Non-invasive approaches for phenotyping of enhanced performance traits in bean

Non-destructive, whole-plant phenotyping reveals dynamic changes in water use efficiency, photosynthesis, and rhizosphere acidification of sorghum accessions under osmotic stress

Noninvasive phenotyping can quantify dynamic plant growth processes at higher temporal resolution than destructive phenotyping and can reveal phenomena that would be missed by end-point analysis alone. Additionally, whole-plant phenotyping can identify growth conditions that are optimal for both above- and below-ground tissues. However, noninvasive, whole-plant phenotyping approaches available today are generally expensive, complex, and non-modular. We developed a low-cost and versatile approach to noninvasively measure whole-plant physiology over time by growing plants in isolated hydroponic chambers. We demonstrate the versatility of our approach by measuring whole-plant biomass accumulation, water use, and water use efficiency every two days on unstressed and osmotically stressed sorghum accessions. We identified relationships between root zone acidification and photosynthesis on whole-plant water use efficiency over time. Our system can be implemented using cheap, basic components, requires no specific technical expertise, and should be suitable for any non-aquatic vascular plant species.

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.

3D U-Net Segmentation Improves Root System Reconstruction from 3D MRI Images in Automated and Manual Virtual Reality Work Flows

Magnetic resonance imaging (MRI) is used to image root systems grown in opaque soil. However, reconstruction of root system architecture (RSA) from 3-dimensional (3D) MRI images is challenging. Low resolution and poor contrast-to-noise ratios (CNRs) hinder automated reconstruction. Hence, manual reconstruction is still widely used. Here, we evaluate a novel 2-step work flow for automated RSA reconstruction. In the first step, a 3D U-Net segments MRI images into root and soil in super-resolution. In the second step, an automated tracing algorithm reconstructs the root systems from the segmented images. We evaluated the merits of both steps for an MRI dataset of 8 lupine root systems, by comparing the automated reconstructions to manual reconstructions of unaltered and segmented MRI images derived with a novel virtual reality system. We found that the U-Net segmentation offers profound benefits in manual reconstruction: reconstruction speed was doubled (+97%) for images with low CNR and increased by 27% for images with high CNR. Reconstructed root lengths were increased by 20% and 3%, respectively. Therefore, we propose to use U-Net segmentation as a principal image preprocessing step in manual work flows. The root length derived by the tracing algorithm was lower than in both manual reconstruction methods, but segmentation allowed automated processing of otherwise not readily usable MRI images. Nonetheless, model-based functional root traits revealed similar hydraulic behavior of automated and manual reconstructions. Future studies will aim to establish a hybrid work flow that utilizes automated reconstructions as scaffolds that can be manually corrected.

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

Background

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

Methods

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

Results

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

Conclusion

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

In situ pod growth rate reveals contrasting diurnal sensitivity to water deficit in Phaseolus vulgaris

The development of reproductive tissues determines plant fecundity and yield. Loading of resources into the developing reproductive tissue is thought to be under the co-limiting effects of source and sink strength. The dynamics of this co-limitation are unknown, largely due to an inability to measure the flux of resources into a developing sink. Here we use nuclear magnetic resonance (NMR) sensors to measure sink strength by quantifying rates of pod dry matter accumulation (pod loading) in Phaseolus vulgaris at 13-min intervals across the diel period. Rates of pod loading showed contrasting variation across light and dark periods during the onset of water deficit. In addition, rates of pod loading appeared decoupled from net photosynthetic rates when adjusted to the plant scale. Combined, these observations illustrate that the rate of pod development varies under water limitation and that continuous, non-invasive methodologies to measure sink strength provide insight into the governing processes that determine the development of reproductive tissues.

Exploitation of Plant Growth Promoting Bacteria for Sustainable Agriculture: Hierarchical Approach to Link Laboratory and Field Experiments

To feed a world population, which will reach 9.7 billion in 2050, agricultural production will have to increase by 35–56%. Therefore, more food is urgently needed. Yield improvements for any given crop would require adequate fertilizer, water, and plant protection from pests and disease, but their further abuse will be economically disadvantageous and will have a negative impact on the environment. Using even more agricultural inputs is simply not possible, and the availability of arable land will be increasingly reduced due to climate changes. To improve agricultural production without further consumption of natural resources, farmers have a powerful ally: the beneficial microorganisms inhabiting the rhizosphere. However, to fully exploit the benefits of these microorganisms and therefore to widely market microbial-based products, there are still gaps that need to be filled, and here we will describe some critical issues that should be better addressed.

Linking the Spectra of Decomposing Litter to Ecosystem Processes: Tandem Close-Range Hyperspectral Imagery and Decomposition Metrics

Efforts to monitor terrestrial decomposition dynamics at broad spatial scales are hampered by the lack of a cost-effective and scalable means to track the decomposition process. Recent advances in remote sensing have enabled the simulation of litter spectra throughout decomposition for grasses in general, yet unique decomposition pathways are hypothesized to create subtly different litter spectral signatures with unique ecosystem functional significance. The objectives of this study were to improve spectra–decomposition linkages and thereby enable the more comprehensive monitoring of ecosystem processes such as nutrient and carbon cycles. Using close-range hyperspectral imaging, litter spectra and multiple decomposition metrics were concurrently monitored in four classes of naturally decayed litter under four decomposition treatments. The first principal component accounted for approximately 94% of spectral variation in the close-range imagery and was attributed to the progression of decomposition. Decomposition-induced spectral changes were moderately correlated with the leaf carbon to nitrogen ratio (R2 = 0.52) and sodium hydroxide extractables (R2 = 0.45) but had no correlation with carbon dioxide flux. Temperature and humidity strongly influenced the decomposition process but did not influence spectral variability or the patterns of surface decomposition. The outcome of the study is that litter spectra are linked to important metrics of decomposition and thus remote sensing could be utilized to assess decomposition dynamics and the implications for nutrient recycling at broad spatial scales. A secondary study outcome is the need to resolve methodological challenges related to inducing unique decomposition pathways in a lab environment. Improving decomposition treatments that mimic real-world conditions of temperature, humidity, insolation, and the decomposer community will enable an improved understanding of the impacts of climatic change, which are expected to strongly affect microbially mediated decomposition.

Root-omics for drought tolerance in cool-season grain legumes

Root traits can be exploited to increase the physiological efficiency of crop water use under drought. Root length, root hairs, root branching, root diameter, and root proliferation rate are genetically defined traits that can help to improve the water productivity potential of crops. Recently, high-throughput phenotyping techniques/platforms have been used to screen the germplasm of major cool-season grain legumes for root traits and their impact on different physiological processes, including nutrient uptake and yield potential. Advances in omics approaches have led to the dissection of genomic, proteomic, and metabolomic structures of these traits. This knowledge facilitates breeders to improve the water productivity and nutrient uptake of cultivars under limited soil moisture conditions in major cool-season grain legumes that usually face terminal drought. This review discusses the advances in root traits and their potential for developing drought-tolerant cultivars in cool-season grain legumes.

Genetics and genomics of root system variation in adaptation to drought stress in cereal crops

Cereals are important crops worldwide that help meet food demands and nutritional needs. In recent years, cereal production has been challenged globally by frequent droughts and hot spells. A plant's root is the most relevant organ for the plant adaptation to stress conditions, playing pivotal roles in anchorage and the acquisition of soil-based resources. Thus, dissecting root system variations and trait selection for enhancing yield and sustainability under drought stress conditions should aid in future global food security. This review highlights the variations in root system attributes and their interplay with shoot architecture features to face water scarcity and maintain thus yield of major cereal crops. Further, we compile the root-related drought responsive quantitative trait loci/genes in cereal crops including their interspecies relationships using microsynteny to facilitate comparative genomic analyses. We then discuss the potential of an integrated strategy combining genomics and phenomics at genetic and epigenetic levels to explore natural genetic diversity as a basis for knowledge-based genome editing. Finally, we present an outline to establish innovative breeding leads for the rapid and optimized selection of root traits necessary to develop resilient crop varieties.

Low field, time domain NMR in the agriculture and agrifood sectors: An overview of applications in plants, foods and biofuels

In this contribution, a selective overview of low field, time-domain NMR (TD-NMR) applications in the agriculture and agrifood sectors is presented. The first applications of commercial TD-NMR instruments were in food and agriculture domains. Many of these earlier methods have now been recognized as standard methods by several international agencies. Since 2000, several new applications have been developed, using state of the art instruments, new pulse sequences and new signal processing methods. TD-NMR is expected, in the coming years, to become even more important in quality control of fresh food and agricultural products, as well as for a wide range of food-processed products. TD-NMR systems provide excellent means to collect data relevant for use in the agricultural environment and the bioenergy industry. Data and information collected by TD-NMR systems thus may support decision makers in business and public organizations.

Field Phenomics: Will It Enable Crop Improvement?

Field phenomics has been identified as a promising enabling technology to assist plant breeders with the development of improved cultivars for farmers. Yet, despite much investment, there are few examples demonstrating the application of phenomics within a plant breeding program. We review recent progress in field phenomics and highlight the importance of targeting breeders' needs, rather than perceived technology needs, through developing and enhancing partnerships between phenomics researchers and plant breeders.

Active and Passive Electro-Optical Sensors for Health Assessment in Food Crops

In agriculture, early detection of plant stresses is advantageous in preventing crop yield losses. Remote sensors are increasingly being utilized for crop health monitoring, offering non-destructive, spatialized detection and the quantification of plant diseases at various levels of measurement. Advances in sensor technologies have promoted the development of novel techniques for precision agriculture. As in situ techniques are surpassed by multispectral imaging, refinement of hyperspectral imaging and the promising emergence of light detection and ranging (LIDAR), remote sensing will define the future of biotic and abiotic plant stress detection, crop yield estimation and product quality. The added value of LIDAR-based systems stems from their greater flexibility in capturing data, high rate of data delivery and suitability for a high level of automation while overcoming the shortcomings of passive systems limited by atmospheric conditions, changes in light, viewing angle and canopy structure. In particular, a multi-sensor systems approach and associated data fusion techniques (i.e., blending LIDAR with existing electro-optical sensors) offer increased accuracy in plant disease detection by focusing on traditional optimal estimation and the adoption of artificial intelligence techniques for spatially and temporally distributed big data. When applied across different platforms (handheld, ground-based, airborne, ground/aerial robotic vehicles or satellites), these electro-optical sensors offer new avenues to predict and react to plant stress and disease. This review examines the key sensor characteristics, platform integration options and data analysis techniques recently proposed in the field of precision agriculture and highlights the key challenges and benefits of each concept towards informing future research in this very important and rapidly growing field.

Gloxinia-An Open-Source Sensing Platform to Monitor the Dynamic Responses of Plants

The study of the dynamic responses of plants to short-term environmental changes is becoming increasingly important in basic plant science, phenotyping, breeding, crop management, and modelling. These short-term variations are crucial in plant adaptation to new environments and, consequently, in plant fitness and productivity. Scalable, versatile, accurate, and low-cost data-logging solutions are necessary to advance these fields and complement existing sensing platforms such as high-throughput phenotyping. However, current data logging and sensing platforms do not meet the requirements to monitor these responses. Therefore, a new modular data logging platform was designed, named Gloxinia. Different sensor boards are interconnected depending upon the needs, with the potential to scale to hundreds of sensors in a distributed sensor system. To demonstrate the architecture, two sensor boards were designed—one for single-ended measurements and one for lock-in amplifier based measurements, named Sylvatica and Planalta, respectively. To evaluate the performance of the system in small setups, a small-scale trial was conducted in a growth chamber. Expected plant dynamics were successfully captured, indicating proper operation of the system. Though a large scale trial was not performed, we expect the system to scale very well to larger setups. Additionally, the platform is open-source, enabling other users to easily build upon our work and perform application-specific optimisations.

3D characterization of walnut morphological traits using X-ray computed tomography

BACKGROUND

Walnuts are grown worldwide in temperate areas and producers are facing an increasing demand. In a climate change context, the industry also needs cultivars that provide fruits of quality. This quality includes satisfactory filling ratio, thicker shell, ease of cracking, smooth shell and round-shaped walnut, and larger nut size. These desirable traits have been analysed so far using calipers or micrometers, but it takes a lot of time and requires the destruction of the sample. A challenge to take up is to develop an accurate, fast and non-destructive method for quality-related and morphometric trait measurements of walnuts, that are used to characterize new cultivars or collections in any germplasm management process.

RESULTS

In this study, we develop a method to measure different morphological traits on several walnuts simultaneously such as morphometric traits (nut length, nut face and profile diameters), traits that previously required opening the nut (shell thickness, kernel volume and filling kernel/nut ratio) and traits that previously were difficult to quantify (shell rugosity, nut sphericity, nut surface area and nut shape). These measurements were obtained from reconstructed 3D images acquired by X-ray computed tomography (CT). A workflow was created including several steps: noise elimination, walnut individualization, properties extraction and quantification of the different parts of the fruit. This method was applied to characterize 50 walnuts of a part of the INRAE walnut germplasm collection made of 161 unique accessions, obtained from the 2018 harvest. Our results indicate that 50 walnuts are sufficient to phenotype the fruit quality of one accession using X-ray CT and to find correlations between the morphometric traits. Our imaging workflow is suitable for any walnut size or shape and provides new and more accurate measurements.

CONCLUSIONS

The fast and accurate measurement of quantitative traits is of utmost importance to conduct quantitative genetic analyses or cultivar characterization. Our imaging workflow is well adapted for accurate phenotypic characterization of a various range of traits and could be easily applied to other important nut crops.

Connecting the dots between computational tools to analyse soil-root water relations

In recent years, many computational tools, such as image analysis, data management, process-based simulation, and upscaling tools, have been developed to help quantify and understand water flow in the soil-root system, at multiple scales (tissue, organ, plant, and population). Several of these tools work together or at least are compatible. However, for the uninformed researcher, they might seem disconnected, forming an unclear and disorganized succession of tools. In this article, we show how different studies can be further developed by connecting them to analyse soil-root water relations in a comprehensive and structured network. This 'explicit network of soil-root computational tools' informs readers about existing tools and helps them understand how their data (past and future) might fit within the network. We also demonstrate the novel possibilities of scale-consistent parameterizations made possible by the network with a set of case studies from the literature. Finally, we discuss existing gaps in the network and how we can move forward to fill them.

Plant Phenotyping: Past, Present, and Future

A plant develops the dynamic phenotypes from the interaction of the plant with the environment. Understanding these processes that span plant's lifetime in a permanently changing environment is essential for the advancement of basic plant science and its translation into application including breeding and crop management. The plant research community was thus confronted with the need to accurately measure diverse traits of an increasingly large number of plants to help plants to adapt to resource-limiting environment and low-input agriculture. In this overview, we outline the development of plant phenotyping as a multidisciplinary field. We sketch the technological advancement that laid the foundation for the development of phenotyping centers and evaluate the upcoming challenges for further advancement of plant phenotyping specifically with respect to standardization of data acquisition and reusability. Finally, we describe the development of the plant phenotyping community as an essential step to integrate the community and effectively use the emerging synergies.

Using Plant Phenomics to Exploit the Gains of Genomics

Agricultural scientists face the dual challenge of breeding input-responsive, widely adoptable and climate-resilient varieties of crop plants and developing such varieties at a faster pace. Integrating the gains of genomics with modern-day phenomics will lead to increased breeding efficiency which in turn offers great promise to develop such varieties rapidly. Plant phenotyping techniques have impressively evolved during the last two decades. The low-cost, automated and semi-automated methods for data acquisition, storage and analysis are now available which allow precise quantitative analysis of plant structure and function; and genetic dissection of complex traits. Appropriate plant types can now be quickly developed that respond favorably to low input and resource-limited environments and address the challenges of subsistence agriculture. The present review focuses on the need of systematic, rapid, minimal invasive and low-cost plant phenotyping. It also discusses its evolution to modern day high throughput phenotyping (HTP), traits amenable to HTP, integration of HTP with genomics and the scope of utilizing these tools for crop improvement.

Plant Phenotyping: Past, Present, and Future

A plant develops the dynamic phenotypes from the interaction of the plant with the environment. Understanding these processes that span plant's lifetime in a permanently changing environment is essential for the advancement of basic plant science and its translation into application including breeding and crop management. The plant research community was thus confronted with the need to accurately measure diverse traits of an increasingly large number of plants to help plants to adapt to resource-limiting environment and low-input agriculture. In this overview, we outline the development of plant phenotyping as a multidisciplinary field. We sketch the technological advancement that laid the foundation for the development of phenotyping centers and evaluate the upcoming challenges for further advancement of plant phenotyping specifically with respect to standardization of data acquisition and reusability. Finally, we describe the development of the plant phenotyping community as an essential step to integrate the community and effectively use the emerging synergies.

Source-Sink Relationships in Crop Plants and Their Influence on Yield Development and Nutritional Quality

For seed crops, yield is the cumulative result of both source and sink strength for photoassimilates and nutrients over the course of seed development. Source strength for photoassimilates is dictated by both net photosynthetic rate and the rate of photoassimilate remobilisation from source tissues. This review focuses on the current understanding of how the source-sink relationship in crop plants influences rates of yield development and the resilience of yield and nutritional quality. We present the limitations of current approaches to accurately measure sink strength and emphasize differences in coordination between photosynthesis and yield under varying environmental conditions. We highlight the potential to exploit source-sink dynamics, in order to improve yields and emphasize the importance of resilience in yield and nutritional quality with implications for plant breeding strategies.

Mapping of novel salt tolerance QTL in an Excalibur × Kukri doubled haploid wheat population

KEY MESSAGE

Novel QTL for salinity tolerance traits have been detected using non-destructive and destructive phenotyping in bread wheat and were shown to be linked to improvements in yield in saline fields. Soil salinity is a major limitation to cereal production. Breeding new salt-tolerant cultivars has the potential to improve cereal crop yields. In this study, a doubled haploid bread wheat mapping population, derived from the bi-parental cross of Excalibur × Kukri, was grown in a glasshouse under control and salinity treatments and evaluated using high-throughput non-destructive imaging technology. Quantitative trait locus (QTL) analysis of this population detected multiple QTL under salt and control treatments. Of these, six QTL were detected in the salt treatment including one for maintenance of shoot growth under salinity (QG(1-5).asl-7A), one for leaf Na+ exclusion (QNa.asl-7A) and four for leaf K+ accumulation (QK.asl-2B.1, QK.asl-2B.2, QK.asl-5A and QK:Na.asl-6A). The beneficial allele for QG(1-5).asl-7A (the maintenance of shoot growth under salinity) was present in six out of 44 mainly Australian bread and durum wheat cultivars. The effect of each QTL allele on grain yield was tested in a range of salinity concentrations at three field sites across 2 years. In six out of nine field trials with different levels of salinity stress, lines with alleles for Na+ exclusion and/or K+ maintenance at three QTL (QNa.asl-7A, QK.asl-2B.2 and QK:Na.asl-6A) excluded more Na+ or accumulated more K+ compared to lines without these alleles. Importantly, the QK.asl-2B.2 allele for higher K+ accumulation was found to be associated with higher grain yield at all field sites. Several alleles at other QTL were associated with higher grain yields at selected field sites.

RhizoChamber-Monitor: a robotic platform and software enabling characterization of root growth

BACKGROUND

In order to efficiently determine genotypic differences in rooting patterns of crops, novel hardware and software are needed simultaneously to characterize dynamics of root development.

RESULTS

We describe a prototype robotic monitoring platform-the RhizoChamber-Monitor for analyzing growth patterns of plant roots automatically. The RhizoChamber-Monitor comprises an automatic imaging system for acquiring sequential images of roots which grow on a cloth substrate in custom rhizoboxes, an automatic irrigation system and a flexible shading arrangement. A customized image processing software was developed to analyze the spatio-temporal dynamics of root growth from time-course images of multiple plants. This software can quantify overall growth of roots and extract detailed growth traits (e.g. dynamics of length and diameter) of primary roots and of individual lateral roots automatically. It can also identify local growth traits of lateral roots (pseudo-mean-length and pseudo-maximum-length) semi-automatically. Two cotton genotypes were used to test both the physical platform and the analysis software.

CONCLUSIONS

The combination of hardware and software is expected to facilitate quantification of root geometry and its spatio-temporal growth patterns, and therefore to provide opportunities for high-throughput root phenotyping in support of crop breeding to optimize root architecture.

Diversity in root growth responses to moisture deficit in young faba bean (Vicia faba L.) plants

BACKGROUND

Soil moisture deficiency causes yield reduction and instability in faba bean (Vicia faba L.) production. The extent of sensitivity to drought stress varies across accessions originating from diverse moisture regimes of the world. Hence, we conducted successive greenhouse experiments in pots and rhizotrons to explore diversity in root responses to soil water deficit.

METHODS

A set of 89 accessions from wet and dry growing regions of the world was defined according to the Focused Identification of Germplasm Strategy and screened in a perlite-sand medium under well watered conditions in a greenhouse experiment. Stomatal conductance, canopy temperature, chlorophyll concentration, and root and shoot dry weights were recorded during the fifth week of growth. Eight accessions representing the range of responses were selected for further investigation. Starting five days after germination, they were subjected to a root phenotyping experiment using the automated phenotyping platform GROWSCREEN-Rhizo. The rhizotrons were filled with peat-soil under well watered and water limited conditions. Root architectural traits were recorded five, 12, and 19 days after the treatment (DAT) began.

RESULTS

In the germplasm survey, accessions from dry regions showed significantly higher values of chlorophyll concentration, shoot and root dry weights than those from wet regions. Root and shoot dry weight as well as seed weight, and chlorophyll concentration were positively correlated with each other. Accession DS70622 combined higher values of root and shoot dry weight than the rest. The experiment in GROWSCREEN-Rhizo showed large differences in root response to water deficit. The accession by treatment interactions in taproot and second order lateral root lengths were significant at 12 and 19 DAT, and the taproot length was reduced up to 57% by drought. The longest and deepest root systems under both treatment conditions were recorded by DS70622 and DS11320, and total root length of DS70622 was three times longer than that of WS99501, the shortest rooted accession. The maximum horizontal distribution of a root system and root surface coverage were positively correlated with taproot and total root lengths and root system depth. DS70622 and WS99501 combined maximum and minimum values of these traits, respectively. Thus, roots of DS70622 and DS11320, from dry regions, showed drought-avoidance characteristics whereas those of WS99501 and Mèlodie/2, from wet regions, showed the opposite.

DISCUSSION

The combination of the germplasm survey and use of GROWSCREEN-Rhizo allowed exploring of adaptive traits and detection of root phenotypic markers for potential drought avoidance. The greater root system depth and root surface coverage, exemplified by DS70622 and DS11320, can now be tested as new sources of drought tolerance.

Plant phenomics: an overview of image acquisition technologies and image data analysis algorithms

The study of phenomes or phenomics has been a central part of biology. The field of automatic phenotype acquisition technologies based on images has seen an important advance in the last years. As with other high-throughput technologies, it addresses a common set of problems, including data acquisition and analysis. In this review, we give an overview of the main systems developed to acquire images. We give an in-depth analysis of image processing with its major issues and the algorithms that are being used or emerging as useful to obtain data out of images in an automatic fashion.

NGS-Based Genotyping, High-Throughput Phenotyping and Genome-Wide Association Studies Laid the Foundations for Next-Generation Breeding in Horticultural Crops

Demographic trends and changes to climate require a more efficient use of plant genetic resources in breeding programs. Indeed, the release of high-yielding varieties has resulted in crop genetic erosion and loss of diversity. This has produced an increased susceptibility to severe stresses and a reduction of several food quality parameters. Next generation sequencing (NGS) technologies are being increasingly used to explore “gene space” and to provide high-resolution profiling of nucleotide variation within germplasm collections. On the other hand, advances in high-throughput phenotyping are bridging the genotype-to-phenotype gap in crop selection. The combination of allelic and phenotypic data points via genome-wide association studies is facilitating the discovery of genetic loci that are associated with key agronomic traits. In this review, we provide a brief overview on the latest NGS-based and phenotyping technologies and on their role to unlocking the genetic potential of vegetable crops; then, we discuss the paradigm shift that is underway in horticultural crop breeding.

Toward Genomics-Based Breeding in C3 Cool-Season Perennial Grasses

Most important food and feed crops in the world belong to the C3 grass family. The future of food security is highly reliant on achieving genetic gains of those grasses. Conventional breeding methods have already reached a plateau for improving major crops. Genomics tools and resources have opened an avenue to explore genome-wide variability and make use of the variation for enhancing genetic gains in breeding programs. Major C3 annual cereal breeding programs are well equipped with genomic tools; however, genomic research of C3 cool-season perennial grasses is lagging behind. In this review, we discuss the currently available genomics tools and approaches useful for C3 cool-season perennial grass breeding. Along with a general review, we emphasize the discussion focusing on forage grasses that were considered orphan and have little or no genetic information available. Transcriptome sequencing and genotype-by-sequencing technology for genome-wide marker detection using next-generation sequencing (NGS) are very promising as genomics tools. Most C3 cool-season perennial grass members have no prior genetic information; thus NGS technology will enhance collinear study with other C3 model grasses like Brachypodium and rice. Transcriptomics data can be used for identification of functional genes and molecular markers, i.e., polymorphism markers and simple sequence repeats (SSRs). Genome-wide association study with NGS-based markers will facilitate marker identification for marker-assisted selection. With limited genetic information, genomic selection holds great promise to breeders for attaining maximum genetic gain of the cool-season C3 perennial grasses. Application of all these tools can ensure better genetic gains, reduce length of selection cycles, and facilitate cultivar development to meet the future demand for food and fodder.

Phylogenetic studies and comparative chloroplast genome analyses elucidate the basal position of halophyte Nitraria sibirica (Nitrariaceae) in the Sapindales

Nitraria sibirica is a halophyte and belongs to the family Nitrariaceae. The chloroplast genome of Nitraria sibirica (159,466 bp) has a quadripartite structure, which consists of a large single-copy (87,914 bp) region, a small single-copy (18,316 bp) region, and a pair of inverted repeats (26,618 bp). Sequencing analyses indicate that the chloroplast genome contains 113 distinct genes, including 79 peptide-coding genes, 30 transfer RNA genes, and 4 ribosomal RNA genes. We also identified 105 perfect simple sequence repeats, 12 most divergent non-coding regions, and 6 most divergent coding regions when compared to the chloroplast genomes of the Sapindales plants. Phylogenetic analyses using the concatenated amino acid sequences of 58 protein-coding genes from 48 species suggest that the 'basal' position of Nitraria sibirica belongs to the Sapindales clade. We also found that the inverted repeat expansion resulted in a duplication of rps19 in Nitraria sibirica when comparing its chloroplast genome structure with Theobroma cacao, Vitis vinifera, Eucalyptus erythrocorys and Arabidopsis thaliana. The duplication of rps19 gene was consistent with that in Zanthoxylum piperitum, Azadirachta indica, Sapindus mukorossi and Citrus sinensis, all of which belong to the order Sapindales, but different from most Rosids plants. In summary, the analyses of Nitraria sibirica chloroplast genome not only provide insights into comparative genome analysis, but also pave the way for a better understanding of the phylogenetic relationships within the Sapindales.

Emerging Avenues for Utilization of Exotic Germplasm

Breeders have been successful in increasing crop performance by exploiting genetic diversity over time. However, the reported annual yield increases are not sufficient in view of rapid human population growth and global environmental changes. Exotic germplasm possesses high levels of genetic diversity for valuable traits. However, only a small fraction of naturally occurring genetic diversity is utilized. Moreover, the yield gap between elite and exotic germplasm widens, which increases the effort needed to use exotic germplasm and to identify beneficial alleles and for their introgression. The advent of high-throughput genotyping and phenotyping technologies together with emerging biotechnologies provide new opportunities to explore exotic genetic variation. This review will summarize potential challenges for utilization of exotic germplasm and provide solutions.

PhenoCurve: capturing dynamic phenotype-environment relationships using phenomics data

Motivation

Phenomics is essential for understanding the mechanisms that regulate or influence growth, fitness, and development. Techniques have been developed to conduct high-throughput large-scale phenotyping on animals, plants and humans, aiming to bridge the gap between genomics, gene functions and traits. Although new developments in phenotyping techniques are exciting, we are limited by the tools to analyze fully the massive phenotype data, especially the dynamic relationships between phenotypes and environments.

Results

We present a new algorithm called PhenoCurve, a knowledge-based curve fitting algorithm, aiming to identify the complex relationships between phenotypes and environments, thus studying both values and trends of phenomics data. The results on both real and simulated data showed that PhenoCurve has the best performance among all the six tested methods. Its application to photosynthesis hysteresis pattern identification reveals new functions of core genes that control photosynthetic efficiency in response to varying environmental conditions, which are critical for understanding plant energy storage and improving crop productivity.

Availability and Implementation

Software is available at phenomics.uky.edu/PhenoCurve.

Contact

chen.jin@uky.edu or kramerd8@cns.msu.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

A size-adjustable radiofrequency coil for investigating plants in a Halbach magnet

A radio-frequency coil with adjustable distance has been developed and tested for in-situ examination of growing plants. The Helmholtz-based coil design reduces laborious tuning and matching efforts encountered with solenoids wound around a growing stem or branch. Relaxation experiments were performed on tomato plants and winter wheat under controlled light irradiation. Changes in signal amplitude and in relaxation times T₂ were recorded over day and night cycles. Peaks in distributions of relaxation times were attributed to different tissue components of two different plants.

3D Imaging Systems for Agricultural Applications

The development of the concepts of precision agriculture and viticulture since the last three decades has shown the need to use first 2D image acquisition techniques and dedicated image processing. More and more needs concern now 3D images and information. The main ideas of this chapter is thus to present some innovations of the 3D tools and methods in the agronomic domain. This chapter will particularly focus on two main subjects such as the 3D characterization of crop using Shape from Focus or Structure from Motion techniques and the 3D use for root phenotyping using rhizotron system. Results presented show that 3D information allows to better characterize crucial crop morphometric parameters using proxy-detection or phenotyping methods.

RADIX: rhizoslide platform allowing high throughput digital image analysis of root system expansion

BACKGROUND

Phenotyping of genotype-by-environment interactions in the root-zone is of major importance for crop improvement as the spatial distribution of a plant's root system is crucial for a plant to access water and nutrient resources of the soil. However, so far it is unclear to what extent genetic variations in root system responses to spatially varying soil resources can be utilized for breeding applications. Among others, one limiting factor is the absence of phenotyping platforms allowing the analysis of such interactions.

RESULTS

We developed a system that is able to (a) monitor root and shoot growth synchronously, (b) investigate their dynamic responses and (c) analyse the effect of heterogeneous N distribution to parts of the root system in a split-nutrient setup with a throughput (200 individual maize plants at once) sufficient for mapping of quantitative trait loci or for screens of multiple environmental factors. In a test trial, 24 maize genotypes were grown under split nitrogen conditions and the response of shoot and root growth was investigated. An almost double elongation rate of crown and lateral roots was observed under high N for all genotypes. The intensity of genotype-specific responses varied strongly. For example, elongation of crown roots differed almost two times between the fastest and slowest growing genotype. A stronger selective root placement in the high-N compartment was related to an increased shoot development indicating that early vigour might be related to a more intense foraging behaviour.

CONCLUSION

To our knowledge, RADIX is the only system currently existing which allows studying the differential response of crown roots to split-nutrient application to quantify foraging behaviour in genome mapping or selection experiments. In doing so, changes in root and shoot development and the connection to plant performance can be investigated.

Sun-induced chlorophyll fluorescence from high-resolution imaging spectroscopy data to quantify spatio-temporal patterns of photosynthetic function in crop canopies

Passive detection of sun-induced chlorophyll fluorescence (SIF) using spectroscopy has been proposed as a proxy to quantify changes in photochemical efficiency at canopy level under natural light conditions. In this study, we explored the use of imaging spectroscopy to quantify spatio-temporal dynamics of SIF within crop canopies and its sensitivity to track patterns of photosynthetic activity originating from the interaction between vegetation structure and incoming radiation as well as variations in plant function. SIF was retrieved using the Fraunhofer Line Depth (FLD) principle from imaging spectroscopy data acquired at different time scales a few metres above several crop canopies growing under natural illumination. We report the first maps of canopy SIF in high spatial resolution. Changes of SIF were monitored at different time scales ranging from quick variations under induced stress conditions to seasonal dynamics. Natural changes were primarily determined by varying levels and distribution of photosynthetic active radiation (PAR). However, this relationship changed throughout the day demonstrating an additional physiological component modulating spatio-temporal patterns of SIF emission. We successfully used detailed SIF maps to track changes in the canopy's photochemical activity under field conditions, providing a new tool to evaluate complex patterns of photosynthesis within the canopy.

RhizoTubes as a new tool for high throughput imaging of plant root development and architecture: test, comparison with pot grown plants and validation

BACKGROUND

In order to maintain high yields while saving water and preserving non-renewable resources and thus limiting the use of chemical fertilizer, it is crucial to select plants with more efficient root systems. This could be achieved through an optimization of both root architecture and root uptake ability and/or through the improvement of positive plant interactions with microorganisms in the rhizosphere. The development of devices suitable for high-throughput phenotyping of root structures remains a major bottleneck.

RESULTS

Rhizotrons suitable for plant growth in controlled conditions and non-invasive image acquisition of plant shoot and root systems (RhizoTubes) are described. These RhizoTubes allow growing one to six plants simultaneously, having a maximum height of 1.1 m, up to 8 weeks, depending on plant species. Both shoot and root compartment can be imaged automatically and non-destructively throughout the experiment thanks to an imaging cabin (RhizoCab). RhizoCab contains robots and imaging equipment for obtaining high-resolution pictures of plant roots. Using this versatile experimental setup, we illustrate how some morphometric root traits can be determined for various species including model (Medicago truncatula), crops (Pisum sativum, Brassica napus, Vitis vinifera, Triticum aestivum) and weed (Vulpia myuros) species grown under non-limiting conditions or submitted to various abiotic and biotic constraints. The measurement of the root phenotypic traits using this system was compared to that obtained using "classic" growth conditions in pots.

CONCLUSIONS

This integrated system, to include 1200 Rhizotubes, will allow high-throughput phenotyping of plant shoots and roots under various abiotic and biotic environmental conditions. Our system allows an easy visualization or extraction of roots and measurement of root traits for high-throughput or kinetic analyses. The utility of this system for studying root system architecture will greatly facilitate the identification of genetic and environmental determinants of key root traits involved in crop responses to stresses, including interactions with soil microorganisms.

Plant Phenotyping using Probabilistic Topic Models: Uncovering the Hyperspectral Language of Plants

Modern phenotyping and plant disease detection methods, based on optical sensors and information technology, provide promising approaches to plant research and precision farming. In particular, hyperspectral imaging have been found to reveal physiological and structural characteristics in plants and to allow for tracking physiological dynamics due to environmental effects. In this work, we present an approach to plant phenotyping that integrates non-invasive sensors, computer vision, as well as data mining techniques and allows for monitoring how plants respond to stress. To uncover latent hyperspectral characteristics of diseased plants reliably and in an easy-to-understand way, we "wordify" the hyperspectral images, i.e., we turn the images into a corpus of text documents. Then, we apply probabilistic topic models, a well-established natural language processing technique that identifies content and topics of documents. Based on recent regularized topic models, we demonstrate that one can track automatically the development of three foliar diseases of barley. We also present a visualization of the topics that provides plant scientists an intuitive tool for hyperspectral imaging. In short, our analysis and visualization of characteristic topics found during symptom development and disease progress reveal the hyperspectral language of plant diseases.

Observation of plant-pathogen interaction by simultaneous hyperspectral imaging reflection and transmission measurements

Hyperspectral imaging sensors are valuable tools for plant disease detection and plant phenotyping. Reflectance properties are influenced by plant pathogens and resistance responses, but changes of transmission characteristics of plants are less described. In this study we used simultaneously recorded reflectance and transmittance imaging data of resistant and susceptible barley genotypes that were inoculated with Blumeria graminis f. sp. hordei to evaluate the added value of imaging transmission, reflection and absorption for characterisation of disease development. These datasets were statistically analysed using principal component analysis, and compared with visual and molecular disease estimation. Reflection measurement performed significantly better for early detection of powdery mildew infection, colonies could be detected 2 days before symptoms became visible in RGB images. Transmission data could be used to detect powdery mildew 2 days after symptoms becoming visible in reflection based RGB images. Additionally distinct transmission changes occurred at 580-650nm for pixels containing disease symptoms. It could be shown that the additional information of the transmission data allows for a clearer spatial differentiation and localisation between powdery mildew symptoms and necrotic tissue on the leaf then purely reflectance based data. Thus the information of both measurement approaches are complementary: reflectance based measurements facilitate an early detection, and transmission measurements provide additional information to better understand and quantify the complex spatio-temporal dynamics of plant-pathogen interactions.

Field Scanalyzer: An automated robotic field phenotyping platform for detailed crop monitoring

Current approaches to field phenotyping are laborious or permit the use of only a few sensors at a time. In an effort to overcome this, a fully automated robotic field phenotyping platform with a dedicated sensor array that may be accurately positioned in three dimensions and mounted on fixed rails has been established, to facilitate continual and high-throughput monitoring of crop performance. Employed sensors comprise of high-resolution visible, chlorophyll fluorescence and thermal infrared cameras, two hyperspectral imagers and dual 3D laser scanners. The sensor array facilitates specific growth measurements and identification of key growth stages with dense temporal and spectral resolution. Together, this platform produces a detailed description of canopy development across the crops entire lifecycle, with a high-degree of accuracy and reproducibility.

Sun-induced fluorescence - a new probe of photosynthesis: First maps from the imaging spectrometer HyPlant

Variations in photosynthesis still cause substantial uncertainties in predicting photosynthetic CO2 uptake rates and monitoring plant stress. Changes in actual photosynthesis that are not related to greenness of vegetation are difficult to measure by reflectance based optical remote sensing techniques. Several activities are underway to evaluate the sun-induced fluorescence signal on the ground and on a coarse spatial scale using space-borne imaging spectrometers. Intermediate-scale observations using airborne-based imaging spectroscopy, which are critical to bridge the existing gap between small-scale field studies and global observations, are still insufficient. Here we present the first validated maps of sun-induced fluorescence in that critical, intermediate spatial resolution, employing the novel airborne imaging spectrometer HyPlant. HyPlant has an unprecedented spectral resolution, which allows for the first time quantifying sun-induced fluorescence fluxes in physical units according to the Fraunhofer Line Depth Principle that exploits solar and atmospheric absorption bands. Maps of sun-induced fluorescence show a large spatial variability between different vegetation types, which complement classical remote sensing approaches. Different crop types largely differ in emitting fluorescence that additionally changes within the seasonal cycle and thus may be related to the seasonal activation and deactivation of the photosynthetic machinery. We argue that sun-induced fluorescence emission is related to two processes: (i) the total absorbed radiation by photosynthetically active chlorophyll; and (ii) the functional status of actual photosynthesis and vegetation stress.

Dissecting spatiotemporal biomass accumulation in barley under different water regimes using high-throughput image analysis

Phenotyping large numbers of genotypes still represents the rate-limiting step in many plant genetic experiments and in breeding. To address this issue, novel automated phenotyping technologies have been developed. We investigated for a core set of barley cultivars if high-throughput image analysis can help to dissect vegetative biomass accumulation in response to two different watering regimes under semi-controlled greenhouse conditions. We found that experiments, treatments, genotypes and genotype by environment interaction (G × E) can be characterized at any time point by certain digital traits. Biomass accumulation under control and stress conditions was highly heritable. Growth model-derived maximum vegetative biomass (K max), inflection point (I) and regrowth rate (k) were identified as promising candidate traits for genome-wide association studies. Drought stress symptoms can be visualized, dissected and modelled. Especially the highly heritable regrowth rate, which had the biggest influence on biomass accumulation in stress treatment, seems promising for future studies to improve drought tolerance in different crop species. A proof of concept study revealed potential correlations between digital traits obtained from pot experiments under greenhouse conditions and agronomic traits from field experiments. Overall, non-invasive, imaging-based phenotyping platforms under greenhouse conditions offer excellent possibilities for trait discovery, trait development and industrial applications.

Volatile organic compounds as non-invasive markers for plant phenotyping

Plants emit a great variety of volatile organic compounds (VOCs) that can actively participate in plant growth and protection against biotic and abiotic stresses. VOC emissions are strongly dependent on environmental conditions; the greatest ambiguity is whether or not the predicted change in climate will influence and modify plant-pest interactions that are mediated by VOCs. The constitutive and induced emission patterns between plant genotypes, species, and taxa are highly variable and can be used as pheno(chemo)typic markers to distinguish between different origins and provenances. In recent years significant progress has been made in molecular and genetic plant breeding. However, there is actually a lack of knowledge in functionally linking genotypes and phenotypes, particularly in analyses of plant-environment interactions. Plant phenotyping, the assessment of complex plant traits such as growth, development, tolerance, resistance, etc., has become a major bottleneck, and quantitative information on genotype-environment relationships is the key to addressing major future challenges. With increasing demand to support and accelerate progress in breeding for novel traits, the plant research community faces the need to measure accurately increasingly large numbers of plants and plant traits. In this review article, we focus on the promising outlook of VOC phenotyping as a fast and non-invasive measure of phenotypic dynamics. The basic principle is to define plant phenotypes according to their disease resistance and stress tolerance, which in turn will help in improving the performance and yield of economically relevant plants.

Root phenotyping: from component trait in the lab to breeding

In the last decade cheaper and faster sequencing methods have resulted in an enormous increase in genomic data. High throughput genotyping, genotyping by sequencing and genomic breeding are becoming a standard in plant breeding. As a result, the collection of phenotypic data is increasingly becoming a limiting factor in plant breeding. Genetic studies on root traits are being hampered by the complexity of these traits and the inaccessibility of the rhizosphere. With an increasing interest in phenotyping, breeders and scientists try to overcome these limitations, resulting in impressive developments in automated phenotyping platforms. Recently, many such platforms have been thoroughly described, yet their efficiency to increase genetic gain often remains undiscussed. This efficiency depends on the heritability of the phenotyped traits as well as the correlation of these traits with agronomically relevant breeding targets. This review provides an overview of the latest developments in root phenotyping and describes the environmental and genetic factors influencing root phenotype and heritability. It also intends to give direction to future phenotyping and breeding strategies for optimizing root system functioning. A quantitative framework to determine the efficiency of phenotyping platforms for genetic gain is described. By increasing heritability, managing effects caused by interactions between genotype and environment and by quantifying the genetic relation between traits phenotyped in platforms and ultimate breeding targets, phenotyping platforms can be utilized to their maximum potential.

Magnetic resonance imaging of sugar beet taproots in soil reveals growth reduction and morphological changes during foliar Cercospora beticola infestation

Cercospora leaf spot (CLS) infection can cause severe yield loss in sugar beet. Introduction of Cercospora-resistant varieties in breeding programmes is important for plant protection to reduce both fungicide applications and the risk of the development of fungal resistance. However, in vivo monitoring of the sugar-containing taproots at early stages of foliar symptoms and the characterization of the temporal development of disease progression has proven difficult. Non-invasive magnetic resonance imaging (MRI) measurements were conducted to quantify taproot development of genotypes with high (HS) and low (LS) levels of susceptibility after foliar Cercospora inoculation. Fourteen days post-inoculation (dpi) the ratio of infected leaf area was still low (~7%) in both the HS and LS genotypes. However, during this period, the volumetric growth of the taproot had already started to decrease. Additionally, inoculated plants showed a reduction of the increase in width of inner cambial rings while the width of outer rings increased slightly compared with non-inoculated plants. This response partly compensated for the reduced development of inner rings that had a vascular connection with Cercospora-inoculated leaves. Hence, alterations in taproot anatomical features such as volume and cambial ring development can be non-invasively detected already at 14 dpi, providing information on the early impact of the infection on whole-plant performance. All these findings show that MRI is a suitable tool to identify promising candidate parent lines with improved resistance to Cercospora, for example with comparatively lower taproot growth reduction at early stages of canopy infection, for future introduction into breeing programmes.

Root Traits and Phenotyping Strategies for Plant Improvement

Roots are crucial for nutrient and water acquisition and can be targeted to enhance plant productivity under a broad range of growing conditions. A current challenge for plant breeding is the limited ability to phenotype and select for desirable root characteristics due to their underground location. Plant breeding efforts aimed at modifying root traits can result in novel, more stress-tolerant crops and increased yield by enhancing the capacity of the plant for soil exploration and, thus, water and nutrient acquisition. Available approaches for root phenotyping in laboratory, greenhouse and field encompass simple agar plates to labor-intensive root digging (i.e., shovelomics) and soil boring methods, the construction of underground root observation stations and sophisticated computer-assisted root imaging. Here, we summarize root architectural traits relevant to crop productivity, survey root phenotyping strategies and describe their advantages, limitations and practical value for crop and forage breeding programs.

Accuracy analysis of a multi-view stereo approach for phenotyping of tomato plants at the organ level

Accessing a plant's 3D geometry has become of significant importance for phenotyping during the last few years. Close-up laser scanning is an established method to acquire 3D plant shapes in real time with high detail, but it is stationary and has high investment costs. 3D reconstruction from images using structure from motion (SfM) and multi-view stereo (MVS) is a flexible cost-effective method, but requires post-processing procedures. The aim of this study is to evaluate the potential measuring accuracy of an SfM- and MVS-based photogrammetric method for the task of organ-level plant phenotyping. For this, reference data are provided by a high-accuracy close-up laser scanner. Using both methods, point clouds of several tomato plants were reconstructed at six following days. The parameters leaf area, main stem height and convex hull of the complete plant were extracted from the 3D point clouds and compared to the reference data regarding accuracy and correlation. These parameters were chosen regarding the demands of current phenotyping scenarios. The study shows that the photogrammetric approach is highly suitable for the presented monitoring scenario, yielding high correlations to the reference measurements. This cost-effective 3D reconstruction method depicts an alternative to an expensive laser scanner in the studied scenarios with potential for automated procedures.

Hyperspectral phenotyping on the microscopic scale: towards automated characterization of plant-pathogen interactions

BACKGROUND

The detection and characterization of resistance reactions of crop plants against fungal pathogens are essential to select resistant genotypes. In breeding practice phenotyping of plant genotypes is realized by time consuming and expensive visual rating. In this context hyperspectral imaging (HSI) is a promising non-invasive sensor technique in order to accelerate and to automate classical phenotyping methods. A hyperspectral microscope was established to determine spectral changes on the leaf and cellular level of barley (Hordeum vulgare) during resistance reactions against powdery mildew (Blumeria graminis f.sp. hordei, isolate K1). Experiments were conducted with near isogenic barley lines of cv. Ingrid, including the susceptible wild type (WT), mildew locus a 12 (Mla12 based resistance) and the resistant mildew locus o 3 (mlo3 based resistance), respectively. The reflection of inoculated and non-inoculated leaves was recorded daily with a hyperspectral linescanner in the visual (400 - 700 nm) and near infrared (700 - 1000 nm) range 3 to 14 days after inoculation.

RESULTS

Data analysis showed no significant differences in spectral signatures between non-inoculated genotypes. Barley leaves of the near-isogenic genotypes, inoculated with B. graminis f.sp. hordei differed in the spectral reflectance over time, respectively. The susceptible genotypes (WT, Mla12) showed an increase in reflectance in the visible range according to symptom development. However, the spectral signature of the resistant mlo-genotype did not show significant changes over the experimental period. In addition, a recent data driven approach for automated discovery of disease specific signatures, which is based on a new representation of the data using Simplex Volume Maximization (SiVM) was applied. The automated approach - evaluated in only a fraction of time revealed results similar to the time and labor intensive manually assessed hyperspectral signatures. The new representation determined by SiVM was also used to generate intuitive and easy to interpretable summaries, e.g. fingerprints or traces of hyperspectral dynamics of the different genotypes.

CONCLUSION

With this HSI based and data driven phenotyping approach an evaluation of host-pathogen interactions over time and a discrimination of barley genotypes differing in susceptibility to powdery mildew is possible.

A portable NMR sensor to measure dynamic changes in the amount of water in living stems or fruit and its potential to measure sap flow

Nuclear magnetic resonance (NMR) and NMR imaging (magnetic resonance imaging) offer the possibility to quantitatively and non-invasively measure the presence and movement of water. Unfortunately, traditional NMR hardware is expensive, poorly suited for plants, and because of its bulk and complexity, not suitable for use in the field. But does it need to be? We here explore how novel, small-scale portable NMR devices can be used as a flow sensor to directly measure xylem sap flow in a poplar tree (Populus nigra L.), or in a dendrometer-like fashion to measure dynamic changes in the absolute water content of fruit or stems. For the latter purpose we monitored the diurnal pattern of growth, expansion and shrinkage in a model fruit (bean pod, Phaseolus vulgaris L.) and in the stem of an oak tree (Quercus robur L.). We compared changes in absolute stem water content, as measured by the NMR sensor, against stem diameter variations as measured by a set of conventional point dendrometers, to test how well the sensitivities of the two methods compare and to investigate how well diurnal changes in trunk absolute water content correlate with the concomitant diurnal variations in stem diameter. Our results confirm the existence of a strong correlation between the two parameters, but also suggest that dynamic changes in oak stem water content could be larger than is apparent on the basis of the stem diameter variation alone.

Plant photosynthesis phenomics data quality control

MOTIVATION

Plant phenomics, the collection of large-scale plant phenotype data, is growing exponentially. The resources have become essential component of modern plant science. Such complex datasets are critical for understanding the mechanisms governing energy intake and storage in plants, and this is essential for improving crop productivity. However, a major issue facing these efforts is the determination of the quality of phenotypic data. Automated methods are needed to identify and characterize alterations caused by system errors, all of which are difficult to remove in the data collection step and distinguish them from more interesting cases of altered biological responses.

RESULTS

As a step towards solving this problem, we have developed a coarse-to-refined model called dynamic filter to identify abnormalities in plant photosynthesis phenotype data by comparing light responses of photosynthesis using a simplified kinetic model of photosynthesis. Dynamic filter employs an expectation-maximization process to adjust the kinetic model in coarse and refined regions to identify both abnormalities and biological outliers. The experimental results show that our algorithm can effectively identify most of the abnormalities in both real and synthetic datasets.

AVAILABILITY AND IMPLEMENTATION

Software available at www.msu.edu/%7Ejinchen/DynamicFilter .

HyperART: non-invasive quantification of leaf traits using hyperspectral absorption-reflectance-transmittance imaging

BACKGROUND

Combined assessment of leaf reflectance and transmittance is currently limited to spot (point) measurements. This study introduces a tailor-made hyperspectral absorption-reflectance-transmittance imaging (HyperART) system, yielding a non-invasive determination of both reflectance and transmittance of the whole leaf. We addressed its applicability for analysing plant traits, i.e. assessing Cercospora beticola disease severity or leaf chlorophyll content. To test the accuracy of the obtained data, these were compared with reflectance and transmittance measurements of selected leaves acquired by the point spectroradiometer ASD FieldSpec, equipped with the FluoWat device.

RESULTS

The working principle of the HyperART system relies on the upward redirection of transmitted and reflected light (range of 400 to 2500 nm) of a plant sample towards two line scanners. By using both the reflectance and transmittance image, an image of leaf absorption can be calculated. The comparison with the dynamically high-resolution ASD FieldSpec data showed good correlation, underlying the accuracy of the HyperART system. Our experiments showed that variation in both leaf chlorophyll content of four different crop species, due to different fertilization regimes during growth, and fungal symptoms on sugar beet leaves could be accurately estimated and monitored. The use of leaf reflectance and transmittance, as well as their sum (by which the non-absorbed radiation is calculated) obtained by the HyperART system gave considerably improved results in classification of Cercospora leaf spot disease and determination of chlorophyll content.

CONCLUSIONS

The HyperART system offers the possibility for non-invasive and accurate mapping of leaf transmittance and absorption, significantly expanding the applicability of reflectance, based on mapping spectroscopy, in plant sciences. Therefore, the HyperART system may be readily employed for non-invasive determination of the spatio-temporal dynamics of various plant properties.