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48,453 grants matching “machine learning”
CI-ADDO-EN: Flexible Machine Learning for Natural Language in the MALLET Toolkit
$650,000Andrew K McCallum · University Of Massachusetts Amherst · · FY2010 · CSE
Major: CAIRA - A Creative Artificially-Intuitive and Reasoning Agent in the Context of Ensemble Music Improvisation
$650,000Jonas Braasch · Rensselaer Polytechnic Institute · · FY2010 · CSE
** AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** THIS RESEARCH PROJECT AIMS TO STUDY HOW AND HOW WELL LOCAL SMALL FARMERS IN NORTH/CENTRAL FLORIDA USE SOCIAL MEDIA FOR THEIR AGRICULTURAL BUSINESSES, AND FIND OUT HOW TO OPTIMIZE THEIR SPECIFIC OPERATIONAL STRATEGIES USING ADVANCED SOCIAL MEDIA MARKETING TECHNIQUES. THE STRATEGIC USE OF SOCIAL MEDIA BY SMALL FARMERS CAN SIGNIFICANTLY BOOST REGIONAL ECONOMIES AS THEY REPRESENT THE MAJORITY OF THE LOCAL FARM AND RURAL COMMUNITY. IT FOSTERS SUSTAINABLE AGRICULTURE, ECONOMIC GROWTH, AND VITALITY IN RURAL AREAS IN NORTH/CENTRAL FLORIDA.THIS PROJECT WILL CONDUCT COMPREHENSIVE INTERVIEWS AND COLLECT SPECIALLY DESIGNED SURVEYS TO UNDERSTAND SOCIAL MEDIA ADOPTIONS AMONG LOCAL SMALL FARMERS. IT WILL ALSO LEVERAGE AI, MACHINE LEARNING, AND OPERATIONS RESEARCH METHODS TO CREATE TOOLS AND APPROACHES FOR SMALL FARMERS TO FULLY TAKE ADVANTAGE OF SOCIAL MEDIA TO IMPROVE THEIR BUSINESSES.THE GOAL OF THIS RESEARCH PROJECTIS TO ASSIST SMALL-SCALE AGRICULTURAL ENTERPRISES IN NORTHEAST AND CENTRAL FLORIDA IN OPTIMIZING THEIR OPERATIONAL STRATEGIES THROUGH THE UTILIZATION OF ADVANCED SOCIAL MEDIA MARKETING TECHNIQUES AND SUBSEQUENTLY ENHANCE THEIR INCOMES. IF SUCCESSFUL, THIS PROJECT IS EXPECTED TO HELP INCREASE THE ECONOMIC ACTIVITIES IN THE REGION, DRIVE SALES IN LOCAL RURAL AGRICULTURE BUSINESSES AND BENEFIT THE LOCAL COMMUNITY BY INCREASING FARM INCOME AND CIRCULATING MONEY LOCALLY. THE DIRECT RESULTS AND SOCIAL BENEFITS WILL BE THREE FOLDED. FIRST, THE PROJECT WILL HELP INCREASE THE ADOPTION OF SOCIAL MEDIA AMONG SMALL TO MEDIUM FARMERS TO HELP THEM REACH OUT TO CUSTOMERS; SECOND, IT WILL HELP SMALL TO MEDIUM FARMERS INCREASE ENGAGEMENT OF LOCAL CONSUMERS OF AGRICULTURAL PRODUCES AND SERVICES; THIRD, EVENTUALLY IT CAN HELP ENHANCE THE CONNECTION BETWEEN SMALL TO MEDIUM FARMERS AND LOCAL CONSUMERS AMONG THE LOCAL COMMUNITIES IN THE NORTHEASTERN AND CENTRAL FLORIDA REGION.
$650,000The University Of Central Florida Board Of Trustees · · FY2024 · National Institute of Food and Agriculture
** AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** ALABAMA IS A HIGH POVERTY STATE WITH SIGNIFICANT PORTIONS, THE BLACK BELT COUNTIES, AFFECTED BY THE LACK OF AVAILABLE, AFFORDABLE, AND NUTRITIOUS FOOD. THIS PROJECT ADDRESSES THE ISSUE OF FOOD AND NUTRITION SECURITY AMONG DISADVANTAGED AND MINORITY HOUSEHOLDS AND COMMUNITIES THAT ARE IMPACTED BY DISASTERS SUCH AS EXTREME WEATHER EVENTS (HURRICANES, TORNADOES, AND FLOODS) AND ALSO HIGHLIGHTED BY THE ONGOING PANDEMIC. THE LONG-TERM GOAL IS TO APPLY MACHINE LEARNING AND DIGITAL TECHNIQUES TO TRANSFORM ALABAMA EMERGENCY FOOD DISTRIBUTION CENTERS STRATEGICALLY, MANAGERIALLY, TECHNOLOGICALLY, AND SOCIALLY TO POSITION THEM AS SUSTAINABLE FOOD DISTRIBUTION AND COLLECTION SYSTEMS CAPABLE OF PROVIDING SUFFICIENT AND NUTRITIOUS FOODS TO PEOPLE IN NEED. OUR TEAM WILL ENGAGE IN RESEARCH, EXTENSION, AND EDUCATIONAL ACTIVITIES TO:1) DEVELOP DIGITAL AND MACHINE LEARNING TECHNOLOGIES TO UNDERSTAND FOOD DEMAND AT ALABAMA EMERGENCY FOOD DISTRIBUTION CENTERS TOWARD A CONSUMER-CENTRIC ENVIRONMENT, 2) DEVELOP DIGITAL TECHNOLOGIES TO DETECT FOOD SUPPLY AT ALABAMA EMERGENCY FOOD DISTRIBUTION CENTERS TOWARD A HIGHER OPERATIONAL EFFICIENCY ENVIRONMENT, 3) INCREASE THE ALABAMA EMERGENCY FOOD DISTRIBUTION SYSTEM'S CAPACITY FOR PROVIDING HEALTHY FOOD TO THE FOOD-INSECURE, 4) STRENGTHEN THE AI/MACHINE LEARNING EDUCATION AT TUSKEGEE UNIVERSITY BY INTEGRATING EDUCATION WITH RESEARCH AND EXTENSION. THE PROPOSED PROJECT WILL ENHANCE EFFECTIVE FOOD PANTRY-BASED EXTENSION PROGRAMS THAT STRESS AND REINFORCE HUMAN CAPACITY BUILDING. OUR PROJECT HAS AN ADVISORY BOARD REPRESENTING EMERGENCY FOOD DISTRIBUTORS, PROFESSIONALS, AND BLACK FARMERS AND HAS A PROJECT EVALUATOR. THE OUTCOME OF OUR EFFORTS WILL BE AN EFFICIENT AND CONSUMER-CENTRIC FOOD DISTRIBUTION SYSTEM THAT ENABLES THE FOOD-INSECURITY COMMUNITY LIKELIER TO ACCRUE NUTRITIOUS FOOD.
$650,000Tuskegee University · · FY2023 · National Institute of Food and Agriculture
Collaborative Research: URoL:ASC: Using the Rules of Antibiotic Resistance Development to Inform Wastewater Mitigation Strategies
$650,000Liqing Zhang · Virginia Polytechnic Institute And State University · · FY2023 · BIO
CAREER: Toward Reliable Nonadiabatic Dynamics in Condensed Matter and Nanoscale Systems
$650,000Alexey V Akimov · Suny At Buffalo · · FY2021 · MPS
Low-cost detection of dementia using electronic health records data: validation and testing of the eRADAR algorithm in a pragmatic, patient-centered trial.
$650,000Sascha Dublin · Kaiser Foundation Research Institute · R01 · FY2020 · AG
CAREER: Frontiers of Distributed Machine Learning with Communication, Computation and Data Constraints
$650,000Gauri Joshi · Carnegie Mellon University · · FY2021 · CSE
** AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** CONSUMERS ARE BECOMING MORE ENVIRONMENTALLY CONSCIOUS AND WITHIN THE LAST THREE TO FIVE YEARS,BRANDS AND RETAILERS HAVE BEGUN AGGRESSIVE MARKETING CAMPAIGNS FOR SUSTAINABLE PRODUCTS.SUSTAINABILITY PROGRAMS SUCH AS THE U.S. COTTON TRUST PROTOCOL IS AVAILABLE TO PRODUCERS TO INCREASECONSUMER AWARENESS OF THE SUSTAINABLE FARMING PRACTICES USED IN U.S. COTTON PRODUCTION. THEGROWER PARTICIPATION RATE INTO THESE PROGRAMS IS LOW DUE TO TIME, DATA PRIVACY, LACK OF GENERALINTEREST, AND FEAR OF THE INFORMATION BEING USED AGAINST THEM.THIS PROJECT HAS THREE OBJECTIVES: 1) IDENTIFY KEY DEMOGRAPHIC FACTORS THAT PLAY A ROLE IN PRODUCERPARTICIPATION OF SUSTAINABILITY PROGRAMS USING DIAL-TESTING TECHNOLOGY, 2) EVALUATE CONSUMERPREFERENCES AND WILLINGNESS TO PAY (WTP) FOR SUSTAINABLY PRODUCED COTTON PRODUCTS AND SEGMENTCONSUMERS BY PRODUCT TYPE USING MACHINE LEARNING METHODS, AND 3) EVALUATE BRAND AND RETAILERPERCEPTIONS IN THE SUPPLY CHAIN TO DETERMINE WTP AND OPTIMAL CONTRACT DESIGN USING A BAYESIANFRAMEWORK TO ANALYZE SURVEY RESPONSES.PARTICIPATION IN SUSTAINABILITY PROGRAMS WILL HAVE A SIGNIFICANT IMPACT ON GROWER COMPETITIVENESS INTHE GLOBAL SUPPLY CHAIN AND THE RESULTS OF THIS STUDY WILL IMPROVE THE ADOPTION RATE OF GROWERS INTOTHE U.S. COTTON TRUST PROTOCOL. PRODUCER INVOLVEMENT MAY ALSO INCREASE THE CONSUMPTION OF COTTONBY CONSUMERS IN THE MARKET IF COTTON PRODUCTS ARE PERCEIVED AS MORE SUSTAINABLE THAN MAN-MADEFABRICS LIKE POLYESTER.
$649,999Texas Tech University System · · FY2023 · National Institute of Food and Agriculture
NSF Convergence Accelerator Track M: Targeted Insect Sensing and Control
$649,999Shailendra Singh · Farmsense Inc. · · FY2024 · TIP
** AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** THIS PROJECT ASSESSES HOW WETLAND REGULATION AFFECTS US RURAL ECONOMIC DEVELOPMENT. THE PROJECT'S FIRST COMPONENT BUILDS A MACHINE LEARNING MODEL TO PREDICT WHICH WETLANDS THE CLEAN WATER ACT REGULATES UNDER THE SUPREME COURT'S 2023 SACKETT DECISION, WHICH CREATED A NEW LANDSCAPE IN WETLAND LAW. THE PROJECT'S SECOND COMPONENT ESTIMATES THE CAUSAL IMPACT OF WETLAND REGULATION ON RURAL LAND MARKETS. IT ANALYZES IMPACTS OF WETLAND REGULATION ON LAND VALUES, DEVELOPMENT, AND TRANSACTIONS. IT FOCUSES ON VACANT PARCELS IN PRIMARILY RURAL AREAS, THOUGH USES RESIDENTIAL, COMMERCIAL, AND INDUSTRIAL LAND USES AS BENCHMARKS. THE PROJECT'S THIRD COMPONENT PROVIDES A PROOF-OF-CONCEPT INVESTIGATION OF HOW MACHINE LEARNING MODELS PREDICTING WETLAND REGULATION CAN SUPPORT AND IMPROVE THAT REGULATION. THE THIRD COMPONENT INVESTIGATES THE POTENTIAL OF DECISION SUPPORT TOOLS TO SUPPORT ENFORCEMENT AND DETECTION OF VIOLATIONS OF WETLAND REGULATION, AND DETERMINATIONS OF WHICH WETLANDS ARE JURISDICTIONAL. WE CONSIDER APPLICATIONS TO STATE AND FEDERAL WETLAND LAW. THE THREE COMPONENTS HAVE IMPORTANT IMPLICATIONS FOR ENVIRONMENTAL AND RESOURCE ECONOMICS AND FOR RURAL ECONOMIC DEVELOPMENT. NATIONAL MEDIA ASSERT THAT RECENT CLEAN WATER ACT CHANGES HAVE REMOVED REGULATION FOR OVER HALF OF US WETLANDS. WETLANDS CAN MITIGATE FLOODS, IMPROVE WATER QUALITY, AND PROVIDE OTHER IMPORTANT ECOSYSTEM SERVICES, AND THUS RECENT CHANGES IN REGULATION POTENTIALLY HAVE LARGE CONSEQUENCES FOR THE WELL-BEING OF RURAL COMMUNITIES. MANY RANCHING, FARMING, AND OTHER AGRICULTURAL ORGANIZATIONS HAVE ARGUED IN PUBLISHED REPORTS, MEDIA STORIES, AMICUS CURIAE BRIEFS AT THE SUPREME COURT, AND OTHER DOMAINS THAT WETLAND REGULATION PLAYS A CENTRAL ROLE IN RURAL ECONOMIC WELL-BEING.
$649,999Regents Of The University Of California, The · · FY2024 · National Institute of Food and Agriculture
Multi-site External Validation and Improvement of a Clinical Screening Tool for Future Firearm Violence
$649,996Jason Elliott Goldstick · University Of Michigan At Ann Arbor · R01 · FY2021 · CE
** AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** ACCURATE PREDICTION OF NITROUS OXIDE (N2O) EMISSIONS FROM AGRICULTURAL SOILS IS A PERSISTENT CHALLENGE FOR EXISTING PREDICTION TOOLS, ESPECIALLY BIOGEOCHEMICAL MODELS. BIASED PREDICTIONS AND THE LARGE GLOBAL WARMING POTENTIAL OF N2O LEAD TO SUBSTANTIAL UNCERTAINTY IN ASSESSMENTS OF THE MITIGATION POTENTIAL OF CLIMATE-SMART CROPPING SYSTEMS. THE RECENT EMERGENCE OF HIGH-FREQUENCY N2O MEASUREMENTS OFFERS A NOVEL OPPORTUNITY TO ADDRESS THIS CHALLENGE: DATA FROM NEAR-CONTINUOUS AUTOMATED CHAMBER METHODOLOGIES ARE NOW SUFFICIENT TO INFORM A NEW CLASS OF MACHINE LEARNING (ML) APPROACHES FOR IMPROVED N2O FLUX PREDICTIONS. WE PROPOSE THE FIRST-EVER EFFORT TO COLLATE DENSE MEASUREMENT FLUXES FROM ALL AVAILABLE SITES WORLDWIDE IN ORDER TO 1) CREATE A PUBLIC DATABASE OF HIGH-FREQUENCY N2O FLUXES FROM INTENSIVELY CROPPED SYSTEMS THAT CAN BE USED BY US AND OTHERS TO IMPROVE QUANTITATIVE N2O MODELS; 2) DEVELOP ADVANCED DEEP LEARNING MODELS FOR TIME-SERIES N2O PREDICTIONS THAT CAN BE TESTED AGAINST AUTOMATED AND NON-AUTOMATED N2O FLUX DATA; AND 3) USE ML-PROCESS BASED HYBRID MODELING TO MEET PREDICTOR DATA NEEDS FOR ML AND INFORM DATA NEEDS FOR FURTHER PROCESS-LEVEL MODEL DEVELOPMENT. OUR RESEARCH ADDRESSES PRIORITIES IN THE AFRI BIOENERGY, NATURAL RESOURCES, AND ENVIRONMENT PRIORITY AREA AND CONTRIBUTES TO MANAGING A DOMINANT COMPONENT OF CLIMATE-SMART AGRICULTURE - SOIL-BASED N2O FLUXES. OUR PROJECT TEAM BRINGS TOGETHER EXPERTISE IN SOIL BIOGEOCHEMISTRY AND AGRICULTURAL GREENHOUSE GAS FLUXES TOGETHER WITH ADVANCED DATA AND PROCESS MODELING TO ADDRESS ONE OF THE MOST RECALCITRANT PROBLEMS FACING THE DESIGN OF CLIMATE SMART CROPPING SYSTEMS TODAY.
$649,996University Of Tennessee · · FY2023 · National Institute of Food and Agriculture
CAREER: CAS-Climate: Data-driven Coupled-Cluster for Biomimetic CO2 Capture
$649,995Konstantinos Vogiatzis · University Of Tennessee Knoxville · · FY2022 · MPS
Multi-site External Validation and Improvement of a Clinical Screening Tool for Future Firearm Violence
$649,991Jason Elliott Goldstick · University Of Michigan At Ann Arbor · R01 · FY2020 · CE
LEVERAGING HIGH-THROUGHPUT COMPUTATION AND MACHINE LEARNING TO DISCOVER AND UNDERSTAND LOW-TEMPERATURE FAST OXYGEN CONDUCTORS
$649,991University Of Wisconsin System · · FY2019 · Department of Energy
** AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** IN 2020, CHINA HAD ACCOUNTED FOR 20% OF THE UNITED STATES' TOTAL AGRICULTURAL EXPORTS. CHINA'SHUGE AND UNEXPECTED IMPORTS OF US CORN, SOYBEAN, AND PORK PRODUCTS MAY OR MAY NOT CONTINUEIN THE NEXT DECADE. THERE IS A LACK OF RELIABLE STATISTICS ON CHINA'S KEY AGRICULTURAL COMMODITIES.THIS CREATES PRICE VOLATILITY AND MIGHT REDUCE THE TRANSPARENCY OF US AND WORLD AGRICULTURALMARKETS.WE PROPOSE TO DEVELOP RELIABLE DATA/STATISTICS FOR CORN, SOYBEANS, AND PORK. FIRST, WE WILL USEPRICE, TRADE, AND SATELLITE DATA TO GET RELIABLE STATISTICS ON PRODUCTION, CONSUMPTION AND STOCKS.SECOND, WE WILL WORK WITH OUR PARTNERS IN THE HUAZHONG AGRICULTURAL UNIVERSITY TO CONDUCTPLANTING INTENTION SURVEYS IN KEY COMMODITY GROWING AREAS TO UNDERSTAND THE ECONOMIC ANDPOLICY DETERMINANTS OF CHINESE CROP PRODUCERS' DECISIONS.WE PROPOSE TO DEVELOP MACHINE LEARNING (ML) MODELS TO FORECAST CHINA'S AGRICULTURAL IMPORTS.WE WILL COMPARE THE PREDICTION ACCURACIES OF ML MODELS WITH TRADITIONAL GRAVITY MODELS, WHICHLEADS TO AN UNDERSTANDING OF THE APPLICABILITY OF ML TECHNIQUES IN FORECASTING AGRICULTURAL TRADEAND QUANTIFYING THE IMPACTS OF TRADE AND ECONOMIC POLICIES.WE HAVE SHOWN THE APPLICABILITY OF ML METHODS IN PREDICTING AGRICULTURAL TRADE FLOWS AND ALSOIN PREDICTING STOCK LEVELS WHEN SOME ECONOMIC DATA IS ACCURATE, AND SOME IS NOT. THE PROPOSEDDATA COLLECTION AND MODELING SYSTEM WILL PROVIDE INSIGHTS INTO WORLD AGRICULTURAL TRADE PATTERNSAND ENHANCE OUR ABILITY TO QUANTIFY FUTURE SHOCKS TO THE GLOBAL AGRICULTURAL MARKETS. THE ULTIMATEGOAL IS TO IMPROVE THE LONG-TERM SUSTAINABILITY AND RESILIENCY OF US AGRICULTURE AND FOOD SYSTEMS.
$649,980Cornell University · · FY2023 · National Institute of Food and Agriculture
** 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.
$649,977North Carolina State University · · FY2022 · National Institute of Food and Agriculture
AITF: Learning and Adapting Sparse Recovery Algorithms for RF Spectrum Sensing
$649,963John N Wright · Columbia University · · FY2017 · CSE
TRADITIONAL CROP BREEDING RELIES ON TIME-CONSUMING AND INEFFICIENT MANUAL FIELD OBSERVATIONS OR SAMPLINGS. UNMANNED AERIAL VEHICLE (UAV)-BASED HIGH-THROUGHPUT PHENOTYPING PROMISES TO ALLEVIATE THE PHENOTYPING BOTTLENECK AND PROVIDE THE CAPABILITY FOR REPEATED NON-DESTRUCTIVE MEASUREMENT OF THOUSANDS OF GENOTYPES GROWN IN FIELD EXPERIMENT PLOTS IN CROP SELECTION CYCLES. MAJOR CHALLENGES REMAIN TO ENABLE MORE SOPHISTICATED ANALYSES AND DEVELOPMENT OF INTEGRATED DECISION-MAKING SYSTEMS THAT CAN GREATLY ACCELERATE CROP IMPROVEMENT AND PHENOTYPING. WE PROPOSE TO DEVELOP A DIGITAL RICE SELECTION SYSTEM THAT INTEGRATES UAV IMAGING, MACHINE LEARNING, AND MULTI-TRAIT DECISION-MAKING. OBJECTIVES ARE: 1) QUANTIFY KEY PHENOLOGICAL, MORPHOLOGICAL AND ARCHITECTURAL TRAITS THAT CAPTURE RICE GROWTH AND DEVELOPMENT; 2) ACQUIRE MULTI-VIEWPOINT UAV IMAGES OF RICE GENOTYPES DURING CRITICAL GROWTH STAGES; 3) DEVELOP ADVANCED IMAGE ANALYSIS ALGORITHMS TO DERIVE KEY PHENOLOGICAL, MORPHOLOGICAL AND ARCHITECTURAL TRAITS FOR CRITICAL RICE GROWTH STAGES, AND 4) DEVELOP A DIGITAL RICE SELECTION SYSTEM THAT SCREENS FOR BEST-PERFORMING GENOTYPES THROUGH DATA INTEGRATION AND MULTI-TRAIT DECISION MAKING.GROUND TRUTH DATA FOR KEY TRAITS DURING CRITICAL RICE GROWTH STAGES WILL BE COLLECTED ALONG WITH HIGH-RESOLUTION RGB/MULTISPECTRAL UAV IMAGES FROM MULTIPLE CAMERA ANGLES. WE WILL DEVELOP (1) ADVANCED MACHINE LEARNING ALGORITHMS TO EXTRACT KEY TRAITS, (2) MULTI-TRAIT-BASED MACHINE LEARNING MODELS TO ESTIMATE FINAL ABOVEGROUND BIOMASS AND GRAIN YIELD, AND (3) A MULTI-CRITERIA DECISION-MAKING SYSTEM TO SELECT BEST-PERFORMING RICE GENOTYPES. THE PROPOSED PROJECT REPRESENTS A MAJOR EFFORT IN DELIVERING AN INTEGRATED UAV IMAGERY-BASED DECISION-MAKING SYSTEM TO RICE BREEDERS AND RESEARCHERS AND WILL BE AN INDISPENSABLE TOOL TO GREATLY IMPROVE RICE BREEDING AND PHENOTYPING EFFICIENCY.
$649,955Texas A&M Agrilife Research · · FY2022 · National Institute of Food and Agriculture
**AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** TILLAGE IS AN ESSENTIAL FARMING PRACTICE THAT IS CLOSELY TIED TO PRODUCTION COST, CROP YIELD, AND ENVIRONMENTAL SUSTAINABILITY. THE U.S. MIDWEST HAS SEEN A RECENT TREND OF SHIFTING FROM CONVENTIONAL TILLAGE TO MORE CONSERVATION TILLAGE (E.G., NO-TILL), THOUGH THE ADOPTION RATE OF NO-TILL IS STILL LOW (~35% BY 2017) AND ITS CHANGE IS RELATIVELY STAGNANT. HOWEVER, THERE IS STILL NO CONSENSUS IN THE EXISTING SCIENTIFIC LITERATURE ABOUT THE IMPACTS OF CHANGING TILLAGE PRACTICES ON CROP PRODUCTION AND ENVIRONMENTAL SUSTAINABILITY, MAINLY BECAUSE OF THE SPATIALLY VARYING SOIL, WEATHER, AND MANAGEMENT CONDITIONS. HIGH-RESOLUTION SPATIALLY-EXPLICIT ASSESSMENTS OF TILLAGE IMPACTS ARE MOSTLY LACKING IN THE U.S. MIDWEST, AND ARE CRITICALLY NEEDED FOR FARMERS TO MAKE THE MOST INFORMED DECISIONS FOR THEIR FIELDS.IN THIS PROJECT, WE AIM TO INNOVATIVELY INTEGRATE META-ANALYSIS, AIRBORNE AND SATELLITE DATA, AND PROCESS-BASED MODELING TO CONDUCT HIGH SPATIOTEMPORAL ASSESSMENTS OF TILLAGE IMPACTS ON CROP PRODUCTIVITY AND ENVIRONMENTAL SUSTAINABILITY IN THE U.S. MIDWEST. THE FOLLOWING FOUR MAJOR MIDWEST STATES WILL BE INCLUDED AS OUR STUDY DOMAIN: ILLINOIS, INDIANA, IOWA, AND MINNESOTA. IN PARTICULAR, WE WILL STUDY THE TILLAGE IMPACTS ON CROP YIELD OF CORN AND SOYBEAN (THE TWO DOMINANT CROPS GROWN IN THE U.S. MIDWEST), AND SEVERAL ENVIRONMENTAL SUSTAINABILITY METRICS: GREENHOUSE GAS EMISSION, NITROGEN LEACHING, CHANGES IN SOIL ORGANIC CARBON, AND SOIL EROSION. WE AIM TO ACHIEVE THE FOLLOWING OBJECTIVES: (1) COLLECT HISTORICAL FIELD DATA FROM THE LITERATURE TO CONDUCT META-ANALYSES TO QUANTIFY IMPACTS OF VARIOUS TILLAGE PRACTICES ON CROP YIELD AND ENVIRONMENTAL SUSTAINABILITY METRICS; (2) USE AIRBORNE HYPERSPECTRAL IMAGING DATA, MULTI-SENSOR FUSED SATELLITE DATA, AND MACHINE-LEARNING ALGORITHMS TO MAP FIELD-SCALE TILLAGE PRACTICES FOR THE HISTORICAL PERIOD (2000-2023) FOR THE FOUR MAJOR MIDWEST STATES, AND QUANTIFY TEMPORAL TRENDS OF TILLAGE PRACTICES AT THE FIELD SCALE; (3) USE LONG-TERM EXPERIMENTAL DATA, META-ANALYSIS RESULTS, AND SATELLITE-BASED MEASUREMENTS TO CONSTRAIN AND VALIDATE THE ECOSYS MODEL SIMULATION ON TILLAGE IMPACTS FOR CROP YIELD AND ENVIRONMENTAL SUSTAINABILITY; AND (4) QUANTIFY THE IMPACTS OF TILLAGE PRACTICES ON CROP PRODUCTION AND ENVIRONMENTAL SUSTAINABILITY USING THE CONSTRAINED ECOSYS MODEL AND SATELLITE-BASED TILLAGE MAPS FOR THE MAJOR MIDWEST STATES AND CONDUCT SUITABILITY ASSESSMENT OF TILLAGE PRACTICES TO SUPPORT POLICY DESIGN AND FARMERS' DECISION MAKING.
$649,950University Of Illinois · · FY2023 · National Institute of Food and Agriculture
THANKS TO TECHNOLOGICAL ADVANCES, ANIMAL GENETICISTS HAVE AN EVER-EXPANDING TOOL CHEST WITH WHICH TO STUDY THE INHERITANCE OF TRAITS IN LIVESTOCK IN ORDER TO IMPROVE PRODUCTION. OUR LONG-RANGE GOAL IS TO DEVELOP INTEGRATED RESOURCES THAT LEVERAGE PRIOR INVESTMENTS IN CYBERINFRASTRUCTURE TO HELP MAXIMIZE THE UTILITY OF GENOTYPE-TO-PHENOTYPE DATA TO FUNCTIONALLY ANNOTATE LIVESTOCK GENOMES. THE OBJECTIVES OF THIS PARTICULAR APPLICATION ARE: 1) DEVELOPMENT OF MACHINE LEARNING-ASSISTED DATA CURATION AND AUTOMATED SEMANTIC ANNOTATION, AND 2) MANUAL CURATION OF GENOTYPE/PHENOTYPE, CORRELATION, AND HERITABILITY DATA. WITH THE GROWING VOLUME AND BREADTH OF INFORMATION, IT IS INCREASINGLY DIFFICULT FOR CURATORS TO KEEP ABREAST OF PUBLICATIONS. THESE COMPLEMENTARY OBJECTIVES TARGET THE NEED TO EFFICIENTLY COLLECT AND COMPREHEND LARGE AMOUNTS OF GENOTYPE/PHENOTYPE ASSOCIATION AND CORRELATION/HERITABILITY DATA THAT ARE BEING PUBLISHED AT AN ACCELERATING RATE. FIRST, WE EXPECT TO BEGIN TO AUTOMATE THE FUNCTIONAL ANNOTATION OF LIVESTOCK GENOMES BY APPLYING ARTIFICIAL INTELLIGENCE TECHNIQUES TO THE CURATION OF PUBLISHED QTL/VARIANT ASSOCIATION DATA INTO THE ANIMAL QTLDB, AND GENETIC AND PHENOTYPIC CORRELATION AND HERITABILITY DATA INTO THE ANIMAL CORRDB, FOR MULTIPLE LIVESTOCK SPECIES. SECOND, WE EXPECT TO DEVELOP ARTIFICIAL INTELLIGENCE TOOLS TO EXPEDITE ONTOLOGY DEVELOPMENT. THIRD, WE EXPECT TO DEVELOP INTELLIGENT RETRIEVAL TOOLS THAT CAN ANSWER QUERIES SEMANTICALLY. FOURTH, WE EXPECT TO CURATE GENOTYPE/PHENOTYPE AND CORRELATION/HERITABILITY DATA AND TO EXPAND RELEVANT ONTOLOGIES. TAKEN TOGETHER, OUR EFFORTS ARE EXPECTED TO GENERATE POSITIVE LONG-TERM EFFECTS ON RESEARCHERS' ABILITY TO TRANSFER KNOWLEDGE AND ANALYZE QTL/ASSOCIATION DATA TO ADDRESS ISSUES OF ECONOMIC AND HEALTH IMPORTANCE IN LIVESTOCK SPECIES.
$649,941Iowa State University Of Science And Technology · · FY2022 · National Institute of Food and Agriculture
Discovery of early immunologic biomarkers for risk of PTLDS through machine learning-assisted broad temporal profiling of humoral immune response
$649,935Neal Walter Woodbury · Arizona State University-Tempe Campus · R01 · FY2023 · AI
NSF Convergence Accelerator Track L: Intelligent Nature-inspired Olfactory Sensors Engineered to Sniff (iNOSES)
$649,930Joanna Aizenberg · Harvard University · · FY2024 · TIP
Enhancements to the GMOD Suite of Genome Annotation and Visualization Tools
$649,922Ian H Holmes · University Of California Berkeley · R01 · FY2025 · HG