Walking rows of soybeans in the mid-summer heat is an exhausting but essential chore in breeding new cultivars.
Walking rows of soybeans in the mid-summer heat is an exhausting but essential chore in breeding new cultivars. Researchers brave the heat daily during crucial parts of the growing season to look for plants showing desirable traits, such as early pod maturity. But without a way to automate detection of these traits, breeders can’t test as many plots as they’d like in a given year, elongating the time it takes to bring new cultivars to market.
In a new study from the University of Illinois, researchers predict soybean maturity date within two days using drone images and artificial intelligence, greatly reducing the need for boots on the ground.
“Assessing pod maturity is very time consuming and prone to errors. It’s a scoring system based on the color of the pod, so it is also subject to human bias,” says Nicolas Martin, assistant professor in the Department of Crop Sciences at Illinois and co-author on the study. “Many research groups are trying to use drone pictures to assess maturity, but can’t do it at scale. So we came up with a more precise way to do that. It was really cool, actually.”
Rodrigo Trevisan, a doctoral student working with Martin, trained computers to detect changes in canopy color from drone images collected across five trials, three growing seasons, and two countries. Importantly, he was able to account for “bad” images to maintain accuracy.
Read more at University of Illinois College of Agricultural, Consumer and Environmental Sciences
Photo Credit: jcesar2015 via Pixabay
