Rapid progress in proximal sensing has enabled vast increases in the quantity and quality of data available for characterizing crop responses. Data analysis is rapidly becoming the rate limiting step, especially for estimating biologically meaningful plant phenotypes. Inverse modeling and data assimilation show promise for allowing researches to maximize the useful information extracted from proximal sensing. This symposium examines how simulation modeling can be merged with proximal sensing, with potential applications for high-throughput phenotyping.