Harnessing artificial intelligence to decode the rhizosphere microbiome
Juan Ma, Jiangfang Qiao, Yanyong Cao, Zeqiang Cheng
Abstract
The rhizosphere microbiome plays crucial roles in plant health by regulating nutrient cycling and enhancing stress resilience. However, due to its complexity, the rhizosphere microbiome is quite challenging to analyze using conventional approaches. Recent advances in artificial intelligence (AI) offer unprecedented opportunities to decipher intricate microbial interactions and leverage their potential for crop breeding. In this review, we assess AI methodologies derived from human microbiome studies that address foundational data challenges, including high dimensionality, compositionality, and sparsity. Next, we examine the uses of these methods for the functional prediction of microbial traits. We then shift our focus to the rhizosphere, exploring AI-driven approaches for predictive modeling of rhizosphere dynamics, integrating plant phenotypic and microbiome data, and designing synthetic microbial communities (SynComs). Finally, we discuss the major challenges and future prospects of using AI in rhizosphere microbiome research. Specifically, we propose an emerging AI paradigm that integrates complementary inside-out (hologenome-based genomic selection) and outside-in (SynCom design) strategies, powered by transformative technologies such as federated learning, large language models, digital twins, and autonomous AI agents. This review underscores the potential for AI to revolutionize microbiome science and crop improvement.