̫ӳ researchers honored internationally for AI-powered wood identification research
Contact: Chloe Madison
STARKVILLE, Miss.—An international wood society is recognizing Mississippi State researchers with a prestigious award for groundbreaking work harnessing artificial intelligence to identify wood species.
The team, led by Frank Owens, associate professor in ̫ӳ’s Department of Sustainable Bioproducts, is the recipient of the George Marra Award from the Society of Wood Science and Technology which was presented at the organization’s recent conference in Portoroz, Slovenia. Owens and collaborators, including Assistant Professor Adriana Costa, Department Head Rubin Shmulsky, and Team Leader of the USDA Forest Products Laboratory’s Center for Wood Anatomy Research Alex Wiedenhoeft, were recognized for their 2023 article “,” published in Wood and Fiber Science.
“Every year, the Society of Wood Science and Technology gives the George Marra award to authors that demonstrate excellence in research and writing,” Owens said. “I know and respect many of SWST’s members, so it’s gratifying that our work was recognized by a peer group as worthy of an award.”
Owens, Costa and Shmulsky, who are also scientists in the university’s Forest and Wildlife Research Center, are pioneers in leveraging this technology to advance the field of wood identification.
“Around 2010, research on using computer-vision, a type of artificial intelligence, for wood identification started. As researchers rushed to gather data and publish, we noticed the quality of wood specimen surface preparation varied from image to image and publication to publication,” Owens said. “Sometimes the knifing or sanding quality was so bad you couldn’t see the features needed to identify the wood specimen. Our team had always considered high-quality surface preparation to be essential for training and testing accurate models, figuring that if we humans couldn’t see the wood features adequately, maybe the computer couldn’t either. No one had really tested that assumption. We decided it was important to investigate further.”
The team captured thousands of cross-sectional images of tropical woods under magnification to train the model. They tested the model on images of wood specimens prepared across progressively coarser sanding grits—from flawless to extremely scratchy. They were the first group to identify the predictive accuracy of the model drops as the coarseness of the sanding increases.
Costa, who focused on preparing samples and capturing the thousands of images needed to train the model, said it’s an honor to receive the award during her first year as an assistant professor.
“I always tell my students to be patient with their craft,” she said. “Every project will have its challenges, and sometimes when you’re solving a problem, it will lead to a new project. Keep going, work ethically and don’t take short cuts.”
Other collaborators on this project included University of Wisconsin Model Developer Prabu Ravindran; Assistant Professor Brunela Pollastrelli Rodrigues of Clemson University, who sanded and imaged the testing specimens; and Manuel Chavesta and Rolando Montenegro of Universidad Nacional Agraria La Molina, who provided expertise on the tropical wood species used in the project.
To learn more about the Department of Sustainable Bioproducts in ̫ӳ’s College of Forest Resources, visit . To learn more about the Forest and Wildlife Research Center, visit .
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