Improving throughput and accuracy of plant phenotyping is core to continued advances in breeding to ensure genetic gain to meet global food demand. Current manual phenotyping requires enormous investments in time, cost, and labor as quantitative values are required for thousands of genetic varieties across different environments. In soybean, a genotype’s maturity governs the geography for which it is adapted and has an impact on yield, which must be controlled for in breeding to realize genetic gain. In this work, we developed and validated a method for highthroughput phenotyping of soybean maturity using high resolution, visual, RGB, imagery collected using an unmanned aerial vehicle (UAV). We illustrate a method to automatically derive maturity date by modeling change through time of a quantitative assessment of canopy greenness on a per plot basis. The efficacy of the analytical framework is compared to the manual scoring system by evaluating phenotypic and genetic correlations and genetic repeatability measures. Analysis of replicated experiments from multiple locations yielded high phenotypic correlations (R<sup>2</sup> = 0.85 - 0.96) between manual and UAV derived maturity scores. Heritability of the maturity estimates from the proposed remote sensing method is comparable to that of manual scoring. Implementation of this system has allowed for improved scale, cost efficiencies and data quality for soy maturity data collected via UAV remote sensing.