FRESH PRODUCE HAS BEEN REPEATEDLY LINKED TO HIGH-PROFILE FOODBORNE DISEASE OUTBREAKS, LEADING TO ILLNESS, LOSS OF LIFE, SIGNIFICANT ECONOMIC LOSS, AND EROSION OF CONSUMER CONFIDENCE. CONVENTIONAL METHODS FOR RISK ASSESSMENT HAVE ATTEMPTED TO MODEL IMPORTANT EVENTS LEADING TO PRODUCE CONTAMINATION BY HUMAN PATHOGENS. HOWEVER, THE CRITICAL NEED FOR EARLY IDENTIFICATION OF EMERGING PRODUCE SAFETY RISKS AND EARLY WARNING TO THE PUBLIC HAS NOT BEEN MET. IN THIS AGE OF THE INTERNET OF THINGS (IOT), THE USE OF THE INTERNET, ESPECIALLY REAL-TIME SOCIAL MEDIA AND ITS RAPID PROLIFERATION AND DISSEMINATION, AND THE EMERGENCE OF GAME-CHANGING BIG DATA TECHNOLOGIES HAVE PROVIDED UNPRECEDENTED OPPORTUNITIES TO DETECT EMERGING PRODUCE SAFETY ISSUES AND ALERT THE PUBLIC AT AN EARLY STAGE. THE OVERALL GOAL OF THIS STUDY IS TO DEVELOP AN INNOVATIVE BIG DATA ANALYTICS INFRASTRUCTURE FOR FRESH PRODUCE SAFETY RISK PREDICTION AND EARLY WARNING BASED ON CYBER-INFORMATICS TECHNOLOGIES THAT EXPLOIT MULTI-SOURCE BIG DATA, INCLUDING SOCIAL MEDIA, NEWS MEDIA, AND GOVERNMENT REPORTS, TO REDUCE THE INCIDENCE OF FOODBORNE DISEASES ASSOCIATED WITH CONSUMPTION OF FRESH PRODUCE. THE SPECIFIC OBJECTIVES INCLUDE TO: 1) DEVELOP A REAL-TIME DATA RETRIEVAL MECHANISM TO EXTRACT RELEVANT INFORMATION FROM DIVERSE DIGITAL ON-LINE SOURCES, 2) DESIGN BIG DATA STORAGE FUSING RISK PATTERN DATA SETS, 3) DISCOVER EVENT PATTERNS ABOUT SAFETY RISKS IN FRESH PRODUCE CHAINS, 4) DESIGN MACHINE LEARNING MODELS FOR PREDICTING OUTBREAKS EARLY, AND 5) IMPLEMENT A WEB-BASED EARLY WARNING INTERFACE FOR STAKEHOLDERS TO VISUALLY EXPLORE LEVELS OF RISKS.