Artificial Intelligence Approaches for Uncovering Plant–Microbial Interactions

Plant–microbe interactions play a vital role in plant health, growth, and resilience to environmental stresses, making their study essential for advancements in agriculture, biotechnology, and ecosystem management. Artificial intelligence (AI) has revolutionized multiple scientific disciplines, including plant–microbe research, by enabling the analysis of complex, high-dimensional biological datasets. The integration of AI-driven approaches with multi-omics data analysis offers an unprecedented opportunity to unravel the intricate relationships between plants and their associated microbial communities. AI methodologies such as machine learning, deep learning, and network-based models facilitate the identification of microbial communities, the prediction of their functional roles, and the discovery of novel plant–microbe associations. These techniques enhance our understanding of symbiotic relationships, plant defense mechanisms, and microbial contributions to plant growth and stress tolerance. By harnessing data from genomics, metagenomics, transcriptomics, and imaging, AI uncovers hidden patterns and correlations that were previously challenging to detect. Furthermore, AI-powered models aid in optimizing microbial consortia to enhance plant productivity and improve disease resistance. This review highlights the latest AI methodologies applied to plant–microbial interaction research, showcasing their potential to accelerate discoveries and promote agricultural sustainability. Future research should prioritize the integration of AI with multi-omics data, the refinement of predictive models, and the development of user-friendly tools for researchers and agronomists.