By Marylee Williams
Invasive plants wreak havoc on farms and natural ecosystems. They can destroy crops by crowding out native plants and competing for resources, resulting in millions of dollars in annual losses.
Tracking down, monitoring and ultimately eradicating invasive species is a priority for people working in agriculture, conservation and ecology, but the task is time-consuming and costly. Artificial intelligence could help, but scientists lack the data needed to develop a tool that can effectively identify and monitor these plants.
To help fill this data gap, researchers at Carnegie Mellon University worked with scientists at a conservation ranch in Montana to develop a method that trains machine learning models to detect invasive species more effectively with limited data, allowing researchers to create AI tools that can detect and monitor invasive species.
The project currently focuses on leafy spurge, a noxious weed with small green flowers rapidly spreading across the pastures and grasslands of the Great Plains. It is toxic to livestock and can render whole hayfields inedible. Research into leafy spurge estimates that it causes more than $35 million in losses annually in the country's beef and hay production.
"These invasive plants are a serious problem," said Ruslan Salakhutdinov, a faculty member in CMU's School of Computer Science. "Leafy spurge can destroy the ecosystems around it. Building a machine learning tool to help identify it was tough because we didn't have massive amounts of data on this plant, even online. It became a problem of trying to build accurate models with limited data, and the solution has a big impact on the ecology and environment."
Salakhutdinov, the UPMC Professor of Computer Science in CMU's Machine Learning Department (MLD), worked on the project with Brandon Trabucco, an MLD doctoral student; Max Gurinas at Harvard University; and Kyle Doherty, a staff scientist at MPG Ranch, which manages more than 15,000 acres of conservation property in Western Montana for research.
Researchers in SCS wanted to leverage new generative AI tools to improve existing models trained to detect leafy spurge using existing drone images collected at MPG Ranch. Salakhutdinov and Trabucco also wondered if using synthetic images of leafy spurge made with AI could create the needed data to make the models work better.
Source : cmu.edu