scholarly journals Assessing Root System Architecture of Wheat Seedlings Using A High-Throughput Root Phenotyping System

2019 ◽  
Author(s):  
E. Adeleke ◽  
R. Millas ◽  
W. McNeal ◽  
J Faris ◽  
A. Taheri
Keyword(s):  
Root System ◽  
System Architecture ◽  
Root Traits ◽  
Wheat Seedling ◽  
Root System Architecture ◽  
Rapid Screening ◽  
Wheat Seedlings ◽  
Ploidy Levels ◽  
Seedling Traits ◽  
Root Phenotyping

AbstractBackground and aimsRoot system architecture is a vital part of the plant that has been shown to vary between species and within species based on response to genotypic and/or environmental influences. The root traits of wheat seedlings is critical for the establishment and evidently linked to plant height and seed yield. However, plant breeders have not efficiently developed the role of RSA in wheat selection due to the difficulty of studying root traits.MethodsWe set up a root phenotyping platform to characterize RSA in 34 wheat accessions. The phenotyping pipeline consists of the germination paper-based moisture replacement system, image capture units, and root-image processing software. The 34 accessions from two different wheat ploidy levels (hexaploids and tetraploids), were characterized in ten replicates. A total of 19 root traits were quantified from the root architecture generated.ResultsThis pipeline allowed for rapid screening of 340 wheat seedlings within 10days. Also, at least one line from each ploidy (6x and 4x) showed significant differences (P < 0.05) in measured traits except in mean seminal count. Our result also showed strong correlation (0.8) between total root length, maximum depth and convex hull area.ConclusionsThis phenotyping pipeline has the advantage and capacity to increase screening potential at early stages of plant development leading to characterization of wheat seedling traits that can be further examined using QTL analysis in populations generated from the examined accessions.

scholarly journals Variation Analysis of Root System Development in Wheat Seedlings Using Root Phenotyping System

Agronomy ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 206 ◽  
Author(s):  
Ekundayo Adeleke ◽  
Reneth Millas ◽  
Waymon McNeal ◽  
Justin Faris ◽  
Ali Taheri
Keyword(s):  
Root System ◽  
System Development ◽  
Root Traits ◽  
Wheat Seedling ◽  
Root System Architecture ◽  
Rapid Screening ◽  
Wheat Seedlings ◽  
Ploidy Levels ◽  
Root System Development ◽  
Root Phenotyping

Root system architecture is a vital part of the plant that has been shown to vary between species and within species based on response to genotypic and/or environmental influences. The root traits of wheat seedlings are critical for their establishment in soil and evidently linked to plant height and seed yield. However, plant breeders have not efficiently developed the role of RSA in wheat selection due to the difficulty of studying root traits. We set up a root phenotyping platform to characterize RSA in 34 wheat accessions. The phenotyping pipeline consists of the germination paper-based moisture replacement system, image capture units, and root-image processing software. The 34 accessions from two different wheat ploidy levels (hexaploids and tetraploids), were characterized in ten replicates. A total of 19 root traits were quantified from the root architecture generated. This pipeline allowed for rapid screening of 340 wheat seedlings within 10 days. At least one line from each ploidy (6× and 4×) showed significant differences (p < 0.05) in measured traits, except for mean seminal count. Our result also showed a strong correlation (0.8) between total root length, maximum depth and convex hull area. This phenotyping pipeline has the advantage and capacity to increase screening potential at early stages of plant development, leading to the characterization of wheat seedling traits that can be further examined using QTL analysis in populations generated from the examined accessions.

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 Trait-Based Root Phenotyping as a Necessary Tool for Crop Selection and Improvement

Agronomy ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1328
Author(s):  
Rebecca K. McGrail ◽  
David A. Van Sanford ◽  
David H. McNear
Keyword(s):  
Root System ◽  
System Architecture ◽  
Production Systems ◽  
Root Traits ◽  
Root System Architecture ◽  
Root Decomposition ◽  
Crop Breeding ◽  
Organic Acid Production ◽  
Nutritional Qualities ◽  
Chemical Traits

Most of the effort of crop breeding has focused on the expression of aboveground traits with the goals of increasing yield and disease resistance, decreasing height in grains, and improvement of nutritional qualities. The role of roots in supporting these goals has been largely ignored. With the increasing need to produce more food, feed, fiber, and fuel on less land and with fewer inputs, the next advance in plant breeding must include greater consideration of roots. Root traits are an untapped source of phenotypic variation that will prove essential for breeders working to increase yields and the provisioning of ecosystem services. Roots are dynamic, and their structure and the composition of metabolites introduced to the rhizosphere change as the plant develops and in response to environmental, biotic, and edaphic factors. The assessment of physical qualities of root system architecture will allow breeding for desired root placement in the soil profile, such as deeper roots in no-till production systems plagued with drought or shallow roots systems for accessing nutrients. Combining the assessment of physical characteristics with chemical traits, including enzymes and organic acid production, will provide a better understanding of biogeochemical mechanisms by which roots acquire resources. Lastly, information on the structural and elemental composition of the roots will help better predict root decomposition, their contribution to soil organic carbon pools, and the subsequent benefits provided to the following crop. Breeding can no longer continue with a narrow focus on aboveground traits, and breeding for belowground traits cannot only focus on root system architecture. Incorporation of root biogeochemical traits into breeding will permit the creation of germplasm with the required traits to meet production needs in a variety of soil types and projected climate scenarios.

scholarly journals Genetic Dissection of the Seminal Root System Architecture in Mediterranean Durum Wheat Landraces by Genome-Wide Association Study

Agronomy ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. 364 ◽  
Author(s):  
Martina Roselló ◽  
Conxita Royo ◽  
Miguel Sanchez-Garcia ◽  
Jose Miguel Soriano
Keyword(s):  
Durum Wheat ◽  
Root System ◽  
System Architecture ◽  
Eastern Mediterranean Region ◽  
Root Traits ◽  
Root System Architecture ◽  
Seminal Root ◽  
Large Variability ◽  
Wheat Landraces ◽  
Durum Wheat Landraces

Roots are crucial for adaptation to drought stress. However, phenotyping root systems is a difficult and time-consuming task due to the special feature of the traits in the process of being analyzed. Correlations between root system architecture (RSA) at the early stages of development and in adult plants have been reported. In this study, the seminal RSA was analysed on a collection of 160 durum wheat landraces from 21 Mediterranean countries and 18 modern cultivars. The landraces showed large variability in RSA, and differences in root traits were found between previously identified genetic subpopulations. Landraces from the eastern Mediterranean region, which is the driest and warmest within the Mediterranean Basin, showed the largest seminal root size in terms of root length, surface, and volume and the widest root angle, whereas landraces from eastern Balkan countries showed the lowest values. Correlations were found between RSA and yield-related traits in a very dry environment. The identification of molecular markers linked to the traits of interest detected 233 marker-trait associations for 10 RSA traits and grouped them in 82 genome regions named marker-train association quantitative trait loci (MTA-QTLs). Our results support the use of ancient local germplasm to widen the genetic background for root traits in breeding programs.

scholarly journals Genetic Bases of Root System Architecture in Durum Wheat Seedlings

2016 ◽  
Vol 10 (1) ◽  
pp. 25-28
Author(s):  
Ghasemali Nazemi ◽  
Silvio Salvi
Keyword(s):  
Durum Wheat ◽  
Root System ◽  
System Architecture ◽  
Root Length ◽  
Recombinant Inbred Lines ◽  
Primary Root ◽  
Root System Architecture ◽  
Total Root Length ◽  
Wheat Seedlings ◽  
Significant Difference

Root system architecture (RSA) traits are characterized by constitutive genetic inheritance components which may enable to predict the root phenotypes based on genetic information. The research presented in this study aimed at the identification of traits and genes that underlie root system architecture (RSA) in a population of 176 recombinant inbred lines (RILs) derived from the cross between two durum wheat cvs. Meridiana and Claudio, in order to eventually contribute to the genetic improvement of this species. The following seedling-stage RSA traits were: primary root length, seminal root length, total root length, diameter of primary and seminal roots. Results of ANOVA showed a significant difference among durum wheat cultivars for all traits and the largest heritability was observed for total root length (30.7%). In total, 14 novel QTLs for RSA traits were identified, and both parents contributed favorable alleles to the population.International Journal of Life Sciences 10 (1) : 2016; 25-28

scholarly journals Soybean Root System Architecture Trait Study through Genotypic, Phenotypic, and Shape-Based Clusters

2020 ◽  
Vol 2020 ◽  
pp. 1-23 ◽  
Author(s):  
Kevin G. Falk ◽  
Talukder Zaki Jubery ◽  
Jamie A. O’Rourke ◽  
Arti Singh ◽  
Soumik Sarkar ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Root System ◽  
System Architecture ◽  
Fourier Transformation ◽  
Root Traits ◽  
Root System Architecture ◽  
Neural Net ◽  
Measurement Variation ◽  
Transformation Methods ◽  
Morphology Parameters

We report a root system architecture (RSA) traits examination of a larger scale soybean accession set to study trait genetic diversity. Suffering from the limitation of scale, scope, and susceptibility to measurement variation, RSA traits are tedious to phenotype. Combining 35,448 SNPs with an imaging phenotyping platform, 292 accessions (replications=14) were studied for RSA traits to decipher the genetic diversity. Based on literature search for root shape and morphology parameters, we used an ideotype-based approach to develop informative root (iRoot) categories using root traits. The RSA traits displayed genetic variability for root shape, length, number, mass, and angle. Soybean accessions clustered into eight genotype- and phenotype-based clusters and displayed similarity. Genotype-based clusters correlated with geographical origins. SNP profiles indicated that much of US origin genotypes lack genetic diversity for RSA traits, while diverse accession could infuse useful genetic variation for these traits. Shape-based clusters were created by integrating convolution neural net and Fourier transformation methods, enabling trait cataloging for breeding and research applications. The combination of genetic and phenotypic analyses in conjunction with machine learning and mathematical models provides opportunities for targeted root trait breeding efforts to maximize the beneficial genetic diversity for future genetic gains.

scholarly journals Identification of QTL and Underlying Genes for Root System Architecture associated with Nitrate Nutrition in Hexaploid Wheat

2019 ◽  
Author(s):  
Marcus Griffiths ◽  
Jonathan A. Atkinson ◽  
Laura-Jayne Gardiner ◽  
Ranjan Swarup ◽  
Michael P. Pound ◽  
...  
Keyword(s):  
Root System ◽  
System Architecture ◽  
Root System Architecture ◽  
Plant Phenotyping ◽  
Adaptive Responses ◽  
Potential Yield ◽  
Seedling Traits ◽  
Nitrate Treatment ◽  
Root Angle ◽  
Trait Locus

AbstractThe root system architecture (RSA) of a crop has a profound effect on the uptake of nutrients and consequently the potential yield. However, little is known about the genetic basis of RSA and resource adaptive responses in wheat (Triticum aestivum L.). Here, a high-throughput germination paper plant phenotyping system was used to identify seedling traits in a wheat doubled haploid mapping population, Savannah × Rialto. Significant genotypic and nitrate-N treatment variation was found across the population for seedling traits with distinct trait grouping for root size-related traits and root distribution-related traits. Quantitative trait locus (QTL) analysis identified a total of 59 seedling trait QTLs. Across two nitrate treatments, 27 root QTLs were specific to the nitrate treatment. Transcriptomic analyses for one of the QTLs on chromosome 2D found under low nitrate conditions was pursued revealing gene enrichment in N-related biological processes and 17 candidate up-regulated genes with possible involvement in a root angle response. Together, these findings provide genetic insight into root system architecture and plant adaptive responses to nitrate and provide targets that could help improve N capture in wheat.

scholarly journals Exploiting natural variation in crown root traits via genome-wide association studies in maize

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Houmiao Wang ◽  
Xiao Tang ◽  
Xiaoyi Yang ◽  
Yingying Fan ◽  
Yang Xu ◽  
...  
Keyword(s):  
Root System ◽  
Candidate Genes ◽  
System Architecture ◽  
Root Traits ◽  
Root System Architecture ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Crown Root ◽  
Genome Wide ◽  
Maize Varieties

Abstract Background Root system architecture (RSA), which is determined by the crown root angle (CRA), crown root diameter (CRD), and crown root number (CRN), is an important factor affecting the ability of plants to obtain nutrients and water from the soil. However, the genetic mechanisms regulating crown root traits in the field remain unclear. Methods In this study, the CRA, CRD, and CRN of 316 diverse maize inbred lines were analysed in three field trials. Substantial phenotypic variations were observed for the three crown root traits in all environments. A genome-wide association study was conducted using two single-locus methods (GLM and MLM) and three multi-locus methods (FarmCPU, FASTmrMLM, and FASTmrEMMA) with 140,421 SNP. Results A total of 38 QTL including 126 SNPs were detected for CRA, CRD, and CRN. Additionally, 113 candidate genes within 50 kb of the significant SNPs were identified. Combining the gene annotation information and the expression profiles, 3 genes including GRMZM2G141205 (IAA), GRMZM2G138511 (HSP) and GRMZM2G175910 (cytokinin-O-glucosyltransferase) were selected as potentially candidate genes related to crown root development. Moreover, GRMZM2G141205, encoding an AUX/IAA transcriptional regulator, was resequenced in all tested lines. Five variants were identified as significantly associated with CRN in different environments. Four haplotypes were detected based on these significant variants, and Hap1 has more CRN. Conclusions These findings may be useful for clarifying the genetic basis of maize root system architecture. Furthermore, the identified candidate genes and variants may be relevant for breeding new maize varieties with root traits suitable for diverse environmental conditions.

Determination of root system architecture variation of drought adapted sorghum genotypes using high throughput root phenotyping

Rhizosphere ◽  
2021 ◽  
pp. 100370
Author(s):  
Habtamu Demelash ◽  
Taye Tadesse ◽  
Temesegen Menamo ◽  
Ahadu Menzir
Keyword(s):  
Root System ◽  
High Throughput ◽  
System Architecture ◽  
Root System Architecture ◽  
Root Phenotyping ◽  
Sorghum Genotypes

scholarly journals Affordable and robust phenotyping framework to analyse root system architecture of soil-grown plants

2019 ◽  
Author(s):  
Thibaut Bontpart ◽  
Cristobal Concha ◽  
Valerio Giuffrida ◽  
Ingrid Robertson ◽  
Kassahun Admkie ◽  
...  
Keyword(s):  
Root System ◽  
Low Income ◽  
System Architecture ◽  
Imaging System ◽  
Soil Surface ◽  
Root Systems ◽  
Root System Architecture ◽  
Low Income Countries ◽  
Climate Resilience ◽  
Root Phenotyping

AbstractThe analysis of root system growth, root phenotyping, is important to inform efforts to enhance plant resource acquisition from soils. However, root phenotyping remains challenging due to soil opacity and requires systems that optimize root visibility and image acquisition. Previously reported systems require costly and bespoke materials not available in most countries, where breeders need tools to select varieties best adapted to local soils and field conditions. Here, we present an affordable soil-based growth container (rhizobox) and imaging system to phenotype root development in greenhouses or shelters. All components of the system are made from commodity components, locally available worldwide to facilitate the adoption of this affordable technology in low-income countries. The rhizobox is large enough (~6000 cm2 visible soil) to not restrict vertical root system growth for at least seven weeks after sowing, yet light enough (~21 kg) to be routinely moved manually. Support structures and an imaging station, with five cameras covering the whole soil surface, complement the rhizoboxes. Images are acquired via the Phenotiki sensor interface, collected, stitched and analysed. Root system architecture (RSA) parameters are quantified without intervention. RSA of a dicot (chickpea, Cicer arietinum L.) and a monocot (barley, Hordeum vulgare L.) species, which exhibit contrasting root systems, were analysed. The affordable system is relevant for efforts in Ethiopia and elsewhere to enhance yields and climate resilience of chickpea and other crops for improved food security.Significance StatementAn affordable system to characterize root system architecture of soil-grown plants was developed. Using commodity components, this will enable local efforts world-wide to breed for enhanced root systems.

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