Automatic Scoring of Rhizoctonia Crown and Root Rot Affected Sugar Beet Fields from Orthorectified UAV Images Using Machine Learning.

Rhizoctonia crown and root rot (RCRR), caused by Rhizoctonia solani, can cause severe yield and quality losses in sugar beet. The most common strategy to control the disease is the development of resistant varieties. In the breeding process, field experiments with artificial inoculation are carried out to evaluate the performance of genotypes and varieties. The phenotyping process in breeding trials requires constant monitoring and scoring by skilled experts. This work is time demanding and shows bias and heterogeneity according to the experience and capacity of each individual person. Optical sensors and artificial intelligence have demonstrated great potential to achieve higher accuracy than human raters and the possibility to standardize phenotyping applications. A workflow combining red-green-blue and multispectral imagery coupled to an unmanned aerial vehicle (UAV), as well as machine learning techniques, was applied to score diseased plants and plots affected by RCRR. Georeferenced annotation of UAV-orthorectified images was carried out. With the annotated images, five convolutional neural networks were trained to score individual plants. The training was carried out with different image analysis strategies and data augmentation. The custom convolutional neural network trained from scratch together with pretrained MobileNet showed the best precision in scoring RCRR (0.73 to 0.85). The average per plot of spectral information was used to score the plots, and the benefit of adding the information obtained from the score of individual plants was compared. For this purpose, machine learning models were trained together with data management strategies, and the best-performing model was chosen. A combined pipeline of random forest and k-nearest neighbors has shown the best weighted precision (0.67). This research provides a reliable workflow for detecting and scoring RCRR based on aerial imagery. RCRR is often distributed heterogeneously in trial plots; therefore, considering the information from individual plants of the plots showed a significant improvement in UAV-based automated monitoring routines.

Integrated Nematode Management in a World in Transition: Constraints, Policy, Processes, and Technologies for the Future.

Plant-parasitic nematodes are one of the most insidious pests limiting agricultural production, parasitizing mostly belowground and occasionally aboveground plant parts. They are an important and underestimated component of the estimated 30% yield loss inflicted on crops globally by biotic constraints. Nematode damage is intensified by interactions with biotic and abiotic factors constraints: soilborne pathogens, soil fertility degradation, reduced soil biodiversity, climate variability, and policies influencing the development of improved management options. This review focuses on the following topics: (a) biotic and abiotic constraints, (b) modification of production systems, (c) agricultural policies, (d) the microbiome, (e) genetic solutions, and (f) remote sensing. Improving integrated nematode management (INM) across all scales of agricultural production and along the Global North-Global South divide, where inequalities influence access to technology, is discussed. The importance of the integration of technological development in INM is critical to improving food security and human well-being in the future.

Workflow for phenotyping sugar beet roots by automated evaluation of cell characteristics and tissue arrangement using digital image processing.

BACKGROUND: Cell characteristics, including cell type, size, shape, packing, cell-to-cell-adhesion, intercellular space, and cell wall thickness, influence the physical characteristics of plant tissues. Genotypic differences were found concerning damage susceptibility related to beet texture for sugar beet (Beta vulgaris). Sugar beet storage roots are characterized by heterogeneous tissue with several cambium rings surrounded by small-celled vascular tissue and big-celled sugar-storing parenchyma between the rings. This study presents a procedure for phenotyping heterogeneous tissues like beetroots by imaging. RESULTS: Ten Beta genotypes (nine sugar beet and one fodder beet) were included to establish a pipeline for the automated histologic evaluation of cell characteristics and tissue arrangement using digital image processing written in the programming language R. The identification of cells has been validated by comparison with manual cell identification. Cells are reliably discriminated from intercellular spaces, and cells with similar morphological features are assigned to biological tissue types. CONCLUSIONS: Genotypic differences in cell diameter and cell arrangement can straightforwardly be phenotyped by the presented workflow. The presented routine can further identify genotypic differences in cell diameter and cell arrangement during early growth stages and between sugar storage capabilities.

Fusarium Head Blight on Wheat: Biology, Modern Detection and Diagnosis and Integrated Disease Management.

Fusarium head blight (FHB) is a major threat for wheat production worldwide. Most reviews focus on Fusarium graminearum as a main causal agent of FHB. However, different Fusarium species are involved in this disease complex. These species differ in their geographic adaptation and mycotoxin profile. The incidence of FHB epidemics is highly correlated with weather conditions, especially rainy days with warm temperatures at anthesis and an abundance of primary inoculum. Yield losses due to the disease can reach up to 80% of the crop. This review summarizes the Fusarium species involved in the FHB disease complex with the corresponding mycotoxin profiles, disease cycle, diagnostic methods, the history of FHB epidemics, and the management strategy of the disease. In addition, it discusses the role of remote sensing technology in the integrated management of the disease. This technology can accelerate the phenotyping process in the breeding programs aiming at FHB-resistant varieties. Moreover, it can support the decision-making strategies to apply fungicides via monitoring and early detection of the diseases under field conditions. It can also be used for selective harvest to avoid mycotoxin-contaminated plots in the field.

Hyperspectral Imaging in the UV Range Allows for Differentiation of Sugar Beet Diseases Based on Changes in Secondary Plant Metabolites.

Fungal infections trigger defense or signaling responses in plants, leading to various changes in plant metabolites. The changes in metabolites, for example chlorophyll or flavonoids, have long been detectable using time-consuming destructive analytical methods including high-performance liquid chromatography or photometric determination. Recent plant phenotyping studies have revealed that hyperspectral imaging (HSI) in the UV range can be used to link spectral changes with changes in plant metabolites. To compare established destructive analytical methods with new nondestructive hyperspectral measurements, the interaction between sugar beet leaves and the pathogens Cercospora beticola, which causes Cercospora leaf spot disease (CLS), and Uromyces betae, which causes sugar beet rust (BR), was investigated. With the help of destructive analyses, we showed that both diseases have different effects on chlorophylls, carotenoids, flavonoids, and several phenols. Nondestructive hyperspectral measurements in the UV range revealed different effects of CLS and BR on plant metabolites resulting in distinct reflectance patterns. Both diseases resulted in specific spectral changes that allowed differentiation between the two diseases. Machine learning algorithms enabled the differentiation between the symptom classes and recognition of the two sugar beet diseases. Feature importance analysis identified specific wavelengths important to the classification, highlighting the utility of the UV range. The study demonstrates that HSI in the UV range is a promising, nondestructive tool to investigate the influence of plant diseases on plant physiology and biochemistry.

Disease Incidence and Severity of Cercospora Leaf Spot in Sugar Beet Assessed by Multispectral Unmanned Aerial Images and Machine Learning.

Disease incidence (DI) and metrics of disease severity are relevant parameters for decision making in plant protection and plant breeding. To develop automated and sensor-based routines, a sugar beet variety trial was inoculated with Cercospora beticola and monitored with a multispectral camera system mounted to an unmanned aerial vehicle (UAV) over the vegetation period. A pipeline based on machine learning methods was established for image data analysis and extraction of disease-relevant parameters. Features based on the digital surface model, vegetation indices, shadow condition, and image resolution improved classification performance in comparison with using single multispectral channels in 12 and 6% of diseased and soil regions, respectively. With a postprocessing step, area-related parameters were computed after classification. Results of this pipeline also included extraction of DI and disease severity (DS) from UAV data. The calculated area under disease progress curve of DS was 2,810.4 to 7,058.8%.days for human visual scoring and 1,400.5 to 4,343.2%.days for UAV-based scoring. Moreover, a sharper differentiation of varieties compared with visual scoring was observed in area-related parameters such as area of complete foliage (AF), area of healthy foliage (AH), and mean area of lesion by unit of foliage ([Formula: see text]). These advantages provide the option to replace the laborious work of visual disease assessments in the field with a more precise, nondestructive assessment via multispectral data acquired by UAV flights.[Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Agricultural plant cataloging and establishment of a data framework from UAV-based crop images by computer vision.

BACKGROUND: Unmanned aerial vehicle (UAV)-based image retrieval in modern agriculture enables gathering large amounts of spatially referenced crop image data. In large-scale experiments, however, UAV images suffer from containing a multitudinous amount of crops in a complex canopy architecture. Especially for the observation of temporal effects, this complicates the recognition of individual plants over several images and the extraction of relevant information tremendously. RESULTS: In this work, we present a hands-on workflow for the automatized temporal and spatial identification and individualization of crop images from UAVs abbreviated as "cataloging" based on comprehensible computer vision methods. We evaluate the workflow on 2 real-world datasets. One dataset is recorded for observation of Cercospora leaf spot-a fungal disease-in sugar beet over an entire growing cycle. The other one deals with harvest prediction of cauliflower plants. The plant catalog is utilized for the extraction of single plant images seen over multiple time points. This gathers a large-scale spatiotemporal image dataset that in turn can be applied to train further machine learning models including various data layers. CONCLUSION: The presented approach improves analysis and interpretation of UAV data in agriculture significantly. By validation with some reference data, our method shows an accuracy that is similar to more complex deep learning-based recognition techniques. Our workflow is able to automatize plant cataloging and training image extraction, especially for large datasets.

Is there a risk to honeybees from use of thiamethoxam as a sugar beet seed treatment?

The ban imposed by the European Union on the use of neonicotinoids as sugar beet seed treatments was based on the exposure of bees to residues of neonicotinoids in pollen and nectar of succeeding crops. To address this concern, residues of thiamethoxam (TMX) and clothianidin (CTD) were analyzed in soil collected from fields planted in at least the previous year with thiamethoxam-treated sugar beet seed. This soil monitoring program was conducted at 94 sites across Germany in two separate years. In addition, a succeeding crop study assessed residues in soil, guttation fluid, pollen, and nectar sampled from untreated succeeding crops planted in the season after thiamethoxam seed-treated sugar beet at eight field sites across five countries. The overall mean residues observed in soil monitoring were 8.0 ± 0.5 µg TMX + CTD/kg in the season after the use of treated sugar beet seed. Residue values decreased with increasing time interval between the latest thiamethoxam or clothianidin application before sugar beet drilling and with lower application frequency. Residues were detected in guttation fluid (2.0-37.7 µg TMX/L); however, the risk to pollinators from this route of exposure is likely to be low, based on the reported levels of consumption. Residues of thiamethoxam and clothianidin in pollen and nectar sampled from the succeeding crops were detected at or below the limit of quantification (0.5-1 µg a.i./kg) in 86.7% of pollen and 98.6% of nectar samples and, unlike guttation fluid residues, were not correlated with measured soil residues. Residues in pollen and nectar are lower than reported sublethal adverse effect concentrations in studies with honeybee and bumble bee individuals and colonies fed only thiamethoxam-treated sucrose, and are lower than those reported to result in no effects in honeybees, bumble bees, and solitary bees foraging on seed-treated crops. Integr Environ Assess Manag 2022;18:709-721. © 2021 SYNGENTA. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Pesticide use in sugar beet cultivation in Germany and assessment of the associated environmental risks using the risk indicator SYNOPS-GIS.

BACKGROUND: The assessment of the environmental risks for pesticides is a current topic of the European Union (EU) strategy 'Farm to Fork'. Therefore, an analysis of the status quo of pesticide use from 2010 to 2015 and the associated environmental risks was performed for sugar beet cultivation in Germany. Based on this assessment, crop protection strategies should be developed that contribute to risk reduction. RESULTS: Pesticide use data from 2314 randomly chosen sugar beet growing farms were available from annual farm surveys from 2010 until 2015. Possible environmental risks from pesticide applications were calculated with the model SYNOPS-GIS. Each pesticide application pattern was combined with several model fields. The concentrations of active ingredients in the non-target compartments, namely soil, neighboring surface waters and field margins, were used to determined risk indices (exposure toxicity ratios, ETRs) for different terrestrial and aquatic reference species. ETRs were mainly lower than a risk threshold used throughout this study (ETR = 1). The risks caused by herbicide use were studied in more detail since herbicides are applied on nearly all fields. The aquatic risks posed by herbicides were independent of specific active ingredients or application patterns. Instead, certain combinations of active ingredients, application dates and field-specific environmental conditions provoked higher risks. The aquatic risks were strongly influenced by the distance of the fields to surface waters. CONCLUSIONS: Further risk mitigation seems possible by combining field-specific measures and technical options. © 2021 Society of Chemical Industry.

A Hyperspectral Library of Foliar Diseases of Wheat.

This work established a hyperspectral library of important foliar diseases of wheat induced by different fungal pathogens, representing a time series from infection to symptom appearance for the purpose of detecting spectral changes. The data were generated under controlled conditions at the leaf scale. The transition from healthy to diseased leaf tissue was assessed, and spectral shifts were identified and used in combination with histological investigations to define developmental stages in pathogenesis for each disease. The spectral signatures of each plant disease that indicate a specific developmental stage during pathogenesis, defined as turning points, were combined into a spectral library. Machine learning analysis methods were applied and compared to test the potential of this library to detect and quantify foliar diseases in hyperspectral images. All evaluated classifiers had high accuracy (≤99%) for the detection and identification of both biotrophic and necrotrophic fungi. The potential of applying spectral analysis methods in combination with a spectral library for the detection and identification of plant diseases is demonstrated. Further evaluation and development of these algorithms should contribute to a robust detection and identification system for plant diseases at different developmental stages and the promotion and development of site-specific management techniques for plant diseases under field conditions.

Evidence for the association of target-site resistance in cyp51 with reduced DMI sensitivity in European Cercospora beticola field isolates.

BACKGROUND: Cercospora leaf spot caused by Cercospora beticola is the most relevant foliar disease in sugar beet cultivation. In the last decade a decreasing sensitivity of C. beticola towards demethylation inhibitors (DMIs) occurred. Different mechanisms mediating a reduced sensitivity towards DMIs have been identified in different plant pathogens to date, such as target site mutations, over-expression or active excretion of the fungicide. RESULTS: A sequencing of the cytochrome P450-dependent sterol 14α-demethylase gene sequence (cyp51) of diverse C. beticola isolates collected in different European countries with reduced DMI sensitivity was performed in order to find a possible correlation of mutations with higher EC50 values. The amino acid alterations Y464S, L144F and I309T combined with L144F were found to be associated with a reduced sensitivity. Furthermore, mutations I387M, M145W and M145W with E460Q were found uniquely. Additionally, constitutive and fungicide triggered expression of cyp51 was assayed by means of RT-qPCR. A very strong induction of cyp51 mRNA expression in sensitive isolates suggests that the fungal cells upregulate expression to maintain ergosterol biosynthesis in DMI presence. The less intensive cyp51 induction in isolates with higher EC50 values underlines the possible correlation of the found target-site mutations with reduced sensitivity. CONCLUSION: This study provides new results about possible alterations in the target gene mediating reduced sensitivity of C. beticola towards DMIs and hypothesized a fungicide induced over-expression of the target enzyme CYP51 as natural reaction of the fungus to fungicide application. © 2020 Society of Chemical Industry.

Technical workflows for hyperspectral plant image assessment and processing on the greenhouse and laboratory scale.

BACKGROUND: The use of hyperspectral cameras is well established in the field of plant phenotyping, especially as a part of high-throughput routines in greenhouses. Nevertheless, the workflows used differ depending on the applied camera, the plants being imaged, the experience of the users, and the measurement set-up. RESULTS: This review describes a general workflow for the assessment and processing of hyperspectral plant data at greenhouse and laboratory scale. Aiming at a detailed description of possible error sources, a comprehensive literature review of possibilities to overcome these errors and influences is provided. The processing of hyperspectral data of plants starting from the hardware sensor calibration, the software processing steps to overcome sensor inaccuracies, and the preparation for machine learning is shown and described in detail. Furthermore, plant traits extracted from spectral hypercubes are categorized to standardize the terms used when describing hyperspectral traits in plant phenotyping. A scientific data perspective is introduced covering information for canopy, single organs, plant development, and also combined traits coming from spectral and 3D measuring devices. CONCLUSIONS: This publication provides a structured overview on implementing hyperspectral imaging into biological studies at greenhouse and laboratory scale. Workflows have been categorized to define a trait-level scale according to their metrological level and the processing complexity. A general workflow is shown to outline procedures and requirements to provide fully calibrated data of the highest quality. This is essential for differentiation of the smallest changes from hyperspectral reflectance of plants, to track and trace hyperspectral development as an answer to biotic or abiotic stresses.

Statistical shape analysis of tap roots: a methodological case study on laser scanned sugar beets.

BACKGROUND: The efficient and robust statistical analysis of the shape of plant organs of different cultivars is an important investigation issue in plant breeding and enables a robust cultivar description within the breeding progress. Laserscanning is a highly accurate and high resolution technique to acquire the 3D shape of plant surfaces. The computation of a shape based principal component analysis (PCA) built on concepts from continuum mechanics has proven to be an effective tool for a qualitative and quantitative shape examination. RESULTS: The shape based PCA was used for a statistical analysis of 140 sugar beet roots of different cultivars. The calculation of the mean sugar beet root shape and the description of the main variations was possible. Furthermore, unknown and individual tap roots could be attributed to their cultivar by means of a robust classification tool based on the PCA results. CONCLUSION: The method demonstrates that it is possible to identify principal modes of root shape variations automatically and to quantify associated variances out of laserscanned 3D sugar beet tap root models. The introduced approach is not limited to the 3D shape description by laser scanning. A transfer to 3D MRI or radar data is also conceivable.

Fusarium Head Blight: Effect of Infection Timing on Spread of Fusarium graminearum and Spatial Distribution of Deoxynivalenol within Wheat Spikes.

Most studies of Fusarium head blight (FHB) focused on wheat infection at anthesis. Less is known about infections at later stages. In this study, the effect of infection timing on the development of FHB and the distribution of fungal biomass and deoxynivalenol (DON) along wheat spikes was investigated. Under greenhouse conditions, two wheat varieties were point-inoculated with Fusarium graminearum starting from anthesis until 25 days after anthesis. The fungus and fungal DNA were isolated from the centers and the bases of all the spikes but not from the tips for all inoculation times and both varieties. In each variety, the amount of fungal DNA and the content of DON and deoxynivalenol-3-glucoside (DON-3-G) were higher in the center than in the base for all inoculation times. A positive correlation was found between the content of fungal DNA and DON in the centers as well as the bases of both varieties. This study showed that F. graminearum grows downward within infected wheat spikes and that the accumulation of DON is largely confined to the colonized tissue. Moreover, F. graminearum was able to infect wheat kernels and cause contamination with mycotoxins even when inoculated 25 days after anthesis.

A Systematic Review on the Value of Infrared Thermography in the Early Detection of Periprosthetic Joint Infections. / Infrarotthermografische Untersuchungen nach endoprothetischem Gelenkersatz ­ ein systematisches Review.

BACKGROUND: Variations in the temperature of body and skin are symptoms of many pathological changes. Although joint replacement surgery of hip and knee has been very successful in recent decades, periprosthetic infection is a growing problem and the number one reason for revision. While many studies have investigated changes in blood levels, investigation of temperature has not been performed on a regular basis. The objective of this work is to determine whether reference literature exists for the infrared thermographic examination in knee and hip arthroplasty and if reference values can be derived for the methodology or if there is a peri- and postoperative benefit. MATERIAL UND METHODS: By means of a systematic online database search and based on the Cochrane, PICOT and PRISMA guidelines, this systematic review retrieved 254 studies. All publications with thermographic examination in arthroplasty of the hip and knee were imbedded. 249 studies were excluded due to the defined inclusion and exclusion criteria and five studies with 251 patients have finally been included in the evaluation process. This was followed by an analysis and discussion of the methodology. RESULTS AND CONCLUSION: Infrared thermography is a useful tool in the perioperative care of patients after arthroplasty of the knee and hip joint. The technology is portable, easy to use and non-invasive. Based only on these few publications, values can be derived, which provide a guidance for the thermographic aftercare in arthroplasty surgery.

Assessment of Fusarium Infection and Mycotoxin Contamination of Wheat Kernels and Flour Using Hyperspectral Imaging.

Fusarium head blight (FHB) epidemics in wheat and contamination with Fusarium mycotoxins has become an increasing problem over the last decades. This prompted the need for non-invasive and non-destructive techniques to screen cereal grains for Fusarium infection, which is usually accompanied by mycotoxin contamination. This study tested the potential of hyperspectral imaging to monitor the infection of wheat kernels and flour with three Fusarium species. Kernels of two wheat varieties inoculated at anthesis with F. graminearum, F. culmorum, and F. poae were investigated. Hyperspectral images of kernels and flour were taken in the visible-near infrared (VIS-NIR) (400-1000 nm) and short-wave infrared (SWIR) (1000-2500 nm) ranges. The fungal DNA and mycotoxin contents were quantified. Spectral reflectance of Fusarium-damaged kernels (FDK) was significantly higher than non-inoculated ones. In contrast, spectral reflectance of flour from non-inoculated kernels was higher than that of FDK in the VIS and lower in the NIR and SWIR ranges. Spectral reflectance of kernels was positively correlated with fungal DNA and deoxynivalenol (DON) contents. In the case of the flour, this correlation exceeded r = -0.80 in the VIS range. Remarkable peaks of correlation appeared at 1193, 1231, 1446 to 1465, and 1742 to 2500 nm in the SWIR range.

Quantitative and qualitative phenotyping of disease resistance of crops by hyperspectral sensors: seamless interlocking of phytopathology, sensors, and machine learning is needed!

Determination and characterization of resistance reactions of crops against fungal pathogens are essential to select resistant genotypes. In plant breeding, phenotyping of genotypes is realized by time consuming and expensive visual plant ratings. During resistance reactions and during pathogenesis plants initiate different structural and biochemical defence mechanisms, which partly affect the optical properties of plant organs. Recently, intensive research has been conducted to develop innovative optical methods for an assessment of compatible and incompatible plant pathogen interaction. These approaches, combining classical phytopathology or microbiology with technology driven methods - such as sensors, robotics, machine learning, and artificial intelligence - are summarized by the term digital phenotyping. In contrast to common visual rating, detection and assessment methods, optical sensors in combination with advanced data analysis methods are able to retrieve pathogen induced changes in the physiology of susceptible or resistant plants non-invasively and objectively. Phenotyping disease resistance aims different tasks. In an early breeding step, a qualitative assessment and characterization of specific resistance action is aimed to link it, for example, to a genetic marker. Later, during greenhouse and field screening, the assessment of the level of susceptibility of different genotypes is relevant. Within this review, recent advances of digital phenotyping technologies for the detection of subtle resistance reactions and resistance breeding are highlighted and methodological requirements are critically discussed.

Comparison and Combination of Thermal, Fluorescence, and Hyperspectral Imaging for Monitoring Fusarium Head Blight of Wheat on Spikelet Scale.

Optical sensors have shown high capabilities to improve the detection and monitoring of plant disease development. This study was designed to compare the feasibility of different sensors to characterize Fusarium head blight (FHB) caused by Fusarium graminearum and Fusarium culmorum. Under controlled conditions, time-series measurements were performed with infrared thermography (IRT), chlorophyll fluorescence imaging (CFI), and hyperspectral imaging (HSI) starting 3 days after inoculation (dai). IRT allowed the visualization of temperature differences within the infected spikelets beginning 5 dai. At the same time, a disorder of the photosynthetic activity was confirmed by CFI via maximal fluorescence yields of spikelets (Fm) 5 dai. Pigment-specific simple ratio PSSRa and PSSRb derived from HSI allowed discrimination between Fusarium-infected and non-inoculated spikelets 3 dai. This effect on assimilation started earlier and was more pronounced with F. graminearum. Except the maximum temperature difference (MTD), all parameters derived from different sensors were significantly correlated with each other and with disease severity (DS). A support vector machine (SVM) classification of parameters derived from IRT, CFI, or HSI allowed the differentiation between non-inoculated and infected spikelets 3 dai with an accuracy of 78, 56 and 78%, respectively. Combining the IRT-HSI or CFI-HSI parameters improved the accuracy to 89% 30 dai.

Discovering coherency of specific gene expression and optical reflectance properties of barley genotypes differing for resistance reactions against powdery mildew.

Hyperspectral imaging has proved its potential for evaluating complex plant-pathogen interactions. However, a closer link of the spectral signatures and genotypic characteristics remains elusive. Here, we show relation between gene expression profiles and specific wavebands from reflectance during three barley-powdery mildew interactions. Significant synergistic effects between the hyperspectral signal and the corresponding gene activities has been shown using the linear discriminant analysis (LDA). Combining the data sets of hyperspectral signatures and gene expression profiles allowed a more precise differentiation of the three investigated barley-Bgh interactions independent from the time after inoculation. This shows significant synergistic effects between the hyperspectral signal and the corresponding gene activities. To analyze this coherency between spectral reflectance and seven different gene expression profiles, relevant wavelength bands and reflectance intensities for each gene were computed using the Relief algorithm. Instancing, xylanase activity was indicated by relevant wavelengths around 710 nm, which are characterized by leaf and cell structures. HvRuBisCO activity underlines relevant wavebands in the green and red range, elucidating the coherency of RuBisCO to the photosynthesis apparatus and in the NIR range due to the influence of RuBisCO on barley leaf cell development. These findings provide the first insights to links between gene expression and spectral reflectance that can be used for an efficient non-invasive phenotyping of plant resistance and enables new insights into plant-pathogen interactions.

Screening of Barley Resistance Against Powdery Mildew by Simultaneous High-Throughput Enzyme Activity Signature Profiling and Multispectral Imaging.

Molecular marker analysis allow for a rapid and advanced pre-selection and resistance screenings in plant breeding processes. During the phenotyping process, optical sensors have proved their potential to determine and assess the function of the genotype of the breeding material. Thereby, biomarkers for specific disease resistance traits provide valuable information for calibrating optical sensor approaches during early plant-pathogen interactions. In this context, the combination of physiological, metabolic phenotyping and phenomic profiles could establish efficient identification and quantification of relevant genotypes within breeding processes. Experiments were conducted with near-isogenic lines of H. vulgare (susceptible, mildew locus o (mlo) and Mildew locus a (Mla) resistant). Multispectral imaging of barley plants was daily conducted 0-8 days after inoculation (dai) in a high-throughput facility with 10 wavelength bands from 400 to 1,000 nm. In parallel, the temporal dynamics of the activities of invertase isoenzymes, as key sink specific enzymes that irreversibly cleave the transport sugar sucrose into the hexose monomers, were profiled in a semi high-throughput approach. The activities of cell wall, cytosolic and vacuole invertase revealed specific dynamics of the activity signatures for susceptible genotypes and genotypes with mlo and Mla based resistances 0-120 hours after inoculation (hai). These patterns could be used to differentiate between interaction types and revealed an early influence of Blumeria graminis f.sp. hordei (Bgh) conidia on the specific invertase activity already 0.5 hai. During this early powdery mildew pathogenesis, the reflectance intensity increased in the blue bands and at 690 nm. The Mla resistant plants showed an increased reflectance at 680 and 710 nm and a decreased reflectance in the near infrared bands from 3 dai. Applying a Support Vector Machine classification as a supervised machine learning approach, the pixelwise identification and quantification of powdery mildew diseased barley tissue and hypersensitive response spots were established. This enables an automatic identification of the barley-powdery mildew interaction. The study established a proof-of-concept for plant resistance phenotyping with multispectral imaging in high-throughput. The combination of invertase analysis and multispectral imaging showed to be a complementing validation system. This will provide a deeper understanding of optical data and its implementation into disease resistance screening.