Recent Advances in Diagnosing and Managing Phytoplasma Diseases

Abstract

Phytoplasmas are obligate intracellular parasitic bacteria that infect over 1000 plant species globally, causing devastating diseases characterized by yellowing, witches’ broom, phyllody, and significant yield losses in economically important crops. The unculturable nature of these pathogens has historically hindered their study; however, advances in molecular biology and genomics have substantially accelerated progress over the past two decades. This review provides a comprehensive overview of current knowledge on phytoplasma diseases and control technologies. In terms of taxonomy, phytoplasmas are currently classified into 37 16Sr groups with over 150 subgroups based on 16S rRNA gene analysis, and approximately 50 ‘Candidatus Phytoplasma’ species have been formally named. Genomic studies have revealed that phytoplasmas possess highly reduced genomes (530–1350 kb) lacking many essential metabolic pathways, reflecting their obligate parasitic lifestyle. Regarding pathogenesis, secreted effector proteins such as SAP (Secreted Aster Yellows Witches’ Broom Protein), TENGU (tengu-su inducer), and SWP (Secreted Wheat Blue Dwarf Protein) manipulate plant hormone signaling and developmental processes, leading to characteristic disease symptoms. Detection technologies have evolved from traditional microscopy to molecular methods, including nested PCR, real-time quantitative PCR, loop-mediated isothermal amplification (LAMP), and CRISPR/Cas-based systems (Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR-associated protein), with AI-based image recognition and remote sensing emerging as promising tools for large-scale field monitoring. Integrated management strategies encompassing agricultural practices, insect vector control, biological control agents, induced resistance, and breeding for resistance are discussed. Finally, future research directions, including functional genomics, microbiome-based approaches, and precision agriculture technologies, are highlighted. This review aims to provide researchers and practitioners with a systematic reference for understanding phytoplasma biology and developing effective disease management strategies.

1. Introduction

Phytoplasmas belong to the class Mollicutes and are obligate parasitic prokaryotes that have lost their cell walls and a substantial number of metabolic genes during evolution. Their genomes are highly reduced (530–1350 kb), encoding only one-quarter to one-fifth the number of genes found in other bacteria, and they are entirely dependent on host plants and insect vectors for survival. These unique biological characteristics have greatly hindered in-depth research on these pathogens [1,2]. Phytoplasmas colonize the phloem sieve tubes of plants and cause characteristic symptoms such as yellowing, witches’ broom, stunting, and phyllody by disrupting hormone balance and metabolic systems, which can lead to plant death in severe cases. Phytoplasmas are primarily transmitted by phloem-feeding insect vectors, including leafhoppers, planthoppers, and psyllids, as well as through grafting and dodder parasitism [1,2]. The latent period in insect vectors generally ranges from 10 to 84 days, while symptom development in plant hosts typically occurs within 3 to 10 days post-inoculation [3]. However, these periods are highly variable depending on the specific phytoplasma strain, host plant species, vector species, and environmental conditions, particularly temperature. In some host–pathogen combinations, the incubation period in plants can extend to several months or even over one year [4]. These specialized biological features and diverse transmission routes have resulted in the widespread occurrence of phytoplasma diseases in over 100 countries and regions worldwide, posing a serious threat to agricultural and forestry production [3,5,6]. Phytoplasma diseases cause devastating yield losses across diverse cropping systems. In vegetable production, yield losses can reach up to 100% in susceptible crops such as cucumber, tomato, pepper, and potato [5,7,8]. Lethal yellowing disease has killed millions of coconut palms in the Caribbean over the past four decades, with Jamaica alone losing more than seven million palm trees by 1980 [9]. In Europe, grapevine yellows diseases, particularly Flavescence dorée and Bois noir, have caused severe damage to viticulture for several decades, leading to significant economic losses and quality deterioration [10]. Paulownia witches’ broom disease affected 880,000 hectares of timber production in China by 2006, causing billions of dollars in economic losses [11]. In China, jujube witches’ broom disease caused by ‘Candidatus Phytoplasma ziziphi’ remains a severe threat to jujube production, with recent molecular studies revealing genetic diversity among phytoplasma strains from different resistant and susceptible cultivars [12].

In recent years, significant breakthroughs have been achieved in phytoplasma research: an internationally recognized classification system based on 16S rRNA has been established, whole-genome sequencing has revealed mechanisms of adaptive evolution, the discovery of effector proteins has elucidated the molecular basis of pathogenicity, and quantitative detection methods have enabled precise measurement of phytoplasma titers and their dynamics during infection [13]. However, the pathogenic mechanisms remain incompletely understood, and critical challenges persist in resistant cultivar breeding, biological control, and early warning systems. This review summarizes recent advances in phytoplasma taxonomy and biological characteristics, major disease types, transmission pathways, pathogenic mechanisms, detection technologies, and integrated control strategies. Furthermore, emerging technologies, including microbiomics, novel molecular detection methods, and artificial intelligence, are highlighted, aiming to provide a reference for future research and scientific management of phytoplasma diseases.

2. Taxonomy and Biological Characteristics of Phytoplasmas

2.1. Evolution and Current Status of the Classification System

The classification of phytoplasmas has long been both a research focus and a challenge in this field. Since phytoplasmas cannot be cultured on artificial media, traditional culture-dependent classification methods, such as phenotypic characterization and biochemical testing, are inapplicable. Early classification relied primarily on electron microscopy, serological reactions, and host responses; however, these methods suffered from low accuracy and poor reproducibility [14].

In 1992, Seemüller first proposed a phytoplasma classification system based on restriction fragment length polymorphism (RFLP) analysis of the 16S rRNA gene, using 17 restriction enzymes to digest PCR-amplified 16S rRNA genes and grouping phytoplasmas according to the similarity of their restriction enzyme digestion patterns. This system classifies phytoplasmas into distinct 16Sr groups using 17 restriction enzymes, with similarity coefficients ≥0.85 assigned to the same group and ≥0.97 assigned to the same subgroup [15]. This classification scheme is simple, stable, and reproducible, and it rapidly became the international standard. To date, the International Phytoplasma Working Group has recognized 37 16Sr groups, from 16SrI to 16SrXXXIX, comprising over 150 subgroups, with approximately 50 named ‘Candidatus Phytoplasma’ species [16]. These groups are closely associated with important agricultural and forestry diseases. Among them, the 16SrI group (aster yellows group) has the widest distribution and broadest host range, including various vegetables and ornamental plants. The 16SrV group (elm yellows group) includes elm yellows, jujube witches’ broom, and flavescence dorée of grapevine, representing important diseases of fruit trees and grapevines [17]. The 16SrXI group mainly infects gramineous crops including rice and maize. The 16SrX group (apple proliferation group) includes apple proliferation disease caused by Candidatus Phytoplasma mali (16SrX-A), pear decline caused by Candidatus Phytoplasma pyri (16SrX-C), and European stone fruit yellows caused by Candidatus Phytoplasma prunorum (16SrX-B), representing major threats to pome and stone fruit production [18]. The 16SrXII group includes the coconut lethal yellowing cluster, which poses a serious threat to the coconut industry in tropical regions [19]. The 16SrXXXII group predominantly affects trees in the Salicaceae family, causing diseases such as willow witches’ broom.

In addition to the 16S rRNA gene, researchers have developed classification markers based on other conserved genes, including secY, secA, tuf, rp (ribosomal protein genes), and groEL, for more refined classification, strain differentiation, and phylogenetic analysis [18]. In recent years, the application of whole-genome sequencing technology has provided new perspectives for phytoplasma classification. Phylogenomic analysis based on core genomes can more accurately reveal the evolutionary relationships among phytoplasmas [20]. Cho [21] conducted a comparative whole-genome analysis of 11 strains within the 16SrI group and identified issues with intragenic variation and unequal site weighting in the RFLP classification based on the 16S rRNA gene. They proposed species delineation criteria of average nucleotide identity (ANI) >95% and shared coding genes >78%, and developed a multilocus sequence analysis (MLSA) system based on five marker genes including dnaD, degV, and secY [20]. Kirdat [22] demonstrated that phylogenetic trees constructed from core gene sets could effectively distinguish strains with highly similar 16S rRNA gene sequences. The 2022 revised guidelines for phytoplasma classification [23] officially adopted dual criteria of ANI ≥95% combined with 16S rRNA gene identity ≥98.65% for new species identification. Currently, over 55 phytoplasma genomes have been deposited in the NCBI database; however, these cover less than 20% of named species, indicating the need to expand genome sequencing coverage to improve the classification system.

2.2. Biological Characteristics

Phytoplasmas possess unique biological characteristics that have resulted from long-term adaptation to obligate parasitism. In terms of morphology and ultrastructure, phytoplasma cells exhibit pronounced pleomorphism, appearing spherical, elliptical, or filamentous, with diameters typically ranging from 200 to 800 nm. Due to the absence of a cell wall, phytoplasmas are enclosed only by a trilayered membrane structure, rendering them highly sensitive to osmotic pressure changes [24]. Electron microscopy has revealed that phytoplasmas tend to aggregate in clusters within phloem sieve tubes or attach to sieve tube walls and companion cells. Various membrane proteins are distributed on the cell membrane, and these play important roles in interactions with hosts and insect vectors.

Regarding genomic features, phytoplasmas exhibit extreme genome reduction. Their genome sizes range from only 530 to 1350 kb, with exceptionally low GC content, typically between 21% and 29%, which is considerably lower than that of other bacteria [25]. The number of coding genes ranges from only 470 to 1050, as most biosynthetic pathways for amino acids, fatty acids, and nucleotides have been lost during evolution; consequently, these essential nutrients must be acquired from hosts or vectors [25]. Genomes also contain numerous transposable elements and repetitive sequences, resulting in considerable genomic instability, which may be associated with rapid evolution and adaptation to different host environments [25].

The metabolic characteristics of phytoplasmas similarly reflect adaptation to their parasitic lifestyle. Due to the lack of a complete tricarboxylic acid cycle and respiratory chain, phytoplasmas rely primarily on glycolysis for ATP (adenosine triphosphate) production [2,26]. The absence of multiple metabolic enzyme genes renders phytoplasmas dependent on hosts for essential nutrients including amino acids, nucleotides, and fatty acids, which is the fundamental reason for their inability to be cultured in vitro [26]. Studies have revealed that phytoplasmas possess multiple ABC (ATP-binding cassette) transporter systems and phosphotransferase systems that are specialized for acquiring various nutrients from host cells, ensuring their survival and reproduction within hosts [23]. Regarding growth and reproduction characteristics, phytoplasma concentrations within host plants display distinct seasonal fluctuation patterns, typically increasing during the vigorous growth period in spring and summer and decreasing in autumn and winter [27]. This pattern is closely related to phloem metabolic activity and assimilate transport, reflecting the dependence of phytoplasmas on host physiological status. Phytoplasma reproduction is relatively slow, with symptom development typically requiring weeks to months after inoculation; the length of this incubation period is influenced by multiple factors including temperature, plant species, and phytoplasma strain.

3. Major Phytoplasma Disease Types and Epidemiological Patterns

3.1. Major Disease Types

Phytoplasmas can infect an extremely wide range of plant species, including cereals, vegetables, fruit trees, and ornamental plants. Different phytoplasma groups exhibit varying degrees of host specificity and geographical distribution. The following section presents several important phytoplasma diseases and the typical genomic characteristics of major phytoplasma pathogens. Major phytoplasma diseases and their typical symptoms are summarized in

Table 1. Major phytoplasma diseases and their typical symptoms.

Phytoplasma Group	Disease Name	Main Plant Hosts	Typical Symptoms
16SrI	Aster yellows [28]	Aster, lettuce, carrot, celery, onion	Yellowing, stunting, witches’ broom
	Chrysanthemum yellows [29]	Chrysanthemum (Chrysanthemum spp.)	Virescence, phyllody, flower size reduction
	Tomato big bud [30]	Tomato, pepper, eggplant	Bud enlargement, leaf malformation, sterility
	Mulberry dwarf [31]	Mulberry (Morus spp.)	Yellowing, stunting, leaf shrinkage
	Carrot proliferation [32]	Carrot (Daucus carota)	Hairy root, leaf proliferation, bitter root
	Maize bushy stunt [33]	Maize (Zea mays)	Severe stunting, excessive tillering, sterility
	Strawberry green petal [34]	Strawberry (Fragaria × ananassa)	Virescence, phyllody, fruit malformation
16SrII	Peanut witches’ broom [35]	Peanut (Arachis hypogaea)	Witches’ broom, little leaf, sterility
	Faba bean phyllody [15]	Faba bean (Vicia faba)	Phyllody, virescence, pod malformation
	Lime witches’ broom [36]	Lime, lemon (Citrus spp.)	Witches’ broom, little leaf, tree decline
16SrV	Jujube witches’ broom [37]	Jujube (Ziziphus jujuba)	Excessive sprouting, little leaf, sterility
	Elm yellows [12]	Elm (Ulmus spp.)	Leaf yellowing, premature defoliation, death
	Grapevine flavescence dorée [38]	Grapevine (Vitis vinifera)	Leaf curling, yellowing, poor fruit quality
16SrVI	Paulownia witches’ broom [37]	Paulownia (Paulownia spp.)	Yellowing, decline, branch dieback
	Chinaberry decline [39]	Chinaberry (Melia azedarach)	Yellowing, decline, branch dieback
16SrX	Pear decline [37]	Pear (Pyrus spp.)	Decline, leaf curling, reduced fruit quality
	European stone fruit yellows [40]	Apricot, peach, plum (Prunus spp.)	Yellowing, stunting, fruit drop
	Apple proliferation [41]	Apple (Malus domestica)	Stipule enlargement, witches’ broom, small fruit
16SrXI	Rice orange leaf [42]	Rice (Oryza sativa)	Orange-yellow leaves, stunting, sterility
	Sugarcane white leaf [43]	Sugarcane (Saccharum spp.)	White leaf, phyllody, stunting
16SrXII	Coconut lethal yellowing [19]	Coconut (Cocos nucifera)	Premature fruit drop, leaf yellowing, death
	Cape St. Paul wilt [44]	Coconut, oil palm	Leaf wilting, inflorescence necrosis, death
	Oil palm yellowing [44]	Oil palm (Elaeis guineensis)	Yellowing, fruit abortion, decline
16SrXXXII	Willow witches’ broom [41]	Willow (Salix spp.)	Witches’ broom, little leaf, stunting
	Poplar witches’ broom [45]	Poplar (Populus spp.)	Witches’ broom, leaf chlorosis, decline

, while the genomic characteristics of representative phytoplasma pathogens are presented in

Table 2. Genomic characteristics of major phytoplasma pathogens.

16Sr Group	Candidatus Species	Representative Strain	Genome Size (kb)	GC%	GenBank Accession	Associated Diseases
16SrI	Ca. P. asteris	OY-M/AY-WB	706–861	26.9–27.7	AP006628/CP000061	Aster yellows, chrysanthemum yellows, tomato big bud, mulberry dwarf, maize bushy stunt [46,47]
16SrII	Ca. P. aurantifolia	WBDL	~650	24.0	AJWL00000000	Peanut witches’ broom, faba bean phyllody, lime witches’ broom [48]
16SrV	Ca. P. ziziphi	Jwb-nky	751	23.3	CP025121	Jujube witches’ broom [49]
16SrV	Ca. P. ulmi	ULW	~660	26.6	JPLR00000000	Elm yellows [50]
16SrV	Ca. P. vitis	FD-C	~670	22.4	CCSE00000000	Grapevine flavescence dorée [51]
16SrX	Ca. P. mali	AT	602	21.4	CU469464	Apple proliferation [52]
16SrX	Ca. P. pyri	PD1	~600	21.0	FR863631	Pear decline [53]
16SrX	Ca. P. prunorum	ESFY-G1	~600	21.0	FR863634	European stone fruit yellows [53]
16SrXI	Ca. P. oryzae	MMbita1	567	23.0	NZ LSYZ00000000	Rice orange leaf [54]
16SrXI	Ca. P.sacchari	SCWL	~540	21.0	NQO000000000	Sugarcane white leaf [55]
16SrXII	Ca. P. australiense	PAa/SLY	880–960	27.0–27.4	AM422018/CP002548	Coconut lethal yellowing, Cape St. Paul wilt [56]
16SrXXXII	Ca. P. malaysianum	PPWB-MY	~680	25.0	PHHS00000000	Willow witches’ broom [48]

Genomic data were obtained from the NCBI Genome database.

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3.2. Transmission and Epidemiological Patterns

The occurrence and spread of phytoplasma diseases result from the combined effects of pathogens, hosts, insect vectors, and environmental factors, exhibiting distinct spatiotemporal distribution patterns. Insect vector transmission is the primary mode of phytoplasma spread, with phloem-feeding insects, particularly those in the families Cicadellidae (leafhoppers), Delphacidae (planthoppers), and Psyllidae (psyllids), playing decisive roles in disease epidemiology [57]. Phytoplasmas undergo a complex and precise circulative propagative cycle within their vectors [26,58]. When insects feed on phytoplasma-infected host plants, the pathogens enter the digestive tract along with phloem sap, subsequently cross the intestinal epithelial cells into the hemolymph system, multiply and spread within the hemolymph, and ultimately colonize the salivary glands, from which they are injected into new host plants during subsequent feeding [12]. This complete circulation process requires a latent period of 10 to 40 days, after which the vector carries and continuously transmits phytoplasmas for its entire lifespan. This persistent transmission characteristic makes vector control a critical component of disease management [58]. The host specificity of vectors largely determines the epidemiological patterns of phytoplasma diseases. The host specificity of vectors largely determines the epidemiological patterns of phytoplasma diseases [4]. Taking jujube witches’ broom disease as an example, its primary vector is Hishimonus sellatus, which produces one to two generations annually in Shandong and Hebei provinces, with adult population peaks occurring in May–June and August–September, coinciding closely with the peak periods of disease transmission. Overwintering is a critical phase in the epidemiological cycle of phytoplasma diseases. Phytoplasmas can survive winter in the phloem of perennial host plants, including fruit trees, woody ornamentals, and herbaceous perennials, serving as primary inoculum sources for the following growing season [58]. The overwintering strategy of insect vectors varies among species: some overwinter as eggs, others as nymphs or adults in plant debris, soil, or on alternative host plants. For example, Scaphoideus titanus, the vector of grapevine Flavescence dorée, overwinters as eggs inserted into grapevine bark, while Cacopsylla species, vectors of apple proliferation and pear decline, overwinter as adults on conifers before returning to fruit trees in spring [58]. Understanding overwintering biology is essential for developing effective early-season vector management strategies.

In addition to insect vector transmission, vegetative propagation methods such as grafting, layering, and division can directly transmit phytoplasmas, representing an important route for disease spread in perennial crops including fruit trees and ornamental plants. Consequently, the use of disease-free planting material and implementation of strict quarantine measures are essential for preventing disease dissemination [59]. Parasitic plants such as dodder (Cuscuta spp.) can also transmit phytoplasmas between different hosts and are frequently used as experimental transmission tools in research [9]. Although the contribution of dodder transmission to disease epidemics is relatively limited under natural conditions, its role should not be overlooked in certain ecosystems.

Weeds and other wild plant species play a significant role in the epidemiology of phytoplasma diseases as alternative or reservoir hosts. Similarly, Tamarix aphylla has been identified as a reservoir host for diverse phytoplasmas (16SrI-B, 16SrII-D, and 16SrVI-A) in arid regions of Iran, with disease incidence reaching up to 72% [60]. Many weed species can harbor phytoplasmas, often without showing obvious symptoms, serving as inoculum sources for infection of cultivated crops [58]. For example, in vineyards affected by Bois noir disease, weeds such as Convolvulus arvensis (field bindweed) and Urtica dioica (stinging nettle) have been identified as important reservoir hosts for ‘Candidatus Phytoplasma solani’ [61]. Similarly, various weed species in the families Asteraceae and Fabaceae have been reported as hosts for aster yellows phytoplasmas, facilitating disease spread to vegetable crops [32]. The proximity of weed hosts to cultivated fields increases the risk of phytoplasma transmission by providing breeding and feeding sites for insect vectors. Therefore, weed management in and around crop fields is an important component of integrated phytoplasma disease management strategies.

The epidemiology of phytoplasma diseases is influenced by multiple interacting factors. Climatic conditions, particularly temperature and humidity, directly affect vector reproduction and phytoplasma multiplication rates. Warm and humid climates generally favor disease occurrence, and studies have demonstrated a significant positive correlation between spring–summer temperatures and phytoplasma concentrations [62]. Cultivation practices also significantly influence disease incidence; large-scale monoculture, high planting density, and inadequate field management all increase the risk of disease outbreaks [36]. In recent years, global climate change has led to the expansion of vector distribution ranges, causing some phytoplasma diseases that were previously confined to specific regions to spread into new areas, presenting new challenges for plant protection efforts worldwide [63].

4. Molecular Mechanisms of Pathogenicity

Research on the molecular mechanisms of phytoplasma pathogenicity has been a focus of recent studies. Although phytoplasmas are not amenable to genetic manipulation, researchers have progressively elucidated the molecular mechanisms by which phytoplasmas manipulate host plant development and metabolism through genomics, transcriptomics, and proteomics approaches. Among these mechanisms, effector proteins and membrane proteins play pivotal roles.

4.1. Effector Proteins and Host Manipulation

Effectors are proteins secreted by phytoplasmas into host cells that precisely manipulate plant development and defense systems by targeting key host proteins, inducing phenotypic changes favorable for pathogen survival and transmission [64]. These effectors represent the core molecular arsenal of phytoplasma pathogenicity. The major effectors identified to date include:

Phytoplasma effector proteins and their host manipulation mechanisms are illustrated in Figure 1. SAP11 (Secreted AY-WB Protein 11) was among the first phytoplasma effectors to be extensively studied. It interacts with plant TCP (TEOSINTE BRANCHED 1/CYCLOIDEA/PCF) transcription factors, interfering with apical dominance and leaf development, resulting in witches’ broom and little leaf symptoms [65]. SAP11 also degrades TCP transcription factors and suppresses the jasmonic acid biosynthesis pathway, thereby reducing plant defenses against insects and enhancing vector fitness. This creates a tripartite mutualistic relationship among phytoplasmas, plants, and insect vectors [65].

SAP54/PHYL1 (PHYLlody 1) is the key effector responsible for phyllody symptoms. It degrades MADS-box transcription factors (such as APETALA1 and SEPALLATA) through the 26S proteasome pathway by interacting with RAD23 (RADIATION SENSITIVE23), a proteasome shuttle factor that delivers ubiquitinated substrates to the proteasome; these transcription factors control floral organ development [66]. When MADS-box transcription factors are degraded, floral organs are transformed into leaf-like structures. These leafy flowers are more suitable for vector oviposition and feeding, thereby promoting phytoplasma transmission [67].

TENGU (tengu-su inducer) is an important effector that causes plant stunting. It interferes with auxin signaling pathways, leading to stunted growth. Studies have shown that TENGU interacts with plant JAZ (JASMONATE ZIM-DOMAIN) proteins, suppressing jasmonic acid signaling while activating auxin-responsive gene expression, resulting in abnormal plant development [67]. Expression of TENGU in Arabidopsis thaliana reproduced the stunting phenotype observed in phytoplasma-infected plants, confirming its role in pathogenicity.

Research has revealed that different phytoplasma strains may carry different members of the SWP family, which are conserved in sequence but exhibit functional diversity. SWP disrupts polar auxin transport and signal transduction, leading to the failure of lateral bud inhibition and triggering excessive lateral shoot proliferation. Functional studies have demonstrated that SWP not only affects shoot branching patterns but may also be involved in regulating root development. Furthermore, SWP may act synergistically with other effectors such as SAP11 to collectively manipulate plant hormone networks, maximizing alterations in host phenotype [68].

4.2. Membrane Proteins and Vector Interactions

Phytoplasma membrane proteins play an indispensable role in the interactions between phytoplasmas and their insect vectors, directly determining whether phytoplasmas can successfully colonize vectors and achieve effective transmission. Immunodominant membrane protein (Imp) is one of the most abundant proteins on the phytoplasma cell membrane. After phytoplasmas are acquired by insect vectors, Imp interacts with vector cytoskeletal proteins such as actin and tubulin, facilitating phytoplasma passage through intestinal epithelial cells into the hemolymph system [69]. The antigenic variation of Imp has been hypothesized to help phytoplasmas evade recognition by the vector immune system, although this mechanism has been demonstrated in only a limited number of phytoplasma–vector systems [69]. Antigenic membrane protein (Amp) is also involved in phytoplasma circulation and colonization within vectors. Studies have shown that the Amp gene family exhibits considerable sequence diversity, and differences in Amp sequences among phytoplasma strains may be closely associated with vector specificity [69]. Phytoplasma membrane proteins also possess important adhesion functions, enabling stable attachment to insect salivary gland cell surfaces and completing final colonization within vectors. Experiments in specific phytoplasma–vector systems have demonstrated that strains lacking certain membrane proteins show significantly reduced ability to colonize vectors, which may lead to impaired transmission capability [70]. Research on membrane proteins holds significant theoretical and practical implications for understanding phytoplasma–vector specific recognition mechanisms. While transmission-blocking strategies such as antibody-based blocking techniques or RNA interference technologies have been proposed, their practical application remains in early stages of development and requires further validation [24].

4.3. Hormone Balance and Metabolic Interference

Phytoplasma infection causes significant alterations in plant hormone levels and metabolic pathways, representing a critical physiological basis for the development of phytoplasma disease symptoms.

Studies have demonstrated that the content and distribution of multiple hormones undergo marked changes following phytoplasma infection, although the specific patterns vary depending on the host plant species and phytoplasma strain involved. Cytokinin concentrations increase substantially, leading to excessive lateral bud sprouting and the formation of witches’ broom symptoms; for example, elevated cytokinin levels have been documented in coconut palms affected by lethal yellowing and in Chinese jujube infected with jujube witches’ broom phytoplasma [71,72]. Auxin distribution patterns become abnormal, resulting in plant stunting and malformed organ development [67,68]. Decreased jasmonic acid levels weaken plant defenses against insects, facilitating vector feeding and reproduction on infected plants [65,67]. Experimental studies in Arabidopsis thaliana have demonstrated that suppressed gibberellin synthesis further exacerbates plant stunting, while elevated salicylic acid levels activate defense responses that nevertheless fail to effectively eliminate phytoplasmas [73]. These hormonal changes may result either from direct effector protein activity or from plant responses to biotic stress. For example, it has been experimentally demonstrated that SAP11 can directly suppress the expression of jasmonic acid biosynthesis enzyme genes, thereby reducing jasmonic acid levels and enhancing insect vector reproduction [65], whereas elevated cytokinin may originate from phytoplasma synthesis or induced overproduction by the plant [68].

Metabolomic studies have revealed that phytoplasma infection causes extensive and profound changes in both primary and secondary metabolic pathways [74,75]. Regarding carbohydrate metabolism, sugars accumulate substantially in phloem sieve tubes, potentially providing abundant nutrients for phytoplasmas while disrupting plant source-sink relationships and affecting normal photosynthate allocation [76,77]. Amino acid metabolism is similarly affected, with certain amino acids such as proline and glutamate showing significantly elevated levels, possibly related to plant stress responses [75,78]. Secondary metabolite changes exhibit complex patterns: the levels of defensive secondary metabolites including phenolic compounds and flavonoids show divergent responses, with increases in some compounds reflecting plant defense responses while decreases in others may be associated with suppression of anti-insect defenses [74,78]. These coordinated changes in hormones and metabolism not only directly cause various pathological symptoms in plants but also profoundly influence plant–vector interactions, ultimately creating an ecosystem favorable for phytoplasma survival and efficient transmission—exemplifying the sophisticated strategies by which pathogens manipulate their hosts [75].

5. Detection Technologies for Phytoplasma Diseases

Phytoplasma detection technologies have evolved from morphological observation to molecular detection, and can be broadly classified into four categories: traditional detection methods, immunological methods, molecular biological methods, and AI-based image recognition and remote sensing methods.

The major detection technologies for phytoplasma diseases are summarized in

Table 3. Major Detection Technologies for Phytoplasma Diseases.

Detection Method	Advantages	Disadvantages	Reference Examples
Direct Observation	Simple operation, low cost, no special equipment required	Limited accuracy, easily confused with viral diseases, nutrient deficiencies, or herbicide damage, suitable only for preliminary assessment	Mycoplasma-like organisms in mulberry dwarf disease [31]
Electron Microscopy	Direct visualization of phytoplasma morphology and distribution, high reliability of results	Expensive equipment, high technical requirements, tedious sample preparation, unsuitable for large-scale detection	PCR (polymerase chain reaction) detection and differentiation of paulownia witches’ broom phytoplasma [37]
Histochemical Staining	Simple and rapid operation, controllable cost, preliminary localization of phytoplasma distribution	Insufficient specificity, prone to false positives, unable to identify species	DAPI fluorescence staining for jujube witches’ broom phytoplasma detection [80]
Serological Methods	Simple operation, moderate cost, suitable for preliminary screening of batch samples	Sensitivity and specificity limited by antibody quality, cross-reactivity exists, difficult to achieve precise identification	ELISA detection of coconut lethal yellowing disease [13]
Nucleic Acid Hybridization	High specificity, enables in situ tissue localization detection	Low sensitivity, tedious and time-consuming operation, high cost, difficult to promote application	PCR-based detection and differentiation of phytoplasma [81]
Conventional PCR	High sensitivity, strong specificity, short detection cycle, mature technology	Qualitative detection only, requires electrophoresis verification, prone to contamination, cannot distinguish live from dead cells	Nested PCR detection of grapevine Flavescence dorée phytoplasma using 16S rRNA gene [82]
Real-time Quantitative PCR (qPCR)	Extremely high sensitivity, strong specificity, accurate quantification, no electrophoresis required, high detection efficiency	High equipment and reagent costs, susceptible to inhibitors, cannot distinguish live from dead cells, depends on known gene sequences	SYBR Green qPCR quantitative detection of Ampelopsis grossedentata phytoplasma
Digital PCR (dPCR)	Absolute quantification without standard curves, higher sensitivity for low-abundance detection, effectively distinguishes similar sequences	High equipment cost, low single-run throughput, strict requirements for sample quality and technical operation	ddPCR (droplet digital PCR) detection of potato purple top phytoplasma
Loop-mediated Isothermal Amplification (LAMP)	Isothermal amplification without thermal cycler, simple and rapid operation, visual product detection, strong anti-interference capability	Complex primer design, specificity easily affected, amplification products prone to contamination causing false positives, difficult to quantify accurately	LAMP rapid field detection of apple proliferation disease
CRISPR/Cas Detection	Extremely high sensitivity reaching single-copy level, strong specificity, rapid results in 15–30 min, visual results (fluorescence or test strips), no thermal cycler required, suitable for field detection	Requires design of specific crRNA (CRISPR RNA), relatively high reagent costs, technology popularization still needs time	AIOD-CRISPR detection of multiple phytoplasmas [83]
High-throughput Sequencing (NGS/HTS)	No preset targets required, can discover unknown phytoplasmas, simultaneous detection of multiple pathogens, suitable for mixed infection diagnosis and new disease identification	Expensive equipment, complex data analysis, longer detection cycle (1–3 days), high cost	Metagenomic detection of paulownia witches’ broom [84]
Microfluidic Chips	Integration, automation, low reagent consumption, simultaneous detection of multiple targets, low contamination risk	High chip manufacturing cost, strict sample preprocessing requirements, limited throughput	Multiplex microfluidic platform for multiple pathogen detection [85]
AI (artificial intelligence) Image Recognition and Remote Sensing	Enables rapid large-scale screening, identifies early symptoms, UAV-mounted multispectral sensors can detect before symptoms appear, accuracy can exceed 95%	Requires large amounts of labeled training data, model generalization affected by environment, relatively high equipment cost	UAV multispectral detection of sugarcane white leaf disease [86]

. Traditional detection methods include symptom observation, electron microscopy, and histochemical staining. Although symptom observation is simple and practical, phytoplasma disease symptoms are easily confused with viral diseases and nutrient deficiencies, resulting in high misdiagnosis rates. Electron microscopy enables direct observation of phytoplasma morphology and distribution, but the equipment is expensive and sample preparation is tedious, making it unsuitable for routine detection [31]. Histochemical methods such as 4′,6-diamidino-2-phenylindole (DAPI) fluorescence staining are simple to perform but lack specificity, serving only as preliminary screening tools [79].

Immunological methods, represented by ELISA, utilize specific antigen–antibody reactions for detection. These methods are simple to operate, moderately priced, and suitable for batch screening; however, sensitivity is limited by antibody quality, and serological cross-reactivity exists among different phytoplasma strains, making precise identification difficult [87].

Molecular biological methods represent the mainstream technology for current phytoplasma detection. Conventional PCR and nested PCR offer high sensitivity and strong specificity, representing the most widely applied detection methods, though they provide only qualitative detection and require gel electrophoresis verification [88]. Real-time quantitative PCR (qPCR) achieves quantitative detection while maintaining high sensitivity, eliminates the need for electrophoresis, and significantly improves detection efficiency, although equipment and reagent costs are relatively high [89]. Digital PCR (dPCR) enables absolute quantification with distinct advantages for low-copy sample detection, but has lower throughput and higher costs [90]. Loop-mediated isothermal amplification (LAMP) requires no thermal cycler, and results can be interpreted directly through color changes, making it suitable for rapid field detection; however, primer design is complex and the risk of false positives is relatively high [91].

In addition to the methods described above, the combination of isothermal amplification with CRISPR/Cas systems has opened new avenues for on-site rapid phytoplasma detection. This technology utilizes the trans-cleavage activity activated upon target sequence recognition by Cas12a protein, coupled with fluorescent probes or lateral flow assay strips for signal output. The AIOD-CRISPR method established by Li [83] can complete phytoplasma detection within 15 min with a sensitivity of 3.37 × 10² copies/reaction, enabling visual interpretation of results and eliminating dependence on sophisticated instruments. Similarly, recombinase polymerase amplification (RPA) can complete nucleic acid amplification under isothermal conditions of 37–42 °C, and its combination with CRISPR can further enhance detection efficiency [92]. Although these methods have not yet matched the sensitivity of nested PCR and qPCR, their rapid, portable, and visual characteristics provide distinct advantages for field screening and primary-level detection. For precise diagnosis of emerging diseases or mixed infections, technologies such as high-throughput sequencing and digital PCR remain irreplaceable and can be used complementarily with rapid detection methods according to practical requirements [93].

High-throughput sequencing (NGS/HTS) technologies have revolutionized phytoplasma detection and characterization by enabling comprehensive pathogen identification without prior knowledge of the target sequences. Metagenomic approaches can simultaneously detect multiple phytoplasmas and identify novel strains in mixed infections, providing valuable insights for epidemiological studies [13]. However, the high equipment cost, complex bioinformatics analysis requirements, and relatively long turnaround time limit their application to specialized research laboratories rather than routine diagnostics. Microfluidic chip technology represents another promising advancement, integrating sample preparation, nucleic acid amplification, and signal detection into a miniaturized platform. These “lab-on-a-chip” devices offer advantages including reduced reagent consumption, shortened analysis time, and potential for automation and portability [71]. Although current microfluidic systems for phytoplasma detection remain largely in the research stage, their integration with isothermal amplification methods such as LAMP or RPA holds promise for developing next-generation point-of-care diagnostic devices.

In addition, AI-based image recognition and remote sensing technologies have emerged as promising non-destructive approaches for large-scale phytoplasma disease monitoring. Deep learning models, particularly convolutional neural networks (CNNs), have achieved accuracy rates exceeding 95% in plant disease detection from visual symptoms [94,95]. Unmanned aerial vehicles (UAVs) equipped with multispectral or hyperspectral sensors enable rapid surveillance of orchards and fields, facilitating early detection of disease symptoms before they become visually apparent. For example, Narmilan successfully applied UAV-based multispectral imaging combined with machine learning classifiers to detect sugarcane white leaf disease [86]. These technologies are particularly valuable for rapid, large-scale field screening and early warning systems.

From a practical application perspective, appropriate methods should be selected for different detection scenarios: rapid methods such as LAMP, RPA, or CRISPR/Cas are suitable for field screening due to their simple operation and minimal equipment requirements; nested PCR combined with sequencing analysis is recommended for laboratory confirmation; qPCR or dPCR can be employed for epidemiological investigations and quantitative studies; high-throughput sequencing technology can assist in new disease identification and mixed infection diagnosis; and taxonomic studies require integration of multi-locus sequencing with phylogenetic analysis [96].

6. Integrated Management of Phytoplasma Diseases

The management of phytoplasma diseases represents a worldwide challenge (

Table 4. Major Control Measures for Phytoplasma Diseases.

Control Type	Key Measures	Measure Details
Prevention and Detection	Monitoring and Preventive Measures	Systematic disease and vector monitoring combined with meteorological forecasting; source control via disease-free nurseries, quarantine, resistant varieties, and optimized cultivation [58].
Agricultural Control	Breeding and Promotion of Resistant Varieties	Breeding resistant varieties is the most economical, effective, and environmentally friendly control measure. Significant differences exist in resistance to phytoplasmas among different plant varieties. Screening methods for resistant varieties include field natural infection identification and artificial inoculation identification [97].
	Use of Disease-free Seedlings and Strict Quarantine	Seedlings are the main pathway for long-distance transmission of phytoplasma diseases. Establishing disease-free seedling propagation systems is a key measure to prevent disease spread [98].
	Timely Removal of Infected Plants	Infected plants are the main source of primary inoculum in the field. Timely detection and thorough removal of infected plants (including root systems), followed by centralized burning or deep burial, can significantly reduce field inoculum levels and disease transmission [99].
	Strengthening Cultivation Management to Enhance Plant Vigor	Vigorous plants have stronger disease resistance [9]. Balanced fertilization with increased organic fertilizer application, reasonable N-P-K ratios, and supplementation of trace elements such as zinc, boron, and iron can significantly improve plant nutrition levels and resistance.
	Adjusting Planting Structure	Avoid large-scale monoculture cropping, implement diversified variety planting to reduce the risk of large-scale disease outbreaks [100]. In high-incidence areas, consider switching to resistant or non-host crops.
Physical Control	Insect-proof Net Barriers	Using insect-proof nets in seedling greenhouses, glasshouses, and open-field cultivation can effectively block insect vectors and cut off transmission pathways [26].
	Yellow Sticky Trap Control	Hanging yellow sticky traps can attract and kill insect vectors such as leafhoppers and planthoppers. Hanging 20–30 yellow boards per mu (667 m2) at 50–80 cm above ground level can significantly reduce vector population density [4].
	Heat Treatment for Pathogen Elimination	Utilizing the sensitivity of phytoplasmas to high temperatures, heat treatment can effectively eliminate phytoplasmas from seedlings [89]. Common heat treatment methods include hot water immersion, hot air treatment, and steam heat treatment.
	Surgical Therapy	For fruit tree diseases such as jujube witches’ broom, surgical methods, including girdling and bark scraping, can be used to block phytoplasma transport and spread within the tree [101].
Chemical Control	Chemical Control of Insect Vectors	Controlling vector population density is an effective measure to cut off transmission pathways [101].
	Antibiotic Treatment	Tetracycline antibiotics such as oxytetracycline, tetracycline, and doxycycline have inhibitory effects on phytoplasmas [101]. Main application methods: trunk injection and foliar spraying [102]. Note: Antibiotic use in plant production is permitted only in certain countries and is prohibited in many regions including the European Union.
Biological Control	Antagonistic Microorganisms	Certain microorganisms can inhibit phytoplasma growth or induce plant resistance [103]. Well-studied antagonistic microorganisms include Bacillus, Pseudomonas, Streptomyces, and Trichoderma [104].
	Plant-derived Preparations	Active substances extracted from plants have inhibitory effects on phytoplasmas and are environmentally safe [105]. These include matrine, baicalin, Stellera chamaejasme extracts, and plant essential oils.
	Induced Resistance Agents	Certain chemicals or biological agents can activate the plant’s own defense system and enhance resistance to phytoplasmas [105]. Examples include benzothiadiazole (BTH, also known as acibenzolar-S-methyl), salicylic acid (SA), chitosan, and glutathione-oligosaccharin formulations. BTH has been shown to induce systemic acquired resistance (SAR) and reduce phytoplasma titers in various host plants [106].
	Biological Control of Insect Vectors	Using natural enemies and entomopathogenic microorganisms to control vector populations can indirectly control phytoplasma transmission [107]. Entomopathogenic fungi such as Beauveria bassiana and Metarhizium anisopliae, as well as parasitoid wasps and predatory insects, have shown effectiveness against leafhopper vectors [108].
	RNA Interference (RNAi) Technology	By designing double-stranded RNA or small interfering RNA targeting key phytoplasma genes, the expression of these genes can be specifically inhibited, thereby reducing phytoplasma pathogenicity or transmission capacity [109].
	Microbiome Regulation	Phytoplasma infection significantly alters host endophytic microbial community structure. Inoculation with IAA-producing endophytes or application of synthetic microbial communities can promote growth recovery in infected plants. Beneficial endophytes function through mechanisms including competitive exclusion, induction of systemic resistance, and production of antimicrobial substances [110,111].
Integrated Management System	Critical Period Control	Focus efforts on control during critical periods of phytoplasma disease occurrence and transmission [9].
	Regional Joint Prevention and Control	Phytoplasma diseases are characterized by rapid spread and wide dispersal range, necessitating implementation of regional unified control [112].
	Control Efficacy Evaluation	Regularly evaluate control efficacy and adjust control strategies in a timely manner [112].

). Because phytoplasmas parasitize plant phloem, they are difficult to reach with chemical agents; transmission pathways are complex, involving multiple insect vectors; and effective therapeutic agents are lacking, rendering traditional single-control measures of limited efficacy [45]. Therefore, a strategy of “prevention first, integrated management” must be adopted, organically combining agricultural, physical, chemical, and biological control measures to establish a comprehensive prevention and control system [6]. Additionally, emerging technologies such as microbiome regulation and nano-delivery systems are opening new avenues for green management of phytoplasma diseases; relevant research progress and future directions will be discussed in detail in the following chapter.

Different control measures possess distinct characteristics and applicable ranges. Agricultural control serves as the foundation for phytoplasma disease management, offering long-term effectiveness and sustainability, though results are relatively slow to manifest and resistant variety breeding requires extended cycles [26]. Physical control methods such as insect-proof net barriers and yellow sticky traps are simple, intuitive, and environmentally friendly, but are only suitable for protected cultivation or small-scale applications, with limited potential for large-scale field implementation. Chemical control produces rapid and obvious effects, nsecticides such as neonicotinoids and pyrethroids have proven effective in reducing vector populations and limiting phytoplasma transmission [113], but antibiotic agents pose risks of environmental residues and resistance development, and cannot eradicate phytoplasmas parasitizing the phloem [114]. Biological control aligns with the direction of green agricultural development and is environmentally friendly and sustainable; however, current challenges include unstable control efficacy, lengthy product development cycles, and immature field application technologies [115]. Therefore, future phytoplasma disease management should adhere to the principle of “prevention first, integrated strategies,” emphasizing synergistic integration of multiple technologies to establish an integrated management system combining “resistant varieties + healthy seedlings + vector control + biological control,” thereby achieving unity of economic, ecological, and social benefits [45].

In recent years, the rapid development of microbiomics has opened new directions for phytoplasma disease management. In the field of microbiomics, Zhang [109] systematically analyzed changes in endophytic bacterial communities following paulownia witches’ broom phytoplasma infection, discovering that phytoplasma infection significantly disrupted the composition and function of host endophytic microbiota, and identified 11 microbial markers applicable for disease prediction. Nguyen [111] demonstrated that the indole-3-acetic acid-producing endophytes Delftia lacustris and Rahnella aquatilis, isolated from rice roots, significantly promoted growth recovery in sugarcane white leaf disease-infected seedlings, indicating that rebuilding healthy microbiomes through inoculation with beneficial endophytes represents a highly promising ecological management strategy.

7. Current Challenges and Future Perspectives

Current detection technologies face bottlenecks including difficulties in early diagnosis, insufficient sensitivity, and low standardization levels. Phytoplasma latent periods range from 10 to 84 days, and detection sensitivity is inadequate for low-concentration infections. Field rapid detection technologies still require improvements in accuracy, portability, and cost. Detection methods lack unified standards and quality control systems, resulting in poor comparability of results across different laboratories. Development of multiplex detection technologies lags behind, and high-throughput technologies for simultaneous detection of multiple phytoplasmas remain imperfect, constraining the efficiency of mixed infection diagnosis and large-scale screening. Future efforts should focus on developing ultra-sensitive rapid detection platforms based on cutting-edge technologies such as CRISPR-Cas systems and nano-biosensors to achieve single-molecule-level point-of-care field diagnosis [111]. Multi-omics technologies including metabolomics, proteomics, and hyperspectral imaging should be utilized to screen for latent period-specific biomarkers, breaking through the limitations of symptom-dependent diagnosis. High-throughput multiplex detection platforms should be established using liquid-phase chip and microfluidic chip technologies to enable simultaneous detection of multiple phytoplasmas. Concurrently, standardization of detection methods should be advanced through development of unified detection standards, establishment of reference material libraries and quality control systems, ensuring comparability of results across different laboratories.

At the control technology level, challenges include unstable efficacy, lack of resistant varieties, and dependence on chemical pesticides for vector management. Antibiotics can only temporarily suppress symptoms without achieving eradication [116]; furthermore, antibiotic use in plant production poses significant risks including contribution to antimicrobial resistance and is prohibited in many regions such as the European Union, while physical and biological control efficacy is highly influenced by environmental conditions. Most economic crops lack highly resistant varieties, and resistance breeding progress remains slow [26]. Vector management relies primarily on chemical insecticides, with insufficient research on ecological regulation. Integrated management systems remain incomplete, with low integration of individual technologies and lack of customized solutions for different crops and regions, resulting in suboptimal control efficiency and high costs [117]. To address these challenges, it is necessary to screen and develop efficient biological control agents including antagonistic microorganisms, plant-derived fungicides, and phage therapy, achieving field control efficacy exceeding 70% through optimization of formulations and application technologies. RNA interference agents and transgenic disease-resistant plants should be developed to target key phytoplasma genes for precision control. Ecological management systems based on habitat management, natural enemy conservation and utilization, and symbiont regulation should be constructed to establish sustainable vector management models. Nano-agent delivery systems should be developed to improve targeting and bioavailability while reducing application rates and environmental pollution. Meanwhile, advances in microbiomics and nanotechnology are opening new directions for phytoplasma disease management. Studies have shown that plant endophytic microbial communities are closely associated with phytoplasma infection; by screening beneficial endophytes with antagonistic activity or resistance-inducing functions, it may be possible to construct microbial barriers against phytoplasma infection. Regulation of insect vector symbiotic microorganisms also provides new approaches for blocking transmission chains. Concurrently, nanocarrier technologies can significantly improve phloem penetration and sustained-release properties of agents such as antibiotics, enhancing control efficiency while reducing environmental risks. Materials including chitosan nanoparticles and mesoporous silica have demonstrated promising application potential in agent delivery. However, translating these emerging technologies from laboratory to field application still faces challenges including efficacy validation, ecological safety assessment, and cost control.

At the basic research level, pathogenic mechanisms have not been fully elucidated. The effectors discovered thus far represent only the tip of the iceberg, with the functions of numerous effectors remaining unknown. Key questions regarding synergistic interactions among effectors and how phytoplasmas precisely regulate host hormone balance require in-depth investigation. Resistance genes in resistant varieties have not been successfully cloned, molecular mechanisms remain unclear, and molecular markers applicable for breeding are lacking. Research on phytoplasma–vector interactions is insufficient; most vector species have not been accurately identified, and the molecular basis of transmission specificity remains unclear. Because phytoplasmas cannot be genetically transformed, functional gene verification faces significant technical barriers, severely constraining in-depth molecular mechanism studies. Future efforts should focus on systematic identification of effector protein repertoires and construction of effector–host interaction networks, elucidation of the molecular basis of phytoplasma–plant–insect tripartite interactions, and utilization of gene editing technologies such as CRISPR for functional genomics research to overcome genetic transformation bottlenecks [112]. Regarding resistance breeding, germplasm resources should be extensively collected and evaluated, genome-wide association studies should be utilized for precise identification of resistance genes and mining and cloning of plant disease resistance genes, and molecular marker-assisted breeding tools should be developed. CRISPR/Cas9 technology can be employed for targeted knockout of susceptibility genes or introduction of resistance genes, genomic selection technologies can be introduced to improve breeding efficiency, and rootstock resistance breeding strategies can be developed for grafted crops.

At the extension and application level, farmers have insufficient awareness of diseases and low motivation for control. Grassroots detection capabilities are weak, technical services are inadequate, and effective technical support is difficult to provide. The lack of dedicated control funding and compensation policies for infected tree removal has impacted control efforts. The absence of cross-regional joint prevention and control mechanisms, with each locality operating independently, has resulted in repeated disease transmission between regions, seriously affecting overall control effectiveness [6]. Construction of integrated management systems should establish intelligent monitoring and early warning platforms integrating the Internet of Things, big data, and artificial intelligence, develop zone-specific and category-specific control technology models, and formulate differentiated strategies. Efforts should strengthen social service system development, enhance grassroots technical service capabilities, and improve policy support and regional joint prevention and control mechanisms. Facing climate change challenges, long-term monitoring systems need to be established to track changes in phytoplasma and vector distribution, systematically assess the impacts of climate change on disease epidemiology, develop climate-adaptive control strategies including adjustment of control timing, optimization of measures, and breeding of adaptable varieties, and strengthen international scientific cooperation and information sharing to prevent cross-border transmission risks [93].

Finally, artificial intelligence technologies are providing new approaches for early diagnosis and precision management of phytoplasma diseases. In terms of image recognition, deep learning models based on convolutional neural networks (CNN) have achieved accuracy rates exceeding 95% in plant disease detection [94,95]. Singh [118] systematically reviewed advances in the application of deep learning for plant disease classification, detection, and segmentation. Specifically for phytoplasma diseases, Narmilan [86] utilized unmanned aerial vehicles equipped with high-resolution multispectral sensors, combined with supervised machine learning classifiers, to achieve pixel-level segmentation and automatic classification of early and severe symptoms of sugarcane white leaf disease, providing technical support for large-scale rapid screening. Regarding epidemic prediction, algorithms such as random forest, XGBoost, and long short-term memory networks (LSTM) can integrate multi-source information including meteorological data, vector population density, and historical disease incidence data to construct predictive models [95]. Furthermore, the integration of Internet of Things and AI technologies is driving the development of intelligent plant protection platforms that unify “monitoring–diagnosis–decision-making–control” [119]. However, the application of AI technologies in the phytoplasma field still faces challenges including the lack of high-quality annotated datasets and the impact of complex field environments on model generalization capability. Future efforts should focus on strengthening the construction of phytoplasma disease-specific datasets and developing lightweight, highly robust recognition models.

8. Conclusions

The core challenge in phytoplasma disease management lies in the unique biological characteristics of these pathogens: the inability to culture them in vitro constrains basic research, their obligate parasitic nature makes eradication difficult, and insect vector transmission increases control complexity. Current research has achieved important breakthroughs in molecular identification, pathogenic mechanisms, and detection technologies; however, a significant gap remains between understanding pathogenic mechanisms and achieving effective control.

Breakthroughs in phytoplasma management depend on coordinated advances in three key areas. First, deepening the analysis of effector–host interaction networks to provide the molecular foundation for targeted intervention. Second, developing early diagnostic technologies and intelligent monitoring systems to achieve “early detection, early control.” Third, innovating green management technologies, particularly the organic integration of RNA interference, biological control, and resistance breeding. Single technologies cannot effectively control phytoplasma diseases; it is essential to establish an integrated management system encompassing “monitoring and early warning–ecological regulation–precision application–resistant varieties.” Meanwhile, with the development of emerging technologies such as microbiomics, nanotechnology, CRISPR-based rapid detection, and artificial intelligence, phytoplasma disease management is expected to transition from experience-based approaches toward precision and intelligent strategies.

Notably, climate change is altering the distribution patterns of phytoplasmas and insect vectors, potentially leading to new disease outbreaks. This necessitates the establishment of long-term monitoring mechanisms, strengthening of cross-regional joint prevention and control, advancement of international cooperation, and transition from regional control to global coordinated management. Phytoplasma disease management is a long-term systematic endeavor requiring in-depth basic research, technological innovation breakthroughs, improvement of extension systems, and policy support, ultimately achieving a strategic transformation from reactive response to proactive prevention.