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  • New imaging, machine-learning methods speed effort to reduce crops' need for water

    Two researchers in a stand of sorghum.

    From left, Andrew Leakey, Jiayang (Kevin) Xie and their colleagues developed an improved method for analyzing features of plant leaves that contribute to water-use efficiency in crops like corn, sorghum (pictured) and Setaria. They used advanced statistical approaches to identify regions of the genome and lists of genes that contribute to these traits.

    Photo by L. Brian Stauffer

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  • Editor’s notes

    To reach Andrew Leakey, email leakey@illinois.edu.

    The paper “Optical topometry and machine learning to rapidly phenotype stomatal patterning traits for maize QTL mapping” is available online and from the U. of I. News Bureau. DOI: 10.1093/plphys/kiab299

    The paper “Correlation and co-localization of QTL for stomatal density, canopy temperature, and productivity with and without drought stress in Setaria” is available online and from the U. of I. News Bureau. DOI: 10.1093/jxb/erab166

    The paper “Machine learning enabled phenotyping for GWAS and TWAS of WUE traits in 869 field-grown sorghum accessions” is available online and from the U. of I. News Bureau. DOI: 10.1093/plphys/kiab346

    The paper “Phenotyping stomatal closure by thermal imaging for GWAS and TWAS of water use efficiency-related genes” is available online and from the U. of I. News Bureau. DOI: 10.1093/plphys/kiab395