Next Article in Journal
Identification of Potential QTLs Related to Grain Size in Rice
Next Article in Special Issue
Genetic Divergence and Population Structure of Xanthomonas albilineans Strains Infecting Saccharum spp. Hybrid and Saccharum officinarum
Previous Article in Journal
Recommendations for the Assessment of Potential Environmental Effects of Genome-Editing Applications in Plants in the EU
Previous Article in Special Issue
Citrus Canker Pathogen, Its Mechanism of Infection, Eradication, and Impacts
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Plants
Volume 12
Issue 9
10.3390/plants12091765
plants-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Detection, Diagnosis, and Preventive Management of the Bacterial Plant Pathogen Pseudomonas syringae

by
Piao Yang
1,
Lijing Zhao
1,
Yu Gary Gao
2,3 and
Ye Xia
1,*
1
Department of Plant Pathology, College of Food, Agricultural, and Environmental Science, The Ohio State University, Columbus, OH 43210, USA
2
OSU South Centers, The Ohio State University, 1864 Shyville Road, Piketon, OH 45661, USA
3
Department of Extension, College of Food, Agricultural, and Environmental Sciences, The Ohio State University, Columbus, OH 43210, USA
*
Author to whom correspondence should be addressed.
Plants 2023, 12(9), 1765; https://doi.org/10.3390/plants12091765
Submission received: 15 February 2023 / Revised: 1 April 2023 / Accepted: 14 April 2023 / Published: 25 April 2023
(This article belongs to the Special Issue Detection and Diagnostics of Bacterial Plant Pathogens)
Browse Figures
Versions Notes

Abstract

:
Plant diseases caused by the pathogen Pseudomonas syringae are serious problems for various plant species worldwide. Accurate detection and diagnosis of P. syringae infections are critical for the effective management of these plant diseases. In this review, we summarize the current methods for the detection and diagnosis of P. syringae, including traditional techniques such as culture isolation and microscopy, and relatively newer techniques such as PCR and ELISA. It should be noted that each method has its advantages and disadvantages, and the choice of each method depends on the specific requirements, resources of each laboratory, and field settings. We also discuss the future trends in this field, such as the need for more sensitive and specific methods to detect the pathogens at low concentrations and the methods that can be used to diagnose P. syringae infections that are co-existing with other pathogens. Modern technologies such as genomics and proteomics could lead to the development of new methods of highly accurate detection and diagnosis based on the analysis of genetic and protein markers of the pathogens. Furthermore, using machine learning algorithms to analyze large data sets could yield new insights into the biology of P. syringae and novel diagnostic strategies. This review could enhance our understanding of P. syringae and help foster the development of more effective management techniques of the diseases caused by related pathogens.

1. Introduction

Plant pathogen detection recognizes the presence of plant pathogens in a specific location or area. The process involves observing visible disease symptoms in plants, collecting plant samples for further laboratory analysis, or using remote sensing techniques to detect the presence of pathogens [1,2]. Plant pathogen detection aims to identify the presence of plant pathogens as early as possible so control measures can be implemented to alleviate their impacts on crop production [3]. On the other hand, plant pathogen diagnosis refers to identifying the specific cause of a plant disease. It involves the identification of specific disease-causing pathogens by biochemical, molecular, and other techniques [4]. Plant pathogen diagnosis aims to find the specific pathogen responsible for the specific disease so proper control measures can be implemented to limit the further spread of the pathogen and disease. Plant pathogen detection and diagnosis are critical to understanding and managing plant diseases. They are associated with applying multiple techniques and approaches to identify and understand the presence and cause of plant diseases [5,6].
Plant diseases are a significant constraint to crop production worldwide and exert particularly severe impacts in developing countries, where agricultural systems may be less resilient than in developed ones [7,8,9,10]. Although bacteria evolved billions of years ago [11], they had not been demonstrated to cause plant diseases until the late 19th century [12]. Bacterial plant diseases can reduce crop yields and debase the quality of harvested crops, thus leading to significant quality and economic losses for farmers and agricultural industries [13,14,15]. Studying bacterial plant pathogens helps identify the ways to detect, diagnose, prevent, and control those destructive plant diseases, such as using resistant crop varieties, applying chemicals or biological control agents, and implementing good agricultural practices [9,16]. By further understanding the biology and epidemiology of bacterial plant diseases, researchers can upgrade their strategies to reduce the impacts of these diseases on crop production and improve global food security. Alongside developing control measures, it is also essential to study bacterial plant diseases to understand the factors contributing to their emergence and spread [17]. These efforts can involve the identification of the genetic and environmental factors that influence plant disease development and the roles that different plant hosts, vectors, and reservoirs play in the transmission of bacterial plant pathogens.
Rapid detection and correct diagnosis of bacterial plant pathogens and diseases are increasingly essential for protecting global food security. By detecting and diagnosing these pathogens and diseases early, it is possible to implement control measures such as the application of chemicals or biological control agents or the implementation of other agricultural practices to reduce the impacts of these diseases on crop production [18]. Furthermore, bacterial plant pathogens can sometimes result in the contamination of human food with harmful pathogens [19]. By detecting and diagnosing these pathogens early, it is possible to implement the control measures to prevent food contamination and improve food safety [20]. Bacterial plant diseases can also sometimes lead to the extinction of plant species, particularly rare or endangered species [21]. By noticing these pathogens earlier, it is helpful to develop strategies to protect cultures or production directly.
There are challenges in detecting and diagnosing bacterial plant pathogens, such as P. syringae [22]. One challenge is the need for rapid and correct diagnosis of bacterial plant pathogens. However, traditional methods, such as biochemical or molecular techniques, can be time-consuming and may not provide rapid results [23]. Another challenge is adapting to the fluctuating environmental conditions [24]. Bacterial plant pathogens can be influenced by various factors, including temperature, humidity, soil conditions, etc., which can vary over time and space [20]. Therefore, it is difficult to accurately diagnose and control bacterial plant diseases as the effective control measures may vary, depending on the specific environmental conditions. Diagnosis and control require the development of flexible and adaptable diagnostic and control strategies tailored to the specific environmental conditions in which the diseases are occurring. There is also a need to account for the diversity of bacterial plant pathogens. A wide range of pathogens can cause bacterial plant diseases [25]. This diversity can make it difficult to accurately diagnose and control bacterial plant pathogens and diseases. The effective diagnostic and control strategies may vary depending on the pathogen involved.

2. P. syringae as a Bacterial Plant Pathogen

P. syringae, a Gram-negative, rod-shaped bacterium that can cause severe damage to many plant species, is a significant concern for plant health and crop production [26]. It is classified as a hemibiotrophic pathogen that initially feeds on living plant tissues and later causes the death of plant cells [27]. The P. syringae phylogenetic group includes more than 60 pathovars and 15 recognized bacterial species [28]. Each pathovar of P. syringae infects a distinct group of host plants and is known for its diverse host-specific interactions with the plants [29,30]. As early as 1939, the P. syringae pv. primulae was reported to cause necrotic leaf spots on primrose plants in the USA (Figure 1A) [31]. In 1961, the P. syringae pv. tomato was reported to cause necrotic leaf spots on tomato plants in the UK (Figure 1A) [32]. The P. syringae pv. tomato DC3000 is also pathogenic to Arabidopsis plants and has become a model pathogen for probing disease susceptibility and hormone signaling in plants [27]. Up to 2009, Japan witnessed the highest level of occurrence of plant diseases caused by P. syringae, followed by the USA (Figure 1B). Japan reported/deposited 18 different pathovars of P. syringae to the National Collection of Plant Pathogenic Bacteria (NCPPB), and the USA reported/deposited 9 different pathovars of P. syringae to NCPPB (Figure 1B), which increased our understanding of the occurrence/distribution of P. syringae on a world-scale view.
The life cycle of P. syringae involves a range of different stages and modes of transmission [33]. P. syringae can be transmitted through seeds, water, vector insects, and infected plant debris. Once inside the plants, P. syringae can multiply and produce toxins that harm plant tissues. The infected plants can develop characteristic symptoms, such as lesions or discoloration on diseased leaves and necrosis spots on diseased fruits. P. syringae can survive in plant debris in the environment for extended periods and easily infect susceptible host plants through wounds or natural openings (Figure 2). It is worth noting that the life cycle of P. syringae can vary depending on the pathovar (strain) of the bacterium and the plant species it infects (Table 1). P. syringae is typically characterized by its ability to infect only specific areas of plants, such as foliar tissues and fruits. Some pathovars of P. syringae are more virulent or have a broader host range than others, affecting how the bacterium spreads and causes diseases [34].
P. syringae has been extensively studied since the early 1980s, and it is often used as a model for understanding various aspects of bacterial pathogenicity, including molecular mechanisms of plant-microbe interactions, microbial ecology, and epidemiology [27,30]. Genomic studies have revealed specific genomic characteristics that contribute to the virulence of P. syringae. Currently, it has been found that P. syringae deploys three vital strategies to harm plants: it can survive and adapt to the surface of plants, it can suppress the plant’s immune system at different stages of infection, and it can establish a water-filled space in the plant tissues, which provides it with the access to water and nutrients [30,92,93,94].
There are various techniques available for the detection and diagnosis of P. syringae. These techniques can be broadly classified into several categories: conventional (visual examination, microscopy, culture plate or phage typing), molecular (RPA, LAMP, NGS, FISH or PCR), serological (FCM, ELISA, IF or immunoStrip), biomarker-based (plant metabolite profiling, pathogen metabolite profiling, or microbiome analysis), vision-based (hyperspectral imaging or spectroscopic imaging) and AI (artificial intelligence). Different techniques have different advantages and limitations depending on the sample type, pathovar diversity, cost-effectiveness, etc. Conventional, molecular, and serological techniques are widely used nowadays for the detection and diagnosis of P. syringae.

3. Detection and Diagnosis of P. syringae with Conventional Methods

Visual examination is commonly used to detect and diagnose plant diseases caused by diverse pathogens [95] (Figure 3). The advantages of visual examinations are as follows: (1) It is relatively quick and easy to perform, requiring no specialized equipment or training. (2) It allows the observer to examine the location and extent of the disease symptoms on the plants. (3) It can be performed in the field, allowing for real-time pathogen and disease assessment (Table 2). However, there are also some limitations to visual examination for plant pathogen detection and diagnosis: (1) It may not be sensitive enough to detect pathogens at the initial stages when symptoms may not be visible. (2) It is subjective, as different people may interpret the same symptoms differently. (3) It is affected by environmental conditions, such as lighting and weather, making it difficult to assess disease symptoms accurately. (4) It is not suitable for detecting diseases caused by pathogens not visible to the naked eye, such as viruses or bacteria that colonize inside the plant tissues (Table 2). In summary, one traditional method for identifying plant pathogens is through visual examination, but this approach is generally only practical after significant damage has already occurred. Moreover, treatments are often ineffective once visible damage has already taken place. Farmers need to be able to identify a pathogen before the symptom becomes apparent to prevent irreparable harm to crops [96,97].
Microscopy examination is a technique that uses a microscope to magnify and observe small objects, including plant tissues and cells (Figure 3) [17,18,19]. Under a microscope, P. syringae bacteria are rod-shaped and have a characteristic appearance. They are typically 1–2 μm in width and 2–5 μm in length. P. syringae cells are gram-negative, which means they have a thin cell wall and are stained pink by a Gram stain. The bacteria are motile and often have a single polar flagellum, which helps them move around [146,147]. In the context of plant pathogen detection and diagnosis of P. syringae, microscopy has several advantages: (1) High resolution: Microscopes can supply high-resolution images of plant tissues and cells, allowing for the detection of subtle features and abnormalities that may be indicative of disease. (2) Versatility: many diverse types of microscopes are available, each with unique capabilities, making microscopy a versatile tool for plant pathogen detection and diagnosis. (3) Accessibility: Microscopes are widely available and relatively inexpensive, making them accessible to researchers and practitioners in many different settings (Table 2). However, there are also some limitations to using microscopy for plant pathogen detection and diagnosis: (1) Sample preparation: Microscopy requires the preparation of thin sections or slides of plant tissues or cells, which can be time-consuming and may not be suitable for all types of samples. (2) Shallow depth of field: Microscopes have a limited depth of field, making it challenging to visualize objects that are not in focus. (3) Limited spatial resolution: Microscopes have a limited spatial resolution, making it difficult to distinguish between closely spaced objects or features. (4) Operator skill: The accuracy and usefulness of microscopy depend on the skill of the operator, who must be appropriately trained to properly prepare and observe samples [17,18,19] (Table 2).
Culture plates are a commonly used tool in P. syringae detection and diagnosis. They are flat dishes typically made of glass or plastic and are used to culture and preserve microbes, such as bacteria, fungi, and yeast (Figure 3) [103,104]. On a culture plate, P. syringae can form colonies that are typically smooth, circular, and slightly raised, with a glossy or opaque appearance. The color of the colonies can vary depending on the type of agar and the specific strain of P. syringae, but they are usually white, yellow, or cream-colored [148]. Culture plates have several advantages for plant pathogen detection and diagnosis: (1) Culture plates are relatively inexpensive and easy to use. (2) They supply a controlled environment for growing and keeping microbial cultures, which helps to ensure accurate and consistent results. (3) Culture plates allow for isolating individual microbial species, which is important for identifying and characterizing specific pathogens. (4) They can be used to identify the presence of a pathogen or co-existing pathogens in a sample by observing the growth of each pathogen on the individual culture plate (Table 2). However, culture plates also have some limitations: (1) Culture plates are not suitable for detecting all types of pathogens, as some pathogens may not grow well in culture. (2) They require a relatively long time to produce results, as microbial cultures need time to grow and develop. (3) Culture plates are prone to contamination, leading to false positives or negatives. (4) They do not supply information about the pathogenicity or virulence of a pathogen, which is essential for understanding its potential impacts on plants (Table 2). Overall, a culture plate is a useful tool for detecting and diagnosing plant pathogens. Still, culture plates should be combined with other techniques to provide a more complete picture of the pathogen and its impact on plants [103,104].
Phage typing is a method used to identify and characterize bacterial phages, which are viruses that infect bacteria. In plant pathogen detection and diagnosis, phage typing can be used to identify and differentiate bacterial pathogens that cause plant diseases (Figure 3) [105,106,107]. Advantages of phage typing include: (1) High specificity: Phage typing can be used to accurately distinguish between different bacterial strains, even those closely related. (2) Rapid results: Phage typing can supply results within a few days, making it a faster alternative than other methods, such as bacterial culture and biochemical testing. (3) Cost-effective: Phage typing is generally less expensive than other methods, such as DNA sequencing (Table 2). Limitations of phage typing include: (1) Limited to certain bacteria: Phage typing can be used only to identify bacteria susceptible to phage infection, which limits its applicability to a narrow range of bacterial pathogens. (2) Requirement for phage collection: Phage typing requires a collection of phages specific to the target bacteria, which may not be available for all bacterial species. (3) Limited resolution: Phage typing may not be able to distinguish between closely related bacterial strains, which can limit its accuracy. (4) Risk of contamination: Phage typing requires the handling of potentially infectious materials, which carries a risk of contamination (Table 2) [105,106,107].

4. Detection and Diagnosis of P. syringae by Molecular and Genetic Methods

Recombinase polymerase amplification (RPA) is a rapid, sensitive, and specific nucleic acid amplification technique that has been used in plant pathogen detection and diagnosis (Figure 3) [108,109,110]. RPA uses recombinase enzymes with accessory proteins to unwind and anneal primers to the target DNA or RNA [149]. Some advantages of RPA are as follows: (1) High sensitivity: RPA can detect low levels of target DNA or RNA, making it suitable for detecting pathogens at the preliminary stages of infection. (2) High specificity: RPA can distinguish between closely related pathogens, making it helpful in identifying specific pathogens in complex mixtures. (3) Rapid turnaround time: RPA can provide results within hours, making it useful for rapid diagnosis in plant disease outbreaks. (4) Easy to use: RPA does not require specialized equipment or complex protocols, making it accessible to many laboratories (Table 2). Some limitations of RPA are: (1) Limited multiplexing: RPA is not well suited for multiplexing (detecting multiple targets in a single assay), as it relies on specific primers and probes to detect target DNA or RNA. (2) Low throughput: RPA is not as efficient as the other amplification techniques, such as PCR, regarding the amount of DNA or RNA that can be amplified in a single reaction. (3) Poor stability: RPA reagents are prone to degradation, requiring frequent preparation and handling. (4) High cost: RPA reagents are more expensive than those used in other amplification techniques, such as PCR (Table 2). Overall, RPA has many potential applications in plant pathogen detection and diagnosis, but its limitations should be considered when choosing the most appropriate amplification technique for a given application [108,109,110].
Polymerase chain reaction (PCR) is a common laboratory technique widely used in plant pathogen detection and diagnosis as well as in many other fields of research and medicine (Figure 3) [104,116,117]. PCR amplifies small segments of genetic material using a polymerase enzyme and specific primers. For instance, PCR can detect P. syringae using primers based on different gene regions, such as the 16S–23S rDNA inter-transcribed spacer region [150]. There are several advantages to using PCR for plant pathogen detection and diagnosis: (1) Sensitivity: PCR can detect tiny amounts of DNA, making it extremely sensitive and able to detect even trace amounts of pathogens. (2) Specificity: PCR can specifically amplify a targeted region of DNA, making it highly specific and able to distinguish between different pathogens and genetic variations. (3) Speed: PCR can amplify DNA very quickly, making it possible to obtain results within a few hours or even minutes. (4) Versatility: PCR can be adapted to various applications and used to amplify DNA from various sources, including plant tissues, environmental samples, and clinical specimens (Table 2). There are also some limitations in using PCR for plant pathogen detection and diagnosis: (1) Cost: PCR requires specialized equipment and reagents, which can be expensive. (2) Complexity: PCR requires careful optimization and execution and can be challenging for those not experienced with the technique. (3) Limited sensitivity: In some cases, PCR may not be able to detect exceptionally low levels of pathogen DNA in a complex matrix. (4) False positives: PCR can produce false positive results if contaminants are present in the sample or if there are errors in the amplification process (Table 2). Overall, PCR is a powerful and widely used tool in plant pathogen detection and diagnosis, but it is important to consider both the advantages and limitations of the technique when designing and interpreting experiments [104,116,117].
Loop-mediated isothermal amplification (LAMP) is a rapid, sensitive, and specific method for amplifying DNA that has been widely used to detect and diagnose plant pathogens (Figure 3) [39,42]. For instance, LAMP can detect P. syringae using primers based on different genes, such as type III effector genes [151] or enolase genes [152]. Some advantages of LAMP for plant pathogen detection and diagnosis: (1) High sensitivity: LAMP can detect extremely low levels of target DNA, making it helpful in detecting pathogens at an early stage of infection. (2) Specificity: LAMP is specific to the target DNA, so it is unlikely to produce false positives or cross-react with other DNA sequences. (3) Rapid turnaround time: LAMP can amplify DNA in as little as 60 min, making it faster than the other amplification methods, such as PCR. (4) Simplicity: LAMP requires minimal equipment and can be performed at a single temperature, making it easy to use in various settings (Table 2). LAMP’s limitations for plant pathogen detection and diagnosis: (1) Limited multiplexing capabilities: LAMP is typically limited to amplifying a single target DNA sequence at a time, so it is not suitable for analyzing multiple targets simultaneously. (2) Poor performance with complex DNA templates: LAMP can be less efficient at amplifying DNA from complex samples, such as those holding multiple pathogens or contaminants. (3) Inability to detect DNA deletions or insertions: LAMP does not detect changes in the DNA sequence, such as deletions or insertions, so it may not be suitable for certain types of genetic analysis. (4) Limited commercial availability: LAMP kits and reagents are not as widely available as those for the other amplification methods, such as PCR (Table 2) [108,111].
Next-generation sequencing (NGS) is a high-throughput DNA/RNA sequencing technology that allows scientists to sequence copious amounts of DNA in a single experiment rapidly and accurately. NGS has revolutionized the field of genomics by providing researchers with the ability to analyze entire genomes, exomes (the part of the genome that codes for proteins), and transcriptomes (the set of all RNA molecules in one cell or a population of cells) at an unprecedented level of detail. NGS is used in various applications, including genomic sequencing, gene expression analysis, metagenomic analysis, genetic variation analysis, gene function analysis, etc. NGS is an important tool in many areas of research, including genetics, genomics, and medicine, and has contributed to numerous scientific discoveries and innovations (Figure 3) [112,113]. The analysis of the plant-associated microbiome compositions and functions through the NGS approach, such as metagenomic analysis, refers to the collective genetic/genomic information of the microorganisms that live in and on a plant, which has the potential to be a powerful tool in the detection and diagnosis of P. syringae or those pathogens co-existing with P. syringae (Figure 3) [128,134]. For instance, NGS can detect P. syringae by sequencing its whole genome or specific regions, such as the 16S rRNA gene or multilocus sequence typing (MLST) loci. There are several advantages of using next-generation sequencing (NGS) in plant pathogen detection and diagnosis: (1) High throughput: NGS allows researchers to analyze large amounts of DNA data in a single experiment, which is useful for detecting and diagnosing plant pathogens that affect many plants, such as P. syringae and the other pathogens co-existing with P. syringae. (2) High accuracy: NGS generates high-quality, reliable, accurate data, which is essential for accurately detecting and diagnosing plant pathogens. (3) Multiplexing: NGS allows researchers to analyze multiple samples simultaneously, useful for studying plant pathogens and diseases in different environments or at various stages of development (Table 2). There are also some limitations to using NGS in plant pathogen detection and diagnosis: (1) Cost: NGS can be expensive, especially when substantial amounts of data or multiple samples are analyzed. (2) Complexity: NGS requires specialized equipment and expertise, which can be a barrier to using this technology in some research settings. (3) Data analysis: NGS generates enormous amounts of data that can be difficult to analyze and interpret, especially for researchers with limited bioinformatics experience. (4) Sensitivity: NGS may not be sensitive enough to detect low levels of pathogen-associated DNA, which can limit its usefulness in some cases (Table 2). Overall, NGS is a powerful tool for detecting and diagnosing bacterial pathogens, such as P. syringae, but it is essential to take both the advantages and limitations into consideration when designing and implementing studies [112,113].
Fluorescence in Situ Hybridization (FISH) is a molecular biology technique that uses fluorescent probes to detect and locate specific DNA or RNA sequences in cells or tissues (Figure 3). FISH can detect DNA and RNA by hybridizing complementary probes that are labeled with different fluorophores and visualize the location and distribution of target sequences by using a fluorescence microscope [153]. FISH has several advantages for detecting and diagnosing P. syringae [114,115]: (1) High sensitivity: FISH can detect single copies of DNA or RNA sequences in cells, making it more sensitive than the other techniques such as microscopy. (2) High specificity: FISH can detect specific DNA or RNA sequences with high accuracy, making it more specific than techniques such as PCR, that amplify all DNA or RNA sequences in a sample. (3) Rapid: FISH can be completed relatively quickly compared to the other techniques, such as DNA sequencing. (4) Easy to visualize: FISH uses fluorescent probes, which allow researchers to easily visualize the presence of specific DNA or RNA sequences under a fluorescence microscope (Table 2). Limitations: (1) Expensive: FISH requires specialized equipment and reagents, making it more expensive than the other techniques. (2) Time-consuming: FISH requires careful preparation of samples, which can be time-consuming. (3) Requires trained personnel: FISH requires specialized training to perform and interpret, which can be a limitation in some settings. (4) Limited to specific sequences: FISH can only detect specific sequences of DNA or RNA designed as probes, so it is limited to the available sequences (Table 2). Overall, FISH is a powerful technique for plant pathogen detection and diagnosis, but it has certain limitations that should be considered when selecting the appropriate method for a given study [114,115].

5. Detection and Diagnosis of P. syringae with Serological Methods

Flow cytometry is a technique that uses lasers and specialized detectors to measure the physical and chemical characteristics of cells or particles suspended in a fluid. Flow cytometry can detect cells or particles by labeling them with fluorescent markers that bind to specific molecules, such as DNA, RNA, proteins, or antibodies. Flow cytometry is often used in plant pathogen detection and diagnosis as it allows researchers to rapidly and accurately quantify and analyze large numbers of cells or particles (Figure 3) [118,119,120]. For instance, flow cytometry can detect P. syringae by staining them with fluorescent antibodies that recognize specific antigens on their surface [154]. There are several advantages to using flow cytometry in P. syringae detection and diagnosis: (1) High throughput: Flow cytometry can analyze thousands of cells or particles per second, making it a highly efficient and rapid method for analyzing large samples. (2) Multiple parameters: Flow cytometry can simultaneously measure multiple physical and chemical characteristics of cells or particles, allowing researchers to obtain a comprehensive and detailed sample analysis. (3) Sensitivity: Flow cytometry is highly sensitive and can detect small changes in cell or particle characteristics, making it useful for detecting pathogens at the early stages of diseases or identifying subtle changes in cell function. (4) Accuracy: Flow cytometry is an accurate method with low error rates and high reproducibility for analyzing cells or particles (Table 2). There are also some limitations to using flow cytometry in plant pathogen detection and diagnosis: (1) Complexity: Flow cytometry requires specialized equipment and trained personnel to run it, making it a more complex and expensive method than the other techniques. (2) Sample preparation: Flow cytometry requires samples to be suspended in a fluid, which can be a time-consuming and labor-intensive process for plant samples. (3) Limited cell types: Flow cytometry is the most effective approach for analyzing cells or particles suspended in a fluid and may not be suitable for analyzing solid tissue samples or cells with thick walls. (4) Interference: Flow cytometry can be influenced by factors such as particles or contaminants in the samples, which can affect the accuracy of the results (Table 2) [118,119,120].
Enzyme-linked immunosorbent assays (ELISAs) are a type of immunoassay widely used in plant pathogen detection and diagnosis (Figure 3) [121,122,123]. ELISAs can detect molecules by binding them to a solid surface, such as a microplate well, and adding an enzyme-labeled antibody that recognizes the target molecule [155]. For instance, ELISAs can detect P. syringae using antibodies that recognize specific antigens on the surface, such as the lipopolysaccharides or flagellins [156]. Some advantages of ELISAs for plant pathogen detection and diagnosis are as follows: (1) Sensitivity: ELISAs are highly sensitive and can detect very low levels of specific antibodies or antigens in a sample. This makes them useful for detecting plant pathogens in the preliminary stages when the levels of specific markers may be low. (2) Specificity: ELISAs are specific for the antibodies or antigens they are designed to detect, allowing for the accurate identification of specific plant pathogens. (3) Speed: ELISAs can be completed relatively quickly, often within a few hours, making them a convenient and efficient tool for plant pathogen diagnosis. (4) Versatility: ELISAs can be modified to detect a wide range of plant pathogens, making them versatile for plant pathogen and disease diagnosis (Table 2). Some limitations of ELISAs for plant pathogen detection and diagnosis: (1) Complexity: ELISAs require a series of steps and specialized equipment, making them more complex and time-consuming compared to other diagnostic techniques. (2) Cost: ELISAs need specialized reagents and equipment, which can make them more expensive than the other diagnostic techniques. (3) Limited scope: ELISAs are limited to detecting specific antibodies or antigens, which may not be present in all plant pathogens. This can limit their usefulness for the diagnosis of some plant pathogens. (4) False positives: ELISAs can sometimes produce false positive results, which may lead to incorrect diagnoses. This can be a particular concern when ELISAs are used to diagnose rare or unusual plant pathogens (Table 2) [121,122,123].
Immunofluorescence is a technique that uses antibodies labeled with fluorescent dyes to detect and visualize specific proteins or other molecules in a sample. Immunofluorescence can detect antigens using a microscope that excites the fluorescent molecules and captures their emitted light [157]. In plant pathogen detection and diagnosis, immunofluorescence can be used to identify the presence of specific pathogen-associated molecular patterns (PAMPs) or effector proteins, which are secreted by pathogens and contribute to disease development (Figure 3) [102,124,125]. Some advantages of immunofluorescence for plant pathogen detection and diagnosis are the following: (1) High sensitivity: Immunofluorescence is highly sensitive and can detect low levels of specific molecules in a sample. (2) High specificity: Immunofluorescence is specific to the target molecule and can differentiate between closely related molecules. (3) Multiplexing: Immunofluorescence can detect multiple target molecules in the same sample, allowing for the analysis of multiple aspects of the disease process at once. (4) Ease of use: Immunofluorescence is a relatively simple and straightforward technique that can be performed in most research laboratories (Table 2). However, there are also some limitations to using immunofluorescence for plant pathogen detection and diagnosis: (1) Requirement for specific antibodies: Immunofluorescence needs specific antibodies that recognize the target molecule, which can be challenging to obtain or produce. (2) Limited to surface-exposed molecules: Immunofluorescence can only detect molecules that are accessible on the surface of cells or tissues and may not be able to detect molecules that are hidden inside cells or tissues. (3) Interference from the other molecules: Immunofluorescence signals can be masked or confused by the other molecules in the sample that cross-react with the antibodies. (4) Need for specialized equipment: Immunofluorescence requires the use of specialized equipment, such as fluorescence microscopes, which can be expensive and may not be available in all laboratories (Table 2) [102,124,125].
ImmunoStrip is a rapid diagnostic test that uses lateral flow technology to detect specific plant pathogens in a sample. ImmunoStrip can detect antigens using a sample pad that absorbs the liquid sample and transfers it to a test zone where the antigen binds to a capture antibody coated on a membrane [158]. ImmunoStrip tests are designed to be simple, fast, and easy to use, and they can provide results within a few hours (Figure 3) [122,126]. One of the main advantages of ImmunoStrip is that it is a non-destructive method of testing, which means that it does not require the significant destruction of plant tissues. This makes ImmunoStrip an attractive option for P. syringae detection and diagnosis, particularly in cases where plant tissue is limited or valuable (Table 2). However, there are also some limitations to ImmunoStrip. One limitation is that ImmunoStrip is a qualitative test, which means it can only detect the presence or absence of a specific pathogen rather than quantifying the amount of pathogen present in the sample. Another limitation is that ImmunoStrip may only be suitable for detecting some types of plant pathogens as it depends on the availability of specific antibodies that can bind to the pathogen of interest. In addition, ImmunoStrip tests can be affected by the presence of other substances in the sample, such as contaminants or plant compounds, which may interfere with the test results. Finally, ImmunoStrip tests may not be as sensitive as some other diagnostic methods, such as PCR (polymerase chain reaction), which can detect trace amounts of the pathogen in a sample (Table 2) [122,126].

6. Detection and Diagnosis of P. syringae with Biomarker-Based Methods

A biomarker is a measurable indicator of a biological state or condition that helps distinguish a diseased plant from a healthy one [159]. Biomarkers can be used to develop accurate and sensitive methods in the detection and diagnosis of plant pathogens [160]. For instance, biomarker-based methods can detect P. syringae by using biomarkers that are specific to the infection or metabolism, such as lipopolysaccharides (LPS), flagellins (FLG), quorum sensing signals (QSS), coronatine (COR), or salicylic acid (SA) [161]. Plant metabolite profiling (metabolomics) is a technique that involves the analysis of the metabolites present in plant tissues, which involves using nuclear magnetic resonance (NMR) or mass-spectrometry (MS) coupled with liquid chromatography (LC) or gas chromatography (GC) [162,163,164]. This technique uses the metabolites as the biomarkers to detect and diagnose plant pathogens, including P. syringae (or those pathogens co-existing with P. syringae), by identifying changes in the levels of specific metabolites that are associated with disease development (Figure 3) [127,128,129]. There are several advantages to using plant metabolite profiling in plant pathogen detection and diagnosis: (1) High sensitivity: Plant metabolite profiling can detect slight changes in the levels of specific metabolites, which may be indicative of early stages of disease development. (2) Multiplexing capability: Plant metabolite profiling can simultaneously measure the levels of multiple metabolites, providing a comprehensive view of the plant’s metabolic status. (3) High-throughput: Plant metabolite profiling can be performed on large numbers of samples in a brief period, making it a high-throughput technique (Table 2). However, there are several limitations to using plant metabolite profiling in plant pathogen detection and diagnosis: (1) Complexity: Plant metabolite profiles can be complex with hundreds or thousands of different metabolites present. This complexity can make it difficult to interpret the results of plant metabolite profiling studies. (2) False positives: Plant metabolite profiling can generate false positive results, as changes in the levels of certain metabolites may not always be directly related to disease development. (3) Limited specificity: Plant metabolite profiling may not be specific for a particular pathogen, as changes in metabolite levels may be caused by other factors such as environmental conditions or genetic variations. (4) Expensive: Plant metabolite profiling can be expensive due to the specialized equipment and expertise required (Table 2). Overall, plant metabolite profiling is a powerful tool for plant pathogen detection and diagnosis, but its effectiveness is limited by the complexity of plant metabolism and the potential for false positive results [127,128,129].
In addition to plant metabolite profiling, we can also carry out plant pathogen metabolite profiling. There are several advantages of using pathogen metabolite profiling in P. syringae detection and diagnosis (Figure 3) [130,131,132,133]: (1) Specificity: Metabolite profiling is specific to the pathogen of interest, which makes it a highly reliable method for detecting and diagnosing plant pathogens. (2) Early detection: Metabolite profiling can detect plant pathogens at an early stage, which is useful for implementing timely control measures. (3) Multiplexing: Metabolite profiling allows researchers to analyze multiple samples simultaneously, which is useful for studying plant pathogens in different environments or at various stages of development (Table 2). There are also some limitations to using pathogen metabolite profiling in plant pathogen detection and diagnosis: (1) Cost: Metabolite profiling can be expensive, especially when analyzing large amounts of data or multiple samples. (2) Complexity: Metabolite profiling requires specialized equipment and expertise, which can be a barrier to using this technology in some research settings. (3) Data analysis: Metabolite profiling generates substantial amounts of data that can be difficult to analyze and interpret, especially for researchers with limited bioinformatics experience. (4) Sensitivity: Metabolite profiling may not be sensitive enough to detect low levels of disease-associated metabolites, limiting its usefulness in some cases (Table 2). Overall, pathogen metabolite profiling is a powerful tool for detecting and diagnosing plant pathogens, but it is important to consider both the advantages and limitations of this technology when designing and implementing studies [130,131,132,133].
We can also carry out the plant-associated microbiome metabolite analysis. Some of the potential advantages of microbiome metabolite analysis in this context: (1) Early detection: Changes in the microbiome can occur before visible symptoms of the disease appear, allowing for the detection of pathogens in their early stages. (2) Broad coverage: Microbiome analysis can provide information about a wide range of microorganisms that may be present in or on a plant, including bacteria, fungi, and viruses. (3) Potential for early warning: Changes in the microbiome can serve as an early warning of impending pathogens and diseases, allowing for the implementation of preventive measures (Table 2). However, there are also some limitations to microbiome analysis in plant pathogen detection and diagnosis: (1) Complexity: The microbiome is complex and dynamic and can be influenced by various factors such as the environment, host genetics, and other microorganisms. (2) Limited understanding: Our understanding of the roles of individual microorganisms in the microbiome is still limited, and more research is needed to fully understand the relationships between different microorganisms and the hosts. (3) Technical challenges: Analyzing the microbiome requires specialized equipment and expertise, and the data generated can be complex and challenging to interpret. (4) Limited predictive power: While changes in the microbiome can be correlated with plant pathogens and diseases, they may not necessarily be causally linked. Further research is needed to understand the specific mechanisms by which the microbiome and related metabolites may influence plant health (Table 2) [128,134].

7. Detection and Diagnosis of P. syringae with Vision-Based Methods

Hyperspectral imaging is a technique that involves the acquisition and analysis of spectral data from a scene or object using a spectrometer or the other imaging instrument (Figure 3) [135,136,137,138]. In plant pathogen detection and diagnosis, hyperspectral imaging has several advantages, including the following: (1) Rapid: Hyperspectral imaging can provide rapid results, allowing researchers to quickly identify diseased plants and take appropriate actions. (2) High-throughput: Hyperspectral imaging allows researchers to analyze large numbers of plants quickly and efficiently, making it a high-throughput method for pathogen detection and diagnosis. (3) High spatial and spectral resolution: Hyperspectral imaging provides high spatial and spectral resolution, allowing researchers to analyze small features and subtle changes in plant tissues (Table 2). Despite these advantages, hyperspectral imaging has several limitations in plant pathogen detection and diagnosis: (1) Complex data analysis: The large amount of data generated by hyperspectral imaging can be difficult to analyze, requiring specialized software and expertise. (2) Cost: Hyperspectral imaging equipment can be expensive, limiting its use in some research settings. (3) Limited penetration depth: Hyperspectral imaging is limited in its ability to penetrate deep into plant tissues, making it less effective for detecting pathogens that affect internal plant organs. (4) Environmental factors: Hyperspectral imaging can be affected by environmental factors such as light intensity and atmospheric conditions, which can affect the accuracy of the results (Table 2). Overall, hyperspectral imaging is a powerful tool for plant pathogen detection and diagnosis, but it is important to consider its advantages and limitations in the context of specific research goals and objectives [135,136,137,138].
Spectroscopic imaging is a technique that involves the use of spectroscopy to obtain images of samples based on their spectral properties. Spectroscopic imaging has several advantages and limitations in the detection and diagnosis of P. syringae (Figure 3) [139,140]. Advantages of spectroscopic imaging in plant pathogen detection and diagnosis: (1) High spatial resolution: Spectroscopic imaging can provide high spatial resolution images, allowing for the detection of small changes in plant tissues that may not be visible using other techniques. (2) High sensitivity: Spectroscopic imaging is highly sensitive and can detect small changes in the spectral properties of plant tissues that may be indicative of disease. (3) Rapid analysis: Spectroscopic imaging can provide rapid analysis of plant samples, making it useful for large-scale pathogen and disease screening and diagnosis (Table 2). Limitations of spectroscopic imaging in plant pathogen detection and diagnosis: (1) Limited depth penetration: Spectroscopic imaging is limited by the depth of penetration of the spectroscopic signal, which can be affected by the size and density of the plant tissues. (2) Complex sample preparation: Spectroscopic imaging may require complex sample preparation techniques, such as slicing or sectioning the sample, which can be time-consuming and labor-intensive. (3) Need for calibration: Spectroscopic imaging relies on calibrated standards, which may be difficult to obtain for certain plant pathogens. (4) Interference from external factors: Spectroscopic imaging can be affected by external factors, such as temperature and humidity, which can impact the accuracy of the results (Table 2). Overall, spectroscopic imaging is a valuable tool for plant pathogen detection and diagnosis, but it has certain limitations that should be considered when using it for these purposes [139,140].

8. Detection and Diagnosis of P. syringae by AI (Artificial Intelligence) Methods

Machine learning is a type of artificial intelligence that uses image processing, algorithms, and statistical models to allow systems to learn from data without being explicitly programmed (Figure 3) [1,2,165]. There are several advantages to using machine learning techniques in plant pathogen detection and diagnosis. One advantage is that machine learning algorithms can analyze large amounts of data quickly and accurately, making it possible to analyze large datasets and identify patterns and trends that might not be easily discernible by humans. Additionally, machine-learning algorithms can be trained to recognize specific features or patterns in data, allowing them to accurately classify and predict outcomes in complex systems (Table 2). A study by Wang et al. (2022) used machine learning to analyze the genomic data from 101 P. syringae isolates and generated a predictive model for virulence affecting beans based on the information from 13 genes [144]. There are also some limitations to using machine learning in plant pathogen detection and diagnosis. One limitation is that machine-learning algorithms can only work with the data provided to them. Hence, the accuracy of their predictions is highly dependent on the quality and relevance of the data. Additionally, machine-learning algorithms may not be able to adapt to new or changing conditions, and they may require significant amounts of data and computing power to work effectively. Finally, there may be ethical concerns about using machine-learning algorithms to make decisions about plant health, as these decisions may significantly affect agriculture and food production (Table 2) [141,142,143,144,145]. Overall, machine-learning technology has the potential to significantly improve our ability to detect, diagnose, and control plant pathogens and can be a valuable tool for improving global food security [1,2,165]. Machine learning is thus far mainly used through image processing to detect and diagnose plant diseases. However, it has the potential to predict disease emergencies and development if previous years of disease development and epidemiology data are used for training the machine. A study by Li et al. (2022) used hyperspectral imaging and machine learning to classify healthy and diseased kiwi leaves infected by Psa, a strain of P. syringae that causes kiwifruit canker [166].

9. Koch’s Postulates as Golden Rules in the Detection and Diagnosis of P. syringae

Koch’s postulates are a set of criteria used to prove the causal relationship between a specific microorganism and a particular disease. These postulates were developed by Robert Koch, a German bacteriologist, in the late 19th century, and they are still widely used in the detection and diagnosis of bacterial plant pathogens [167].The four postulates are as follows: (1) The microorganism must be present in every case of the disease. (2) The microorganism must be isolated from the infected plant and grown in pure culture. (3) The disease must be reproduced when the isolated microorganism is introduced into a healthy plant. (4) The same microorganism must be reisolated from the experimentally infected plant (Figure 4).
To apply Koch’s postulates in detecting and diagnosing a bacterial plant pathogen, a researcher would first find the presence of the pathogenic microorganism in a plant and then isolate the suspected causative microorganism from the infected plant tissue. The microorganism would then be grown in pure culture and used to infect a healthy plant, which should result in the development of the same disease symptom. Finally, the same microorganism should be isolated from the infected plant and confirmed to be consistent with the original isolate. If all four postulates are satisfied, it can be concluded that the microorganism is the causal agent of the disease [169].
While Koch’s postulates are widely used and considered a reliable method for establishing the causal relationship between a microorganism and a plant disease, they are not always applicable in all cases. For example, some microorganisms may be difficult to grow in pure culture, or the disease may not be reproducible in all cases. In these situations, alternative methods should be used to prove a causal relationship [170].

10. Conclusions

P. syringae is a bacterial plant pathogen that causes a wide range of diseases in very diverse plant species. These diseases, which can cause severe economic consequences, include bacterial speck, bacterial spot, bacterial canker, etc. Accurate and timely detection and diagnosis of P. syringae infections are critical for managing and controlling these diseases. Several methods can be used for the detection and diagnosis of P. syringae infections in plants. These methods can be broadly divided into traditional techniques and modern techniques. Traditional techniques for detecting and diagnosing P. syringae infections include culture isolation and microscopy approaches. Culture isolation involves the cultivation of P. syringae from plant tissues on specialized media. Microscopy involves the examination of plant tissues under a microscope to identify the presence of P. syringae cells. Modern techniques for the detection and diagnosis of P. syringae infections include PCR (polymerase chain reaction), ELISA (enzyme-linked immunosorbent assay), next-generation sequencing, etc. PCR is a molecular technique that can amplify and detect specific DNA sequences, including those of P. syringae. Serology involves the use of antibodies to detect the presence of P. syringae antigens in plant tissues. ELISA is a biochemical technique that can detect the presence of specific proteins, including P. syringae antigens, in plant tissues. Each method of detection and diagnosis has its advantages and disadvantages. Traditional techniques, such as culture isolation and microscopy are relatively simple and inexpensive. However, they can be time-consuming and may not be sensitive enough to detect low concentrations of P. syringae. Modern techniques, such as PCR and ELISA, have the advantage of being more sensitive and specific than traditional techniques. They can detect low concentrations of P. syringae and provide rapid results. However, they can be more expensive and require specialized equipment and training. In conclusion, different methods are available for the detection and diagnosis of P. syringae infections in plants. The choice of methods depends on the specific needs and resources of the laboratory or field setting. It is important to carefully consider the advantages and disadvantages of each method to select the most appropriate method for a given situation. Methods of detecting and diagnosing P. syringae will continue to evolve to meet the needs and challenges of the agricultural and greater scientific communities.

11. Future Directions on Preventive Management of P. syringae

The conventional plant disease triangle is a model used to understand the complex interactions between a plant, a pathogen, and the environment that can lead to the development of a plant disease [168] (Figure 5A). The three components of the plant disease triangle are: (1) The host plant: This refers to the plant susceptible to the disease. The host plant has specific characteristics that make it more or less prone to the infection by a particular pathogen. (2) The pathogen: This refers to the microorganism that causes the disease. Pathogens can be bacteria, viruses, fungi, or other types of microorganisms. (3) The environment: This refers to the physical, chemical, and biological factors that promote the pathogenicity of pathogens. These factors include temperature, humidity, light, and nutrient availability [168] (Figure 5A).
The plant disease triangle model helps to illustrate that the development of plant disease results from the interactions between these three components. By understanding the relationships between these components, scientists and farmers can develop effective strategies for preventing and controlling plant diseases. However, the conventional plant disease triangle does not account for the potential contribution of beneficial microbes [168] (Figure 5A). Beneficial microbes are microorganisms that have a positive impact on the health and fitness of plants. Beneficial microbes can be found in the soil, on the surface of plants, and within plant tissues, including bacteria, fungi, viruses, and other microorganisms [174,175]. These microbes can promote plant growth and enhance plant defense by providing essential nutrients, protecting them from harmful pathogens, and increasing their resistance to abiotic stresses [9,176]. More and more studies have proved the significance and potential of beneficial microbes in promoting plant fitness [9,174,177,178,179]. Therefore, we propose a modified model called the plant fitness tetrahedron by integrating the beneficial microbes (Figure 5B). The presence of beneficial microbes is essential for the health and fitness of plants, and their importance has been increasingly recognized in agriculture and plant science [9,174,177,178,179]. There are several well-known ways in which beneficial microbes can enhance plant fitness. For example, some beneficial microbes can fix nitrogen, making it available to plants in a form they can use [180,181]. The other beneficial microbes can help plants absorb and use other essential nutrients, such as phosphorus and sulfur [182,183,184]. In addition, beneficial microbes can help plants defend themselves against harmful pathogens by competing for resources and secreting antimicrobial compounds [9,174]. Finally, beneficial microbes can help plants cope with environmental stressors, such as drought and extreme temperatures, by modulating their metabolism and signaling pathways; and protecting against plant’s oxidative damage [185,186,187,188].
Since the start of agriculture, farmers have continuously been improving their practices for combating various plagues suffered by crops [16,173,189,190]. Our growing understanding of the interactions between pathogens and hosts, which began with discovering the causes of plant diseases in the early 19th century, has allowed us to develop many methods for controlling specific plant diseases. Based on our more profound understanding of plant disease control, we have developed a set of general principles that can assist us in addressing new problems with any crop in certain environmental conditions [16,172,173,191]. Initially outlined by H. H. Whetzel and modified by various authors, these principles have been widely accepted and taught to plant pathology students, researchers, and other stakeholders globally [192]. In this review paper, we termed the core principles as the plant disease management hexagon (Figure 5C). The avoidance principle is to prevent plant diseases by selecting a time or location where the environment is not favorable for the pathogen infection or where there is no pathogenic inoculum. The exclusion principle is to prevent the introduction of the pathogenic inoculum. The eradication principle is to eliminate, inactivate, or destroy the pathogenic inoculum. The protection principle is to prevent infection through toxicants or other barriers to pathogenic infection. The resistance principle is to harness plant cultivars with inborn resistance or tolerance to pathogenic infections. The therapy principle is to cure plants already under the pathogenic infection [191]. The plant disease management hexagon is based on a comprehensive understanding of the interactions between the pathogen, the beneficial microbes, the host plant, and the environment. The effective management of plant diseases requires a combination of those principles tailored to the specific needs of each plant and the environment in which it is grown (Figure 5).
To effectively control P. syringae infections, in addition to the effective pathogen detections approaches, it is crucial to use disease-free seeds, practice crop rotation, and apply chemical or biological control agents as needed [34]. The plant fitness tetrahedron and the plant disease management hexagon (Figure 5) should also be considered to sustainably obtain effective controls.

Author Contributions

P.Y. and Y.X. conceived and wrote the manuscript. L.Z. and Y.G.G. helped with the drafting and editing of the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

We acknowledge the funding supports from OARDC Internal Grants Program 2021009, USDA Hatch Project OHO01511, and USDA-NIFA AWD-112532.

Data Availability Statement

Not applicable.

Acknowledgments

We thank Xia’s lab members for their constructive discussions and insightful suggestions. Unfortunately, we could not include citations for all relevant articles in this review due to limited space. We apologize for any oversight.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Mohanty, S.P.; Hughes, D.P.; Salathé, M. Using Deep Learning for Image-Based Plant Disease Detection. Front. Plant Sci. 2016, 7, 1419. [Google Scholar] [CrossRef]
  2. Liu, J.; Wang, X. Plant Diseases and Pests Detection Based on Deep Learning: A Review. Plant Methods 2021, 17, 22. [Google Scholar] [CrossRef] [PubMed]
  3. Harakannanavar, S.S.; Rudagi, J.M.; Puranikmath, V.I.; Siddiqua, A.; Pramodhini, R. Plant Leaf Disease Detection Using Computer Vision and Machine Learning Algorithms. Glob. Transit. Proc. 2022, 3, 305–310. [Google Scholar] [CrossRef]
  4. Plant Disease Diagnosis. Available online: https://www.apsnet.org/edcenter/disimpactmngmnt/casestudies/Pages/PlantDiseaseDiagnosis.aspx (accessed on 3 February 2023).
  5. Meena, A.K.; Godara, S.L.; Meena, P.N. Detection and Diagnosis of Plant Diseases; Scientific Publishers: Jodhpur, India, 2020; ISBN 978-93-89184-42-6. [Google Scholar]
  6. Gullino, M.L.; Bonants, P.J.M. (Eds.) Detection and Diagnostics of Plant Pathogens; Springer: Dordrecht, The Netherlands, 2014; ISBN 978-94-017-9019-2. [Google Scholar]
  7. FAO. The State of Food and Agriculture 2021: Making Agrifood Systems More Resilient to Shocks and Stresses; The State of Food and Agriculture (SOFA); FAO: Rome, Italy, 2021; ISBN 978-92-5-134329-6. [Google Scholar]
  8. Yang, P.; Chen, Y.; Wu, H.; Fang, W.; Liang, Q.; Zheng, Y.; Olsson, S.; Zhang, D.; Zhou, J.; Wang, Z.; et al. The 5-Oxoprolinase Is Required for Conidiation, Sexual Reproduction, Virulence and Deoxynivalenol Production of Fusarium graminearum. Curr. Genet. 2018, 64, 285–301. [Google Scholar] [CrossRef]
  9. Yang, P.; Zhao, Z.; Fan, J.; Liang, Y.; Bernier, M.C.; Gao, Y.; Zhao, L.; Opiyo, S.O.; Xia, Y. Bacillus proteolyticus OSUB18 Triggers Induced Systemic Resistance against Bacterial and Fungal Pathogens in Arabidopsis. Front. Plant Sci. 2023, 14, 1078100. [Google Scholar] [CrossRef]
  10. Zhao, L.; Yang, P.; Li, W.; Zhao, Z.; Xia, Y. First Report of Trichoderma crassum Causing Leaf Spot on Tomato (Solanum lycopersicum Cv. M82) in Ohio. Plant Dis. 2023, 107, 582. [Google Scholar] [CrossRef] [PubMed]
  11. Cavalazzi, B.; Lemelle, L.; Simionovici, A.; Cady, S.L.; Russell, M.J.; Bailo, E.; Canteri, R.; Enrico, E.; Manceau, A.; Maris, A.; et al. Cellular Remains in a ~3.42-Billion-Year-Old Subseafloor Hydrothermal Environment. Sci. Adv. 2021, 7, eabf3963. [Google Scholar] [CrossRef]
  12. Ehrlich, P. Address in Pathology, on Chemiotherapy. Br. Med. J. 1913, 2, 353–359. [Google Scholar] [CrossRef] [PubMed]
  13. Zhao, Z.; Yang, X.; Lü, S.; Fan, J.; Opiyo, S.; Yang, P.; Mangold, J.; Mackey, D.; Xia, Y. Deciphering the Novel Role of AtMIN7 in Cuticle Formation and Defense against the Bacterial Pathogen Infection. Int. J. Mol. Sci. 2020, 21, 5547. [Google Scholar] [CrossRef]
  14. Zhao, Z.; Fan, J.; Gao, Y.G.; Wang, Z.; Yang, P.; Liang, Y.; Opiyo, S.; Xia, Y. Arabidopsis Plasma Membrane ATPase AHA5 Is Negatively Involved in PAMP-Triggered Immunity. Int. J. Mol. Sci. 2022, 23, 3857. [Google Scholar] [CrossRef] [PubMed]
  15. Zhao, Z.; Fan, J.; Yang, P.; Wang, Z.; Stephen, O.; Mackey, D.; Ye, X. Involvement of Arabidopsis Acyl Carrier Protein 1 in PAMP-Triggered Immunity. Mol. Plant-Microbe Interact. 2022, 35, 681–693. [Google Scholar] [CrossRef]
  16. Plant Disease Management Strategies. Available online: https://www.apsnet.org/edcenter/disimpactmngmnt/topc/EpidemiologyTemporal/Pages/ManagementStrategies.aspx (accessed on 3 February 2023).
  17. Buttimer, C.; McAuliffe, O.; Ross, R.P.; Hill, C.; O’Mahony, J.; Coffey, A. Bacteriophages and Bacterial Plant Diseases. Front. Microbiol. 2017, 8, 34. [Google Scholar] [CrossRef]
  18. Rapid and Accurate Disease Diagnosis as a Key Component to Successful Plant Disease Management. Available online: https://extension.unh.edu/blog/2022/02/rapid-accurate-disease-diagnosis-key-component-successful-plant-disease-management (accessed on 3 February 2023).
  19. Rizzo, D.M.; Lichtveld, M.; Mazet, J.A.K.; Togami, E.; Miller, S.A. Plant Health and Its Effects on Food Safety and Security in a One Health Framework: Four Case Studies. One Health Outlook 2021, 3, 6. [Google Scholar] [CrossRef]
  20. Ristaino, J.B.; Anderson, P.K.; Bebber, D.P.; Brauman, K.A.; Cunniffe, N.J.; Fedoroff, N.V.; Finegold, C.; Garrett, K.A.; Gilligan, C.A.; Jones, C.M.; et al. The Persistent Threat of Emerging Plant Disease Pandemics to Global Food Security. Proc. Natl. Acad. Sci. USA 2021, 118, e2022239118. [Google Scholar] [CrossRef]
  21. Mooney, H.A.; Cleland, E.E. The Evolutionary Impact of Invasive Species. Proc. Natl. Acad. Sci. USA 2001, 98, 5446–5451. [Google Scholar] [CrossRef] [PubMed]
  22. López, M.M.; Llop, P.; Olmos, A.; Marco-Noales, E.; Cambra, M.; Bertolini, E. Are Molecular Tools Solving the Challenges Posed by Detection of Plant Pathogenic Bacteria and Viruses? Curr. Issues Mol. Biol. 2009, 11, 13–46. [Google Scholar] [PubMed]
  23. Khiyami, M.A.; Almoammar, H.; Awad, Y.M.; Alghuthaymi, M.A.; Abd-Elsalam, K.A. Plant Pathogen Nanodiagnostic Techniques: Forthcoming Changes? Biotechnol. Biotechnol. Equip. 2014, 28, 775–785. [Google Scholar] [CrossRef]
  24. Buja, I.; Sabella, E.; Monteduro, A.G.; Chiriacò, M.S.; De Bellis, L.; Luvisi, A.; Maruccio, G. Advances in Plant Disease Detection and Monitoring: From Traditional Assays to In-Field Diagnostics. Sensors 2021, 21, 2129. [Google Scholar] [CrossRef] [PubMed]
  25. Pfeilmeier, S.; Caly, D.L.; Malone, J.G. Bacterial Pathogenesis of Plants: Future Challenges from a Microbial Perspective. Mol. Plant Pathol. 2016, 17, 1298–1313. [Google Scholar] [CrossRef] [PubMed]
  26. Katagiri, F.; Thilmony, R.; He, S.Y. The Arabidopsis thaliana-Pseudomonas syringae Interaction. In Arabidopsis Book; The American Society of Plant Biologists: Rockville, MD, USA, 2002; Volume 2002. [Google Scholar] [CrossRef]
  27. Xin, X.-F.; He, S.Y. Pseudomonas syringae pv. tomato Dc3000: A Model Pathogen for Probing Disease Susceptibility and Hormone Signaling in Plants. Annu. Rev. Phytopathol. 2013, 51, 473–498. [Google Scholar] [CrossRef] [PubMed]
  28. Gomila, M.; Busquets, A.; Mulet, M.; García-Valdés, E.; Lalucat, J. Clarification of Taxonomic Status within the Pseudomonas syringae Species Group Based on a Phylogenomic Analysis. Front. Microbiol. 2017, 8, 2422. [Google Scholar] [CrossRef] [PubMed]
  29. Hirano, S.S.; Upper, C.D. Bacteria in the Leaf Ecosystem with Emphasis On Pseudomonas syringae—A Pathogen, Ice Nucleus, and Epiphyte. Microbiol. Mol. Biol. Rev. 2000, 64, 624–653. [Google Scholar] [CrossRef] [PubMed]
  30. Xin, X.-F.; Kvitko, B.; He, S.Y. Pseudomonas syringae: What It Takes to Be a Pathogen. Nat. Rev. Microbiol. 2018, 16, 316–328. [Google Scholar] [CrossRef] [PubMed]
  31. Gardan, L.; Shafik, H.; Belouin, S.; Broch, R.; Grimont, F.; Grimont, P.A.D.Y. 1999 DNA Relatedness among the Pathovars of Pseudomonas syringae and Description of Pseudomonas tremae sp. nov. and Pseudomonas cannabina sp. nov. (Ex Sutic and Dowson 1959). Int. J. Syst. Evol. Microbiol. 1999, 49, 469–478. [Google Scholar] [CrossRef] [PubMed]
  32. Preston, G.M. Pseudomonas syringae pv. tomato: The Right Pathogen, of the Right Plant, at the Right Time. Mol. Plant Pathol. 2000, 1, 263–275. [Google Scholar] [CrossRef]
  33. Morris, C.E.; Sands, D.C.; Vinatzer, B.A.; Glaux, C.; Guilbaud, C.; Buffière, A.; Yan, S.; Dominguez, H.; Thompson, B.M. The Life History of the Plant Pathogen Pseudomonas syringae Is Linked to the Water Cycle. ISME J. 2008, 2, 321–334. [Google Scholar] [CrossRef] [PubMed]
  34. Jones, J.B.; Zitter, T.A.; Momol, T.M.; Miller, S.A. Compendium of Tomato Diseases and Pests, 2nd ed.; Jeffrey, B.J., Thomas, A.Z., Timur, M.M., Sally, A.M., Eds.; Diseases and Pests Compendium Series; The American Phytopathological Society: Saint Paul, MN, USA, 2016; ISBN 978-0-89054-434-1. [Google Scholar]
  35. Takikawa, Y.; Tsuyumu, S.; Goto, M. Occurrence of Bacterial Leaf Spot of Maple Incited by Pseudomonas Syrinigae pv. aceris in Japan. Jpn. J. Phytopathol. 1991, 57, 724–728. [Google Scholar] [CrossRef]
  36. Takikawa, Y.; Serizawa, S.; Ichikawa, T.; Tsuyumu, S.; Goto, M. Pseudomonas syringae pv. actinidiae pv. nov.: The Causal Bacterium of Canker of Kiwifruit in Japan. Ann. Phytopathol. Soc. Jpn. 1989, 55, 437–444. [Google Scholar] [CrossRef]
  37. Durgapal, J.C.; Singh, B. Taxonomy of Pseudomonads Pathogenic to Horse-Chestnut, Wild Fig and Wild Cherry in India. Indian Phytopathol. 1980, 33, 533–535. [Google Scholar]
  38. Hendson, M.; Hildebrand, D.C.; Schroth, M.N. Relatedness of Pseudomonas syringae pv. tomato, Pseudomonas syringae pv. maculicola and Pseudomonas syringae pv. antirrhini. J. Appl. Bacteriol. 1992, 73, 455–464. [Google Scholar] [CrossRef]
  39. Gardan, L.; Cottin, S.; Bollet, C.; Hunault, G. Phenotypic Heterogeneity of Pseudomonas syringae van Hall. Res. Microbiol. 1991, 142, 995–1003. [Google Scholar] [CrossRef]
  40. Maraite, H.; Weyns, J. Pseudomonas syringae pv. aptata and pv. atrofaciens, Specific Pathovars or Members of pv. Syringae? In Pseudomonas Syringae Pathovars and Related Pathogens; Rudolph, K., Burr, T.J., Mansfield, J.W., Stead, D., Vivian, A., von Kietzell, J., Eds.; Developments in Plant Pathology; Springer: Dordrecht, The Netherlands, 1997; pp. 515–520. ISBN 978-94-011-5472-7. [Google Scholar]
  41. Iacobellis, N.S.; Figliuolo, G.; Janse, J.; Scortichini, M.; Ciuffreda, G. Characterization of Pseudomonas syringae pv. atrofaciens. In Pseudomonas syringae Pathovars and Related Pathogens; Rudolph, K., Burr, T.J., Mansfield, J.W., Stead, D., Vivian, A., von Kietzell, J., Eds.; Developments in Plant Pathology; Springer: Dordrecht, The Netherlands, 1997; pp. 500–504. ISBN 978-94-011-5472-7. [Google Scholar]
  42. Sato, M.; Nishiyama, K.; Shirata, A. Involvement of Plasmid DNA in the Productivity of Coronatine by Pseudomonas syringae pv. atropurpurea. Jpn. J. Phytopathol. 1983, 49, 522–528. [Google Scholar] [CrossRef]
  43. Psallidas, P.G. Pseudomonas syringae pv. avellanae pathovar nov., the Bacterium Causing Canker Disease on Corylus avellana. Plant Pathol. 1993, 42, 358–363. [Google Scholar] [CrossRef]
  44. Ménard, M.; Sutra, L.; Luisetti, J.; Prunier, J.P.; Gardan, L. Pseudomonas syringae pv. avii (pv. nov.), the Causal Agent of Bacterial Canker of Wild Cherries (Prunus avium) in France. Eur. J. Plant Pathol. 2003, 109, 565–576. [Google Scholar] [CrossRef]
  45. Roberts, S.j. A Note on Pseudomonas syringae pv. berberidis Infections of Berberis: Comparative Infection Studies in Attached and Detached Leaves. J. Appl. Bacteriol. 1985, 59, 369–374. [Google Scholar] [CrossRef]
  46. Takahashi, K.; Nishiyama, K.; Sato, M. Pseudomonas syringae pv. broussonetiae pv. nov., the Causal Agent of Bacterial Blight of Paper Mulberry (Broussonetia kazinoki×B. papyrifera). Jpn. J. Phytopathol. 1996, 62, 17–22. [Google Scholar] [CrossRef]
  47. Sato, M.; Watanabe, K.; Yazawa, M.; Takikawa, Y.; Nishiyama, K. Detection of New Ethylene-Producing Bacteria, Pseudomonas syringae pvs. cannabina and sesami, by PCR Amplification of Genes for the Ethylene-Forming Enzyme. Phytopathology 1997, 87, 1192–1196. [Google Scholar] [CrossRef] [PubMed]
  48. Takanashi, K.; Shimizu, K. Pseudomonas syringae pv. castaneae pv. nov., causal agent of bacterial canker of chestnut (Castanea crenata Sieb. et Zucc.). Ann. Phytopathol. Soc. Jpn. 1989, 55, 397–403. [Google Scholar] [CrossRef]
  49. Kamiunten, H.; Nakao, T.; Oshida, S. Pseudomonas syringae pv. cerasicola, pv. nov., the Causal Agent of Bacterial Gall of Cherry Tree. J. Gen. Plant Pathol. 2000, 66, 219–224. [Google Scholar] [CrossRef]
  50. Lavermicocca, P.; Lonigro, S.L.; Evidente, A.; Andolfi, A. Bacteriocin Production by Pseudomonas syringae pv. ciccaronei NCPPB2355. Isolation and Partial Characterization of the Antimicrobial Compound. J. Appl. Microbiol. 1999, 86, 257–265. [Google Scholar] [CrossRef]
  51. Toben, H.-M.; Rudoph, K. Pseudomonas syringae pv. coriandricola, Incitant of Bacterial Umbel Blight and Seed Decay of Coriander (Coriandrum sativum L.) in Germany. J. Phytopathol. 1996, 144, 169–178. [Google Scholar] [CrossRef]
  52. Harper, S.; Zewdie, N.; Brown, I.R.; Mansfield, J.W. Histological, Physiological and Genetical Studies of the Responses of Leaves and Pods of Phaseolus vulgaris to Three Races of Pseudomonas syringae pv. phaseolicola and to Pseudomonas syringae pv. coronafaciens. Physiol. Mol. Plant Pathol. 1987, 31, 153–172. [Google Scholar] [CrossRef]
  53. Scortichini, M.; Rossi, M.P.; Loreti, S.; Bosco, A.; Fiori, M.; Jackson, R.W.; Stead, D.E.; Aspin, A.; Marchesi, U.; Zini, M.; et al. Pseudomonas syringae pv. coryli, the Causal Agent of Bacterial Twig Dieback of Corylus avellana. Phytopathology 2005, 95, 1316–1324. [Google Scholar] [CrossRef] [PubMed]
  54. He, X.-Y.; Goto, M. Bacterial Needle Blight of Chinese Fir (Cunninghamia lanceolate Hook) Caused by Pseudomonas syringae pv. cunninghamiae pv. nov. Jpn. J. Phytopathol. 1995, 61, 38–40. [Google Scholar] [CrossRef]
  55. Oginh, C.; Kubo, Y.; Higuchi, H.; Takikawa, Y. Bacterial Gall Diseases of Himeyuzuriha (Daphniphyllum teijsmanni Z.) Caused by Pseudomonas syringae pv. daphniphylli pv. nov. J. Jpn. For. Soc. 1990, 72, 17–22. [Google Scholar] [CrossRef]
  56. Zdorovenko, E.L.; Zatonsky, G.V.; Kocharova, N.A.; Shashkov, A.S.; Knirel, Y.A.; Ovod, V.V. Structure of the O-Polysaccharide of Pseudomonas syringae pv. delphinii NCPPB 1879T Having Side Chains of 3-Acetamido-3,6-Dideoxy-D-Galactose Residues. Biochemistry 2002, 67, 558–565. [Google Scholar] [CrossRef]
  57. Ogimi, C.; Higuchi, H.; Takikawa, Y. Bacterial Gall Disease of Kakuremino (Dendropanax trifidus Mak.) Caused by Pseudomonas syrigae pv. dendropanacis pv. nov. Ann. Phytopathol. Soc. Jpn. 1988, 54, 296–302. [Google Scholar] [CrossRef]
  58. Young, J.M.; Triggs, C.m. Evaluation of Determinative Tests for Pathovars of Pseudomonas syringae van Hall 1902. J. Appl. Bacteriol. 1994, 77, 195–207. [Google Scholar] [CrossRef] [PubMed]
  59. Kamiunten, H. Loss of a Plasmid in Pseudomonas syringae pv. eriobotryae Is Correlated with Change of Symptoms. Jpn. J. Phytopathol. 1990, 56, 645–650. [Google Scholar] [CrossRef]
  60. Kairu, G.M. Biochemical and Pathogenic Differences between Kenyan and Brazilian Isolates of Pseudomonas syringae pv. garcae. Plant Pathol. 1997, 46, 239–246. [Google Scholar] [CrossRef]
  61. Osman, S.F.; Fett, W.F.; Fishman, M.L. Exopolysaccharides of the Phytopathogen Pseudomonas syringae pv. glycinea. J. Bacteriol. 1986, 166, 66–71. [Google Scholar] [CrossRef]
  62. Arsenijević, M.; Vennete, R.J.; Maširević, S. Pseudomonas syringae pv. helianthi (Kawamura 1934) Dye, Wilkie et Young 1978, a Pathogen of Sunflower (Helianthus annuus L.). J. Phytopathol. 1994, 142, 199–208. [Google Scholar] [CrossRef]
  63. Fukuda, T.; Azegami, K.; Tabei, H. Histological studies on bacterial black node of barley and wheat caused by Pseudomonas syringae pv. japonica. Ann. Phytopathol. Soc. Jpn. 1990, 56, 252–256. [Google Scholar] [CrossRef]
  64. Yedidia, I.; Shoresh, M.; Kerem, Z.; Benhamou, N.; Kapulnik, Y.; Chet, I. Concomitant Induction of Systemic Resistance to Pseudomonas syringae pv. lachrymans in Cucumber by Trichoderma asperellum (T-203) and Accumulation of Phytoalexins. Appl. Environ. Microbiol. 2003, 69, 7343–7353. [Google Scholar] [CrossRef]
  65. Cuppels, D.A.; Ainsworth, T. Molecular and Physiological Characterization of Pseudomonas syringae pv. tomato and Pseudomonas syringae pv. maculicola Strains That Produce the Phytotoxin Coronatine. Appl. Environ. Microbiol. 1995, 61, 3530–3536. [Google Scholar] [CrossRef] [PubMed]
  66. Kiba, A.; Imanaka, Y.; Nakano, M.; Galis, I.; Hojo, Y.; Shinya, T.; Ohnishi, K.; Hikichi, Y. Silencing of Nicotiana benthamiana SEC14 Phospholipid Transfer Protein Reduced Jasmonic Acid Dependent Defense against Pseudomonas syringae. Plant Biotechnol. 2016, 33, 111–115. [Google Scholar] [CrossRef]
  67. Ratanapo, S.; Ngamjunyaporn, W.; Chulavatnatol, M. Interaction of a Mulberry Leaf Lectin with a Phytopathogenic Bacterium, P. syringae Pv mori. Plant Sci. 2001, 160, 739–744. [Google Scholar] [CrossRef]
  68. Sundin, G.W.; Jones, A.L.; Olson, B.D. Overwintering and Population Dynamics of Pseudomonas syringae pv. syringae and P.s. pv. morsprunorum on Sweet and Sour Cherry Trees. Can. J. Plant Pathol. 1988, 10, 281–288. [Google Scholar] [CrossRef]
  69. Ogimi, C.; Higuchi, H. Bacterial gall of yamamomo (Myrica rubra S. et Z.) caused by Pseudomonas syringae pv. myricae pv. nov. Ann. Phytopathol. Soc. Jpn. 1981, 47, 443–448. [Google Scholar] [CrossRef]
  70. Kuwata, H. Pseudomonas syringae pv. oryzae pv. nov., Causal Agent of Bacterial Halo Blight of Rice. Jpn. J. Phytopathol. 1985, 51, 212–218. [Google Scholar] [CrossRef]
  71. Kerkoud, M.; Manceau, C.; Paulin, J.P. Rapid Diagnosis of Pseudomonas syringae pv. papulans, the Causal Agent of Blister Spot of Apple, by Polymerase Chain Reaction Using Specifically Designed HrpL Gene Primers. Phytopathology 2002, 92, 1077–1083. [Google Scholar] [CrossRef] [PubMed]
  72. Jose, P.A.; Krishnamoorthy, R.; Kwon, S.-W.; Janahiraman, V.; Senthilkumar, M.; Gopal, N.O.; Kumutha, K.; Anandham, R. Interference in Quorum Sensing and Virulence of the Phytopathogen Pseudomonas syringae pv. passiflorae by Bacillus and Variovorax Species. BioControl 2019, 64, 423–433. [Google Scholar] [CrossRef]
  73. Young, J.M. Pseudomonas syringae pv. persicae from Nectarine, Peach, and Japanese Plum in New Zealand1. EPPO Bull. 1988, 18, 141–151. [Google Scholar] [CrossRef]
  74. Lindgren, P.B.; Peet, R.C.; Panopoulos, N.J. Gene Cluster of Pseudomonas syringae pv. “phaseolicola” Controls Pathogenicity of Bean Plants and Hypersensitivity of Nonhost Plants. J. Bacteriol. 1986, 168, 512–522. [Google Scholar] [CrossRef] [PubMed]
  75. Roberts, S.J. Variation within Pseudomonas syringae pv. philadelphi, the Cause of a Leaf Spot of Philadelphus spp. J. Appl. Bacteriol. 1985, 59, 283–290. [Google Scholar] [CrossRef]
  76. Goto, M. Pseudomonas syringae pv. photiniae pv. nov., the Causal Agent of Bacterial Leaf Spot of Photinia glabra Maxim. Jpn. J. Phytopathol. 1983, 49, 457–462. [Google Scholar] [CrossRef]
  77. Taylor, J.D.; Bevan, J.R.; Crute, I.R.; Reader, S.L. Genetic Relationship between Races of Pseudomonas syringae pv. pisi and Cultivars of Pisum sativum. Plant Pathol. 1989, 38, 364–375. [Google Scholar] [CrossRef]
  78. Samson, R.; Shafik, H.; Benjama, A.; Gardan, L. Description of the Bacterium Causing Blight of Leek as Pseudomonas syringae pv. porri (pv. nov.). Phytopathology 1998, 88, 844–850. [Google Scholar] [CrossRef] [PubMed]
  79. Ogimi, C.; Kawano, C.; Higuchi, H.; Takikawa, Y. Bacterial Gall Disease of Sharinbai (Rhaphiolepis umbellata MARINO) Caused by Pseudomonas syringae pv. rhaphiolepidis pv. nov. J. Jpn. For. Soc. 1992, 74, 308–313. [Google Scholar] [CrossRef]
  80. Ovod, V.V.; Zdorovenko, E.L.; Shashkov, A.S.; Kocharova, N.A.; Knirel, Y.A. Structure of the O Polysaccharide and Serological Classification of Pseudomonas syringae pv. ribicola NCPPB 1010. Eur. J. Biochem. 2000, 267, 2372–2379. [Google Scholar] [CrossRef]
  81. Janse, J.D. Pathovar Discrimination within Pseudomonas syringae subsp. Savastanoi Using Whole Cell Fatty Acids and Pathogenicity as Criteria. Syst. Appl. Microbiol. 1991, 14, 79–84. [Google Scholar] [CrossRef]
  82. Firdous, S.S.; Asghar, R.; Irfan-ul-Haque, M.; Waheed, A.; Afzal, S.N.; Mirza, M.Y. Pathogenesis of Pseudomonas syringae pv. sesami Associated with Sesame (Sesamum indicum L.) Bacterial Leaf Spot. Pak. J. Bot. 2009, 41, 927–934. [Google Scholar]
  83. SATO, M.; WATANABE, K.; SATO, Y. Pseudomonas syringae pv. solidagae pv. nov., the Causal Agent of Bacterial Leaf Spot of Tall Goldenrod Solidago altissima L. J. Gen. Plant Pathol. 2001, 67, 303–308. [Google Scholar] [CrossRef]
  84. Barta, T.M.; Willis, D.K. Biological and Molecular Evidence That Pseudomonas syringae Pathovars coronafaciens, striafaciens and garcae Are Likely the Same Pathovar. J. Phytopathol. 2005, 153, 492–499. [Google Scholar] [CrossRef]
  85. Rahimian, H. The Occurrence of Bacterial Red Streak of Sugarcane Caused by Pseudomonas syringae pv. syringae in Iran. J. Phytopathol. 1995, 143, 321–324. [Google Scholar] [CrossRef]
  86. Turner, J.G.; Taha, R.R. Contribution of Tabtoxin to the Pathogenicity of Pseudomonas syringae pv. tabaci. Physiol. Plant Pathol. 1984, 25, 55–69. [Google Scholar] [CrossRef]
  87. Scortichini, M.; Marchesi, U.; Di Prospero, P. Genetic Relatedness among Pseudomonas avellanae, P. syringae pv. theae and P.s. pv. Actinidiae, and Their Identification. Eur. J. Plant Pathol. 2002, 108, 269–278. [Google Scholar] [CrossRef]
  88. Ogimi, C.; Higuchi, H.; Takikawa, Y. Bacterial Gall Disease of Urajiroenoki (Trema Orientalis BLUME) Caused by Pseudomonas syringae pv. Tremae pv. nov. J. Jpn. For. Soc. 1988, 70, 441–446. [Google Scholar] [CrossRef]
  89. Shinohara, H.; Moriwaki, J.; Kadota, I.; Nishiyama, K. Ability of pathotype strains of Pseudomonas syringae to produce coronatine. Ann. Phytopathol. Soc. Jpn. 1999, 65, 629–634. [Google Scholar] [CrossRef]
  90. Stead, D.E.; Stanford, H.; Aspin, A.; Weller, S.A. First Record of Pseudomonas syringae pv. viburni in the UK. Plant Pathol. 2006, 55, 571. [Google Scholar] [CrossRef]
  91. Bowden, R.L.; Percich, J. Etiology of Bacterial Leaf Streak of Wild Rice. Phytopathology 1983, 73, 640–645. [Google Scholar] [CrossRef]
  92. Xin, X.-F.; Nomura, K.; Aung, K.; Velásquez, A.C.; Yao, J.; Boutrot, F.; Chang, J.H.; Zipfel, C.; He, S.Y. Bacteria Establish an Aqueous Living Space in Plants Crucial for Virulence. Nature 2016, 539, 524–529. [Google Scholar] [CrossRef]
  93. Hu, Y.; Ding, Y.; Cai, B.; Qin, X.; Wu, J.; Yuan, M.; Wan, S.; Zhao, Y.; Xin, X.-F. Bacterial Effectors Manipulate Plant Abscisic Acid Signaling for Creation of an Aqueous Apoplast. Cell Host Microbe 2022, 30, 518–529.e6. [Google Scholar] [CrossRef]
  94. Yuan, M.; Jiang, Z.; Bi, G.; Nomura, K.; Liu, M.; Wang, Y.; Cai, B.; Zhou, J.-M.; He, S.Y.; Xin, X.-F. Pattern-Recognition Receptors Are Required for NLR-Mediated Plant Immunity. Nature 2021, 592, 105–109. [Google Scholar] [CrossRef]
  95. Tyson, J.L.; Vergara, M.J.; Butler, R.C.; Seelye, J.F.; Morgan, E.R. Survival, Growth and Detection of Pseudomonas syringae pv. actinidiae in Actinidia in Vitro Cultures. N. Z. J. Crop Hortic. Sci. 2018, 46, 319–333. [Google Scholar] [CrossRef]
  96. Authority (EFSA), E.F.S.; Vogelaar, M.; Schenk, M.; Delbianco, A.; Graziosi, I.; Vos, S. Pest Survey Card on Pseudomonas syringae pv. actinidiae. EFSA Support. Publ. 2020, 17, 1986E. [Google Scholar] [CrossRef]
  97. Whalen, M.C.; Innes, R.W.; Bent, A.F.; Staskawicz, B.J. Identification of Pseudomonas syringae Pathogens of Arabidopsis and a Bacterial Locus Determining Avirulence on Both Arabidopsis and Soybean. Plant Cell 1991, 3, 49–59. [Google Scholar] [CrossRef] [PubMed]
  98. Sankaran, S.; Mishra, A.; Ehsani, R.; Davis, C. A Review of Advanced Techniques for Detecting Plant Diseases. Comput. Electron. Agric. 2010, 72, 1–13. [Google Scholar] [CrossRef]
  99. Narayanasamy, P. Microbial Plant Pathogens-Detection and Disease Diagnosis: Bacterial and Phytoplasmal Pathogens, Vol.2; Springer: Dordrecht, The Netherlands, 2011; ISBN 978-90-481-9768-2. [Google Scholar]
  100. Roos, I.M.M.; Hattingh, M.J. Scanning Electron Microscopy of Pseudomonas syringae Pv, morsprunorum on Sweet Cherry Leaves. J. Phytopathol. 1983, 108, 18–25. [Google Scholar] [CrossRef]
  101. Silcock, D.J.; Waterhouse, R.N.; Glover, L.A.; Prosser, J.I.; Killham, K. Detection of a Single Genetically Modified Bacterial Cell in Soil by Using Charge Coupled Device-Enhanced Microscopy. Appl. Environ. Microbiol. 1992, 58, 2444–2448. [Google Scholar] [CrossRef] [PubMed]
  102. Van Vuurde, J.W.L.; Franken, A.A.J.M.; Birnbaum, Y.; Jochems, G. Characteristics of Immunofluorescence Microscopy and of Dilution-Plating to Detect Pseudomonas syringae pv. phaseolicola in Bean Seed Lots and for Risk Assessment of Field Incidence of Halo Blight. Neth. J. Plant Pathol. 1991, 97, 233–244. [Google Scholar] [CrossRef]
  103. Mohan, S.K.; Schaad, N.W. An Improved Agar Plating Assay for Detecting Pseudomonas syringae pv. syringae and P. s. pv. phaseolicola in Contaminated Bean Seed. Phytopathology 1987, 77, 1390–1395. [Google Scholar] [CrossRef]
  104. Schaad, N.W.; Cheong, S.S.; Tamaki, S.; Hatziloukas, E.; Panopoulos, N.J. A Combined Biological and Enzymatic Amplification (BIO-PCR) Technique to Detect Pseudomonas syringae pv. phaseolicola in Bean Seed Extracts. Phytopathology 1995, 85, 243–246. [Google Scholar] [CrossRef]
  105. Gitaitis, R.D.; Sasser, M.J.; Beaver, R.W.; McInnes, T.B.; Stall, R.E. Pectolytic Xanthomonads in Mixed Infections with Pseudomonas syringae pv. syringae, P. syringae pv. tomato, and Xanthomonas campestris pv. vesicatoria in Tomato and Pepper Transplants. Phytopathology 1987, 77, 611–615. [Google Scholar] [CrossRef]
  106. Frampton, R.A.; Taylor, C.; Holguín Moreno, A.V.; Visnovsky, S.B.; Petty, N.K.; Pitman, A.R.; Fineran, P.C. Identification of Bacteriophages for Biocontrol of the Kiwifruit Canker Phytopathogen Pseudomonas syringae pv. actinidiae. Appl. Environ. Microbiol. 2014, 80, 2216–2228. [Google Scholar] [CrossRef]
  107. Martino, G.; Holtappels, D.; Vallino, M.; Chiapello, M.; Turina, M.; Lavigne, R.; Wagemans, J.; Ciuffo, M. Molecular Characterization and Taxonomic Assignment of Three Phage Isolates from a Collection Infecting Pseudomonas syringae pv. actinidiae and P. syringae pv. phaseolicola from Northern Italy. Viruses 2021, 13, 2083. [Google Scholar] [CrossRef] [PubMed]
  108. Chen, Z.-D.; Kang, H.-J.; Chai, A.-L.; Shi, Y.-X.; Xie, X.-W.; Li, L.; Li, B.-J. Development of a Loop-Mediated Isothermal Amplification (LAMP) Assay for Rapid Detection of Pseudomonas syringae pv. tomato in Planta. Eur. J. Plant Pathol. 2020, 156, 739–750. [Google Scholar] [CrossRef]
  109. Lobato, I.M.; O’Sullivan, C.K. Recombinase Polymerase Amplification: Basics, Applications and Recent Advances. TrAC Trends Anal. Chem. 2018, 98, 19–35. [Google Scholar] [CrossRef] [PubMed]
  110. Strayer-Scherer, A.; Jones, J.B.; Paret, M.L. Paret Recombinase Polymerase Amplification Assay for Field Detection of Tomato Bacterial Spot Pathogens. Phytopathology 2019, 109, 690–700. [Google Scholar] [CrossRef] [PubMed]
  111. Kant, P.; Fruzangohar, M.; Mann, R.; Rodoni, B.; Hollaway, G.; Rosewarne, G. Development and Application of a Loop-Mediated Isothermal Amplification (LAMP) Assay for the Detection of Pseudomonas syringae Pathovars pisi and syringae. Agriculture 2021, 11, 875. [Google Scholar] [CrossRef]
  112. Díaz-Cruz, G.A.; Smith, C.M.; Wiebe, K.F.; Villanueva, S.M.; Klonowski, A.R.; Cassone, B.J. Applications of Next-Generation Sequencing for Large-Scale Pathogen Diagnoses in Soybean. Plant Dis. 2019, 103, 1075–1083. [Google Scholar] [CrossRef]
  113. Hu, S.; Li, X.; Yin, X.; Li, R.; Zhang, R.; Zang, J.; Liu, Y. Species-Specific Identification of Pseudomonas Based on 16S-23S RRNA Gene Internal Transcribed Spacer (ITS) and Its Combined Application with next-Generation Sequencing. BMC Microbiol. 2022, 22, 188. [Google Scholar] [CrossRef] [PubMed]
  114. Cuppels, D.A.; Moore, R.A.; Morris, V.L. Construction and Use of a Nonradioactive DNA Hybridization Probe for Detection of Pseudomonas syringae pv. tomato on Tomato Plants. Appl. Environ. Microbiol. 1990, 56, 1743–1749. [Google Scholar] [CrossRef] [PubMed]
  115. Li, Q.; Yan, Q.; Chen, J.; He, Y.; Wang, J.; Zhang, H.; Yu, Z.; Li, L. Molecular Characterization of an Ice Nucleation Protein Variant (InaQ) from Pseudomonas syringae and the Analysis of Its Transmembrane Transport Activity in Escherichia coli. Int. J. Biol. Sci. 2012, 8, 1097–1108. [Google Scholar] [CrossRef]
  116. Muvingi, M.; Муфарo, М.; Slovareva, O.Y.; Юрьевна, С.О.; Zargar, M.; Мейсам, З. Identification of Pseudomonas fuscovaginae, Pseudomonas syringae and Xanthomonas translucens in wheat seeds using PCR. RUDN J. Agron. Anim. Ind. 2022, 17, 473–483. [Google Scholar] [CrossRef]
  117. Balestra, G.M.; Taratufolo, M.C.; Vinatzer, B.A.; Mazzaglia, A. A Multiplex PCR Assay for Detection of Pseudomonas syringae pv. actinidiae and Differentiation of Populations with Different Geographic Origin. Plant Dis. 2013, 97, 472–478. [Google Scholar] [CrossRef]
  118. Rufián, J.S.; Sánchez-Romero, M.-A.; López-Márquez, D.; Macho, A.P.; Mansfield, J.W.; Arnold, D.L.; Ruiz-Albert, J.; Casadesús, J.; Beuzón, C.R. Pseudomonas syringae Differentiates into Phenotypically Distinct Subpopulations During Colonization of a Plant Host. Environ. Microbiol. 2016, 18, 3593–3605. [Google Scholar] [CrossRef]
  119. Khan, M.M.T.; Pyle, B.H.; Camper, A.K. Specific and Rapid Enumeration of Viable but Nonculturable and Viable-Culturable Gram-Negative Bacteria by Using Flow Cytometry. Appl. Environ. Microbiol. 2010, 76, 5088–5096. [Google Scholar] [CrossRef]
  120. Rufián, J.S.; Ruiz-Albert, J.; Beuzón, C.R. Fluorescently Labeled Pseudomonas syringae DC3000 and 1449b Wild-Type Strains Constitutively Expressing Either EGFP, ECFP, or DsRED. Micropublication Biol. 2022, 2022, 595. [Google Scholar] [CrossRef]
  121. Cimmino, A.; Iannaccone, M.; Petriccione, M.; Masi, M.; Evidente, M.; Capparelli, R.; Scortichini, M.; Evidente, A. An ELISA Method to Identify the Phytotoxic Pseudomonas syringae pv. actinidiae Exopolysaccharides: A Tool for Rapid Immunochemical Detection of Kiwifruit Bacterial Canker. Phytochem. Lett. 2017, 19, 136–140. [Google Scholar] [CrossRef]
  122. Wang, W.; Feng, M.; Kong, D.; Liu, L.; Song, S.; Xu, C. Development of an Immunochromatographic Strip for the Rapid Detection of Pseudomonas syringae pv. maculicola in Broccoli and Radish Seeds. Food Agric. Immunol. 2015, 26, 738–745. [Google Scholar] [CrossRef]
  123. Bonants, P. Advances in Testing Seed Health. In Advances in Seed Science and Technology for More Sustainable Crop Production; Burleigh Dodds Science Publishing Limited: Cambridge, UK, 2022; pp. 233–264. [Google Scholar] [CrossRef]
  124. Riffaud, C.M.-H.; Morris, C.E. Detection of Pseudomonas syringae pv. aptata in Irrigation Water Retention Basins by Immunofluorescence Colony-Staining. Eur. J. Plant Pathol. 2002, 108, 539–545. [Google Scholar] [CrossRef]
  125. Jung, H.-C.; Lebeault, J.-M.; Pan, J.-G. Surface Display of Zymomonas Mobilis Levansucrase by Using the Ice-Nucleation Protein of Pseudomonas syringae. Nat. Biotechnol. 1998, 16, 576–580. [Google Scholar] [CrossRef] [PubMed]
  126. Chen, H.; Hu, Y.; Qin, K.; Yang, X.; Jia, Z.; Li, Q.; Chen, H.; Yang, H. A Serological Approach for the Identification of the Effector Hopz5 of Pseudomonas syringae pv. actinidiae: A Tool for the Rapid Immunodetection of Kiwifruit Bacterial Canker. J. Plant Pathol. 2018, 100, 171–177. [Google Scholar] [CrossRef]
  127. Rico, A.; McCraw, S.L.; Preston, G.M. The Metabolic Interface between Pseudomonas syringae and Plant Cells. Curr. Opin. Microbiol. 2011, 14, 31–38. [Google Scholar] [CrossRef] [PubMed]
  128. Purahong, W.; Orrù, L.; Donati, I.; Perpetuini, G.; Cellini, A.; Lamontanara, A.; Michelotti, V.; Tacconi, G.; Spinelli, F. Plant Microbiome and Its Link to Plant Health: Host Species, Organs and Pseudomonas syringae pv. actinidiae Infection Shaping Bacterial Phyllosphere Communities of Kiwifruit Plants. Front. Plant Sci. 2018, 9, 1563. [Google Scholar] [CrossRef]
  129. López-Gresa, M.P.; Torres, C.; Campos, L.; Lisón, P.; Rodrigo, I.; Bellés, J.M.; Conejero, V. Identification of Defence Metabolites in Tomato Plants Infected by the Bacterial Pathogen Pseudomonas syringae. Environ. Exp. Bot. 2011, 74, 216–228. [Google Scholar] [CrossRef]
  130. Scortichini, M.; Ferrante, P.; Marcelletti, S.; Petriccione, M. Omics, Epidemiology and Integrated Approach for the Coexistence with Bacterial Canker of Kiwifruit, Caused by Pseudomonas syringae pv. actinidiae. Ital. J. Agron. 2014, 9, 163–165. [Google Scholar] [CrossRef]
  131. Nobori, T.; Wang, Y.; Wu, J.; Stolze, S.C.; Tsuda, Y.; Finkemeier, I.; Nakagami, H.; Tsuda, K. In Planta Bacterial Multi-Omics Analysis Illuminates Regulatory Principles Underlying Plant-Pathogen Interactions. bioRxiv 2019. [Google Scholar] [CrossRef]
  132. Guilbaud, C.; Morris, C.E.; Barakat, M.; Ortet, P.; Berge, O. Isolation and Identification of Pseudomonas syringae Facilitated by a PCR Targeting the Whole P. syringae Group. FEMS Microbiol. Ecol. 2016, 92, fiv146. [Google Scholar] [CrossRef]
  133. Bultreys, A.; Gheysen, I.; Maraite, H.; de Hoffmann, E. Characterization of Fluorescent and Nonfluorescent Peptide Siderophores Produced by Pseudomonas syringae Strains and Their Potential Use in Strain Identification. Appl. Environ. Microbiol. 2001, 67, 1718–1727. [Google Scholar] [CrossRef] [PubMed]
  134. Song, S.; Liu, Y.; Wang, N.R.; Haney, C.H. Mechanisms in Plant–Microbiome Interactions: Lessons from Model Systems. Curr. Opin. Plant Biol. 2021, 62, 102003. [Google Scholar] [CrossRef] [PubMed]
  135. Rodríguez-Moreno, L.; Pineda, M.; Soukupová, J.; Macho, A.P.; Beuzón, C.R.; Barón, M.; Ramos, C. Early Detection of Bean Infection by Pseudomonas syringae in Asymptomatic Leaf Areas Using Chlorophyll Fluorescence Imaging. Photosynth. Res. 2008, 96, 27–35. [Google Scholar] [CrossRef]
  136. Iqbal, M.J.; Goodwin, P.H.; Leonardos, E.D.; Grodzinski, B. Spatial and Temporal Changes in Chlorophyll Fluorescence Images of Nicotiana benthamiana Leaves Following Inoculation with Pseudomonas syringae pv. tabaci. Plant Pathol. 2012, 61, 1052–1062. [Google Scholar] [CrossRef]
  137. Huang, L. Pseudomonas syringae pv. actinidiae Chemotaxis and the Kiwifruit Phyllosphere. Ph.D. Thesis, Open Access Te Herenga Waka-Victoria University of Wellington, Wellington, New Zealand, 2022. [Google Scholar]
  138. Hupp, S.; Rosenkranz, M.; Bonfig, K.; Pandey, C.; Roitsch, T. Noninvasive Phenotyping of Plant–Pathogen Interaction: Consecutive In Situ Imaging of Fluorescing Pseudomonas syringae, Plant Phenolic Fluorescence, and Chlorophyll Fluorescence in Arabidopsis Leaves. Front. Plant Sci. 2019, 10, 1239. [Google Scholar] [CrossRef] [PubMed]
  139. Taylor, J.; Mowat, A.; Bollen, A.; Whelan, B. Early Season Detection and Mapping of Pseudomonas syringae pv. actinidae Infected Kiwifruit (Actinidia sp.) Orchards. N. Z. J. Crop Hortic. Sci. 2014, 42, 303–311. [Google Scholar] [CrossRef]
  140. Cebula, Z.; Żołędowska, S.; Dziąbowska, K.; Skwarecka, M.; Malinowska, N.; Białobrzeska, W.; Czaczyk, E.; Siuzdak, K.; Sawczak, M.; Bogdanowicz, R.; et al. Detection of the Plant Pathogen Pseudomonas syringae pv. lachrymans on Antibody-Modified Gold Electrodes by Electrochemical Impedance Spectroscopy. Sensors 2019, 19, 5411. [Google Scholar] [CrossRef] [PubMed]
  141. Li, B.; Hulin, M.T.; Brain, P.; Mansfield, J.W.; Jackson, R.W.; Harrison, R.J. Rapid, Automated Detection of Stem Canker Symptoms in Woody Perennials Using Artificial Neural Network Analysis. Plant Methods 2015, 11, 57. [Google Scholar] [CrossRef]
  142. Furci, L.; Pascual-Pardo, D.; Ton, J. A Rapid and Non-Destructive Method for Spatial–Temporal Quantification of Colonization by Pseudomonas syringae pv. tomato DC3000 in Arabidopsis and Tomato. Plant Methods 2021, 17, 126. [Google Scholar] [CrossRef]
  143. Zhang, Z.; Zhang, Q.; Cui, H.; Li, Y.; Xu, N.; Lu, T.; Chen, J.; Penuelas, J.; Hu, B.; Qian, H. Composition Identification and Functional Verification of Bacterial Community in Disease-Suppressive Soils by Machine Learning. Environ. Microbiol. 2022, 24, 3405–3419. [Google Scholar] [CrossRef]
  144. Almeida, R.N.D.; Greenberg, M.; Bundalovic-Torma, C.; Martel, A.; Wang, P.W.; Middleton, M.A.; Chatterton, S.; Desveaux, D.; Guttman, D.S. Predictive Modeling of Pseudomonas syringae Virulence on Bean Using Gradient Boosted Decision Trees. PLoS Pathog. 2022, 18, e1010716. [Google Scholar] [CrossRef] [PubMed]
  145. Qin, Z.; Zhang, J.E.; Jiang, Y.P.; Wang, R.L.; Wu, R.S. Predicting the Potential Distribution of Pseudomonas syringae pv. actinidiae in China Using Ensemble Models. Plant Pathol. 2020, 69, 120–131. [Google Scholar] [CrossRef]
  146. Misas-Villamil, J.C.; Kolodziejek, I.; van der Hoorn, R.A.L. Pseudomonas syringae Colonizes Distant Tissues in Nicotiana benthamiana through Xylem Vessels. Plant J. 2011, 67, 774–782. [Google Scholar] [CrossRef] [PubMed]
  147. Farias, G.A.; Olmedilla, A.; Gallegos, M. Visualization and Characterization of Pseudomonas syringae pv. tomato DC3000 Pellicles. Microb. Biotechnol. 2019, 12, 688–702. [Google Scholar] [CrossRef] [PubMed]
  148. Dorati, F.; Barrett, G.A.; Sanchez-Contreras, M.; Arseneault, T.; José, M.S.; Studholme, D.J.; Murillo, J.; Caballero, P.; Waterfield, N.R.; Arnold, D.L.; et al. Coping with Environmental Eukaryotes; Identification of Pseudomonas syringae Genes during the Interaction with Alternative Hosts or Predators. Microorganisms 2018, 6, 32. [Google Scholar] [CrossRef]
  149. Daher, R.K.; Stewart, G.; Boissinot, M.; Bergeron, M.G. Recombinase Polymerase Amplification for Diagnostic Applications. Clin. Chem. 2016, 62, 947–958. [Google Scholar] [CrossRef]
  150. Rees-George, J.; Vanneste, J.L.; Cornish, D.A.; Pushparajah, I.P.S.; Yu, J.; Templeton, M.D.; Everett, K.R. Detection of Pseudomonas syringae pv. actinidiae Using Polymerase Chain Reaction (PCR) Primers Based on the 16S–23S RDNA Intertranscribed Spacer Region and Comparison with PCR Primers Based on Other Gene Regions. Plant Pathol. 2010, 59, 453–464. [Google Scholar] [CrossRef]
  151. Suzaki, K.; Sawada, H.; Kisaki, G. Loop-Mediated Isothermal Amplification of Bacterial Effector Genes to Detect Pseudomonas syringae pv. actinidiae Biovars 1 and 3. J. Gen. Plant Pathol. 2022, 88, 2–9. [Google Scholar] [CrossRef]
  152. Li, G.-R.; Huang, G.-M.; Zhu, L.-H.; Lv, D.; Cao, B.; Liao, F.; Luo, J.-F. Loop-Mediated Isothermal Amplification (LAMP) Detection of Phytophthora hibernalis, P. syringae and P. cambivora. J. Plant Pathol. 2019, 101, 51–57. [Google Scholar] [CrossRef]
  153. Huber, D.; Voith von Voithenberg, L.; Kaigala, G.V. Fluorescence in Situ Hybridization (FISH): History, Limitations and What to Expect from Micro-Scale FISH? Micro Nano Eng. 2018, 1, 15–24. [Google Scholar] [CrossRef]
  154. Kanegane, H.; Hoshino, A.; Okano, T.; Yasumi, T.; Wada, T.; Takada, H.; Okada, S.; Yamashita, M.; Yeh, T.-W.; Nishikomori, R.; et al. Flow Cytometry-Based Diagnosis of Primary Immunodeficiency Diseases. Allergol. Int. 2018, 67, 43–54. [Google Scholar] [CrossRef] [PubMed]
  155. Stepaniak, L.; Sørhaug, T.; Jedrychowski, L. ANALYSIS | Immunochemical. In Encyclopedia of Dairy Sciences; Roginski, H., Ed.; Elsevier: Oxford, UK, 2002; pp. 62–67. ISBN 978-0-12-227235-6. [Google Scholar]
  156. Gonzalez, A.; Gaines, M.; Gallegos, L.Y.; Guevara, R.; Gomez, F.A. Enzyme-Linked Immunosorbent Assays (ELISA) Based on Thread, Paper, and Fabric. Electrophoresis 2018, 39, 476–484. [Google Scholar] [CrossRef] [PubMed]
  157. Chan, H.S.L.; Haddad, G.; Zheng, L.; Bradley, G.; Dalton, W.S.; Ling, V. Sensitive Immunofluorescence Detection of the Expression of P-Glycoprotein in Malignant Cells. Cytometry 1997, 29, 65–75. [Google Scholar] [CrossRef]
  158. Tian, X.; Zhao, Z.; Wang, H.; Qin, Y.; Wang, D.; Li, Y.; Wang, X.; Wang, L. An Immunochromatographic Test Strip for On-Site Detection of Ralstonia solanacearum in Tobacco Leaves and Soil. Anal. Biochem. 2022, 641, 114561. [Google Scholar] [CrossRef]
  159. Strimbu, K.; Tavel, J.A. What Are Biomarkers? Curr. Opin. HIV AIDS 2010, 5, 463–466. [Google Scholar] [CrossRef]
  160. Bakare, O.O.; Keyster, M.; Pretorius, A. Identification of Biomarkers for the Accurate and Sensitive Diagnosis of Three Bacterial Pneumonia Pathogens Using in Silico Approaches. BMC Mol. Cell Biol. 2020, 21, 82. [Google Scholar] [CrossRef]
  161. Dyussembayev, K.; Sambasivam, P.; Bar, I.; Brownlie, J.C.; Shiddiky, M.J.A.; Ford, R. Biosensor Technologies for Early Detection and Quantification of Plant Pathogens. Front. Chem. 2021, 9, 636245. [Google Scholar] [CrossRef]
  162. Steinhauser, D.; Kopka, J. Methods, Applications and Concepts of Metabolite Profiling: Primary Metabolism. In Plant Systems Biology; Baginsky, S., Fernie, A.R., Eds.; Experientia Supplementum; Birkhäuser: Basel, Switzerland, 2007; pp. 171–194. ISBN 978-3-7643-7439-6. [Google Scholar]
  163. Shah, S.H.; Kraus, W.E.; Newgard, C.B. Metabolomic Profiling for Identification of Novel Biomarkers and Mechanisms Related to Common Cardiovascular Diseases: Form and Function. Circulation 2012, 126, 1110–1120. [Google Scholar] [CrossRef]
  164. Hazrati, H.; Kudsk, P.; Ding, L.; Uthe, H.; Fomsgaard, I.S. Integrated LC–MS and GC–MS-Based Metabolomics Reveal the Effects of Plant Competition on the Rye Metabolome. J. Agric. Food Chem. 2022, 70, 3056–3066. [Google Scholar] [CrossRef]
  165. Ferentinos, K.P. Deep Learning Models for Plant Disease Detection and Diagnosis. Comput. Electron. Agric. 2018, 145, 311–318. [Google Scholar] [CrossRef]
  166. Reis-Pereira, M.; Tosin, R.; Martins, R.; Neves Dos Santos, F.; Tavares, F.; Cunha, M. Kiwi Plant Canker Diagnosis Using Hyperspectral Signal Processing and Machine Learning: Detecting Symptoms Caused by Pseudomonas syringae pv. actinidiae. Plants 2022, 11, 2154. [Google Scholar] [CrossRef] [PubMed]
  167. Byrd, A.L.; Segre, J.A. Adapting Koch’s Postulates. Science 2016, 351, 224–226. [Google Scholar] [CrossRef] [PubMed]
  168. Agrios, G.N. Plant Pathology, 5th ed.; Elsevier Academic Press: Amsterdam, The Netherland; Boston, MA, USA, 2005; ISBN 978-0-12-044565-3. [Google Scholar]
  169. Segre, J.A. What Does It Take to Satisfy Koch’s Postulates Two Centuries Later? Microbial Genomics and Propionibacteria Acnes. J. Investig. Dermatol. 2013, 133, 2141–2142. [Google Scholar] [CrossRef]
  170. Bhunjun, C.S.; Phillips, A.J.L.; Jayawardena, R.S.; Promputtha, I.; Hyde, K.D. Importance of Molecular Data to Identify Fungal Plant Pathogens and Guidelines for Pathogenicity Testing Based on Koch’s Postulates. Pathogens 2021, 10, 1096. [Google Scholar] [CrossRef]
  171. Scholthof, K.-B.G. The Disease Triangle: Pathogens, the Environment and Society. Nat. Rev. Microbiol. 2007, 5, 152–156. [Google Scholar] [CrossRef]
  172. The Disease Triangle: A Plant Pathological Paradigm Revisited. Available online: https://www.apsnet.org/edcenter/foreducators/TeachingNotes/Pages/DiseaseTriangle.aspx (accessed on 6 January 2023).
  173. Fry, W.E. Principles of Plant Disease Management; Academic Press: Cambridge, MA, USA, 2012; ISBN 978-0-08-091830-3. [Google Scholar]
  174. Yang, P.; Bokros, N.; Debolt, S.; Zhao, Z.; Xia, Y. Genome Sequence Source of Bacillus amyloliquefaciens Strain GD4a, a Bacterial Endophyte Associated with Switchgrass Plants. Phytobiomes J. 2022, 6, 354–357. [Google Scholar] [CrossRef]
  175. Koskey, G.; Mburu, S.W.; Awino, R.; Njeru, E.M.; Maingi, J.M. Potential Use of Beneficial Microorganisms for Soil Amelioration, Phytopathogen Biocontrol, and Sustainable Crop Production in Smallholder Agroecosystems. Front. Sustain. Food Syst. 2021, 5, 606308. [Google Scholar] [CrossRef]
  176. Romano, I.; Ventorino, V.; Pepe, O. Effectiveness of Plant Beneficial Microbes: Overview of the Methodological Approaches for the Assessment of Root Colonization and Persistence. Front. Plant Sci. 2020, 11, 6. [Google Scholar] [CrossRef]
  177. Sahib, M.R.; Yang, P.; Bokros, N.; Shapiro, N.; Woyke, T.; Kyrpides, N.C.; Xia, Y.; DeBolt, S. Improved Draft Genome Sequence of Microbacterium sp. Strain LKL04, a Bacterial Endophyte Associated with Switchgrass Plants. Microbiol. Resour. Announc. 2019, 8, e00927-19. [Google Scholar] [CrossRef] [PubMed]
  178. Zhao, Z.; Bokros, N.; DeBolt, S.; Yang, P.; Xia, Y. Genome Sequence Resource of Bacillus sp. RRD69, a Beneficial Bacterial Endophyte Isolated from Switchgrass Plants. Mol. Plant-Microbe Interact. 2021, 34, 1320–1323. [Google Scholar] [CrossRef] [PubMed]
  179. Pieterse, C.M.J.; Zamioudis, C.; Berendsen, R.L.; Weller, D.M.; Van Wees, S.C.M.; Bakker, P.A.H.M. Induced Systemic Resistance by Beneficial Microbes. Annu. Rev. Phytopathol. 2014, 52, 347–375. [Google Scholar] [CrossRef]
  180. Dellagi, A.; Quillere, I.; Hirel, B. Beneficial Soil-Borne Bacteria and Fungi: A Promising Way to Improve Plant Nitrogen Acquisition. J. Exp. Bot. 2020, 71, 4469–4479. [Google Scholar] [CrossRef]
  181. Rosenblueth, M.; Ormeño-Orrillo, E.; López-López, A.; Rogel, M.A.; Reyes-Hernández, B.J.; Martínez-Romero, J.C.; Reddy, P.M.; Martínez-Romero, E. Nitrogen Fixation in Cereals. Front. Microbiol. 2018, 9, 1794. [Google Scholar] [CrossRef] [PubMed]
  182. Fox, A.; Kwapinski, W.; Griffiths, B.S.; Schmalenberger, A. The Role of Sulfur- and Phosphorus-Mobilizing Bacteria in Biochar-Induced Growth Promotion of Lolium perenne. FEMS Microbiol. Ecol. 2014, 90, 78–91. [Google Scholar] [CrossRef]
  183. Gahan, J.; Schmalenberger, A. The Role of Bacteria and Mycorrhiza in Plant Sulfur Supply. Front. Plant Sci. 2014, 5, 723. [Google Scholar] [CrossRef] [PubMed]
  184. Alori, E.T.; Glick, B.R.; Babalola, O.O. Microbial Phosphorus Solubilization and Its Potential for Use in Sustainable Agriculture. Front. Microbiol. 2017, 8, 971. [Google Scholar] [CrossRef]
  185. Moretti, L.G.; Crusciol, C.A.C.; Bossolani, J.W.; Calonego, J.C.; Moreira, A.; Garcia, A.; Momesso, L.; Kuramae, E.E.; Hungria, M. Beneficial Microbial Species and Metabolites Alleviate Soybean Oxidative Damage and Increase Grain Yield during Short Dry Spells. Eur. J. Agron. 2021, 127, 126293. [Google Scholar] [CrossRef]
  186. Abdelaal, K.; AlKahtani, M.; Attia, K.; Hafez, Y.; Király, L.; Künstler, A. The Role of Plant Growth-Promoting Bacteria in Alleviating the Adverse Effects of Drought on Plants. Biology 2021, 10, 520. [Google Scholar] [CrossRef]
  187. Munir, N.; Hanif, M.; Abideen, Z.; Sohail, M.; El-Keblawy, A.; Radicetti, E.; Mancinelli, R.; Haider, G. Mechanisms and Strategies of Plant Microbiome Interactions to Mitigate Abiotic Stresses. Agronomy 2022, 12, 2069. [Google Scholar] [CrossRef]
  188. Porter, S.S.; Bantay, R.; Friel, C.A.; Garoutte, A.; Gdanetz, K.; Ibarreta, K.; Moore, B.M.; Shetty, P.; Siler, E.; Friesen, M.L. Beneficial Microbes Ameliorate Abiotic and Biotic Sources of Stress on Plants. Funct. Ecol. 2020, 34, 2075–2086. [Google Scholar] [CrossRef]
  189. Jain, A.; Sarsaiya, S.; Wu, Q.; Lu, Y.; Shi, J. A Review of Plant Leaf Fungal Diseases and Its Environment Speciation. Bioengineered 2019, 10, 409–424. [Google Scholar] [CrossRef] [PubMed]
  190. Chen, X.; Selvaraj, P.; Lin, L.; Fang, W.; Wu, C.; Yang, P.; Zhang, J.; Abubakar, Y.S.; Yang, F.; Lu, G.; et al. Rab7/Retromer-Based Endolysosomal Trafficking Facilitates Effector Secretion and Host Invasion in Rice Blast. bioRxiv 2022. [Google Scholar] [CrossRef]
  191. Gilligan, C.A. An Epidemiological Framework for Disease Management. In Advances in Botanical Research; Academic Press: Cambridge, MA, USA, 2002; Volume 38, pp. 1–64. [Google Scholar]
  192. Newhall, A.G. Herbert Hice Whetzel: Pioneer American Plant Pathologist. Annu. Rev. Phytopathol. 1980, 18, 27–36. [Google Scholar] [CrossRef]
Plants 12 01765 g001 550
Figure 1. Occurrence of plant diseases caused by P. syringae. Data were retrieved from NCPPB (National Collection of Plant Pathogenic Bacteria, https://www.fera.co.uk/ncppb, accessed on 15 February 2023). (A) Landmark discoveries of pathovars of P. syringae. In the horizontal timeline, we only highlight those pathovars deposited to NCPPB. Due to space constraints, we were unable to cover all the significant discoveries that occurred along the timeline. (B) P. syringae pathovars deposited in NCPPB. The occurrence data were presented in a world map view. The color bar indicates the counts of different pathovars of P. syringae identified from the specific country until 2009. The gradient of the color (from lighter to darker orange) indicates the number/types of pathovars reported (from lower to higher level).
Figure 1. Occurrence of plant diseases caused by P. syringae. Data were retrieved from NCPPB (National Collection of Plant Pathogenic Bacteria, https://www.fera.co.uk/ncppb, accessed on 15 February 2023). (A) Landmark discoveries of pathovars of P. syringae. In the horizontal timeline, we only highlight those pathovars deposited to NCPPB. Due to space constraints, we were unable to cover all the significant discoveries that occurred along the timeline. (B) P. syringae pathovars deposited in NCPPB. The occurrence data were presented in a world map view. The color bar indicates the counts of different pathovars of P. syringae identified from the specific country until 2009. The gradient of the color (from lighter to darker orange) indicates the number/types of pathovars reported (from lower to higher level).
Plants 12 01765 g001
Plants 12 01765 g002 550
Figure 2. The life cycle of P. syringae. The diagram was adapted from [27] with some modifications and updates. The figure was created with BioRender.com, accessed on 15 February 2023.
Figure 2. The life cycle of P. syringae. The diagram was adapted from [27] with some modifications and updates. The figure was created with BioRender.com, accessed on 15 February 2023.
Plants 12 01765 g002
Plants 12 01765 g003 550
Figure 3. Methods in detection and diagnosis of P. syringae in the life history of a plant. The figure was created with BioRender.com, accessed on 15 February 2023.
Figure 3. Methods in detection and diagnosis of P. syringae in the life history of a plant. The figure was created with BioRender.com, accessed on 15 February 2023.
Plants 12 01765 g003
Plants 12 01765 g004 550
Figure 4. Koch’s postulates as golden rules in the detection and diagnosis of P. syringae. This figure was adapted from reference [168] with some modifications and additions. The figure was created with BioRender.com, accessed on 15 February 2023.
Figure 4. Koch’s postulates as golden rules in the detection and diagnosis of P. syringae. This figure was adapted from reference [168] with some modifications and additions. The figure was created with BioRender.com, accessed on 15 February 2023.
Plants 12 01765 g004
Plants 12 01765 g005 550
Figure 5. The conventional and current plant protection principles. (A) The conventional plant disease triangle model was adapted from reference [168]. (B) The current plant fitness tetrahedron model was adapted from references [171,172]. (C) The current plant disease management hexagon model was adapted from reference [173].
Figure 5. The conventional and current plant protection principles. (A) The conventional plant disease triangle model was adapted from reference [168]. (B) The current plant fitness tetrahedron model was adapted from references [171,172]. (C) The current plant disease management hexagon model was adapted from reference [173].
Plants 12 01765 g005
Table
Table 1. Documentary records of plant diseases caused by P. syringae. Data were retrieved from NCPPB (National Collection of Plant Pathogenic Bacteria, https://www.fera.co.uk/ncppb, accessed on 15 February 2023).
Table 1. Documentary records of plant diseases caused by P. syringae. Data were retrieved from NCPPB (National Collection of Plant Pathogenic Bacteria, https://www.fera.co.uk/ncppb, accessed on 15 February 2023).
PathovarCountryHost Plant SymptomsYear References
pv. acerisJapan Acer buergerianum Miq. ‘Tohkaeda’(trident maple)Necrotic leaf spots1990NCPPB, [35]
pv. actinidiaeJapanActinidia chinensis (kiwifruit)Stem cankers1990NCPPB, [36]
pv. aesculiIndiaAesculus indica (horse chestnut)Stem cankers1990NCPPB, [37]
pv. antirrhiniUKAntirrhinum majus (snapdragon)Necrotic leaf spots1966NCPPB, [38]
pv. apii USAApium graveolens var. dulce (celery)Necrotic leaf spots1964NCPPB, [39]
pv. aptata USABeta vulgaris (sugar beet)Tissue blights1961NCPPB, [40]
pv. atrofaciensNew ZealandTriticum aestivum (bread wheat)Glume rots1974NCPPB, [41]
pv. atropurpureaJapanLolium multiflorum (ryegrass)Shoot-tip diebacks1971NCPPB, [42]
pv. avellanaeGreeceCorylus avellana (hazel)Stem cankers1987NCPPB, [43]
pv. avii UKPrunus avium (wild cherry)Necrotic leaf spots1959NCPPB, [44]
pv. berberidisNew ZealandBerberis sp. (barberry)Necrotic leaf spots1975NCPPB, [45]
pv. broussonetiaeJapanBroussonetia kazinoki (kozo) Shoot blights2008NCPPB, [46]
pv. cannabinaHungaryCannabis sativa (hemp)Leaf and stem rots1960NCPPB, [47]
pv. castaneaeJapanCastanea crenata (chestnut)Leaf blights2008NCPPB, [48]
pv. cerasicolaJapanPrunus × yedoensis (cherry tree)Galls on trunks and twigs.2008NCPPB, [49]
pv. ciccaronei ItalyCeratonia siliqua (carob)Stem cankers1971NCPPB, [50]
pv. coriandricolaGermanyCoriandrum sativum var. micocarpur (coriander)Necrotic leaf spots1991NCPPB, [51]
pv. coronafaciens UKAvena sativa (oat)Leaf blights1958NCPPB, [52]
pv. coryliItalyCorylus avellena (hazel)Stem cankers2001NCPPB, [53]
pv. cunninghamiaeChina Cunninghamia lanceolata (Chinese fir)Small brown spots with yellow halos on needles (leaves) 2008NCPPB, [54]
pv. daphniphylli JapanDaphniphyllum teijsmanni (himeyuzuriha)Galls on trunks and twigs.1989NCPPB, [55]
pv. delphinii New ZealandDelphinium sp. (candle larspur)Stem cankers1966NCPPB, [56]
pv. dendropanacisJapanDendropanax trifidus (ivy)Stem cankers1986NCPPB, [57]
pv. dysoxyli New ZealandDysoxylum sp. (kohekohe)Frost damages1966NCPPB, [58]
pv. eriobotryaeUSAEriobotrya japonica (loquat)Spots and blisters on fruit1970NCPPB, [59]
pv. garcaeBrazilCoffea arabica (coffee)Leaf and stem rots1958NCPPB, [60]
pv. glycineaNew ZealandGlycine max (soybean)Leaf blights1971NCPPB, [61]
pv. helianthiMexicoHelianthus annuus (sunflower)Necrotic leaf spots1974NCPPB, [62]
pv. hibisci USAHibiscus rosa seinensis (hibiscus)Necrotic leaf spots1990NCPPB, [31]
pv. japonicaJapanHordeum vulgare (barley)Leaf blights1979NCPPB, [63]
pv. lachrymans HungaryCucumis sativus (cucumber)Necrotic leaf spots1960NCPPB, [64]
pv. maculicolaNew ZealandBrassica oleracea var. botrytis (cauliflower)Necrotic leaf spots1967NCPPB, [65]
pv. melleaJapanNicotiana tabacum (tobacco)Necrotic leaf spots1971NCPPB, [66]
pv. moriHungaryMorus alba (mulberry)Necrotic leaf spots1961NCPPB, [67]
pv. morsprunorumSwitzerlandPrunus armeniaca (apricot)Dead dormant buds1971NCPPB, [68]
pv. myricaeJapanMyrica rubra (yumberry)Necrotic leaf spots1981NCPPB, [69]
pv. oryzaeJapanOryza sativa (rice)Sheath brown rots1990NCPPB, [70]
pv. papulans CanadaMalus sylvestris (forest apple)Blister spots1975NCPPB, [71]
pv. passifloraeNew ZealandPassiflora edulis (passion fruit)Necrotic leaf spots1963NCPPB, [72]
pv. persicaeFrancePrunus persica (peach)Stem cankers1975NCPPB, [73]
pv. phaseolicolaCanadaPhaseolus vulgaris (bean)Necrotic leaf spots1941NCPPB, [74]
pv. philadelphi UKPhiladelphus coronarius (dogwood)Necrotic leaf spots1983NCPPB, [75]
pv. photiniaeJapanPhotinia glabra (Japanese photinia)Necrotic leaf spots1990NCPPB, [76]
pv. pisiNew ZealandPisum sativum (pea)Necrotic leaf spots1974NCPPB, [77]
pv. porriFranceAllium porrum (leek)Leaf blights1985NCPPB, [78]
pv. primulaeUSAPrimula sp. (primrose)Necrotic leaf spots1939NCPPB, [31]
pv. rhaphiolepidisJapanRaphiolepis umbellata (yeddo hawthorne)Necrotic leaf spots1989NCPPB, [79]
pv. ribicolaUSARibes aureum (golden currant)Necrotic leaf spots1961NCPPB, [80]
pv. neriiSpainNerium oleander (oleander)Brown leaf galls1983NCPPB, [81]
pv. sesami GreeceSesamum indicum (sesame)Necrotic leaf spots1961NCPPB, [82]
pv. solidagae JapanSolidago altissima (goldenrod)Defoliation and terminal diebacks2009NCPPB, [83]
pv. striafaciens USAAvena sp. (oats)Stripe blights1966NCPPB, [84]
pv. syringaeJapanHordeum vulgare (barley)Leaf blights1979NCPPB, [85]
pv. tabaci AustraliaGlycine max (soybean)Necrotic leaf spots1975NCPPB, [86]
pv. tagetisZimbabweTagetes erecta (marigold)Necrotic leaf spots1972NCPPB, [1]
pv. theaeJapanThea sinensis (tea plant)Shoot blights1974NCPPB, [87]
pv. tomatoUKLycopersicon esculentum (tomato)Necrotic leaf spots1961NCPPB, [32]
pv. tremaeJapanTrema orientalis (charcoal-tree)Necrotic leaf spots1986NCPPB, [88]
pv. ulmi YugoslaviaUlmus sp. (elm)Necrotic leaf spots1959NCPPB, [89]
pv. viburniUSAViburnum sp. (cranberry bush)Leaf and stem spots1966NCPPB, [90]
pv. zizaniaeUSAZizania aquatica (wild rice)Leaf streaks1990NCPPB, [91]
Table
Table 2. Comparison of methods for detection and diagnosis. This table was adapted from references [24,98,99] with some modifications and updates.
Table 2. Comparison of methods for detection and diagnosis. This table was adapted from references [24,98,99] with some modifications and updates.
Method TypeMethodAdvantagesLimitationsReferences
ConventionalVisual examinationQuick and easy to perform, onsite disease detection and diagnosisSubjective, not sensitive enough at early stages[96,97]
ConventionalMicroscopyHigh resolution, versatilitySample preparation, sample size, shallow depth of field[100,101,102]
ConventionalCulture plateRelatively inexpensive, easy to use, isolation of individual bacterial speciesProne to contamination, not suitable for unculturable bacteria[103,104]
ConventionalPhage typingHigh specificityLimited to certain bacteria, limited resolution, risk of contamination[105,106,107]
MolecularRPA (recombinase polymerase amplification)High sensitivity, high specificity, rapid turnaround time, onsite disease detection and diagnosisLimited multiplexing, low throughput, poor stability, high cost[108,109,110]
MolecularLAMP (loop-mediated isothermal amplification)Onsite disease detection and diagnosis, simplicityLimited multiplexing, limited commercial availability[108,111]
MolecularNGS (next-generation sequencing)High throughput, large-scale, high resolution, versatilityTechnical expertise, sample quality, data analysis, limited access[112,113]
MolecularFISH (fluorescence in-situ hybridization)High sensitivity, high specificity, rapid, Easy to visualizePhotobleaching, autofluorescence, limited to specific sequences[114,115]
MolecularPCR (polymerase chain reaction)Ease of use, quantitation possible, sensitivity, specificity, speed, versatilityPCR system affects the effectiveness, complexity, false positives[104,116,117]
SerologicalFCM (flow cytometry)High throughput, multiple parameters, sensitivity, accuracyComplexity, sample preparation, limited cell types, interference[118,119,120]
SerologicalELISA (enzyme-linked immunosorbent assay)Speed, ease of use, testing seed health, sensitivity, specificity, versatilityExpensive, complexity, limited scope, false positives[121,122,123]
SerologicalIF (immunofluorescence)Sensitive and visualizable, multiplexing, ease of usePhotobleaching, requirement for specific antibodies, limited to surface-exposed molecules, need for specialized equipment[102,124,125]
SerologicalImmunoStripRapid, sensitive, specific, portable, easy to useSpecificity varies among products, expensive, a qualitative test, [122,126]
Biomarker-basedPlant metabolite profilingHigh specificity, early detection, high sensitivity, multiplexing capability, High throughputExpensive, incomplete database, data analysis skills, complexity[127,128,129]
Biomarker-basedPathogen metabolite profilingHigh specificity, early detection, high sensitivity, multiplexing capability, high throughputExpensive, incomplete database, data analysis skills, limited to specific stages of infection, limited to specific pathogens[130,131,132,133]
Biomarker-basedMicrobiome analysisHigh throughput, early detection, broad coverageExpensive, incomplete database, data analysis skills, complexity, limited understanding, technical challenges[128,134]
Vision-basedHyperspectral imagingEarly-detection, can be used to study the effects of various factors, such as environmental conditions or treatment, on plant growth and developmentExpensive, can be affected by factors such as light intensity and wavelength, may require the use of specialized equipment and software[135,136,137,138]
Vision-basedSpectroscopic imagingEarly-detection, non-destructive, high spatial resolution, high sensitivity, rapid analysisExpensive, limited depth penetration, complex sample preparation, need for calibration, interference from external factors[139,140]
AI (artificial intelligence)Machine learningSpeed, accuracy, cost-effectivenessLack of database, lack of algorithms, lack of understanding, dependence on high-quality data, lack of interpretability, bias[141,142,143,144,145]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Yang, P.; Zhao, L.; Gao, Y.G.; Xia, Y. Detection, Diagnosis, and Preventive Management of the Bacterial Plant Pathogen Pseudomonas syringae. Plants 2023, 12, 1765. https://doi.org/10.3390/plants12091765

AMA Style

Yang P, Zhao L, Gao YG, Xia Y. Detection, Diagnosis, and Preventive Management of the Bacterial Plant Pathogen Pseudomonas syringae. Plants. 2023; 12(9):1765. https://doi.org/10.3390/plants12091765

Chicago/Turabian Style

Yang, Piao, Lijing Zhao, Yu Gary Gao, and Ye Xia. 2023. "Detection, Diagnosis, and Preventive Management of the Bacterial Plant Pathogen Pseudomonas syringae" Plants 12, no. 9: 1765. https://doi.org/10.3390/plants12091765

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop