Yufeng Ge

This paper introduces a regression-based framework to predict flowering time from sparse UAV observations, reducing the need for repeated data collection.

This study evaluates maize leaf morphology and canopy architecture to identify optimal leaf shapes for maximizing light interception and photosynthetic efficiency.

This study uses CRISPR-Cas9 to generate sorghum mutants and analyzes their nitrogen response and growth trajectory using high-throughput phenotyping.

This study uses data-driven methods to analyze hyperspectral leaf reflectance data from maize plants, identifying genetic markers correlated with reflectance patterns and chlorophyll content.

The paper identifies a historically balanced locus under recent directional selection in maize linked to nitrogen response traits, revealing genetic changes associated with modern breeding practices.