Water deficit is currently one of the most significant limiting factors for global agricultural productivity, a factor further exacerbated by global climate change according to a 2019 water report from the Food and Agriculture Organization (FAO) of the United Nations. As a result, researchers worldwide have been working to improve water-use efficiency in crops to better cope with water-scarce conditions. 

Scientists have developed and deployed a series of new imaging and machine-learning tools to discover attributes that contribute to water-use efficiency in crop plants during photosynthesis and to reveal the genetic basis of variation in those traits.

The findings are described in a series of four research papers led by University of Illinois Urbana-Champaign graduate students Jiayang (Kevin) Xie and Parthiban Prakash, and postdoctoral researchers John Ferguson, Samuel Fernandes and Charles Pignon.