Katelin D. Pearson|Gil Nelson|Myla F. J. Aronson|Pierre Bonnet|Laura Brenskelle|Charles C. Davis|Ellen G. Denny|Elizabeth R. Ellwood|Hervé Goëau|J. Mason Heberling|Alexis Joly|Titouan Lorieul|Susan J. Mazer|Emily K. Meineke|Brian J. Stucky|Patrick W. Sweeney|Alexander E. White|Pamela S. Soltis
Machine Learning Using Digitized Herbarium Specimens to Advance Phenological Research
California Polytechnic State University|Florida Museum of Natural History|Rutgers, The State University of New Jersey|Botany and Modelling of Plant Architecture and Vegetation|Institut de Recherche pour le Développement|Centre de Coopération Internationale en Recherche Agronomique pour le Développement|Florida Museum of Natural History|University of Florida|Harvard University|University of Arizona|Natural History Museum of Los Angeles County|Botany and Modelling of Plant Architecture and Vegetation|Institut de Recherche pour le Développement|Centre de Coopération Internationale en Recherche Agronomique pour le Développement|Carnegie Museum of Natural History|Montpellier Laboratory of Informatics, Robotics and Microelectronics|Montpellier Laboratory of Informatics, Robotics and Microelectronics|University of California, Santa Barbara|University of California, Davis|University of Florida|Florida Museum of Natural History|American Museum of Natural History|Smithsonian Institution|Florida Museum of Natural History|University of Florida
abstract machine learning ml great potential drive scientific discovery harvesting data images herbarium specimenspreserved plant material curated natural history collectionsbut ml techniques recently applied rich resource ml particularly strong prospects study plant phenological events growth reproduction major indicator climate change driver ecological processes critical determinant plant fitness plant phenology important frontier application ml techniques science society present article describe generalized modular ml workflow extracting phenological data images herbarium specimens discuss advantages limitations potential future improvements workflow strategic research investment specimenbased ml methods along aggregation herbarium specimen data may give rise better understanding life earth