How do you identify sick oaks? For a long time, detecting unhealthy oaks and identifying the disease afflicting them required a lot of manual labor. Scientists often looked out of airplanes or walked through forests in a bid to detect and find visible symptoms. Even then, one couldn’t really be sure. New research attempts to find a solution to this long-standing problem. A study published in the journal Proceedings of the National Academy of Sciences (PNAS) describes how a team of scientists used remote sensing, spectroscopy and machine learning to not only identify unhealthy oaks before visual symptoms appeared, but to also distinguish between drought stress and oak wilt, a deadly fungal disease. The team monitored sick trees and, as symptoms progressed, observed physiological changes in them while also keeping an eye on how they reflected light. Once the researchers established a link between the two, they used the data to train a machine learning model that can now tell if an oak is sick, and if it suffers from drought stress or oak wilt. “We obtained spectroscopic information in many wavelengths from light reflected from plants,” Jeannine Cavender-Bares, a co-author of the study and Distinguished McKnight University Professor of ecology, evolution and behavior at the University of Minnesota, told Mongabay in a video interview. “When we do this, we get a spectral fingerprint of the plant, which allows us to detect disease when we couple it with machine learning models.” Scientists have developed a machine learning model that can…This article was originally published on Mongabay
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