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."

My dad is a wildlife biologist, and during road trips we took when I was growing up he spent a lot of time talking about the grasses and trees along the highway. It was a game he played, trying to correctly identify the passing greenery from the driver's seat of a moving car. As a carsick-prone kid wedged into the back seat of a Ford F150, I found this supremely lame. As an adult--specifically, one who just spoke with a paleobotanist--I now know something about my father's roadtripping habit: Identifying leaves isn't easy. "I've looked at tens of thousands of living and fossil leaves," says that paleobotanist, Peter Wilf of Penn State's College of Earth and Mineral Sciences.

Can software identify complex personality traits simply by analysing your face? Faception, a start-up based in Tel Aviv, Israel, courted controversy this week when it claimed its tech does just that. And not just broad categories such as introvert or extrovert: Faception claims it can spot terrorists, paedophiles – and brand promoters. "Using automated feature extraction is standard for face recognition and emotion recognition," says Raia Hadsell, a machine vision engineer at Google DeepMind. The controversial part is what happens next.

If you find a new dinosaur the next time you stick a shovel in the dirt, you'll be famous. But pity the paleobotanists -- they find new leaf fossils every time they dig. Lack of fame is the least of their problems, though. A central obstacle botanists face is the inability to identify all those fossils. Leaves are naturally complex, with an astounding variety of vein and shape patterns. Comprehensive knoweldge and identification are virtually impossible.

Posted by A'ndrea Elyse Messer-Penn State on March 8, 2016 You are free to share this article under the Attribution 4.0 International license. Identifying an isolated leaf, especially if preserved as a fossil, can be a painstaking process for botanists. A new computer program that learns to categorize leaves into large evolutionary categories could help. Researchers "trained" a machine-learning algorithm to identify leaves based on a set of nearly 7,600 digital images of leaves that had been chemically treated to emphasize their shape and venation. The software discerned relevant patterns so well from that set of examples that it went on to identify the family of novel leaf images with greater than 70 percent accuracy (a rate 13 times better than chance) and the order with about 60 percent accuracy.