GrowScreen-PaGe, a non-invasive, high-throughput phenotyping system based on germination paper to quantify crop phenotypic diversity and plasticity of root traits under varying nutrient supply

2017 ◽  
Vol 44 (1) ◽  
pp. 76 ◽  
Author(s):  
Tania Gioia ◽  
Anna Galinski ◽  
Henning Lenz ◽  
Carmen Müller ◽  
Jonas Lentz ◽  
...  
Keyword(s):  
High Throughput ◽  
Phenotypic Diversity ◽  
Crop Improvement ◽  
Root Traits ◽  
Root Systems ◽  
Non Invasive ◽  
Large Sets ◽  
High Throughput Phenotyping ◽  
Improvement Programs ◽  
Root Phenotyping

New techniques and approaches have been developed for root phenotyping recently; however, rapid and repeatable non-invasive root phenotyping remains challenging. Here, we present GrowScreen-PaGe, a non-invasive, high-throughput phenotyping system (4 plants min–1) based on flat germination paper. GrowScreen-PaGe allows the acquisition of time series of the developing root systems of 500 plants, thereby enabling to quantify short-term variations in root system. The choice of germination paper was found to be crucial and paper ☓ root interaction should be considered when comparing data from different studies on germination paper. The system is suitable for phenotyping dicot and monocot plant species. The potential of the system for high-throughput phenotyping was shown by investigating phenotypic diversity of root traits in a collection of 180 rapeseed accessions and of 52 barley genotypes grown under control and nutrient-starved conditions. Most traits showed a large variation linked to both genotype and treatment. In general, root length traits contributed more than shape and branching related traits in separating the genotypes. Overall, results showed that GrowScreen-PaGe will be a powerful resource to investigate root systems and root plasticity of large sets of plants and to explore the molecular and genetic root traits of various species including for crop improvement programs.

scholarly journals Phenotyping Root Systems in a Set of Japonica Rice Accessions: Can Structural Traits Predict the Response to Drought?

Rice ◽  
2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Paulo Henrique Ramos Guimarães ◽  
Isabela Pereira de Lima ◽  
Adriano Pereira de Castro ◽  
Anna Cristina Lanna ◽  
Patrícia Guimarães Santos Melo ◽  
...  
Keyword(s):  
Root System ◽  
Water Deficit ◽  
High Throughput ◽  
Root Traits ◽  
Root Systems ◽  
Japonica Rice ◽  
Plastic Response ◽  
Reproductive Phase ◽  
High Throughput Phenotyping ◽  
Structural Traits

Abstract Background The root system plays a major role in plant growth and development and root system architecture is reported to be the main trait related to plant adaptation to drought. However, phenotyping root systems in situ is not suited to high-throughput methods, leading to the development of non-destructive methods for evaluations in more or less controlled root environments. This study used a root phenotyping platform with a panel of 20 japonica rice accessions in order to: (i) assess their genetic diversity for a set of structural and morphological root traits and classify the different types; (ii) analyze the plastic response of their root system to a water deficit at reproductive phase and (iii) explore the ability of the platform for high-throughput phenotyping of root structure and morphology. Results High variability for the studied root traits was found in the reduced set of accessions. Using eight selected traits under irrigated conditions, five root clusters were found that differed in root thickness, branching index and the pattern of fine and thick root distribution along the profile. When water deficit occurred at reproductive phase, some accessions significantly reduced root growth compared to the irrigated treatment, while others stimulated it. It was found that root cluster, as defined under irrigated conditions, could not predict the plastic response of roots under drought. Conclusions This study revealed the possibility of reconstructing the structure of root systems from scanned images. It was thus possible to significantly class root systems according to simple structural traits, opening up the way for using such a platform for medium to high-throughput phenotyping. The study also highlighted the uncoupling between root structures under non-limiting water conditions and their response to drought.

scholarly journals Ground-Penetrating Radar as phenotyping tool for characterizing intraspecific variability in root traits of a widespread conifer

2021 ◽  
Author(s):  
Erica Lombardi ◽  
Juan Pedro Ferrio ◽  
Ulises Rodríguez-Robles ◽  
Víctor Resco de Dios ◽  
Jordi Voltas
Keyword(s):  
High Throughput ◽  
Ground Penetrating Radar ◽  
Intraspecific Variability ◽  
Root Traits ◽  
Root Systems ◽  
Mediterranean Forests ◽  
High Throughput Phenotyping ◽  
The Mediterranean ◽  
Ground Penetrating ◽  
Root Frequency

Abstract Background and Aim Drought is the main abiotic stress affecting Mediterranean forests. Root systems are responsible for water uptake, but intraspecific variability in tree root morphology is poorly understood mainly owing to sampling difficulties. The aim of this study was to gain knowledge on the adaptive relevance of rooting traits for a widespread pine using a non-invasive, high-throughput phenotyping technique. Methods Ground-Penetrating Radar (GPR) was used to characterize variability in coarse root features (depth, diameter and frequency) among populations of the Mediterranean conifer Pinus halepensis evaluated in a common garden. GPR records were examined in relation to aboveground growth and climate variables at origin of populations. Results Variability was detected for root traits among 56 range-wide populations categorized into 16 ecotypes. Root diameter decreased eastward within the Mediterranean basin. In turn, root frequency, but not depth and diameter, decreased following a northward gradient. Root traits also varied with climatic variables at origin such as the ratio of summer to annual precipitation, summer temperature or solar radiation. Particularly, root frequency increased with aridity, whereas root depth and diameter were maximum for ecotypes occupying the thermal midpoint of the species distribution range. Conclusion GPR is a high-throughput phenotyping tool that allows detection of intraspecific variation in root traits of P. halepensis and its dependencies on eco-geographic characteristics at origin, thereby informing on the adaptive relevance of root systems for the species. It is also potentially suited for inferring population divergence in resource allocation above- and belowground in forest genetic trials.

scholarly journals Bottlenecks and opportunities in field-based high-throughput phenotyping for heat and drought stress

2021 ◽  
Author(s):  
Nathan T Hein ◽  
Ignacio A Ciampitti ◽  
S V Krishna Jagadish
Keyword(s):  
Remote Sensing ◽  
Drought Stress ◽  
High Throughput ◽  
Complex Traits ◽  
Crop Improvement ◽  
Grain Filling ◽  
Time Of Day ◽  
Water Soluble ◽  
High Throughput Phenotyping ◽  
Improvement Programs

Abstract Flowering and grain-filling stages are highly sensitive to heat and drought stress exposure, leading to significant loss in crop yields. Therefore, phenotyping to enhance resilience to these abiotic stresses is critical for sustaining genetic gains in crop improvement programs. However, traditional methods for screening traits related to these stresses are slow, laborious, and often expensive. Remote sensing provides opportunities to introduce low-cost, less-biased, high-throughput phenotyping methods to capture large genetic diversity to facilitate enhancement of stress resilience in crops. This review focuses on four key physiological traits or processes that are critical in understanding crop responses to drought and heat stress during reproductive and grain-filling periods. Specifically, these traits include: i) time-of-day of flowering, to escape these stresses during flowering, ii) optimizing photosynthetic efficiency, iii) storage and translocation of water-soluble carbohydrates, and iv) yield and yield components to provide in-season yield estimates. An overview of current advances in remote sensing in capturing these traits, limitations with existing technology and future direction of research to develop high-throughput phenotyping approaches for these traits are discussed in this review. In the future, phenotyping these complex traits will require sensor advancement, high-quality imagery combined with machine learning methods, and efforts in transdisciplinary science to foster integration across disciplines.

scholarly journals PhenoImage: an open-source GUI for plant image analysis

2020 ◽  
Author(s):  
Feiyu Zhu ◽  
Manny Saluja ◽  
Jaspinder Singh ◽  
Puneet Paul ◽  
Scott E. Sattler ◽  
...  
Keyword(s):  
Image Analysis ◽  
Open Source ◽  
Plant Species ◽  
High Throughput ◽  
Large Scale ◽  
Imaging System ◽  
Crop Improvement ◽  
Plant Phenotyping ◽  
High Throughput Phenotyping ◽  
Improvement Programs

AbstractHigh-throughput genotyping coupled with molecular breeding approaches has dramatically accelerated crop improvement programs. More recently, improved plant phenotyping methods have led to a shift from manual measurements to automated platforms with increased scalability and resolution. Considerable effort has also gone into the development of large-scale downstream processing of the imaging datasets derived from high-throughput phenotyping (HTP) platforms. However, most available tools require some programing skills. We developed PhenoImage – an open-source GUI based cross-platform solution for HTP image processing with the aim to make image analysis accessible to users with either little or no programming skills. The open-source nature provides the possibility to extend its usability to meet user-specific requirements. The availability of multiple functions and filtering parameters provides flexibility to analyze images from a wide variety of plant species and platforms. PhenoImage can be run on a personal computer as well as on high-performance computing clusters. To test the efficacy of the application, we analyzed the LemnaTec Imaging system derived RGB and fluorescence shoot images from two plant species: sorghum and wheat differing in their physical attributes. In the study, we discuss the development, implementation, and working of the PhenoImage.HighlightPhenoImage is an open-source application designed for analyzing images derived from high-throughput phenotyping.

scholarly journals High-throughput phenotyping (HTP) identifies seedling root traits linked to variation in seed yield and nutrient capture in field-grown oilseed rape (Brassica napusL.)

2016 ◽  
Vol 118 (4) ◽  
pp. 655-665 ◽  
Author(s):  
C. L. Thomas ◽  
N. S. Graham ◽  
R. Hayden ◽  
M. C. Meacham ◽  
K. Neugebauer ◽  
...  
Keyword(s):  
Oilseed Rape ◽  
Seed Yield ◽  
High Throughput ◽  
Root Traits ◽  
Seedling Root ◽  
High Throughput Phenotyping

scholarly journals ChronoRoot: High-throughput phenotyping by deep segmentation networks reveals novel temporal parameters of plant root system architecture

2020 ◽  
Author(s):  
Nicolás Gaggion ◽  
Federico Ariel ◽  
Vladimir Daric ◽  
Éric Lambert ◽  
Simon Legendre ◽  
...  
Keyword(s):  
Deep Learning ◽  
Root System ◽  
High Throughput ◽  
System Architecture ◽  
Root Traits ◽  
Root System Architecture ◽  
Machine Intelligence ◽  
Reconstruction Process ◽  
High Throughput Phenotyping ◽  
3D Printed

ABSTRACTDeep learning methods have outperformed previous techniques in most computer vision tasks, including image-based plant phenotyping. However, massive data collection of root traits and the development of associated artificial intelligence approaches have been hampered by the inaccessibility of the rhizosphere. Here we present ChronoRoot, a system which combines 3D printed open-hardware with deep segmentation networks for high temporal resolution phenotyping of plant roots in agarized medium. We developed a novel deep learning based root extraction method which leverages the latest advances in convolutional neural networks for image segmentation, and incorporates temporal consistency into the root system architecture reconstruction process. Automatic extraction of phenotypic parameters from sequences of images allowed a comprehensive characterization of the root system growth dynamics. Furthermore, novel time-associated parameters emerged from the analysis of spectral features derived from temporal signals. Altogether, our work shows that the combination of machine intelligence methods and a 3D-printed device expands the possibilities of root high-throughput phenotyping for genetics and natural variation studies as well as the screening of clock-related mutants, revealing novel root traits.

scholarly journals High-Throughput Phenotyping: A Platform to Accelerate Crop Improvement

Phenomics ◽  
2021 ◽  
Author(s):  
Sumit Jangra ◽  
Vrantika Chaudhary ◽  
Ram C. Yadav ◽  
Neelam R. Yadav
Keyword(s):  
High Throughput ◽  
Crop Improvement ◽  
High Throughput Phenotyping

scholarly journals Ground-penetrating Radar as Phenotyping Tool for Characterizing Intraspecific Variability in Root Traits of Pinus Halepensis

2021 ◽  
Author(s):  
Erica Lombardi ◽  
Juan Pedro Ferrio ◽  
Ulises Rodríguez-Robles ◽  
Víctor Resco de Dios ◽  
Jordi Voltas
Keyword(s):  
High Throughput ◽  
Pinus Halepensis ◽  
Genotypic Variation ◽  
Intraspecific Variability ◽  
Root Traits ◽  
Root Systems ◽  
Population Divergence ◽  
Coarse Root ◽  
The Mediterranean ◽  
Root Frequency

Abstract Background and AimDrought is the main factor limiting Mediterranean forest ecosystem productivity. Root systems are responsible for water uptake but intraspecific variability in root morphology is poorly understood, mainly due to sampling complexity. The main aim of this study was to gain knowledge on the adaptive relevance of rooting traits for a widespread conifer using a non-invasive high-throughput technique.MethodsGround-Penetrating Radar (GPR) was used to characterize variability in coarse root features (frequency, depth, and diameter) among populations of the Mediterranean pine Pinus halepensis evaluated in a common garden. GPR records were analysed in relation to aboveground growth and also climate variables at origin of populations.ResultsGenotypic variability was detected for root traits among 56 range-wide populations categorized into 16 ecotypes. Root diameter of populations decreased eastward within the Mediterranean basin. Root frequency, but not depth and diameter, decreased following a northward gradient. Genotypic variation in root traits varied with climatic variables at origin such as summer to annual precipitation ratio, summer temperature and solar radiation. Particularly, root frequency increased with aridity, whereas root depth and diameter were maximum in ecotypes occupying the thermal midpoint of the species distribution range.Conclusion GPR is a high-throughput phenotyping tool that allows detection of intraspecific variation in root traits of Aleppo pine and its dependencies of eco-geographic characteristics at origin, thereby informing on the adaptive relevance of root systems for the species. It is also potentially suited for inferring population divergence in resource allocation above and belowground in forest genetic trials.

scholarly journals High-Throughput Phenotyping and Random Regression Models Reveal Temporal Genetic Control of Soybean Biomass Production

2021 ◽  
Vol 12 ◽  
Author(s):  
Fabiana Freitas Moreira ◽  
Hinayah Rojas de Oliveira ◽  
Miguel Angel Lopez ◽  
Bilal Jamal Abughali ◽  
Guilherme Gomes ◽  
...  
Keyword(s):  
High Throughput ◽  
Regression Models ◽  
Genetic Architecture ◽  
Crop Improvement ◽  
Random Regression ◽  
Genome Wide Association Studies ◽  
Breeding Values ◽  
High Throughput Phenotyping ◽  
General Response ◽  
Over Time

Understanding temporal accumulation of soybean above-ground biomass (AGB) has the potential to contribute to yield gains and the development of stress-resilient cultivars. Our main objectives were to develop a high-throughput phenotyping method to predict soybean AGB over time and to reveal its temporal quantitative genomic properties. A subset of the SoyNAM population (n = 383) was grown in multi-environment trials and destructive AGB measurements were collected along with multispectral and RGB imaging from 27 to 83 days after planting (DAP). We used machine-learning methods for phenotypic prediction of AGB, genomic prediction of breeding values, and genome-wide association studies (GWAS) based on random regression models (RRM). RRM enable the study of changes in genetic variability over time and further allow selection of individuals when aiming to alter the general response shapes over time. AGB phenotypic predictions were high (R2 = 0.92–0.94). Narrow-sense heritabilities estimated over time ranged from low to moderate (from 0.02 at 44 DAP to 0.28 at 33 DAP). AGB from adjacent DAP had highest genetic correlations compared to those DAP further apart. We observed high accuracies and low biases of prediction indicating that genomic breeding values for AGB can be predicted over specific time intervals. Genomic regions associated with AGB varied with time, and no genetic markers were significant in all time points evaluated. Thus, RRM seem a powerful tool for modeling the temporal genetic architecture of soybean AGB and can provide useful information for crop improvement. This study provides a basis for future studies to combine phenotyping and genomic analyses to understand the genetic architecture of complex longitudinal traits in plants.

scholarly journals Exploiting High-Throughput Indoor Phenotyping to Characterize the Founders of a Structured B. napus Breeding Population

2022 ◽  
Vol 12 ◽  
Author(s):  
Jana Ebersbach ◽  
Nazifa Azam Khan ◽  
Ian McQuillan ◽  
Erin E. Higgins ◽  
Kyla Horner ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Drought Stress ◽  
High Throughput ◽  
Complex Traits ◽  
Phenotypic Diversity ◽  
Crop Improvement ◽  
Breeding Population ◽  
Supervised Machine Learning ◽  
Oilseed Crop

Phenotyping is considered a significant bottleneck impeding fast and efficient crop improvement. Similar to many crops, Brassica napus, an internationally important oilseed crop, suffers from low genetic diversity, and will require exploitation of diverse genetic resources to develop locally adapted, high yielding and stress resistant cultivars. A pilot study was completed to assess the feasibility of using indoor high-throughput phenotyping (HTP), semi-automated image processing, and machine learning to capture the phenotypic diversity of agronomically important traits in a diverse B. napus breeding population, SKBnNAM, introduced here for the first time. The experiment comprised 50 spring-type B. napus lines, grown and phenotyped in six replicates under two treatment conditions (control and drought) over 38 days in a LemnaTec Scanalyzer 3D facility. Growth traits including plant height, width, projected leaf area, and estimated biovolume were extracted and derived through processing of RGB and NIR images. Anthesis was automatically and accurately scored (97% accuracy) and the number of flowers per plant and day was approximated alongside relevant canopy traits (width, angle). Further, supervised machine learning was used to predict the total number of raceme branches from flower attributes with 91% accuracy (linear regression and Huber regression algorithms) and to identify mild drought stress, a complex trait which typically has to be empirically scored (0.85 area under the receiver operating characteristic curve, random forest classifier algorithm). The study demonstrates the potential of HTP, image processing and computer vision for effective characterization of agronomic trait diversity in B. napus, although limitations of the platform did create significant variation that limited the utility of the data. However, the results underscore the value of machine learning for phenotyping studies, particularly for complex traits such as drought stress resistance.

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