Modern plant breeding for commercial agriculture is an essential tool for maintaining the supply of food, food stocks, and biofuels for both our nation and the larger world

Even a modest improvement in agronomic performance and yield of a commercial crop can have significant impact. However, there is a key area in which plant breeding efforts have lagged: the root system. Roots play a vital role in water uptake, nutrient absorption, and stress resilience, yet the effort required to study them in-situ has limited phenotyping at scale.traditionally, root phenotyping has been labor-intensive and difficult to scale, using manual root tracing techniques that require trained plant scientists and countless hours to complete. Root data from a single test plot can potentially take months to process. Due to this labor and time constraint, root phenotyping with legacy methods is inherently incompatible with modern plant breeding programs. The rhizomatic application provides a major step forward in analyzing roots using a combination of deep learning AI and classical image processing techniques, however IT was designed for modest academic studies rather than large scale breeding studies. In this effort, IT will be expanded to support these large datasets by transitioning to a multi-processing cloud service, developing new characterization tools, andoptimizing the processing of root images. The end result of this project will be a user-friendly tool that can automatically characterize roots in thousands of images efficiently, replacing months of labor with mere hours of processing and enabling plant breeders to produce new varieties with positive root traits. These crops will be more productive and resilient, able to withstand drought and disease and to grow quickly and in adverse environments, leading to better and more stable yields in americas farms.