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When subjected to loads, granular materials tend to yield and exhibit some localized particle reorganizations. Due to the complex disordered structure of granular materials, it is challenging to identify the key preexisting defects in the static, unloaded structure that eventually promotes dynamical particle rearrangements once a load is applied. Here, based on discrete element simulations of an archetypal frictional granular material model, we introduce a machine learning framework that pinpoints such structural defects with unprecedented accuracy. We show that the optimal structural fingerprint of plastic flow defects depends on strain, wherein the plastic flow is governed by short-range defects at low strain but become dominated by medium-range defects at high strain. [ABSTRACT FROM AUTHOR]