** AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** UNDERSTANDING PLANT ROOTS IS CRITICAL TO INCREASING PLANT AND CROP EFFICIENCY AND RESILIANCE. HOWEVER, STUDYING ROOTS IS EXTREMELY CHALLENGING. OUR CURRENT TOOLS AND MECHANISMS ARE VERY LIMITED IN WHAT ROOT INFORMATION THEY CAN COLLECT AND THE SCALE IN WHICH THIS ROOT INFORMATION CAN BE OBTAINED. RECENT ADVANCES IN MACHINE-LEARNING COMBINED WITH IMPROVED IMAGING TECHNOLOGY HAS OPENED DOORS TO COLLECT AND EXPLORE ROOT CHARACTERISTICS OF FIELD-GROWN PLANTS MORE EASILY AND EFFICIENTLY.THIS INCLUDES ROOT IMAGERY FROM VARIOUS IMAGING SOURCES INCLUDING X-RAY COMPUTED TOMOGRAPHY (X-RAY CT) SCANS OF PART OF OR WHOLE ROOT STRUCTURES. HOWEVER, A CRITICAL BOTTLENECK FOR USING THIS BIG DATA, SPECIFICALLY X-RAY CT IMAGERY OF ROOTS, IS THE INABILITY OF MACHINE-LEARNING ALGORITHMS TO RECOGNIZE AND DIFFERENTIATE ROOT FEATURES FROM THOSE OF ORGANIC MATTER AND OTHER NOISE EMBEDDED IN THE IMAGERY. IN THIS PROJECT, WE PROPOSE TO DEVELOP AND APPLY AN APPROACH THAT COUPLES ROOT ARCHITECTURE MODELING WITH GRAPH CONVOLUTIONAL NEURAL NETWORKS (GCNNS) FOR IMPROVED DETECTION AND CHARACTERIZATION OF SWITCHGRASS ROOTS FROM X-RAY CT SCANS OF SOIL CORES. WE WILL DEVELOP, IMPLEMENT AND SHARE NEW MACHINE LEARNING METHODS TO AUTOMATE THE UNDERSTANDING OF X-RAY CT SCANS OF ROOTS. THE OUTCOMES OF THIS RESEARCH WILL INCLUDE A NOVEL ROOT PHENOTYPING FRAMEWORK WITH HIGHLY STREAMLINED WORKFLOW THAT WILL TRANSFORM OUR ABILITY TO DETECT AND EXTRACT FEATURES OF ROOT TRAITS FROM SCANNED SOIL CORE IMAGES FROM THE FIELD.