In Pennsylvania, computer algorithms looking at images of thousands of preserved plants have learned to automatically identify species that have been pressed, dried and mounted on herbarium sheets. This is the first attempt to use deep learning — an artificial-intelligence technique that teaches neural networks using large, complex data sets — to tackle the difficult task of identifying species in natural-history collections.
It’s unlikely to be the last attempt, says palaeobotanist Peter Wilf of Pennsylvania State University in University Park. “This kind of work is the future; this is where we’re going in natural history.”
Natural-history museums around the world are racing to digitize their collections, depositing images of their specimens into open databases that researchers anywhere can rifle through. Because some samples are centuries old, that data can paint a portrait of how plants have adapted to shifting climates — an area of growing interest in the face of climate change concerns.