Plant phenology – the timing of plant life-cycle events, such as leaf growth, flowering, and fruiting – plays a fundamental role in shaping terrestrial ecosystems. The timing of plant phenology not only affects the fitness of individual plants, it also impacts the fitness and behaviors of organisms dependent on plants, which in terrestrial ecosystems includes nearly all animals, either directly or indirectly. Thus, changes in plant phenology can trigger dramatic, and sometimes devastating, consequences for ecosystems and human economic interests and health. Plant phenological data are therefore indispensable for understanding ecosystem function, detecting ecosystem changes, and predicting the impacts of ongoing climate and land use changes. Given the importance of plant phenology, continuing local, regional and national data collection efforts have generated large volumes of phenological data. However, these data are surprisingly heterogeneous, difficult to integrate, and thus remain largely inaccessible for broader research. At the same time, community science and specimen digitization infrastructure have produced massive, rapidly expanding collections of herbarium specimens and in situ plant photographs, which contain a wealth of virtually untapped historical and contemporary phenological information. This project will use machine learning approaches to extract phenological data from plant photographs and digitized specimens. These data will then be integrated with phenolog