POSTHARVEST HANDLING OF GREEN ASPARAGUS, ESPECIALLY GRADING AND SORTING, IS EXTREMELY LABOR-INTENSIVE. THERE IS AN URGENT NEED TO DEVELOP LABOR-SAVING, COST-EFFECTIVE TECHNOLOGY FOR THE UNITED STATES (U.S.) ASPARAGUS INDUSTRY TO STAY SUSTAINABLE AND COMPETITIVE. CURRENTLY, THERE HAS BEEN A LACK OF RESEARCH INTO DEVELOPING DEDICATED, COST-EFFECTIVE TECHNOLOGY FOR FULL-SURFACE QUALITY GRADING AND SORTING OF GREEN ASPARAGUS, ESPECIALLY FOR SMALL PRODUCERS. THIS PROJECT IS TO ADVANCE MACHINE VISION AND ARTIFICIAL INTELLIGENCE (AI) FOR HIGH-SPEED QUALITY GRADING AND SORTING OF FRESH GREEN ASPARAGUS, WITH THE GOAL OF TRANSFERRING THE AUTOMATED SORTING TECHNOLOGY TO COMMERCIAL PRODUCERS. IN THIS PROJECT, WE WILL DESIGN AND CONSTRUCT AN INNOVATIVE HARDWARE PLATFORM CAPABLE OF FULL-SURFACE QUALITY DETECTION OF ASPARAGUS SPEARS, DEVELOP AND EVALUATE AN AI-POWERED ALGORITHM PIPELINE FOR REAL-TIME QUALITY DETECTION AND GRADING OF FRESH ASPARAGUS. MOREOVER, WE WILL DESIGN EFFECTIVE SORTING MECHANISMS FOR RELIABLE AND EFFICIENT SEPARATION OF GREEN ASPARAGUS AND DEVELOP AND EVALUATE AN INTEGRATED SYSTEM FOR HIGH-SPEED QUALITY GRADING AND SORTING OF GREEN ASPARAGUS. THE SUCCESS OF THIS PROJECT WILL EMPOWER SMALL U.S. ASPARAGUS PRODUCERS TO REDUCE OR MINIMIZE LABOR COSTS AND INCREASE PROFIT MARGINS, AND CONSISTENTLY SUPPLY HIGH-QUALITY SPEARS, LEADING TO ENHANCED CONSUMER ACCEPTANCE AND SATISFACTION AND EVENTUALLY LONG-TERM FINANCIAL BENEFITS.