With sleap-roots, SLEAP can process biological traits of root systems like depth, mass, and angle of growth.The Salk group evaluated the sleap-roots bundle in a range of plants, including crop plants like soybeans, rice, and canola, as well as the model plant species Arabidopsis thaliana– a flowering weed in the mustard household. Across the variety of plants trialed, they discovered the novel SLEAP-based approach exceeded existing practices by annotating 1.5 times much faster, training the AI design 10 times faster, and predicting plant structure on brand-new information 10 times faster, all with the exact same or much better accuracy than before.Together with huge genome sequencing efforts for elucidating the genotype data in big numbers of crop varieties, these phenotypic information, such as a plants root system growing especially deep in soil, can be theorized to understand the genes accountable for creating that especially deep root system.SLEAP and sleap-roots instantly discover landmarks across the entire root system architecture.”We have already been able to create the most substantial catalog of plant root system phenotypes to date, which is truly accelerating our research to create carbon-capturing plants that combat environment change,” states Busch, the Hess Chair in Plant Science at Salk.
“We developed a robust procedure verified in numerous plant types that cuts down on analysis time and human error, while highlighting ease of access and ease-of-use– and it needed no changes to the actual SLEAP software,” states first author Elizabeth Berrigan, a bioinformatics expert in Buschs lab.Impact of SLEAP on Plant BreedingWithout modifying the standard technology of SLEAP, the researchers developed a downloadable toolkit for SLEAP called sleap-roots (readily available as open-source software here). With sleap-roots, SLEAP can process biological characteristics of root systems like depth, mass, and angle of growth.The Salk group tested the sleap-roots bundle in a range of plants, including crop plants like soybeans, rice, and canola, as well as the model plant types Arabidopsis thaliana– a blooming weed in the mustard family. Across the variety of plants trialed, they discovered the unique SLEAP-based technique exceeded existing practices by annotating 1.5 times quicker, training the AI design 10 times faster, and forecasting plant structure on brand-new information 10 times much faster, all with the same or much better accuracy than before.Together with huge genome sequencing efforts for elucidating the genotype data in big numbers of crop ranges, these phenotypic information, such as a plants root system growing specifically deep in soil, can be theorized to understand the genes accountable for developing that especially deep root system.SLEAP and sleap-roots automatically spot landmarks throughout the entire root system architecture.”We have actually already been able to develop the most extensive brochure of plant root system phenotypes to date, which is truly accelerating our research study to develop carbon-capturing plants that combat environment change,” says Busch, the Hess Chair in Plant Science at Salk. Efforts to improve, expand, and share SLEAP and sleap-roots will continue for years to come, however its use in Salks Harnessing Plants Initiative is already speeding up plant designs and assisting the Institute make an impact on environment change.Reference: “Fast and Efficient Root Phenotyping via Pose Estimation” by Elizabeth M. Berrigan, Lin Wang, Hannah Carrillo, Kimberly Echegoyen, Mikayla Kappes, Jorge Torres, Angel Ai-Perreira, Erica McCoy, Emily Shane, Charles D. Copeland, Lauren Ragel, Charidimos Georgousakis, Sanghwa Lee, Dawn Reynolds, Avery Talgo, Juan Gonzalez, Ling Zhang, Ashish B. Rajurkar, Michel Ruiz, Erin Daniels, Liezl Maree, Shree Pariyar, Wolfgang Busch and Talmo D. Pereira, 12 April 2024, Plant Phenomics.DOI: 10.34133/ plantphenomics.0175 Other authors include Lin Wang, Hannah Carrillo, Kimberly Echegoyen, Mikayla Kappes, Jorge Torres, Angel Ai-Perreira, Erica McCoy, Emily Shane, Charles Copeland, Lauren Ragel, Charidimos Georgousakis, Sanghwa Lee, Dawn Reynolds, Avery Talgo, Juan Gonzalez, Ling Zhang, Ashish Rajurkar, Michel Ruiz, Erin Daniels, Liezl Maree, and Shree Pariyar of Salk.The work was supported by the Bezos Earth Fund, the Hess Corporation, the TED Audacious Project, and the National Institutes of Health (RF1MH132653).
