ACCURATE INVENTORY OF SEEDLING STOCK IS CRUCIAL TO COMMERCIAL FOREST TREE NURSERIES FOR PLANNING OF STOCK SHIPMENTS AND OUTPLANTINGS. THE U.S. FORESTRY TREE NURSERY INDUSTRY CURRENTLY RELIES ON MANUAL LABOR FOR SAMPLING-BASED SEEDLING INVENTORIES ON A LARGE SCALE AT EVERY NURSERY. THE PROCESS IS LABOR-INTENSIVE, TIME-CONSUMING, ERROR-PRONE, AND ERGONOMICALLY POOR FOR WORKERS. AS THE U.S. FARM LABOR SUPPLY IS EXPECTED TO CONTINUE TO DECLINE IN THE LONG TERM, AN AUTOMATED SEEDLING INVENTORY TECHNOLOGY IS NEEDED TO MEET THE NATIONAL AND GLOBAL GOALS FOR SUSTAINABLE PRACTICE. THE MAIN OBJECTIVE OF THIS PROJECT IS TO DEVELOP AND EVALUATE AN AI-BASED GROUND ROBOTIC VISION SYSTEM FOR AUTOMATED INVENTORY AND QUALITY ASSESSMENT (I.E., STEM DIAMETER, SHOOT HEIGHT, AND HEALTH STATUS) OF BAREROOT PINE SEEDLINGS AT STAND LEVEL. DEEP CONVOLUTIONAL NEURAL NETWORKS, ACTIVELY-ILLUMINATED 3D STEREO IMAGING, AND FIELD ROBOTICS WILL BE INTEGRATED INTO AN AUTONOMOUS GROUND-BASED SEEDLING DETECTION AND MEASUREMENT SYSTEM. THE SYSTEM PERFORMANCE WILL BE EXTENSIVELY EVALUATED AGAINST MULTIPLE FACTORS, I.E., YEAR, LOCATION, PINE SPECIES, SEEDLOT AND GROWTH STAGE. THE SECONDARY OBJECTIVE IS TO DEVELOP AND EVALUATE A GIS DECISION SUPPORT SYSTEM THAT ENABLES MANAGEMENT, VISUALIZATION, AND SPATIAL ANALYSIS OF THE AUTOMATED SEEDLING INVENTORY AND QUALITY DATA.THE PROPOSED PRECISION SENSING AND INFORMATION MANAGEMENT TECHNOLOGIES WILL PROVIDE NEAR-TERM AND LONG-TERM SOLUTIONS FOR NURSERY MANAGERS TO IMPROVE EFFICIENCY, PROFITABILITY AND SUSTAINABILITY OF FORESTRY TREE SEEDLING PRODUCTION.