** AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** RISING LABOR COSTS ARE THRENTENING THE PROFITABILITY OF THE UNITED STATES APPLE INDUSTRY. IT IS IMPERATIVE TO DEVELOP COST- AND LABOR-SAVING TECHNOLOGY TO MAXIMIZE THE NET PROFIT OF APPLE INDUSTRY STAKEHOLDERS AND ENSURE THE CONTINUED COMPETITIVENESS OF THE FRUIT INDUSTRY. AUTOMATED, IN-FIELD SORTING OF HARVESTED FRUIT, PRIOR TO POSTHARVEST OPERATIONS, CAN ACHIEVE ECONOMIC BENEFITS OF HUNDREDS OF MILLIONS OF DOLLARS FOR APPLE GROWERS AND PACKERS. CURRENTLY THERE IS NO COMMERICAL AUTOMATION TECHNOLOGY FOR IN-FIELD SORTING OF APPLES. ALTHOUGH MACHINE VISION TECHNOLOGY HAS BEEN ADOPTED FOR AUTOMATED FRUIT SORTING AT PACKINGHOUSES, CRITICAL CHALLENGES EXIST IN DEVELOPING MACHINE VISION SYSTEMS FOR ORCHARD APPLICATION. TO FAIL THE GAP, BUILDING UPON PREVIOUS RESEARCH, OUR TEAM PROPOSE TO DEVELOP AN AI (ARTIFICIAL INTELLIGENCE) -ENHANCED MULTISPECTRAL VISION-BASED IN-FIELD APPLE SORTING SYSTEM. OUR LONG-RANGE GOAL IS TO DEVELOP MACHINE VISION TECHNOLOGY FOR COMMERCIAL IN-FIELD GRADING AND SORTING OF TREE FRUITS. SPECIFIC OBJECTIVES OF THE PROPOSED RESEARCH ARE TO: 1) DEVELOP A MULTISPECTRAL IMAGING SYSTEM INTEGRATED WITH A SCREW CONVEYOR FOR FULL-SURFACE FRUIT INSPECTION, 2) DEVELOP DEEP LEARNING MODELS FOR GRADING APPLES ACCORDING TO SIZE, COLOR AND SURFACE DEFECTS, 3) DEVELOP A SORTER TO EFFICIENTLY SORT APPLES INTO THREE GRADES , 4) DEVELOP USER-FRIENDLY, OPEN-SOURCED SOFTWARE PROGRAMS FOR SYSTEM OPERATION AND REAL-TIME VISUALIZATION, AND 5) PERFORM SYSTEM INTEGRATION AND CONDUCT FIELD TESTS AND DEMONSTRATION OF THE INNOVATIVE IN-FIELD APPLE SORTING MACHINE PROTOTYPE IN COMMERCIAL ORCHARDS.