Consistent, or robust, phenotypes are crucial to agriculture. Crops that are more uniform are more easily harvested and processed into predictable, desirable products. Some crops in the fields are nearly genetically identical, so one would expect identical phenotypes. However, across the same field, individuals vary due to stochastic noise that arises during development and differences in their microenvironments. Among traits that influence yields of many crops, is robustness in timing of emergence, with increased consistency in emergence associated with higher yields. However, the genetic architecture underlying robustness in emergence is poorly understood, limiting the ability to select for robustness. To map genes contributing to phenotypic robustness, an accurate measure of trait robustness is needed, using large sample sizes are required to estimate variance. Using both controlled and field environments, we aim to quantify robustness in emergence across species to identify conserved genes contributing to this trait. In Arabidopsis thaliana seed size and hypocotyl length were used proxies of emergence, measured with a large degree of replication in a controlled environment. In Hordeum vulgare, we are building a pipeline to measure emergence in the field using drone images. This involves gathering high resolution, multispectral imagery to create a normalized difference vegetation index, highlighting living plant material over the background soil. Plants are then able to be counted among each genotype to measure emergence robustness. Using these phenotypic data, we will perform association mapping to identify QTL underlying these traits, focusing on shared mechanisms across species.