**AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** THE RESEARCH OBJECTIVE IS DESIGN, DEVELOPMENT, AND FIELD-TESTING OF AN ARTIFICIALLY INTELLIGENT METHOD TO PREDICT THE YIELD AND OIL CONTENT OF FLAX FROM A NUMBER OF MORPHOLOGICAL TRAITS BEFORE HARVESTING. SUCCESS IN THIS PROJECT WILL RESULT IN A QUANTUM LEAP FOR FLAX BREEDING PROGRAMS BY BRINGING IN A SYSTEMATIC DATA-DRIVEN AUTONOMOUS APPROACH, IN LIEU OF CONVENTIONAL HEURISTIC-BASED DECISION MAKING. NDSU HOSTS THE ONLY FLAX BREEDING PROGRAM IN THE US AND NORTH DAKOTA IS THE LARGEST PRODUCER OF FLAX (91% OF US PRODUCTION), WHICH WILL BE USED AS THE TESTBED IN THIS PROJECT. THE PROJECT REQUIRES PRECISION DATA COLLECTION ON MORPHOLOGICAL TRAITS THROUGHOUT THE ENTIRE LIFE CYCLE OF THE CROP. A COOPERATIVE TEAM OF UNMANNED AERIAL SYSTEMS (UASS) AND UNMANNED GROUND VEHICLES (UGVS) WILL BE EMPLOYED. THE UGVS WILL ALSO BE LOADED WITH A HYPERSPECTRAL CAMERA TO PREDICT THE OIL CONTENT EVEN BEFORE HARVESTING. THE SCHEDULING AND OPERATION OF THE UAS-UGV TEAM WILL BE DICTATED BY A DATA ANALYTICS ENGINE. THE VIDEOS COLLECTED BY THE UAS/UGV WILL BE PROCESSED TO EXTRACT VARIOUS MORPHOLOGICAL TRAITS AND TO PREDICT THE FINAL YIELD OF THE CROP THROUGH AN INTEGRATED MACHINE LEARNING MODEL. HYPERSPECTRAL IMAGES WILL BE ANALYZED USING MACHINE LEARNING TO PREDICT THE OIL CONTENT OF EACH PLOT. IF SUCCESSFUL, THIS PREDICTIVE MODEL FOR YIELD AND OIL CONTENT WILL ALLOW A BREEDER TO SUBSTANTIALLY LOWER THE COSTS OF THE BREEDING PROGRAM, AND HEREBY IMPROVE THE QUALITY OF THE NEW CROP VARIETY. IN COLLABORATION WITH AMERIFLAX, THIS FRAMEWORK WILL BE TESTED IN REAL-WORLD SETTINGS.