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

Background: Wheat is 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 (?CT). The image analysis pipeline developed automatically identiies plant material of interest in ?CT 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 diferent water regimes. Temperature has a negative efect on spike height and grain number with the middle of the spike being the most afected region. The data also conirmed 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 efects 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.publishersversionPeer reviewe

Phenotyping Fusarium head blight through seed morphology characteristics using RGB imaging

Fusarium head blight (FHB) is an economically important disease affecting wheat and thus poses a major threat to wheat production. Several studies have evaluated the effectiveness of image analysis methods to predict FHB using disease-infected grains; however, few have looked at the final application, considering the relationship between cost and benefit, resolution, and accuracy. The conventional screening of FHB resistance of large-scale samples is still dependent on low-throughput visual inspections. This study aims to compare the performance of two cost-benefit seed image analysis methods, the free software "SmartGrain " and the fully automated commercially available instrument "Cgrain Value (TM) " by assessing 16 seed morphological traits of winter wheat to predict FHB. The analysis was carried out on a seed set of FHB which was visually assessed as to the severity. The dataset is composed of 432 winter wheat genotypes that were greenhouse-inoculated. The predictions from each method, in addition to the predictions combined from the results of both methods, were compared with the disease visual scores. The results showed that Cgrain Value (TM) had a higher prediction accuracy of R (2) = 0.52 compared with SmartGrain for which R (2) = 0.30 for all morphological traits. However, the results combined from both methods showed the greatest prediction performance of R (2) = 0.58. Additionally, a subpart of the morphological traits, namely, width, length, thickness, and color features, showed a higher correlation with the visual scores compared with the other traits. Overall, both methods were related to the visual scores. This study shows that these affordable imaging methods could be effective to predict FHB in seeds and enable us to distinguish minor differences in seed morphology, which could lead to a precise performance selection of disease-free seeds/grains

High-throughput phenotyping of plant leaf morphological, physiological, and biochemical traits on multiple scales using optical sensing

Acquisition of plant phenotypic information facilitates plant breeding, sheds light on gene action, and can be applied to optimize the quality of agricultural and forestry products. Because leaves often show the fastest responses to external environmental stimuli, leaf phenotypic traits are indicators of plant growth, health, and stress levels. Combination of new imaging sensors, image processing, and data analytics permits measurement over the full life span of plants at high temporal resolution and at several organizational levels from organs to individual plants to field populations of plants. We review the optical sensors and associated data analytics used for measuring morphological, physiological, and biochemical traits of plant leaves on multiple scales. We summarize the characteristics, advantages and limitations of optical sensing and data-processing methods applied in various plant phenotyping scenarios. Finally, we discuss the future prospects of plant leaf phenotyping research. This review aims to help researchers choose appropriate optical sensors and data processing methods to acquire plant leaf phenotypes rapidly, accurately, and cost-effectively

Yielding to the image: how phenotyping reproductive growth can assist crop improvement and production

Reproductive organs are the main reason we grow and harvest most plant species as crops, yet they receive less attention from phenotyping due to their complexity and inaccessibility for analysis. This review highlights recent progress towards the quantitative high-throughput phenotyping of reproductive development, focusing on three impactful areas that are pivotal for plant breeding and crop production. First, we look at phenotyping phenology, summarizing the indirect and direct approaches that are available. This is essential for analysis of genotype by environment, and to enable effective management interpretation and agronomy and physiological interventions. Second, we look at pollen development and production, in addition to anther characteristics, these are critical points of vulnerability for yield loss when stress occurs before and during flowering, and are of particular interest for hybrid technology development. Third, we elaborate on phenotyping yield components, indirectly or directly during the season, with a numerical or growth related approach and post-harvest processing. Finally, we summarise the opportunities and challenges ahead for phenotyping reproductive growth and their feasibility and impact, with emphasis on plant breeding applications and targeted yield increases

Large-scale field phenotyping using backpack LiDAR and CropQuant-3D to measure structural variation in wheat

Plant phenomics bridges the gap between traits of agricultural importance and genomic information. Limitations of current field-based phenotyping solutions include mobility, affordability, throughput, accuracy, scalability, and the ability to analyze big data collected. Here, we present a large-scale phenotyping solution that combines a commercial backpack Light Detection and Ranging (LiDAR) device and our analytic software, CropQuant-3D, which have been applied jointly to phenotype wheat (Triticum aestivum) and associated 3D trait analysis. The use of LiDAR can acquire millions of 3D points to represent spatial features of crops, and CropQuant-3D can extract meaningful traits from large, complex point clouds. In a case study examining the response of wheat varieties to three different levels of nitrogen fertilization in field experiments, the combined solution differentiated significant genotype and treatment effects on crop growth and structural variation in the canopy, with strong correlations with manual measurements. Hence, we demonstrate that this system could consistently perform 3D trait analysis at a larger scale and more quickly than heretofore possible and addresses challenges in mobility, throughput, and scalability. To ensure our work could reach non-expert users, we developed an open-source graphical user interface for CropQuant-3D. We, therefore, believe that the combined system is easy-to-use and could be used as a reliable research tool in multi-location phenotyping for both crop research and breeding. Furthermore, together with the fast maturity of LiDAR technologies, the system has the potential for further development in accuracy and affordability, contributing to the resolution of the phenotyping bottleneck and exploiting available genomic resources more effectively

Robotic Technologies for High-Throughput Plant Phenotyping: Contemporary Reviews and Future Perspectives

Phenotyping plants is an essential component of any effort to develop new crop varieties. As plant breeders seek to increase crop productivity and produce more food for the future, the amount of phenotype information they require will also increase. Traditional plant phenotyping relying on manual measurement is laborious, time-consuming, error-prone, and costly. Plant phenotyping robots have emerged as a high-throughput technology to measure morphological, chemical and physiological properties of large number of plants. Several robotic systems have been developed to fulfill different phenotyping missions. In particular, robotic phenotyping has the potential to enable efficient monitoring of changes in plant traits over time in both controlled environments and in the field. The operation of these robots can be challenging as a result of the dynamic nature of plants and the agricultural environments. Here we discuss developments in phenotyping robots, and the challenges which have been overcome and others which remain outstanding. In addition, some perspective applications of the phenotyping robots are also presented. We optimistically anticipate that autonomous and robotic systems will make great leaps forward in the next 10 years to advance the plant phenotyping research into a new era

An automated, cost-effective and scalable, flood-and-drain based root phenotyping system for cereals

Background: Genetic studies on the molecular mechanisms of the regulation of root growth require the characterisation of a specific root phenotype to be linked with a certain genotype. Such studies using classical labour-intensive methods are severely hindered due to the technical limitations that are associated with the impeded observation of the root system of a plant during its growth. The aim of the research presented here was to develop a reliable, cost-effective method for the analysis of a plant root phenotype that would enable the precise characterisation of the root system architecture of cereals. Results: The presented method describes a complete system for automatic supplementation and continuous sensing of culture solution supplied to plants that are grown in transparent tubes containing a solid substrate. The presented system comprises the comprehensive pipeline consisting of a modular-based and remotely-controlled plant growth system and customized imaging setup for root and shoot phenotyping. The system enables an easy extension of the experimental capacity in order to form a combined platform that is comprised of parallel modules, each holding up to 48 plants. The conducted experiments focused on the selection of the most suitable conditions for phenotyping studies in barley: an optimal size of the glass beads, diameters of the acrylic tubes, composition of a medium, and a rate of the medium flow. Conclusions: The developed system enables an efficient, accurate and highly repeatable analysis of the morphological features of the root system of cereals. Because a simple and fully-automated control system is used, the experimental conditions can easily be normalised for different species of cereals. The scalability of the module-based system allows its capacity to be adjusted in order to meet the requirements of a particular experiment

Analysis of Alterations in Cellular Architecture by cryo-Soft X-ray Tomography and Ultrastructure Expansion Microscopy

The central aim of this thesis was to visualize changes in cellular architecture induced by environmental factors such as drug treatment or viral infection. High-resolution microscopic analysis of such alterations is crucial to elucidate the mechanisms of disease, as well as to develop potential therapies. The importance thereof was clearly underscored by the COVID 19 pandemic, whose causative agent, the novel severe acute respiratory syndrome coronavirus 2 (SARS CoV 2), infected and killed millions of people across the globe. By using high-resolution 3D imaging by cryo soft X ray tomography (cryo SXT), rapid changes in the cellular ultrastructure upon infection with SARS CoV 2 or feline infectious peritonitis virus (FIPV) could be visualized. The changes induced by viral infection were affected by treatment with FDA-approved drugs, which had previously emerged as candidates from drug-repurposing screens to combat SARS CoV 2. Cryo SXT analysis revealed formation of lysosome-associated dark-rimmed vesicles (DRVs), which were demonstrated to be multilamellar lipid deposits by transmission electron microscopy. The observed striking interplay of drug- and virus-induced alterations on the level of size and number of lysosome-associated DRVs suggests involvement of lysosomal function in the inhibition of the viruses by the drugs. It is likely that this inhibition is either due to impairment of lysosomal escape or due to reduced lipid availability for membrane remodeling essential for virus replication. To comprehensively test these models, complementary screening tools are required, which allow ultrastructural analysis at a higher throughput than cryo SXT. For that, the recently developed ultrastructure expansion microscopy (U ExM) is a promising technique. Comparing the structural preservation of cell organelles after the U ExM sample preparation to that of cells acquired under near-native conditions in cryo SXT, it emerged that the morphology of large organelles such as nuclei and plasma membrane can be visualized efficiently by U ExM, while very small or lipid-dense structures were more difficult to preserve accurately. These results highlight the benefits of cryo SXT, but also the potential of U ExM to complement cryo SXT with its higher throughput of ultrastructure visualization, even if its resolution is lower than that of cryo SXT. Therefore, U ExM can be used for experimental characterization for cryo SXT, as one step towards more efficiency of cryo SXT. Another step is to automate the segmentation of cryo SXT tomograms. To that end, a deep learning platform was trained and validated on a large pool of cryo SXT data acquired in this work. This convoluted neural network performs full annotation of cellular features in cryo SXT tomograms within less than half an hour on consumer-grade GPUs, thereby significantly reducing the time required for data analysis. Taken together, these results illustrate how cryo SXT, U ExM and deep learning can be used complementarily to address highly relevant biological questions

Phenotyping Fusarium head blight through seed morphology characteristics using RGB imaging

Fusarium head blight (FHB) is an economically important disease affecting wheat and thus poses a major threat to wheat production. Several studies have evaluated the effectiveness of image analysis methods to predict FHB using disease-infected grains; however, few have looked at the final application, considering the relationship between cost and benefit, resolution, and accuracy. The conventional screening of FHB resistance of large-scale samples is still dependent on low-throughput visual inspections. This study aims to compare the performance of two cost–benefit seed image analysis methods, the free software “SmartGrain” and the fully automated commercially available instrument “Cgrain Value™” by assessing 16 seed morphological traits of winter wheat to predict FHB. The analysis was carried out on a seed set of FHB which was visually assessed as to the severity. The dataset is composed of 432 winter wheat genotypes that were greenhouse-inoculated. The predictions from each method, in addition to the predictions combined from the results of both methods, were compared with the disease visual scores. The results showed that Cgrain Value™ had a higher prediction accuracy of R2 = 0.52 compared with SmartGrain for which R2 = 0.30 for all morphological traits. However, the results combined from both methods showed the greatest prediction performance of R2 = 0.58. Additionally, a subpart of the morphological traits, namely, width, length, thickness, and color features, showed a higher correlation with the visual scores compared with the other traits. Overall, both methods were related to the visual scores. This study shows that these affordable imaging methods could be effective to predict FHB in seeds and enable us to distinguish minor differences in seed morphology, which could lead to a precise performance selection of disease-free seeds/grains