From phenotyping to genetic mapping: identifying water-stress adaptations in legume root traits.

BACKGROUND: Climate change induces perturbation in the global water cycle, profoundly impacting water availability for agriculture and therefore global food security. Water stress encompasses both drought (i.e. water scarcity) that causes the drying of soil and subsequent plant desiccation, and flooding, which results in excess soil water and hypoxia for plant roots. Terrestrial plants have evolved diverse mechanisms to cope with soil water stress, with the root system serving as the first line of defense. The responses of roots to water stress can involve both structural and physiological changes, and their plasticity is a vital feature of these adaptations. Genetic methodologies have been extensively employed to identify numerous genetic loci linked to water stress-responsive root traits. This knowledge is immensely important for developing crops with optimal root systems that enhance yield and guarantee food security under water stress conditions. RESULTS: This review focused on the latest insights into modifications in the root system architecture and anatomical features of legume roots in response to drought and flooding stresses. Special attention was given to recent breakthroughs in understanding the genetic underpinnings of legume root development under water stress. The review also described various root phenotyping techniques and examples of their applications in different legume species. Finally, the prevailing challenges and prospective research avenues in this dynamic field as well as the potential for using root system architecture as a breeding target are discussed. CONCLUSIONS: This review integrated the latest knowledge of the genetic components governing the adaptability of legume roots to water stress, providing a reference for using root traits as the new crop breeding targets.

Three-dimensional image analysis specifies the root distribution for drought avoidance in the early growth stage of rice.

BACKGROUND AND AIMS: Root system architecture (RSA) plays a key role in plant adaptation to drought, because deep rooting enables better water uptake than shallow rooting under terminal drought. Understanding RSA during early plant development is essential for improving crop yields, because early drought can affect subsequent shoot growth. Herein, we demonstrate that root distribution in the topsoil significantly impacts shoot growth during the early stages of rice (Oryza sativa) development under drought, as assessed through three-dimensional image analysis. METHODS: We used 109 F12 recombinant inbred lines obtained from a cross between shallow-rooting lowland rice and deep-rooting upland rice, representing a population with diverse RSA. We applied a moderate drought during the early development of rice grown in a plant pot (25 cm in height) by stopping irrigation 14 days after sowing. Time-series RSA at 14, 21 and 28 days after sowing was visualized by X-ray computed tomography and, subsequently, compared between drought and well-watered conditions. After this analysis, we investigated drought-avoidant RSA further by testing 20 randomly selected recombinant inbred lines in drought conditions. KEY RESULTS: We inferred the root location that most influences shoot growth using a hierarchical Bayes approach: the root segment depth that impacted shoot growth positively ranged between 1.7 and 3.4 cm in drought conditions and between 0.0 and 1.7 cm in well-watered conditions. Drought-avoidant recombinant inbred lines had a higher root density in the lower layers of the topsoil compared with the others. CONCLUSIONS: Fine classification of soil layers using three-dimensional image analysis revealed that increasing root density in the lower layers of the topsoil, rather than in the subsoil, is advantageous for drought avoidance during the early growth stage of rice.

Evidence for root adaptation to a spatially discontinuous water availability in the absence of external water potential gradients.

We hereby show that root systems adapt to a spatially discontinuous pattern of water availability even when the gradients of water potential across them are vanishingly small. A paper microfluidic approach allowed us to expose the entire root system of Brassica rapa plants to a square array of water sources, separated by dry areas. Gradients in the concentration of water vapor across the root system were as small as 10-4⋅mM⋅m-1 (∼4 orders of magnitude smaller than in conventional hydrotropism assays). Despite such minuscule gradients (which greatly limit the possible influence of the well-understood gradient-driven hydrotropic response), our results show that 1) individual roots as well as the root system as a whole adapt to the pattern of water availability to maximize access to water, and that 2) this adaptation increases as water sources become more rare. These results suggest that either plant roots are more sensitive to water gradients than humanmade water sensors by 3-5 orders of magnitude, or they might have developed, like other organisms, mechanisms for water foraging that allow them to find water in the absence of an external gradient in water potential.

Melatonin mitigates cadmium toxicity by promoting root development, delaying root senescence, and regulating cadmium transport in cotton.

Cd ions are absorbed and transported from the soil by crop roots, which are the first organ to be exposed to Cd. This results in an increase in cadmium ions in crops, significantly affecting crop growth and yield. Exogenous melatonin (MT) can help reduce cadmium (Cd) stress in cotton, but the specific contribution of roots to this process remains unclear. In order to address this knowledge gap, an in-situ root phenotyping study was conducted to investigate the the phenotype and lifespan of roots under cadmium stress (Cd) and melatonin treatment (Cd + MT). The results showed that MT alleviated the decreases in plant height, leaf area, SPAD value, stem diameter, stomatal conductance and net photosynthetic rate under Cd stress, which further promoted the biomass accumulation in various cotton organs. What is more, the Cd + MT treatment increased root volume, surface area, and length under Cd stress by 25.63 %, 10.58 %, and 21.89 %, respectively, compared with Cd treatment. Interestingly, compared to Cd treatment, Cd + MT treatment also significantly extended the lifespan of roots and root hairs by 6.68 days and 2.18 days, respectively. In addition, Cd + MT treatment reduced the transport of Cd from roots to shoots, particularly to bolls, and decreased the Cd bioconcentration factor in bolls by 61.17 %, compared to Cd treatment. In conclusion, these findings show that applying MT externally helps reduce Cd stress by delaying root senescence, promoting root development and regulating Cd transport. This method can be an effective approach to managing Cd stress in cotton.

Early Identification of Root Damages Caused by Western Corn Rootworms Using a Minimally Invasive Root Phenotyping Robot-MISIRoot.

Western corn rootworm (WCR) is one of the most devastating corn rootworm species in North America because of its ability to cause severe production loss and grain quality damage. To control the loss, it is important to identify the infection of WCR at an early stage. Because the root system is the earliest feeding source of the WCR at the larvae stage, assessing the direct damage in the root system is crucial to achieving early detection. Most of the current methods still necessitate uprooting the entire plant, which could cause permanent destruction and a loss of the original root's structural information. To measure the root damages caused by WCR non-destructively, this study utilized MISIRoot, a minimally invasive and in situ automatic plant root phenotyping robot to collect not only high-resolution images but also 3D positions of the roots without uprooting. To identify roots in the images and to study how the damages were distributed in different types of roots, a deep convolution neural network model was trained to differentiate the relatively thick and thin roots. In addition, a color camera was used to capture the above-ground morphological features, such as the leaf color, plant height, and side-view leaf area. To check if the plant shoot had any visible symptoms in the inoculated group compared to the control group, several vegetation indices were calculated based on the RGB color. Additionally, the shoot morphological features were fed into a PLS-DA model to differentiate the two groups. Results showed that none of the above-ground features or models output a statistically significant difference between the two groups at the 95% confidence level. On the contrary, many of the root structural features measured using MISIRoot could successfully differentiate the two groups with the smallest t-test p-value of 1.5791 × 10-6. The promising outcomes were solid proof of the effectiveness of MISIRoot as a potential solution for identifying WCR infestations before the plant shoot showed significant symptoms.

Optimisation of root traits to provide enhanced ecosystem services in agricultural systems: A focus on cover crops.

Roots are the interface between the plant and the soil and play a central role in multiple ecosystem processes. With intensification of agricultural practices, rhizosphere processes are being disrupted and are causing degradation of the physical, chemical and biotic properties of soil. However, cover crops, a group of plants that provide ecosystem services, can be utilised during fallow periods or used as an intercrop to restore soil health. The effectiveness of ecosystem services provided by cover crops varies widely as very little breeding has occurred in these species. Improvement of ecosystem service performance is rarely considered as a breeding trait due to the complexities and challenges of belowground evaluation. Advancements in root phenotyping and genetic tools are critical in accelerating ecosystem service improvement in cover crops. In this study, we provide an overview of the range of belowground ecosystem services provided by cover crop roots: (1) soil structural remediation, (2) capture of soil resources and (3) maintenance of the rhizosphere and building of organic matter content. Based on the ecosystem services described, we outline current and promising phenotyping technologies and breeding strategies in cover crops that can enhance agricultural sustainability through improvement of root traits.

Plant Root Phenotyping Using Deep Conditional GANs and Binary Semantic Segmentation.

This paper develops an approach to perform binary semantic segmentation on Arabidopsis thaliana root images for plant root phenotyping using a conditional generative adversarial network (cGAN) to address pixel-wise class imbalance. Specifically, we use Pix2PixHD, an image-to-image translation cGAN, to generate realistic and high resolution images of plant roots and annotations similar to the original dataset. Furthermore, we use our trained cGAN to triple the size of our original root dataset to reduce pixel-wise class imbalance. We then feed both the original and generated datasets into SegNet to semantically segment the root pixels from the background. Furthermore, we postprocess our segmentation results to close small, apparent gaps along the main and lateral roots. Lastly, we present a comparison of our binary semantic segmentation approach with the state-of-the-art in root segmentation. Our efforts demonstrate that cGAN can produce realistic and high resolution root images, reduce pixel-wise class imbalance, and our segmentation model yields high testing accuracy (of over 99%), low cross entropy error (of less than 2%), high Dice Score (of near 0.80), and low inference time for near real-time processing.

Digging roots is easier with AI.

The scale of root quantification in research is often limited by the time required for sampling, measurement, and processing samples. Recent developments in convolutional neural networks (CNNs) have made faster and more accurate plant image analysis possible, which may significantly reduce the time required for root measurement, but challenges remain in making these methods accessible to researchers without an in-depth knowledge of machine learning. We analyzed root images acquired from three destructive root samplings using the RootPainter CNN software that features an interface for corrective annotation for easier use. Root scans with and without non-root debris were used to test if training a model (i.e. learning from labeled examples) can effectively exclude the debris by comparing the end results with measurements from clean images. Root images acquired from soil profile walls and the cross-section of soil cores were also used for training, and the derived measurements were compared with manual measurements. After 200 min of training on each dataset, significant relationships between manual measurements and RootPainter-derived data were noted for monolith (R2=0.99), profile wall (R2=0.76), and core-break (R2=0.57). The rooting density derived from images with debris was not significantly different from that derived from clean images after processing with RootPainter. Rooting density was also successfully calculated from both profile wall and soil core images, and in each case the gradient of root density with depth was not significantly different from manual counts. Differences in root-length density (RLD) between crops with contrasting root systems were captured using automatic segmentation at soil profiles with high RLD (1-5 cm cm-3) as well with low RLD (0.1-0.3 cm cm-3). Our results demonstrate that the proposed approach using CNN can lead to substantial reductions in root sample processing workloads, increasing the potential scale of future root investigations.

Root phenotyping: important and minimum information required for root modeling in crop plants.

As plants cannot relocate, they require effective root systems for water and nutrient uptake. Root development plasticity enables plants to adapt to different environmental conditions. Research on improvements in crop root systems is limited in comparison with that in shoots as the former are difficult to image. Breeding more effective root systems is proposed as the "second green revolution". There are several recent publications on root system architecture (RSA), but the methods used to analyze the RSA have not been standardized. Here, we introduce traditional and current root-imaging methods and discuss root structure phenotyping. Some important root structures have not been standardized as roots are easily affected by rhizosphere conditions and exhibit greater plasticity than shoots; moreover, root morphology significantly varies even in the same genotype. For these reasons, it is difficult to define the ideal root systems for breeding. In this review, we introduce several types of software to analyze roots and identify important root parameters by modeling to simplify the root system characterization. These parameters can be extracted from photographs captured in the field. This modeling approach is applicable to various legacy root data stored in old or unpublished formats. Standardization of RSA data could help estimate root ideotypes.

MyROOT: a method and software for the semiautomatic measurement of primary root length in Arabidopsis seedlings.

Root analysis is essential for both academic and agricultural research. Despite the great advances in root phenotyping and imaging, calculating root length is still performed manually and involves considerable amounts of labor and time. To overcome these limitations, we developed MyROOT, a software for the semiautomatic quantification of root growth of seedlings growing directly on agar plates. Our method automatically determines the scale from the image of the plate, and subsequently measures the root length of the individual plants. To this aim, MyROOT combines a bottom-up root tracking approach with a hypocotyl detection algorithm. At the same time as providing accurate root measurements, MyROOT also significantly minimizes the user intervention required during the process. Using Arabidopsis, we tested MyROOT with seedlings from different growth stages and experimental conditions. When comparing the data obtained from this software with that of manual root measurements, we found a high correlation between both methods (R2  = 0.997). When compared with previous developed software with similar features (BRAT and EZ-Rhizo), MyROOT offered an improved accuracy for root length measurements. Therefore, MyROOT will be of great use to the plant science community by permitting high-throughput root length measurements while saving both labor and time.

Exploiting natural variation in root system architecture via genome-wide association studies.

Root growth and development has become an important research topic for breeders and researchers based on a growing need to adapt plants to changing and more demanding environmental conditions worldwide. Over the last few years, genome-wide association studies (GWASs) became an important tool to identify the link between traits in the field and their genetic background. Here we give an overview of the current literature concerning GWASs performed on root system architecture (RSA) in plants. We summarize which root traits and approaches have been used for GWAS, mentioning their respective success rate towards a successful gene discovery. Furthermore, we zoom in on the current technical hurdles in root phenotyping and GWAS, and discuss future possibilities in this field of research.

Root phenotypes of young wheat plants grown in controlled environments show inconsistent correlation with mature root traits in the field.

Using a field to lab approach, mature deep-rooting traits in wheat were correlated to root phenotypes measured on young plants from controlled conditions. Mature deep-rooting root traits of 20 wheat genotypes at maturity were established via coring in three field trials across 2 years. Field traits were correlated to phenotypes expressed by the 20 genotypes after growth in four commonly used lab screens: (i) soil tubes for root emergence, elongation, length, and branching at four ages to 34 days after sowing (DAS); (ii) paper pouches 7 DAS and (iii) agar chambers for primary root (PR) number and angles at 8 DAS; and (iv) soil baskets for PR and nodal root (NR) number and angle at 42 DAS. Correlations between lab and field root traits (r2=0.45-0.73) were highly inconsistent, with many traits uncorrelated and no one lab phenotype correlating similarly across three field experiments. Phenotypes most positively associated with deep field roots were: longest PR and NR axiles from the soil tube screen at 20 DAS; and narrow PR angle and wide NR angle from soil baskets at 42 DAS. Paper and agar PR angles were positively and significantly correlated to each other, but only wide outer PRs in the paper screen correlated positively to shallower field root traits. NR phenotypes in soil baskets were not predicted by PR phenotypes in any screen, suggesting independent developmental controls and value in measuring both root types in lab screens. Strong temporal and edaphic effects on mature root traits, and a lack of understanding of root trait changes during plant development, are major challenges in creating controlled-environment root screens for mature root traits in the field.

Laser ablation tomography for visualization of root colonization by edaphic organisms.

Soil biota have important effects on crop productivity, but can be difficult to study in situ. Laser ablation tomography (LAT) is a novel method that allows for rapid, three-dimensional quantitative and qualitative analysis of root anatomy, providing new opportunities to investigate interactions between roots and edaphic organisms. LAT was used for analysis of maize roots colonized by arbuscular mycorrhizal fungi, maize roots herbivorized by western corn rootworm, barley roots parasitized by cereal cyst nematode, and common bean roots damaged by Fusarium. UV excitation of root tissues affected by edaphic organisms resulted in differential autofluorescence emission, facilitating the classification of tissues and anatomical features. Samples were spatially resolved in three dimensions, enabling quantification of the volume and distribution of fungal colonization, western corn rootworm damage, nematode feeding sites, tissue compromised by Fusarium, and as well as root anatomical phenotypes. Owing to its capability for high-throughput sample imaging, LAT serves as an excellent tool to conduct large, quantitative screens to characterize genetic control of root anatomy and interactions with edaphic organisms. Additionally, this technology improves interpretation of root-organism interactions in relatively large, opaque root segments, providing opportunities for novel research investigating the effects of root anatomical phenes on associations with edaphic organisms.

Genomic regions responsible for seminal and crown root lengths identified by 2D & 3D root system image analysis.

BACKGROUND: Genetic improvement of root system architecture is a promising approach for improved uptake of water and mineral nutrients distributed unevenly in the soil. To identify genomic regions associated with the length of different root types in rice, we quantified root system architecture in a set of 26 chromosome segment substitution lines derived from a cross between lowland indica rice, IR64, and upland tropical japonica rice, Kinandang Patong, (IK-CSSLs), using 2D & 3D root phenotyping platforms. RESULTS: Lengths of seminal and crown roots in the IK-CSSLs grown under hydroponic conditions were measured by 2D image analysis (RootReader2D). Twelve CSSLs showed significantly longer seminal root length than the recurrent parent IR64. Of these, 8 CSSLs also exhibited longer total length of the three longest crown roots compared to IR64. Three-dimensional image analysis (RootReader3D) for these CSSLs grown in gellan gum revealed that only one CSSL, SL1003, showed significantly longer total root length than IR64. To characterize the root morphology of SL1003 under soil conditions, SL1003 was grown in Turface, a soil-like growth media, and roots were quantified using RootReader3D. SL1003 had larger total root length and increased total crown root length than did IR64, although its seminal root length was similar to that of IR64. The larger TRL in SL1003 may be due to increased crown root length. CONCLUSIONS: SL1003 carries an introgression from Kinandang Patong on the long arm of chromosome 1 in the genetic background of IR64. We conclude that this region harbors a QTL controlling crown root elongation.

A new method for the rapid characterization of root growth and distribution using digital image correlation.

Rapidly determining root growth patterns is biologically important and technically challenging. Current methods focus on direct observation of roots and require destructive excavations or time-consuming root tracing. We developed a novel methodology based on analyzing soil particle displacement, rather than direct observation of roots. This inferred root growth method uses digital image correlation (DIC) analysis, an established and high-throughput method used in many engineering and science disciplines. By applying DIC analyses to repeated images of plants grown in clear window boxes, we produced visually intuitive and quantifiable strain maps, indicating the magnitude and direction of soil movement. From this, we could infer root growth and rapidly quantify root system metrics. Strain measures were closely associated with the spatial distribution of roots and correlated with root length measured using conventional approaches. The method also allowed for the detection of root proliferation in nutrient-enriched soil patches, indicating its suitability for quantifying biological patterns. This novel application of DIC in root biology is effective, scalable, low cost, flexible and complementary to existing technologies. This method offers a new tool for answering questions in plant biology and will be particularly useful in studies involving temporal dynamics of root processes.

Morphological and genetic characterisation of the root system architecture of selected barley recombinant chromosome substitution lines using an integrated phenotyping approach.

Discoveries on the genetics of resource acquisition efficiency are limited by the ability to measure plant roots in sufficient number and with adequate genotypic variability. This paper presents a root phenotyping study that explores ways to combine live imaging and computer algorithms for model-based extraction of root growth parameters. The study is based on a subset of barley Recombinant Chromosome Substitution Lines (RCSLs) and a combinatorial approach was designed for fast identification of the regions of the genome that contribute the most to variations in root system architecture (RSA). Results showed there was a strong genotypic variation in root growth parameters within the set of genotypes studied. The chromosomal regions associated with primary root growth differed from the regions of the genome associated with changes in lateral root growth. The concepts presented here are discussed in the context of identifying root QTL and its potential to assist breeding for novel crops with improved root systems.

From promise to application: root traits for enhanced nutrient capture in rice breeding.

Improving nutrient uptake is an objective in crop breeding, especially in tropical areas where infertile soils dominate and farmers may not have the resources to improve soil fertility through fertilizer application. Scientific endeavors to understand the genetic basis of nutrient acquisition have mostly followed reverse genetic approaches. This has undoubtedly led to improved understanding of basic principles in root development and nutrient transport. However, little evidence suggests that the genes identified are actively utilized in breeding programs, and the bottleneck has been the failure to establish links between allelic variation for identified genes and performance in the field. Screening experiments typically reveal large genotypic variation in performance under nutrient deficiency, strongly suggesting the presence of superior alleles for genes controlling root growth and/or nutrient uptake processes. Progress in sequencing technology has enabled characterizations of allelic variation across whole genomes and an international effort has recently culminated in the sequencing of 3000 rice genomes from the International Rice Research Institute genebank. Queries of the 3000 rice sequence database offer immediate possibilities to assess the extent to which allelic variation exists for candidate genes. By selecting subsets of accessions, allelic effects can be tested, diagnostic markers developed, and new donors identified. Technological and conceptual advances in phenotyping of root traits offer improved possibilities to assure that trait-allele associations are established in ways that link to field performance. Genotype-to-phenotype relationships can thus be predicted and tested with unprecedented precision, facilitating the discovery and transfer of beneficial nutrition-related alleles and associated markers into existing breeding pipelines.

Evolving technologies for growing, imaging and analyzing 3D root system architecture of crop plants.

A plant's ability to maintain or improve its yield under limiting conditions, such as nutrient deficiency or drought, can be strongly influenced by root system architecture (RSA), the three-dimensional distribution of the different root types in the soil. The ability to image, track and quantify these root system attributes in a dynamic fashion is a useful tool in assessing desirable genetic and physiological root traits. Recent advances in imaging technology and phenotyping software have resulted in substantive progress in describing and quantifying RSA. We have designed a hydroponic growth system which retains the three-dimensional RSA of the plant root system, while allowing for aeration, solution replenishment and the imposition of nutrient treatments, as well as high-quality imaging of the root system. The simplicity and flexibility of the system allows for modifications tailored to the RSA of different crop species and improved throughput. This paper details the recent improvements and innovations in our root growth and imaging system which allows for greater image sensitivity (detection of fine roots and other root details), higher efficiency, and a broad array of growing conditions for plants that more closely mimic those found under field conditions.