RSAtrace3D: robust vectorization software for measuring monocot root system architecture

Active breeding programs specifically for root system architecture (RSA) phenotypes remain rare; however, breeding for branch and taproot types in the perennial crop alfalfa is ongoing. Phenotyping in this and other crops for active RSA breeding has mostly used visual scoring of specific traits or subjective classification into different root types. While image-based methods have been developed, translation to applied breeding is limited. This research is aimed at developing and comparing image-based RSA phenotyping methods using machine and deep learning algorithms for objective classification of 617 root images from mature alfalfa plants collected from the field to support the ongoing breeding efforts. Our results show that unsupervised machine learning tends to incorrectly classify roots into a normal distribution with most lines predicted as the intermediate root type. Encouragingly, random forest and TensorFlow-based neural networks can classify the root types into branch-type, taproot-type, and an intermediate taproot-branch type with 86% accuracy. With image augmentation, the prediction accuracy was improved to 97%. Coupling the predicted root type with its prediction probability will give breeders a confidence level for better decisions to advance the best and exclude the worst lines from their breeding program. This machine and deep learning approach enables accurate classification of the RSA phenotypes for genomic breeding of climate-resilient alfalfa.

AimsSelection for optimal root system architecture (RSA) is important to ensure genetic gains in the sustainable production of wheat (Triticum aestivum L.). Here we examine the hypothesis that past wheat breeding has led to changes in RSA and that future breeding efforts can focus directly on RSA to improve adaptation to target environments.MethodsWe conducted field trials using diverse wheat varieties, including modern and historic UK varieties and non-UK landraces, tested under contrasting tillage regimes (non-inversion tillage versus conventional ploughing) for two trial years or different seeding rates (standard versus high rate) for one trial year. We used field excavation, washing and measurement of root crowns (‘shovelomics’) to characterise RSA traits, including: numbers of seminal, crown and nodal roots per plant, and crown root growth angle.ResultsWe found differences among genotypes for all root traits. Modern varieties generally had fewer roots per plant than historic varieties. On average, there were fewer crown roots and root angles were wider under shallow non-inversion tillage compared with conventional ploughing. Crown root numbers per plant also tended to be smaller at a high seeding rate compared with the standard. There were significant genotype-by-year, genotype-by-tillage and genotype-by-seeding-rate interactions for many root traits.ConclusionsSmaller root systems are likely to be a result of past selection that facilitated historical yield increases by reducing below-ground competition within the crop. The effects of crop management practices on RSA depend on genotype, suggesting that future breeding could select for improved RSA traits in resource-efficient farming systems.

Roots are the main sensing organ, initiating multiple signaling pathways in response to abiotic factors, including nutrients, drought, and salt stress. A focus on improving the root system architecture is a key strategy to mitigate these stresses in wheat crop. In the present study, a diversity panel comprising indigenous landraces and historical cultivars from Pakistan was characterized for the root system architecture (RSA) and important loci were identified using a genome-wide association study (GWAS). RSA of the diversity panel was characterized 30 days after sowing in brunch tubes, and root images were taken. A high-throughput root imaging analysis using Rhizovision software was performed by setting the scale to extract the eight RSA traits and four plant biomass-related traits. GWAS identified 323 association signals for 12 root and biomass traits present on all wheat chromosomes, while the most important and reliable genetic loci (based on pleotropic loci and candidate genes) were identified on chromosomes 2A, 2B, 5A, 5D, 6A, 7B, and 7D for RSA. SNP annotation and transcriptome profiling identified nine candidate genes regulating the RSA and plant biomass traits, including ROOTLESS WITH UNDETECTABLE MERISTEM1, MYB TRANSCRIPTION FACTOR4, BRASSINOSTEROID INSENSITIVE1, SLENDER RICE1, AUXIN-RESPONSIVE FACTOR25, SCARECROW, NARROW LEAF2, PIN-FORMED1 AND PHOSPHATE TRANSCRIPTION FACTOR1. This study provided pre-breeding information for deep-rooting genotypes and associated markers that will accelerate the incorporation of such traits in breeding.

The root system architecture (RSA) determines plant growth and yield. The characterization of optimal RSA and discovery of genetic loci or candidate genes that control root traits are therefore important research goals. However, the hidden nature of the root system makes it difficult to perform nondestructive, rapid analyses of RSA. In this study, we developed an automated, nondestructive, high-throughput root phenotyping platform (Root-HTP) and a corresponding data processing pipeline for efficient, large-scale characterization of wheat (Triticum aestivum L.) RSA. This system is capable of tracking root growth dynamics and RSA variation across all wheat developmental stages. In situ phenotyping using Root-HTP extracted 47 RSA traits, including 33 novel traits in wheat and 23 novel traits in other crops. We used root trait data from the phenotyping system and yield trait data to conduct a genome-wide association study (GWAS) of 155 wheat accessions, which identified 2,650 SNPs and 233 quantitative trait loci (QTLs) associated with aspects of root architecture. The candidate gene TaMYB93 was detected in a QTL for root tortuosity, and EMS mutants confirmed its effect on RSA in wheat. We explored the relationship between root- and yield-related traits and identified 20 root-related QTLs that were also associated with yield traits. Furthermore, we have built a predictive model for wheat yield based on 18 RSA traits and propose a parsimonious RSA ideotype associated with high yields. The data generated from this study provide insight into the genetic architecture of wheat RSA and support for RSA ideotype-based wheat breeding and yield prediction.

Characterising shoot and root system trait variability and contribution to genotypic variability in juvenile cassava (Manihot esculenta Crantz) plants

In the emerging new agricultural context, a drastic reduction in fertilizer usage is required. A promising way to maintain high crop yields while reducing fertilizer inputs is to breed new varieties with optimized root system architecture (RSA), designed to reach soil resources more efficiently. This relies on identifying key traits that underlie genotypic variability and plasticity of RSA in response to nutrient availability. The aim of our study was to characterize the RSA plasticity in response to nitrogen limitation of a set of contrasted oilseed rape genotypes, by using the ArchiSimple model parameters as screening traits. Eight accessions of Brassica napus were grown in long tubes in the greenhouse, under two contrasting levels of nitrogen availability. After plant excavation, roots were scanned at high resolution. Six RSA traits relative to root diameter, elongation rate and branching were measured, as well as nine growth and biomass allocation traits. The plasticity of each trait to nitrogen availability was estimated. Nitrogen-limited plants were characterized by a strong reduction in total biomass and leaf area. Even if the architecture traits were shown to be less plastic than allocation traits, significant nitrogen and genotype effects were highlighted on each RSA trait, except the root minimal diameter. Thus, the RSA of nitrogen-limited plants was primarily characterised by a reduced lateral root density, a smaller primary root diameter, associated with a stronger root dominance. Among the RSA traits measured, the inter-branch distance showed the highest plasticity with a level of 70%, in the same range as the most plastic allocation traits. This work suggests that lateral root density plays the key role in the adaptation of the root system to nitrogen availability and highlights inter-branch distance as a major target trait for breeding new varieties, better adapted to low input systems.

BackgroundTraditional rice (Oryza sativa) varieties are valuable resources for the improvement of drought resistance. qDTY3.2 is a drought-yield quantitative trait locus that was identified in a population derived from the traditional variety Moroberekan and the drought-susceptible variety Swarna. In this study, our aim was to characterize the physiological mechanisms associated with qDTY3.2. Our approach was to phenotype fifteen BC2F3:4 lines for shoot and root drought resistance-related traits as compared to Swarna in the field under well-watered and drought stress conditions. Four BC2F3:4 lines contrasting for yield under drought were selected for detailed characterization of shoot morphology, water use related traits, flowering time and root system architecture in the field as well as in controlled environments (lysimeters in a greenhouse, and gel imaging platform in a growth chamber).ResultsAcross five field experiments, grain yield correlated significantly with root growth along the soil profile, flowering time, and canopy temperature under drought conditions. The four selected BC2F3:4 lines showed earlier flowering time, reduced distribution of root growth to shallow soil layers which resulted in lower water uptake (between 0 and 30 cm) and drought-induced increased distribution of root growth to deep soil layers (between 30 and 60 cm) as compared to Swarna in the field. Root system architecture phenotypes were confirmed in whole root systems in lysimeters, and corresponded to higher numbers of root tips in a gel imaging platform, highlighting the potential stability of some root traits across different growth stages and systems.ConclusionsWe conclude that earlier flowering time, reduced shallow root growth, and drought-induced increased deep root growth are associated with the presence of qDTY3.2 since these phenotypes were consistently observed in the selected QTL lines with full introgression of qDTY3.2. We hypothesize that the qDTY3.2 associated RSA phenotypes led to better use of water and metabolic resources which, combined with earlier flowering time, improved yield under drought.

Understanding the root system architecture (RSA) is necessary for elucidation of plant growth patterns in response to environmental stimuli and hormonal signals. Ethylene, a gaseous phytohormone, modulates root developmental plasticity, including primary root elongation, lateral root formation, and root hair growth. We present a protocol for mapping ethylene-specific RSA traits in Arabidopsis thaliana using a hydroponic growth system. Arabidopsis seedlingsgrow on a polypropylene mesh supported by polycarbonate wedges in a magenta box-based setup. Wetreat seedlings with ethylene or its precursor, thenspread root system on agar plates with an art brush. High-resolution images are recorded and analyzed withfree ImageJ software. This protocol allows detailed RSA analysisunder controlled ethylene treatments and can be adapted for other plant species.

Root system architecture (RSA) is an essential characteristic for efficient water and nutrient absorption in terrestrial plants; its plasticity enables plants to respond to different soil environments. Better understanding of root plasticity is important in developing stress-tolerant crops. Non-invasive techniques that can measure roots in soils nondestructively, such as X-ray computed tomography (CT), are useful to evaluate RSA plasticity. However, although RSA plasticity can be measured by tracking individual root growth, only a few methods are available for tracking individual roots from time-series three-dimensional (3D) images. We developed a semi-automatic workflow that tracks individual root growth by vectorizing RSA from time-series 3D images via two major steps. The first step involves 3D alignment of the time-series RSA images by iterative closest point registration with point clouds generated by high-intensity particles in potted soils. This alignment ensures that the time-series RSA images overlap. The second step consists of backward prediction of vectorization, which is based on the phenomenon that the root length of the RSA vector at the earlier time point is shorter than that at the last time point. In other words, when CT scanning is performed at time point A and again at time point B for the same pot, the CT data and RSA vectors at time points A and B will almost overlap, but not where the roots have grown. We assumed that given a manually created RSA vector at the last time point of the time series, all RSA vectors except those at the last time point could be automatically predicted by referring to the corresponding RSA images. Using 21 time-series CT volumes of a potted plant of upland rice (Oryza sativa), this workflow revealed that the root elongation speed increased with age. Compared with a workflow that does not use backward prediction, the workflow with backward prediction reduced the manual labor time by 95%. We developed a workflow to efficiently generate time-series RSA vectors from time-series X-ray CT volumes. We named this workflow 'RSAtrace4D' and are confident that it can be applied to the time-series analysis of RSA development and plasticity.

Influences of cerium oxide nanoparticles and salinity on common bean (Phaseolus vulgaris) growth, physiology, and root system architectural and anatomical traits

Root system architecture is a resource with untapped potential for agricultural improvements. The presented data describes the root system architecture of water efficient maize lines to different nitrogen fertilizer rates and plant density at two locations in North West Province of South Africa. The experiment was carried out during the 2015/16 and 2016/17 planting seasons. The root system architecture traits brace root angle, brace root number, brace root branch depth, crown root angle, crown root number, crown root branch depth and number of lateral roots were scored with the aid of shovelomics score board. ANOVA was used to analyze the data set and means separated with DMRT (p ≤ 0.05).The regression analysis was used to determine the relationship among nitrogen fertilizer and root architecture system.

Water uptake by roots is a key adaptation of plants to aerial life. Water uptake depends on root system architecture (RSA) and tissue hydraulic properties that, together, shape the root hydraulic architecture. This work investigates how the interplay between conductivities along radial (e.g. aquaporins) and axial (e.g. xylem vessels) pathways determines the water transport properties of highly branched RSAs as found in adult Arabidopsis (Arabidopsis thaliana) plants. A hydraulic model named HydroRoot was developed, based on multi-scale tree graph representations of RSAs. Root water flow was measured by the pressure chamber technique after successive cuts of a same root system from the tip toward the base. HydroRoot model inversion in corresponding RSAs allowed us to concomitantly determine radial and axial conductivities, providing evidence that the latter is often overestimated by classical evaluation based on the Hagen–Poiseuille law. Organizing principles of Arabidopsis primary and lateral root growth and branching were determined and used to apply the HydroRoot model to an extended set of simulated RSAs. Sensitivity analyses revealed that water transport can be co-limited by radial and axial conductances throughout the whole RSA. The number of roots that can be sectioned (intercepted) at a given distance from the base was defined as an accessible and informative indicator of RSA. The overall set of experimental and theoretical procedures was applied to plants mutated in ESKIMO1 and previously shown to have xylem collapse. This approach will be instrumental to dissect the root water transport phenotype of plants with intricate alterations in root growth or transport functions.

The root system is critical for the survival of nearly all land plants and a key target for improving abiotic stress tolerance, nutrient accumulation, and yield in crop species. Although many methods of root phenotyping exist, within field studies, one of the most popular methods is the extraction and measurement of the upper portion of the root system, known as the root crown, followed by trait quantification based on manual measurements or 2D imaging. However, 2D techniques are inherently limited by the information available from single points of view. Here, we used X-ray computed tomography to generate highly accurate 3D models of maize root crowns and created computational pipelines capable of measuring 71 features from each sample. This approach improves estimates of the genetic contribution to root system architecture and is refined enough to detect various changes in global root system architecture over developmental time as well as more subtle changes in root distributions as a result of environmental differences. We demonstrate that root pulling force, a high-throughput method of root extraction that provides an estimate of root mass, is associated with multiple 3D traits from our pipeline. Our combined methodology can therefore be used to calibrate and interpret root pulling force measurements across a range of experimental contexts or scaled up as a stand-alone approach in large genetic studies of root system architecture.

BackgroundPhosphorus (P) is an important in ensuring plant morphogenesis and grain quality, therefore an efficient root system is crucial for P-uptake. Identification of useful loci for root morphological and P uptake related traits at seedling stage is important for wheat breeding. The aims of this study were to evaluate phenotypic diversity of Yangmai 16/Zhongmai 895 derived doubled haploid (DH) population for root system architecture (RSA) and biomass related traits (BRT) in different P treatments at seedling stage using hydroponic culture, and to identify QTL using 660 K SNP array based high-density genetic map.ResultsAll traits showed significant variations among the DH lines with high heritabilities (0.76 to 0.91) and high correlations (r = 0.59 to 0.98) among all traits. Inclusive composite interval mapping (ICIM) identified 34 QTL with 4.64–20.41% of the phenotypic variances individually, and the log of odds (LOD) values ranging from 2.59 to 10.43. Seven QTL clusters (C1 to C7) were mapped on chromosomes 3DL, 4BS, 4DS, 6BL, 7AS, 7AL and 7BL, cluster C5 on chromosome 7AS (AX-109955164 - AX-109445593) with pleiotropic effect played key role in modulating root length (RL), root tips number (RTN) and root surface area (ROSA) under low P condition, with the favorable allele from Zhongmai 895.ConclusionsThis study carried out an imaging pipeline-based rapid phenotyping of RSA and BRT traits in hydroponic culture. It is an efficient approach for screening of large populations under different nutrient conditions. Four QTL on chromosomes 6BL (2) and 7AL (2) identified in low P treatment showed positive additive effects contributed by Zhongmai 895, indicating that Zhongmai 895 could be used as parent for P-deficient breeding. The most stable QTL QRRS.caas-4DS for ratio of root to shoot dry weight (RRS) harbored the stable genetic region with high phenotypic effect, and QTL clusters on 7A might be used for speedy selection of genotypes for P-uptake. SNPs closely linked to QTLs and clusters could be used to improve nutrient-use efficiency.

Chapter 3 - Molecular advances in plant root system architecture response and redesigning for improved performance under unfavorable environments