Fossil-sorting robots will help researchers study oceans and climate – ScienceDaily

Researchers have designed and demonstrated a robot capable of sorting, manipulating and identifying microscopic marine fossils. The new technology automates a lengthy process that plays a key role in improving our understanding of the world’s oceans and climate – both today and in the prehistoric past.

“The beauty of this technology is that it’s made from relatively inexpensive off-the-shelf components, and we’re open-sourcing both the designs and the software for artificial intelligence,” says Edgar Lobaton, co-author of an article on the work and associate professor in Electrical and Computer Engineering from North Carolina State University. “Our goal is to make this tool widely available so that as many researchers as possible can use it to improve our understanding of oceans, biodiversity and climate.”

The technology, called Forabot, uses robotics and artificial intelligence to physically manipulate the remains of organisms called foraminifera, or forams, so that those remains can be isolated, imaged, and identified.

Forams are protists, neither plants nor animals, and have been widespread in our oceans for more than 100 million years. When forams die, they leave their tiny shells, most less than a millimeter wide. These shells give scientists insight into the properties of the oceans as they existed when the Forams lived. For example, different types of Foram species thrive in different types of marine environments, and chemical measurements can tell scientists everything from the chemistry of the ocean to its temperature when the shell was formed.

However, evaluating foram shells and fossils is both tedious and time-consuming. For this reason, a team of engineers and paleoceanography experts developed Forabot to automate the process.

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“At this point, Forabot is able to identify six different foram types and process 27 forams per hour – but it never gets boring and it never gets tired,” says Lobaton. “This is a proof-of-concept prototype, so we’re going to expand the number of foram species it can identify. And we’re optimistic that we’ll also be able to improve the number of forams it can process per hour.

“Also, at this point, the forabot has a 79% accuracy rate in identifying forams, which is better than most trained humans.”

“Once Forabot is optimized, it will be a valuable research tool, allowing student foram pickers to better spend their time learning more advanced skills,” said Tom Marchitto, co-author of the publication and professor of geological sciences from the University of Colorado, Boulder. “By using taxonomic knowledge from the community to train the robot, we can also improve the consistency of foram identification across research groups.”

This is how Forabot works. First, users need to wash and sieve a sample of hundreds of forams. This leaves users with a pile that looks like sand. The foram sample is then placed in a container called an isolation tower. A needle at the bottom of the isolation tower then protrudes up through the sample and lifts a single foram up where it is removed from the tower by aspiration. The suction pulls the foram to a separate container, the picture tower, which is equipped with an automated, high-resolution camera that captures multiple images of the foram. After the images have been taken, the foram is lifted again with a needle until it can be suctioned off and placed in the appropriate container in a sorting station. “The idea is that our AI can tell what type of foram it is from the images and sort them accordingly,” says Lobaton.

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“We are publishing in an open-source journal and are including the blueprints and AI software in the supplemental materials to this paper,” adds Lobaton. “Hopefully people will take advantage of this. The next step for us is to expand the types of forams the system can identify and work on optimizing the speed of operation.”


The work was carried out with support from the National Science Foundation under grant number 1829930.

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Materials provided by North Carolina State University. Originally written by Matt Shipman. Note: Content can be edited for style and length.

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