Non-invasive assessment of cultivar and sex of Cannabis sativa L. by means of hyperspectral measurement.

Cannabis sativa L. is a versatile crop attracting increasing attention for food, fiber, and medical uses. As a dioecious species, males and females are visually indistinguishable during early growth. For seed or cannabinoid production, a higher number of female plants is economically advantageous. Currently, sex determination is labor-intensive and costly. Instead, we used rapid and non-destructive hyperspectral measurement, an emerging means of assessing plant physiological status, to reliably differentiate males and females. One industrial hemp (low tetrahydrocannabinol [THC]) cultivar was pre-grown in trays before transfer to the field in control soil. Reflectance spectra were acquired from leaves during flowering and machine learning algorithms applied allowed sex classification, which was best using a radial basis function (RBF) network. Eight industrial hemp (low THC) cultivars were field grown on fertilized and control soil. Reflectance spectra were acquired from leaves at early development when the plants of all cultivars had developed between four and six leaf pairs and in three cases only flower buds were visible (start of flowering). Machine learning algorithms were applied, allowing sex classification, differentiation of cultivars and fertilizer regime, again with best results for RBF networks. Differentiating nutrient status and varietal identity is feasible with high prediction accuracy. Sex classification was error-free at flowering but less accurate (between 60% and 87%) when using spectra from leaves at early growth stages. This was influenced by both cultivar and soil conditions, reflecting developmental differences between cultivars related to nutritional status. Hyperspectral measurement combined with machine learning algorithms is valuable for non-invasive assessment of C. sativa cultivar and sex. This approach can potentially improve regulatory security and productivity of cannabis farming.

Dynamics and genetic regulation of leaf nutrient concentration in barley based on hyperspectral imaging and machine learning.

Biofortification, the enrichment of nutrients in crop plants, is of increasing importance to improve human health. The wild barley nested association mapping (NAM) population HEB-25 was developed to improve agronomic traits including nutrient concentration. Here, we evaluated the potential of high-throughput hyperspectral imaging in HEB-25 to predict leaf concentration of 15 mineral nutrients, sampled from two field experiments and four developmental stages. Particularly accurate predictions were obtained by partial least squares regression (PLS) modeling of leaf concentrations for N, P and K reaching coefficients of determination of 0.90, 0.75 and 0.89, respectively. We recognized nutrient-specific patterns of variation of leaf nutrient concentration between developmental stages. A number of quantitative trait loci (QTL) associated with the simultaneous expression of leaf nutrients were detected, indicating their potential co-regulation in barley. For example, the wild barley allele of QTL-4H-1 simultaneously increased leaf concentration of N, P, K and Cu. Similar effects of the same QTL were previously reported for nutrient concentrations in grains, supporting a potential parallel regulation of N, P, K and Cu in leaves and grains of HEB-25. Our study provides a new approach for nutrient assessment in large-scale field experiments to ultimately select genes and genotypes supporting plant biofortification.

Genome-wide association study reveals the genetic complexity of fructan accumulation patterns in barley grain.

We profiled the grain oligosaccharide content of 154 two-row spring barley genotypes and quantified 27 compounds, mainly inulin- and neoseries-type fructans, showing differential abundance. Clustering revealed two profile groups where the 'high' set contained greater amounts of sugar monomers, sucrose, and overall fructans, but lower fructosylraffinose. A genome-wide association study (GWAS) identified a significant association for the variability of two fructan types: neoseries-DP7 and inulin-DP9, which showed increased strength when applying a novel compound ratio-GWAS approach. Gene models within this region included three known fructan biosynthesis genes (fructan:fructan 1-fructosyltransferase, sucrose:sucrose 1-fructosyltransferase, and sucrose:fructan 6-fructosyltransferase). Two other genes in this region, 6(G)-fructosyltransferase and vacuolar invertase1, have not previously been linked to fructan biosynthesis and showed expression patterns distinct from those of the other three genes, including exclusive expression of 6(G)-fructosyltransferase in outer grain tissues at the storage phase. From exome capture data, several single nucleotide polymorphisms related to inulin- and neoseries-type fructan variability were identified in fructan:fructan 1-fructosyltransferase and 6(G)-fructosyltransferase genes. Co-expression analyses uncovered potential regulators of fructan biosynthesis including transcription factors. Our results provide the first scientific evidence for the distinct biosynthesis of neoseries-type fructans during barley grain maturation and reveal novel gene candidates likely to be involved in the differential biosynthesis of various types of fructan in barley.

"Macrobot": An Automated Segmentation-Based System for Powdery Mildew Disease Quantification.

Managing plant diseases is increasingly difficult due to reasons such as intensifying the field production, climatic change-driven expansion of pests, redraw and loss of effectiveness of pesticides, rapid breakdown of the disease resistance in the field, and other factors. The substantial progress in genomics of both plants and pathogens, achieved in the last decades, has the potential to counteract this negative trend, however, only when the genomic data is supported by relevant phenotypic data that allows linking the genomic information to specific traits. We have developed a set of methods and equipment and combined them into a "Macrophenomics facility." The pipeline has been optimized for the quantification of powdery mildew infection symptoms on wheat and barley, but it can be adapted to other diseases and host plants. The Macrophenomics pipeline scores the visible powdery mildew disease symptoms, typically 5-7 days after inoculation (dai), in a highly automated manner. The system can precisely and reproducibly quantify the percentage of the infected leaf area with a theoretical throughput of up to 10000 individual samples per day, making it appropriate for phenotyping of large germplasm collections and crossing populations.

Evaluating the suitability of hyper- and multispectral imaging to detect foliar symptoms of the grapevine trunk disease Esca in vineyards.

BACKGROUND: Grapevine trunk diseases (GTDs) such as Esca are among the most devastating threats to viticulture. Due to the lack of efficient preventive and curative treatments, Esca causes severe economic losses worldwide. Since symptoms do not develop consecutively, the true incidence of the disease in a vineyard is difficult to assess. Therefore, an annual monitoring is required. In this context, automatic detection of symptoms could be a great relief for winegrowers. Spectral sensors have proven to be successful in disease detection, allowing a non-destructive, objective, and fast data acquisition. The aim of this study is to evaluate the feasibility of the in-field detection of foliar Esca symptoms over three consecutive years using ground-based hyperspectral and airborne multispectral imaging. RESULTS: Hyperspectral disease detection models have been successfully developed using either original field data or manually annotated data. In a next step, these models were applied on plant scale. While the model using annotated data performed better during development, the model using original data showed higher classification accuracies when applied in practical work. Moreover, the transferability of disease detection models to unknown data was tested. Although the visible and near-infrared (VNIR) range showed promising results, the transfer of such models is challenging. Initial results indicate that external symptoms could be detected pre-symptomatically, but this needs further evaluation. Furthermore, an application specific multispectral approach was simulated by identifying the most important wavelengths for the differentiation tasks, which was then compared to real multispectral data. Even though the ground-based multispectral disease detection was successful, airborne detection remains difficult. CONCLUSIONS: In this study, ground-based hyperspectral and airborne multispectral approaches for the detection of foliar Esca symptoms are presented. Both sensor systems seem to be suitable for the in-field detection of the disease, even though airborne data acquisition has to be further optimized. Our disease detection approaches could facilitate monitoring plant phenotypes in a vineyard.

Optimizing the procedure of grain nutrient predictions in barley via hyperspectral imaging.

Hyperspectral imaging enables researchers and plant breeders to analyze various traits of interest like nutritional value in high throughput. In order to achieve this, the optimal design of a reliable calibration model, linking the measured spectra with the investigated traits, is necessary. In the present study we investigated the impact of different regression models, calibration set sizes and calibration set compositions on prediction performance. For this purpose, we analyzed concentrations of six globally relevant grain nutrients of the wild barley population HEB-YIELD as case study. The data comprised 1,593 plots, grown in 2015 and 2016 at the locations Dundee and Halle, which have been entirely analyzed through traditional laboratory methods and hyperspectral imaging. The results indicated that a linear regression model based on partial least squares outperformed neural networks in this particular data modelling task. There existed a positive relationship between the number of samples in a calibration model and prediction performance, with a local optimum at a calibration set size of ~40% of the total data. The inclusion of samples from several years and locations could clearly improve the predictions of the investigated nutrient traits at small calibration set sizes. It should be stated that the expansion of calibration models with additional samples is only useful as long as they are able to increase trait variability. Models obtained in a certain environment were only to a limited extent transferable to other environments. They should therefore be successively upgraded with new calibration data to enable a reliable prediction of the desired traits. The presented results will assist the design and conceptualization of future hyperspectral imaging projects in order to achieve reliable predictions. It will in general help to establish practical applications of hyperspectral imaging systems, for instance in plant breeding concepts.

Genetic dissection of grain elements predicted by hyperspectral imaging associated with yield-related traits in a wild barley NAM population.

Enhancing the accumulation of essential mineral elements in cereal grains is of prime importance for combating human malnutrition. Biofortification by breeding holds great potential for improving nutrient accumulation in grains. However, conventional breeding approaches require element analysis of many grain samples, which causes high costs. Here we applied hyperspectral imaging to estimate the concentration of 15 grain elements (C, B, Ca, Cd, Cu, Fe, K, Mg, Mn, Mo, N, Na, P, S, Zn) in high-throughput in the wild barley nested association mapping (NAM) population HEB-25, comprising 1,420 BC1S3 lines derived from crossing 25 wild barley accessions with the cultivar 'Barke'. Nutrient concentrations varied largely with a multitude of lines having higher micronutrient concentration than 'Barke'. In a genome-wide association study (GWAS), we located 75 quantitative trait locus (QTL) hotspots, whereof many could be explained by major genes such as NO APICAL MERISTEM-1 (NAM-1) and PHOTOPERIOD 1 (Ppd-H1). The GWAS approach revealed exotic alleles that were able to increase grain element concentrations. Remarkably, a QTL linked to GIBBERELLIN 20 OXIDASE 2 (HvGA20ox2) significantly increased several grain elements without yield loss. We conclude that introgressing promising exotic alleles into elite breeding material can assist in improving the nutritional value of barley grains.

Phenoliner: A New Field Phenotyping Platform for Grapevine Research.

In grapevine research the acquisition of phenotypic data is largely restricted to the field due to its perennial nature and size. The methodologies used to assess morphological traits and phenology are mainly limited to visual scoring. Some measurements for biotic and abiotic stress, as well as for quality assessments, are done by invasive measures. The new evolving sensor technologies provide the opportunity to perform non-destructive evaluations of phenotypic traits using different field phenotyping platforms. One of the biggest technical challenges for field phenotyping of grapevines are the varying light conditions and the background. In the present study the Phenoliner is presented, which represents a novel type of a robust field phenotyping platform. The vehicle is based on a grape harvester following the concept of a moveable tunnel. The tunnel it is equipped with different sensor systems (RGB and NIR camera system, hyperspectral camera, RTK-GPS, orientation sensor) and an artificial broadband light source. It is independent from external light conditions and in combination with artificial background, the Phenoliner enables standardised acquisition of high-quality, geo-referenced sensor data.

Improved classification accuracy of powdery mildew infection levels of wine grapes by spatial-spectral analysis of hyperspectral images.

BACKGROUND: Hyperspectral imaging is an emerging means of assessing plant vitality, stress parameters, nutrition status, and diseases. Extraction of target values from the high-dimensional datasets either relies on pixel-wise processing of the full spectral information, appropriate selection of individual bands, or calculation of spectral indices. Limitations of such approaches are reduced classification accuracy, reduced robustness due to spatial variation of the spectral information across the surface of the objects measured as well as a loss of information intrinsic to band selection and use of spectral indices. In this paper we present an improved spatial-spectral segmentation approach for the analysis of hyperspectral imaging data and its application for the prediction of powdery mildew infection levels (disease severity) of intact Chardonnay grape bunches shortly before veraison. RESULTS: Instead of calculating texture features (spatial features) for the huge number of spectral bands independently, dimensionality reduction by means of Linear Discriminant Analysis (LDA) was applied first to derive a few descriptive image bands. Subsequent classification was based on modified Random Forest classifiers and selective extraction of texture parameters from the integral image representation of the image bands generated. Dimensionality reduction, integral images, and the selective feature extraction led to improved classification accuracies of up to [Formula: see text] for detached berries used as a reference sample (training dataset). Our approach was validated by predicting infection levels for a sample of 30 intact bunches. Classification accuracy improved with the number of decision trees of the Random Forest classifier. These results corresponded with qPCR results. An accuracy of 0.87 was achieved in classification of healthy, infected, and severely diseased bunches. However, discrimination between visually healthy and infected bunches proved to be challenging for a few samples, perhaps due to colonized berries or sparse mycelia hidden within the bunch or airborne conidia on the berries that were detected by qPCR. CONCLUSIONS: An advanced approach to hyperspectral image classification based on combined spatial and spectral image features, potentially applicable to many available hyperspectral sensor technologies, has been developed and validated to improve the detection of powdery mildew infection levels of Chardonnay grape bunches. The spatial-spectral approach improved especially the detection of light infection levels compared with pixel-wise spectral data analysis. This approach is expected to improve the speed and accuracy of disease detection once the thresholds for fungal biomass detected by hyperspectral imaging are established; it can also facilitate monitoring in plant phenotyping of grapevine and additional crops.

Non-invasive Presymptomatic Detection of Cercospora beticola Infection and Identification of Early Metabolic Responses in Sugar Beet.

Cercospora beticola is an economically significant fungal pathogen of sugar beet, and is the causative pathogen of Cercospora leaf spot. Selected host genotypes with contrasting degree of susceptibility to the disease have been exploited to characterize the patterns of metabolite responses to fungal infection, and to devise a pre-symptomatic, non-invasive method of detecting the presence of the pathogen. Sugar beet genotypes were analyzed for metabolite profiles and hyperspectral signatures. Correlation of data matrices from both approaches facilitated identification of candidates for metabolic markers. Hyperspectral imaging was highly predictive with a classification accuracy of 98.5-99.9% in detecting C. beticola. Metabolite analysis revealed metabolites altered by the host as part of a successful defense response: these were L-DOPA, 12-hydroxyjasmonic acid 12-O-ß-D-glucoside, pantothenic acid, and 5-O-feruloylquinic acid. The accumulation of glucosylvitexin in the resistant cultivar suggests it acts as a constitutively produced protectant. The study establishes a proof-of-concept for an unbiased, presymptomatic and non-invasive detection system for the presence of C. beticola. The test needs to be validated with a larger set of genotypes, to be scalable to the level of a crop improvement program, aiming to speed up the selection for resistant cultivars of sugar beet. Untargeted metabolic profiling is a valuable tool to identify metabolites which correlate with hyperspectral data.

Label-free proteome profiling reveals developmental-dependent patterns in young barley grains.

UNLABELLED: Due to its importance as a cereal crop worldwide, high interest in the determination of factors influencing barley grain quality exists. This study focusses on the elucidation of protein networks affecting early grain developmental processes. NanoLC-based separation coupled to label-free MS detection was applied to gain insights into biochemical processes during five different grain developmental phases (pre-storage until storage phase, 3days to 16days after flowering). Multivariate statistics revealed two distinct developmental patterns during the analysed grain developmental phases: proteins showed either highest abundance in the middle phase of development - in the transition phase - or at later developmental stages - within the storage phase. Verification of developmental patterns observed by proteomic analysis was done by applying hypothesis-driven approaches, namely Western Blot analysis and enzyme assays. High general metabolic activity of the grain with regard to protein synthesis, cell cycle regulation, defence against oxidative stress, and energy production via photosynthesis was observed in the transition phase. Proteins upregulated in the storage phase are related towards storage protein accumulation, and interestingly to the defence of storage reserves against pathogens. A mixed regulatory pattern for most enzymes detected in our study points to regulatory mechanisms at the level of protein isoforms. BIOLOGICAL SIGNIFICANCE: In-depth understanding of early grain developmental processes of cereal caryopses is of high importance as they influence final grain weight and quality. Our knowledge about these processes is still limited, especially on proteome level. To identify key mechanisms in early barley grain development, a label-free data-independent proteomics acquisition approach has been applied. Our data clearly show, that proteins either exhibit highest expression during cellularization and the switch to the storage phase (transition phase, 5-7 DAF), or during storage product accumulation (10-16 DAF). The results highlight versatile cellular metabolic activity in the transition phase and strong convergence towards storage product accumulation in the storage phase. Notably, both phases are characterized by particular protective mechanism, such as scavenging of oxidative stress and defence against pathogens, during the transition and the storage phase, respectively.

Mass Spectrometry Imaging of Metabolites in Barley Grain Tissues.

Higher plants are composed of a multitude of tissues with particular functions, reflected by distinct profiles of transcripts, proteins, and metabolites. Although the rapid development of "omics" technologies has advanced plant science tremendously within recent years, analysis is frequently performed on whole organ or whole plant extracts, causing the loss of spatial information. Mass spectrometry-based imaging (MSI) approaches have become a powerful tool to decipher spatially resolved molecular information. Matrix-assisted laser desorption/ionization (MALDI) is the most widespread ionization method utilized for MSI and has recently been applied to plant science. A range of different plant organs and tissues has been successfully analyzed by MSI, and patterns of various classes of metabolites from primary and secondary metabolism have been obtained. This protocol describes a method for analysis of spatial metabolite distributions in cryosections of developing barley grains. Detailed procedures for sample preparation, mass spectrometry measurement, and data analysis are provided. © 2016 by John Wiley & Sons, Inc.

HyphArea--automated analysis of spatiotemporal fungal patterns.

In phytopathology quantitative measurements are rarely used to assess crop plant disease symptoms. Instead, a qualitative valuation by eye is often the method of choice. In order to close the gap between subjective human inspection and objective quantitative results, the development of an automated analysis system that is capable of recognizing and characterizing the growth patterns of fungal hyphae in micrograph images was developed. This system should enable the efficient screening of different host-pathogen combinations (e.g., barley-Blumeria graminis, barley-Rhynchosporium secalis) using different microscopy technologies (e.g., bright field, fluorescence). An image segmentation algorithm was developed for gray-scale image data that achieved good results with several microscope imaging protocols. Furthermore, adaptability towards different host-pathogen systems was obtained by using a classification that is based on a genetic algorithm. The developed software system was named HyphArea, since the quantification of the area covered by a hyphal colony is the basic task and prerequisite for all further morphological and statistical analyses in this context. By means of a typical use case the utilization and basic properties of HyphArea could be demonstrated. It was possible to detect statistically significant differences between the growth of an R. secalis wild-type strain and a virulence mutant.

Barley grain development toward an integrative view.

Seeds are complex structures composed of several maternal and filial tissues which undergo rapid changes during development. In this review, the barley grain is taken as a cereal seed model. Following a brief description of the developing grain, recent progress in grain development modeling is described. 3-D/4-D models based on histological sections or nondestructive NMR measurements can be used to integrate a variety of datasets. Extensive transcriptome data are taken as a frame to augment our understanding of various molecular-physiological processes. Discussed are maternal influences on grain development and the role of different tissues (pericarp, nucellus, nucellar projection, endosperm, endosperm transfer cells). Programmed cell death (PCD) is taken to pinpoint tissue specificities and the importance of remobilization processes for grain development. Transcriptome data have also been used to derive transcriptional networks underlying differentiation and maturation in endosperm and embryo. They suggest that the "maturation hormone" ABA is important also in early grain development. Massive storage product synthesis during maturation is dependent on sufficient energy, which can only be provided by specific metabolic adaptations due to severe oxygen deficiencies within the seed. To integrate the great variety of data from different research areas in complex, predictive computational modeling as part of a systems biology approach is an important challenge of the future. First attempts of modeling barley grain metabolism are summarized.

A high-throughput screening system for barley/powdery mildew interactions based on automated analysis of light micrographs.

BACKGROUND: To find candidate genes that potentially influence the susceptibility or resistance of crop plants to powdery mildew fungi, an assay system based on transient-induced gene silencing (TIGS) as well as transient over-expression in single epidermal cells of barley has been developed. However, this system relies on quantitative microscopic analysis of the barley/powdery mildew interaction and will only become a high-throughput tool of phenomics upon automation of the most time-consuming steps. RESULTS: We have developed a high-throughput screening system based on a motorized microscope which evaluates the specimens fully automatically. A large-scale double-blind verification of the system showed an excellent agreement of manual and automated analysis and proved the system to work dependably. Furthermore, in a series of bombardment experiments an RNAi construct targeting the Mlo gene was included, which is expected to phenocopy resistance mediated by recessive loss-of-function alleles such as mlo5. In most cases, the automated analysis system recorded a shift towards resistance upon RNAi of Mlo, thus providing proof of concept for its usefulness in detecting gene-target effects. CONCLUSION: Besides saving labor and enabling a screening of thousands of candidate genes, this system offers continuous operation of expensive laboratory equipment and provides a less subjective analysis as well as a complete and enduring documentation of the experimental raw data in terms of digital images. In general, it proves the concept of enabling available microscope hardware to handle challenging screening tasks fully automatically.

Correlation-maximizing surrogate gene space for visual mining of gene expression patterns in developing barley endosperm tissue.

BACKGROUND: Micro- and macroarray technologies help acquire thousands of gene expression patterns covering important biological processes during plant ontogeny. Particularly, faithful visualization methods are beneficial for revealing interesting gene expression patterns and functional relationships of coexpressed genes. Such screening helps to gain deeper insights into regulatory behavior and cellular responses, as will be discussed for expression data of developing barley endosperm tissue. For that purpose, high-throughput multidimensional scaling (HiT-MDS), a recent method for similarity-preserving data embedding, is substantially refined and used for (a) assessing the quality and reliability of centroid gene expression patterns, and for (b) derivation of functional relationships of coexpressed genes of endosperm tissue during barley grain development (0-26 days after flowering). RESULTS: Temporal expression profiles of 4824 genes at 14 time points are faithfully embedded into two-dimensional displays. Thereby, similar shapes of coexpressed genes get closely grouped by a correlation-based similarity measure. As a main result, by using power transformation of correlation terms, a characteristic cloud of points with bipolar sandglass shape is obtained that is inherently connected to expression patterns of pre-storage, intermediate and storage phase of endosperm development. CONCLUSION: The new HiT-MDS-2 method helps to create global views of expression patterns and to validate centroids obtained from clustering programs. Furthermore, functional gene annotation for developing endosperm barley tissue is successfully mapped to the visualization, making easy localization of major centroids of enriched functional categories possible.