IT HAS BEEN PREDICTED THAT THE WORLD POPULATION WILL REACH 10 BILLION PEOPLE BY 2050, DEMANDING CRITICAL IMPROVEMENTS OF TODAY'S AGRICULTURAL SYSTEMS. THIS PROPOSAL ADDRESSES SEVERAL NOVEL TECHNIQUES THAT MAY EXTEND TO A RANGE OF FRUIT, BUT THE FOCUS WILL BE ON VITICULTURE FOR EASE OF ACCESS. THERE ARE 1,392 GRAPE FARMS IN NEW YORK STATE ALONE WITH 39,216 ACRES OF VINEYARDS, GENERATING MORE THAN $4.8 BILLION IN ANNUAL REVENUE. THESE PRODUCERS DEPEND ON PREDICTIONS OF HOW MUCH FRUIT IS COMING IN FROM THE VINEYARD TO PREDICT REVENUE AND ALLOCATE RESOURCES SUCH AS HARVEST AND WINERY LABOR, TANK SPACE, BOTTLES, AND OTHER REQUIRED SHIPPING-CAPABLE PACKAGING. IMPROVED METHODS FOR SENSING AND PROCESSING DATA IN THE FIELD WILL LEAD TO MORE ACCURATE YIELD PREDICTIONS WELL AHEAD OF THE HARVEST, WITHOUT ADDED LABOR COST. SUCH SYSTEMS MAY FURTHER PERMIT MORE TARGETED APPLICATION OF FERTILIZERS AND PESTICIDES REDUCING COST AND ENVIRONMENTAL IMPACT.STATE-OF-THE-ART PRECISION AGRICULTURE FOCUSES LARGELY ON AUTOMATED VISUAL ASSESSMENT OF CROPS IN THE FIELD USING HIGH-END CAMERA AND LASER RANGING SYSTEMS. HOWEVER, IN MANY SCENARIOS, INCLUDING WHEN HEAVY FOLIAGE IS PRESENT, USE OF REMOTE VISION IS NOT ADEQUATE TO DETERMINE IMPORTANT MEASURES SUCH AS CROP GROWTH, RIPENESS, AND HEALTH. THIS PROPOSAL AIMS TO AUGMENT SUCH CYBER PHYSICAL SYSTEMS THROUGH INEXPENSIVE VISON-BASED TECHNIQUES DEPLOYED BEFORE FOLIAGE BECOMES EXCESSIVE, AND THROUGH NOVEL SOFT AND TOUCH SENSITIVE TECHNOLOGIES THAT CAN OPERATE IN CLOSE PROXIMITY TO THE CROP. SPECIFICALLY, THE WORK PROPOSED INVOLVES FOUR INTEGRATIVE THRUSTS: 1) SOFT MANIPULATORS CAPABLE OF SAFELY OBTAINING CLOSE-RANGE DATA ON THE FRUIT, 2) A SOFT, POROUS VEIL WITH INTEGRATED ULTRASONIC TRANSDUCERS FOR DETECTION OF MICROBES AND FRUIT RIPENESS, 3) USE OF LOW-END CAMERAS AND MACHINE LEARNING TECHNIQUES FOR AUTOMATED EARLY SEASON CLUSTER COUNTING, AND 3) MODELING FRAMEWORKS TO INTERPRET DIVERSE MULTI-MODAL SENSOR INFORMATION FOR IMPROVED YIELD PREDICATION AND TARGETED PESTICIDE APPLICATION. THE FULL RANGE OF ACQUIRED DATA WILL INCLUDE CLUSTER NUMBER, VISUAL APPEARANCE, GEOMETRY, WEIGHT, TEMPERATURE, ELASTIC MODULUS, THERMAL CONDUCTIVITY, AND HUMIDITY PROCESSED INTO INFORMATION ON LEAF-AREA, BERRY COUNT, BERRY HUE, BERRY SOUNDNESS, BERRY CONTACT, SURFACE WETNESS, MICROBES, CLUSTER CLOSURE, SIZE, AND COMPACTNESS. TO DEMONSTRATE THE ADVANTAGES OF INTRODUCING THESE TECHNOLOGIES TO AGRICULTURE, THE PIS WILL DEVELOP AN INTEGRATED ROBOTIC SOLUTION FOR IMPROVED VINEYARD MANAGEMENT. TRACKING SPECIFIC CLUSTERS OVER DAYS AND WEEKS MAY SIGNIFICANTLY ENHANCE YIELD PREDICTIONS AND ADDITIONALLY EASE GATHERING OF SUBSEQUENT DATA. THESE RESEARCH EFFORTS WILL CULMINATE IN SEVERAL IN-FIELD DEMONSTRATIONS, AS WELL AS A DECISION SUPPORT SYSTEM THAT ALLOWS FARMERS TO MAKE USE OF DATA COLLECTED FROM MULTIPLE SYSTEMS IN THE FIELD. THIS WORK REPRESENTS A TRANSFORMATIVE STEP TOWARDS SUPERIOR CYBER PHYSICAL SYSTEMS FOR AGRICULTURE, THROUGH THE COMBINED USE OF REAL-TIME QUANTITATIVE GLOBAL- AND QUALITATIVE LOCAL DATA.