From museum to laptop: Visual leaf library a new tool for identifying
plants

Date:
March 15, 2022
Source:
Penn State
Summary:
Fossil plants reveal the evolution of green life on Earth, but the
most abundant samples that are found -- fossil leaves -- are also
the most challenging to identify. A large, open-access visual leaf
library provides a new resource to help scientists recognize and
classify these leaves.



FULL STORY ========================================================================== Fossil plants reveal the evolution of green life on Earth, but the
most abundant samples that are found -- fossil leaves -- are also the
most challenging to identify. A large, open-access visual leaf library developed by a Penn State-led team provides a new resource to help
scientists recognize and classify these leaves.


==========================================================================
"The complexity of leaves is off the charts, and the terminology we have
to describe them is only the tiniest beginning of what is needed," said
Peter Wilf, professor of geosciences at Penn State. "Researchers need much
more accessible visual references to study what the differences are among
the many plant groups, so we can put more of that into words. There are a
lot of plant families that look superficially similar, and this collection provides an opportunity to see new patterns." Studying fossil and modern leaves traditionally requires research visits to museum collections, which requires funding, planning and time for travel to several locations. More museums are putting leaf collections online, but often these images
are low resolution, are hard to access in quantity, have uninformative filenames, or the leaves are photographed with other plant parts and
labels that make rapid comparisons challenging, the scientists said.

The scientists combined images of modern and fossil leaves from several prominent collections, including several not previously online in any
format, and spent thousands of hours formatting the data to create a
single, merged, open-access dataset with standardized, easily searchable filenames and high- resolution images. They reported in PhytoKeys that
the dataset is available from the Figshare Plus repository.

The dataset contains 30,252 images, including 26,176 images of cleared
and x- rayed leaves and 4,076 fossil leaves. Cleared leaves are specimens
that have been chemically bleached, stained and mounted on slides to
reveal vein patterns. Each image represents a vouchered museum specimen.

"What we have done here is to make this massive educational resource
available to everyone by vetting and standardizing all these images from different legacy sources," Wilf said. "It took 15 years for us all to
do that and convert all the filenames, but now you can have the whole
package on your desktop with a single browser click. Every filename has
the key information embedded, in the same order for rapid alpha-sorting: family, genus, species, and specimen number. The filenames can be rapidly searched in seconds for the item you are interested in and the images
viewed using standard tools, such as the Windows search bar. All images
are original resolution; nothing is downsampled." The dataset is a
potential resource not just to train students but also machine learning programs. Feeding vetted training data to learning algorithms allows
them to better identify leaves and find important visual patterns that
humans may have overlooked or been unable to see.



==========================================================================
"For scientists studying botanical subjects, particularly fields such
as paleobotany, these tools can most reliably be used to facilitate and multiply the impact of human expertise," said Jacob Rose, a doctoral
student at Brown University, who worked closely with Wilf to create the dataset. His adviser, Thomas Serre, professor in computer science at
Brown, also contributed. "Using these models as a starting point for
an expert to either accept, reject or scrutinize further could soon
prove to be a profound example of using technology to expand the value
that is possible for a single scientist to produce as well as what is
possible for us as a society to learn about the natural world, both in
scale and precision." Machine learning may be especially important for paleobotanists, who most often find isolated fossil leaves without seeds,
fruit or flowers that could help identify plants. Further compounding
the challenge, many of the individual fossils represent plants that
are extinct.

The new dataset is a promising option for training machine learning
because it contains examples of modern and fossil leaves vetted at
least to the family level, a higher taxonomic classification that is
the standard first target for fossil-leaf identification. The Fagaceae
family, for example, includes beeches, chestnuts and oaks.

The dataset includes images from the Jack A. Wolfe and Leo J. Hickey contributions to the National Cleared Leaf Collection and the Scott Wing
X-Ray collection at the Smithsonian National Museum of National History, Washington, D.C., and the Daniel I. Axelrod Cleared Leaf Collection at the University of California Museum of Paleontology, Berkeley. Also included
are fossil images from various sites in North and South America. The
largest contribution is from the Florissant Fossil Beds National Monument
in Colorado.

"This database makes the information in these collections available
to people around the world in a form that is easier to search than the
original and more amenable to digital analyses," said Scott Wing, research geologist and curator of paleobotany at the Smithsonian. "We think the
database will encourage new research and also open the museum collections
to people." Also contributing were Xiaoyu Zou, undergraduate student,
Penn State; Herbert Meyer, paleontologist, Florissant Fossil Beds National Monument; Rohit Saha, former graduate student, Brown University; Rube'n
Cu'neo, director, Museum of Paleontology Egidio Feruglio, Argentina;
Michael Donovan, paleobotany collections manager, Cleveland Museum
of National History; Diane Erwin, senior museum scientist, University
of California, Berkeley; M. Alejandra Gandolfo, associate professor,
Cornell University; Erika Gonza'lez-Akre, project manager, Smithsonian Conservation Biology Institute; Fabiany Herrera, assistant curator of paleobotany, Field Museum of National History; Shusheng Hu, paleobotany collections manager, Yale Peabody Museum of Natural History; Ari Iglesias, researcher, National University of Comahue, Argentina; and Talia Karim, collections manager of invertebrate paleontology, University of Colorado
Museum of Natural History.

The National Science Foundation and the National Park Service provided
funding for this work.


========================================================================== Story Source: Materials provided by Penn_State. Original written by
Matthew Carroll. Note: Content may be edited for style and length.


========================================================================== Related Multimedia:
* Pairs_of_modern_and_fossil_leaves ========================================================================== Journal Reference:
1. Peter Wilf, Scott L. Wing, Herbert W. Meyer, Jacob A. Rose,
Rohit Saha,
Thomas Serre, N.Rube'n Cu'neo, Michael P. Donovan, Diane
M. Erwin, Maria A. Gandolfo, Erika Gonzalez-Akre, Fabiany Herrera,
Shusheng Hu, Ari Iglesias, Kirk R. Johnson, Talia S. Karim, Xiaoyu
Zou. An image dataset of cleared, x-rayed, and fossil leaves
vetted to plant family for human and machine learning. PhytoKeys,
2021; 187: 93 DOI: 10.3897/ PhytoKeys.187.72350 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/03/220315162808.htm

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