact SNPs per sorghum gene. We have implemented a public search database for this new genetic resource of 30,285 distinct sorghum genes containing medium‐ or high‐impact EMS‐induced mutations. Seedstock for a select 486 of the 600 described mutants are publicly available in the Germplasm Resources Information Network (GRIN) database.
... While core collections for drought tolerance have been assembled, gains in limited water and rainfed cotton productivity have largely been incremental and reflective of improvements in yield potential under non-stressed conditions. Thus, future genetic gain in abiotic stress resistance will require a combination of traditional plant breeding and new breeding methods such as genomic selection, as well as the integration of panomics (Weckwerth et al., 2020), novel field-based phenomics (White et al., 2012;Zhao et al., 2019), and the application of machine learning and artificial intelligence to breeding for complex plant traits (Niazian and Niedbała, 2020;Nabwire et al., 2021). However, these methods are currently prohibitively expensive and/or will require a core collection of diverse germplasm to efficiently assess whether they will be effective in cotton breeding. ...
Article
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Global plant breeding activities are reliant on the available genetic variation held in extant varieties and germplasm collections. Throughout the mid- to late 1900s, germplasm collecting efforts were prioritized for breeding programs to archive precious material before it disappeared and led to the development of the numerous large germplasm resources now available in different countries. In recent decades, however, the maintenance and particularly the expansion of these germplasm resources have come under threat, and there has been a significant decline in investment in further collecting expeditions, an increase in global biosecurity restrictions, and restrictions placed on the open exchange of some commercial germplasm between breeders. The large size of most genebank collections, as well as constraints surrounding the availability and reliability of accurate germplasm passport data and physical or genetic characterization of the accessions in collections, limits germplasm utilization by plant breeders. To overcome these constraints, core collections, defined as a representative subset of the total germplasm collection, have gained popularity. Core collections aim to increase germplasm utilization by containing highly characterized germplasm that attempts to capture the majority of the variation in a whole collection. With the recent availability of many new genetic tools, the potential to unlock the value of these resources can now be realized. The Commonwealth Scientific and Industrial Research Organisation (CSIRO) cotton breeding program supplies 100% of the cotton cultivars grown in Australia. The program is reliant on the use of plant genetic resources for the development of improved cotton varieties to address emerging challenges in pest and disease resistance as well as the global changes occurring in the climate. Currently, the CSIRO germplasm collection is actively maintained but underutilized by plant breeders. This review presents an overview of the Australian cotton germplasm resources and discusses the appropriateness of a core collection for cotton breeding programs.
... The major challenge for fast genetic progress is to connect genetic variants (genotype) to their expression in observable traits (phenotype) and to predict plant phenotypes from genetic information [116]. The enormous advances in genome sequencing of plants are providing massive genomic data collections, but the lack of efficient methods to collect rapid, high-quality, and high volumes of phenotypic data has become a bottleneck in genomics-assisted breeding [117][118][119]. It would be ideal to have fast and reliable phenomics tools to select for yield potential and drought tolerance in the earliest generations of the breeding program [120]. ...
Article
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Wheat and rice are two main staple food crops that may suffer from yield losses due to drought episodes that are increasingly impacted by climate change, in addition to new epidemic outbreaks. Sustainable intensification of production will rely on several strategies, such as efficient use of water and variety improvement. This review updates the latest findings regarding complementary approaches in agronomy, genetics, and phenomics to cope with climate change challenges. The agronomic approach focuses on a case study examining alternative rice water management practices, with their impact on greenhouse gas emissions and biodiversity for ecosystem services. The genetic approach reviews in depth the latest technologies to achieve fungal disease resistance, as well as the use of landraces to increase the genetic diversity of new varieties. The phenomics approach explores recent advances in high-throughput remote sensing technologies useful in detecting both biotic and abiotic stress effects on breeding programs. The complementary nature of all these technologies indicates that only interdisciplinary work will ensure significant steps towards a more sustainable agriculture under future climate change scenarios.
... Prediction accuracy of GS models depends upon the quality of phenotypic data collected on the training population (Beyene et al., 2019). However, advancements in phenotyping has lagged compared with recent advancements in genomics (White et al., 2012). During the last decade, several high throughput phenotyping (HTP) tools have been developed to cope with the phenotyping bottleneck White & Conley, 2013). ...
Thesis
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Classical plant breeding has evolved considerably during the last century. However, the rate of genetic gain is insufficient to cope with a 2% annual increase in the human population. Plant breeders and scientists are under pressure to develop new varieties and crops having higher yield, higher nutritional value, climate resilience, and disease and insect resistance. The solution requires the merging of new techniques with previously used tools and breeder’s skills. The main goal of this research was to explore the potential of genomics, phenomics, machine and deep learning tools in a wheat (Triticum aestivum L.) breeding program. Grain yield and grain protein content (GPC) are two traits very important in hard red spring wheat breeding, yet difficult to select for due to their well-known negative correlation. A nested association mapping population was used to map the regions controlling the stability of grain protein content. Analytical methods for complex datasets traditionally used in other disciplines represent an opportunity for improving prediction accuracy. We predicted five different quantitative traits with varying genetic architecture using cross-validations, independent validations, and different sets of SNP markers. Deep learning models gave 0 to 5% higher prediction accuracy than rrBLUP. Screening for end-use quality traits is usually secondary to grain yield due to high labor needs, cost of testing, and large seed requirements for phenotyping. Genomic selection provides an alternative to predict performance using genome-wide markers under forward and across location predictions, where previous years dataset can be used to build the models. The prediction accuracies for different traits varied from 0.45-0.81, 0.29-0.55, and 0.27-0.50 under cross-validation, forward, and across location predictions. Genomics and phenomics have the potential to revolutionize the field of plant breeding. Incorporation of secondary correlated traits in GS models has been demonstrated to improve accuracy. In another study, ability to predict GPC and grain yield was assessed using secondary traits, univariate, covariate, and multivariate GS models for within and across cycle predictions. An increased prediction ability for GPC and grain yield with the inclusion of secondary traits demonstrates the potential to improve the genetic gain per unit time and cost in wheat breeding.
... The main advantage of GS is that it does not require phenotypic information from the validation set to aid in the selection process; however, initially the model training requires both, phenotypic and marker data. Hence, precise phenotypic information is very crucial (White et al. 2012, Cobb et al. 2013, Crain et al. 2018 for obtaining accurate predictions. ...
Article
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One of the biggest challenges that breeders face is the development of improved cultivars in changing climate conditions posing extra challenges to their labor. On the other hand, the availability of data generated with automated systems offers an opportunity to characterize genetically and phenotypically genotypes with high detail. Modern sequencing technologies delivering hundreds of thousands of molecular makers, offered the opportunity of selecting genotypes without the need of observing these in fields and this methodology was coined as Genomic Selection (GS). More recently, sophisticated automated phenotyping platforms depending on sensors able to measure a large number of plant features were also developed and have shown potential in plant breeding applications. These modern phenotyping systems that attempt to efficiently deliver phenotypic information on secondary traits are also know as high-throughput phenotyping platforms (HTPPs). The integration of HTPP with GS models opened a new research front to improve the efficiency of the selection methods based on genomic data only, specially of those traits depending on a large number of genes with small effects (complex traits). However, there are still remaining some issues to solve for developing a robust methodology able to combine in an efficient and informed way these two high dimensional data types. In this document, we provide an overview of the statistical analysis of the data derived of the HTTPs for improving the predictive ability of conventional GS models. First, we provide a brief introduction showing the utility of genomic data in plant breeding applications. Then, we provide an overview of the field-based HTPPs considering the light detection and ranging, and the unmanned aerial vehicles and how the image data derived from these platforms can be used to accelerate genetic gains. After that, we discuss about the extension of the conventional GS models to allow the incorpora-tion of data derived of the HTPPs as main effects and also in interaction with environmental factors. The availability of several sources of information have opened a venue to investigate besides the univariate or single trait model, models based on multiple traits and also models that consider multiple time measures allowing longitudinal GS studies. Finally, we provide some conclusions as well as we mention some the current issues that do not allow to fully exploit the potential of HTTPs in plant breeding applications. © 2021, Brazilian Society of Plant Breeding. All rights reserved.
... For example, an automated phenotyping platform for the monitoring of three-dimensional plant growth in a greenhouse has enabled the genetic dissection of growth processes using a dynamic model (Campbell et al., 2018). In a field experiment, highthroughput phenotyping using unmanned aerial vehicles (UAVs) (Yang et al., 2017) and tractors (White et al., 2012) was used to measure plant growth. Among growth traits, the leaf area index (LAI) is often investigated because it is accessible from high-throughput phenotyping (Verger et al., 2014;Liu et al., 2017) while being sensitive to the environment, directly determining amount of light absorption, and thus affecting biomass production and yield. ...
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... Identification of key secondary traits, together with the deployment of affordable high-throughput platforms, have improved data collection in many ways for its use in breeding programs (Araus and Cairns, 2014;Moreira et al., 2019). Research has demonstrated that individual wavelengths of the electromagnetic spectrum can be associated with physiological traits in a rapid, non-destructive, and cost-effective manner (White et al., 2012). A primary challenge in associating wavebands for trait prediction is the high correlation among neighboring bands (multicollinearity). ...
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... To gain insights into complex traits, a study of large numbers of genotyped accessions across multiple environments is required to identify genotype-by-environment (G × E) interactions [31]. This is particularly important in quinoa, due to the large G × E interactions that have been reported [32,33]. ...
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Citation: Stanschewski, C.S.; Rey, E.; Fiene, G.; Craine, E.B.; Wellman, G.; Melino, V.J.; Patiranage, D.S.R.; Johansen, K.; Schmöckel, S.M.; Bertero, D.; et al. Quinoa Abstract: Quinoa is a crop originating in the Andes but grown more widely and with the genetic potential for significant further expansion. Due to the phenotypic plasticity of quinoa, varieties need to be assessed across years and multiple locations. To improve comparability among field trials across the globe and to facilitate collaborations, components of the trials need to be kept consistent, including the type and methods of data collected. Here, an internationally open-access framework for phenotyping a wide range of quinoa features is proposed to facilitate the systematic agronomic, physiological and genetic characterization of quinoa for crop adaptation and improvement. Mature plant phenotyping is a central aspect of this paper, including detailed descriptions and the provision of phenotyping cards to facilitate consistency in data collection. High-throughput methods for multi-temporal phenotyping based on remote sensing technologies are described. Tools for higher-throughput post-harvest phenotyping of seeds are presented. A guideline for approaching quinoa field trials including the collection of environmental data and designing layouts with statistical robustness is suggested. To move towards developing resources for quinoa in line with major cereal crops, a database was created. The Quinoa Germinate Platform will serve as a central repository of data for quinoa researchers globally.
... 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 for the estimation of various plant traits using nonintrusive and non-destructive technology (White et al., 2012;Rufo et al., 2021). Remote sensing has attracted growing interest in breeding programs 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). ...
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Low temperature freezing stress has adverse effects on wheat seedling growth and final yield. The traditional method to evaluate the wheat injury caused by the freezing stress is by visual observations, which is time-consuming and laborious. Therefore, to effectively and efficiently quantify the wheat freezing injury in the field environments, a high-throughput phenotyping system was developed in this paper , namely, RGB FREEZING INJURY SYSTEM. The system is able to automatically collect, processing, and analyze the wheat images collected using a mobile phenotype cabin in the field conditions. A data management system was also developed to store and manage the original images and the calculated phenotypic data in the system. A group of 128 wheat varieties were planted with replicates under a freezing environment. Canopy images of the wheat were collected at the seedling stage and three image features were extracted for each wheat samples, including ExG, ExR and ExV. The results show that the developed methods can clearly distinguish wheat samples with different wheat freezing injury scores. The automatic phenotypic analysis method of freezing injury provides a solution for high-throughput phenotypic analysis of field wheat and can quantify the stress caused by freezing injury at the seedling stage. The method has a certain guiding significance for wheat breeding.
Chapter
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Thesis
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Chapter
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The influences of cotton plant relative turgidity (RT), solar radiation (RS), and air temperature at plant height (TA) on leaf temperature (TL,) and leaf minus air temperature (TL — TA) were studied during two crop seasons. The daily data show that (a) a decrease in relative turgidity from 83 to 59% resulted in a 3.6C increase in leaf temperature, and (b) a unit increase in solar radiation (from about 0.5 to 1.5 ly min⁻¹) resulted in a 9 to 10C increase in leaf temperature. These same changes in relative turgidity and solar radiation resulted in 2.7 to 3.7 and 8 to 10C increases, respectively, in (TL — TA). Seasonal average (TL — TA) was 4C. Leaf temperature and (TL — TA) could be estimated from the seasonal data with an average standard error of 1C by RT, RS, and TA in linear multiple regression analyses. The results of these studies show that variations in plant moisture stress significantly alter leaf temperature and leaf minus air temperature. However, variations in insolation must be carefully monitored under intermittent cloud conditions to account for their influences on plant leaf temperature. Please view the pdf by using the Full Text (PDF) link under 'View' to the left. Copyright © . .