X-ray computed tomography to study rice (Oryza sativa L.) panicle development

Size measurement and filled/unfilled detection of rice grains using backlight image processing

Measurements of rice physical traits, such as length, width, and percentage of filled/unfilled grains, are essential steps of rice breeding. A new approach for measuring the physical traits of rice grains for breeding purposes was presented in this study, utilizing image processing techniques. Backlight photography was used to capture a grayscale image of a group of rice grains, which was then analyzed using a clustering algorithm to differentiate between filled and unfilled grains based on their grayscale values. The impact of backlight intensity on the accuracy of the method was also investigated. The results show that the proposed method has excellent accuracy and high efficiency. The mean absolute percentage error of the method was 0.24% and 1.36% in calculating the total number of grain particles and distinguishing the number of filled grains, respectively. The grain size was also measured with a little margin of error. The mean absolute percentage error of grain length measurement was 1.11%, while the measurement error of grain width was 4.03%. The method was found to be highly accurate, non-destructive, and cost-effective when compared to conventional methods, making it a promising approach for characterizing physical traits for crop breeding.

A non-destructive coconut fruit and seed traits extraction method based on Micro-CT and deeplabV3+ model

With the completion of the coconut gene map and the gradual improvement of related molecular biology tools, molecular marker-assisted breeding of coconut has become the next focus of coconut breeding, and accurate coconut phenotypic traits measurement will provide technical support for screening and identifying the correspondence between genotype and phenotype. A Micro-CT system was developed to measure coconut fruits and seeds automatically and nondestructively to acquire the 3D model and phenotyping traits. A deeplabv3+ model with an Xception backbone was used to segment the sectional image of coconut fruits and seeds automatically. Compared with the structural-light system measurement, the mean absolute percentage error of the fruit volume and surface area measurements by the Micro-CT system was 1.87% and 2.24%, respectively, and the squares of the correlation coefficients were 0.977 and 0.964, respectively. In addition, compared with the manual measurements, the mean absolute percentage error of the automatic copra weight and total biomass measurements was 8.85% and 25.19%, respectively, and the adjusted squares of the correlation coefficients were 0.922 and 0.721, respectively. The Micro-CT system can nondestructively obtain up to 21 agronomic traits and 57 digital traits precisely.

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.

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.

Sonication increases the porosity of uncooked rice kernels affording softer textural properties, loss of intrinsic nutrients and increased uptake capacity during fortification

This work investigates the effect of sonication on brown and milled rice grains of both waxy and non-waxy varieties. We report herein the microstructural analysis of uncooked rice kernels under sonication and its effect on the textural properties. X-ray computed tomography results showed the formation of microporous surfaces and the creation of cracks and fissures. Sonication increased the % porosity of the rice samples allowing for easy penetration of water during the cooking process and promotes softer texture. Moreover, the effect of sonication in brown rice resulted to the decrease in endogenous iron and phosphorus contents but increased its capacity for iron uptake through fortification when sonicated rice is soaked in the mineral solution.

Crop Phenomics and High-Throughput Phenotyping: Past Decades, Current Challenges, and Future Perspectives

Since whole-genome sequencing of many crops has been achieved, crop functional genomics studies have stepped into the big-data and high-throughput era. However, acquisition of large-scale phenotypic data has become one of the major bottlenecks hindering crop breeding and functional genomics studies. Nevertheless, recent technological advances provide us potential solutions to relieve this bottleneck and to explore advanced methods for large-scale phenotyping data acquisition and processing in the coming years. In this article, we review the major progress on high-throughput phenotyping in controlled environments and field conditions as well as its use for post-harvest yield and quality assessment in the past decades. We then discuss the latest multi-omics research combining high-throughput phenotyping with genetic studies. Finally, we propose some conceptual challenges and provide our perspectives on how to bridge the phenotype-genotype gap. It is no doubt that accurate high-throughput phenotyping will accelerate plant genetic improvements and promote the next green revolution in crop breeding.

Nondestructive 3D Image Analysis Pipeline to Extract Rice Grain Traits Using X-Ray Computed Tomography

The traits of rice panicles play important roles in yield assessment, variety classification, rice breeding, and cultivation management. Most traditional grain phenotyping methods require threshing and thus are time-consuming and labor-intensive; moreover, these methods cannot obtain 3D grain traits. In this work, based on X-ray computed tomography, we proposed an image analysis method to extract twenty-two 3D grain traits. After 104 samples were tested, the R ² values between the extracted and manual measurements of the grain number and grain length were 0.980 and 0.960, respectively. We also found a high correlation between the total grain volume and weight. In addition, the extracted 3D grain traits were used to classify the rice varieties, and the support vector machine classifier had a higher recognition accuracy than the stepwise discriminant analysis and random forest classifiers. In conclusion, we developed a 3D image analysis pipeline to extract rice grain traits using X-ray computed tomography that can provide more 3D grain information and could benefit future research on rice functional genomics and rice breeding.

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.

Novel Imaging Techniques to Analyze Panicle Architecture

Panicle architecture is known to directly influence grain yield in rice, and thus is an important trait for rice varietal improvement. However, spike branching consequences trigger variation in number of superior and inferior grains and thus affect grain quality. The genetics behind the length of both primary and secondary branches were studied resulting in the identification of cloned genes. Extending this knowledge to include other physiological parameters of panicle architecture is not yet well studied, and it requires high-throughput imaging techniques that are accurate. In this chapter we put the spotlight on Panicle Trait Phenotyping Tool (P-TRAP), a freely available platform independent software to analyze the panicle architecture of rice, as one of such methods that can be used to generate a comprehensive and reproducible panicle architecture data and identify superior breeding lines. P-TRAP measures 15 panicle structure and nine spikelet traits. These quantitative traits can be used in genome-wide association studies to understand their genetic basis.

Investigating the microstructure of plant leaves in 3D with lab-based X-ray computed tomography

BACKGROUND

Leaf cellular architecture plays an important role in setting limits for carbon assimilation and, thus, photosynthetic performance. However, the low density, fine structure, and sensitivity to desiccation of plant tissue has presented challenges to its quantification. Classical methods of tissue fixation and embedding prior to 2D microscopy of sections is both laborious and susceptible to artefacts that can skew the values obtained. Here we report an image analysis pipeline that provides quantitative descriptors of plant leaf intercellular airspace using lab-based X-ray computed tomography (microCT). We demonstrate successful visualisation and quantification of differences in leaf intercellular airspace in 3D for a range of species (including both dicots and monocots) and provide a comparison with a standard 2D analysis of leaf sections.

RESULTS

We used the microCT image pipeline to obtain estimates of leaf porosity and mesophyll exposed surface area (S_mes) for three dicot species (Arabidopsis, tomato and pea) and three monocot grasses (barley, oat and rice). The imaging pipeline consisted of (1) a masking operation to remove the background airspace surrounding the leaf, (2) segmentation by an automated threshold in ImageJ and then (3) quantification of the extracted pores using the ImageJ 'Analyze Particles' tool. Arabidopsis had the highest porosity and lowest S_mes for the dicot species whereas barley had the highest porosity and the highest S_mes for the grass species. Comparison of porosity and S_mes estimates from 3D microCT analysis and 2D analysis of sections indicates that both methods provide a comparable estimate of porosity but the 2D method may underestimate S_mes by almost 50%. A deeper study of porosity revealed similarities and differences in the asymmetric distribution of airspace between the species analysed.

CONCLUSIONS

Our results demonstrate the utility of high resolution imaging of leaf intercellular airspace networks by lab-based microCT and provide quantitative data on descriptors of leaf cellular architecture. They indicate there is a range of porosity and S_mes values in different species and that there is not a simple relationship between these parameters, suggesting the importance of cell size, shape and packing in the determination of cellular parameters proposed to influence leaf photosynthetic performance.

Non-destructive determination of floral staging in cereals using X-ray micro computed tomography (µCT)

BACKGROUND

Accurate floral staging is required to aid research into pollen and flower development, in particular male development. Pollen development is highly sensitive to stress and is critical for crop yields. Research into male development under environmental change is important to help target increased yields. This is hindered in monocots as the flower develops internally in the pseudostem. Floral staging studies therefore typically rely on destructive analysis, such as removal from the plant, fixation, staining and sectioning. This time-consuming analysis therefore prevents follow up studies and analysis past the point of the floral staging.

RESULTS

This study focuses on using X-ray µCT scanning to allow quick and detailed non-destructive internal 3D phenotypic information to allow accurate staging of Arabidopsis thaliana L. and Barley (Hordeum vulgare L.) flowers. X-ray µCT has previously relied on fixation methods for above ground tissue, therefore two contrast agents (Lugol's iodine and Bismuth) were observed in Arabidopsis and Barley in planta to circumvent this step. 3D models and 2D slices were generated from the X-ray µCT images providing insightful information normally only available through destructive time-consuming processes such as sectioning and microscopy. Barley growth and development was also monitored over three weeks by X-ray µCT to observe flower development in situ. By measuring spike size in the developing tillers accurate non-destructive staging at the flower and anther stages could be performed; this staging was confirmed using traditional destructive microscopic analysis.

CONCLUSION

The use of X-ray micro computed tomography (µCT) scanning of living plant tissue offers immense benefits for plant phenotyping, for successive developmental measurements and for accurate developmental timing for scientific measurements. Nevertheless, X-ray µCT remains underused in plant sciences, especially in above-ground organs, despite its unique potential in delivering detailed non-destructive internal 3D phenotypic information. This work represents a novel application of X-ray µCT that could enhance research undertaken in monocot species to enable effective non-destructive staging and developmental analysis for molecular genetic studies and to determine effects of stresses at particular growth stages.

Non-destructive, high-content analysis of wheat grain traits using X-ray micro computed tomography

BACKGROUND

Wheat is one of the most widely grown crop in temperate climates for food and animal feed. In order to meet the demands of the predicted population increase in an ever-changing climate, wheat production needs to dramatically increase. Spike and grain traits are critical determinants of final yield and grain uniformity a commercially desired trait, but their analysis is laborious and often requires destructive harvest. One of the current challenges is to develop an accurate, non-destructive method for spike and grain trait analysis capable of handling large populations.

RESULTS

In this study we describe the development of a robust method for the accurate extraction and measurement of spike and grain morphometric parameters from images acquired by X-ray micro-computed tomography (μCT). The image analysis pipeline developed automatically identifies plant material of interest in μCT images, performs image analysis, and extracts morphometric data. As a proof of principle, this integrated methodology was used to analyse the spikes from a population of wheat plants subjected to high temperatures under two different water regimes. Temperature has a negative effect on spike height and grain number with the middle of the spike being the most affected region. The data also confirmed that increased grain volume was correlated with the decrease in grain number under mild stress.

CONCLUSIONS

Being able to quickly measure plant phenotypes in a non-destructive manner is crucial to advance our understanding of gene function and the effects of the environment. We report on the development of an image analysis pipeline capable of accurately and reliably extracting spike and grain traits from crops without the loss of positional information. This methodology was applied to the analysis of wheat spikes can be readily applied to other economically important crop species.