Artificial intelligence helps scientists engineer plants to fight climate change

 The Intergovernmental Panel on Climate Change (IPCC) declared that removing carbon from the atmosphere is now essential to fighting climate change and limiting global temperature rise. To support these efforts, Salk scientists are harnessing plants' natural ability to draw carbon dioxide out of the air by optimizing their root systems to store more carbon for a longer period of time.

To design these climate-saving plants, scientists in Salk's Harnessing Plants Initiative are using a sophisticated new research tool called SLEAP -- an easy-to-use artificial intelligence (AI) software that tracks multiple features of root growth. Created by Salk Fellow Talmo Pereira, SLEAP was initially designed to track animal movement in the lab. Now, Pereira has teamed up with plant scientist and Salk colleague Professor Wolfgang Busch to apply SLEAP to plants.

In a study published in Plant Phenomics on April 12, 2024, Busch and Pereira debut a new protocol for using SLEAP to analyze plant root phenotypes -- how deep and wide they grow, how massive their root systems become, and other physical qualities that, prior to SLEAP, were tedious to measure. The application of SLEAP to plants has already enabled researchers to establish the most extensive catalog of plant root system phenotypes to date.

What's more, tracking these physical root system characteristics helps scientists find genes affiliated with those characteristics, as well as whether multiple root characteristics are determined by the same genes or independently. This allows the Salk team to determine what genes are most beneficial to their plant designs.

"This collaboration is truly a testament to what makes Salk science so special and impactful," says Pereira. "We're not just 'borrowing' from different disciplines -- we're really putting them on equal footing in order to create something greater than the sum of its parts."

Prior to using SLEAP, tracking the physical characteristics of both plants and animals required a lot of labor that slowed the scientific process. If researchers wanted to analyze an image of a plant, they would need to manually flag the parts of the image that were and weren't plant -- frame-by-frame, part-by-part, pixel-by-pixel. Only then could older AI models be applied to process the image and gather data about the plant's structure.

Source: ScienceDaily