The plant microbiome, which encompasses diverse communities of bacteria, fungi, archaea, viruses and oomycetes that colonise plant surfaces and internal tissues, is a critical determinant of plant health, resilience and productivity. This review systematically examines how plant-associated microbial communities modulate plant immune signalling pathways, with particular emphasis on pattern-triggered immunity (PTI), effector-triggered immunity (ETI), systemic acquired resistance (SAR) and induced systemic resistance (ISR). Critical evaluation of the molecular mechanisms through which beneficial microorganisms prime host defences, including microbe-associated molecular pattern (MAMP) perception, salicylate and jasmonate signalling cascades, reactive oxygen species (ROS) dynamics and transcriptional reprogramming of immune networks. Additionally, we address pathogen-mediated immune suppression strategies, including effector-driven interference, hormonal hijacking and pathobiome-associated dysbiosis, drawing explicit connections to the limitations of classical Koch's postulates in the context of community-level disease causation. This review further explores how environmental factors, including climate change, drought and agricultural intensification, reshape the microbiome composition and immune competence. Translational applications, including synthetic microbial communities (SynComs), microbiome-assisted breeding and microbiome restoration strategies, have been evaluated for field efficacy, scalability and regulatory feasibility. Current methodological limitations and future research priorities are discussed to guide the development of microbiome-informed, climate-resilient and sustainable disease management frameworks. This review highlights the transformative potential of harnessing plant microbiomes for next-generation agricultural protection strategies.
A. Daunde, S. Yogendra, V. Gholve et al.· Plant Science Today· 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
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