** AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** GROWERS RELY ON EXTENSION AGENTS TO INFORM THEM OF APPROPRIATE METHODOLOGIES AND TIMING FOR PEST MANAGEMENT PRACTICES. IN TURN, EXTENSION AGENTS OFTEN RELY ON DECISION-SUPPORT TOOLS TO PROVIDE PREDICTIONS OF PEST PHENOLOGY. ALTHOUGH PHENOLOGICAL MODELING HAS RECENTLY SEEN MAJOR ADVANCES, LARGELY DUE TO SOPHISTICATED MACHINE LEARNING MODELS, THOSE MODELS REMAIN LARGELY INACCESSIBLE TO EXTENSION AGENTS SINCE THEY REQUIRE EXTENSIVE DATA SCIENCE EXPERTISE TO USE AND ARE NOT YET INTEGRATED INTO EXISTING DECISION SUPPORT TOOLS. HERE, WE PROPOSE THE DEVELOPMENT OF A DECISION-SUPPORT PLATFORM THAT AUTOMATES THE USE OF MACHINE LEARNING MODELS TO MAKE PREDICTIONS ABOUT PEST PHENOLOGY. THE CENTRAL OBJECTIVES OF THIS PROJECT ARE TO PERFORM RESEARCH AND DEVELOPMENT FOR MACHINE LEARNING MODELS YIELDING OPTIMAL PREDICTIVE PERFORMANCE OF PEST PHENOLOGY, DEVELOP A CONTAINERIZED SOFTWARE PLATFORM, VALIDATE THE RESULTS WITH COLLECTION OF NEW DATA, AND COMMUNICATE WITH END USERS FOR FEEDBACK AND TRAINING. AS A TEST CASE, WE WILL UTILIZE 18 YEARS OF DATA ON THREE APHID PESTS FROM THE MIDWEST SUCTION TRAP NETWORK, CREATING A WEB-READY DECISION-SUPPORT TOOL FOR THESE PESTS. THE TOOL WILL BE BUILT USING MODERN BEST PRACTICES IN SOFTWARE DEVELOPMENT, FACILITATING ADAPTATION TO OTHER PESTS FOR WHICH SIMILAR DATASETS EXIST. THIS PROPOSED PROJECT ALIGNS WITH THE DSFAS PROGRAM AREA PRIORITY WITH A FOCUS ON THE PLANT HEALTH AND PRODUCTION AND PLANT PRODUCTS NIFA PRIORITY AREA BY INTEGRATING ARTIFICIAL INTELLIGENCE WITH MODERN SOFTWARE ENGINEERING TECHNOLOGIES TO MAXIMIZE UTILIZATION OF EXISTING DATA AND HELPING FARMERS MAKE INFORMED AND ECONOMICAL DECISIONS ABOUT MANAGING RESOURCES TO CONTROL APHID PESTS.