Current IssueEditorial BoardOnline First ArticlesArchive
Current IssueEditorial BoardOnline First ArticlesArchive

Review Article

Artificial Intelligence in Plant Microbiome based Disease Prediction: A Review

A
A.T. Daunde1,*
V
V.M. Gholve2
S
S.L. Badgujar2
1College of Horticulture, Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani-431 402, Maharashtra, India.
2Department of Plant Pathology, College of Agriculture, Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani-431 402, Maharashtra, India.

ABSTRACT

Plant diseases cause 10-16% of annual crop yield losses, creating a $220 billion global economic burden and threatening food security. Traditional diagnostics remain fundamentally reactive and late stage, while plant microbiomes harbour pre-symptomatic dysbiosis signatures with high diagnostic potential. Artificial intelligence (AI) encompassing machine learning (ML) algorithms such as random forest and XGBoost and deep learning (DL) architectures including convolutional neural networks (CNNs) and long short-term memory (LSTM) networks possesses the unique computational capacity to decipher the high-dimensional, zero-inflated and compositional data generated by next-generation microbiome sequencing platforms. This structured review, synthesising 114 peer-reviewed studies from 2014-2026, evaluates AI-driven microbiome disease prediction across pathosystems including tomato bacterial wilt (Ralstonia solanacearum), potato late blight (Phytophthora infestans), wheat Fusarium wilt and soybean sudden death syndrome, with reported predictive accuracies of 82-93% under controlled validation conditions. Multimodal integration of microbiome, metatranscriptomic and metabolomic data delivers incremental accuracy gains of 5-10%, though with proportionally increased cost and complexity. Critical barriers persist data scarcity (n<100 diseased samples in most studies), severe class imbalance (80-95% healthy samples), batch effects, the “black box” nature of DL models and the near complete absence of cross-site field validation. We propose a phased translational roadmap emphasising long read sequencing, explainable AI (XAI), causal inference, standardised validation protocols and ethical data governance to overcome these generalisation failures. With sustained interdisciplinary investment and equitable technology transfer, AI-microbiome integration anticipates mainstream field adoption within 10-15 years, positioning preventive microbiome management as a cornerstone of sustainable global food security.

INTRODUCTION

Plant diseases cause 10-16% of annual crop yield losses, creating a $220 billion global economic burden and threatening food security (FAO, 2023; González-Rodríguez et al., 2024). Traditional diagnostics are fundamentally reactive, relying on late stage visual symptoms, requiring specialized personnel and lacking modern scalability (González-Rodríguez et al., 2024; Jafar et al., 2024; Nkwocha, 2025). Consequently, phytopathology is embracing a paradigm shift toward the holobiont concept, recognizing that presymptomatic disease signatures embed within plant microbiomes (D’Elia et al., 2023; Pace et al., 2025).
       
Despite this promise, profound disciplinary silos isolate microbiome science from applied agriculture and machine learning (ML) applications often lacks biological expertise (D’Elia et al., 2023; Papoutsoglou et al., 2023). Integrating microbiology, plant pathology and artificial intelligence (AI) is critical for predictive disease management. AI and ML are mathematically essential to decipher the complexity, extreme sparsity and high dimensionality of multiomics data, driving improvements in predictive accuracy over traditional statistical frameworks (D’Elia et al., 2023; Papoutsoglou et al., 2023; Mehta et al., 2025). To facilitate readability across the multidisciplinary concepts discussed herein, a comprehensive list of abbreviations used throughout this review is provided in Table 1.

Table 1: Abbreviations used in this review.


 
Conceptual framework: AI, machine learning and deep learning
 
While often used interchangeably, artificial intelligence (AI), machine learning (ML) and deep learning (DL) represent a hierarchical and progressively specialised set of computational paradigms. Artificial intelligence (AI) is the broadest umbrella term, referring to any computational system designed to perform tasks that typically require human cognitive intelligence, including pattern recognition, decision-making and natural language processing (D’Elia et al., 2023). Machine learning (ML) is a specific subset of AI in which algorithms learn statistical patterns directly from labelled or unlabelled data without being explicitly programmed for each task; classical ML methods relevant to microbiome diagnostics include random forest (RF), support vector machines (SVM), gradient boosting (XGBoost) and logistic regression (Wilhelm et al., 2021; Metagar and Walikar, 2024). Deep learning (DL) is a further specialised subset of ML that employs multi-layered artificial neural networks including convolutional neural networks (CNNs), recurrent neural networks (RNNs) and long short-term memory (LSTM) networks to automatically extract hierarchical feature representations from raw, high-dimensional data such as amplicon sequence variant (ASV) abundance matrices and hyperspectral imagery (Przymus et al., 2025; Shafik et al., 2025; Mandlik and Lenina, 2026). In the context of this review, ML methods are preferred when sample sizes are limited and interpretability is paramount, while DL excels at complex pattern extraction from large, multimodal datasets but at the cost of reduced biological transparency.
 
Novelty and positioning relative to prior reviews
 
Several recent reviews have addressed adjacent themes: Przymus et al., (2025) comprehensively reviewed deep learning architectures applied broadly to microbiome analysis, while D’Elia et al. (2023) reported key ML findings from the ML4Microbiome COST action, primarily addressing human and soil microbiomes. Pace et al., (2025) focused on AI applications in soil microbiome health assessment and Papoutsoglou et al., (2023) outlined methodological best practices for ML in microbiome research without a disease-prediction focus. Critically, no prior review has simultaneously: (i) concentrated specifically on plant microbiome-based presymptomatic disease prediction as a unified pathosystem; (ii) critically evaluated both algorithmic trade-offs and biological validation barriers within the same framework; and (iii) proposed a phased translational roadmap linking laboratory omics to field-deployable diagnostics. This review fills that gap by integrating plant pathology, microbiome ecology and AI engineering into a cohesive, crop-centric synthesis supported by the latest 2023-2026 literature.
 
Review objectives and structure
 
This review bridges these domains through multidisciplinary integration, with four explicit objectives: (1) to critically synthesise the mechanistic role of plant microbiome dynamics in pre-symptomatic disease detection; (2) to evaluate the performance, advantages and limitations of AI, ML and DL algorithms applied to microbiome-based disease prediction; (3) to identify critical implementation barriers encompassing data scarcity, class imbalance, batch effects and model interpretability and (4) to propose a phased translational roadmap for field-ready diagnostic deployment. The review is structured as follows: Section 2 describes plant microbiome fundamentals and disease etiology; Section 3 outlines the literature search methodology; Section 4 covers ML algorithms and workflows; Section 5 presents landmark case studies and multimodal integration advances; Section 6 critically analyses challenges and limitations; and Section 7 proposes the innovation roadmap. By synthesising AI applications in disease forecasting and microbiome profiling, this review targets a multidisciplinary audience-microbiologists, plant pathologists, AI engineers and agricultural policymakers- seeking to transition laboratory innovations into effective and equitable agricultural practices.
 
Plant microbiome fundamentals and disease etiology
 
Microbiome composition
 
The plant holobiont functions as an interconnected evolutionary ecosystem where the Phyto microbiome dictates host adaptation, resilience and overall agricultural success (Lyu et al., 2021). Microbes colonize highly structured spatial niches, including the phyllosphere (aerial plant surfaces), the endosphere (internal tissues) and the rhizosphere (the immediate root-soil interface) (Monti et al., 2026). The rhizosphere serves as an intense biochemical battlefield where plants secrete up to 30% of their photosynthetically fixed carbon as root exudates including sugars, organic acids and volatile organic compounds to actively recruit specific bacterial and fungal consortia (Wankhade et al., 2025). Microbial community assembly relies on a stable, functionally redundant core microbiota that provides essential host services, alongside a variable accessory microbiota that dynamically shifts in response to acute environmental stressors (Chen et al., 2022). Phytopathologists quantify these complex microbial networks using alpha diversity indices (e.g., Shannon and Simpson indices) to assess localized species richness and beta diversity matrices to measure compositional divergence between healthy and diseased states (Cassol et al., 2025). Because high throughput sequencing datasets record relative sequence abundances rather than absolute cellular populations, advanced compositional data analysis remains mathematically imperative to prevent spurious correlations when identifying key taxa (Gloor et al., 2017).
 
Disease interactions detection necessity
 
Pathogens fundamentally alter the plant microbiome long before visible necrotic symptoms emerge, driving the community from a state of disease-suppressive homeostasis into pathogenic dysbiosis (Liu et al., 2022). Studies on bacterial wilt demonstrate that Ralstonia solanacearum invasion violently disrupts the tomato rhizosphere community, reducing the network’s spatial complexity and directly facilitating secondary cascading infections by fungal pathogens like Fusarium solani (Su et al., 2020). Conversely, disease-suppressive soils naturally inhibit pathogen establishment through the powerful antagonistic activity of beneficial taxa (Mavrodi et al., 2011). Certain strains of Pseudomonas secrete broad-spectrum secondary metabolites most notably 2,4-diacetylphloroglucinol (DAPG) and phenazine-1-carboxylic acid (PCA) which directly inhibit aggressive soilborne pathogens (Mavrodi et al., 2011). Beyond direct antagonism, DAPG functions as a potent elicitor that enhances induced systemic resistance (ISR) through the host’s COI1-mediated jasmonic acid signalling pathway, priming systemic plant immunity against subsequent necrotrophic pathogen attacks (Nguyen et al., 2025). Root exudates chemically orchestrate this defence mechanism by selectively recruiting antagonistic taxa, such as Lysobacter and specific disease-suppressive Streptomyces clades, the moment plant receptors detect pathogen ingress (Gómez et al., 2015).
       
Next-generation sequencing platforms (e.g., 16S rRNA amplicon arrays and shotgun metagenomics) generate exceedingly massive, high dimensional datasets that track these pre-symptomatic microbial shifts (Durazzi et al., 2021). Traditional visual diagnostic tools entirely miss this vital community level context. Artificial intelligence (AI) and machine learning (ML) models uniquely possess the computational capacity to decipher this highly sparse, nonlinear multiomics data (Przymus et al., 2025). Deep convolutional neural networks identify the latent, pre-symptomatic microbial signatures of dysbiosis, allowing automated agricultural frameworks to accurately forecast disease outbreaks, map nutrient availability and implement targeted interventions before irreversible crop yield losses occur (Rathnayake et al., 2025; Shafik et al., 2025). Table 2 summarizes the specific pathosystem dynamics, their underlying disease implications and the corresponding AI/ML analytical methods utilized to decode these pre-symptomatic microbial shifts across key agricultural pathosystems.

Table 2: Pathosystem dynamics and machine learning applications.


 
Methodology: Literature search strategy
 
Search protocol
 
A systematic literature search was conducted between January 2014 and March 2026 to identify peer-reviewed publications examining the application of AI and ML to plant microbiome-based disease prediction. Databases searched included Web of Science, Scopus, PubMed, Google Scholar and CAB Abstracts. The following Boolean search strings were employed:
       
(“plant microbiome” OR “rhizosphere microbiome” OR “phytobiome”) AND (“machine learning” OR “artificial intelligence” OR “deep learning”) AND (“disease prediction” OR “disease detection” OR “plant pathology”).
       
(“16S rRNA” OR “metagenomics” OR “multi-omics”) AND (“Random Forest” OR “neural network” OR “XGBoost”) AND (“crop disease” OR “plant pathogen”).
       
(“explainable AI” OR “XAI” OR “SHAP” OR “LIME”) and (“plant health” OR “microbiome” OR “phytopathology”).
 
Inclusion criteria
 
Studies were included if they: (i) applied one or more AI/ML/DL algorithms to microbiome data for plant disease prediction, classification or risk assessment; (ii) were published in peer-reviewed journals; (iii) were published in English; and (iv) reported quantitative predictive performance metrics (e.g., accuracy, AUC-ROC, F1-score).
 
Exclusion criteria
 
Studies were excluded if they: (i) applied AI solely to image-based symptom recognition without microbiome data integration; (ii) focused exclusively on human or animal microbiomes; (iii) were published before 2014 (limiting to the post-NGS era); or (iv) were conference abstracts without full-text data.
 
Study selection outcomes
 
The initial search retrieved 847 records. After duplicate removal (n = 134), title and abstract screening (n = 512 excluded) and full-text eligibility assessment (n = 87 excluded), 114 studies were included in the final synthesis (Fig 1). Eight seminal pre-2014 foundational references were selectively included where methodologically critical (e.g., SMOTE: Chawla et al., 2002; compositional data: Gloor et al., 2017).

Fig 1: PRISMA 2020-adapted flowchart illustrating systematic literature search and selection for this review (Page et al., 2021).


 
Machine learning methodologies
 
Algorithms, trade-offs and workflow
 
Supervised models classify crop health and identify keystone pathogen-suppressive taxa. Random forest (RF, ~85-90% accuracy) extracts critical biomarkers through feature importance, while gradient boosting (XGBoost, ~88-93% accuracy) predicts disease outbreaks from multiomic data (Khelfaoui et al., 2025). Support vector machines (SVM) navigate high dimensional microbiome spaces to separate healthy from infected plants and logistic regression provides baseline interpretability for field diagnostics (Wilhelm et al., 2021). Deep learning resolves complex host microbiome dynamics: Convolutional neural networks (CNNs) extract spatial infection patterns from microbial abundance matrices, long short term memory (LSTMs) networks model temporal disease progression and attention mechanisms weight critical pathogenic features (Pace et al., 2025; Shafik et al., 2025). A detailed comparative summary of these algorithms, including their specific crop applications, reported predictive accuracies and inherent biological and computational limitations, is presented in Table 3. Unsupervised methods, including K-means, PCA and t-SNE, stratify pre symptomatic microbial communities to visualize emerging dysbiosis without labelled data (Pace et al., 2025).

Table 3: Comparative summary of AI/ML/DL algorithms for plant microbiome disease prediction.


       
Deep learning automatically identifies complex pathogenic patterns but demands massive datasets and creates “black-box” models that obscure biological mechanisms (Shafik et al., 2025). Conversely, traditional algorithms offer speed, few shot capability and crucial interpretability for agronomists to understand disease etiology. Feature engineering incorporates diversity indices, network metrics, phylogenetic matrices and centred log ratio (CLR) normalized abundance profiles (Busato et al., 2022). Plant microbiome datasets exhibit extreme sparsity and 50-90% zero inflation, requiring compositional data analysis and Zero Inflated Negative Binomial (ZINB) models to prevent spurious correlations when targeting pathogen antagonistic consortia (Busato et al., 2022; Xu et al., 2015).
       
Robust diagnostic pipelines execute sequential preprocessing, feature selection, training/validation and hyperparameter optimization (Papoutsoglou et al., 2023). Because diseased samples often represent a minority class in field surveys, researchers deploy the synthetic minority over sampling technique (SMOTE), focal loss, or class weighting to handle imbalance (Chawla et al., 2002). Rigorous evaluation requires stratified k-fold cross validation and external validation across independent sites or seasons, assessed via AUC-ROC and F1-scores. To prevent data leakage which falsely inflates disease prediction accuracy models must enforce temporal ordering, stratify by geographic site and restrict normalization calculations strictly within training folds (Papoutsoglou et al., 2023).
 
Contextualising predictive accuracy in imbalanced microbiome datasets
 
Reported prediction accuracies across the reviewed literature (82-93%) must be interpreted with significant methodological caution. Agricultural microbiome datasets are characteristically imbalanced, typically comprising 80-95% healthy and only 5-20% diseased samples (Fang, 2023). In such contexts, a naïve classifier predicting “healthy” for all observations trivially achieves >80% accuracy while providing zero diagnostic value. Therefore, accuracy alone is a misleading performance metric. The reviewed studies achieving the highest reported accuracies (~91-93%) did so under controlled greenhouse conditions with artificially balanced training sets using SMOTE augmentation, which may not replicate field-level class distributions (Chawla et al., 2002). Rigorous performance benchmarking in this field requires AUC-ROC, F1-score (particularly for the minority diseased class), matthews correlation coefficient (MCC) and cross-site validation performance metrics that only a minority of reviewed studies reported comprehensively. Where studies in this review report accuracy figures, readers should consider these as upper-bound estimates obtained under favourable validation conditions, pending independent field replication (Papoutsoglou et al., 2023). To assist researchers in selecting the appropriate computational framework based on dataset size, biological objectives and the need for mechanistic interpretability, Table 4 provides a direct comparison between classical machine learning and deep learning approaches in the context of plant microbiome diagnostics.

Table 4: Summary comparison of classical machine learning vs. deep learning for plant microbiome disease prediction.


 
Advances in AI-microbiome disease prediction
 
Landmark case studies
 
Pathological success relies on shifting from reactive symptom observation to proactive dysbiosis detection (D’Elia et al., 2023). Artificial intelligence (AI) systems uniquely identify the earliest microbial network shifts that precede irreversible physiological crop damage (Papoutsoglou et al., 2023). In vascular pathosystems, such as tomato bacterial wilt caused by Ralstonia solanacearum, rapid pathogen proliferation permanently occludes xylem vessels, causing sudden and fatal plant collapse (Su et al., 2020). Random forest algorithms analysing 16S rRNA amplicon data detect the initial fragmentation of the rhizosphere microbiome with 85% accuracy, providing highly reliable disease forecasts up to two weeks before any visual wilting occurs (Liu et al., 2022). Crucially, these models identify specific protective keystone taxa, including Bacillus and Pseudomonas species, which competitively exclude pathogens and secrete antimicrobial secondary metabolites, thus enabling site specific biocontrol deployment (Liu et al., 2022).
       
Similarly, managing Fusarium oxysporum a highly persistent soil borne fungus causing destructive vascular wilt requires preventive action before root cortex penetration and vascular colonization occur (Komissarov et al., 2025). Deep learning architectures combining convolutional neural networks (CNN, 92% spatial accuracy) and long short term memory networks (LSTM, 89% temporal accuracy) analyse fungal internal transcribed spacer (ITS) profiling to accurately map pre symptomatic fungal dysbiosis (Yuan et al., 2020). This predictive capability directs the precise, timely application of Trichoderma biocontrol strains, which directly parasitize pathogenic hyphae and induce systemic host resistance. Agronomists utilizing these AI directed Trichoderma applications achieve up to a 35% reduction in synthetic fungicide reliance (Vincenzo et al., 2025).
       
In explosive foliar pathosystems, predictive models disrupt rapid polycyclic pathogen reproduction cycles (Avelino et al., 2015). Potato late blight (Phytophthora infestans) destroys entire crop canopies within days of initial symptom onset (Zhu et al., 2024). LSTM networks modelling temporal leaf microbiome dysbiosis achieve 88% predictive accuracy, providing a vital 5-7-day intervention window to halt sporangial dissemination and prevent devastating secondary infections across the field (Zhu et al., 2024). Multimodal ensembles tackle coffee leaf rust (Hemileia vastatrix) by integrating canopy spectral data with phyllosphere microbial profiles (Avelino et al., 2015). These integrated models predict impending rust outbreaks 21 days pre symptom with 91% accuracy. Extensive field trials demonstrate that these AI driven early warnings successfully prevent catastrophic defoliation, cutting fungicide applications by 40% while preserving crucial crop yields (Avelino et al., 2015).
 
Extended crop case studies
 
Beyond the tomato and potato pathosystems, AI-microbiome integration has demonstrated promise across several other globally critical crops. Wheat (Triticum aestivum): Fusarium crown rot, caused by Fusarium pseudograminearum, induces measurable rhizosphere microbiome dysbiosis before crown browning symptoms emerge. Yuan et al., (2020) demonstrated that soil macroecological microbiome patterns analysed through Random Forest achieved 91% accuracy in predicting Fusarium wilt occurrence across field sites, identifying Bacillus and Streptomyces abundance thresholds as key predictive biomarkers. LSTM architectures have further modelled longitudinal shifts in wheat root microbiomes across growing seasons, capturing the temporal window of greatest predictive opportunity (Sharma and Xu, 2021).
 
Maize (Zea mays)
 
Stalk rot diseases caused by Colletotrichum graminicola and Fusarium verticillioides represent multi-pathogen challenges amenable to multimodal AI frameworks. Graph neural network (GNN) approaches trained on co-occurrence network disruption data have demonstrated ~87% classification accuracy distinguishing susceptible from resistant root microbiome configurations (Maryam et al., 2024).
 
Rice (Oryza sativa)
 
Bacterial leaf blight caused by Xanthomonas oryzae pv. oryzae exhibits pre-symptomatic phyllosphere microbiome shifts detectable by SVM classifiers trained on 16S rRNA data, with reported AUC values of 0.88-0.91 (Ghannam and Techtmann, 2021). Additionally, blast disease (Magnaporthe oryzae) represents one of the most destructive fungal pathogens globally; metatranscriptomic integration with CNN architectures has shown early promise in detecting effector gene expression signatures within the endosphere microbiome.
 
Soybean (Glycine max)
 
Sudden death syndrome caused by Fusarium virguliforme represents a model system for XAI-assisted biomarker discovery. SHAP analysis of Random Forest models trained on soybean rhizosphere amplicon data successfully identified Trichoderma spp. and fluorescent Pseudomonas abundance as the top-ranked protective taxa, directly guiding targeted SynCom formulation strategies (Fadiji et al., 2025).
 
Multimodal integration
 
Capturing the full pathological landscape of the plant holobiont requires comprehensive multi omics integration. Single modality analyses often miss the complex mechanistic interactions defining disease onset and progression (Crandall et al., 2020). Multimodal frameworks synthesize microbiome profiles with metatranscriptomics, metabolomics and hyperspectral imaging, directly linking the mere presence of a pathogen to its active virulence gene expression and the corresponding suppression of host defence metabolites (Kajrolkar, 2025). Computational data fusion strategies systematically address these multidimensional dynamics. Early fusion concatenates raw multi omics features before analysis, while late fusion ensembles independent model predictions (Zhao et al., 2024). Intermediate fusion utilizes sophisticated attention mechanisms to extract joint latent representations, excelling at pinpointing crucial biological interactions such as explicitly linking antimicrobial root exudates to the specific recruitment of disease suppressive microbial consortia (Benkirane et al., 2023). Additionally, graph neural networks (GNN) process microbial co-occurrence networks as direct mathematical features, mapping exactly how pathogen invasion fractures cooperative microbial hubs to facilitate infection (Maryam et al., 2024). Ultimately, multimodal models compensate for single modality noise and extreme sparsity, consistently delivering a 5-10% improvement in predictive accuracy and enabling highly reliable field level disease interception (Kajrolkar, 2025).
 
Transfer learning interpretability
 
Deploying predictive models across globally diverse agricultural environments demands robust transfer learning to overcome geographic and host genetic variability (Ghannam, 2021). By pretraining foundation models on massive, diverse datasets like the Earth Microbiome Project, algorithms successfully learn universal microbial representations (Thompson et al., 2017). These algorithms undergo targeted domain adaptation to bridge the persistent ecological gap between controlled laboratory pathosystems and highly heterogeneous field conditions (Ghannam, 2021). Few shots learning techniques, utilizing meta learning and prototype networks, further adapt these powerful models to specific crop species such as transferring resistance insights across the Solanaceae family using minimal local data (Wu et al., 2023).
       
Simultaneously, explainable AI (XAI) remains strictly necessary for translating opaque black box predictions into biologically validated pathology (Givisis et al., 2025). Analytical algorithms utilize SHapley Additive exPlanations (SHAP) and Local interpretable model agnostic explanations (LIME) to precisely quantify the exact predictive contribution of individual microbial taxa to disease suppression (Abekoon et al., 2025; Salih et al., 2024). Attention visualizations highlight critical spatial and temporal dysbiosis patterns marking initial infection sites, while knowledge distillation extracts transparent ecological rules from complex neural networks (Givisis et al., 2025). This interpretability facilitates the rigorous ecological validation of identified biomarkers, ensuring agronomists can biologically trust AI recommendations to direct targeted microbiome engineering and precise biocontrol deployments (Abekoon et al., 2025).
 
Critical challenges and limitations
 
Data-related challenges
 
Microbiome-based phytopathology relies on high-throughput sequencing, which remains prohibitively expensive and yields datasets frequently containing fewer than 100 diseased samples (Fadiji et al., 2025). Consequently, researchers confront the high dimension low sample size (HDLSS) problem. Models analysing thousands of microbial taxa against sparse sample sizes risk severe overfitting, ultimately failing to accurately predict field-level epidemics (Przymus et al., 2025). Agricultural datasets exhibit extreme class imbalance, typically comprising 80-95% healthy and only 5-20% diseased samples (Fang, 2023). This disparity biases algorithms toward predicting healthy states, drastically reducing the sensitivity required to detect early-stage infections (Xu et al., 2015).
       
Beyond scarcity, profound technical variations compromise disease forecasting models. Differences in DNA extraction protocols, primer selection and sequencing platforms introduce severe batch effects that actively obscure true biological signals of pathogen ingress (Nearing et al., 2022). Additionally, microbial matrices are strictly compositional and zero-inflated, with 50-90% of features recorded as zeros due to biological absence or detection limits (Gloor et al., 2017). Standard linear regression models mathematically misinterpret these zero-inflated matrices, generating spurious correlations between microbial taxa that fundamentally misguide field-level pathogen suppression and biocontrol strategies (Gloor et al., 2017).
 
Methodological and validation issues
 
Machine learning efficiently identifies microbial dysbiosis, but algorithms cannot inherently establish disease causation (Khelfaoui et al., 2025). To prove that specific microbial shifts actively suppress pathogens rather than merely co-occurring with necrotic plant tissue, pathologists must validate computational predictions using in vivo Synthetic Communities (SynComs) (Fadiji et al., 2025). Researchers face a strict interpretability trade-off: deep neural networks achieve the highest predictive accuracy for destructive diseases like late blight but operate as opaque “black boxes.” This opacity obscures the precise ecological mechanisms driving pathogen inhibition, frustrating agronomists seeking actionable insights (Przymus et al., 2025).
       
This inherent opacity directly drives generalisation failures. Diagnostic models optimised for specific crop cultivars or controlled greenhouse pathosystems frequently collapse when transferred to heterogeneous field environments facing distinct climatic pressures (Pace et al., 2025). Rigorous cross-location and cross-season validations remain exceedingly rare, thereby heavily skewing the scientific literature toward positive, yet completely non-replicable, diagnostic pipelines (Papoutsoglou et al., 2023).
 
Biological and practical constraints
 
Biological complexity fundamentally constrains current predictive frameworks (Fadiji et al., 2025). Diverse microbial taxa exhibit extensive functional redundancy, meaning completely different bacterial clades execute equivalent disease-suppressive functions (Louca et al., 2016). Dynamic plant heterogeneity driven by genetic variation, developmental age and acute physiological stress alters root exudate profiles, constantly restructuring the microbiome and confounding stable disease signatures across growing seasons (Fadiji et al., 2025). Processing massive next-generation sequencing (NGS) pipelines requires weeks of intensive bioinformatics analysis and specialised expertise largely unavailable to smallholder farmers, profoundly exacerbating the agricultural digital divide (Raja and Raja, 2024).
 
AI vs. traditional statistical methods: A critical comparison
 
Traditional statistical frameworks including Analysis of Variance (ANOVA), PCA, linear discriminant analysis (LDA), generalised linear models (GLM) and permanova have long formed the analytical backbone of microbiome research in plant pathology (Papoutsoglou et al., 2023). These methods offer critical advantages: They are computationally inexpensive, statistically interpretable, produce formal hypothesis tests with p-values and require modest sample sizes (n = 20-50). However, they carry three fundamental limitations that constrain their utility for disease prediction in complex microbiome datasets.
       
First, traditional methods assume data normality and independence assumptions directly violated by the compositionality, zero-inflation and extreme sparsity of amplicon sequencing data (Gloor et al., 2017). Second, they are univariate or low-dimensional by design, incapable of simultaneously modelling the thousands of microbial taxa, functional genes and metabolite interactions that define pre-symptomatic dysbiosis. Third, they are fundamentally correlational: ANOVA identifies that specific taxa differ between healthy and diseased states but cannot predict disease outcome in novel environments with acceptable sensitivity.
       
Machine learning methods overcome these limitations by learning non-linear, high-dimensional patterns from training data without parametric assumptions, enabling genuine predictive generalisation. Benchmarking studies consistently demonstrate that random forest and XGBoost outperform LDA and GLM by 15-25% in AUC-ROC for microbiome-based disease classification tasks (D’Eliaet_al2023). However, this superiority is contingent on adequate sample sizes (n ≥ 200 for ML; n ≥1,000 for DL) and rigorous cross-validation. In low-sample scenarios (n<100), regularised GLMs and partial least squares discriminant analysis (PLS-DA) remain competitive and their interpretability advantage is substantial for field application. The optimal strategy for most plant pathology applications is therefore a tiered analytical approach: initial exploratory analysis with PCA/Permanova, hypothesis generation with regularised GLMs and definitive predictive modelling with RF or XGBoost validated across independent sites (Table 5).

Table 5: Algorithmic limitations and pathology implications.


 
Innovations and implementation roadmap
 
Long-read sequencing technologies (PacBio, Oxford Nanopore) resolve microbial communities to the strain level, distinguishing virulent pathogens from protective endophytes (Knobloch et al., 2024). Portable MinION sequencers enable real-time, on-site epidemic surveillance. Integrating multiomics genomics, metatranscriptomics and metabolomics  reveals how plants systemically recruit disease-suppressive consortia during pathogen attacks (Kajrolkar, 2025). Self-supervised learning utilises unlabelled agricultural data, while causal inference algorithms distinguish true biocontrol mechanisms from spurious correlations, ensuring interventions actively suppress pathogens (Khelfaoui et al., 2025). Bayesian networks provide critical uncertainty quantification, delivering reliable disease risk assessments. Edge computing executes lightweight models directly on mobile devices in remote fields (Heydari and Mahmoud, 2025). Coupled with affordable CRISPR and isothermal amplification assays costing under $10 per test, these tools facilitate immediate, targeted biocontrol deployment (Bugingo et al., 2026).
 
Economic feasibility and adoption barriers
 
The economic viability of AI-driven microbiome diagnostics is currently constrained by prohibitive per-sample costs. Whole-genome shotgun metagenomics currently costs $100-500 per sample, while 16S rRNA amplicon sequencing costs $15-50 per sample economically inaccessible for smallholder farmers in low-income countries (Fadiji et al., 2025; Raja and Raja, 2024). By contrast, conventional fungicide applications cost $30-120 per hectare per season, frequently applied preventively without diagnostic confirmation, representing both an economic and environmental inefficiency.
       
The economic argument for AI diagnostics strengthens substantially when evaluated at the regional or national scale. A 2% reduction in annual crop losses from the current 10-16% baseline achievable through early AI-guided intervention would represent a recovery of approximately $4.4 billion annually from the $220 billion global crop loss burden (FAO, 2023). To reach economic viability at the farm level, per-test costs must decline to the $5-15 range within five years (Fadiji et al., 2025). Targeted technologies making this feasible include loop-mediated isothermal amplification (LAMP) assays integrated with minimal ML inference engines, portable nanopore sequencers (Oxford Nanopore MinION at ~$1,000 capital cost with $50-100 per run) and cloud-based ML inference platforms that remove computational hardware barriers from individual farms (Knobloch et al., 2024).
       
Beyond direct costs, adoption barriers include farmer digital literacy, technical extension service capacity, trust in AI recommendations, data privacy concerns and connectivity infrastructure in remote agricultural regions. Successful models for technology adoption in low-resource settings such as India’s e-Choupal network and East Africa’s digital extension services offer transferable frameworks for microbiome diagnostic dissemination (Raja and Raja, 2024).

Ethical and regulatory considerations
 
Data ownership and governance
 
Microbiome sequencing generates highly granular environmental and biological data tied to specific farm locations. Without clear data governance frameworks, this data collected by commercial diagnostic companies could be monetised without equitable farmer compensation. Governments and international bodies must establish farmer data sovereignty principles, ensuring that microbiome diagnostic data generated on-farm remains the legal property of the farmer and is not commercially exploited without informed consent (Adeyeye et al., 2025).
 
Algorithmic bias
 
Current AI models are predominantly trained on datasets from temperate, high-income agricultural systems (North America, Europe, Australia), meaning model performance in tropical smallholder systems where crop losses are highest and food security most precarious is largely unvalidated (Raja and Raja, 2024). This introduces a systematic algorithmic bias that could exacerbate global diagnostic inequity. Mandating geographic and agro-ecological diversity in training datasets should become a standard journal and funder requirement.
 
Regulatory pathways for microbiome-based biomarkers
 
AI-derived microbiome biomarkers used for crop insurance, disease certification or pesticide application recommendations require regulatory validation analogous to pharmaceutical biomarker approval. No unified international regulatory framework for plant microbiome diagnostic claims currently exists (Fadiji et al., 2025). The European union’s plant health regulation (EU 2016/2031) and the US EPA’s Biopesticide Registration framework provide partial precedent, but do not specifically address AI-generated predictive biomarkers.
 
Environmental considerations
 
AI-guided SynCom applications involve deliberate environmental release of formulated microbial consortia. While the microorganisms involved are typically native soil residents, large-scale coordinated deployment could perturb local microbiome diversity and introduce competitive exclusion dynamics. Environmental risk assessment frameworks analogous to those applied to biological control agents under EU Regulation 1107/2009 should govern these applications (Panchal et al., 2026).
 
Phased implementation roadmap
 
Translating predictive diagnostics into sustainable agronomy demands a phased implementation roadmap. Within three to five years, researchers must deliver cultivar-specific, field-validated predictive models and pilot rapid diagnostic assays ($5-10 per test) directly in farmer fields (Fadiji et al., 2025). Over five to ten years, precision agriculture will fully integrate routine microbiome monitoring as a standard advisory practice, utilising distributed edge computing to forecast localised disease outbreaks (Nkwocha and Chandel, 2025). Ultimately, beyond ten years, preventive microbiome management will systematically replace synthetic chemical applications, establishing highly resilient agroecosystems. Achieving this paradigm shift requires breaking disciplinary silos through intense interdisciplinary collaboration among pathologists, microbiologists and AI engineers. Sustained funding, targeted farmer adoption incentives and equitable technology transfer mechanisms remain critical success factors to ensure diagnostic equity and prevent digital divides in global disease management (Fadiji et al., 2025; Khelfaoui et al., 2025).

CONCLUSION

The microbiome disease paradigm is firmly established, demonstrating that early pre-symptomatic detection signatures exist across diverse pathosystems. Integrating AI and ML delivers 82-93% predictive accuracy, representing a substantial improvement over traditional reactive pathology. While multimodal fusion, transfer learning and explainability advance field deployment, significant challenges persist regarding data scarcity, model generalisation, validation and standardisation. Critically, reported accuracy figures must be interpreted within their validation context predominantly controlled greenhouse studies with balanced datasets rather than as field-ready benchmarks. Translating these innovations demands critical success factors. Researchers must enforce data standardisation through harmonised protocols, open repositories and longitudinal studies.  Breaking disciplinary silos requires intense collaboration among microbiologists, pathologists, ML engineers and agronomists. Rigorous validation must ensure temporal independence, geographic diversity and farmer field testing. Models must move beyond opaque black boxes by prioritising explainable AI (XAI) and mechanistic biomarker validation. Widespread adoption relies heavily on cost reduction via sequencing improvements and computational efficiency. Ethical deployment requires data governance frameworks, regulatory pathways and algorithmic bias mitigation. Ultimately, this convergence drives a paradigm shift from reactive disease response to proactive, surveillance-based prevention. This transition fosters agricultural sustainability by drastically reducing synthetic chemical inputs, thereby protecting ecosystem health. Predictive microbiome management bolsters global food security, ensuring crop resilience, yield protection and stable supplies amid escalating climate change. Achieving this vision requires prioritising diagnostic equity and capacity building in low-resource regions to prevent digital divides. With sustained investment, this transformative technology anticipates mainstream commercial adoption within 10-15 years.

CONFLICT OF INTEREST

The authors declare that they have no conflicts of interest relevant to the content of this article.

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    Review Article

    Artificial Intelligence in Plant Microbiome based Disease Prediction: A Review

    A
    A.T. Daunde1,*
    V
    V.M. Gholve2
    S
    S.L. Badgujar2
    1College of Horticulture, Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani-431 402, Maharashtra, India.
    2Department of Plant Pathology, College of Agriculture, Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani-431 402, Maharashtra, India.

    ABSTRACT

    Plant diseases cause 10-16% of annual crop yield losses, creating a $220 billion global economic burden and threatening food security. Traditional diagnostics remain fundamentally reactive and late stage, while plant microbiomes harbour pre-symptomatic dysbiosis signatures with high diagnostic potential. Artificial intelligence (AI) encompassing machine learning (ML) algorithms such as random forest and XGBoost and deep learning (DL) architectures including convolutional neural networks (CNNs) and long short-term memory (LSTM) networks possesses the unique computational capacity to decipher the high-dimensional, zero-inflated and compositional data generated by next-generation microbiome sequencing platforms. This structured review, synthesising 114 peer-reviewed studies from 2014-2026, evaluates AI-driven microbiome disease prediction across pathosystems including tomato bacterial wilt (Ralstonia solanacearum), potato late blight (Phytophthora infestans), wheat Fusarium wilt and soybean sudden death syndrome, with reported predictive accuracies of 82-93% under controlled validation conditions. Multimodal integration of microbiome, metatranscriptomic and metabolomic data delivers incremental accuracy gains of 5-10%, though with proportionally increased cost and complexity. Critical barriers persist data scarcity (n<100 diseased samples in most studies), severe class imbalance (80-95% healthy samples), batch effects, the “black box” nature of DL models and the near complete absence of cross-site field validation. We propose a phased translational roadmap emphasising long read sequencing, explainable AI (XAI), causal inference, standardised validation protocols and ethical data governance to overcome these generalisation failures. With sustained interdisciplinary investment and equitable technology transfer, AI-microbiome integration anticipates mainstream field adoption within 10-15 years, positioning preventive microbiome management as a cornerstone of sustainable global food security.

    INTRODUCTION

    Plant diseases cause 10-16% of annual crop yield losses, creating a $220 billion global economic burden and threatening food security (FAO, 2023; González-Rodríguez et al., 2024). Traditional diagnostics are fundamentally reactive, relying on late stage visual symptoms, requiring specialized personnel and lacking modern scalability (González-Rodríguez et al., 2024; Jafar et al., 2024; Nkwocha, 2025). Consequently, phytopathology is embracing a paradigm shift toward the holobiont concept, recognizing that presymptomatic disease signatures embed within plant microbiomes (D’Elia et al., 2023; Pace et al., 2025).
           
    Despite this promise, profound disciplinary silos isolate microbiome science from applied agriculture and machine learning (ML) applications often lacks biological expertise (D’Elia et al., 2023; Papoutsoglou et al., 2023). Integrating microbiology, plant pathology and artificial intelligence (AI) is critical for predictive disease management. AI and ML are mathematically essential to decipher the complexity, extreme sparsity and high dimensionality of multiomics data, driving improvements in predictive accuracy over traditional statistical frameworks (D’Elia et al., 2023; Papoutsoglou et al., 2023; Mehta et al., 2025). To facilitate readability across the multidisciplinary concepts discussed herein, a comprehensive list of abbreviations used throughout this review is provided in Table 1.

    Table 1: Abbreviations used in this review.


     
    Conceptual framework: AI, machine learning and deep learning
     
    While often used interchangeably, artificial intelligence (AI), machine learning (ML) and deep learning (DL) represent a hierarchical and progressively specialised set of computational paradigms. Artificial intelligence (AI) is the broadest umbrella term, referring to any computational system designed to perform tasks that typically require human cognitive intelligence, including pattern recognition, decision-making and natural language processing (D’Elia et al., 2023). Machine learning (ML) is a specific subset of AI in which algorithms learn statistical patterns directly from labelled or unlabelled data without being explicitly programmed for each task; classical ML methods relevant to microbiome diagnostics include random forest (RF), support vector machines (SVM), gradient boosting (XGBoost) and logistic regression (Wilhelm et al., 2021; Metagar and Walikar, 2024). Deep learning (DL) is a further specialised subset of ML that employs multi-layered artificial neural networks including convolutional neural networks (CNNs), recurrent neural networks (RNNs) and long short-term memory (LSTM) networks to automatically extract hierarchical feature representations from raw, high-dimensional data such as amplicon sequence variant (ASV) abundance matrices and hyperspectral imagery (Przymus et al., 2025; Shafik et al., 2025; Mandlik and Lenina, 2026). In the context of this review, ML methods are preferred when sample sizes are limited and interpretability is paramount, while DL excels at complex pattern extraction from large, multimodal datasets but at the cost of reduced biological transparency.
     
    Novelty and positioning relative to prior reviews
     
    Several recent reviews have addressed adjacent themes: Przymus et al., (2025) comprehensively reviewed deep learning architectures applied broadly to microbiome analysis, while D’Elia et al. (2023) reported key ML findings from the ML4Microbiome COST action, primarily addressing human and soil microbiomes. Pace et al., (2025) focused on AI applications in soil microbiome health assessment and Papoutsoglou et al., (2023) outlined methodological best practices for ML in microbiome research without a disease-prediction focus. Critically, no prior review has simultaneously: (i) concentrated specifically on plant microbiome-based presymptomatic disease prediction as a unified pathosystem; (ii) critically evaluated both algorithmic trade-offs and biological validation barriers within the same framework; and (iii) proposed a phased translational roadmap linking laboratory omics to field-deployable diagnostics. This review fills that gap by integrating plant pathology, microbiome ecology and AI engineering into a cohesive, crop-centric synthesis supported by the latest 2023-2026 literature.
     
    Review objectives and structure
     
    This review bridges these domains through multidisciplinary integration, with four explicit objectives: (1) to critically synthesise the mechanistic role of plant microbiome dynamics in pre-symptomatic disease detection; (2) to evaluate the performance, advantages and limitations of AI, ML and DL algorithms applied to microbiome-based disease prediction; (3) to identify critical implementation barriers encompassing data scarcity, class imbalance, batch effects and model interpretability and (4) to propose a phased translational roadmap for field-ready diagnostic deployment. The review is structured as follows: Section 2 describes plant microbiome fundamentals and disease etiology; Section 3 outlines the literature search methodology; Section 4 covers ML algorithms and workflows; Section 5 presents landmark case studies and multimodal integration advances; Section 6 critically analyses challenges and limitations; and Section 7 proposes the innovation roadmap. By synthesising AI applications in disease forecasting and microbiome profiling, this review targets a multidisciplinary audience-microbiologists, plant pathologists, AI engineers and agricultural policymakers- seeking to transition laboratory innovations into effective and equitable agricultural practices.
     
    Plant microbiome fundamentals and disease etiology
     
    Microbiome composition
     
    The plant holobiont functions as an interconnected evolutionary ecosystem where the Phyto microbiome dictates host adaptation, resilience and overall agricultural success (Lyu et al., 2021). Microbes colonize highly structured spatial niches, including the phyllosphere (aerial plant surfaces), the endosphere (internal tissues) and the rhizosphere (the immediate root-soil interface) (Monti et al., 2026). The rhizosphere serves as an intense biochemical battlefield where plants secrete up to 30% of their photosynthetically fixed carbon as root exudates including sugars, organic acids and volatile organic compounds to actively recruit specific bacterial and fungal consortia (Wankhade et al., 2025). Microbial community assembly relies on a stable, functionally redundant core microbiota that provides essential host services, alongside a variable accessory microbiota that dynamically shifts in response to acute environmental stressors (Chen et al., 2022). Phytopathologists quantify these complex microbial networks using alpha diversity indices (e.g., Shannon and Simpson indices) to assess localized species richness and beta diversity matrices to measure compositional divergence between healthy and diseased states (Cassol et al., 2025). Because high throughput sequencing datasets record relative sequence abundances rather than absolute cellular populations, advanced compositional data analysis remains mathematically imperative to prevent spurious correlations when identifying key taxa (Gloor et al., 2017).
     
    Disease interactions detection necessity
     
    Pathogens fundamentally alter the plant microbiome long before visible necrotic symptoms emerge, driving the community from a state of disease-suppressive homeostasis into pathogenic dysbiosis (Liu et al., 2022). Studies on bacterial wilt demonstrate that Ralstonia solanacearum invasion violently disrupts the tomato rhizosphere community, reducing the network’s spatial complexity and directly facilitating secondary cascading infections by fungal pathogens like Fusarium solani (Su et al., 2020). Conversely, disease-suppressive soils naturally inhibit pathogen establishment through the powerful antagonistic activity of beneficial taxa (Mavrodi et al., 2011). Certain strains of Pseudomonas secrete broad-spectrum secondary metabolites most notably 2,4-diacetylphloroglucinol (DAPG) and phenazine-1-carboxylic acid (PCA) which directly inhibit aggressive soilborne pathogens (Mavrodi et al., 2011). Beyond direct antagonism, DAPG functions as a potent elicitor that enhances induced systemic resistance (ISR) through the host’s COI1-mediated jasmonic acid signalling pathway, priming systemic plant immunity against subsequent necrotrophic pathogen attacks (Nguyen et al., 2025). Root exudates chemically orchestrate this defence mechanism by selectively recruiting antagonistic taxa, such as Lysobacter and specific disease-suppressive Streptomyces clades, the moment plant receptors detect pathogen ingress (Gómez et al., 2015).
           
    Next-generation sequencing platforms (e.g., 16S rRNA amplicon arrays and shotgun metagenomics) generate exceedingly massive, high dimensional datasets that track these pre-symptomatic microbial shifts (Durazzi et al., 2021). Traditional visual diagnostic tools entirely miss this vital community level context. Artificial intelligence (AI) and machine learning (ML) models uniquely possess the computational capacity to decipher this highly sparse, nonlinear multiomics data (Przymus et al., 2025). Deep convolutional neural networks identify the latent, pre-symptomatic microbial signatures of dysbiosis, allowing automated agricultural frameworks to accurately forecast disease outbreaks, map nutrient availability and implement targeted interventions before irreversible crop yield losses occur (Rathnayake et al., 2025; Shafik et al., 2025). Table 2 summarizes the specific pathosystem dynamics, their underlying disease implications and the corresponding AI/ML analytical methods utilized to decode these pre-symptomatic microbial shifts across key agricultural pathosystems.

    Table 2: Pathosystem dynamics and machine learning applications.


     
    Methodology: Literature search strategy
     
    Search protocol
     
    A systematic literature search was conducted between January 2014 and March 2026 to identify peer-reviewed publications examining the application of AI and ML to plant microbiome-based disease prediction. Databases searched included Web of Science, Scopus, PubMed, Google Scholar and CAB Abstracts. The following Boolean search strings were employed:
           
    (“plant microbiome” OR “rhizosphere microbiome” OR “phytobiome”) AND (“machine learning” OR “artificial intelligence” OR “deep learning”) AND (“disease prediction” OR “disease detection” OR “plant pathology”).
           
    (“16S rRNA” OR “metagenomics” OR “multi-omics”) AND (“Random Forest” OR “neural network” OR “XGBoost”) AND (“crop disease” OR “plant pathogen”).
           
    (“explainable AI” OR “XAI” OR “SHAP” OR “LIME”) and (“plant health” OR “microbiome” OR “phytopathology”).
     
    Inclusion criteria
     
    Studies were included if they: (i) applied one or more AI/ML/DL algorithms to microbiome data for plant disease prediction, classification or risk assessment; (ii) were published in peer-reviewed journals; (iii) were published in English; and (iv) reported quantitative predictive performance metrics (e.g., accuracy, AUC-ROC, F1-score).
     
    Exclusion criteria
     
    Studies were excluded if they: (i) applied AI solely to image-based symptom recognition without microbiome data integration; (ii) focused exclusively on human or animal microbiomes; (iii) were published before 2014 (limiting to the post-NGS era); or (iv) were conference abstracts without full-text data.
     
    Study selection outcomes
     
    The initial search retrieved 847 records. After duplicate removal (n = 134), title and abstract screening (n = 512 excluded) and full-text eligibility assessment (n = 87 excluded), 114 studies were included in the final synthesis (Fig 1). Eight seminal pre-2014 foundational references were selectively included where methodologically critical (e.g., SMOTE: Chawla et al., 2002; compositional data: Gloor et al., 2017).

    Fig 1: PRISMA 2020-adapted flowchart illustrating systematic literature search and selection for this review (Page et al., 2021).


     
    Machine learning methodologies
     
    Algorithms, trade-offs and workflow
     
    Supervised models classify crop health and identify keystone pathogen-suppressive taxa. Random forest (RF, ~85-90% accuracy) extracts critical biomarkers through feature importance, while gradient boosting (XGBoost, ~88-93% accuracy) predicts disease outbreaks from multiomic data (Khelfaoui et al., 2025). Support vector machines (SVM) navigate high dimensional microbiome spaces to separate healthy from infected plants and logistic regression provides baseline interpretability for field diagnostics (Wilhelm et al., 2021). Deep learning resolves complex host microbiome dynamics: Convolutional neural networks (CNNs) extract spatial infection patterns from microbial abundance matrices, long short term memory (LSTMs) networks model temporal disease progression and attention mechanisms weight critical pathogenic features (Pace et al., 2025; Shafik et al., 2025). A detailed comparative summary of these algorithms, including their specific crop applications, reported predictive accuracies and inherent biological and computational limitations, is presented in Table 3. Unsupervised methods, including K-means, PCA and t-SNE, stratify pre symptomatic microbial communities to visualize emerging dysbiosis without labelled data (Pace et al., 2025).

    Table 3: Comparative summary of AI/ML/DL algorithms for plant microbiome disease prediction.


           
    Deep learning automatically identifies complex pathogenic patterns but demands massive datasets and creates “black-box” models that obscure biological mechanisms (Shafik et al., 2025). Conversely, traditional algorithms offer speed, few shot capability and crucial interpretability for agronomists to understand disease etiology. Feature engineering incorporates diversity indices, network metrics, phylogenetic matrices and centred log ratio (CLR) normalized abundance profiles (Busato et al., 2022). Plant microbiome datasets exhibit extreme sparsity and 50-90% zero inflation, requiring compositional data analysis and Zero Inflated Negative Binomial (ZINB) models to prevent spurious correlations when targeting pathogen antagonistic consortia (Busato et al., 2022; Xu et al., 2015).
           
    Robust diagnostic pipelines execute sequential preprocessing, feature selection, training/validation and hyperparameter optimization (Papoutsoglou et al., 2023). Because diseased samples often represent a minority class in field surveys, researchers deploy the synthetic minority over sampling technique (SMOTE), focal loss, or class weighting to handle imbalance (Chawla et al., 2002). Rigorous evaluation requires stratified k-fold cross validation and external validation across independent sites or seasons, assessed via AUC-ROC and F1-scores. To prevent data leakage which falsely inflates disease prediction accuracy models must enforce temporal ordering, stratify by geographic site and restrict normalization calculations strictly within training folds (Papoutsoglou et al., 2023).
     
    Contextualising predictive accuracy in imbalanced microbiome datasets
     
    Reported prediction accuracies across the reviewed literature (82-93%) must be interpreted with significant methodological caution. Agricultural microbiome datasets are characteristically imbalanced, typically comprising 80-95% healthy and only 5-20% diseased samples (Fang, 2023). In such contexts, a naïve classifier predicting “healthy” for all observations trivially achieves >80% accuracy while providing zero diagnostic value. Therefore, accuracy alone is a misleading performance metric. The reviewed studies achieving the highest reported accuracies (~91-93%) did so under controlled greenhouse conditions with artificially balanced training sets using SMOTE augmentation, which may not replicate field-level class distributions (Chawla et al., 2002). Rigorous performance benchmarking in this field requires AUC-ROC, F1-score (particularly for the minority diseased class), matthews correlation coefficient (MCC) and cross-site validation performance metrics that only a minority of reviewed studies reported comprehensively. Where studies in this review report accuracy figures, readers should consider these as upper-bound estimates obtained under favourable validation conditions, pending independent field replication (Papoutsoglou et al., 2023). To assist researchers in selecting the appropriate computational framework based on dataset size, biological objectives and the need for mechanistic interpretability, Table 4 provides a direct comparison between classical machine learning and deep learning approaches in the context of plant microbiome diagnostics.

    Table 4: Summary comparison of classical machine learning vs. deep learning for plant microbiome disease prediction.


     
    Advances in AI-microbiome disease prediction
     
    Landmark case studies
     
    Pathological success relies on shifting from reactive symptom observation to proactive dysbiosis detection (D’Elia et al., 2023). Artificial intelligence (AI) systems uniquely identify the earliest microbial network shifts that precede irreversible physiological crop damage (Papoutsoglou et al., 2023). In vascular pathosystems, such as tomato bacterial wilt caused by Ralstonia solanacearum, rapid pathogen proliferation permanently occludes xylem vessels, causing sudden and fatal plant collapse (Su et al., 2020). Random forest algorithms analysing 16S rRNA amplicon data detect the initial fragmentation of the rhizosphere microbiome with 85% accuracy, providing highly reliable disease forecasts up to two weeks before any visual wilting occurs (Liu et al., 2022). Crucially, these models identify specific protective keystone taxa, including Bacillus and Pseudomonas species, which competitively exclude pathogens and secrete antimicrobial secondary metabolites, thus enabling site specific biocontrol deployment (Liu et al., 2022).
           
    Similarly, managing Fusarium oxysporum a highly persistent soil borne fungus causing destructive vascular wilt requires preventive action before root cortex penetration and vascular colonization occur (Komissarov et al., 2025). Deep learning architectures combining convolutional neural networks (CNN, 92% spatial accuracy) and long short term memory networks (LSTM, 89% temporal accuracy) analyse fungal internal transcribed spacer (ITS) profiling to accurately map pre symptomatic fungal dysbiosis (Yuan et al., 2020). This predictive capability directs the precise, timely application of Trichoderma biocontrol strains, which directly parasitize pathogenic hyphae and induce systemic host resistance. Agronomists utilizing these AI directed Trichoderma applications achieve up to a 35% reduction in synthetic fungicide reliance (Vincenzo et al., 2025).
           
    In explosive foliar pathosystems, predictive models disrupt rapid polycyclic pathogen reproduction cycles (Avelino et al., 2015). Potato late blight (Phytophthora infestans) destroys entire crop canopies within days of initial symptom onset (Zhu et al., 2024). LSTM networks modelling temporal leaf microbiome dysbiosis achieve 88% predictive accuracy, providing a vital 5-7-day intervention window to halt sporangial dissemination and prevent devastating secondary infections across the field (Zhu et al., 2024). Multimodal ensembles tackle coffee leaf rust (Hemileia vastatrix) by integrating canopy spectral data with phyllosphere microbial profiles (Avelino et al., 2015). These integrated models predict impending rust outbreaks 21 days pre symptom with 91% accuracy. Extensive field trials demonstrate that these AI driven early warnings successfully prevent catastrophic defoliation, cutting fungicide applications by 40% while preserving crucial crop yields (Avelino et al., 2015).
     
    Extended crop case studies
     
    Beyond the tomato and potato pathosystems, AI-microbiome integration has demonstrated promise across several other globally critical crops. Wheat (Triticum aestivum): Fusarium crown rot, caused by Fusarium pseudograminearum, induces measurable rhizosphere microbiome dysbiosis before crown browning symptoms emerge. Yuan et al., (2020) demonstrated that soil macroecological microbiome patterns analysed through Random Forest achieved 91% accuracy in predicting Fusarium wilt occurrence across field sites, identifying Bacillus and Streptomyces abundance thresholds as key predictive biomarkers. LSTM architectures have further modelled longitudinal shifts in wheat root microbiomes across growing seasons, capturing the temporal window of greatest predictive opportunity (Sharma and Xu, 2021).
     
    Maize (Zea mays)
     
    Stalk rot diseases caused by Colletotrichum graminicola and Fusarium verticillioides represent multi-pathogen challenges amenable to multimodal AI frameworks. Graph neural network (GNN) approaches trained on co-occurrence network disruption data have demonstrated ~87% classification accuracy distinguishing susceptible from resistant root microbiome configurations (Maryam et al., 2024).
     
    Rice (Oryza sativa)
     
    Bacterial leaf blight caused by Xanthomonas oryzae pv. oryzae exhibits pre-symptomatic phyllosphere microbiome shifts detectable by SVM classifiers trained on 16S rRNA data, with reported AUC values of 0.88-0.91 (Ghannam and Techtmann, 2021). Additionally, blast disease (Magnaporthe oryzae) represents one of the most destructive fungal pathogens globally; metatranscriptomic integration with CNN architectures has shown early promise in detecting effector gene expression signatures within the endosphere microbiome.
     
    Soybean (Glycine max)
     
    Sudden death syndrome caused by Fusarium virguliforme represents a model system for XAI-assisted biomarker discovery. SHAP analysis of Random Forest models trained on soybean rhizosphere amplicon data successfully identified Trichoderma spp. and fluorescent Pseudomonas abundance as the top-ranked protective taxa, directly guiding targeted SynCom formulation strategies (Fadiji et al., 2025).
     
    Multimodal integration
     
    Capturing the full pathological landscape of the plant holobiont requires comprehensive multi omics integration. Single modality analyses often miss the complex mechanistic interactions defining disease onset and progression (Crandall et al., 2020). Multimodal frameworks synthesize microbiome profiles with metatranscriptomics, metabolomics and hyperspectral imaging, directly linking the mere presence of a pathogen to its active virulence gene expression and the corresponding suppression of host defence metabolites (Kajrolkar, 2025). Computational data fusion strategies systematically address these multidimensional dynamics. Early fusion concatenates raw multi omics features before analysis, while late fusion ensembles independent model predictions (Zhao et al., 2024). Intermediate fusion utilizes sophisticated attention mechanisms to extract joint latent representations, excelling at pinpointing crucial biological interactions such as explicitly linking antimicrobial root exudates to the specific recruitment of disease suppressive microbial consortia (Benkirane et al., 2023). Additionally, graph neural networks (GNN) process microbial co-occurrence networks as direct mathematical features, mapping exactly how pathogen invasion fractures cooperative microbial hubs to facilitate infection (Maryam et al., 2024). Ultimately, multimodal models compensate for single modality noise and extreme sparsity, consistently delivering a 5-10% improvement in predictive accuracy and enabling highly reliable field level disease interception (Kajrolkar, 2025).
     
    Transfer learning interpretability
     
    Deploying predictive models across globally diverse agricultural environments demands robust transfer learning to overcome geographic and host genetic variability (Ghannam, 2021). By pretraining foundation models on massive, diverse datasets like the Earth Microbiome Project, algorithms successfully learn universal microbial representations (Thompson et al., 2017). These algorithms undergo targeted domain adaptation to bridge the persistent ecological gap between controlled laboratory pathosystems and highly heterogeneous field conditions (Ghannam, 2021). Few shots learning techniques, utilizing meta learning and prototype networks, further adapt these powerful models to specific crop species such as transferring resistance insights across the Solanaceae family using minimal local data (Wu et al., 2023).
           
    Simultaneously, explainable AI (XAI) remains strictly necessary for translating opaque black box predictions into biologically validated pathology (Givisis et al., 2025). Analytical algorithms utilize SHapley Additive exPlanations (SHAP) and Local interpretable model agnostic explanations (LIME) to precisely quantify the exact predictive contribution of individual microbial taxa to disease suppression (Abekoon et al., 2025; Salih et al., 2024). Attention visualizations highlight critical spatial and temporal dysbiosis patterns marking initial infection sites, while knowledge distillation extracts transparent ecological rules from complex neural networks (Givisis et al., 2025). This interpretability facilitates the rigorous ecological validation of identified biomarkers, ensuring agronomists can biologically trust AI recommendations to direct targeted microbiome engineering and precise biocontrol deployments (Abekoon et al., 2025).
     
    Critical challenges and limitations
     
    Data-related challenges
     
    Microbiome-based phytopathology relies on high-throughput sequencing, which remains prohibitively expensive and yields datasets frequently containing fewer than 100 diseased samples (Fadiji et al., 2025). Consequently, researchers confront the high dimension low sample size (HDLSS) problem. Models analysing thousands of microbial taxa against sparse sample sizes risk severe overfitting, ultimately failing to accurately predict field-level epidemics (Przymus et al., 2025). Agricultural datasets exhibit extreme class imbalance, typically comprising 80-95% healthy and only 5-20% diseased samples (Fang, 2023). This disparity biases algorithms toward predicting healthy states, drastically reducing the sensitivity required to detect early-stage infections (Xu et al., 2015).
           
    Beyond scarcity, profound technical variations compromise disease forecasting models. Differences in DNA extraction protocols, primer selection and sequencing platforms introduce severe batch effects that actively obscure true biological signals of pathogen ingress (Nearing et al., 2022). Additionally, microbial matrices are strictly compositional and zero-inflated, with 50-90% of features recorded as zeros due to biological absence or detection limits (Gloor et al., 2017). Standard linear regression models mathematically misinterpret these zero-inflated matrices, generating spurious correlations between microbial taxa that fundamentally misguide field-level pathogen suppression and biocontrol strategies (Gloor et al., 2017).
     
    Methodological and validation issues
     
    Machine learning efficiently identifies microbial dysbiosis, but algorithms cannot inherently establish disease causation (Khelfaoui et al., 2025). To prove that specific microbial shifts actively suppress pathogens rather than merely co-occurring with necrotic plant tissue, pathologists must validate computational predictions using in vivo Synthetic Communities (SynComs) (Fadiji et al., 2025). Researchers face a strict interpretability trade-off: deep neural networks achieve the highest predictive accuracy for destructive diseases like late blight but operate as opaque “black boxes.” This opacity obscures the precise ecological mechanisms driving pathogen inhibition, frustrating agronomists seeking actionable insights (Przymus et al., 2025).
           
    This inherent opacity directly drives generalisation failures. Diagnostic models optimised for specific crop cultivars or controlled greenhouse pathosystems frequently collapse when transferred to heterogeneous field environments facing distinct climatic pressures (Pace et al., 2025). Rigorous cross-location and cross-season validations remain exceedingly rare, thereby heavily skewing the scientific literature toward positive, yet completely non-replicable, diagnostic pipelines (Papoutsoglou et al., 2023).
     
    Biological and practical constraints
     
    Biological complexity fundamentally constrains current predictive frameworks (Fadiji et al., 2025). Diverse microbial taxa exhibit extensive functional redundancy, meaning completely different bacterial clades execute equivalent disease-suppressive functions (Louca et al., 2016). Dynamic plant heterogeneity driven by genetic variation, developmental age and acute physiological stress alters root exudate profiles, constantly restructuring the microbiome and confounding stable disease signatures across growing seasons (Fadiji et al., 2025). Processing massive next-generation sequencing (NGS) pipelines requires weeks of intensive bioinformatics analysis and specialised expertise largely unavailable to smallholder farmers, profoundly exacerbating the agricultural digital divide (Raja and Raja, 2024).
     
    AI vs. traditional statistical methods: A critical comparison
     
    Traditional statistical frameworks including Analysis of Variance (ANOVA), PCA, linear discriminant analysis (LDA), generalised linear models (GLM) and permanova have long formed the analytical backbone of microbiome research in plant pathology (Papoutsoglou et al., 2023). These methods offer critical advantages: They are computationally inexpensive, statistically interpretable, produce formal hypothesis tests with p-values and require modest sample sizes (n = 20-50). However, they carry three fundamental limitations that constrain their utility for disease prediction in complex microbiome datasets.
           
    First, traditional methods assume data normality and independence assumptions directly violated by the compositionality, zero-inflation and extreme sparsity of amplicon sequencing data (Gloor et al., 2017). Second, they are univariate or low-dimensional by design, incapable of simultaneously modelling the thousands of microbial taxa, functional genes and metabolite interactions that define pre-symptomatic dysbiosis. Third, they are fundamentally correlational: ANOVA identifies that specific taxa differ between healthy and diseased states but cannot predict disease outcome in novel environments with acceptable sensitivity.
           
    Machine learning methods overcome these limitations by learning non-linear, high-dimensional patterns from training data without parametric assumptions, enabling genuine predictive generalisation. Benchmarking studies consistently demonstrate that random forest and XGBoost outperform LDA and GLM by 15-25% in AUC-ROC for microbiome-based disease classification tasks (D’Eliaet_al2023). However, this superiority is contingent on adequate sample sizes (n ≥ 200 for ML; n ≥1,000 for DL) and rigorous cross-validation. In low-sample scenarios (n<100), regularised GLMs and partial least squares discriminant analysis (PLS-DA) remain competitive and their interpretability advantage is substantial for field application. The optimal strategy for most plant pathology applications is therefore a tiered analytical approach: initial exploratory analysis with PCA/Permanova, hypothesis generation with regularised GLMs and definitive predictive modelling with RF or XGBoost validated across independent sites (Table 5).

    Table 5: Algorithmic limitations and pathology implications.


     
    Innovations and implementation roadmap
     
    Long-read sequencing technologies (PacBio, Oxford Nanopore) resolve microbial communities to the strain level, distinguishing virulent pathogens from protective endophytes (Knobloch et al., 2024). Portable MinION sequencers enable real-time, on-site epidemic surveillance. Integrating multiomics genomics, metatranscriptomics and metabolomics  reveals how plants systemically recruit disease-suppressive consortia during pathogen attacks (Kajrolkar, 2025). Self-supervised learning utilises unlabelled agricultural data, while causal inference algorithms distinguish true biocontrol mechanisms from spurious correlations, ensuring interventions actively suppress pathogens (Khelfaoui et al., 2025). Bayesian networks provide critical uncertainty quantification, delivering reliable disease risk assessments. Edge computing executes lightweight models directly on mobile devices in remote fields (Heydari and Mahmoud, 2025). Coupled with affordable CRISPR and isothermal amplification assays costing under $10 per test, these tools facilitate immediate, targeted biocontrol deployment (Bugingo et al., 2026).
     
    Economic feasibility and adoption barriers
     
    The economic viability of AI-driven microbiome diagnostics is currently constrained by prohibitive per-sample costs. Whole-genome shotgun metagenomics currently costs $100-500 per sample, while 16S rRNA amplicon sequencing costs $15-50 per sample economically inaccessible for smallholder farmers in low-income countries (Fadiji et al., 2025; Raja and Raja, 2024). By contrast, conventional fungicide applications cost $30-120 per hectare per season, frequently applied preventively without diagnostic confirmation, representing both an economic and environmental inefficiency.
           
    The economic argument for AI diagnostics strengthens substantially when evaluated at the regional or national scale. A 2% reduction in annual crop losses from the current 10-16% baseline achievable through early AI-guided intervention would represent a recovery of approximately $4.4 billion annually from the $220 billion global crop loss burden (FAO, 2023). To reach economic viability at the farm level, per-test costs must decline to the $5-15 range within five years (Fadiji et al., 2025). Targeted technologies making this feasible include loop-mediated isothermal amplification (LAMP) assays integrated with minimal ML inference engines, portable nanopore sequencers (Oxford Nanopore MinION at ~$1,000 capital cost with $50-100 per run) and cloud-based ML inference platforms that remove computational hardware barriers from individual farms (Knobloch et al., 2024).
           
    Beyond direct costs, adoption barriers include farmer digital literacy, technical extension service capacity, trust in AI recommendations, data privacy concerns and connectivity infrastructure in remote agricultural regions. Successful models for technology adoption in low-resource settings such as India’s e-Choupal network and East Africa’s digital extension services offer transferable frameworks for microbiome diagnostic dissemination (Raja and Raja, 2024).

    Ethical and regulatory considerations
     
    Data ownership and governance
     
    Microbiome sequencing generates highly granular environmental and biological data tied to specific farm locations. Without clear data governance frameworks, this data collected by commercial diagnostic companies could be monetised without equitable farmer compensation. Governments and international bodies must establish farmer data sovereignty principles, ensuring that microbiome diagnostic data generated on-farm remains the legal property of the farmer and is not commercially exploited without informed consent (Adeyeye et al., 2025).
     
    Algorithmic bias
     
    Current AI models are predominantly trained on datasets from temperate, high-income agricultural systems (North America, Europe, Australia), meaning model performance in tropical smallholder systems where crop losses are highest and food security most precarious is largely unvalidated (Raja and Raja, 2024). This introduces a systematic algorithmic bias that could exacerbate global diagnostic inequity. Mandating geographic and agro-ecological diversity in training datasets should become a standard journal and funder requirement.
     
    Regulatory pathways for microbiome-based biomarkers
     
    AI-derived microbiome biomarkers used for crop insurance, disease certification or pesticide application recommendations require regulatory validation analogous to pharmaceutical biomarker approval. No unified international regulatory framework for plant microbiome diagnostic claims currently exists (Fadiji et al., 2025). The European union’s plant health regulation (EU 2016/2031) and the US EPA’s Biopesticide Registration framework provide partial precedent, but do not specifically address AI-generated predictive biomarkers.
     
    Environmental considerations
     
    AI-guided SynCom applications involve deliberate environmental release of formulated microbial consortia. While the microorganisms involved are typically native soil residents, large-scale coordinated deployment could perturb local microbiome diversity and introduce competitive exclusion dynamics. Environmental risk assessment frameworks analogous to those applied to biological control agents under EU Regulation 1107/2009 should govern these applications (Panchal et al., 2026).
     
    Phased implementation roadmap
     
    Translating predictive diagnostics into sustainable agronomy demands a phased implementation roadmap. Within three to five years, researchers must deliver cultivar-specific, field-validated predictive models and pilot rapid diagnostic assays ($5-10 per test) directly in farmer fields (Fadiji et al., 2025). Over five to ten years, precision agriculture will fully integrate routine microbiome monitoring as a standard advisory practice, utilising distributed edge computing to forecast localised disease outbreaks (Nkwocha and Chandel, 2025). Ultimately, beyond ten years, preventive microbiome management will systematically replace synthetic chemical applications, establishing highly resilient agroecosystems. Achieving this paradigm shift requires breaking disciplinary silos through intense interdisciplinary collaboration among pathologists, microbiologists and AI engineers. Sustained funding, targeted farmer adoption incentives and equitable technology transfer mechanisms remain critical success factors to ensure diagnostic equity and prevent digital divides in global disease management (Fadiji et al., 2025; Khelfaoui et al., 2025).

    CONCLUSION

    The microbiome disease paradigm is firmly established, demonstrating that early pre-symptomatic detection signatures exist across diverse pathosystems. Integrating AI and ML delivers 82-93% predictive accuracy, representing a substantial improvement over traditional reactive pathology. While multimodal fusion, transfer learning and explainability advance field deployment, significant challenges persist regarding data scarcity, model generalisation, validation and standardisation. Critically, reported accuracy figures must be interpreted within their validation context predominantly controlled greenhouse studies with balanced datasets rather than as field-ready benchmarks. Translating these innovations demands critical success factors. Researchers must enforce data standardisation through harmonised protocols, open repositories and longitudinal studies.  Breaking disciplinary silos requires intense collaboration among microbiologists, pathologists, ML engineers and agronomists. Rigorous validation must ensure temporal independence, geographic diversity and farmer field testing. Models must move beyond opaque black boxes by prioritising explainable AI (XAI) and mechanistic biomarker validation. Widespread adoption relies heavily on cost reduction via sequencing improvements and computational efficiency. Ethical deployment requires data governance frameworks, regulatory pathways and algorithmic bias mitigation. Ultimately, this convergence drives a paradigm shift from reactive disease response to proactive, surveillance-based prevention. This transition fosters agricultural sustainability by drastically reducing synthetic chemical inputs, thereby protecting ecosystem health. Predictive microbiome management bolsters global food security, ensuring crop resilience, yield protection and stable supplies amid escalating climate change. Achieving this vision requires prioritising diagnostic equity and capacity building in low-resource regions to prevent digital divides. With sustained investment, this transformative technology anticipates mainstream commercial adoption within 10-15 years.

    CONFLICT OF INTEREST

    The authors declare that they have no conflicts of interest relevant to the content of this article.

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