Machine learning (ML) can accelerate the extraction of phenological data from herbarium

specimens; however, no studies have assessed whether ML-derived phenological data can be used

reliably to evaluate ecological patterns. In this study, 709 herbarium specimens representing a

widespread annual herb, Streptanthus tortuosus, were scored both manually by human observers

and by a mask R-CNN object detection model to (1) evaluate the concordance between ML and

manually-derived phenological data and (2) determine whether ML-derived data can be used to

reliably assess phenological patterns. The ML model generally underestimated the number of

reproductive structures present on each specimen; however, when these counts were used to provide

a quantitative estimate of the phenological stage of plants on a given sheet (i.e., the phenological

index or PI), the ML and manually-derived PI’s were highly concordant. Moreover, herbarium

specimen age had no effect on the estimated PI of a given sheet. Finally, including ML-derived PIs as

predictor variables in phenological models produced estimates of the phenological sensitivity of this

species to climate, temporal shifts in flowering time, and the rate of phenological progression that are

indistinguishable from those produced by models based on data provided by human observers. This

study demonstrates that phenological data extracted using machine learning can be used reliably to

estimate the phenological stage of herbarium specimens and to detect phenological patterns.