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

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.

Quantitative phenotyping of crop roots with spectral electrical impedance tomography: a rhizotron study with optimized measurement design

BACKGROUND

Root systems are key contributors to plant health, resilience, and, ultimately, yield of agricultural crops. To optimize plant performance, phenotyping trials are conducted to breed plants with diverse root traits. However, traditional analysis methods are often labour-intensive and invasive to the root system, therefore limiting high-throughput phenotyping. Spectral electrical impedance tomography (sEIT) could help as a non-invasive and cost-efficient alternative to optical root analysis, potentially providing 2D or 3D spatio-temporal information on root development and activity. Although impedance measurements have been shown to be sensitive to root biomass, nutrient status, and diurnal activity, only few attempts have been made to employ tomographic algorithms to recover spatially resolved information on root systems. In this study, we aim to establish relationships between tomographic electrical polarization signatures and root traits of different fine root systems (maize, pinto bean, black bean, and soy bean) under hydroponic conditions.

RESULTS

Our results show that, with the use of an optimized data acquisition scheme, sEIT is capable of providing spatially resolved information on root biomass and root surface area for all investigated root systems. We found strong correlations between the total polarization strength and the root biomass (R 2 = 0.82 ) and root surface area (R 2 = 0.8 ). Our findings suggest that the captured polarization signature is dominated by cell-scale polarization processes. Additionally, we demonstrate that the resolution characteristics of the measurement scheme can have a significant impact on the tomographic reconstruction of root traits.

CONCLUSION

Our findings showcase that sEIT is a promising tool for the tomographic reconstruction of root traits in high-throughput root phenotyping trials and should be evaluated as a substitute for traditional, often time-consuming, root characterization methods.

CT image-based 3D inflorescence estimation of Chrysanthemum seticuspe

To study plant organs, it is necessary to investigate the three-dimensional (3D) structures of plants. In recent years, non-destructive measurements through computed tomography (CT) have been used to understand the 3D structures of plants. In this study, we use the Chrysanthemum seticuspe capitulum inflorescence as an example and focus on contact points between the receptacles and florets within the 3D capitulum inflorescence bud structure to investigate the 3D arrangement of the florets on the receptacle. To determine the 3D order of the contact points, we constructed slice images from the CT volume data and detected the receptacles and florets in the image. However, because each CT sample comprises hundreds of slice images to be processed and each C. seticuspe capitulum inflorescence comprises several florets, manually detecting the receptacles and florets is labor-intensive. Therefore, we propose an automatic contact point detection method based on CT slice images using image recognition techniques. The proposed method improves the accuracy of contact point detection using prior knowledge that contact points exist only around the receptacle. In addition, the integration of the detection results enables the estimation of the 3D position of the contact points. According to the experimental results, we confirmed that the proposed method can detect contacts on slice images with high accuracy and estimate their 3D positions through clustering. Additionally, the sample-independent experiments showed that the proposed method achieved the same detection accuracy as sample-dependent experiments.

Convolutional neural networks combined with conventional filtering to semantically segment plant roots in rapidly scanned X-ray computed tomography volumes with high noise levels

BACKGROUND

X-ray computed tomography (CT) is a powerful tool for measuring plant root growth in soil. However, a rapid scan with larger pots, which is required for throughput-prioritized crop breeding, results in high noise levels, low resolution, and blurred root segments in the CT volumes. Moreover, while plant root segmentation is essential for root quantification, detailed conditional studies on segmenting noisy root segments are scarce. The present study aimed to investigate the effects of scanning time and deep learning-based restoration of image quality on semantic segmentation of blurry rice (Oryza sativa) root segments in CT volumes.

RESULTS

VoxResNet, a convolutional neural network-based voxel-wise residual network, was used as the segmentation model. The training efficiency of the model was compared using CT volumes obtained at scan times of 33, 66, 150, 300, and 600 s. The learning efficiencies of the samples were similar, except for scan times of 33 and 66 s. In addition, The noise levels of predicted volumes differd among scanning conditions, indicating that the noise level of a scan time ≥ 150 s does not affect the model training efficiency. Conventional filtering methods, such as median filtering and edge detection, increased the training efficiency by approximately 10% under any conditions. However, the training efficiency of 33 and 66 s-scanned samples remained relatively low. We concluded that scan time must be at least 150 s to not affect segmentation. Finally, we constructed a semantic segmentation model for 150 s-scanned CT volumes, for which the Dice loss reached 0.093. This model could not predict the lateral roots, which were not included in the training data. This limitation will be addressed by preparing appropriate training data.

CONCLUSIONS

A semantic segmentation model can be constructed even with rapidly scanned CT volumes with high noise levels. Given that scanning times ≥ 150 s did not affect the segmentation results, this technique holds promise for rapid and low-dose scanning. This study offers insights into images other than CT volumes with high noise levels that are challenging to determine when annotating.

Non-destructive real-time monitoring of underground root development with distributed fiber optic sensing

Crop genetic engineering for better root systems can offer practical solutions for food security and carbon sequestration; however, soil layers prevent the direct visualization of plant roots, thus posing a challenge to effective phenotyping. Here, we demonstrate an original device with a distributed fiber-optic sensor for fully automated, real-time monitoring of underground root development. We show that spatially encoding an optical fiber with a flexible and durable polymer film in a spiral pattern can significantly enhance sensor detection. After signal processing, the resulting device can detect the penetration of a submillimeter-diameter object in the soil, indicating more than a magnitude higher spatiotemporal resolution than previously reported with underground monitoring techniques. Additionally, we also developed computational models to visualize the roots of tuber crops and monocotyledons and then applied them to radish and rice to compare the results with those of X-ray computed tomography. The device's groundbreaking sensitivity and spatiotemporal resolution enable seamless and laborless phenotyping of root systems that are otherwise invisible underground.

Orchid fruit and root movement analyzed using 2D photographs and a bioinformatics pipeline for processing sequential 3D scans

Premise

Most studies of the movement of orchid fruits and roots during plant development have focused on morphological observations; however, further genetic analysis is required to understand the molecular mechanisms underlying this phenomenon. A precise tool is required to observe these movements and harvest tissue at the correct position and time for transcriptomics research.

Methods

We utilized three-dimensional (3D) micro-computed tomography (CT) scans to capture the movement of fast-growing Erycina pusilla roots, and built an integrated bioinformatics pipeline to process 3D images into 3D time-lapse videos. To record the movement of slowly developing E. pusilla and Phalaenopsis equestris fruits, two-dimensional (2D) photographs were used.

Results

The E. pusilla roots twisted and resupinated multiple times from early development. The first period occurred in the early developmental stage (77-84 days after germination [DAG]) and the subsequent period occurred later in development (140-154 DAG). While E. pusilla fruits twisted 45° from 56-63 days after pollination (DAP), the fruits of P. equestris only began to resupinate a week before dehiscence (133 DAP) and ended a week after dehiscence (161 DAP).

Discussion

Our methods revealed that each orchid root and fruit had an independent direction and degree of torsion from the initial to the final position. Our innovative approaches produced detailed spatial and temporal information on the resupination of roots and fruits during orchid development.

An overview of image-based phenotyping as an adaptive 4.0 technology for studying plant abiotic stress: A bibliometric and literature review

Improving the tolerance of crop species to abiotic stresses that limit plant growth and productivity is essential for mitigating the emerging problems of global warming. In this context, imaged data analysis represents an effective method in the 4.0 technology era, where this method has the non-destructive and recursive characterization of plant phenotypic traits as selection criteria. So, the plant breeders are helped in the development of adapted and climate-resilient crop varieties. Although image-based phenotyping has recently resulted in remarkable improvements for identifying the crop status under a range of growing conditions, the topic of its application for assessing the plant behavioral responses to abiotic stressors has not yet been extensively reviewed. For such a purpose, bibliometric analysis is an ideal analytical concept to analyze the evolution and interplay of image-based phenotyping to abiotic stresses by objectively reviewing the literature in light of existing database. Bibliometricy, a bibliometric analysis was applied using a systematic methodology which involved data mining, mining data improvement and analysis, and manuscript construction. The obtained results indicate that there are 554 documents related to image-based phenotyping to abiotic stress until 5 January 2023. All document showed the future development trends of image-based phenotyping will be mainly centered in the United States, European continent and China. The keywords analysis major focus to the application of 4.0 technology and machine learning in plant breeding, especially to create the tolerant variety under abiotic stresses. Drought and saline become an abiotic stress often using image-based phenotyping. Besides that, the rice, wheat and maize as the main commodities in this topic. In conclusion, the present work provides information on resolutive interactions in developing image-based phenotyping to abiotic stress, especially optimizing high-throughput sensors in image-based phenotyping for the future development.

Rhizosphere frame system enables nondestructive live-imaging of legume-rhizobium interactions in the soil

Most plants interact with various soil microorganisms as they grow through the soil. Root nodule symbiosis by legumes and rhizobia is a well-known phenomenon of plant-microbe interactions in the soil. Although microscopic observations are useful for understanding the infection processes of rhizobia, nondestructive observation methods have not been established for monitoring interactions between rhizobia and soil-grown roots. In this study, we constructed Bradyrhizobium diazoefficiens strains that constitutively express different fluorescent proteins, which allows identification of tagged rhizobia by the type of fluorophores. In addition, we constructed a plant cultivation device, Rhizosphere Frame (RhizoFrame), which is a soil-filled container made of transparent acrylic plates that allows observation of roots growing along the acrylic plates. Combining fluorescent rhizobia with RhizoFrame, we established a live imaging system, RhizoFrame system, that enabled us to track the nodulation processes with fluorescence stereomicroscope while retaining spatial information about roots, rhizobia, and soil. Mixed inoculation with different fluorescent rhizobia using RhizoFrame enabled the visualization of mixed infection of a single nodule with two strains. In addition, observation of transgenic Lotus japonicus expressing auxin-responsive reporter genes indicated that RhizoFrame system could be used for a real-time and nondestructive reporter assay. Thus, the use of RhizoFrame system is expected to enhance the study of the spatiotemporal dynamics of plant-microbe interactions in the soil.

Applications of Microct Imaging to Archaeobotanical Research

The potential applications of microCT scanning in the field of archaeobotany are only just beginning to be explored. The imaging technique can extract new archaeobotanical information from existing archaeobotanical collections as well as create new archaeobotanical assemblages within ancient ceramics and other artefact types. The technique could aid in answering archaeobotanical questions about the early histories of some of the world's most important food crops from geographical regions with amongst the poorest rates of archaeobotanical preservation and where ancient plant exploitation remains poorly understood. This paper reviews current uses of microCT imaging in the investigation of archaeobotanical questions, as well as in cognate fields of geosciences, geoarchaeology, botany and palaeobotany. The technique has to date been used in a small number of novel methodological studies to extract internal anatomical morphologies and three-dimensional quantitative data from a range of food crops, which includes sexually-propagated cereals and legumes, and asexually-propagated underground storage organs (USOs). The large three-dimensional, digital datasets produced by microCT scanning have been shown to aid in taxonomic identification of archaeobotanical specimens, as well as robustly assess domestication status. In the future, as scanning technology, computer processing power and data storage capacities continue to improve, the possible applications of microCT scanning to archaeobotanical studies will only increase with the development of machine and deep learning networks enabling the automation of analyses of large archaeobotanical assemblages.

Automatic segmentation of cotton roots in high-resolution minirhizotron images based on improved OCRNet

Root phenotypic parameters are the important basis for studying the growth state of plants, and root researchers obtain root phenotypic parameters mainly by analyzing root images. With the development of image processing technology, automatic analysis of root phenotypic parameters has become possible. And the automatic segmentation of roots in images is the basis for the automatic analysis of root phenotypic parameters. We collected high-resolution images of cotton roots in a real soil environment using minirhizotrons. The background noise of the minirhizotron images is extremely complex and affects the accuracy of the automatic segmentation of the roots. In order to reduce the influence of the background noise, we improved OCRNet by adding a Global Attention Mechanism (GAM) module to OCRNet to enhance the focus of the model on the root targets. The improved OCRNet model in this paper achieved automatic segmentation of roots in the soil and performed well in the root segmentation of the high-resolution minirhizotron images, achieving an accuracy of 0.9866, a recall of 0.9419, a precision of 0.8887, an F1 score of 0.9146 and an Intersection over Union (IoU) of 0.8426. The method provided a new approach to automatic and accurate root segmentation of high-resolution minirhizotron images.

Studying root-environment interactions in structured microdevices

In negotiating with the environment, plant roots integrate sensory information over space and time, as the basis of decision making in roots under non-uniform conditions. The complexity and dynamic properties of soil across spatial and temporal scales pose a significant technical challenge for research on mechanisms that drive metabolism, growth and development in roots, as well as on inter-organismal networks in the rhizosphere. Synthetic environments, combining microscopic access and manipulation capabilities with soil-like heterogeneity, are needed to elucidate the intriguing tug-of-war that characterises subsurface ecosystems. Microdevices have provided opportunities for innovative approaches to observe, analyse and manipulate plant roots and advanced our understanding of their development, physiology and interactions with the environment. Initially conceived as perfusion platforms for root cultivation under hydroponic conditions, microdevice design has, in recent years, increasingly shifted to better reflect the complex growth conditions in soil. Heterogeneous micro-environments have been created through co-cultivation with microbes, laminar flow-based local stimulation and physical obstacles and constraints. As such, structured microdevices provide an experimental entry point to the complex network behaviour of soil communities.

Local auxin biosynthesis regulates brace root angle and lodging resistance in maize

Root lodging poses a major threat to maize production, resulting in reduced grain yield and quality, and increased harvest costs. Here, we combined expressional, genetic, and cytological studies to demonstrate a role of ZmYUC2 and ZmYUC4 in regulating gravitropic response of the brace root and lodging resistance in maize. We show that both ZmYUC2 and ZmYUC4 are preferentially expressed in root tips with partially overlapping expression patterns, and the protein products of ZmYUC2 and ZmYUC4 are localized in the cytoplasm and endoplasmic reticulum, respectively. The Zmyuc4 single mutant and Zmyuc2/4 double mutant exhibit enlarged brace root angle compared with the wild-type plants, with larger brace root angle being observed in the Zmyuc2/4 double mutant. Consistently, the brace root tips of the Zmyuc4 single mutant and Zmyuc2/4 double mutant accumulate less auxin and are defective in proper reallocation of auxin in response to gravi-stimuli. Furthermore, we show that the Zmyuc4 single mutant and the Zmyuc2/4 double mutant display obviously enhanced root lodging resistance. Our combined results demonstrate that ZmYUC2- and ZmYUC4-mediated local auxin biosynthesis is required for normal gravity response of the brace roots and provide effective targets for breeding root lodging resistant maize cultivars.

An improved U-Net-based in situ root system phenotype segmentation method for plants

The condition of plant root systems plays an important role in plant growth and development. The Minirhizotron method is an important tool to detect the dynamic growth and development of plant root systems. Currently, most researchers use manual methods or software to segment the root system for analysis and study. This method is time-consuming and requires a high level of operation. The complex background and variable environment in soils make traditional automated root system segmentation methods difficult to implement. Inspired by deep learning in medical imaging, which is used to segment pathological regions to help determine diseases, we propose a deep learning method for the root segmentation task. U-Net is chosen as the basis, and the encoder layer is replaced by the ResNet Block, which can reduce the training volume of the model and improve the feature utilization capability; the PSA module is added to the up-sampling part of U-Net to improve the segmentation accuracy of the object through multi-scale features and attention fusion; a new loss function is used to avoid the extreme imbalance and data imbalance problems of backgrounds such as root system and soil. After experimental comparison and analysis, the improved network demonstrates better performance. In the test set of the peanut root segmentation task, a pixel accuracy of 0.9917 and Intersection Over Union of 0.9548 were achieved, with an F1-score of 95.10. Finally, we used the Transfer Learning approach to conduct segmentation experiments on the corn in situ root system dataset. The experiments show that the improved network has a good learning effect and transferability.

Visualization and quantification of coconut using advanced computed tomography postprocessing technology

INTRODUCTION

Computed tomography (CT) is a non-invasive examination tool that is widely used in medicine. In this study, we explored its value in visualizing and quantifying coconut.

MATERIALS AND METHODS

Twelve coconuts were scanned using CT for three months. Axial CT images of the coconuts were obtained using a dual-source CT scanner. In postprocessing process, various three-dimensional models were created by volume rendering (VR), and the plane sections of different angles were obtained through multiplanar reformation (MPR). The morphological parameters and the CT values of the exocarp, mesocarp, endocarp, embryo, bud, solid endosperm, liquid endosperm, and coconut apple were measured. The analysis of variances was used for temporal repeated measures and linear and non-linear regressions were used to analyze the relationship between the data.

RESULTS

The MPR images and VR models provide excellent visualization of the different structures of the coconut. The statistical results showed that the weight of coconut and liquid endosperm volume decreased significantly during the three months, while the CT value of coconut apple decreased slightly. We observed a complete germination of a coconut, its data showed a significant negative correlation between the CT value of the bud and the liquid endosperm volume (y = -2.6955x + 244.91; R2 = 0.9859), and a strong positive correlation between the height and CT value of the bud (y = 1.9576 ln(x) -2.1655; R2 = 0.9691).

CONCLUSION

CT technology can be used for visualization and quantitative analysis of the internal structure of the coconut, and some morphological changes and composition changes of the coconut during the germination process were observed during the three-month experiment. Therefore, CT is a potential tool for analyzing coconuts.

Rice immediately adapts the dynamics of photosynthates translocation to roots in response to changes in soil water environment

Rice is susceptible to abiotic stresses such as drought stress. To enhance drought resistance, elucidating the mechanisms by which rice plants adapt to intermittent drought stress that may occur in the field is an important requirement. Roots are directly exposed to changes in the soil water condition, and their responses to these environmental changes are driven by photosynthates. To visualize the distribution of photosynthates in the root system of rice plants under drought stress and recovery from drought stress, we combined X-ray computed tomography (CT) with open type positron emission tomography (OpenPET) and positron-emitting tracer imaging system (PETIS) with ¹¹C tracer. The short half-life of ¹¹C (20.39 min) allowed us to perform multiple experiments using the same plant, and thus photosynthate translocation was visualized as the same plant was subjected to drought stress and then re-irrigation for recovery. The results revealed that when soil is drier, ¹¹C-photosynthates mainly translocated to the seminal roots, likely to promote elongation of the root with the aim of accessing water stored in the lower soil layers. The photosynthates translocation to seminal roots immediately stopped after rewatering then increased significantly in crown roots. We suggest that when rice plant experiencing drought is re-irrigated from the bottom of pot, the destination of ¹¹C-photosynthates translocation immediately switches from seminal root to crown roots. We reveal that rice roots are responsive to changes in soil water conditions and that rice plants differentially adapts the dynamics of photosynthates translocation to crown roots and seminal roots depending on soil conditions.

Four-dimensional measurement of root system development using time-series three-dimensional volumetric data analysis by backward prediction

BACKGROUND

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.

RESULTS

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%.

CONCLUSIONS

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.

Three-dimensionally visualized rhizoid system of moss, Physcomitrium patens, by refraction-contrast X-ray micro-computed tomography

Land plants have two types of shoot-supporting systems, root system and rhizoid system, in vascular plants and bryophytes. However, since the evolutionary origin of the systems is different, how much they exploit common systems or distinct systems to architect their structures is largely unknown. To understand the regulatory mechanism of how bryophytes architect the rhizoid system responding to environmental factors, we have developed the methodology to visualize and quantitatively analyze the rhizoid system of the moss, Physcomitrium patens, in 3D. The rhizoids having a diameter of 21.3 µm on the average were visualized by refraction-contrast X-ray micro-computed tomography using coherent X-ray optics available at synchrotron radiation facility SPring-8. Three types of shape (ring-shape, line and black circle) observed in tomographic slices of specimens embedded in paraffin were confirmed to be the rhizoids by optical and electron microscopy. Comprehensive automatic segmentation of the rhizoids, which appeared in three different form types in tomograms, was tested by a method using a Canny edge detector or machine learning. The accuracy of output images was evaluated by comparing with the manually segmented ground truth images using measures such as F1 score and Intersection over Union, revealing that the automatic segmentation using machine learning was more effective than that using the Canny edge detector. Thus, machine learning-based skeletonized 3D model revealed quite dense distribution of rhizoids. We successfully visualized the moss rhizoid system in 3D for the first time.

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.

Assessing the Storage Root Development of Cassava with a New Analysis Tool

Storage roots of cassava plants crops are one of the main providers of starch in many South American, African, and Asian countries. Finding varieties with high yields is crucial for growing and breeding. This requires a better understanding of the dynamics of storage root formation, which is usually done by repeated manual evaluation of root types, diameters, and their distribution in excavated roots. We introduce a newly developed method that is capable to analyze the distribution of root diameters automatically, even if root systems display strong variations in root widths and clustering in high numbers. An application study was conducted with cassava roots imaged in a video acquisition box. The root diameter distribution was quantified automatically using an iterative ridge detection approach, which can cope with a wide span of root diameters and clustering. The approach was validated with virtual root models of known geometries and then tested with a time-series of excavated root systems. Based on the retrieved diameter classes, we show plausibly that the dynamics of root type formation can be monitored qualitatively and quantitatively. We conclude that this new method reliably determines important phenotypic traits from storage root crop images. The method is fast and robustly analyses complex root systems and thereby applicable in high-throughput phenotyping and future breeding.

Phenotyping for waterlogging tolerance in crops: current trends and future prospects

Yield losses to waterlogging are expected to become an increasingly costly and frequent issue in some regions of the world. Despite the extensive work that has been carried out examining the molecular and physiological responses to waterlogging, phenotyping for waterlogging tolerance has proven difficult. This difficulty is largely due to the high variability of waterlogging conditions such as duration, temperature, soil type, and growth stage of the crop. In this review, we highlight use of phenotyping to assess and improve waterlogging tolerance in temperate crop species. We start by outlining the experimental methods that have been utilized to impose waterlogging stress, ranging from highly controlled conditions of hydroponic systems to large-scale screenings in the field. We also describe the phenotyping traits used to assess tolerance ranging from survival rates and visual scoring to precise photosynthetic measurements. Finally, we present an overview of the challenges faced in attempting to improve waterlogging tolerance, the trade-offs associated with phenotyping in controlled conditions, limitations of classic phenotyping methods, and future trends using plant-imaging methods. If effectively utilized to increase crop resilience to changing climates, crop phenotyping has a major role to play in global food security.

Recent trends in root phenomics of plant systems with available methods- discrepancies and consonances

The phenotyping of plant roots is a challenging task and poses a major lacuna in plant root research. Roots rhizospheric zone is affected by several environmental cues among which salinity, drought, heavy metal and soil pH are key players. Among biological factors, fungal, nematode and bacterial interactions with roots are vital for improving nutrient uptake efficiency in plants. The subterranean nature of a plant root and the limited number of approaches for root phenotyping offers a great challenge to the plant breeders to select a desirable root trait under different stress conditions. Identification of key root traits can provide a basic understanding for generating crop plants with enhanced ability to withstand various biotic or abiotic stresses. For instance, crops with improved soil exploration potential, phosphate uptake efficiency, water use efficiency and others. Laboratory methods such as hydroponics, rhizotron, rhizoslide and luminescence observatory for roots do not provide precise and desired root quantification attributes. Though 3D imaging by X-ray computed tomography (X-ray-CT) and magnetic resonance imaging techniques are complex, however, it provides the most applicable and practically relevant data for quantifying root system architecture traits. This review outlines the current developments in root studies including recent approaches viz. X-ray-CT, MRI, thermal infrared imaging and minirhizotron. Although root phenotyping is a laborious procedure, it offers multiple advantages by removing discrepancies and providing the actual practical significance of plant roots for breeding programs.

Non-Destructive Measurement of the Pumpkin Rootstock Root Phenotype Using AZURE KINECT

Rootstock grafting is an important method to improve the yield and quality of seedlings. Pumpkin is the rootstock of watermelon, melon, and cucumber, and the root phenotype of rootstock is an important reference for breeding. At present, the root phenotype is mainly measured by scanners, with which it is difficult to achieve non-destructive and in situ measurements. In this work, we propose a method for non-destructive measurement of the root phenotype on the surface layer of the root ball of pumpkin rootstock plug seedlings and an accurate estimation of the surface area, length, and volume of total root using an AZURE KINECT sensor. Firstly, the KINECT is used to capture four-view color and depth images of the root surface, and then multi-view images are spliced to obtain a complete image of the root surface. After preprocessing of the images, we extract the roots from the root ball. For root phenotype measurements, the surface areas of the surface roots and root ball are calculated, followed by calculating root encapsulation. Next, the non-overlapping roots in the surface root image are extracted, and the ratio of the surface area to the skeleton length is used as the average diameter of total root. Based on the high correlation between the surface area of surface root and the surface area of total root, a linear fitting model is established to estimate the surface area, length, and volume of total root. The experiment ultimately showed that the measurement error for the average diameter of total root is less than 30 μm, and consistency with the scanner is higher than 93.3%. The accuracy of the surface area of total root estimation was found to be more than 88.1%, and the accuracy of the root length of total root estimation was observed to be greater than 87.2%. The method proposed in this paper offers similar accuracy to a scanner, which meets the needs of non-destructive root phenotype research. This method is expected to replace root scanners for high-throughput phenotypic measurements and provides a new avenue for root phenotype measurements of pumpkin rootstocks. This technology will provide key basic data for evaluating the root growth of pumpkin rootstocks.

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.

Root dynamic growth strategies in response to salinity

Increasing soil salinization largely impacts crop yield worldwide. To deal with salinity stress, plants exhibit an array of responses, including root system architecture remodelling. Here, we review recent progress in physiological, developmental and cellular mechanisms of root growth responses to salinity. Most recent research in modulation of root branching, root tropisms, as well as in root cell wall modifications under salinity stress, is discussed in the context of the contribution of these responses to overall plant performance. We highlight the power of natural variation approaches revealing novel potential pathways responsible for differences in root salt stress responses. Together, these new findings promote our understanding of how salt shapes the root phenotype, which may provide potential avenues for engineering crops with better yield and survival in saline soils.

Mini review: Targeting below-ground plant performance to improve nitrogen use efficiency (NUE) in barley

Nitrogen (N) fertilizer is one of the major inputs for grain crops including barley and its usage is increasing globally. However, N use efficiency (NUE) is low in cereal crops, leading to higher production costs, unfulfilled grain yield potential and environmental hazards. N uptake is initiated from plant root tips but a very limited number of studies have been conducted on roots relevant to NUE specifically. In this review, we used barley, the fourth most important cereal crop, as the primary study plant to investigate this topic. We first highlighted the recent progress and study gaps in genetic analysis results, primarily, the genome-wide association study (GWAS) regarding both biological and statistical considerations. In addition, different factors contributing to NUE are discussed in terms of root morphological and anatomical traits, as well as physiological mechanisms such as N transporter activities and hormonal regulation.

New approaches to improve crop tolerance to biotic and abiotic stresses

During the last years, a great effort has been dedicated at the development and employment of diverse approaches for achieving more stress-tolerant and climate-flexible crops and sustainable yield increases to meet the food and energy demands of the future. The ongoing climate change is in fact leading to more frequent extreme events with a negative impact on food production, such as increased temperatures, drought, and soil salinization as well as invasive arthropod pests and diseases. In this review, diverse "green strategies" (e.g., chemical priming, root-associated microorganisms), and advanced technologies (e.g., genome editing, high-throughput phenotyping) are described on the basis of the most recent research evidence. Particularly, attention has been focused on the potential use in a context of sustainable and climate-smart agriculture (the so called "next agriculture generation") to improve plant tolerance and resilience to abiotic and biotic stresses. In addition, the gap between the results obtained in controlled experiments and those from application of these technologies in real field conditions (lab to field step) is also discussed.

Machine Learning Algorithms to Distinguish Myocardial Perfusion SPECT Polar Maps

Myocardial perfusion imaging (MPI) plays an important role in patients with suspected and documented coronary artery disease (CAD). Machine Learning (ML) algorithms have been developed for many medical applications with excellent performance. This study used ML algorithms to discern normal and abnormal gated Single Photon Emission Computed Tomography (SPECT) images. We analyzed one thousand and seven polar maps from a database of patients referred to a university hospital for clinically indicated MPI between January 2016 and December 2018. These studies were reported and evaluated by two different expert readers. The image features were extracted from a specific type of polar map segmentation based on horizontal and vertical slices. A senior expert reading was the comparator (gold standard). We used cross-validation to divide the dataset into training and testing subsets, using data augmentation in the training set, and evaluated 04 ML models. All models had accuracy >90% and area under the receiver operating characteristics curve (AUC) >0.80 except for Adaptive Boosting (AUC = 0.77), while all precision and sensitivity obtained were >96 and 92%, respectively. Random Forest had the best performance (AUC: 0.853; accuracy: 0,938; precision: 0.968; sensitivity: 0.963). ML algorithms performed very well in image classification. These models were capable of distinguishing polar maps remarkably into normal and abnormal.

Drought Sensitivity of Sugarcane Cultivars Shapes Rhizosphere Bacterial Community Patterns in Response to Water Stress

Rhizosphere bacteria, the main functional microorganisms inhabiting the roots of terrestrial plants, play important roles in regulating plant growth and environmental stress resistance. However, limited information is available regarding changes occurring within the structure of the root microbial community and the response mechanisms of host plants that improve adaptability to drought stress. In this study, we conducted an experiment on two sugarcane varieties with different drought tolerance levels under drought and control treatments and analyzed the rhizosphere bacterial communities using 16S rRNA high-throughput sequencing. Correlation analysis results clarified the influence of various factors on the rhizosphere bacterial community structure. Drought stress reduced the diversity of the bacterial community in the rhizosphere of sugarcane. Interestingly, the bacterial community of the drought-sensitive sugarcane cultivar GT39 changed more than that of the drought-tolerant cultivar ZZ9. In addition, ZZ9 had a high abundance of drought-resistant bacteria in the rhizosphere under optimal soil water conditions, whereas GT39 accumulated a large number of drought-resistant bacteria only under drought stress. GT39 mainly relied on Actinobacteria in its response to drought stress, and the abundance of this phylum was positively correlated with soil acid phosphatase and protease levels. In contrast, ZZ9 mainly relied on Bacilli in its response to drought stress, and the abundance of this class was positively correlated with only soil acid phosphatase levels. In conclusion, drought stress can significantly reduce the bacterial diversity and increase the abundance of drought-resistant bacteria in the sugarcane rhizosphere. The high abundance of drought-resistant bacteria in the rhizosphere of drought-tolerant cultivars under non-drought conditions is an important factor contributing to the high drought adaptability of these cultivars. Moreover, the core drought-resistant bacteria of the sugarcane rhizosphere and root exudates jointly affect the resistance of sugarcane to drought.

X-ray computed tomography for 3D plant imaging

X-ray computed tomography (CT) is a valuable tool for 3D imaging of plant tissues and organs. Applications include the study of plant development and organ morphogenesis, as well as modeling of transport processes in plants. Some challenges remain, however, including attaining higher contrast for easier quantification, increasing the resolution for imaging subcellular features, and decreasing image acquisition and processing time for high-throughput phenotyping. In addition, phase contrast, multispectral, dark-field, soft X-ray, and time-resolved imaging are emerging. At the same time, a large amount of 3D image data are becoming available, posing challenges for data management. We review recent advances in the area of X-ray CT for plant imaging, and describe opportunities for using such images for studying transport processes in plants.

iPOTs: Internet of Things-based pot system controlling optional treatment of soil water condition for plant phenotyping under drought stress

A cultivation facility that can assist users in controlling the soil water condition is needed for accurately phenotyping plants under drought stress in an artificial environment. Here we report the Internet of Things-based pot system controlling optional treatment of soil water condition (iPOTs), an automatic irrigation system that mimics the drought condition in a growth chamber. The Wi-Fi-enabled iPOTs system allows water supply from the bottom of the pot, based on the soil water level set by the user, and automatically controls the soil water level at a desired depth. The iPOTs also allows users to monitor environmental parameters, such as soil temperature, air temperature, humidity, and light intensity, in each pot. To verify whether the iPOTs mimics the drought condition, we conducted a drought stress test on rice (Oryza sativa L.) varieties and near-isogenic lines, with diverse root system architecture, using the iPOTs system installed in a growth chamber. Similar to the results of a previous drought stress field trial, the growth of shallow-rooted rice accessions was severely affected by drought stress compared with that of deep-rooted accessions. The microclimate data obtained using the iPOTs system increased the accuracy of plant growth evaluation. Transcriptome analysis revealed that pot positions in the growth chamber had little impact on plant growth. Together, these results suggest that the iPOTs system is a reliable platform for phenotyping plants under drought stress.

RSAtrace3D: robust vectorization software for measuring monocot root system architecture

BACKGROUND

The 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.

RESULTS

We 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.

CONCLUSIONS

RSAtrace3D 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.

Image analysis for the automatic phenotyping of Orobanche cumana tubercles on sunflower roots

BACKGROUND

The parasitic plant Orobanche cumana is one of the most important threats to sunflower crops in Europe. Resistant sunflower varieties have been developed, but new O. cumana races have evolved and have overcome introgressed resistance genes, leading to the recurrent need for new resistance methods. Screening for resistance requires the phenotyping of thousands of sunflower plants to various O. cumana races. Most phenotyping experiments have been performed in fields at the later stage of the interaction, requiring time and space. A rapid phenotyping screening method under controlled conditions would need less space and would allow screening for resistance of many sunflower genotypes. Our study proposes a phenotyping tool for the sunflower/O. cumana interaction under controlled conditions through image analysis for broomrape tubercle analysis at early stages of the interaction.

RESULTS

We optimized the phenotyping of sunflower/O. cumana interactions by using rhizotrons (transparent Plexiglas boxes) in a growth chamber to control culture conditions and Orobanche inoculum. We used a Raspberry Pi computer with a picamera for acquiring images of inoculated sunflower roots 3 weeks post inoculation. We set up a macro using ImageJ free software for the automatic counting of the number of tubercles. This phenotyping tool was named RhizOSun. We evaluated five sunflower genotypes inoculated with two O. cumana races and showed that automatic counting of the number of tubercles using RhizOSun was highly correlated with manual time-consuming counting and could be efficiently used for screening sunflower genotypes at the tubercle stage.

CONCLUSION

This method is rapid, accurate and low-cost. It allows rapid imaging of numerous rhizotrons over time, and it enables image tracking of all the data with time kinetics. This paves the way toward automatization of phenotyping in rhizotrons that could be used for other root phenotyping, such as symbiotic nodules on legumes.

Visualization of Arabidopsis Root System Architecture in 3D by Refraction-Contrast X-Ray Micro-Computed Tomography

Plant roots change their morphological traits in order to adapt themselves to different environmental conditions, resulting in alteration of the root system architecture. To understand this mechanism, it is essential to visualize morphology of the entire root system. To reveal effects of long-term alteration of gravity environment on root system development, we have performed an experiment in the International Space Station using Arabidopsis plants and obtained dried root systems grown in rockwool slabs. X-ray computed tomography (CT) technique using industrial X-ray scanners has been introduced to visualize root system architecture of crop species grown in soil in 3D non-invasively. In the case of the present study, however, root system of Arabidopsis is composed of finer roots compared with typical crop plants and rockwool is also composed of fibers having similar dimension to that of the roots. A higher spatial resolution imaging method is required for distinguishing roots from rockwool. Therefore, in the present study, we tested refraction-contrast X-ray micro-CT using coherent X-ray optics available at the beamline of the synchrotron radiation facility SPring-8 for bio-imaging. We have found that wide field of view but with low resolution obtained at the experimental Hutch 3 of this beamline provided an overview map of the root systems, while narrow field of view but with high resolution obtained at the experimental Hutch 1 provided extended architecture of the secondary roots, by clear distinction between roots and individual rockwool fibers, resulting in successful tracing of these roots from their basal regions.

An improved method for the segmentation of roots from X-ray computed tomography 3D images: Rootine v.2

BACKGROUND

X-ray computed tomography is acknowledged as a powerful tool for the study of root system architecture of plants growing in soil. In this paper, we improved the original root segmentation algorithm "Rootine" and present its succeeding version "Rootine v.2". In addition to gray value information, Rootine algorithms are based on shape detection of cylindrical roots. Both algorithms are macros for the ImageJ software and are made freely available to the public. New features in Rootine v.2 are (i) a pot wall detection and removal step to avoid segmentation artefacts for roots growing along the pot wall, (ii) a calculation of the root average gray value based on a histogram analysis, (iii) an automatic calculation of thresholds for hysteresis thresholding of the tubeness image to reduce the number of parameters and (iv) a false negatives recovery based on shape criteria to increase root recovery. We compare the segmentation results of Rootine v.1 and Rootine v.2 with the results of root washing and subsequent analysis with WinRhizo. We use a benchmark dataset of maize roots (Zea mays L. cv. B73) grown in repacked soil for two scenarios with differing soil heterogeneity and image quality.

RESULTS

We demonstrate that Rootine v.2 outperforms its preceding version in terms of root recovery and enables to match better the root diameter distribution data obtained with root washing. Despite a longer processing time, Rootine v.2 comprises less user-defined parameters and shows an overall greater usability.

CONCLUSION

The proposed method facilitates higher root detection accuracy than its predecessor and has the potential for improving high-throughput root phenotyping procedures based on X-ray computed tomography data analysis.

X-Ray CT Phenotyping Reveals Bi-Phasic Growth Phases of Potato Tubers Exposed to Combined Abiotic Stress

As a consequence of climate change, heat waves in combination with extended drought periods will be an increasing threat to crop yield. Therefore, breeding stress tolerant crop plants is an urgent need. Breeding for stress tolerance has benefited from large scale phenotyping, enabling non-invasive, continuous monitoring of plant growth. In case of potato, this is compromised by the fact that tubers grow belowground, making phenotyping of tuber development a challenging task. To determine the growth dynamics of tubers before, during and after stress treatment is nearly impossible with traditional destructive harvesting approaches. In contrast, X-ray Computed Tomography (CT) offers the opportunity to access belowground growth processes. In this study, potato tuber development from initiation until harvest was monitored by CT analysis for five different genotypes under stress conditions. Tuber growth was monitored three times per week via CT analysis. Stress treatment was started when all plants exhibited detectable tubers. Combined heat and drought stress was applied by increasing growth temperature for 2 weeks and simultaneously decreasing daily water supply. CT analysis revealed that tuber growth is inhibited under stress within a week and can resume after the stress has been terminated. After cessation of stress, tubers started growing again and were only slightly and insignificantly smaller than control tubers at the end of the experimental period. These growth characteristics were accompanied by corresponding changes in gene expression and activity of enzymes relevant for starch metabolism which is the driving force for tuber growth. Gene expression and activity of Sucrose Synthase (SuSy) reaffirmed the detrimental impact of the stress on starch biosynthesis. Perception of the stress treatment by the tubers was confirmed by gene expression analysis of potential stress marker genes whose applicability for potato tubers is further discussed. We established a semi-automatic imaging pipeline to analyze potato tuber delevopment in a medium thoughput (5 min per pot). The imaging pipeline presented here can be scaled up to be used in high-throughput phenotyping systems. However, the combination with automated data processing is the key to generate objective data accelerating breeding efforts to improve abiotic stress tolerance of potato genotypes.

Challenges to design-oriented breeding of root system architecture adapted to climate change

Roots are essential organs for capturing water and nutrients from the soil. In particular, root system architecture (RSA) determines the extent of the region of the soil where water and nutrients can be gathered. As global climate change accelerates, it will be important to improve belowground plant parts, as well as aboveground ones, because roots are front-line organs in the response to abiotic stresses such as drought, flooding, and salinity stress. However, using conventional breeding based on phenotypic selection, it is difficult to select breeding lines possessing promising RSAs to adapted to abiotic stress because roots remain hidden underground. Therefore, new breeding strategies that do not require phenotypic selection are necessary. Recent advances in molecular biology and biotechnology can be applied to the design-oriented breeding of RSA without phenotypic selection. Here I summarize recent progress in RSA ideotypes as "design" and RSA-related gene resources as "materials" that will be needed in leveraging these technologies for the RSA breeding. I also highlight the future challenges to design-oriented breeding of RSA and explore solutions to these challenges.

Importance of individual root traits to understand crop root system in agronomic and environmental contexts

Resource acquisition, one of the major functions of roots, can contribute to crop growth and mitigating environmental impacts. The spatio-temporal distribution of roots in the soil in relation to the dynamics of the soil resources is critical in resource acquisition. Root distribution is determined by root system development. The root system consists of many individual roots of different types and ages. Each individual root has specific development, resource acquisition, and transport traits, which change with root growth. The integration of individual root traits in the root system could exhibit crop performance in the various environments via root distribution in the soil. However, the relationship between individual root traits and the pattern of root distribution is complicated. To understand this complicated relationship, we need to evaluate enormous numbers of individual root traits and understand the relationship between individual root development and root distribution as well as the integrated functions of individual root traits along with dynamics of resources in the soil.

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.

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.

Climate-smart crops: key root anatomical traits that confer flooding tolerance

Plants require water, but a deficit or excess of water can negatively impact their growth and functioning. Soil flooding, in which root-zone is filled with excess water, restricts oxygen diffusion into the soil. Global climate change is increasing the risk of crop yield loss caused by flooding, and the development of flooding tolerant crops is urgently needed. Root anatomical traits are essential for plants to adapt to drought and flooding, as they determine the balance between the rates of water and oxygen transport. The stele contains xylem and the cortex contains aerenchyma (gas spaces), which respectively contribute to water uptake from the soil and oxygen supply to the roots; this implies that there is a trade-off between the ratio of cortex and stele sizes with respect to adaptation to drought or flooding. In this review, we analyze recent advances in the understanding of root anatomical traits that confer drought and/or flooding tolerance to plants and illustrate the trade-off between cortex and stele sizes. Moreover, we introduce the progress that has been made in modelling and fully automated analyses of root anatomical traits and discuss how key root anatomical traits can be used to improve crop tolerance to soil flooding.

Visualization of Toyoura sand-grown plant roots by X-ray computer tomography

Plants establish their root system as a three-dimensional structure, which is then used to explore the soil to absorb resources and provide mechanical anchorage. Simplified two-dimensional growth systems, such as agar plates, have been used to study various aspects of plant root biology. However, it remains challenging to study the more realistic three-dimensional structure and function of roots hidden in opaque soil. Here, we optimized X-ray computer tomography (CT)-based visualization of an intact root system by using Toyoura sand, a standard silica sand used in geotechnology research, as a growth substrate. Distinct X-ray attenuation densities of root tissue and Toyoura sand enabled clear image segmentation of the CT data. Sorghum grew especially vigorously in Toyoura sand and it could be used as a model for analyzing root structure optimization in response to mechanical obstacles. The use of Toyoura sand has the potential to link plant root biology and geotechnology applications.