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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.
"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 this work for a research project called Realizing Increased Photosynthetic Efficiency (RIPE). "Now scientists do not need to scratch their heads to figure out which machine learning algorithms to use; they can apply six or more algorithms—for the price of one—to make more accurate predictions."
RIPE - Illinois - Engineering - Crops - Photosynthesis
RIPE, which is led by Illinois, is engineering crops to be more productive by improving photosynthesis, the natural process all plants use to convert sunlight into energy and yields. RIPE is supported by the Bill & Melinda Gates Foundation, the U.S. Foundation for Food and Agriculture Research (FFAR), and the U.K. Government's Department for International Development (DFID).
In a recent study, published in Remote Sensing of Environment, the team introduced spectral analysis as a means to quickly identify photosynthetic improvements that could increase yields. In this 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...
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