Spectral Patterns Reveal Early Resistance Reactions of Barley Against Blumeria graminis f. sp. hordei

Characterization of Rice-Magnaporthe oryzae Interactions by Hyperspectral Imaging

Hyperspectral imaging has the potential to detect, characterize, and quantify plant diseases objectively and nondestructively to improve phenotyping in breeding for disease resistance. In this study, leaf spectral reflectance characteristics of five rice genotypes diseased with blast caused by three Magnaporthe oryzae isolates differing in virulence were compared with visual disease ratings under greenhouse conditions. Spectral information (140 wavebands, range 450 to 850 nm) of infected leaves was recorded with a hyperspectral imaging microscope at 3, 5, and 7 days postinoculation to examine differences in symptom phenotypes and to characterize the compatibility of host-pathogen interactions. Depending on the rice genotype × M. oryzae genotype interaction, blast symptoms varied from tiny necrosis to enlarged lesions with symptom subareas differing in tissue coloration and indicated gene-for-gene-specific interactions. The blast symptom types were differentiated based on their spectral characteristics in the visible/near-infrared range. Symptom-specific spectral signatures and differences in the composition of leaf blast symptom type(s) resulted in unique spectral and spatial patterns of the rice × M. oryzae interactions based on the size, shape, and color of the symptom subareas. Spectral angle mapper classification of spectra enabled (i) discrimination between healthy (green) and diseased tissue of rice genotypes, (ii) classification and quantification of different blast symptom subareas, and (iii) grading of the host-pathogen compatibility (low - intermediate - high). Hyperspectral imaging was more sensitive to small changes in disease resistance than visual disease assessments and enabled the characterization of various types of resistance/susceptibility reactions of tissue subjected to M. oryzae infection.

Hyperspectral imaging-based classification of rice leaf blast severity over multiple growth stages

BACKGROUND

Rice blast, which is prevalent worldwide, represents a serious threat to harvested crop yield and quality. Hyperspectral imaging, an emerging technology used in plant disease research, is a stable, repeatable method for disease grading. Current methods for assessing disease severity have mostly focused on individual growth stages rather than multiple ones. In this study, the spectral reflectance ratio (SRR) of whole leaves were calculated, the sensitive wave bands were selected using the successive projections algorithm (SPA) and the support vector machine (SVM) models were constructed to assess rice leaf blast severity over multiple growth stages.

RESULTS

The average accuracy, micro F1 values, and macro F1 values of the full-spectrum-based SVM model were respectively 94.75%, 0.869, and 0.883 in 2019; 92.92%, 0.823, and 0.808 in 2021; and 88.09%, 0.702, and 0.757 under the 2019-2021 combined model. The SRR-SVM model could be used to evaluate rice leaf blast disease during multiple growth stages and had good generalizability.

CONCLUSIONS

The proposed SRR data analysis method is able to eliminate differences among individuals to some extent, thus allowing for its application to assess rice leaf blast severity over multiple growth stages. Our approach, which can supplement single-stage disease-degree classification, provides a possible direction for future research on the assessment of plant disease severity during multiple growth stages.

Digital plant pathology: a foundation and guide to modern agriculture

Over the last 20 years, researchers in the field of digital plant pathology have chased the goal to implement sensors, machine learning and new technologies into knowledge-based methods for plant phenotyping and plant protection. However, the application of swiftly developing technologies has posed many challenges. Greenhouse and field applications are complex and differ in their study design requirements. Selecting a sensor type (e.g., thermography or hyperspectral imaging), sensor platform (e.g., rovers, unmanned aerial vehicles, or satellites), and the problem-specific spatial and temporal scale adds to the challenge as all pathosystems are unique and differ in their interactions and symptoms, or lack thereof. Adding host-pathogen-environment interactions across time and space increases the complexity even further. Large data sets are necessary to enable a deeper understanding of these interactions. Therefore, modern machine learning methods are developed to realize the fast data analysis of such complex data sets. This reduces not only human effort but also enables an objective data perusal. Especially deep learning approaches show a high potential to identify probable cohesive parameters during plant-pathogen-environment interactions. Unfortunately, the performance and reliability of developed methods are often doubted by the potential user. Gaining their trust is thus needed for real field applications. Linking biological causes to machine learning features and a clear communication, even for non-experts of such results, is a crucial task that will bridge the gap between theory and praxis of a newly developed application. Therefore, we suggest a global connection of experts and data as the basis for defining a common and goal-oriented research roadmap. Such high interconnectivity will likely increase the chances of swift, successful progress in research and practice. A coordination within international excellence clusters will be useful to reduce redundancy of research while supporting the creation and progress of complementary research. With this review, we would like to discuss past research, achievements, as well as recurring and new challenges. Having such a retrospect available, we will attempt to reveal future challenges and provide a possible direction elevating the next decade of research in digital plant pathology.

Identification of a bio-signature for barley resistance against Pyrenophora teres infection based on physiological, molecular and sensor-based phenotyping

Necrotic and chlorotic symptoms induced during Pyrenophora teres infection in barley leaves indicate a compatible interaction that allows the hemi-biotrophic fungus Pyrenophora teres to colonise the host. However, it is unexplored how this fungus affects the physiological responses of resistant and susceptible cultivars during infection. To assess the degree of resistance in four different cultivars, we quantified visible symptoms and fungal DNA and performed expression analyses of genes involved in plant defence and ROS scavenging. To obtain insight into the interaction between fungus and host, we determined the activity of 19 key enzymes of carbohydrate and antioxidant metabolism. The pathogen impact was also phenotyped non-invasively by sensor-based multireflectance and -fluorescence imaging. Symptoms, regulation of stress-related genes and pathogen DNA content distinguished the cultivar Guld as being resistant. Severity of net blotch symptoms was also strongly correlated with the dynamics of enzyme activities already within the first day of infection. In contrast to the resistant cultivar, the three susceptible cultivars showed a higher reflectance over seven spectral bands and higher fluorescence intensities at specific excitation wavelengths. The combination of semi high-throughput physiological and molecular analyses with non-invasive phenotyping enabled the identification of bio-signatures that discriminates the resistant from susceptible cultivars.

Tillage shapes the soil and rhizosphere microbiome of barley-but not its susceptibility towards Blumeria graminis f. sp. hordei

Long-term agricultural practices are assumed to shape the rhizosphere microbiome of crops with implications for plant health. In a long-term field experiment, we investigated the effect of different tillage and fertilization practices on soil and barley rhizosphere microbial communities by means of amplicon sequencing of 16S rRNA gene fragments from total community DNA. Differences in the microbial community composition depending on the tillage practice, but not the fertilization intensity were revealed. To examine whether these soil and rhizosphere microbiome differences influence the plant defense response, barley (cultivar Golden Promise) was grown in field or standard potting soil under greenhouse conditions and challenged with Blumeria graminis f. sp. hordei (Bgh). Amplicon sequence analysis showed that preceding tillage practice, but also aboveground Bgh challenge significantly influenced the microbial community composition. Expression of plant defense-related genes PR1b and PR17b was higher in challenged compared to unchallenged plants. The Bgh infection rates were strikingly lower for barley grown in field soil compared to potting soil. Although previous agricultural management shaped the rhizosphere microbiome, no differences in plant health were observed. We propose therefore that the management-independent higher microbial diversity of field soils compared to potting soils contributed to the low infection rates of barley.

Turfgrass Disease Diagnosis: Past, Present, and Future

Turfgrass is a multibillion-dollar industry severely affected by plant pathogens including fungi, bacteria, viruses, and nematodes. Many of the diseases in turfgrass have similar signs and symptoms, making it difficult to diagnose the specific problem pathogen. Incorrect diagnosis leads to the delay of treatment and excessive use of chemicals. To effectively control these diseases, it is important to have rapid and accurate detection systems in the early stages of infection that harbor relatively low pathogen populations. There are many methods for diagnosing pathogens on turfgrass. Traditional methods include symptoms, morphology, and microscopy identification. These have been followed by nucleic acid detection and onsite detection techniques. Many of these methods allow for rapid diagnosis, some even within the field without much expertise. There are several methods that have great potential, such as high-throughput sequencing and remote sensing. Utilization of these techniques for disease diagnosis allows for faster and accurate disease diagnosis and a reduction in damage and cost of control. Understanding of each of these techniques can allow researchers to select which method is best suited for their pathogen of interest. The objective of this article is to provide an overview of the turfgrass diagnostics efforts used and highlight prospects for disease detection.

Plant disease identification using explainable 3D deep learning on hyperspectral images

BACKGROUND

Hyperspectral imaging is emerging as a promising approach for plant disease identification. The large and possibly redundant information contained in hyperspectral data cubes makes deep learning based identification of plant diseases a natural fit. Here, we deploy a novel 3D deep convolutional neural network (DCNN) that directly assimilates the hyperspectral data. Furthermore, we interrogate the learnt model to produce physiologically meaningful explanations. We focus on an economically important disease, charcoal rot, which is a soil borne fungal disease that affects the yield of soybean crops worldwide.

RESULTS

Based on hyperspectral imaging of inoculated and mock-inoculated stem images, our 3D DCNN has a classification accuracy of 95.73% and an infected class F1 score of 0.87. Using the concept of a saliency map, we visualize the most sensitive pixel locations, and show that the spatial regions with visible disease symptoms are overwhelmingly chosen by the model for classification. We also find that the most sensitive wavelengths used by the model for classification are in the near infrared region (NIR), which is also the commonly used spectral range for determining the vegetative health of a plant.

CONCLUSION

The use of an explainable deep learning model not only provides high accuracy, but also provides physiological insight into model predictions, thus generating confidence in model predictions. These explained predictions lend themselves for eventual use in precision agriculture and research application using automated phenotyping platforms.

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.

Feasibility of Unmanned Aerial Vehicle Optical Imagery for Early Detection and Severity Assessment of Late Blight in Potato

Assessment of disease incidence and severity at farm scale or in agronomic trials is frequently performed based on visual crop inspection, which is a labor intensive task prone to errors associated with its subjectivity. Therefore, alternative methods to relate disease incidence and severity with changes in crop traits are of great interest. Optical imagery in the visible and near-infrared (Vis-NIR) can potentially be used to detect changes in crop traits caused by pathogen development. Also, cameras on-board of Unmanned Aerial Vehicles (UAVs) have flexible data collection capabilities allowing adjustments considering the trade-off between data throughput and its resolution. However, studies focusing on the use of UAV imagery to describe changes in crop traits related to disease infection are still lacking. More specifically, evaluation of late blight (Phytophthora infestans) incidence in potato concerning early discrimination of different disease severity levels has not been extensively reported. In this article, the description of spectral changes related to the development of potato late blight under low disease severity levels is performed using sub-decimeter UAV optical imagery. The main objective was to evaluate the sensitivity of the data acquired regarding early changes in crop traits related to disease incidence. For that, UAV images were acquired on four dates during the growing season (from 37 to 78 days after planting), before and after late blight was detected in the field. The spectral variability observed in each date was summarized using Simplex Volume Maximization (SiVM), and its relationship with experimental treatments (different crop systems) and disease severity levels (evaluated by visual assessment) was determined based on pixel-wise log-likelihood ratio (LLR) calculation. Using this analytical framework it was possible to identify considerable spectral changes related to late blight incidence in different treatments and also to disease severity level as low as between 2.5 and 5.0% of affected leaf area. Comparison of disease incidence and spectral information acquired using UAV (with 4-5 cm of spatial resolution) and ground-based imagery (with 0.1-0.2 cm of spatial resolution) indicate that UAV data allowed identification of patterns comparable to those described by ground-based images, despite some differences concerning the distribution of affected areas detected within the sampling units and an attenuation in the signal measured. Finally, although aggregated information at sampling unit level provided discriminative potential for higher levels of disease development, focusing on spectral information related to disease occurrence increased the discriminative potential of the data acquired.

Innovative technologies for chemical security

Advances across the chemical and biological (life) sciences are increasingly enabled by ideas and tools from sectors outside these disciplines, with information and communication technologies playing a key role across 21st century scientific development. In the face of rapid technological change, the Organisation for the Prohibition of Chemical Weapons (OPCW), the implementing body of the Chemical Weapons Convention (“the Convention”), seeks technological opportunities to strengthen capabilities in the field of chemical disarmament. The OPCW Scientific Advisory Board (SAB) in its review of developments in science and technology examined the potential uses of emerging technologies for the implementation of the Convention at a workshop entitled “Innovative Technologies for Chemical Security”, held from 3 to 5 July 2017, in Rio de Janeiro, Brazil. The event, organized in cooperation with the International Union of Pure and Applied Chemistry (IUPAC), the National Academies of Science, Engineering and Medicine of the United States of America, the Brazilian Academy of Sciences, and the Brazilian Chemical Society, was attended by 45 scientists and engineers from 22 countries. Their insights into the use of innovative technological tools and how they might benefit chemical disarmament and non-proliferation informed the SAB’s report on developments in science and technology for the Fourth Review Conference of the Convention (to be held in November 2018), and are described herein, as are recommendations that the SAB submitted to the OPCW Director-General and the States Parties of the Convention. It is concluded that technologies exist or are under development that could be used for investigations, contingency, assistance and protection, reducing risks to inspectors, and enhancing sampling and analysis.

Hyperspectral Sensors and Imaging Technologies in Phytopathology: State of the Art

Plant disease detection represents a tremendous challenge for research and practical applications. Visual assessment by human raters is time-consuming, expensive, and error prone. Disease rating and plant protection need new and innovative techniques to address forthcoming challenges and trends in agricultural production that require more precision than ever before. Within this context, hyperspectral sensors and imaging techniques-intrinsically tied to efficient data analysis approaches-have shown an enormous potential to provide new insights into plant-pathogen interactions and for the detection of plant diseases. This article provides an overview of hyperspectral sensors and imaging technologies for assessing compatible and incompatible plant-pathogen interactions. Within the progress of digital technologies, the vision, which is increasingly discussed in the society and industry, includes smart and intuitive solutions for assessing plant features in plant phenotyping or for making decisions on plant protection measures in the context of precision agriculture.

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.

Potential of hyperspectral imaging to detect and identify the impact of chemical warfare compounds on plant tissue

The OPCW Member states cover 98% of the global population and landmass. Regrettably, unanticipated chemical warfare agent assaults are reported during the last decades. In addition to the frequent threat situation, the sampling of bio-medical samples from these areas is critical and mainly depends on investigation opportunities of victims. Non-contact sensor technologies are desirable to enable a fast and secure estimation of a situation. Plants react on pollution because of their direct interaction with gases and it is assumed that chemical warfare agents influence plants, respectively. This impact can be analyzed for the detection and characterization of chemical warfare assaults. Nowadays technological progress in digital technologies provides new innovations in detectors, data analysis approaches and software availability which could improve the screening, monitoring and analysis of chemical warfare. Within this context hyperspectral imaging (HSI) is a promising method. Different applications from remote to close range sensing in medicine, food production, military, geography and agriculture do exist already. During the last years HSI showed high potential to determine and assess different plant parameters, e.g. abiotic and biotic stresses by recording the spectral reflectance of plants. Within the present manuscript, the basics principle of HSI as an innovative technique, aspects of recording and analyzing HSI data is presented using wild growing apple leaves which are treated with sulfuric acid, fire or heat. Resulting spectral signatures showed significant changes among the treatments. Especially the shortwave infrared was sensitive to changes due to the different treatments. Furthermore, the calculation of common spectral indices revealed differences due to the treatments which are not visible to the human eye. The results support HSI applications for the detection of chemical warfare agents and elucidate the impact of chemical warfare on plants.

Quantitative assessment of disease severity and rating of barley cultivars based on hyperspectral imaging in a non-invasive, automated phenotyping platform

BACKGROUND

Phenotyping is a bottleneck for the development of new plant cultivars. This study introduces a new hyperspectral phenotyping system, which combines the high throughput of canopy scale measurements with the advantages of high spatial resolution and a controlled measurement environment. Furthermore, the measured barley canopies were grown in large containers (called Mini-Plots), which allow plants to develop field-like phenotypes in greenhouse experiments, without being hindered by pot size.

RESULTS

Six barley cultivars have been investigated via hyperspectral imaging up to 30 days after inoculation with powdery mildew. With a high spatial resolution and stable measurement conditions, it was possible to automatically quantify powdery mildew symptoms through a combination of Simplex Volume Maximization and Support Vector Machines. Detection was feasible as soon as the first symptoms were visible for the human eye during manual rating. An accurate assessment of the disease severity for all cultivars at each measurement day over the course of the experiment was realized. Furthermore, powdery mildew resistance based necrosis of one cultivar was detected as well.

CONCLUSION

The hyperspectral phenotyping system combines the advantages of field based canopy level measurement systems (high throughput, automatization, low manual workload) with those of laboratory based leaf level measurement systems (high spatial resolution, controlled environment, stable conditions for time series measurements). This allows an accurate and objective disease severity assessment without the need for trained experts, who perform visual rating, as well as detection of disease symptoms in early stages. Therefore, it is a promising tool for plant resistance breeding.