Hyperspectral data comprises the full light spectrum; this dataset of continuous spectral information has many applications from understanding the health of the Great Barrier Reef to picking out more productive crop cultivars. To help researchers better predict 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—and they demonstrated that this technique improved the predictive power of a recent study by up to 15 percent, compared to using just one algorithm.

Read the full story from the Realizing Increased Photosynthetic Efficiency project, and find the original report at https://doi.org/10.3389/fpls.2019.00730.