AI News, Satellites, supercomputers, and machine learning provide real-time crop type data

Satellites, supercomputers, and machine learning provide real-time crop type data

'If we want to predict corn or soybean production for Illinois or the entire United States, we have to know where they are being grown,' says Kaiyu Guan, assistant professor in the Department of Natural Resources and Environmental Sciences at the University of Illinois, Blue Waters professor at the National Center for Supercomputing Applications (NCSA), and the principal investigator of the new study.

The researchers argue more timely estimates of crop areas could be used for a variety of monitoring and decision-making applications, including crop insurance, land rental, supply-chain logistics, commodity markets, and more.

'Deep learning approaches have just started to be applied for agricultural applications, and we foresee a huge potential of such technologies for future innovations in this area,' says Jian Peng, assistant professor in the Department of Computer Science at U of I, and a co-author and co-principal investigator of the new study.

Even though it was a relatively small area, analyzing 15 years of satellite data at a 30-meter resolution still required a supercomputer to process tens of terabytes of data.

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