** AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** PRECISION MANAGEMENT OF AGRICULTURAL INPUTS, SUCH AS FRESH WATER, IS CRUCIAL TO SUSTAINABLY MEET INCREASING FOOD PRODUCTION NEEDS. HOWEVER, AUTOMATED SYSTEMS TYPICALLY REQUIRE A POWERED INFRASTRUCTURE (EITHER WIRED OR USING RECHARGEABLE BATTERIES) TO BE INSTALLED AND MAINTAINED WHICH PRESENTS A BARRIER TO WIDESPREAD IMPLEMENTATION. THIS PROJECT AIMS TO FLIP THIS PARADIGM FOR PRECISION SENSING IN AGRICULTURE BY DEVELOPING AN AUTOMATED SOIL-MOISTURE MONITORING SYSTEM THAT CONSISTS OF AUTONOMOUS MOBILE ROBOTS, BOTH UNMANNED AERIAL VEHICLES (UAVS) AND QUADRUPED GROUND ROBOTS, AND BURIED PASSIVE SOIL SENSORS FOR MONITORING SOIL MOISTURE LEVEL. IMPORTANTLY, THE SENSORS REQUIRE NO POWER SOURCE, ARE COMPLETELY UNDERGROUND, AND CAN STAY BURIED IN THE FIELD FOR YEARS AT A TIME. THE MOBILE ROBOTS WILL AUTONOMOUSLY NAVIGATE TO SENSORS, COLLECT DATA TO PRODUCE A SOIL MOISTURE MAP OF THE FIELD, AND RETURN TO RECHARGE THEMSELVES. IN THE FUTURE, THIS MOBILE ROBOTIC PLATFORM CAN BE EXTENDED FOR OTHER SOIL SENSORS (E.G., NUTRIENTS AND ORGANIC CARBON) AND AGRICULTURAL TASKS SUCH AS APPLICATION OF HERBICIDES AND PESTICIDES.OUR PRIMARY OBJECTIVE IS TO DEVELOP AND DEMONSTRATE A ROBOTIC-SENSOR SYSTEM FOR PRECISION IRRIGATION MANAGEMENT. THIS OBJECTIVE BUILDS UPON PRIOR RESEARCH INVESTIGATING WIRELESSLY POWERED SOIL MOISTURE SENSORS. THE SPECIFIC OBJECTIVES FOR THIS PROJECT ARE: 1) DESIGN, SIMULATE, AND CHARACTERIZE THE PASSIVE SOIL MOISTURE SENSING COILS. 2) DEVELOP MACHINE-LEARNING MOTION PLANNING, NAVIGATION, AND LOCALIZATION METHODS FOR THE MOBILE ROBOTS TO AUTONOMOUSLY NAVIGATE TO THE BURIED SENSORS AND LOCALIZE THEM WITH SUFFICIENT PRECISION TO EFFICIENTLY OBTAIN A SOIL MOISTURE MEASUREMENT. 3) DEVELOP A SOIL MOISTURE MAPPING METHOD UTILIZING MULTIPLE INPUTS THAT CAN PROVIDE AN ACCURATE AND SPATIALLY DENSE MAP OF SOIL WATER DEPLETION.THE PASSIVE SOIL MOISTURE SENSORS MAKE USE OF THE RELATIONSHIP BETWEEN SOIL MOISTURE AND THE ELECTRICAL PROPERTIES, CHIEFLY PERMITTIVITY, OF THE SOIL. THE PROPOSED SENSORS WILL CONSIST OF OMNIDIRECTIONAL COILS THAT HAVE A PASSIVE SELF-RESONANCE FREQUENCY. THIS FREQUENCY CHANGES AS A FUNCTION OF SOIL MOISTURE. THE CHANGE IN SELF-RESONANCE FREQUENCY CAN BE READ FROM THE ABOVE GROUND MOBILE ROBOTS (WITHOUT PHYSICALLY CONTACTING THE SENSOR). IMPORTANTLY, THE SENSORS REQUIRE NO ONBOARD POWER, ARE MECHANICALLY ROBUST, AND ARE INDEPENDENT OF ORIENTATION. TO COLLECT SOIL MOISTURE DATA, THE MOBILE ROBOTS MUST BE ABLE TO LOCATE THE BURIED SENSORS WITH SUFFICIENT PRECISION. FIRST, THE ROBOTS WILL USE ONBOARD GPS AND A MAP OF SENSOR LOCATION TO GET RELATIVELY CLOSE TO THE SENSOR, BUT NOT CLOSE ENOUGH TO COLLECT DATA. NEXT, THE FINAL LOCALIZATION STAGE WILL MAKE USE OF THE ELECTROMAGNETIC COUPLING BETWEEN A COIL MOUNTED TO A 3D ROBOTIC ARM ON THE UAV OR QUADRUPED ROBOT AND THE BURIED SENSOR. THIS COUPLING SIGNAL AND SENSOR MODELS WILL BE PROCESSED BY A MACHINE LEARNING ALGORITHM (BAYESIAN ESTIMATOR) TO HOME IN ON THE LOCATION OF THE BURIED SENSOR.,THE LOCALIZATION PHASE WILL ALSO EXPLOIT INFORMATION-THEORETIC MOTION PLANNING TO CONTROL THE MOTION OF THE ROBOTIC ARM TO PRECISELY LOCATE THE SENSOR AND COLLECT SOIL MOISTURE MEASUREMENTS. THESE MEASUREMENTS WILL THEN BE USED TO PRODUCE A SPATIALLY AND TEMPORARILY DENSE MAP OF SOIL WATER DEPLETION (SWD) WHICH COULD BE USED TO INFORM AN AUTOMATED IRRIGATION MANAGEMENT SYSTEM. THE SENSORS WILL DIRECTLY MEASURE VOLUMETRIC WATER CONTENT WHICH WILL THEN BE CONVERTED TO SWD. THE MEASUREMENTS ARE SUBJECT TO SEVERAL SOURCES OF UNCERTAINTY RELATED TO SENSOR DEPTH, POTENTIAL SENSOR DAMAGE, SOIL TYPE (I.E., SENSORS WILL NOT UNDERGO FIELD-SPECIFIC CALIBRATIONS), AND SLOW SENSOR DRIFT. TO ACCOUNT FOR THESE SOURCES OF UNCERTAINTY, WE WILL FURTHER EXPLOIT MACHINE LEARNING THROUGH BAYESIAN INFERENCE. FIRST, WE WILL QUANTIFY THE APPROXIMATE LEVELS OF UNCERTAINTY THROUGH BASIC EXPERIMENTS. THE UNCERTAINTY VALUES WILL BE USED TO CREATE A PRIOR DISTRIBUTION, WHICH WILL THEN BE USED TO UPDATE THE BAYESIAN FILTER. THE FILTER WILL PRODUCE A BEST GUESS AT THE SWD AT A PARTICULAR LOCATION ALONG WITH A LIKELIHOOD VALUE. THE FINAL SYSTEM, INCLUDING SOIL MOISTURE SENSORS, ABOVE-GROUND UAVS AND QUADRUPED ROBOTS, AND SOIL MOISTURE MAPPING WILL BE VALIDATED IN FIELD TESTS OVER ONE GROWING SEASON.