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

Monitoring of parasite Orobanche cumana using Vis-NIR hyperspectral imaging combining with physio-biochemical parameters on host crop Helianthus annuus

KEY MESSAGE

This study provided a non-destructive detection method with Vis-NIR hyperspectral imaging combining with physio-biochemical parameters in Helianthus annuus in response to Orobanche cumana infection that took insights into the monitoring of sunflower weed. Sunflower broomrape (Orobanche cumana Wallr.) is an obligate weed that attaches to the host roots of sunflower (Helianthus annuus L.) leading to a significant reduction in yield worldwide. The emergence of O. cumana shoots after its underground life-cycle causes irreversible damage to the crop. In this study, a fast visual, non-invasive and precise method for monitoring changes in spectral characteristics using visible and near-infrared (Vis-NIR) hyperspectral imaging (HSI) was developed. By combining the bands sensitive to antioxidant enzymes (SOD, GR), non-antioxidant enzymes (GSH, GSH + GSSG), MDA, ROS (O2-, OH-), PAL, and PPO activities obtained from the host leaves, we sought to establish an accurate means of assessing these changes and conducted imaging acquisition using hyperspectral cameras from both infested and non-infested sunflower cultivars, followed by physio-biochemical parameters measurement as well as analyzed the expression of defense related genes. Extreme learning machine (ELM) and convolutional neural network (CNN) models using 3-band images were built to classify infected or non-infected plants in three sunflower cultivars, achieving accuracies of 95.83% and 95.83% for the discrimination of infestation as well as 97.92% and 95.83% of varieties, respectively, indicating the potential of multi-spectral imaging systems for early detection of O. cumana in weed management.

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.

Physiological Genetics Reformed: Bridging the Genome-to-Phenome Gap by Coherent Chemical Fingerprints - the Global Coordinator

Forward-focused molecular genetics is successfully framing DNA diversity and mapping primary gene functions. However, abandoning the classic Linnaean fingerprint link between the phenome and genome by suppressing gene interaction (pleiotropy), has resulted in a genome-to-phenome gap and poor utilization of molecular data. We demonstrate how to bridge this gap by using an example of a barley mutant seed model, where pleiotropy is observed as covarying global molecular patterns that define each endosperm. Global coherence was discovered as a covariate coordinator within and between local genotype specific fingerprints. This implies that any of these fingerprints can select its recombinant global phenotype variant, including composition. Introducing the law of coherence, and the movement of gene complexes by chemical fingerprint traits as selectors, introduces a revolution in understanding physiological molecular genetics and plant-breeding.

Development of Spectral Disease Indices for Southern Corn Rust Detection and Severity Classification

Southern Corn Rust (SCR) is one of the most destructive diseases in corn production, significantly affecting corn quality and yields globally. Field-based fast, nondestructive diagnosis of SCR is critical for smart agriculture applications to reduce pesticide use and ensure food safety. The development of spectral disease indices (SDIs), based on in situ leaf reflectance spectra, has proven to be an effective method in detecting plant diseases in the field. However, little is known about leaf spectral signatures that can assist in the accurate diagnosis of SCR, and no SDIs-based model has been reported for the field-based SCR monitoring. Here, to address those issues, we developed SDIs-based monitoring models to detect SCR-infected leaves and classify SCR damage severity. In detail, we first collected in situ leaf reflectance spectra (350–2500 nm) of healthy and infected corn plants with three severity levels (light, medium, and severe) using a portable spectrometer. Then, the RELIEF-F algorithm was performed to select the most discriminative features (wavelengths) and two band normalized differences for developing SDIs (i.e., health index and severity index) in SCR detection and severity classification, respectively. The leaf reflectance spectra, most sensitive to SCR detection and severity classification, were found in the 572 nm, 766 nm, and 1445 nm wavelength and 575 nm, 640 nm, and 1670 nm wavelength, respectively. These spectral features were associated with leaf pigment and leaf water content. Finally, by employing a support vector machine (SVM), the performances of developed SCR-SDIs were assessed and compared with 38 stress-related vegetation indices (VIs) identified in the literature. The SDIs-based models developed in this study achieved an overall accuracy of 87% and 70% in SCR detection and severity classification, 1.1% and 8.3% higher than the other best VIs-based model under study, respectively. Our results thus suggest that the SCR-SDIs is a promising tool for fast, nondestructive diagnosis of SCR in the field over large areas. To our knowledge, this study represents one of the first few efforts to provide a theoretical basis for remote sensing of SCR at field and larger scales. With the increasing use of unmanned aerial vehicles (UAVs) with hyperspectral measurement capability, more studies should be conducted to expand our developed SCR-SDIs for SCR monitoring at different study sites and growing stages in the future.

Remote Sensing of Diseases

Detection, identification, and quantification of plant diseases by sensor techniques are expected to enable a more precise disease control, as sensors are sensitive, objective, and highly available for disease assessment. Recent progress in sensor technology and data processing is very promising; nevertheless, technical constraints and issues inherent to variability in host-pathogen interactions currently limit the use of sensors in various fields of application. The information from spectral [e.g., RGB (red, green, blue)], multispectral, and hyperspectral sensors that measure reflectance, fluorescence, and emission of radiation or from electronic noses that detect volatile organic compounds released from plants or pathogens, as well as the potential of sensors to characterize the health status of crops, is evaluated based on the recent literature. Phytopathological aspects of remote sensing of plant diseases across different scales and for various purposes are discussed, including spatial disease patterns, epidemic spread of pathogens, crop characteristics, and links to disease control. Future challenges in sensor use are identified.

Close Range Spectral Imaging for Disease Detection in Plants Using Autonomous Platforms: a Review on Recent Studies

Purpose of Review

A short introduction to the spectral imaging (SI) of plants along with a comprehensive overview of the recent research works related to disease detection in plants using autonomous phenotyping platforms is provided. Key benefits and challenges of SI for plant disease detection on robotic platforms are highlighted.

Recent Findings

SI is becoming a potential tool for autonomous platforms for non-destructive plant assessment. This is because it can provide information on the plant pigments such as chlorophylls, anthocyanins and carotenoids and supports quantification of biochemical parameters such as sugars, proteins, different nutrients, water and fat content. A plant suffering from diseases will exhibit different physicochemical parameters compared with a healthy plant, allowing the SI to capture those differences as a function of reflected or absorbed light.

Summary

Potential of SI to non-destructively capture physicochemical parameters in plants makes it a key technique to support disease detection on autonomous platforms. SI can be broadly used for crop disease detection by quantification of physicochemical changes in the plants.

Noninvasive Phenotyping of Plant-Pathogen Interaction: Consecutive In Situ Imaging of Fluorescing Pseudomonas syringae, Plant Phenolic Fluorescence, and Chlorophyll Fluorescence in Arabidopsis Leaves

Plant-pathogen interactions have been widely studied, but mostly from the site of the plant secondary defense. Less is known about the effects of pathogen infection on plant primary metabolism. The possibility to transform a fluorescing protein into prokaryotes is a promising phenotyping tool to follow a bacterial infection in plants in a noninvasive manner. In the present study, virulent and avirulent Pseudomonas syringae strains were transformed with green fluorescent protein (GFP) to follow the spread of bacteria in vivo by imaging Pulse-Amplitude-Modulation (PAM) fluorescence and conventional binocular microscopy. The combination of various wavelengths and filters allowed simultaneous detection of GFP-transformed bacteria, PAM chlorophyll fluorescence, and phenolic fluorescence from pathogen-infected plant leaves. The results show that fluorescence imaging allows spatiotemporal monitoring of pathogen spread as well as phenolic and chlorophyll fluorescence in situ, thus providing a novel means to study complex plant-pathogen interactions and relate the responses of primary and secondary metabolism to pathogen spread and multiplication. The study establishes a deeper understanding of imaging data and their implementation into disease screening.

Detection of Gray Mold Leaf Infections Prior to Visual Symptom Appearance Using a Five-Band Multispectral Sensor

Fungal leaf diseases cause economically important damage to crop plants. Protective treatments help producers to secure good quality crops. In contrast, curative treatments based on visually detectable symptoms are often riskier and less effective because diseased crop plants may develop disease symptoms too late for curative treatments. Therefore, early disease detection prior symptom development would allow an earlier, and therefore more effective, curative management of fungal diseases. Using a five-lens multispectral imager, spectral reflectance of green, blue, red, near infrared (NIR, 840 nm), and rededge (RE, 720 nm) was recorded in time-course experiments of detached tomato leaves inoculated with the fungus Botrytis cinerea and mock infection solution. Linear regression models demonstrate NIR and RE as the two most informative spectral data sets to differentiate pathogen- and mock-inoculated leaf regions of interest (ROI). Under controlled laboratory conditions, bands collecting NIR and RE irradiance showed a lower reflectance intensity of infected tomato leaf tissue when compared with mock-inoculated leaves. Blue and red channels collected higher intensity values in pathogen- than in mock-inoculated ROIs. The reflectance intensities of the green band were not distinguishable between pathogen- and mock infected ROIs. Predictions of linear regressions indicated that gray mold leaf infections could be identified at the earliest at 9 h post infection (hpi) in the most informative bands NIR and RE. Re-analysis of the imagery taken with NIR and RE band allowed to classify infected tissue.