** AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** THIS PROPOSAL WILL BE SUBMITTED TO THE PROGRAM AREA, 'PESTS AND BENEFICIAL SPECIES IN AGRICULTURAL PRODUCTION SYSTEMS (A1112)' AND WE WILL ADDRESS THE FOLLOWING PRIORITIES: 1) BIOTIC AND ABIOTIC FACTORS AFFECTING THE ABUNDANCE OR SPREAD OF AGRICULTURALLY IMPORTANT PLANT PESTS AND 2) ASPECTS OF WEED BIOLOGY THAT IMPACT SEEDBANK DYNAMICS. THE RESEARCH IS NEEDED BASED ON THE LACK OF WEED EMERGENCE MODELS FOR THE SOUTHEASTERN USA, THE LACK OF TOOLS TO ADEQUATELY MODEL EMERGENCE ON A REGIONAL SCALE, AND THE LACK OF TOOLS TO MAKE THIS INFORMATION ACCESSIBLE TO GROWERS. GROWER ACCESS TO ACCURATE WEED EMERGENCE MODELS COULD HELP OBTIMIZE THE TIMING OF WEED MANAGEMENT INPUTS AND REDUCE PEST MANAGEMENT COSTS. THE OVERALL GOAL IS TO DESIGN AND UTILIZE AUTONOMOUS SENSOR STATIONS, DEEP LEARNING METHODOLOGIES, AND ON-LINE PLATFORMS TO ENHANCE OUR ABILITY TO MODEL BROADLEAF WEED EMERGENCE ACROSS DIVERSE REGIONS AND UTLIZE ON-LINE PLATFORMS TO PROVIDE A DECISION-BASED SYSTEM FOR GROWERS TO IMPLEMENT WEED MANAGEMENT. OUR OBJECTIVES ARE TO: 1) TRAIN DEEP LEARNING MODELS TO DETECT AND IDENTIFY WEED SPECIES OF INTEREST IN AGRICULTURAL FIELDS, 2) DEVELOP AND DEPLOY AUTONOMOUS SENSOR STATIONS EQUIPPED WITH DIGITAL IMAGERY AND DEEP LEARNING MODELS FROM OBJECTIVE 1 TO DETECT, IDENTIFY AND COUNT WEEDS OF INTEREST AS THEY EMERGE, 3) UTILIZE DEEP LEARNING METHODOLOGIES AND DATA ACQUIRED FROM THE AUTONOMOUS SENSOR STATIONS FROM OBJECTIVE 2 TO DEVELOP TEMPERATURE-BASED, WEED EMERGENCE MODELS, AND 4) EMBED THE MODELS INTO AN ON-LINE PLATFORM DESIGNED TO HELP GROWERS MANAGE CLIMATE RISK IN AGRICULTURE. T