Root System Architecture Phenotyping Research Articles

Phenotyping Alfalfa (Medicago sativa L.) Root Structure Architecture via Integrating Confident Machine Learning with ResNet-18.

Background: Root system architecture (RSA) is of growing interest in implementing plant improvements with belowground root traits. Modern computing technology applied to images offers new pathways forward to plant trait improvements and selection through RSA analysis (using images to discern/classify root types and traits). However, a major stumbling block to image-based RSA phenotyping is image label noise, which reduces the accuracies of models that take images as direct inputs. To address the label noise problem, this study utilized an artificial intelligence model capable of classifying the RSA of alfalfa (Medicago sativa L.) directly from images and coupled it with downstream label improvement methods. Images were compared with different model outputs with manual root classifications, and confident machine learning (CL) and reactive machine learning (RL) methods were tested to minimize the effects of subjective labeling to improve labeling and prediction accuracies. Results: The CL algorithm modestly improved the Random Forest model's overall prediction accuracy of the Minnesota dataset (1%) while larger gains in accuracy were observed with the ResNet-18 model results. The ResNet-18 cross-population prediction accuracy was improved (~8% to 13%) with CL compared to the original/preprocessed datasets. Training and testing data combinations with the highest accuracies (86%) resulted from the CL- and/or RL-corrected datasets for predicting taproot RSAs. Similarly, the highest accuracies achieved for the intermediate RSA class resulted from corrected data combinations. The highest overall accuracy (~75%) using the ResNet-18 model involved CL on a pooled dataset containing images from both sample locations. Conclusions: ResNet-18 DNN prediction accuracies of alfalfa RSA image labels are increased when CL and RL are employed. By increasing the dataset to reduce overfitting while concurrently finding and correcting image label errors, it is demonstrated here that accuracy increases by as much as ~11% to 13% can be achieved with semi-automated, computer-assisted preprocessing and data cleaning (CL/RL).

Application of an Improved 2-Dimensional High-Throughput Soybean Root Phenotyping Platform to Identify Novel Genetic Variants Regulating Root Architecture Traits.

Nutrient-efficient root system architecture (RSA) is becoming an important breeding objective for generating crop varieties with improved nutrient and water acquisition efficiency. Genetic variants shaping soybean RSA is key in improving nutrient and water acquisition. Here, we report on the use of an improved 2-dimensional high-throughput root phenotyping platform that minimizes background noise by imaging pouch-grown root systems submerged in water. We also developed a background image cleaning Python pipeline that computationally removes images of small pieces of debris and filter paper fibers, which can be erroneously quantified as root tips. This platform was used to phenotype root traits in 286 soybean lines genotyped with 5.4 million single-nucleotide polymorphisms. There was a substantially higher correlation in manually counted number of root tips with computationally quantified root tips (95% correlation), when the background was cleaned of nonroot materials compared to root images without the background corrected (79%). Improvements in our RSA phenotyping pipeline significantly reduced overestimation of the root traits influenced by the number of root tips. Genome-wide association studies conducted on the root phenotypic data and quantitative gene expression analysis of candidate genes resulted in the identification of 3 putative positive regulators of root system depth, total root length and surface area, and root system volume and surface area of thicker roots (DOF1-like zinc finger transcription factor, protein of unknown function, and C2H2 zinc finger protein). We also identified a putative negative regulator (gibberellin 20 oxidase 3) of the total number of lateral roots.

Integrating advancements in root phenotyping and genome-wide association studies to open the root genetics gateway.

Plant adaptation to challenging environmental conditions around the world has made root growth and development an important research area for plant breeders and scientists. Targeted manipulation of root system architecture (RSA) to increase water and nutrient use efficiency can minimize the adverse effects of climate change on crop production. However, phenotyping of RSA is a major bottleneck since the roots are hidden in the soil. Recently the development of 2- and 3D root imaging techniques combined with the genome-wide association studies (GWASs) have opened up new research tools to identify the genetic basis of RSA. These approaches provide a comprehensive understanding of the RSA, by accelerating the identification and characterization of genes involved in root growth and development. This review summarizes the latest developments in phenotyping techniques and GWAS for RSA, which are used to map important genes regulating various aspects of RSA under varying environmental conditions. Furthermore, we discussed about the state-of-the-art image analysis tools integrated with various phenotyping platforms for investigating and quantifying root traits with the highest phenotypic plasticity in both artificial and natural environments which were used for large scale association mapping studies, leading to the identification of RSA phenotypes and their underlying genetics with the greatest potential for RSA improvement. In addition, challenges in root phenotyping and GWAS are also highlighted, along with future research directions employing machine learning and pan-genomics approaches.

Objective Phenotyping of Root System Architecture Using Image Augmentation and Machine Learning in Alfalfa (Medicago sativa L.).

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.

Improving the efficiency of plant root system phenotyping through digitization and automation.

Root system architecture (RSA) determines unevenly distributed water and nutrient availability in soil. Genetic improvement of RSA, therefore, is related to crop production. However, RSA phenotyping has been carried out less frequently than above-ground phenotyping because measuring roots in the soil is difficult and labor intensive. Recent advancements have led to the digitalization of plant measurements; this digital phenotyping has been widely used for measurements of both above-ground and RSA traits. Digital phenotyping for RSA is slower and more difficult than for above-ground traits because the roots are hidden underground. In this review, we summarized recent trends in digital phenotyping for RSA traits. We classified the sample types into three categories: soil block containing roots, section of soil block, and root sample. Examples of the use of digital phenotyping are presented for each category. We also discussed room for improvement in digital phenotyping in each category.

RSAtrace3D: robust vectorization software for measuring monocot root system architecture

BackgroundThe root distribution in the soil is one of the elements that comprise the root system architecture (RSA). In monocots, RSA comprises radicle and crown roots, each of which can be basically represented by a single curve with lateral root branches or approximated using a polyline. Moreover, RSA vectorization (polyline conversion) is useful for RSA phenotyping. However, a robust software that can enable RSA vectorization while using noisy three-dimensional (3D) volumes is unavailable.ResultsWe developed RSAtrace3D, which is a robust 3D RSA vectorization software for monocot RSA phenotyping. It manages the single root (radicle or crown root) as a polyline (a vector), and the set of the polylines represents the entire RSA. RSAtrace3D vectorizes root segments between the two ends of a single root. By utilizing several base points on the root, RSAtrace3D suits noisy images if it is difficult to vectorize it using only two end nodes of the root. Additionally, by employing a simple tracking algorithm that uses the center of gravity (COG) of the root voxels to determine the tracking direction, RSAtrace3D efficiently vectorizes the roots. Thus, RSAtrace3D represents the single root shape more precisely than straight lines or spline curves. As a case study, rice (Oryza sativa) RSA was vectorized from X-ray computed tomography (CT) images, and RSA traits were calculated. In addition, varietal differences in RSA traits were observed. The vector data were 32,000 times more compact than raw X-ray CT images. Therefore, this makes it easier to share data and perform re-analyses. For example, using data from previously conducted studies. For monocot plants, the vectorization and phenotyping algorithm are extendable and suitable for numerous applications.ConclusionsRSAtrace3D is an RSA vectorization software for 3D RSA phenotyping for monocots. Owing to the high expandability of the RSA vectorization and phenotyping algorithm, RSAtrace3D can be applied not only to rice in X-ray CT images but also to other monocots in various 3D images. Since this software is written in Python language, it can be easily modified and will be extensively applied by researchers in this field.

A quick protocol for the identification and characterization of early growth mutants in tomato

Root system architecture (RSA) manipulation may improve water and nutrient capture by plants under normal and extreme climate conditions. With the aim of initiating the genetic dissection of RSA in tomato, we established a defined ontology that allowed the curated annotation of the observed phenotypes on 12 traits at four consecutive growth stages. In addition, we established a quick approach for the molecular identification of the mutations associated with the trait-of-interest by using a whole-genome sequencing approach that does not require the building of an additional mapping population. As a proof-of-concept, we screened 4543 seedlings from 300 tomato M3 lines (Solanum lycopersicum L. cv. Micro-Tom) generated by chemical mutagenesis with ethyl methanesulfonate. We studied the growth and early development of both the root system (primary and lateral roots) and the aerial part of the seedlings as well as the wound-induced adventitious roots emerging from the hypocotyl. We identified 659 individuals (belonging to 203 M3 lines) whose early seedling and RSA phenotypes differed from those of their reference background. We confirmed the genetic segregation of the mutant phenotypes affecting primary root length, seedling viability and early RSA in 31 M4 families derived from 15 M3 lines selected in our screen. Finally, we identified a missense mutation in the SlCESA3 gene causing a seedling-lethal phenotype with short roots. Our results validated the experimental approach used for the identification of tomato mutants during early growth, which will allow the molecular identification of the genes involved.

High-throughput three-dimensional visualization of root system architecture of rice using X-ray computed tomography

BackgroundX-ray computed tomography (CT) allows us to visualize root system architecture (RSA) beneath the soil, non-destructively and in a three-dimensional (3-D) form. However, CT scanning, reconstruction processes, and root isolation from X-ray CT volumes, take considerable time. For genetic analyses, such as quantitative trait locus mapping, which require a large population size, a high-throughput RSA visualization method is required.ResultsWe have developed a high-throughput process flow for the 3-D visualization of rice (Oryza sativa) RSA (consisting of radicle and crown roots), using X-ray CT. The process flow includes use of a uniform particle size, calcined clay to reduce the possibility of visualizing non-root segments, use of a higher tube voltage and current in the X-ray CT scanning to increase root-to-soil contrast, and use of a 3-D median filter and edge detection algorithm to isolate root segments. Using high-performance computing technology, this analysis flow requires only 10 min (33 s, if a rough image is acceptable) for CT scanning and reconstruction, and 2 min for image processing, to visualize rice RSA. This reduced time allowed us to conduct the genetic analysis associated with 3-D RSA phenotyping. In 2-week-old seedlings, 85% and 100% of radicle and crown roots were detected, when 16 cm and 20 cm diameter pots were used, respectively. The X-ray dose per scan was estimated at < 0.09 Gy, which did not impede rice growth. Using the developed process flow, we were able to follow daily RSA development, i.e., 4-D RSA development, of an upland rice variety, over 3 weeks.ConclusionsWe developed a high-throughput process flow for 3-D rice RSA visualization by X-ray CT. The X-ray dose assay on plant growth has shown that this methodology could be applicable for 4-D RSA phenotyping. We named the RSA visualization method ‘RSAvis3D’ and are confident that it represents a potentially efficient application for 3-D RSA phenotyping of various plant species.

Molecular Genetic Analysis of Drought Stress Response Traits in Brachypodium spp.

The root is the organ responsible for the uptake of water and therefore has a very important role in drought tolerance. The aims of the present work were to characterize nine traits of the root system architecture (RSA) and the shoot dry weight (W) of twelve genotypes of Brachypodium spp. under water stress and to establish the relationship between RSA phenotyping traits and SSRs. Two culture media, one standard (SM) and one (PEG) to induced water stress have been used. In SM medium, B. stacei had the highest values of W and all the RSA traits, except the mean diameter of the seminal roots, followed by B. hybridum and B. distachyon. In the PEG medium, root length increased in B. distachyon, decreased in B. hybridum and remained the same in B. stacei. A two-way hierarchical cluster analysis from 117 polymorphic SSRs and the traits of the RSA of the Brachypodium spp. genotypes, was performed. Brachypodium genotypes were separated into three groups corresponding to each species. In the second way of the hierarchical clustering association were observed between five RSA variables and SSR markers, which could be useful in the search for genes or QTLs related to RSA characters.

An Analysis of Soil Coring Strategies to Estimate Root Depth in Maize (Zea mays) and Common Bean (Phaseolus vulgaris).

A soil coring protocol was developed to cooptimize the estimation of root length distribution (RLD) by depth and detection of functionally important variation in root system architecture (RSA) of maize and bean. The functional-structural model OpenSimRoot was used to perform in silico soil coring at six locations on three different maize and bean RSA phenotypes. Results were compared to two seasons of field soil coring and one trench. Two one-sided T-test (TOST) analysis of in silico data suggests a between-row location 5 cm from plant base (location 3), best estimates whole-plot RLD/D of deep, intermediate, and shallow RSA phenotypes, for both maize and bean. Quadratic discriminant analysis indicates location 3 has ~70% categorization accuracy for bean, while an in-row location next to the plant base (location 6) has ~85% categorization accuracy in maize. Analysis of field data suggests the more representative sampling locations vary by year and species. In silico and field studies suggest location 3 is most robust, although variation is significant among seasons, among replications within a field season, and among field soil coring, trench, and simulations. We propose that the characterization of the RLD profile as a dynamic rhizo canopy effectively describes how the RLD profile arises from interactions among an individual plant, its neighbors, and the pedosphere.

Physiological mechanisms contributing to the QTL qDTY3.2 effects on improved performance of rice Moroberekan x Swarna BC2F3:4 lines under drought

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.

Sorghum root-system classification in contrasting P environments reveals three main rooting types and root-architecture-related marker-trait associations.

Roots facilitate acquisition of macro- and micronutrients, which are crucial for plant productivity and anchorage in the soil. Phosphorus (P) is rapidly immobilized in the soil and hardly available for plants. Adaptation to P scarcity relies on changes in root morphology towards rooting systems well suited for topsoil foraging. Root-system architecture (RSA) defines the spatial organization of the network comprising primary, lateral and stem-derived roots and is important for adaptation to stress conditions. RSA phenotyping is a challenging task and essential for understanding root development. In this study, 19 traits describing RSA were analysed in a diversity panel comprising 194 sorghum genotypes, fingerprinted with a 90-k single-nucleotide polymorphism (SNP) array and grown under low and high P availability. Multivariate analysis was conducted and revealed three different RSA types: (1) a small root system; (2) a compact and bushy rooting type; and (3) an exploratory root system, which might benefit plant growth and development if water, nitrogen (N) or P availability is limited. While several genotypes displayed similar rooting types in different environments, others responded to P scarcity positively by developing more exploratory root systems, or negatively with root growth suppression. Genome-wide association studies revealed significant quantitative trait loci (P < 2.9 × 10-6) on chromosomes SBI-02, SBI-03, SBI-05 and SBI-09. Co-localization of significant and suggestive (P < 5.7 × 10-5) associations for several traits indicated hotspots controlling root-system development on chromosomes SBI-02 and SBI-03. Sorghum genotypes with a compact, bushy and shallow root system provide potential adaptation to P scarcity in the field by allowing thorough topsoil foraging, while genotypes with an exploratory root system may be advantageous if N or water is the limiting factor, although such genotypes showed highest P uptake levels under the artificial conditions of the present study.

High-throughput imaging and analysis of root system architecture in Brachypodium distachyon under differential nutrient availability.

Nitrogen (N) and phosphorus (P) deficiency are primary constraints for plant productivity, and root system architecture (RSA) plays a vital role in the acquisition of these nutrients. The genetic determinants of RSA are poorly understood, primarily owing to the complexity of crop genomes and the lack of sufficient RSA phenotyping methods. The objective of this study was to characterize the RSA of two Brachypodium distachyon accessions under different nutrient availability. To do so, we used a high-throughput plant growth and imaging platform, and developed software that quantified 19 different RSA traits. We found significant differences in RSA between two Brachypodium accessions grown on nutrient-rich, low-N and low-P conditions. More specifically, one accession maintained axile root growth under low N, while the other accession maintained lateral root growth under low P. These traits resemble the RSA of crops adapted to low-N and -P conditions, respectively. Furthermore, we found that a number of these traits were highly heritable. This work lays the foundation for future identification of important genetic components of RSA traits under nutrient limitation using a mapping population derived from these two accessions.