20236702139829↗

** AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** ?OVERVIEW: AGRICULTURE AND FORESTRY PROVIDE FOOD, FEED, FIBER, FUEL, LUMBER PRODUCTS, AND ENVIRONMENTAL SERVICES WHILE SUSTAINING RURAL AND URBAN ECONOMIES. BUT US GLOBAL COMPETITIVENESS AND NUTRITION SECURITY ARE AT RISK DUE TO UNPREDICTABLE WEATHER EVENTS, DEGRADING AG-FOREST SYSTEM HEALTH, AND AN AGING AND SKILL-DEFICIT WORKFORCE. TO ADDRESS THESE CHALLENGES, WE PROPOSE TO CREATE AN AI FOR LAND, ECONOMY, AGRICULTURE AND FORESTRY (AI-LEAF) DISCIPLINE, A COMMUNITY OF PRACTICE, AND FUNCTIONING CARBON MARKETS BY IMPROVING UNDERSTANDING OF TRADE-OFFS AND FEEDBACK LOOPS BETWEEN RESILIENCE AND ADAPTATION AND BETWEEN BIOMASS PRODUCTIVITY AND CARBON AND WATER FLUXES, DEVELOPING AI-ENHANCED FLUX AND BIOMASS ESTIMATION METHODS AND SPATIALLY-EXPLICIT MULTISCALE (FIELD-TO-MARKET) DECISION SUPPORT TOOLS FOR AGRICULTURE AND FORESTRY. AI ADVANCES WILL INCLUDE RELIABLE, ACCURATE OUT-OF-SAMPLE PREDICTION FROM SPARSE GROUND-TRUTH MEASUREMENTS WITH CONSIDERATION OF HARD CONSTRAINTS, UNCERTAINTY, AND SPATIOTEMPORAL VARIABILITY. WE PROPOSE A VIRTUOUS CYCLE OF DISCOVERY AND INQUIRY IN FOUNDATIONAL AI (FAI) AND USE-INSPIRED RESEARCH (UIR) THAT CONSIDERS DECISION-MAKING AT DIFFERENT SCALES. FAI RESEARCH INCLUDES COMBINING LEARNING AND AI REASONING, AI-AIDED MULTI-OBJECTIVE DECISION-MAKING, AND GENERALIZATION THEORY, ALONG WITH UIR AREAS OF CARBON AND WATER FLUX ESTIMATION, LAND-USE AND CROPPING SYSTEM CHANGE, BIOMASS PRODUCTIVITY, CARBON MARKETS, MULTI-SCALE DECISION SUPPORT TOOLS, KNOWLEDGE-GUIDED MACHINE LEARNING (KGML), COMPUTER-VISION GUIDED PERCEPTION AND ANALYSIS, AND AI-GUIDED DIGITAL TWINS.INTELLECTUAL MERIT: OUR PROPOSED RESEARCH WILL ADVANCE LAND, ECONOMY, AGRICULTURE AND FORESTRY (LEAF) KNOWLEDGE AND UNDERSTANDING TO CREATE LEAF DECISION SUPPORT SYSTEMS USING KGML FOR RELIABLE OUT-OF-SAMPLE PREDICTION (AI) IN UN- OR UNDER-SAMPLED FIELDS AND PARCELS, AND AI-AIDED MULTISCALE AND MULTICRITERIA DECISION SUPPORT TOOLS FOR EVALUATING TRADEOFFS BETWEEN ALTERNATIVE PRACTICES FOR RESILIENCE AND ADAPTATION UNDER CURRENT AND FUTURE CLIMATE SCENARIOS. IT HAS THE POTENTIAL TO TRANSFORM MACHINE LEARNING FROM A SOFT-CONSTRAINT (E.G., REGULARIZERS) AND MONO-OBJECTIVE (E.G., PREDICTION ACCURACY) PARADIGM TO CONFRONT HARD CONSTRAINTS (E.G., MASS AND ENERGY BALANCE) AND MULTIPLE OBJECTIVES (E.G., DECISION MAKING, PREDICTION ACCURACY AND DOMAIN INTERPRETABILITY, ECONOMIC RETURN, AND ECOSYSTEM SERVICES). LIKE IMAGENET, IT HAS THE TRANSFORMATIVE POTENTIAL TO ADVANCE COMPUTER VISION FROM A HUMAN-VISIBLE SPECTRUM AND POINT-CLOUD-BASED APPROACH TO A SENSOR-RICH (E.G., OPTICAL, THERMAL, MICROWAVE) APPROACH BY PUBLISHING NEW LEAF_IMAGENET BENCHMARK DATA AND USE CASES (ESTIMATE CARBON AND WATER FLUXES, SOIL ORGANIC CARBON, BIOMASS PRODUCTIVITY). OUR CORE TEAM HAS A HISTORY OF SYNERGISTIC RESEARCH AND THE REQUIRED SKILLS, EXPERTISE, AND ACCESS TO DATA AND SENSOR RESOURCES. TO FOSTER STRONG INTERACTIONS ACROSS PROPOSED RESEARCH AREAS, WORKFORCE DEVELOPMENT, AND COLLABORATION NEXUS ACTIV,ITIES, A DEDICATED AI INSTITUTE IS NEEDED TO INTEGRATE THE EXPERTISE OF INVESTIGATORS FROM DIVERSE DISCIPLINES AND INSTITUTES IN CLOSE COLLABORATION WITH STAKEHOLDERS TO CULTIVATE A NEW AI-LEAF DISCIPLINE AND COMMUNITY OF PRACTICE.BROADER IMPACT: THE PROPOSED INSTITUTE WILL BENEFIT SOCIETY BY CATALYZING AN AI-LEAF DISCIPLINE, A COMMUNITY OF PRACTICE, AND BETTER FUNCTIONING CARBON MARKETS. IT WILL ENHANCE THE NATIONAL RESEARCH AND EDUCATIONAL INFRASTRUCTURE BY SHARING CURATED DATASETS AND EASY-TO-USE MULTI-SCALE DECISION SUPPORT TOOLS, INCLUDING AI ADVANCES (E.G., KGML, AI-GUIDED MULTI-OBJECTIVE OPTIMIZATION). IT WILL GROW THE AMERICAN AI WORKFORCE VIA THE INTEGRATION OF AI-LEAF RESEARCH WITH EDUCATION; MENTORING OF PROFESSIONAL, POST-DOCTORAL, GRADUATE, AND UNDERGRADUATE STUDENTS; ENGAGEMENT OF SECONDARY SCHOOL TEACHERS AND STUDENTS; AND CO-DEVELOPMENT AND TRAINING OF FARMERS AND FORESTERS IN THE USE OF AI-INSPIRED TOOLS; WITH CAREFUL CONSIDERATION OF BROADENING PARTICIPATION VIA RECRUITMENT, RETENTION, AND PLACEMENT OF ALL PROGRAM PARTICIPANTS. THE TEAM PURPOSEFULLY INTEGRATED RESEARCH, EDUCATION AND EXTENSION ACROSS MULTIPLE DISCIPLINES AND INSTITUTIONS. COMMUNITY-BUILDING ACTIVITIES INCLUDE SHARED DATA AND TOOLS, INTEGRATION OF PARTNERS, AND KNOWLEDGE TRANSFER VIA CO-CREATION, INDUSTRY CONSORTIA, AND THE IP FRAMEWORK.

Project Grant (B) · National Institute of Food and Agriculture · to Regents of the University of Minnesota (UEI KABJZBBJ4B54) · data as of 2026-09-30

Source: USAspending.gov prime award transactions and FSRS subaward reports, as loaded by SpendQuery (data as of 2026-09-30). Amounts are obligations (money committed), not outlays. The official record on USAspending.gov ↗