** 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 SUCCESS OF ANY FARMING OPERATION IS MEASURED BY FINAL YIELD, AND EFFECTIVE FARM MANAGEMENT REQUIRES KEY DECISIONS THAT IMPROVE PROFIT WITH RESPECT TO YIELD AT THE SUB-FIELD LEVEL. THIS APPROACH IS THE BASIS FOR PRECISION CROP MANAGEMENT. MODERN COMBINE HARVESTERS, WHICH TYPICALLY FEATURE ONBOARD YIELD MONITORS, HAVE INCREASED PRODUCER'S CAPACITY TO CHARACTERIZE YIELD VARIABILITY. HOWEVER, ERRORS THAT TYPICALLY OCCUR IN THE DATA COLLECTION PROCESS MUST BE REMOVED OR CORRECTED TO ENSURE ACCURACY. THIS PROCESS IS CURRENTLY SUBJECTIVE AND TIME-CONSUMING. TO ADDRESS THIS ISSUE, WE PROPOSE THE USE OF AN OPEN-SOURCE AI TOOL THAT CAN DETECT AND CORRECT THE ERRORS IN RAW YIELD DATA. OUR OVERALL OBJECTIVE IS TO INCREASE THE ACCURACY OF YIELD MONITOR DATA, WHICH WILL IMPROVE ANALYTICAL CAPABILITIES AND SUPPORT THE ADOPTION OF PRECISION AGRICULTURAL MANAGEMENT STRATEGIES. THIS OBJECTIVE SUPPORTS OUR LONG-TERM GOAL OF INCREASING THE SUSTAINABILITY OF ROW CROP PRODUCTION. WE HAVE THREE SPECIFIC AIMS FOR THIS PROJECT: (1) DEVELOP METHODS TO IMPROVE THE ACCURACY OF YIELD MONITOR DATA; (2) EVALUATE THE EFFECTIVENESS OF THESE METHODS FOR IMPROVING THE ACCURACY OF YIELD MONITOR DATA; AND (3) MEASURE CURRENT RATES OF AND BARRIERS TO ADOPTING YIELD MAPPING. UPON THE COMPLETION OF THIS PROJECT, WE EXPECT TO HAVE VALIDATED THE PROPOSED AI TOOL THROUGH FIELD TESTING AND COMPARISON AGAINST COMPETING OPTIONS. IF ADOPTED AS THE BASIS FOR FARM DECISION-MAKING, THIS TOOL COULD MAKE WIDE-REACHING IMPACTS IN OPTIMIZING FARM INPUTS, REDUCING FINANCIAL BURDENS TO PRODUCERS, MINIMIZING POTENTIAL ENVIRONMENTAL EFFECTS ON INTENSIVELY MANAGED ROW CROP PRODUCTION, AND GENERATING DATA FOR DECISION SUPPORT TOOLS.