Digital twins (DTs) are increasingly recognized across diverse sectors for their capacity to enhance the control, efficiency, and comprehension of the physical or biological systems they represent. For microbial systems, DTs could allow model-guided improvements of the services provided by the microbial communities in the agrifood chain. While DTs definitions are generally built on the same core idea of bi-directional exchanges between digital and physical counterparts, where realtime data feeds digital models and model-driven insights guide the real system, a wide variety of definitions of what is a DT still co-exist across domains. This variability underscores the need for a clear, system-specific definition of DTs for microbial ecosystems. In this perspective paper, we propose a conceptual framework for microbial system digital twins (MSDTs), defined as a collection of models dynamically linked to the microbiological system through in-line, at-line or off-line data and control flows. We illustrate this framework with examples spanning environmental, bioprocess, plant, animal, food, and human microbial systems, in a One Health perspective. For each ecosystem, we explore the potential applications of MSDTs. We also identify the scientific challenges that remain in experiments, bioinformatics, data science, modeling, control and microbial ecosystem engineering to build accurate MSDTs. We advocate for the development of MSDT in laboratory settings, as a catalyst for interdisciplinary sciences, and we stress practical and ethical issues preventing the generalization of MSDT for large-scale applications. However, high-tech MSDTs in laboratory environments may pave the way for low-tech, generalizable microbial solutions for improved ecosystemic microbial services.