Recent advances in methods for in situ root phenotyping

Transcriptional changes during crown-root development and emergence in barley (Hordeum vulgare L.)

BACKGROUND

Roots play an important role during plant growth and development, ensuring water and nutrient uptake. Understanding the mechanisms regulating their initiation and development opens doors towards root system architecture engineering.

RESULTS

Here, we investigated by RNA-seq analysis the changes in gene expression in the barley stem base of 1 day-after-germination (DAG) and 10DAG seedlings when crown roots are formed. We identified 2,333 genes whose expression was lower in the stem base of 10DAG seedlings compared to 1DAG seedlings. Those genes were mostly related to basal cellular activity such as cell cycle organization, protein biosynthesis, chromatin organization, cytoskeleton organization or nucleotide metabolism. In opposite, 2,932 genes showed up-regulation in the stem base of 10DAG seedlings compared to 1DAG seedlings, and their function was related to phytohormone action, solute transport, redox homeostasis, protein modification, secondary metabolism. Our results highlighted genes that are likely involved in the different steps of crown root formation from initiation to primordia differentiation and emergence, and revealed the activation of different hormonal pathways during this process.

CONCLUSIONS

This whole transcriptomic study is the first study aiming at understanding the molecular mechanisms controlling crown root development in barley. The results shed light on crown root emergence that is likely associated with a strong cell wall modification, death of the cells covering the crown root primordium, and the production of defense molecules that might prevent pathogen infection at the site of root emergence.

Engineering Rapeseed Germination and Root Growth with Mechanical Strength of Polysaccharide Hydrogel

Plant roots are highly sensitive to physical stress in the soil, with appropriate mechanical impedance promoting root elongation and lateral root growth. However, few studies have quantitatively explored the relationship between the mechanical impedance of the growth medium and the phenotypes of plant roots. In this study, we used a tensile machine equipped with a self-made steel needle mimicking the root tip to measure the force needed to penetrate the hydrogel medium (agar, low acyl gellan gum, and κ-carrageenan), providing insights into the force required for the rapeseed root tip to enter the medium following germination. These findings indicate that root penetration length is inversely associated with the mechanical strength of the growth medium, with variations observed in the root system adaptability across different substrates. Specifically, when the gel puncture resistance of the culture medium without adding MS reached approximately 18.4 mN, root penetration and growth were significantly hindered. With the addition of 1/2 MS medium, the polysaccharide concentration is 1.0 wt %, which is more suitable for cultivating rapeseed. This research not only offers a method for quantifying root phenotypes and medium mechanical impedance but also presents an approach for plant growth regulation and crop breeding.

Are high-throughput root phenotyping platforms suitable for informing root system architecture models with genotype-specific parameters? An evaluation based on the root model ArchiSimple and a small panel of wheat cultivars

Given the difficulties in accessing plant roots in situ, high-throughput root phenotyping (HTRP) platforms under controlled conditions have been developed to meet the growing demand for characterizing root system architecture (RSA) for genetic analyses. However, a proper evaluation of their capacity to provide the same estimates for strictly identical root traits across platforms has never been achieved. In this study, we performed such an evaluation based on six major parameters of the RSA model ArchiSimple, using a diversity panel of 14 bread wheat cultivars in two HTRP platforms that had different growth media and non-destructive imaging systems together with a conventional set-up that had a solid growth medium and destructive sampling. Significant effects of the experimental set-up were found for all the parameters and no significant correlations across the diversity panel among the three set-ups could be detected. Differences in temperature, irradiance, and/or the medium in which the plants were growing might partly explain both the differences in the parameter values across the experiments as well as the genotype × set-up interactions. Furthermore, the values and the rankings across genotypes of only a subset of parameters were conserved between contrasting growth stages. As the parameters chosen for our analysis are root traits that have strong impacts on RSA and are close to parameters used in a majority of RSA models, our results highlight the need to carefully consider both developmental and environmental drivers in root phenomics studies.

Improved Transformer for Time Series Senescence Root Recognition

The root is an important organ for plants to obtain nutrients and water, and its phenotypic characteristics are closely related to its functions. Deep-learning-based high-throughput in situ root senescence feature extraction has not yet been published. In light of this, this paper suggests a technique based on the transformer neural network for retrieving cotton's in situ root senescence properties. High-resolution in situ root pictures with various levels of senescence are the main subject of the investigation. By comparing the semantic segmentation of the root system by general convolutional neural networks and transformer neural networks, SegFormer-UN (large) achieves the optimal evaluation metrics with mIoU, mRecall, mPrecision, and mF1 metric values of 81.52%, 86.87%, 90.98%, and 88.81%, respectively. The segmentation results indicate more accurate predictions at the connections of root systems in the segmented images. In contrast to 2 algorithms for cotton root senescence extraction based on deep learning and image processing, the in situ root senescence recognition algorithm using the SegFormer-UN model has a parameter count of 5.81 million and operates at a fast speed, approximately 4 min per image. It can accurately identify senescence roots in the image. We propose that the SegFormer-UN model can rapidly and nondestructively identify senescence root in in situ root images, providing important methodological support for efficient crop senescence research.

Understanding role of roots in plant response to drought: Way forward to climate-resilient crops

Drought stress leads to a significant amount of agricultural crop loss. Thus, with changing climatic conditions, it is important to develop resilience measures in agricultural systems against drought stress. Roots play a crucial role in regulating plant development under drought stress. In this review, we have summarized the studies on the role of roots and root-mediated plant responses. We have also discussed the importance of root system architecture (RSA) and the various structural and anatomical changes that it undergoes to increase survival and productivity under drought. Various genes, transcription factors, and quantitative trait loci involved in regulating root growth and development are also discussed. A summarization of various instruments and software that can be used for high-throughput phenotyping in the field is also provided in this review. More comprehensive studies are required to help build a detailed understanding of RSA and associated traits for breeding drought-resilient cultivars.

Bridging Time-series Image Phenotyping and Functional-Structural Plant Modeling to Predict Adventitious Root System Architecture

Root system architecture (RSA) is an important measure of how plants navigate and interact with the soil environment. However, current methods in studying RSA must make tradeoffs between precision of data and proximity to natural conditions, with root growth in germination papers providing accessibility and high data resolution. Functional-structural plant models (FSPMs) can overcome this tradeoff, though parameterization and evaluation of FSPMs are traditionally based in manual measurements and visual comparison. Here, we applied a germination paper system to study the adventitious RSA and root phenology of Populus trichocarpa stem cuttings using time-series image-based phenotyping augmented by FSPM. We found a significant correlation between timing of root initiation and thermal time at cutting collection (P value = 0.0061, R2 = 0.875), but little correlation with RSA. We also present a use of RhizoVision [1] for automatically extracting FSPM parameters from time series images and evaluating FSPM simulations. A high accuracy of the parameterization was achieved in predicting 2D growth with a sensitivity rate of 83.5%. This accuracy was lost when predicting 3D growth with sensitivity rates of 38.5% to 48.7%, while overall accuracy varied with phenotyping methods. Despite this loss in accuracy, the new method is amenable to high throughput FSPM parameterization and bridges the gap between advances in time-series phenotyping and FSPMs.

Characterizing a Cost-Effective Hydrogel-Based Transparent Soil

Transparent soil (TS) was specifically designed to support root growth in the presence of air, water, and nutrients and allowed the time-resolved phenotyping of roots in vivo. Nevertheless, it is imperative to further optimize the reagent cost of TS to enable its wider utilization. We substituted the costly Phytagel obtained from Sigma with two more economical alternatives, namely Biodee and Coolaber. TS beads from each brand were prepared using 12 different polymer concentrations and seven distinct crosslinker concentrations. A comprehensive assessment encompassing transparency, mechanical characteristics, particle size, porosity, and stability of TS was undertaken. Compared to the Sigma Phytagel brand, both Biodee and Coolaber significantly reduced the transparency and collapse stress of the TS they produced. Consequently, this led to a significant reduction in the allowable width and height of the growth box, although they could still simultaneously exceed 20 cm and 19 cm. There was no notable difference in porosity and stability among the TS samples prepared using the three Phytagel brands. Therefore, it is feasible to consider replacing the Phytagel brand to reduce TS production costs. This study quantified the differences in TS produced using three Phytagel brands at different prices that will better promote the application of TS to root phenotypes.

Integrated phenotyping of root and shoot growth dynamics in maize reveals specific interaction patterns in inbreds and hybrids and in response to drought

In recent years, various automated methods for plant phenotyping addressing roots or shoots have been developed and corresponding platforms have been established to meet the diverse requirements of plant research and breeding. However, most platforms are only either able to phenotype shoots or roots of plants but not both simultaneously. This substantially limits the opportunities offered by a joint assessment of the growth and development dynamics of both organ systems, which are highly interdependent. In order to overcome these limitations, a root phenotyping installation was integrated into an existing automated non-invasive high-throughput shoot phenotyping platform. Thus, the amended platform is now capable of conducting high-throughput phenotyping at the whole-plant level, and it was used to assess the vegetative root and shoot growth dynamics of five maize inbred lines and four hybrids thereof, as well as the responses of five inbred lines to progressive drought stress. The results showed that hybrid vigour (heterosis) occurred simultaneously in roots and shoots and was detectable as early as 4 days after transplanting (4 DAT; i.e., 8 days after seed imbibition) for estimated plant height (EPH), total root length (TRL), and total root volume (TRV). On the other hand, growth dynamics responses to progressive drought were different in roots and shoots. While TRV was significantly reduced 10 days after the onset of the water deficit treatment, the estimated shoot biovolume was significantly reduced about 6 days later, and EPH showed a significant decrease even 2 days later (8 days later than TRV) compared with the control treatment. In contrast to TRV, TRL initially increased in the water deficit period and decreased much later (not earlier than 16 days after the start of the water deficit treatment) compared with the well-watered plants. This may indicate an initial response of the plants to water deficit by forming longer but thinner roots before growth was inhibited by the overall water deficit. The magnitude and the dynamics of the responses were genotype-dependent, as well as under the influence of the water consumption, which was related to plant size.

Early root phenotyping in sweetpotato (Ipomoea batatas L.) uncovers insights into root system architecture variability

Background

We developed a novel, non-destructive, expandable, ebb and flow soilless phenotyping system to deliver a capable way to study early root system architectural traits in stem-derived adventitious roots of sweetpotato (Ipomoea batatas L.). The platform was designed to accommodate up to 12 stems in a relatively small area for root screening. This platform was designed with inexpensive materials and equipped with an automatic watering system.

Methods

To test this platform, we designed a screening experiment for root traits using two contrasting sweetpotato genotypes, 'Covington' and 'NC10-275'. We monitored and imaged root growth, architecture, and branching patterns every five days up to 20 days.

Results

We observed significant differences in both architectural and morphological root traits for both genotypes tested. After 10 days, root length, surface root area, and root volume were higher in 'NC10-275' compared to 'Covington'. However, average root diameter and root branching density were higher in 'Covington'.

Conclusion

These results validated the effective and efficient use of this novel root phenotyping platforming for screening root traits in early stem-derived adventitious roots. This platform allowed for monitoring and 2D imaging of root growth over time with minimal disturbance and no destructive root sampling. This platform can be easily tailored for abiotic stress experiments, and permit root growth mapping and temporal and dynamic root measurements of primary and secondary adventitious roots. This phenotyping platform can be a suitable tool for examining root system architecture and traits of clonally propagated material for a large set of replicates in a relatively small space.

3D segmentation of plant root systems using spatial pyramid pooling and locally adaptive field-of-view inference

Background

The non-invasive 3D-imaging and successive 3D-segmentation of plant root systems has gained interest within fundamental plant research and selectively breeding resilient crops. Currently the state of the art consists of computed tomography (CT) scans and reconstruction followed by an adequate 3D-segmentation process.

Challenge

Generating an exact 3D-segmentation of the roots becomes challenging due to inhomogeneous soil composition, as well as high scale variance in the root structures themselves.

Approach

(1) We address the challenge by combining deep convolutional neural networks (DCNNs) with a weakly supervised learning paradigm. Furthermore, (2) we apply a spatial pyramid pooling (SPP) layer to cope with the scale variance of roots. (3) We generate a fine-tuned training data set with a specialized sub-labeling technique. (4) Finally, to yield fast and high-quality segmentations, we propose a specialized iterative inference algorithm, which locally adapts the field of view (FoV) for the network.

Experiments

We compare our segmentation results against an analytical reference algorithm for root segmentation (RootForce) on a set of roots from Cassava plants and show qualitatively that an increased amount of root voxels and root branches can be segmented.

Results

Our findings show that with the proposed DCNN approach combined with the dynamic inference, much more, and especially fine, root structures can be detected than with a classical analytical reference method.

Conclusion

We show that the application of the proposed DCNN approach leads to better and more robust root segmentation, especially for very small and thin roots.

Response of root and root hair phenotypes of cotton seedlings under high temperature revealed with RhizoPot

Driven by the increase in its frequency and duration, high temperature weather is increasingly seriously affecting crop development. High temperature inhibits the leaf development, flowering, and pollination of cotton, but its effects on the roots and root hair phenotypes and lifespans remain unclear. Thus, this study selected the two cotton varieties Nongda 601 (ND) and Guoxin 9 (GX) as materials and adopted the RhizoPot, an in situ root observation system, to investigate the effects of high temperature (38°C day and 32°C night) on the growth dynamics of the aboveground parts and root phenotypes of cotton at the seedling stage. The results showed that high temperature reduced the net photosynthetic rate and chlorophyll content, decreased the dry matter accumulation and transfer to the root, and lowered the root-shoot ratio (R/S ratio). The root phenotypes changed significantly under high temperature. After 7 d of high temperature stress, the root lengths of ND and GX decreased by 78.14 mm and 59.64 mm, respectively. Their specific root lengths increased by 79.60% and 66.11%, respectively. Their specific root surface areas increased by 418.70 cm²·g^-1 and 433.42 cm²·g^-1, respectively. Their proportions of very fine roots increased to 99.26% and 97.16%, respectively. After the removal of high temperature (RHT), their root lengths tended to increase, and their proportions of very fine roots continued to increase. The root hairs of ND and GX were also significantly affected by high temperature. In particular, the root hair densities of ND and GX decreased by 52.53% and 56.25%, respectively. Their average root hair lengths decreased by 96.62% and 74.29%, respectively. Their root hair lifespans decreased by 7 d and 10 d, respectively. After the RHT, their average root hair lengths failed to recover. A principal component analysis indicated that the root architectures were significantly affected by root hair density, average root hair length, specific root length, and specific root surface area under high temperatures. In summary, cotton adapts to high temperature environments by increasing the specific root length, specific root surface area, and the proportions of very fine roots, and reducing the lifespan of root hairs.

RhizoPot platform: A high-throughput in situ root phenotyping platform with integrated hardware and software

Quantitative analysis of root development is becoming a preferred option in assessing the function of hidden underground roots, especially in studying resistance to abiotic stresses. It can be enhanced by acquiring non-destructive phenotypic information on roots, such as rhizotrons. However, it is challenging to develop high-throughput phenotyping equipment for acquiring and analyzing in situ root images of root development. In this study, the RhizoPot platform, a high-throughput in situ root phenotyping platform integrating plant culture, automatic in situ root image acquisition, and image segmentation, was proposed for quantitative analysis of root development. Plants (1-5) were grown in each RhizoPot, and the growth time depended on the type of plant and the experimental requirements. For example, the growth time of cotton was about 110 days. The imaging control software (RhizoAuto) could automatically and non-destructively image the roots of RhizoPot-cultured plants based on the set time and resolution (50-4800 dpi) and obtain high-resolution (>1200 dpi) images in batches. The improved DeepLabv3+ tool was used for batch processing of root images. The roots were automatically segmented and extracted from the background for analysis of information on radical features using conventional root software (WinRhizo and RhizoVision Explorer). Root morphology, root growth rate, and lifespan analysis were conducted using in situ root images and segmented images. The platform illustrated the dynamic response characteristics of root phenotypes in cotton. In conclusion, the RhizoPot platform has the characteristics of low cost, high-efficiency, and high-throughput, and thus it can effectively monitor the development of plant roots and realize the quantitative analysis of root phenotypes in situ.