High throughput phenotyping of morpho-anatomical stem properties using X-ray computed tomography in sorghum

Noninvasive Abiotic Stress Phenotyping of Vascular Plant in Each Vegetative Organ View

The last decades have witnessed a rapid development of noninvasive plant phenotyping, capable of detecting plant stress scale levels from the subcellular to the whole population scale. However, even with such a broad range, most phenotyping objects are often just concerned with leaves. This review offers a unique perspective of noninvasive plant stress phenotyping from a multi-organ view. First, plant sensing and responding to abiotic stress from the diverse vegetative organs (leaves, stems, and roots) and the interplays between these vital components are analyzed. Then, the corresponding noninvasive optical phenotyping techniques are also provided, which can prompt the practical implementation of appropriate noninvasive phenotyping techniques for each organ. Furthermore, we explore methods for analyzing compound stress situations, as field conditions frequently encompass multiple abiotic stressors. Thus, our work goes beyond the conventional approach of focusing solely on individual plant organs. The novel insights of the multi-organ, noninvasive phenotyping study provide a reference for testing hypotheses concerning the intricate dynamics of plant stress responses, as well as the potential interactive effects among various stressors.

MiTiSegmenter: Software for high throughput segmentation and meshing of microCT data in microtiter plate arrays

Lab-based microCT is a powerful means of visualising the internal structure of physical specimens deployed across the physical sciences, engineering and the arts. As its popularity has grown, demand for bulk digitisation of multiple samples within a single scan has increased. High throughput workflows can increase sample sizes and reduce scan time, yet downstream segmentation and meshing remain a bottleneck. We present MiTiSegmenter as a new tool for the bulk archiving of valuable zooarchaeological and palaeontological remains. We foresee MiTiSegmenter as particularly useful when incorporated into workflows that ultimately require the destructive testing of specimens, including sampling for ancient DNA and proteomics. The software may also play an important role in national museums' ongoing mass digitisation efforts, facilitating the high-speed archiving of specimen 3D morphology across extensive collections with very minimal user intervention or prior training. •We present MiTiSegmenter, a software package for semi-automated image processing and segmentation of array-based batch microCT data.•Implemented in Python, MiTiSegmenter expedites cropping, meshing and exporting samples within stacked microtiter plates, facilitating the rapid digitisation of hundreds-thousands of samples per scan.•We illustrate MiTiSegmenter's capabilities when applied to bulk archiving of valuable zooarchaeological and palaeontological remains.

X-ray driven peanut trait estimation: computer vision aided agri-system transformation

BACKGROUND

In India, raw peanuts are obtained by aggregators from smallholder farms in the form of whole pods and the price is based on a manual estimation of basic peanut pod and kernel characteristics. These methods of raw produce evaluation are slow and can result in procurement irregularities. The procurement delays combined with the lack of storage facilities lead to fungal contaminations and pose a serious threat to food safety in many regions. To address this gap, we investigated whether X-ray technology could be used for the rapid assessment of the key peanut qualities that are important for price estimation.

RESULTS

We generated 1752 individual peanut pod 2D X-ray projections using a computed tomography (CT) system (CTportable160.90). Out of these projections we predicted the kernel weight and shell weight, which are important indicators of the produce price. Two methods for the feature prediction were tested: (i) X-ray image transformation (XRT) and (ii) a trained convolutional neural network (CNN). The prediction power of these methods was tested against the gravimetric measurements of kernel weight and shell weight in diverse peanut pod varieties¹. Both methods predicted the kernel mass with R² > 0.93 (XRT: R² = 0.93 and mean error estimate (MAE) = 0.17, CNN: R² = 0.95 and MAE = 0.14). While the shell weight was predicted more accurately by CNN (R² = 0.91, MAE = 0.09) compared to XRT (R² = 0.78; MAE = 0.08).

CONCLUSION

Our study demonstrated that the X-ray based system is a relevant technology option for the estimation of key peanut produce indicators (Figure 1). The obtained results justify further research to adapt the existing X-ray system for the rapid, accurate and objective peanut procurement process. Fast and accurate estimates of produce value are a necessary pre-requisite to avoid post-harvest losses due to fungal contamination and, at the same time, allow the fair payment to farmers. Additionally, the same technology could also assist crop improvement programs in selecting and developing peanut cultivars with enhanced economic value in a high-throughput manner by skipping the shelling of the pods completely. This study demonstrated the technical feasibility of the approach and is a first step to realize a technology-driven peanut production system transformation of the future.

Application of X-ray computed tomography to analyze the structure of sorghum grain

BACKGROUND

The structural characteristics of whole sorghum kernels are known to affect end-use quality, but traditional evaluation of this structure is two-dimensional (i.e., cross section of a kernel). Current technology offers the potential to consider three-dimensional structural characteristics of grain. X-ray computed tomography (CT) presents one such opportunity to nondestructively extract quantitative data from grain caryopses which can then be related to end-use quality.

RESULTS

Phenotypic measurements were extracted from CT scans of grain sorghum caryopses. Extensive phenotypic variation was found for embryo volume, endosperm hardness, endosperm texture, endosperm volume, pericarp volume, and kernel volume. CT derived estimates were strongly correlated with ground truth measurements enabling the identification of genotypes with superior structural characteristics.

CONCLUSIONS

Presented herein is a phenotyping pipeline developed to quantify three-dimensional structural characteristics from grain sorghum caryopses which increases the throughput efficiency of previously difficult to measure traits. Adaptation of this workflow to other small-seeded crops is possible providing new and unique opportunities for scientists to study grain in a nondestructive manner which will ultimately lead to improvements end-use quality.

Phenotyping Complex Plant Structures with a Large Format Industrial Scale High-Resolution X-Ray Tomography Instrument

Phenotyping specific plant traits is difficult when the samples to be measured are architecturally complex. Inflorescence and root system traits are of great biological interest, but these structures present unique phenotyping challenges due to their often complicated and three-dimensional (3D) forms. We describe how a large industrial scale X-ray tomography (XRT) instrument can be used to scan architecturally complex plant structures for the goal of rapid and accurate measurement of traits that are otherwise cumbersome or not possible to capture by other means. The combination of a large imaging cabinet that can accommodate a wide range of sample size geometries and a variable microfocus reflection X-ray source allows noninvasive X-ray imaging and 3D volume generation of diverse sample types. Specific sample fixturing (mounting) and scanning conditions are presented. These techniques can be moderate to high throughput and still provide unprecedented levels of accuracy and information content in the 3D volume data they generate.

High-Throughput Plant Phenotyping Platform (HT3P) as a Novel Tool for Estimating Agronomic Traits From the Lab to the Field

Food scarcity, population growth, and global climate change have propelled crop yield growth driven by high-throughput phenotyping into the era of big data. However, access to large-scale phenotypic data has now become a critical barrier that phenomics urgently must overcome. Fortunately, the high-throughput plant phenotyping platform (HT3P), employing advanced sensors and data collection systems, can take full advantage of non-destructive and high-throughput methods to monitor, quantify, and evaluate specific phenotypes for large-scale agricultural experiments, and it can effectively perform phenotypic tasks that traditional phenotyping could not do. In this way, HT3Ps are novel and powerful tools, for which various commercial, customized, and even self-developed ones have been recently introduced in rising numbers. Here, we review these HT3Ps in nearly 7 years from greenhouses and growth chambers to the field, and from ground-based proximal phenotyping to aerial large-scale remote sensing. Platform configurations, novelties, operating modes, current developments, as well the strengths and weaknesses of diverse types of HT3Ps are thoroughly and clearly described. Then, miscellaneous combinations of HT3Ps for comparative validation and comprehensive analysis are systematically present, for the first time. Finally, we consider current phenotypic challenges and provide fresh perspectives on future development trends of HT3Ps. This review aims to provide ideas, thoughts, and insights for the optimal selection, exploitation, and utilization of HT3Ps, and thereby pave the way to break through current phenotyping bottlenecks in botany.

Time-dependent mechanical behavior of sweet sorghum stems

Grasses represent the most productive and widely grown crop family across the globe but are susceptible to structural failure (lodging) during growth (e.g., from wind). The mechanisms that contribute to structural failure in grass stems are poorly understood due to a lack of systematic studies of their biomechanical behavior. To this end, this study examines the biomechanical properties of sweet sorghum (Sorghum bicolor (L.) Moench), focusing on the time-dependent behavior of the stems. Specifically, we conducted uniaxial compression tests under ramp and creep loading on pith and stem specimens of the sorghum cultivar Della. The tests demonstrated significantly nonlinear and time-dependent stress-strain behavior in all samples. We surmise that this behavior arises from a combination of poroelasticity due to migration of water through the plant and viscoelasticity due to rearrangement of macromolecular networks, such as cellulose microfibrils and lignin matrices. Overall, our measurements demonstrate that sorghum is not a simple reversible elastic material. As such, a complete understanding of the conditions that lead to stem lodging will require knowledge of sorghum's time-dependent biomechanical properties. Of practical importance, the time-dependent biomechanical properties of the stem influence its mechanical stability under various loading conditions during growth in the field (e.g., different wind speeds).

ROSE-X: an annotated data set for evaluation of 3D plant organ segmentation methods

BACKGROUND

The production and availability of annotated data sets are indispensable for training and evaluation of automatic phenotyping methods. The need for complete 3D models of real plants with organ-level labeling is even more pronounced due to the advances in 3D vision-based phenotyping techniques and the difficulty of full annotation of the intricate 3D plant structure.

RESULTS

We introduce the ROSE-X data set of 11 annotated 3D models of real rosebush plants acquired through X-ray tomography and presented both in volumetric form and as point clouds. The annotation is performed manually to provide ground truth data in the form of organ labels for the voxels corresponding to the plant shoot. This data set is constructed to serve both as training data for supervised learning methods performing organ-level segmentation and as a benchmark to evaluate their performance. The rosebush models in the data set are of high quality and complex architecture with organs frequently touching each other posing a challenge for the current plant organ segmentation methods. We report leaf/stem segmentation results obtained using four baseline methods. The best performance is achieved by the volumetric approach where local features are trained with a random forest classifier, giving Intersection of Union (IoU) values of 97.93% and 86.23% for leaf and stem classes, respectively.

CONCLUSION

We provided an annotated 3D data set of 11 rosebush plants for training and evaluation of organ segmentation methods. We also reported leaf/stem segmentation results of baseline methods, which are open to improvement. The data set, together with the baseline results, has the potential of becoming a significant resource for future studies on automatic plant phenotyping.

Transcriptional Regulation of Sorghum Stem Composition: Key Players Identified Through Co-expression Gene Network and Comparative Genomics Analyses

Most sorghum biomass accumulates in stem secondary cell walls (SCW). As sorghum stems are used as raw materials for various purposes such as feed, energy and fiber reinforced polymers, identifying the genes responsible for SCW establishment is highly important. Taking advantage of studies performed in model species, most of the structural genes contributing at the molecular level to the SCW biosynthesis in sorghum have been proposed while their regulatory factors have mostly not been determined. Validation of the role of several MYB and NAC transcription factors in SCW regulation in Arabidopsis and a few other species has been provided. In this study, we contributed to the recent efforts made in grasses to uncover the mechanisms underlying SCW establishment. We reported updated phylogenies of NAC and MYB in 9 different species and exploited findings from other species to highlight candidate regulators of SCW in sorghum. We acquired expression data during sorghum internode development and used co-expression analyses to determine groups of co-expressed genes that are likely to be involved in SCW establishment. We were able to identify two groups of co-expressed genes presenting multiple evidences of involvement in SCW building. Gene enrichment analysis of MYB and NAC genes provided evidence that while NAC SECONDARY WALL THICKENING PROMOTING FACTOR NST genes and SECONDARY WALL-ASSOCIATED NAC DOMAIN PROTEIN gene functions appear to be conserved in sorghum, NAC master regulators of SCW in sorghum may not be as tissue compartmentalized as in Arabidopsis. We showed that for every homolog of the key SCW MYB in Arabidopsis, a similar role is expected for sorghum. In addition, we unveiled sorghum MYB and NAC that have not been identified to date as being involved in cell wall regulation. Although specific validation of the MYB and NAC genes uncovered in this study is needed, we provide a network of sorghum genes involved in SCW both at the structural and regulatory levels.

A novel rind puncture technique to measure rind thickness and diameter in plant stalks

BACKGROUND

Measurements of rind and culm thickness and stem radius/diameter are important to biomechanical, ecological and physiological plant studies. However, many methods of measuring rind thickness and diameter are labor intensive and induce plant fatality. A novel rind puncture methodology for obtaining measurements of rind thickness and diameter has been developed. The suitability of the new method for implementation in plant studies is presented.

RESULTS

The novel rind puncture technique was used to obtain measurements of rind thickness and diameter for samples of Poison Hemlock (Conium maculatum). The rind puncture measurements were strongly correlated with caliper measurements (R² > 0.97) and photographic image analysis measurements (R² > 0.84). The capacity for high throughput measurements using the rind puncture technique was determined to exceed that of caliper measurements and image analysis techniques.

CONCLUSIONS

The rind puncture technique shows promise as a high throughput method for determining rind thickness and diameter as it is cost effective and non-lethal. The authors are currently working to develop a custom handheld apparatus to allow the novel rind puncture method to be used in field work. High throughput field-based measurements of rind thickness and diameter are needed to help address the problem of stalk lodging (failure of grain crops to remain upright until harvest).

Characterizing 3D inflorescence architecture in grapevine using X-ray imaging and advanced morphometrics: implications for understanding cluster density

Inflorescence architecture provides the scaffold on which flowers and fruits develop, and consequently is a primary trait under investigation in many crop systems. Yet the challenge remains to analyse these complex 3D branching structures with appropriate tools. High information content datasets are required to represent the actual structure and facilitate full analysis of both the geometric and the topological features relevant to phenotypic variation in order to clarify evolutionary and developmental inflorescence patterns. We combined advanced imaging (X-ray tomography) and computational approaches (topological and geometric data analysis and structural simulations) to comprehensively characterize grapevine inflorescence architecture (the rachis and all branches without berries) among 10 wild Vitis species. Clustering and correlation analyses revealed unexpected relationships, for example pedicel branch angles were largely independent of other traits. We identified multivariate traits that typified species, which allowed us to classify species with 78.3% accuracy, versus 10% by chance. Twelve traits had strong signals across phylogenetic clades, providing insight into the evolution of inflorescence architecture. We provide an advanced framework to quantify 3D inflorescence and other branched plant structures that can be used to tease apart subtle, heritable features for a better understanding of genetic and environmental effects on plant phenotypes.

3D Morphological Processing for Wheat Spike Phenotypes Using Computed Tomography Images

Wheat is the main food crop today world-wide. In order to improve its yields, researchers are committed to understand the relationships between wheat genotypes and phenotypes. Compared to progressive technology of wheat gene section identification, wheat trait measurement is mostly done manually in a destructive, labor-intensive and time-consuming way. Therefore, this study will be greatly accelerated and promoted if we can automatically discover wheat phenotype in a nondestructive and fast manner. In this paper, we propose a novel pipeline based on 3D morphological processing to detect wheat spike grains and stem nodes from 3D X-ray micro computed tomography (CT) images. We also introduce a set of newly defined 3D phenotypes, including grain aspect ratio, porosity, Grain-to-Grain distance, and grain angle, which are very difficult to be manually measured. The analysis of the associations among these traits would be very helpful for wheat breeding. Experimental results show that our method is able to count grains more accurately than normal human performance. By analyzing the relationships between traits and environment conditions, we find that the Grain-to-Grain distance, aspect ratio and porosity are more likely affected by the genome than environment (only tested temperature and water conditions). We also find that close grains will inhibit grain volume growth and that the aspect ratio 3.5 may be the best for higher yield in wheat breeding.

Genetic and genomic resources of sorghum to connect genotype with phenotype in contrasting environments

With the recent development of genomic resources and high-throughput phenotyping platforms, the 21st century is primed for major breakthroughs in the discovery, understanding and utilization of plant genetic variation. Significant advances in agriculture remain at the forefront to increase crop production and quality to satisfy the global food demand in a changing climate all while reducing the environmental impacts of the world's food production. Sorghum, a resilient C₄ grain and grass important for food and energy production, is being extensively dissected genetically and phenomically to help connect the relationship between genetic and phenotypic variation. Unlike genetically modified crops such as corn or soybean, sorghum improvement has relied heavily on public research; thus, many of the genetic resources serve a dual purpose for both academic and commercial pursuits. Genetic and genomic resources not only provide the foundation to identify and understand the genes underlying variation, but also serve as novel sources of genetic and phenotypic diversity in plant breeding programs. To better disseminate the collective information of this community, we discuss: (i) the genomic resources of sorghum that are at the disposal of the research community; (ii) the suite of sorghum traits as potential targets for increasing productivity in contrasting environments; and (iii) the prospective approaches and technologies that will help to dissect the genotype-phenotype relationship as well as those that will apply foundational knowledge for sorghum improvement.

Sorghum stem aerenchyma formation is regulated by SbNAC_D during internode development

Sorghum bicolor is a drought-resilient C4 grass used for production of grain, forage, sugar, and biomass. Sorghum genotypes capable of accumulating high levels of stem sucrose have solid stems that contain low levels of aerenchyma. The D-locus on SBI06 modulates the extent of aerenchyma formation in sorghum stems and leaf midribs. A QTL aligned with this locus was identified and fine-mapped in populations derived from BTx623*IS320c, BTx623*R07007, and BTx623*Standard broomcorn. Analysis of coding polymorphisms in the fine-mapped D-locus showed that genotypes that accumulate low levels of aerenchyma encode a truncated NAC transcription factor (Sobic.006G147400, SbNAC_d1), whereas parental lines that accumulate higher levels of stem aerenchyma encode full-length NAC TFs (SbNAC-D). During vegetative stem development, aerenchyma levels are low in nonelongated stem internodes, internode growing zones, and nodes. Aerenchyma levels increase in recently elongated internodes starting at the top of the internode near the center of the stem. SbNAC_D was expressed at low levels in nonelongated internodes and internode growing zones and at higher levels in regions of stem internodes that form aerenchyma. SbXCP1, a gene encoding a cysteine protease involved in programmed cell death, was induced in SbNAC_D genotypes in parallel with aerenchyma formation in sorghum stems but not in SbNAC_d1 genotypes. Several sweet sorghum genotypes encode the recessive SbNAC_d1 allele and have low levels of stem aerenchyma. Based on these results, we propose that SbNAC_D is the D-gene identified by Hilton (1916) and that allelic variation in SbNAC_D modulates the extent of aerenchyma formation in sorghum stems.