A five-phase implementation framework integrating centralized data systems, AI-driven analytics, multi-omics profiling, standardized soil sampling, and feedback-based model retraining within precision agriculture systems is proposed, providing a pathway for translating microbiome insights into field-scale decision support.
Abstract
Background Soil microbiome research has been revolutionized by advances in high-throughput sequencing and multi-omics technologies, generating massive datasets that capture the taxonomic, functional, and metabolic diversity of microbial communities in agricultural soils; however, interpreting these complex datasets and translating them into practical agronomic insights remains challenging. Objectives To critically assess the role of artificial intelligence (AI) in soil microbiome-driven agriculture, focusing on methodological developments, prediction performance, existing limitations, and translational opportunities. Methods A narrative review was conducted to evaluate commonly used AI approaches, including random forest, gradient boosting, support vector machines, and deep learning architectures, alongside key microbiome data types such as amplicon sequencing, metagenomics, and functional gene profiling, with integration of environmental, agronomic, and meteorological datasets. Results The prediction of crop productivity, disease risk, nutrient cycling dynamics, and soil health indicators may be enhanced by AI-assisted integration of microbiome, soil physicochemical, and meteorological data, according to several studies. However, broad generalizations about predictive robustness and generalizability are limited by significant diversity in datasets, validation methods, and model architectures. Discussion To address these limitations, a five-phase implementation framework integrating centralized data systems, AI-driven analytics, multi-omics profiling, standardized soil sampling, and feedback-based model retraining within precision agriculture systems is proposed, providing a pathway for translating microbiome insights into field-scale decision support. Conclusion AI-enabled soil microbiome applications hold significant potential for sustainable agriculture, but future advancements will require large, multisite datasets, improved validation strategies, interpretable modeling approaches, and integration with digital agriculture technologies, highlighting both opportunities and practical constraints.
Livestock production in Africa occurs across highly heterogeneous agroecological and management environments, ranging from extensive pastoral and mixed crop–livestock systems to intensive enterprises. These systems are characterized by seasonal and spatial variation in feed resources, reliance on locally available forage and agricultural by-products, climatic stress, endemic diseases, and the use of indigenous and locally adapted breeds. Such conditions create distinctive microbiome–host interactions that remain poorly represented in global livestock omics research. Although the gut microbiome is central to nutrient utilization, immune function, metabolic homeostasis, and resilience, the functional mechanisms linking microbial communities, diet, host physiology, and productivity in African livestock remain insufficiently characterized. African systems are particularly underrepresented in integrated microbiome–metabolomics datasets, longitudinal studies, and artificial intelligence (AI)-enabled predictive models, limiting the development of context-specific precision nutrition strategies. This review examines the integration of metabolomics and AI with microbiome and host data to advance precision livestock nutrition within an African and One Health context. It identifies both substantial constraints and strategic opportunities. Limited research infrastructure, high-quality regional datasets, computational capacity, and specialized expertise remain major barriers. Conversely, Africa’s diversity of livestock breeds, feed resources, agroecological conditions, and naturally occurring resilience phenotypes provides an important opportunity to identify microbiome–metabolite signatures associated with feed efficiency, disease resilience, climate adaptation, and product quality. Emerging metabolomics and computational capacity, particularly in South Africa, could support regional research networks and continental data infrastructures. Furthermore, the review proposes an Africa-specific approach that develops locally grounded, scalable, and resource-sensitive precision nutrition strategies, strengthening antimicrobial stewardship, animal health, food safety, climate resilience, sustainable livestock production, and broader One Health objectives.
K. T. Ncube, F. Tugizimana· Agriculture· 0 citations
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 less than 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.
A. Daunde, V. Gholve, S. Badgujar· Agricultural Reviews· 0 citations
Soil microbiomes are essential for nutrient cycling, plant health, stress resilience, and sustainable agriculture. Recent advances in high-throughput sequencing, multi-omics technologies, systems biology, and artificial intelligence (AI) have transformed our understanding of plant–microbiome interactions and enabled the development of innovative microbiome engineering strategies. This review provides a comprehensive overview of the mechanisms governing plant-associated soil microbiome assembly, microbial community functions, plant–microbe communication, and microbiome-mediated stress resistance in agricultural ecosystems. Current approaches to plant-associated soil microbiome manipulation and engineering, including microbial inoculants, synthetic microbial communities (SynComs), microbiome transplantation, rhizosphere steering, and synthetic biology-based interventions, are critically examined. The review further discusses the growing role of metagenomics, metabolomics, metatranscriptomics, machine learning (ML), and precision agriculture technologies in improving microbiome characterization, prediction, and management. Particular attention is given to the application of microbiome-based solutions for sustainable crop production, nutrient management, biological control, climate-smart agriculture, and ecosystem restoration. Despite significant progress, challenges related to field-scale variability, colonization stability, biosafety, regulatory frameworks, and data integration continue to limit large-scale implementation. Future advances in precision microbiome engineering are expected to combine ecological principles, multi-omics technologies, AI, and synthetic biology to develop predictive and resilient microbiome-based solutions for sustainable and climate-resilient agriculture.
A. Sadanov, G. Baimakhanova, B. Baimakhanova et al.· Microorganisms· 0 citations
Crop improvement increasingly depends on extracting useful breeding signals from data that span DNA sequence variation, gene regulation, molecular phenotypes, high-throughput field measurements and environmental exposure. Multi-omics can connect genotype to phenotype through intermediate biological layers, while artificial intelligence (AI) and machine-learning methods can model nonlinear, high-dimensional relationships that are difficult to represent with conventional approaches. Yet greater data volume and model complexity do not automatically translate into greater genetic gain. This critical narrative review evaluates how genomics, pangenomics, transcriptomics, epigenomics, proteomics, metabolomics, phenomics and environmental covariates are being integrated with statistical learning, machine learning and deep learning for crop improvement. Literature was selected from accessible scholarly databases and indexes through 13 June 2026, with emphasis on peer-reviewed studies that permit evaluation of predictive value, biological interpretation and breeding relevance. The evidence is strongest where additional modalities capture non-redundant information that is biologically proximal to the target trait or environment, as demonstrated in hybrid prediction, stress adaptation, grain-quality analysis and environment-aware genomic prediction. Conversely, classical genomic best linear unbiased prediction and related models remain competitive in many settings, particularly when sample size is modest, relationships among individuals dominate prediction, or nonlinear signal is weak. Reported AI advantages are sensitive to validation design, relatedness between training and test sets, tissue and developmental stage, environmental transfer, missing modalities and hyperparameter tuning. Pangenomes, single-cell regulatory maps and interpretable multimodal models broaden the biological search space, but evidence for routine breeding utility remains less mature than their mechanistic promise. The most defensible path forward is therefore not unrestricted model escalation, but decision-focused integration: biologically informed feature representation, prospective multi-environment validation, explicit uncertainty, robust missing-data handling and functional validation of discovered mechanisms. Multi-omics and AI are most likely to accelerate crop improvement when evaluated against breeding decisions and realised genetic gain rather than prediction accuracy alone.
This work provides a critical evaluation of the functional gaps between genomic potential and in situ microbial activity and offers a novel synthesis of how multiomics integration and predictive modeling can move beyond species cataloging toward a more robust, evidence‐based framework for environmental sustainability.
The human microbiome functions as a metabolically active organ whose biochemical output is continuously integrated with host physiology. Conventional microbiome surveys, built largely on taxonomic profiling, capture community composition and diversity but resolve neither the functional capacity of these communities nor the bidirectional metabolic exchange that links them to the host. A central limitation is that taxonomy is a poor proxy for function: phylogenetically distinct organisms can perform equivalent reactions, and closely related taxa can diverge metabolically. Resolving host microbiome interactions, therefore, requires integration across heterogeneous, high-dimensional molecular layers, such as metagenomics, metatranscriptomics, proteomics, metabolomics, and host genomic and phenotypic data at a scale and complexity that exceeds classical analytical pipelines. Artificial intelligence (AI) has emerged as a complementary framework for this problem. Machine learning, deep learning, and graph-based models can integrate multi-omics data, infer latent metabolic structure, predict microbial functional potential, and model microbe-metabolite-host relationships as connected networks rather than isolated parts. These approaches have sharpened the discovery of disease-associated microbial and metabolic signatures and candidate therapeutic targets, and they underpin emerging precision medicine applications, including individualized risk stratification, biomarker discovery, and treatment response prediction. Substantial barriers remain, however, including incomplete and non-standardized reference data, limited model interpretability, vulnerability to bias and overfitting, and a shortage of prospective clinical validation. Continued progress in foundation models, real-time microbiome monitoring, and patient-specific metabolic modelling is expected to move the field from descriptive association toward predictive, preventive, and personalized clinical application.
Toyin Tolulope Lawal, James Momoh, M. Odedele et al.· Australian Journal of Biomed...· 0 citations
