High-throughput phenotyping reveals differential transpiration behavior within the banana wild relatives highlighting diversity in drought tolerance

Crop wild relatives, the closely related species of crops, may harbor potentially important sources of new allelic diversity for (a)biotic tolerance or resistance. However, to date wild diversity is only poorly characterized and evaluated. Banana has a large wild diversity but only a narrow proportion is currently used in breeding programs. The main objective of this work was to evaluate genotype-dependent transpiration responses in relation to the environment. By applying continuous high-throughput phenotyping, we were able to construct genotype-specific transpiration response models in relation to light, VPD and soil water potential. We characterized and evaluated 6 (sub)species and discerned four phenotypic clusters. Significant differences were observed in leaf area, cumulative transpiration and transpiration efficiency. We confirmed a general stomatal-driven ‘isohydric’ drought avoidance behavior, but discovered genotypic differences in the onset and intensity of stomatal closure. We pinpointed crucial genotype specific environmental conditions when drought avoidance mechanisms were initiated and when stress kicked in. Differences between (sub)species were more pronounced under certain environmental conditions, illustrating the need for high-throughput dynamic phenotyping, modelling and validation. We conclude that the banana wild relatives contain useful drought tolerance traits, emphasizing the importance of their conservation and potential for use in breeding programs.

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High-throughput phenotyping to dissect genotypic differences in safflower for drought tolerance

PLoS ONE ◽

10.1371/journal.pone.0254908 ◽

2021 ◽

Vol 16 (7) ◽

pp. e0254908

Author(s):

Sameer Joshi ◽

Emily Thoday-Kennedy ◽

Hans D. Daetwyler ◽

Matthew Hayden ◽

German Spangenberg ◽

...

Keyword(s):

Drought Stress ◽

Drought Tolerance ◽

High Throughput ◽

Crop Yields ◽

Geometric Mean ◽

Shoot Biomass ◽

Screening Methods ◽

Tolerance Index ◽

Genotypic Differences ◽

High Throughput Phenotyping

Drought is one of the most severe and unpredictable abiotic stresses, occurring at any growth stage and affecting crop yields worldwide. Therefore, it is essential to develop drought tolerant varieties to ensure sustainable crop production in an ever-changing climate. High-throughput digital phenotyping technologies in tandem with robust screening methods enable precise and faster selection of genotypes for breeding. To investigate the use of digital imaging to reliably phenotype for drought tolerance, a genetically diverse safflower population was screened under different drought stresses at Agriculture Victoria’s high-throughput, automated phenotyping platform, Plant Phenomics Victoria, Horsham. In the first experiment, four treatments, control (90% field capacity; FC), 40% FC at initial branching, 40% FC at flowering and 50% FC at initial branching and flowering, were applied to assess the performance of four safflower genotypes. Based on these results, drought stress using 50% FC at initial branching and flowering stages was chosen to further screen 200 diverse safflower genotypes. Measured plant traits and dry biomass showed high correlations with derived digital traits including estimated shoot biomass, convex hull area, caliper length and minimum area rectangle, indicating the viability of using digital traits as proxy measures for plant growth. Estimated shoot biomass showed close association having moderately high correlation with drought indices yield index, stress tolerance index, geometric mean productivity, and mean productivity. Diverse genotypes were classified into four clusters of drought tolerance based on their performance (seed yield and digitally estimated shoot biomass) under stress. Overall, results show that rapid and precise image-based, high-throughput phenotyping in controlled environments can be used to effectively differentiate response to drought stress in a large numbers of safflower genotypes.

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Validation of UAV-based alfalfa biomass predictability using photogrammetry with fully automatic plot segmentation

Scientific Reports ◽

10.1038/s41598-021-82797-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Zhou Tang ◽

Atit Parajuli ◽

Chunpeng James Chen ◽

Yang Hu ◽

Samuel Revolinski ◽

...

Keyword(s):

Prediction Model ◽

High Throughput ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Selection Process ◽

Forage Legume ◽

Breeding Programs ◽

High Throughput Phenotyping ◽

Fully Automatic ◽

Uav Images

AbstractAlfalfa is the most widely cultivated forage legume, with approximately 30 million hectares planted worldwide. Genetic improvements in alfalfa have been highly successful in developing cultivars with exceptional winter hardiness and disease resistance traits. However, genetic improvements have been limited for complex economically important traits such as biomass. One of the major bottlenecks is the labor-intensive phenotyping burden for biomass selection. In this study, we employed two alfalfa fields to pave a path to overcome the challenge by using UAV images with fully automatic field plot segmentation for high-throughput phenotyping. The first field was used to develop the prediction model and the second field to validate the predictions. The first and second fields had 808 and 1025 plots, respectively. The first field had three harvests with biomass measured in May, July, and September of 2019. The second had one harvest with biomass measured in September of 2019. These two fields were imaged one day before harvesting with a DJI Phantom 4 pro UAV carrying an additional Sentera multispectral camera. Alfalfa plot images were extracted by GRID software to quantify vegetative area based on the Normalized Difference Vegetation Index. The prediction model developed from the first field explained 50–70% (R Square) of biomass variation in the second field by incorporating four features from UAV images: vegetative area, plant height, Normalized Green–Red Difference Index, and Normalized Difference Red Edge Index. This result suggests that UAV-based, high-throughput phenotyping could be used to improve the efficiency of the biomass selection process in alfalfa breeding programs.

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Review: Cost-Effective Unmanned Aerial Vehicle (UAV) Platform for Field Plant Breeding Application

Remote Sensing ◽

10.3390/rs12060998 ◽

2020 ◽

Vol 12 (6) ◽

pp. 998 ◽

Cited By ~ 5

Author(s):

GyuJin Jang ◽

Jaeyoung Kim ◽

Ju-Kyung Yu ◽

Hak-Jin Kim ◽

Yoonha Kim ◽

...

Keyword(s):

Plant Breeding ◽

Unmanned Aerial Vehicle ◽

High Throughput ◽

New Technologies ◽

Cost Effective ◽

New Wave ◽

Breeding Programs ◽

Modern Agriculture ◽

Aerial Vehicle ◽

High Throughput Phenotyping

Utilization of remote sensing is a new wave of modern agriculture that accelerates plant breeding and research, and the performance of farming practices and farm management. High-throughput phenotyping is a key advanced agricultural technology and has been rapidly adopted in plant research. However, technology adoption is not easy due to cost limitations in academia. This article reviews various commercial unmanned aerial vehicle (UAV) platforms as a high-throughput phenotyping technology for plant breeding. It compares known commercial UAV platforms that are cost-effective and manageable in field settings and demonstrates a general workflow for high-throughput phenotyping, including data analysis. The authors expect this article to create opportunities for academics to access new technologies and utilize the information for their research and breeding programs in more workable ways.

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Automated discretization of ‘transpiration restriction to increasing VPD’ features from outdoors high-throughput phenotyping data

Plant Methods ◽

10.1186/s13007-020-00680-8 ◽

2020 ◽

Vol 16 (1) ◽

Author(s):

Soumyashree Kar ◽

Ryokei Tanaka ◽

Lijalem Balcha Korbu ◽

Jana Kholová ◽

Hiroyoshi Iwata ◽

...

Keyword(s):

High Throughput ◽

Transpiration Rate ◽

Genotypic Variation ◽

Water Saving ◽

P Value ◽

Genotypic Differences ◽

Biologically Relevant ◽

Novel Approach ◽

High Throughput Phenotyping ◽

High Vapor

Abstract Background Restricting transpiration under high vapor pressure deficit (VPD) is a promising water-saving trait for drought adaptation. However, it is often measured under controlled conditions and at very low throughput, unsuitable for breeding. A few high-throughput phenotyping (HTP) studies exist, and have considered only maximum transpiration rate in analyzing genotypic differences in this trait. Further, no study has precisely identified the VPD breakpoints where genotypes restrict transpiration under natural conditions. Therefore, outdoors HTP data (15 min frequency) of a chickpea population were used to automate the generation of smooth transpiration profiles, extract informative features of the transpiration response to VPD for optimal genotypic discretization, identify VPD breakpoints, and compare genotypes. Results Fifteen biologically relevant features were extracted from the transpiration rate profiles derived from load cells data. Genotypes were clustered (C1, C2, C3) and 6 most important features (with heritability > 0.5) were selected using unsupervised Random Forest. All the wild relatives were found in C1, while C2 and C3 mostly comprised high TE and low TE lines, respectively. Assessment of the distinct p-value groups within each selected feature revealed highest genotypic variation for the feature representing transpiration response to high VPD condition. Sensitivity analysis on a multi-output neural network model (with R of 0.931, 0.944, 0.953 for C1, C2, C3, respectively) found C1 with the highest water saving ability, that restricted transpiration at relatively low VPD levels, 56% (i.e. 3.52 kPa) or 62% (i.e. 3.90 kPa), depending whether the influence of other environmental variables was minimum or maximum. Also, VPD appeared to have the most striking influence on the transpiration response independently of other environment variable, whereas light, temperature, and relative humidity alone had little/no effect. Conclusion Through this study, we present a novel approach to identifying genotypes with drought-tolerance potential, which overcomes the challenges in HTP of the water-saving trait. The six selected features served as proxy phenotypes for reliable genotypic discretization. The wild chickpeas were found to limit water-loss faster than the water-profligate cultivated ones. Such an analytic approach can be directly used for prescriptive breeding applications, applied to other traits, and help expedite maximized information extraction from HTP data.

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Aerial High-Throughput Phenotyping Enabling Indirect Selection for Grain Yield at the Early-generation Seed-limited Stages in Breeding Programs

10.1101/2020.04.21.054163 ◽

2020 ◽

Author(s):

Margaret R. Krause ◽

Suchismita Mondal ◽

José Crossa ◽

Ravi P. Singh ◽

Francisco Pinto ◽

...

Keyword(s):

Grain Yield ◽

High Throughput ◽

Vegetation Indices ◽

Early Generation ◽

Breeding Programs ◽

Breeding Lines ◽

High Throughput Phenotyping ◽

Seed Increase ◽

Yield Trials ◽

Selection Of

ABSTRACTBreeding programs for wheat and many other crops require one or more generations of seed increase before replicated yield trials can be sown. Extensive phenotyping at this stage of the breeding cycle is challenging due to the small plot size and large number of lines under evaluation. Therefore, breeders typically rely on visual selection of small, unreplicated seed increase plots for the promotion of breeding lines to replicated yield trials. With the development of aerial high-throughput phenotyping technologies, breeders now have the ability to rapidly phenotype thousands of breeding lines for traits that may be useful for indirect selection of grain yield. We evaluated early generation material in the irrigated bread wheat (Triticum aestivum L.) breeding program at the International Maize and Wheat Improvement Center to determine if aerial measurements of vegetation indices assessed on small, unreplicated plots were predictive of grain yield. To test this approach, two sets of 1,008 breeding lines were sown both as replicated yield trials and as small, unreplicated plots during two breeding cycles. Vegetation indices collected with an unmanned aerial vehicle in the small plots were observed to be heritable and moderately correlated with grain yield assessed in replicated yield trials. Furthermore, vegetation indices were more predictive of grain yield than univariate genomic selection, while multi-trait genomic selection approaches that combined genomic information with the aerial phenotypes were found to have the highest predictive abilities overall. A related experiment showed that selection approaches for grain yield based on vegetation indices could be more effective than visual selection; however, selection on the vegetation indices alone would have also driven a directional response in phenology due to confounding between those traits. A restricted selection index was proposed for improving grain yield without affecting the distribution of phenology in the breeding population. The results of these experiments provide a promising outlook for the use of aerial high-throughput phenotyping traits to improve selection at the early-generation seed-limited stage of wheat breeding programs.

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Unraveling the Genetic Architecture of Two Complex, Stomata-Related Drought-Responsive Traits by High-Throughput Physiological Phenotyping and GWAS in Cowpea (Vigna. Unguiculata L. Walp)

Frontiers in Genetics ◽

10.3389/fgene.2021.743758 ◽

2021 ◽

Vol 12 ◽

Author(s):

Xinyi Wu ◽

Ting Sun ◽

Wenzhao Xu ◽

Yudong Sun ◽

Baogen Wang ◽

...

Keyword(s):

Drought Tolerance ◽

Genetic Mapping ◽

Soil Water ◽

Water Relations ◽

High Throughput ◽

Genetic Architecture ◽

Stomatal Closure ◽

Significant Snps ◽

Soil Drought ◽

Stomatal Control

Drought is one of the most devasting and frequent abiotic stresses in agriculture. While many morphological, biochemical and physiological indicators are being used to quantify plant drought responses, stomatal control, and hence the transpiration and photosynthesis regulation through it, is of particular importance in marking the plant capacity of balancing stress response and yield. Due to the difficulties in simultaneous, large-scale measurement of stomatal traits such as sensitivity and speed of stomatal closure under progressive soil drought, forward genetic mapping of these important behaviors has long been unavailable. The recent emerging phenomic technologies offer solutions to identify the water relations of whole plant and assay the stomatal regulation in a dynamic process at the population level. Here, we report high-throughput physiological phenotyping of water relations of 106 cowpea accessions under progressive drought stress, which, in combination of genome-wide association study (GWAS), enables genetic mapping of the complex, stomata-related drought responsive traits “critical soil water content” (θcri) and “slope of transpiration rate declining” (KTr). The 106 accessions showed large variations in θcri and KTr, indicating that they had broad spectrum of stomatal control in response to soil water deficit, which may confer them different levels of drought tolerance. Univariate GWAS identified six and fourteen significant SNPs associated with θcri and KTr, respectively. The detected SNPs distributed in nine chromosomes and accounted for 8.7–21% of the phenotypic variation, suggesting that both stomatal sensitivity to soil drought and the speed of stomatal closure to completion were controlled by multiple genes with moderate effects. Multivariate GWAS detected ten more significant SNPs in addition to confirming eight of the twenty SNPs as detected by univariate GWAS. Integrated, a final set of 30 significant SNPs associated with stomatal closure were reported. Taken together, our work, by combining phenomics and genetics, enables forward genetic mapping of the genetic architecture of stomatal traits related to drought tolerance, which not only provides a basis for molecular breeding of drought resistant cultivars of cowpea, but offers a new methodology to explore the genetic determinants of water budgeting in crops under stressful conditions in the phenomics era.

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Genotypic variation for drought stress response traits in soybean. I. Variation in soybean and wild Glycine spp. for epidermal conductance, osmotic potential, and relative water content

Australian Journal of Agricultural Research ◽

10.1071/ar07159 ◽

2008 ◽

Vol 59 (7) ◽

pp. 656 ◽

Cited By ~ 27

Author(s):

A. T. James ◽

R. J. Lawn ◽

M. Cooper

Keyword(s):

Water Content ◽

Wild Species ◽

Osmotic Potential ◽

Relative Water Content ◽

Stomatal Closure ◽

Genotypic Variation ◽

Soil Water Potential ◽

Genotypic Differences ◽

Relative Water ◽

Soybean Genotypes

Studies were undertaken to assess genotypic variation in soybean and related wild species for traits with putative effects on leaf turgor maintenance in droughted plants. Traits of interest were (i) epidermal conductance (ge) which influences the rate of water loss from stressed leaves after stomatal closure; (ii) osmotic adjustment (OA) as indicated by tissue osmotic potential (π), which potentially affects the capacity to withdraw water at low soil water potential; and (iii) relative water content (RWC) at incipient leaf death (critical relative water content, RWCC), which is a measure of the dehydration tolerance of leaf tissue. The germplasm comprised a diverse set of 58 soybean genotypes, 2 genotypes of the annual wild species G. soja and 9 genotypes representing 6 perennial wild Glycine spp. indigenous/endemic to Australia. Seedling plants were grown in soil-filled beds in the glasshouse and exposed to terminal water deficit stress from the second trifoliolate leaflet stage (21 days after sowing). Measurements were made on well watered plants, moderately stressed plants, and at incipient plant death, in 2 separate studies. In both studies, there were significant genotypic differences in all 3 traits in the stressed plants. However, across the 3 sample times, ge decreased and the absolute magnitude of π increased, indicating that the expression of these traits changed as the plants acclimated to the stress. RWC was therefore used as a covariate to adjust the genotypic values of π and ge in order to facilitate comparison at a consistent plant water status of 70% RWC. There was statistically significant genotypic variation for the adjusted values, ge70 and π70, in both studies, and genotypic correlations between the 2 studies were significant (P < 0.05) and positive for all 3 traits: ge70 (r = 0.48), π70 (r = 0.50), and RWCC (r = 0.53). Among the soybean genotypes, there was at least a 2-fold range in ge70, a 0.7 MPa range in π70, and a 12 percentage point range in RWCC. Some of the perennial wild genotypes exhibited lower values of ge and RWCC and greater OA than soybean and G. soja, consistent with adaptation to drier environments. While the repeatability of measurement between experiments was variable among genotypes, the studies confirmed the existence of genotypic differences for ge, OA, and RWCC in cultivated soybean, with a wider range among the wild germplasm.

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High-Throughput Phenotyping of Soybean Maturity Using Time Series UAV Imagery and Convolutional Neural Networks

10.20944/preprints202009.0458.v1 ◽

2020 ◽

Author(s):

Rodrigo Trevisan ◽

Osvaldo Pérez ◽

Nathan Schmitz ◽

Brian Diers ◽

Nicolas Martin

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

High Throughput ◽

Mean Squared Error ◽

Ground Truth ◽

Breeding Programs ◽

Growing Seasons ◽

Maturity Date ◽

High Throughput Phenotyping ◽

Commercial Breeding

Soybean maturity is a trait of critical importance for the development of new soybean cultivars, nevertheless, its characterization based on visual ratings has many challenges. Unmanned aerial vehicles (UAVs) imagery-based high-throughput phenotyping methodologies have been proposed as an alternative to the traditional visual ratings of pod senescence. However, the lack of scalable and accurate methods to extract the desired information from the images remains a significant bottleneck in breeding programs. The objective of this study was to develop an image-based high-throughput phenotyping system for evaluating soybean maturity in breeding programs. Images were acquired twice a week, starting when the earlier lines began maturation until the latest ones were mature. Two complementary convolutional neural networks (CNN) were developed to predict the maturity date. The first using a single date and the second using the five best image dates identified by the first model. The proposed CNN architecture was validated using more than 15,000 ground truth observations from five trials, including data from three growing seasons and two countries. The trained model showed good generalization capability with a root mean squared error lower than two days in four out of five trials. Four methods of estimating prediction uncertainty showed potential at identifying different sources of errors in the maturity date predictions. The architecture used solves limitations of previous research and can be used at scale in commercial breeding programs.

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