Artificial Intelligence in Plant Microbiome based Disease Prediction: A Review
TL;DR
This structured review, synthesising 114 peer-reviewed studies from 2014-2026, evaluates AI-driven microbiome disease prediction across pathosystems including tomato bacterial wilt, potato late blight, wheat Fusarium wilt and soybean sudden death syndrome, with reported predictive accuracies of 82-93% under controlled validation conditions.
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 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.