To help researchers identify high yielding crop traits, a team from the
University of Illinois have stacked together six high-powered, machine
learning algorithms that are used to interpret hyperspectral data. The team
showed that the technique improved the predictive power of a previous study
by up to 15 percent, compared to using just one algorithm.

A previous study by the team introduced spectral analysis as a means to
quickly identify photosynthetic improvements that could increase yields. In
a new study, published in Frontiers in Plant Science, the team improved
their previous predictions of photosynthetic capacity by as much as 15
percent using machine learning, where computers automatically applied these
six algorithms to their dataset without human help.

"We are empowering scientists from many fields, who are not necessarily
experts in computational analysis, to translate their enormous datasets into
beneficial results," said first author Peng Fu, a postdoctoral researcher at
Illinois, who led the work for the project Realizing Increased
Photosynthetic Efficiency (RIPE). He added that with the stacked algorithm,
"scientists do not need to scratch their heads" to figure out which machine
learning algorithms to use as they can apply six or more algorithms-for the
price of one-to make more accurate predictions.

[www.igb.illinois.edu]
mprove-predictions-yield-boosting-crop-traits