CODLING MOTH (CM) IS THE MOST DEVASTATING PEST PROBLEM CONFRONTING THE US APPLE INDUSTRY, DESPITE INTEGRATED PEST MANAGEMENT (IPM) CONTROL AND OTHER MITIGATION EFFORTS, IT REMAINS A HUGE PROBLEM FOR THE APPLE INDUSTRY IN THE US AND OTHER COUNTRIES WHERE US IMPORTSAPPLES FROM, LIKE CHINA AND ARGENTINA. ONE CM INFESTED APPLE IN A BATCH COULD REDUCE THE RETURN TO PRODUCERS BY 59%. AT THE TURN OF THE MILLENNIUM, US SAW A SURGE IN CM INFESTATION IN APPLES. CURRENT PRACTICES OF DETECTION AND SORTING ARE MOSTLY SUBJECTIVE AND RANDOM, ANDTHEREFORE, PRONE TO SIGNIFICANT ERROR. THEREFORE, THERE IS A CRITICAL NEED TO DEVELOP RELIABLE AND OBJECTIVE METHODS FOR THE DETECTION AND SORTING OF CM INFESTATION USING NON-DESTRUCTIVE METHODS. IN THIS PROJECT, WE HAVE PROPOSED A NONINVASIVE SYSTEM USING SIMULTANEOUS HYPERSPECTRAL IMAGING (HSI) AND ACOUSTIC EMISSION (AE) MEASUREMENTS TO INCREASE THE EFFICIENCY AND ACCURACY OF DETECTING AND CLASSIFYING OF CM INFESTED APPLES. OUR SPECIFIC GOALS INCLUDE:AUTHENTICATE AND CHARACTERIZE AE SIGNAL SOURCE FROM CM-INFESTED APPLES.IMPROVE THE TEST PERFORMANCE OF NONINVASIVE DETECTION OF CM-INFESTED APPLES USING MAIN AND COMBINED HSI AND AE SENSOR DATA BASED ON MACHINE LEARNING APPROACHES.DETERMINE THE IMPACT OF FIELD AND STORAGE CONDITIONS (TEMPERATURES) FOR ACCURATE APPLICATION OF THE TECHNIQUE.HSI AND AE DATA WILL BE ACQUIRED AND STREAMLINED THROUGH PREPROCESSING TO REMOVE NOISE AND REDUNDANT DATA, AND ANALYZED FURTHER USING ADVANCED MACHINE LEARNING APPROACHES TO RECOGNIZE PATTERNS THAT ALLOW DELINEATION BETWEEN SPECTRA AND ACOUSTIC SIGNALS SYNONYMOUS WITH HEALTHY AND CM INFESTED APPLES. HSI AND AE DATA WILL BE CONSIDERED INDIVIDUALLY AND IN A SENSOR DATA FUSION APPROACH.THE RESULT OF THIS STUDY WILL FORM THE FOUNDATION FOR THE DEVELOPMENT OF HARDWARE SYSTEMS THAT CAN BE USED IN ONLINE/INLINE APPLE SORTING FOR CM INFESTATION. THE TOOL CAN ALSO BE USED BY INDEPENDENT AND GOVERNMENT REGULATORY AGENCIES INVOLVED IN APPLE QUALITY AND SAFETY TESTING. THE LONG TERM IMPACT OF THIS STUDY IS THE PROJECTED REDUCTION IN CM INFESTED APPLE FOR LOCAL AND EXPORT MARKETS; AND THE ELIMINATION OF THE DRUDGERY INVOLVED IN APPLE SORTING FOR INFESTATION. SEVERAL HIGHLY QUALIFIED PERSONNEL LIKE UNDERGRADUATE AND GRADUATE STUDENTS AND POST-DOCTORAL RESEARCHER WILL BE TRAINED IN THIS PROCESS, AND SEVERAL PEER-REVIEWED JOURNAL ARTICLES WILL BE PUBLISHED IN HIGH IMPACT FACTOR JOURNALS FOR A WIDER SHARING OF THE RESULTS OF THE PROJECT.