Current and Prospective Methods for Plant Disease Detection

CRISPR-Cas assisted diagnostics of plant viruses and challenges

Plant viruses threaten global food security by infecting commercial crops, highlighting the critical need for efficient virus detection to enable timely preventive measures. Current techniques rely on polymerase chain reaction (PCR) for viral genome amplification and require laboratory conditions. This review explores the applications of CRISPR-Cas assisted diagnostic tools, specifically CRISPR-Cas12a and CRISPR-Cas13a/d systems for plant virus detection and analysis. The CRISPR-Cas12a system can detect viral DNA/RNA amplicons and can be coupled with PCR or isothermal amplification, allowing multiplexed detection in plants with mixed infections. Recent studies have eliminated the need for expensive RNA purification, streamlining the process by providing a visible readout through lateral flow strips. The CRISPR-Cas13a/d system can directly detect viral RNA with minimal preamplification, offering a proportional readout to the viral load. These approaches enable rapid viral diagnostics within 30 min of leaf harvest, making them valuable for onsite field applications. Timely identification of diseases associated with pathogens is crucial for effective treatment; yet developing rapid, specific, sensitive, and cost-effective diagnostic technologies remains challenging. The current gold standard, PCR technology, has drawbacks such as lengthy operational cycles, high costs, and demanding requirements. Here we update the technical advancements of CRISPR-Cas in viral detection, providing insights into future developments, versatile applications, and potential clinical translation. There is a need for approaches enabling field plant viral nucleic acid detection with high sensitivity, specificity, affordability, and portability. Despite challenges, CRISPR-Cas-mediated pathogen diagnostic solutions hold robust capabilities, paving the way for ideal diagnostic tools. Alternative applications in virus research are also explored, acknowledging the technology's limitations and challenges.

Recent advances in biosensors for screening plant pathogens

Worldwide, plant pathogens have been a considerably important cause of economic loss in agriculture especially in the decades of agricultural intensification. The increasing losses in agriculture due to biotic plant diseases have drawn attention towards the development of plant disease analyzing methods. In this context, biosensors have emerged as significantly important tools which help farmers in on-field diagnosis of plant diseases. Compared to traditional methods, biosensors have outstanding features such as being highly sensitive and selective, cost-effective, portable, fast and user-friendly operation, and so on. There are three common types of biosensors including electrochemical, fluorescent, and colorimetric biosensors. In this review, some common biotic plant diseases caused by fungi, bacteria, and viruses are first summarized. Then, current advances in developing biosensors are discussed.

High-Throughput Bioassay for Detection of Latent Fungi in Postharvest Produce

Enormous fresh agricultural produce is wasted annually due to rots caused by pathogenic microorganisms. Most pathogenic fungi attack the harvested produce by penetrating the fruit at the field and remaining quiescent or latent until the fruit ripens or senescence. In this work, a recently developed simple, cost-effective, and high-throughput 96-well plate-based assay was applied to determine the presence of pathogenic fungi in their latent stage. The surface strands immobilized on the 96-well plate, only with the presence of the complementary RNA marker (enoyl-CoA hydratase (ECH)) of the latent fungal-pathogen Colletotrichum gloeosporioides will create a complex with the target and reporter (labeled with the horseradish peroxidase (HRP) enzyme) strands for positive signal generation. The developed assay demonstrated 3.1-fold higher specificity for the latent marker (ECH) of C. gloeosporioides compared to latent markers of other pathogenic fungi. A 2 nM detection limit of target strands was demonstrated, showing a high plate sensitivity, and was further validated with biological samples extracted from latent infection in tomato fruit. The developed assay provides a new economical tool for detecting the presence of latent RNA markers of pathogenic fungi in agricultural produce, ultimately improving postharvest decision-making and reducing postharvest losses.

Fluorescent chemosensors facilitate the visualization of plant health and their living environment in sustainable agriculture

Globally, 91% of plant production encounters diverse environmental stresses that adversely affect their growth, leading to severe yield losses of 50-60%. In this case, monitoring the connection between the environment and plant health can balance population demands with environmental protection and resource distribution. Fluorescent chemosensors have shown great progress in monitoring the health and environment of plants due to their high sensitivity and biocompatibility. However, to date, no comprehensive analysis and systematic summary of fluorescent chemosensors used in monitoring the correlation between plant health and their environment have been reported. Thus, herein, we summarize the current fluorescent chemosensors ranging from their design strategies to applications in monitoring plant-environment interaction processes. First, we highlight the types of fluorescent chemosensors with design strategies to resolve the bottlenecks encountered in monitoring the health and living environment of plants. In addition, the applications of fluorescent small-molecule, nano and supramolecular chemosensors in the visualization of the health and living environment of plants are discussed. Finally, the major challenges and perspectives in this field are presented. This work will provide guidance for the design of efficient fluorescent chemosensors to monitor plant health, and then promote sustainable agricultural development.

Identification of an Isolate of Citrus Tristeza Virus by Nanopore Sequencing in Korea and Development of a CRISPR/Cas12a-Based Assay for Rapid Visual Detection of the Virus

Citrus tristeza virus (CTV) is a highly destructive viral pathogen posing a significant threat to citrus crops worldwide. Disease management and crop protection strategies necessitate the development of rapid and accurate detection methods. In this study, we employed Oxford Nanopore sequencing to detect CTV in Citrus unshiu samples. Subsequently, we developed a specific and sensitive detection assay combining CRISPR/Cas12a with reverse transcription-recombinase polymerase amplification. The CRISPR-Cas12a assay exhibited exceptional specificity for CTV, surpassing conventional RT-PCR by at least 10-fold in sensitivity. Remarkably, the developed assay detected CTV in field samples, with zero false negatives. This diagnostic approach is user-friendly, cost-effective, and offers tremendous potential for rapid onsite detection of CTV. Therefore, the CRISPR-Cas12a assay plays a significant role in managing and preserving citrus trees that are free from viruses in the industry.

Applications of Surface Plasmon Resonance (SPR) to the Study of Diverse Protein-Ligand Interactions

Functional characterization of enzymes/proteins requires determination of the binding affinity of small molecules or other biomolecules with the target proteins. Several available techniques, such as proteomics and drug discovery strategies, require a precise and high-throughput assay for rapid and reliable screening of potential candidates for further testing. Surface plasmon resonance (SPR), a well-established label-free technique, directly measures biomolecular affinities. SPR assays require immobilization of one interacting component (ligand) on a conductive metal (mostly gold or silver) and a continuous flow of solution containing potential binding partner (analyte) across the surface. The SPR phenomenon occurs when polarized light excites the electrons at the interface of the metal and the dielectric medium to generate electromagnetic waves that propagate parallel to the surface. Changes in the refractive index due to interaction between the ligand and analyte are measured by detecting the reflected light, providing real-time data on kinetics and specificity. A prominent use of SPR is identifying compounds in crude plant extracts that bind to specific molecules. Procedures that utilize SPR are becoming increasingly applicable outside the laboratory setting, and SPR imaging and localized SPR (LSPR) are cheaper and more portable alternative for in situ detection of plant or mammalian pathogens and drug discovery studies. LSPR, in particular, has the advantage of direct attachment to test tissues in live-plant studies. Here, we describe three protocols utilizing SPR-based assays for precise analysis of protein-ligand interactions. © 2024 Wiley Periodicals LLC. Basic Protocol 1: SPR comparison of binding affinities of viral reverse transcriptase polymorphisms Basic Protocol 2: SPR screening of crude plant extract for protein-binding agents Basic Protocol 3: Localized SPR-based antigen detection using antibody-conjugated gold nanoparticles.

Development of label-free electrochemical OMP-DNA probe biosensor as a highly sensitive system to detect of citrus huanglongbing

The fabrication of the first label-free electrochemical DNA probe biosensor for highly sensitive detection of Candidatus Liberibacter asiaticus (CLas), as the causal agent of citrus huanglongbing disease, is conducted here. An OMP probe was designed based on the hybridization with its target-specific sequence in the outer membrane protein (OMP) gene of CLas. The characterization of the steps of biosensor fabrication and hybridization process between the immobilized OMP-DNA probe and the target ssDNA oligonucleotides (OMP-complementary and three mismatches OMP or OMP-mutation) was monitored using cyclic voltammetry and electrochemical impedance spectroscopy based on increasing or decreasing in the electron transfer in [Fe (CN)6]3-/4- on the modified gold electrode surface. The biosensor sensitivity indicated that the peak currents were linear over ranges from 20 to 100 nM for OMP-complementary with the detection limit of 0.026 nM (S/N = 3). The absence of any cross-interference with other biological DNA sequences confirmed a high selectivity of fabricated biosensor. Likewise, it showed good specificity in discriminating the mutation oligonucleotides from complementary target DNAs. The functional performance of optimized biosensor was achieved via the hybridization of OMP-DNA probe with extracted DNA from citrus plant infected with CLas. Therefore, fabricated biosensor indicates promise for sensitivity and early detection of citrus huanglongbing disease.

Secreted in Xylem (SIX) genes in Fusarium oxysporum f.sp. cubense (Foc) unravels the potential biomarkers for early detection of Fusarium wilt disease

Secreted in Xylem (SIX) are small effector proteins released by Fusarium oxysporum f.sp. cubense (Foc) into the plant's xylem sap disrupting the host's defence responses causing Fusarium wilt disease resulting in a significant decline in banana crop yields and economic losses. Notably, different races of Foc possess unique sets of SIX genes responsible for their virulence, however, these genes remain underutilized, despite their potential as biomarkers for early disease detection. Herein, we identified seven SIX genes i.e. SIX1, SIX2, SIX4, SIX6, SIX8a, SIX9a and SIX13 present in Foc Tropical Race 4 (FocTR4), while only SIX9b in Foc Race 1 (Foc1). Analysis of SIX gene expression in infected banana roots revealed differential patterns during infection providing valuable insights into host-pathogen interactions, virulence level, and early detection time points. Additionally, a comprehensive analysis of virulent Foc1_C2HIR and FocTR4_C1HIR isolates yielded informative genomic insights. Hence, these discoveries contribute to our comprehension of potential disease control targets in these plants, as well as enhancing plant diagnostics and breeding programs.

Deep learning and content-based filtering techniques for improving plant disease identification and treatment recommendations: A comprehensive review

The importance of identifying plant diseases has risen recently due to the adverse effect they have on agricultutal production. Plant diseases have been a big concern in agriculture, as they affect crop production, and constitute a major threat to global food security. In the domain of modern agriculture, effective plant disease management is vital to ensure healthy crop yields and sustainable practices. Traditional means of identifying plant disease are faced with lots of challenges and the need for better and efficient detection methods cannot be overemphazised. The emergence of advanced technologies, particularly deep learning and content-based filtering techniques, if integrated together can changed the way plant diseases are identified and treated. Such as speedy and correct identification of plant diseases and efficient treatment recommendations which are keys for sustainable food production. In this work, We try to investigate the current state of research, identified gaps and limitations in knowledge, and suggests future directions for researchers, experts and farmers that could help to provide better ways of mitigating plant disease problems.

Few-shot disease recognition algorithm based on supervised contrastive learning

Diseases cause crop yield reduction and quality decline, which has a great impact on agricultural production. Plant disease recognition based on computer vision can help farmers quickly and accurately recognize diseases. However, the occurrence of diseases is random and the collection cost is very high. In many cases, the number of disease samples that can be used to train the disease classifier is small. To address this problem, we propose a few-shot disease recognition algorithm that uses supervised contrastive learning. Our algorithm is divided into two phases: supervised contrastive learning and meta-learning. In the first phase, we use a supervised contrastive learning algorithm to train an encoder with strong generalization capabilities using a large number of samples. In the second phase, we treat this encoder as an extractor of plant disease features and adopt the meta-learning training mechanism to accomplish the few-shot disease recognition tasks by training a nearest-centroid classifier based on distance metrics. The experimental results indicate that the proposed method outperforms the other nine popular few-shot learning algorithms as a comparison in the disease recognition accuracy over the public plant disease dataset PlantVillage. In few-shot potato leaf disease recognition tasks in natural scenarios, the accuracy of the model reaches the accuracy of 79.51% with only 30 training images. The experiment also revealed that, in the contrastive learning phase, the combination of different image augmentation operations has a greater impact on model. Furthermore, the introduction of label information in supervised contrastive learning enables our algorithm to still obtain high accuracy in few-shot disease recognition tasks with smaller batch size, thus allowing us to complete the training with less GPU resource compared to traditional contrastive learning.

PlantPAD: a platform for large-scale image phenomics analysis of disease in plant science

Plant disease, a huge burden, can cause yield loss of up to 100% and thus reduce food security. Actually, smart diagnosing diseases with plant phenomics is crucial for recovering the most yield loss, which usually requires sufficient image information. Hence, phenomics is being pursued as an independent discipline to enable the development of high-throughput phenotyping for plant disease. However, we often face challenges in sharing large-scale image data due to incompatibilities in formats and descriptions provided by different communities, limiting multidisciplinary research exploration. To this end, we build a Plant Phenomics Analysis of Disease (PlantPAD) platform with large-scale information on disease. Our platform contains 421 314 images, 63 crops and 310 diseases. Compared to other databases, PlantPAD has extensive, well-annotated image data and in-depth disease information, and offers pre-trained deep-learning models for accurate plant disease diagnosis. PlantPAD supports various valuable applications across multiple disciplines, including intelligent disease diagnosis, disease education and efficient disease detection and control. Through three applications of PlantPAD, we show the easy-to-use and convenient functions. PlantPAD is mainly oriented towards biologists, computer scientists, plant pathologists, farm managers and pesticide scientists, which may easily explore multidisciplinary research to fight against plant diseases. PlantPAD is freely available at http://plantpad.samlab.cn.

Optical Coherence Tomography as a Non-Invasive Tool for Plant Material Characterization in Agriculture: A Review

Characterizing plant material is crucial in terms of early disease detection, pest control, physiological assessments, and growth monitoring, which are essential parameters to increase production in agriculture and prevent unnecessary economic losses. The conventional methods employed to assess the aforementioned parameters have several limitations, such as invasive inspection, complexity, high time consumption, and costly features. In recent years, optical coherence tomography (OCT), which is an ultra-high resolution, non-invasive, and real-time unique image-based approach has been widely utilized as a significant and potential tool for assessing plant materials in numerous aspects. The obtained OCT cross-sections and volumetrics, as well as the amplitude signals of plant materials, have the capability to reveal vital information in both axial and lateral directions owing to the high resolution of the imaging system. This review discusses recent technological trends and advanced applications of OCT, which have been potentially adapted for numerous agricultural applications, such as non-invasive disease screening, optical signals-based growth speed detection, the structural analysis of plant materials, and microbiological discoveries. Therefore, this review offers a comprehensive exploration of recent advanced OCT technological approaches for agricultural applications, which provides insights into their potential to incorporate OCT technology into numerous industries.

Modelling the In Vitro Growth of Phytopathogenic Filamentous Fungi and Oomycetes: The Gompertz Parameters as Robust Indicators of Propolis Antifungal Action

Propolis is a resinous mixture produced by honeybees, mainly from plant exudates. With a rich chemical composition including many phenolic compounds, mostly responsible for its biological properties, namely antimicrobial ones, propolis may be a promising alternative to synthetic pesticides. The study of propolis from the south of Portugal and of its potential against phytopathogenic agents are still very recent and different methodological approaches hinder a comparison of efficacies. In this context, we aimed to test the value of a mathematical model for the multiparametric characterization of propolis' antifungal action on solid medium assays. An ethanol extract (EE) of a propolis sample harvested in 2016 from Alves (A16) was characterized in terms of phenolic composition and antimicrobial potential against five phytopathogenic species. A16.EE (500-2000 µg/mL) inhibited the mycelial growth of all the species, with Phytophthora cinnamomi and Biscogniauxia mediterranea being the most susceptible and Colletotrichum acutatum being the least affected. The Gompertz mathematical model proved to be a suitable tool for quantitatively describing the growth profiles of fungi and oomycetes, and its parameters exhibit a high level of discrimination. Our results reveal that propolis extracts may have potential applications beyond traditional uses, particularly within the agri-food sector, allowing beekeepers to make their businesses more profitable and diversified.

Novel plant disease detection techniques-a brief review

Plant pathogens cause severe losses to agricultural yield worldwide. Tracking plant health and early disease detection is important to reduce the disease spread and thus economic loss. Though visual scouting has been practiced from former times, detection of asymptomatic disease conditions is difficult. So, DNA-based and serological methods gained importance in plant disease detection. The progress in advanced technologies challenges the development of rapid, non-invasive, and on-field detection techniques such as spectroscopy. This review highlights various direct and indirect ways of detecting plant diseases like Enzyme-linked immunosorbent assay, Lateral flow assays, Polymerase chain reaction, spectroscopic techniques and biosensors. Although these techniques are sensitive and pathogen-specific, they are more laborious and time-intensive. As a consequence, a lot of interest is gained in in-field adaptable point-of-care devices with artificial intelligence-assisted pathogen detection at an early stage. More recently computer-aided techniques like neural networks are gaining significance in plant disease detection by image processing. In addition, a concise report on the latest progress achieved in plant disease detection techniques is provided.

Molecular diagnostic assay for pre-harvest detection of Tilletia indica infection in wheat plants

The current study describes a new diagnostic method for the rapid and accurate detection of Tilletia indica, the pathogen accountable for causing Karnal bunt (KB) disease in wheat. This method uses quantitative real-time polymerase chain reaction (qPCR) and a primer set derived from glyceraldehyde 3-phosphate dehydrogenase (GAPDH) gene of T. indica to identify the presence of the pathogen. The qPCR assay using this primer set was found highly sensitive, with a limit of detection (LOD) value of 4 pg of T. indica DNA. This level of sensitivity allows for the detection of the pathogen even in cases of different growth stages of wheat, where no visible symptoms of infection on the wheat plants can be seen by naked eyes. The study also validated the qPCR assay on ten different wheat cultivars. Overall, this study presents a valuable molecular tool for rapid, specific and sensitive detection of KB fungus in wheat host. This method has practical applications in disease management, screening of wheat genotypes against KB and can aid in the development of strategies to mitigate the impact of Karnal bunt disease on wheat production.

Biogenic zinc oxide nanoparticles: A viable agricultural tool to control plant pathogenic fungi and its potential effects on soil and plants

Fungal and bacterial pathogens represent some of the greatest challenges facing crop production globally and account for about 20-40 % crop losses annually. This review highlights the use of ZnO NPs as antimicrobial agents and explores their mechanisms of actions against disease causing plant fungal pathogens. The behavior of ZnO NPs in soil and their interactions with the soil components were also highlighted. The review discusses the potential effects of ZnO NPs on plants and their mechanisms of action on plants and how these mechanisms are related to their physicochemical properties. In addition, the reduction of ZnO NPs toxicity through surface modification and coating with silica is also addressed. Soil properties play a significant role in the dispersal, aggregation, stability, bioavailability, and transport of ZnO NPs and their release into the soil. The transport of ZnO NPs into the soil might influence soil components and, as a result, plant physiology. The harmful effects of ZnO NPs on plants and fungi are caused by a variety of processes, the most important of which is the formation of reactive oxygen species, lysosomal instability, DNA damage, and the reduction of oxidative stress by direct penetration/liberation of Zn2+ ions in plant/fungal cells. Based on these highlighted areas, this review concludes that ZnO NPs exhibit its antifungal activity via generations of reactive oxygen species, coupled with the inhibition of various metabolic pathways. Despite the numerous advantages of ZnO NPs, there is need to regulate its uses to minimize the harmful effects that may arise from its applications in the soil and plants.

Identification of volatile organic compounds in purple and white soybean flowers by HS-SPME/GC-MS

This study aimed to establish a method for the extraction, enrichment, and identification of volatile organic compounds (VOCs) released by the flowers of purple (BRS 399) and white (DONMARIO 6563) soybean varieties. We tested the Static Headspace (HS) and Solid Phase Microextraction (SPME) methods using various fibre types: PDMS (Polydimethylsiloxane), PDMS/DVB (Divinylbenzene), and PDMS/DVB/CAR (Carboxen). We employed gas chromatography-mass spectrometry (GC-MS) to identify the VOCs. The SPME method with PDMS/DVB and PDMS/DVB/CAR fibres yielded the highest number of extracted compounds for both soybean cultivars. Notably, 67 compounds were detected in Glycine max. L for the first time. Using the developed method, we were able to detect 52 and 57 VOCs in the purple and white soybean varieties, respectively, including ketones, alcohols, aldehydes and benzenoids. In conclusion, the method we developed effectively identified VOCs in soybean flowers, thus enriching our understanding of the interactions between soybean flowers and their pollinators.

Enhancing host-pathogen phenotyping dynamics: early detection of tomato bacterial diseases using hyperspectral point measurement and predictive modeling

Early diagnosis of plant diseases is needed to promote sustainable plant protection strategies. Applied predictive modeling over hyperspectral spectroscopy (HS) data can be an effective, fast, cost-effective approach for improving plant disease diagnosis. This study aimed to investigate the potential of HS point-of-measurement (POM) data for in-situ, non-destructive diagnosis of tomato bacterial speck caused by Pseudomonas syringae pv. tomato (Pst), and bacterial spot, caused by Xanthomonas euvesicatoria (Xeu), on leaves (cv. cherry). Bacterial artificial infection was performed on tomato plants at the same phenological stage. A sensing system composed by a hyperspectral spectrometer, a transmission optical fiber bundle with a slitted probe and a white light source were used for spectral data acquisition, allowing the assessment of 3478 spectral points. An applied predictive classification model was developed, consisting of a normalizing pre-processing strategy allied with a Linear Discriminant Analysis (LDA) for reducing data dimensionality and a supervised machine learning algorithm (Support Vector Machine - SVM) for the classification task. The predicted model achieved classification accuracies of 100% and 74% for Pst and Xeu test set assessments, respectively, before symptom appearance. Model predictions were coherent with host-pathogen interactions mentioned in the literature (e.g., changes in photosynthetic pigment levels, production of bacterial-specific molecules, and activation of plants' defense mechanisms). Furthermore, these results were coherent with visual phenotyping inspection and PCR results. The reported outcomes support the application of spectral point measurements acquired in-vivo for plant disease diagnosis, aiming for more precise and eco-friendly phytosanitary approaches.

CRISPR/Cas-Based Diagnostics in Agricultural Applications

Pests and disease-causing pathogens frequently impede agricultural production. An early and efficient diagnostic tool is crucial for effective disease management. Clustered regularly interspaced short palindromic repeats (CRISPR) and the CRISPR-associated protein (Cas) have recently been harnessed to develop diagnostic tools. The CRISPR/Cas system, composed of the Cas endonuclease and guide RNA, enables precise identification and cleavage of the target nucleic acids. The inherent sensitivity, high specificity, and rapid assay time of the CRISPR/Cas system make it an effective alternative for diagnosing plant pathogens and identifying genetically modified crops. Furthermore, its potential for multiplexing and suitability for point-of-care testing at the field level provide advantages over traditional diagnostic systems such as RT-PCR, LAMP, and NGS. In this review, we discuss the recent developments in CRISPR/Cas based diagnostics and their implications in various agricultural applications. We have also emphasized the major challenges with possible solutions and provided insights into future perspectives and potential applications of the CRISPR/Cas system in agriculture.

Scope of Onsite, Portable Prevention Diagnostic Strategies for Alternaria Infections in Medicinal Plants

Medicinal plants are constantly challenged by different biotic inconveniences, which not only cause yield and economic losses but also affect the quality of products derived from them. Among them, Alternaria pathogens are one of the harmful fungal pathogens in medicinal plants across the globe. Therefore, a fast and accurate detection method in the early stage is needed to avoid significant economic losses. Although traditional methods are available to detect Alternaria, they are more time-consuming and costly and need good expertise. Nevertheless, numerous biochemical- and molecular-based techniques are available for the detection of plant diseases, but their efficacy is constrained by differences in their accuracy, specificity, sensitivity, dependability, and speed in addition to being unsuitable for direct on-field studies. Considering the effect of Alternaria on medicinal plants, the development of novel and early detection measures is required to detect causal Alternaria species accurately, sensitively, and rapidly that can be further applied in fields to speed up the advancement process in detection strategies. In this regard, nanotechnology can be employed to develop portable biosensors suitable for early and correct pathogenic disease detection on the field. It also provides an efficient future scope to convert innovative nanoparticle-derived fabricated biomolecules and biosensor approaches in the diagnostics of disease-causing pathogens in important medicinal plants. In this review, we summarize the traditional methods, including immunological and molecular methods, utilized in plant-disease diagnostics. We also brief advanced automobile and efficient sensing technologies for diagnostics. Here we are proposing an idea with a focus on the development of electrochemical and/or colorimetric properties-based nano-biosensors that could be useful in the early detection of Alternaria and other plant pathogens in important medicinal plants. In addition, we discuss challenges faced during the fabrication of biosensors and new capabilities of the technology that provide information regarding disease management strategies.

Advancing the automation of plant nucleic acid extraction for rapid diagnosis of plant diseases in space

Human space exploration missions will continue the development of sustainable plant cultivation in what are obviously novel habitat settings. Effective pathology mitigation strategies are needed to cope with plant disease outbreaks in any space-based plant growth system. However, few technologies currently exist for space-based diagnosis of plant pathogens. Therefore, we developed a method of extracting plant nucleic acid that will facilitate the rapid diagnosis of plant diseases for future spaceflight applications. The microHomogenizer^™ from Claremont BioSolutions, originally designed for bacterial and animal tissue samples, was evaluated for plant-microbial nucleic acid extractions. The microHomogenizer^™ is an appealing device in that it provides automation and containment capabilities that would be required in spaceflight applications. Three different plant pathosystems were used to assess the versatility of the extraction process. Tomato, lettuce, and pepper plants were respectively inoculated with a fungal plant pathogen, an oomycete pathogen, and a plant viral pathogen. The microHomogenizer^™, along with the developed protocols, proved to be an effective mechanism for producing DNA from all three pathosystems, in that PCR and sequencing of the resulting samples demonstrated clear DNA-based diagnoses. Thus, this investigation advances the efforts to automate nucleic acid extraction for future plant disease diagnosis in space.

Can Metabolomic Approaches Become a Tool for Improving Early Plant Disease Detection and Diagnosis with Modern Remote Sensing Methods? A Review

The various areas of ultra-sensitive remote sensing research equipment development have provided new ways for assessing crop states. However, even the most promising areas of research, such as hyperspectral remote sensing or Raman spectrometry, have not yet led to stable results. In this review, the main methods for early plant disease detection are discussed. The best proven existing techniques for data acquisition are described. It is discussed how they can be applied to new areas of knowledge. The role of metabolomic approaches in the application of modern methods for early plant disease detection and diagnosis is reviewed. A further direction for experimental methodological development is indicated. The ways to increase the efficiency of modern early plant disease detection remote sensing methods through metabolomic data usage are shown. This article provides an overview of modern sensors and technologies for assessing the biochemical state of crops as well as the ways to apply them in synergy with existing data acquisition and analysis technologies for early plant disease detection.

Emerging Frontiers in Nanotechnology for Precision Agriculture: Advancements, Hurdles and Prospects

This review article provides an extensive overview of the emerging frontiers of nanotechnology in precision agriculture, highlighting recent advancements, hurdles, and prospects. The benefits of nanotechnology in this field include the development of advanced nanomaterials for enhanced seed germination and micronutrient supply, along with the alleviation of biotic and abiotic stress. Further, nanotechnology-based fertilizers and pesticides can be delivered in lower dosages, which reduces environmental impacts and human health hazards. Another significant advantage lies in introducing cutting-edge nanodiagnostic systems and nanobiosensors that monitor soil quality parameters, plant diseases, and stress, all of which are critical for precision agriculture. Additionally, this technology has demonstrated potential in reducing agro-waste, synthesizing high-value products, and using methods and devices for tagging, monitoring, and tracking agroproducts. Alongside these developments, cloud computing and smartphone-based biosensors have emerged as crucial data collection and analysis tools. Finally, this review delves into the economic, legal, social, and risk implications of nanotechnology in agriculture, which must be thoroughly examined for the technology’s widespread adoption.

Image-based plant wilting estimation

BACKGROUND

Environmental stress due to climate or pathogens is a major threat to modern agriculture. Plant genetic resistance to these stresses is one way to develop more resilient crops, but accurately quantifying plant phenotypic responses can be challenging. Here we develop and test a set of metrics to quantify plant wilting, which can occur in response to abiotic stress such as heat or drought, or in response to biotic stress caused by pathogenic microbes. These metrics can be useful in genomic studies to identify genes and genomic regions underlying plant resistance to a given stress.

RESULTS

We use two datasets: one of tomatoes inoculated with Ralstonia solanacearum, a soilborne pathogen that causes bacterial wilt disease, and another of soybeans exposed to water stress. For both tomato and soybean, the metrics predict the visual wilting score provided by human experts. Specific to the tomato dataset, we demonstrate that our metrics can capture the genetic difference of bacterium wilt resistance among resistant and susceptible tomato genotypes. In soybean, we show that our metrics can capture the effect of water stress.

CONCLUSION

Our proposed RGB image-based wilting metrics can be useful for identifying plant wilting caused by diverse stresses in different plant species.

Current and emerging trends in techniques for plant pathogen detection

Plant pathogenic microorganisms cause substantial yield losses in several economically important crops, resulting in economic and social adversity. The spread of such plant pathogens and the emergence of new diseases is facilitated by human practices such as monoculture farming and global trade. Therefore, the early detection and identification of pathogens is of utmost importance to reduce the associated agricultural losses. In this review, techniques that are currently available to detect plant pathogens are discussed, including culture-based, PCR-based, sequencing-based, and immunology-based techniques. Their working principles are explained, followed by an overview of the main advantages and disadvantages, and examples of their use in plant pathogen detection. In addition to the more conventional and commonly used techniques, we also point to some recent evolutions in the field of plant pathogen detection. The potential use of point-of-care devices, including biosensors, have gained in popularity. These devices can provide fast analysis, are easy to use, and most importantly can be used for on-site diagnosis, allowing the farmers to take rapid disease management decisions.

Part I: Non-faradaic electrochemical impedance-based DNA biosensor for detecting phytopathogen - Ralstonia solanacearum

Herein, we report for the first time the development of a label-free, non-faradaic, and highly sensitive DNA-based impedimetric sensor using micro-sized gold interdigitated electrodes (IDE) to detect a soil-borne agricultural pathogen Ralstonia solanacearum. A universal 30 oligomer single-stranded DNA (ssDNA) probe lpxC4 having specificity towards R. solanacearum is successfully immobilized on the surface of IDE along with mercaptohexanol. The electrochemical stability of the developed sensor surface is determined using open circuit potential measurements. The DNA probe immobilization protocol is validated using the changes configured on the surface of IDE by contact angle and ATR-FTIR analysis. The DNA target hybridization is detected using non-faradaic electrochemical impedance spectroscopy measurement with an ultra-low sample volume of 10 µL. The non-faradaic approach is verified by studying redox behavior using cyclic voltammetry. We investigate the hybridization of the surface-immobilized label-free probe with the complementary DNA targets obtained from infected eggplant saplings and cross-reactive studies with mismatched DNA strands. Our impedimetric sensor can detect target concentrations as low as 0.1 ng/µL. This standardization and detection of DNA hybridization serves as part I of the work and paves the way for further study in the detection of pathogenic field samples.

Improvement of Akanthomyces lecanii resistance to tebuconazole through UV-C radiation and selective pressure on microbial evolution and growth arenas

Tebuconazole (TEB) is a fungicide widely used in agriculture; however, its constant application has increased the emergence of resistant plant pathogenic fungal strains and reduced the effectiveness of fungi as biological control agents; for instance, the entomopathogenic and hyperparasitic fungus Akanthomyces lecanii, suitable for simultaneous biological control of insect pest and plant pathogenic fungi, is highly sensitive to fungicides. We carried out the induction of resistance to TEB in two wild type strains of A. lecanii by UV radiation and selective pressure in increasing fungicide gradients using a modified Microbial Evolution and Growth Arena (MEGA), to produce A. lecanii strains that can be used as biological control agent in the presence of tebuconazole. Nine UV-induced and three naturally adapted A. lecanii strains were resistant to TEB at the agriculturally recommended dose, and three irradiated strains were resistant to TEB concentration ten times higher; moreover, growth, sporulation rates, production of hydrolytic enzymes, and virulence against the hemipteran Coccus viridis, a major pest of coffee crops, were not affected in the TEB-resistant strains. These A. lecanii TEB-resistant strains would have a greater opportunity to develop and to establish themselves in fields where the fungicide is present and can be used in a combined biological-chemical strategy to improve insect and plant pathogenic fungal control in agriculture. Also, the selective pressure through modified MEGA plate methodology can be used for the adaptation of entomopathogenic filamentous fungi to withstand other chemical or abiotic stresses that limits its effectiveness for pest control.

Multi-point scanning confocal Raman spectroscopy for accurate identification of microorganisms at the single-cell level

Raman spectroscopy has been widely used for microbial analysis due to its exceptional qualities as a rapid, simple, non-invasive, reproducible, and real-time monitoring tool. The Raman spectrum of a cell is a superposition of the spectral information of all biochemical components in the laser focus. In the case where the microbial size is larger than the laser spot size, the Raman spectrum measured from a single-point within a cell cannot capture all biochemical information due to the spatial heterogeneity of microorganisms. In this work, we have proposed a method for the accurate identification of microorganisms using multi-point scanning confocal Raman spectroscopy. Through an image recognition algorithm and the control of a high-precision motorized stage, Raman spectra can be integrated at one time to measure the multi-point biochemical information of microorganisms. This solves the problem that the measured single microbial cells are of different sizes, and the laser spot of the confocal Raman system is not easy to change. Here, the single-cell Raman spectra of three Escherichia coli and seven Lactobacillus species were measured separately. The commonly used supervised classification method, support vector machine (SVM), was applied to compare the data based on the single-point spectra and multi-point scanning spectra. Multi-point spectra showed superior performance in terms of their accuracy and recall rates compared with single-point spectra. The results show that multi-point scanning confocal Raman spectra can be used for more accurate species classification at different taxonomic levels, which is of great importance in species identification.

New trends in methyl salicylate sensing and their implications in agriculture

Methyl salicylate (MeSal) is an organic compound present in plants during stress events and is therefore a key marker for early plant disease detection. It has usually been detected by conventional methods that require bulky and costly equipment, such as gas chromatography or mass spectrometry. Currently, however, chemical sensors provide an alternative for MeSal monitoring, showing good performance for its determination in the vapour or liquid phase. The most promising concepts used in MeSal determination include sensors based on electrochemical and conductometric principles, although other technologies based on mass-sensitive, microwave, or spectrophotometric principles also show promise. The receptor elements or sensitive materials are shown to be part of the key elements in these sensing technologies. A literature survey identified a significant contribution of bioreceptors, including enzymes, odourant-binding proteins or peptides, as well as receptors based on polymers or inorganic materials in MeSal determination. This work reviews these concepts and materials and discusses their future prospects and limitations for application in plant health monitoring.

Identification of geographic origins of Morus alba Linn. through surfaced enhanced Raman spectrometry and machine learning algorithms

The leaves of Morus alba Linn., which is also known as white mulberry, have been commonly used in many of traditional systems of medicine for centuries. In traditional Chinese medicine (TCM), mulberry leaf is mainly used for anti-diabetic purpose due to its enrichment in bioactive compounds such as alkaloids, flavonoids and polysaccharides. However, these components are variable due to the different habitats of the mulberry plant. Therefore, geographic origin is an important feature because it is closely associated with bioactive ingredient composition that further influences medicinal qualities and effects. As a low-cost and non-invasive method, surface enhanced Raman spectrometry (SERS) is able to generate the overall fingerprints of chemical compounds in medicinal plants, which holds the potential for the rapid identification of their geographic origins. In this study, we collected mulberry leaves from five representative provinces in China, namely, Anhui, Guangdong, Hebei, Henan and Jiangsu. SERS spectrometry was applied to characterize the fingerprints of both ethanol and water extracts of mulberry leaves, respectively. Through the combination of SERS spectra and machine learning algorithms, mulberry leaves were well discriminated with high accuracies in terms of their geographic origins, among which the deep learning algorithm convolutional neural network (CNN) showed the best performance. Taken together, our study established a novel method for predicting the geographic origins of mulberry leaves through the combination of SERS spectra with machine learning algorithms, which strengthened the application potential of the method in the quality evaluation, control and assurance of mulberry leaves.

Protein Kinases as Potential Targets Contribute to the Development of Agrochemicals

Using agrochemicals against pest insects, fungi, and weeds plays a major part in maintaining and improving crop yields, which helps to solve the issue of food security. Due to the limited targets and resistance of agrochemicals, protein kinases are regarded as attractive potential targets to develop new agrochemicals. Recently, a lot of investigations have shown the extension of agrochemicals by targeting protein kinases, implying an increasing concern for this kind of method. However, few people have summarized and discussed the targetability of protein kinases contributing to the development of agrochemicals. In this work, we introduce the research on protein kinases as potential targets used in crop protection and discuss the prospects of protein kinases in the field of agrochemical development. This study may not only provide guidance for the contribution of protein kinases to the development of agrochemicals but also help nonprofessionals such as students learn and understand the role of protein kinases quickly.

On-field optical imaging data for the pre-identification and estimation of leaf deformities

Visually nonidentifiable pathological symptoms at an early stage are a major limitation in agricultural plantations. Thickness reduction in palisade parenchyma (PP) and spongy parenchyma (SP) layers is one of the most common symptoms that occur at the early stage of leaf diseases, particularly in apple and persimmon. To visualize variations in PP and SP thickness, we used optical coherence tomography (OCT)-based imaging and analyzed the acquired datasets to determine the threshold parameters for pre-identifying and estimating persimmon and apple leaf abnormalities using an intensity-based depth profiling algorithm. The algorithm identified morphological differences between healthy, apparently-healthy, and infected leaves by applying a threshold in depth profiling to classify them. The qualitative and quantitative results revealed changes and abnormalities in leaf morphology in addition to disease incubation in both apple and persimmon leaves. These can be used to examine how initial symptoms are influenced by disease growth. Thus, these datasets confirm the significance of OCT in identifying disease symptoms nondestructively and providing a benchmark dataset to the agriculture community for future reference.

Nanopore Metagenomics Sequencing for Rapid Diagnosis and Characterization of Lily Viruses

Lilies (Lilium spp.) are one of the most important ornamental flower crops grown in Korea. Most viral diseases in lilies are transmitted by infected bulbs, which cause serious economic losses due to reduced yields. Various diagnostic techniques and high-throughput sequencing methods have been used to detect lily viruses. According to Oxford Nanopore Technologies (ONT), MinION is a compact and portable sequencing device. In this study, three plant viruses, lily mottle, lily symptomless, and plantago asiatica mosaic virus, were detected in lily samples using the ONT platform. As a result of genome assembly of reads obtained through ONT, 100% coverage and 90.3-93.4% identity were obtained. Thus, we show that the ONT platform is a promising tool for the diagnosis and characterization of viruses that infect crops.

Detection of Tip-Burn Stress on Lettuce Grown in an Indoor Environment Using Deep Learning Algorithms

Lettuce grown in indoor farms under fully artificial light is susceptible to a physiological disorder known as tip-burn. A vital factor that controls plant growth in indoor farms is the ability to adjust the growing environment to promote faster crop growth. However, this rapid growth process exacerbates the tip-burn problem, especially for lettuce. This paper presents an automated detection of tip-burn lettuce grown indoors using a deep-learning algorithm based on a one-stage object detector. The tip-burn lettuce images were captured under various light and indoor background conditions (under white, red, and blue LEDs). After augmentation, a total of 2333 images were generated and used for training using three different one-stage detectors, namely, CenterNet, YOLOv4, and YOLOv5. In the training dataset, all the models exhibited a mean average precision (mAP) greater than 80% except for YOLOv4. The most accurate model for detecting tip-burns was YOLOv5, which had the highest mAP of 82.8%. The performance of the trained models was also evaluated on the images taken under different indoor farm light settings, including white, red, and blue LEDs. Again, YOLOv5 was significantly better than CenterNet and YOLOv4. Therefore, detecting tip-burn on lettuce grown in indoor farms under different lighting conditions can be recognized by using deep-learning algorithms with a reliable overall accuracy. Early detection of tip-burn can help growers readjust the lighting and controlled environment parameters to increase the freshness of lettuce grown in plant factories.

The Detection of Pine Wilt Disease: A Literature Review

Pine wilt disease (PWD) is a global quarantine disease of forests that mainly affects Pinaceae species. The disease spreads rapidly. Once infected, pine trees have an extremely high mortality rate. This paper provides a summary of the common techniques used to detect PWD, including morphological-, molecular-, chemical- and physical-based methods. By comprehending the complex relationship among pinewood nematodes, vectors and host pine trees and employing the available approaches for nematode detection, we can improve the implementation of intervention and control measures to effectively reduce the damage caused by PWD. Although conventional techniques allow a reliable diagnosis of the symptomatic phase, the volatile compound detection and remote sensing technology facilitate a rapid diagnosis during asymptomatic stages. Moreover, the remote sensing technology is capable of monitoring PWD over large areas. Therefore, multiple perspective evaluations based on these technologies are crucial for the rapid and effective detection of PWD.

Close-Range Thermography and Reflectance Spectroscopy Support In Vitro and In Vivo Characterization of Colletotrichum spp. Isolates from Mango Fruits

Colletotrichum causing anthracnose in mango is known for its variable virulence that may have an effect on disease development and efficacy of management strategies. In this study, we characterized Colletotrichum spp. isolated from mango fruits under in vitro and in vivo conditions using close-range thermography and reflectance spectroscopy. Twenty-six isolates were phylogenetically characterized to ascertain species using the internal transcribed spacer sequence. Virulence, spectral (in vivo and in vitro), and thermographic responses (in vivo) of these isolates were analyzed. Isolates were grouped into the Colletotrichum gloeosporioides species complex and classified into eight morphotypes. Mycelial growth, conidia production, sporulation abundance, and area under disease progress curve (AUDPC) varied largely among isolates. Disease symptoms were observed 4 days after inoculation (dai), and, for most morphotypes, changes in tissue temperature were registered at 11 dai, with the greatest decrease at 14 dai with pathogen sporulation. In vitro and in vivo morphotypes shared changes in the spectrum range, and main variations were found in the number of informative spectral bands. In vivo average gross reflectance was higher in disease-inoculated tissue than in healthy uninoculated tissue. Morphotype responses varied depending on AUDPC values and postinoculation time. Discriminant analysis of the spectral response using principal component analysis and partial least squares regression explained 94 to 96.3 and 98 to 99.9% of the variance from in vitro and in vivo tests, respectively. Spectral markers were obtained for four distinct morphotype groups. We found three (550 to 650, 650.1 to 790, and 1,300 to 1,400 nm) and two (520 to 830 and 1,100 to 1,450 nm) regions with highly (P < 0.05) discriminant spectral bands for diseased fruits and morphotype characterization.

Kiwi Plant Canker Diagnosis Using Hyperspectral Signal Processing and Machine Learning: Detecting Symptoms Caused by Pseudomonas syringae pv. actinidiae

Pseudomonas syringae pv. actinidiae (Psa) has been responsible for numerous epidemics of bacterial canker of kiwi (BCK), resulting in high losses in kiwi production worldwide. Current diagnostic approaches for this disease usually depend on visible signs of the infection (disease symptoms) to be present. Since these symptoms frequently manifest themselves in the middle to late stages of the infection process, the effectiveness of phytosanitary measures can be compromised. Hyperspectral spectroscopy has the potential to be an effective, non-invasive, rapid, cost-effective, high-throughput approach for improving BCK diagnostics. This study aimed to investigate the potential of hyperspectral UV–VIS reflectance for in-situ, non-destructive discrimination of bacterial canker on kiwi leaves. Spectral reflectance (325–1075 nm) of twenty plants were obtained with a handheld spectroradiometer in two commercial kiwi orchards located in Portugal, for 15 weeks, totaling 504 spectral measurements. Several modeling approaches based on continuous hyperspectral data or specific wavelengths, chosen by different feature selection algorithms, were tested to discriminate BCK on leaves. Spectral separability of asymptomatic and symptomatic leaves was observed in all multi-variate and machine learning models, including the FDA, GLM, PLS, and SVM methods. The combination of a stepwise forward variable selection approach using a support vector machine algorithm with a radial kernel and class weights was selected as the final model. Its overall accuracy was 85%, with a 0.70 kappa score and 0.84 F-measure. These results were coherent with leaves classified as asymptomatic or symptomatic by visual inspection. Overall, the findings herein reported support the implementation of spectral point measurements acquired in situ for crop disease diagnosis.

Discovery and Characterization of a Novel Umbravirus from Paederia scandens Plants Showing Leaf Chlorosis and Yellowing Symptoms

Umbraviruses are a special class of plant viruses that do not encode any viral structural proteins. Here, a novel umbravirus that has been tentatively named Paederia scandens chlorosis yellow virus (PSCYV) was discovered through RNA-seq in Paederia scandens plants showing leaf chlorosis and yellowing symptoms. The PSCYV genome is a 4301 nt positive-sense, single strand RNA that contains four open reading frames (ORFs), i.e., ORF1–4, that encode P1–P4 proteins, respectively. Together, ORF1 and ORF2 are predicted to encode an additional protein, RdRp, through a −1 frameshift mechanism. The P3 protein encoded by ORF3 was predicted to be the viral long-distance movement protein. P4 was determined to function as the viral cell-to-cell movement protein (MP) and transcriptional gene silencing (TGS) suppressor. Both P1 and RdRp function as weak post-transcriptional gene silencing (PTGS) suppressors of PSCYV. The PVX-expression system indicated that all viral proteins may be symptom determinants of PSCYV. Phylogenetic analysis indicated that PSCYV is evolutionarily related to members of the genus Umbravirus in the family Tombusviridae. Furthermore, a cDNA infectious clone of PSCYV was successfully constructed and used to prove that PSCYV can infect both Paederia scandens and Nicotiana benthamiana plants through mechanical inoculation, causing leaf chlorosis and yellowing symptoms. These findings have broadened our understanding of umbraviruses and their host range.

Biosensors for rapid detection of bacterial pathogens in water, food and environment

Conventional techniques (e.g., culture-based method) for bacterial detection typically require a central laboratory and well-trained technicians, which may take several hours or days. However, recent developments within various disciplines of science and engineering have led to a major paradigm shift in how microorganisms can be detected. The analytical sensors which are widely used for medical applications in the literature are being extended for rapid and on-site monitoring of the bacterial pathogens in food, water and the environment. Especially, within the low-resource settings such as low and middle-income countries, due to the advantages of low cost, rapidness and potential for field-testing, their use is indispensable for sustainable development of the regions. Within this context, this paper discusses analytical methods and biosensors which can be used to ensure food safety, water quality and environmental monitoring. In brief, most of our discussion is focused on various rapid sensors including biosensors and microfluidic chips. The analytical performances such as the sensitivity, specificity and usability of these sensors, as well as a brief comparison with the conventional techniques for bacteria detection, form the core part of the discussion. Furthermore, we provide a holistic viewpoint on how future research should focus on exploring the synergy of different sensing technologies by developing an integrated multiplexed, sensitive and accurate sensors that will enable rapid detection for food safety, water and environmental monitoring.

New-Generation Sequencing Technology in Diagnosis of Fungal Plant Pathogens: A Dream Comes True?

The fast and continued progress of high-throughput sequencing (HTS) and the drastic reduction of its costs have boosted new and unpredictable developments in the field of plant pathology. The cost of whole-genome sequencing, which, until few years ago, was prohibitive for many projects, is now so affordable that a new branch, phylogenomics, is being developed. Fungal taxonomy is being deeply influenced by genome comparison, too. It is now easier to discover new genes as potential targets for an accurate diagnosis of new or emerging pathogens, notably those of quarantine concern. Similarly, with the development of metabarcoding and metagenomics techniques, it is now possible to unravel complex diseases or answer crucial questions, such as "What's in my soil?", to a good approximation, including fungi, bacteria, nematodes, etc. The new technologies allow to redraw the approach for disease control strategies considering the pathogens within their environment and deciphering the complex interactions between microorganisms and the cultivated crops. This kind of analysis usually generates big data that need sophisticated bioinformatic tools (machine learning, artificial intelligence) for their management. Herein, examples of the use of new technologies for research in fungal diversity and diagnosis of some fungal pathogens are reported.

The Detection Method of Potato Foliage Diseases in Complex Background Based on Instance Segmentation and Semantic Segmentation

Potato early blight and late blight are devastating diseases that affect potato planting and production. Thus, precise diagnosis of the diseases is critical in treatment application and management of potato farm. However, traditional computer vision technology and pattern recognition methods have certain limitations in the detection of crop diseases. In recent years, the development of deep learning technology and convolutional neural networks has provided new solutions for the rapid and accurate detection of crop diseases. In this study, an integrated framework that combines instance segmentation model, classification model, and semantic segmentation model was devised to realize the segmentation and detection of potato foliage diseases in complex backgrounds. In the first stage, Mask R-CNN was adopted to segment potato leaves in complex backgrounds. In the second stage, VGG16, ResNet50, and InceptionV3 classification models were employed to classify potato leaves. In the third stage, UNet, PSPNet, and DeepLabV3+ semantic segmentation models were applied to divide potato leaves. Finally, the three-stage models were combined to segment and detect the potato leaf diseases. According to the experimental results, the average precision (AP) obtained by the Mask R-CNN network in the first stage was 81.87%, and the precision was 97.13%. At the same time, the accuracy of the classification model in the second stage was 95.33%. The mean intersection over union (MIoU) of the semantic segmentation model in the third stage was 89.91%, and the mean pixel accuracy (MPA) was 94.24%. In short, it not only provides a new model framework for the identification and detection of potato foliage diseases in natural environment, but also lays a theoretical basis for potato disease assessment and classification.

Raman Spectroscopy Applications in Grapevine: Metabolic Analysis of Plants Infected by Two Different Viruses

Grapevine is one of the most cultivated fruit plant among economically relevant species in the world. It is vegetatively propagated and can be attacked by more than 80 viruses with possible detrimental effects on crop yield and wine quality. Preventive measures relying on extensive and robust diagnosis are fundamental to guarantee the use of virus-free grapevine plants and to manage its diseases. New phenotyping techniques for non-invasive identification of biochemical changes occurring during virus infection can be used for rapid diagnostic purposes. Here, we have investigated the potential of Raman spectroscopy (RS) to identify the presence of two different viruses, grapevine fan leaf virus (GFLV) and grapevine rupestris stem pitting-associated virus (GRSPaV) in Vitis vinifera cv. Chardonnay. We showed that RS can discriminate healthy plants from those infected by each of the two viruses, even in the absence of visible symptoms, with accuracy up to 100% and 80% for GFLV and GRSPaV, respectively. Chemometric analyses of the Raman spectra followed by chemical measurements showed that RS could probe a decrease in the carotenoid content in infected leaves, more profoundly altered by GFLV infection. Transcriptional analysis of genes involved in the carotenoid pathway confirmed that this biosynthetic process is altered during infection. These results indicate that RS is a cutting-edge alternative for a real-time dynamic monitoring of pathogens in grapevine plants and can be useful for studying the metabolic changes ensuing from plant stresses.