**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 HAGBERG-PERTEN FALLING NUMBER (FN) TEST IS THE INTERNATIONAL STANDARD FOR MEASURING SPROUTING DAMAGE IN WHEAT. SPROUTED GRAIN PRODUCES STARCH-DEGRADING ENZYMES, RESULTING IN POOR BAKING QUALITY AND HEAVY PRICE DISCOUNTS. BECAUSE ENZYMES ARE CATALYSTS, A LARGE AMOUNT OF SOUND WHEAT CAN BE RUINED BY A SMALL AMOUNT OF SPROUTED WHEAT. WORLDWIDE LOSSES ARE ~$1B/YR. THE CURRENT FN TEST IS SLOW, DESTROYS THE SAMPLE, AND IS CONDUCTED RETROSPECTIVELY. THESE FEATURES MAKE IT UNABLE TO PREVENT CONTAMINATION DURING HARVEST, TRANSPORT, AND STORAGE. A TOOL THAT COMBINES ARTIFICIAL NEURAL NETWORKS (ANN) WITH NEAR-INFRARED (NIR) AND HYPERSPECTRAL IMAGING (HSI) PROMISES TO SOLVE THIS PROBLEM. OUR PRELIMINARY DATA INDICATE THAT ANN CONTINUOUSLY ACHIEVE GREATER PREDICTIVE ACCURACY WITH INCREASING TRAINING SAMPLE SIZE. USING THIS APPROACH, WE PROPOSE TO DEVELOP AN ONLINE COMPUTING TOOL TO TRANSFORM THREE EXISTING DATASETS INTO KNOWLEDGE FOR: I) PREDICTION OF FN BASED ON NIR OF GROUND KERNELS; II) NON-DESTRUCTIVE PREDICTION OF FN FROM HSI OF INTACT KERNELS; AND III) EXTENSION APPLICATIONS IN SPRING AND WINTER WHEAT BREEDING. THE DATASET RESOURCES INCLUDE A USDA PREHARVEST SPROUTING PROJECT WITH 2940 SAMPLES COVERING 320 VARIETIES AND 7 ENVIRONMENTS SPANNING 3 YEARS; WASHINGTON VARIETY TESTING PROGRAM WITH 28,000 SAMPLES COVERING 495 VARIETIES GROWN IN MULTIPLE LOCATIONS FOR 7 YEARS; AND WHEAT MARKET CENTER WITH 1500 SAMPLES OUTSIDE OF PACIFIC NORTHWEST. IF SUCCESSFUL, THIS NEW DATA-APPLICATION WILL PROVIDE WHEAT GROWERS, MILLERS, BAKERS, AND BREEDERS WITH A RAPID TOOL TO: 1) PARTITION SPROUTED/SOUND WHEAT IN REAL-TIME, AND 2) IDENTIFY LINES WITH RESISTANCE TO LOW FN FOR SUSTAINABLE DEVELOPMENT.