Digitizing plant specimens is opening up a whole new world for researchers looking to mine collections from around the world. Computer algorithms trained on the images of thousands of preserved plants have learned to automatically identify species that have been pressed, dried and mounted on herbarium sheets, researchers report. The work, published in BMC Evolutionary Biology on 11 August1, 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 taxonomic 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."

Machine learning algorithms have successfully identified plant species in massive herbaria just by looking at the dried specimens. According to researchers, similar AI approaches could also be used identify the likes of fly larvae and plant fossils. There are roughly 3,000 herbaria in the world, hosting an estimated 350 million specimens -- only a fraction of which has been digitized. But the swelling data sets, along with advances in computing techniques, enticed computer scientist Erick Mata-Montero of the Costa Rica Institute of Technology in Cartago and botanist Pierre Bonnet of the French Agricultural Research Centre for International Development in Montpellier, to see what they could make of the data. Researchers trained... algorithms on more than 260,000 scans of herbarium sheets, encompassing more than 1,000 species.