Harnessing machine learning and big data to fight hunger

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A group of Cornell researchers has received a $1 million grant from the U.S. Agency for International Development to use machine learning to rapidly analyze agricultural and food market conditions, aiming to better predict poverty and undernutrition in some of the world's poorest regions. The method will use open-source, freely available satellite data to measure solar-induced chlorophyll fluorescence (SIF) – photons emitted from plants during the process of photosynthesis, detected by satellite, which can monitor agricultural productivity. It will also consider land-surface temperature, which provides information about crop stress under water deficit or excessive heat, as well as food-price data. "A method that can use near real-time, low-cost or freely available remotely sensed data can speed up the delivery of this information, and sharply reduce the cost," said Chris Barrett, the Stephen B. and Janice G. Ashley Professor of Applied Economics and Management in the Charles H. Dyson School of Applied Economics and Management, and the principal investigator on the three-year grant. "If you are a humanitarian organization trying to really target your resources at the poorest rural areas, this seems a powerful diagnostic tool."