LUMBER GRADING HAS BEEN PERFORMED BY HUMAN GRADERS OR AUTOMATED SYSTEMS. HUMAN GRADERS EXPERIENCE FATIGUE FROM REPETITIVE TASKS AND WORKING CONDITIONS, NEED TO RAPIDLY GRADE THE BOARD ON A FAST PRODUCTION LINE, AND ARE CHALLENGED WITH MENTAL CALCULATIONS. ON THE OTHER HAND, AUTOMATED GRADING SYSTEMS FAIL TO CAPTURE THE NATURAL VARIABILITY WITHIN AND AMONG WOOD SPECIES AND DRYING AND SMALL DEFECTS. THE OVERARCHING GOAL OF THIS STUDY IS TO CREATE ECONOMICALLY RELEVANT INNOVATION IN FOREST AND WOOD SCIENCE BY EMBRACING A FUTURE-ORIENTED INDUSTRY 4.0. THE RATIONALE FOR THE PROPOSED RESEARCH IS THAT DEVELOPING AND IMPLEMENTING MODERN MACHINE-LEARNED MODELS WILL HELP TO STEER THE FOREST PRODUCTS INDUSTRY TOWARDS NEW, COST-EFFECTIVE, AND HIGHLY SUCCESSFUL DIRECTIONS. THE BROADER IMPACTS OF THIS PROJECT ARE JOB CREATION, INDUSTRY STRENGTHENING AND INCREASE AWARENESS OF WOOD PRODUCTS. THIS PROJECT WILL ADVANCE THE SCIENTIFIC UNDERSTANDING OF GRADING LUMBER BOARDS, LUMBER MANUFACTURING AND IMPROVE DATA SCIENCE APPLICABILITY. ULTIMATELY, THIS PROJECT HAS THE POTENTIAL OF DEVELOPING COMMERCIAL, HIGH PERFORMANCE, STRONGER AND STIFFER WOOD PRODUCTS.