Measuring phenotypes is at the core of corn breeding and management recommendation practices. Due to the time and labor intensive nature of manually phenotyping plants, the data used for these efforts has typically focused only on end of season traits such as plant height, ear height, and yield. Development of robust, rapid, and low cost methods to evaluate key morphological features will allow for measurements throughout the growing season. This is beneficial in a breeding context as it allows breeders to understand variation in environmental responsiveness between varieties and to potentially develop varieties more resilient to increasingly extreme weather events. Recent technological advances in drones, sensors, and computational resources are allowing for this to become a reality. We will develop optimized analysis procedures for extracting traits of agronomic importance using unmanned aerial systems. These procedures will be applied to a relatively homogeneous field with variable genotypes, akin to a corn breeding nursery to evaluate plants on a plot basis for variable responses to environmental conditions. We will also use sensors that have high spectral resolution to evaluate plants under a number of stress conditions to identify the unseen signatures of stress that proceed visible phenotypes, allowing for earlier management interventions. Detecting and understanding non-visible early symptoms will also provide valuable information for future physiological and genetic work to understand the specific mechanisms plants use to respond to their environment so that we can create more resilient plants for the future.