** 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 SPREAD OF INVASIVE PLANTPESTS AND PATHOGENS (HEREAFTER PESTS) ARE WELL-KNOWN ECOLOGICAL AND ECONOMIC THREATS TO AGRICULTURE, RESPONSIBLE FOR 10-40% OF CROP YIELD LOSSES GLOBALLY AND RESULTING IN AN ESTIMATED $40 BILLION OF PRODUCTION LOSSES EACH YEAR IN THE UNITED STATES. THE THREAT PESTS POSE TO FOOD SECURITY IS EXPECTED TO INCREASE DUE TO CLIMATE CHANGE AND THE GLOBAL NATURE OF TRADE AND TRAVEL. ALTHOUGH MANY PESTS ARE UNDER REGULATORY CONTROL TO PREVENT AND MITIGATE OUTBREAKS, CONTROL OR ERADICATION AFTER PEST ESTABLISHMENT CAN BE RESOURCE-INTENSIVE, AND SUCCESS REQUIRES RAPID DETECTION AND EFFECTIVE IMPLEMENTATION OF APPROPRIATE STRATEGIES. EVEN PREVENTATIVE PESTICIDE MEASURES CAN BE COSTLY AND ENVIRONMENTALLY DAMAGING IF OVER-APPLIED AND INEFFECTIVE IF UNDER-APPLIED OR INCORRECTLY TIMED, ALL WITH THE POTENTIAL FOR PROMOTING PESTICIDE-RESISTANCEAND LOSS OF NATURAL CONTROLS.RAPID RESPONSES AND DATA-DRIVEN DECISION SUPPORT TOOLS ARE ESSENTIAL THEN FOR UNDERSTANDING AND MITIGATING THREATS POSED BY DAMAGING AGRICULTURAL PESTS. HOWEVER, SPARSE DATA TYPICALLY LIMIT THE ACCURACYAND ITERATIVE IMPROVEMENT OF PEST SPREAD MODELS. THIS RESEARCH WILL COUPLE ADVANCES IN OBJECT DETECTIONUSING MACHINE LEARNING WITHWIDELY-AVAILABLE CROWDSOURCED AND SATELLITE IMAGERY TO BUILD AN AUTOMATED, REPEATABLE PROCESS FOR EXPANDING MAPPING EFFORTS OFSUSCEPTIBLE CROPS (I.E., HOST SPECIES)ESSENTIAL TO FORECASTING PEST SPREAD ACCURATELY AND ACROSS MULTIPLE SCALES. THE RESULTING MAPS OF HOST SPECIES WILL IMPROVE PEST SPREAD MODELS BY ADDRESSING SPARSE DATA CONCERNS AND REDUCING DELAYS IN DATA AVAILABILITY, THEREBY ENABLING THE CONTINUOUS IMPROVEMENT OF PEST SPREAD FORECASTSAND SHORTENING TIME TO DECISION MAKING.WE WILL FOCUS ON SEVERAL ECONOMICALLY AND CULTURALLY SIGNIFICANT FRUIT AND TREE NUT SPECIES THREATENED BY EMERGING PESTS AND CLIMATE CHANGE. WE WILL COLLABORATE WITH USDA ANIMAL AND PLANT HEALTH INSPECTION SERVICE (APHIS), USDA AGRICULTURAL RESEARCH SERVICE (ARS), STATE DEPARTMENTSOF AGRICULTURE, AND GROWERS ASSOCIATIONS TO: (1) IDENTIFY KEY PEST THREATS TO FRUIT AND TREE NUT CROPS, (2) ITERATIVELY DEVELOP AND VALIDATE HOST SPECIES MAPS AND MODEL FORECASTS, (3) CONTINUE CO-DEVELOPING OUR USER-FRIENDLY DECISION SUPPORT TOOL, THE POPS FORECASTING PLATFORM, AND (4) ADD AN ALERT SYSTEM THAT TRANSLATES FORECASTS AND SIMULATIONS INTO ACTIONABLE INSIGHTS FOR CROP PROTECTION.THE ITERATIVE NEAR-TERM FORECASTING SYSTEM, COUPLED WITH DATA INPUTS ENHANCED USING MACHINE LEARNING, WILL REDUCE COSTS FOR PEST SURVEYS AND HELP GROWERS IDENTIFY WHEN AND WHERE TO INTERVENE TO PROTECT THEIR CROPS, THUS REDUCING PRODUCTION LOSSES AND CHEMICAL PESTICIDE INPUTS.