Research & Development
AI-LEAF Spotlight Series
Societal Grand Challenge
Extreme weather occurrences are increasing precipitation variability, narrowing planting windows and potentially impacting yields as early as 2030. This will affect our production of food, fiber, and fuel in the face of increasing demand due to population growth. At the same time, agriculture and forestry have unrealized potential to regenerate their soils, increasing their resilience to extremes and unrealized potential for economic gain which will help the rural economy and our country’s national security. While our forests and farms have significant untapped potential for carbon accrual realizing this potential requires addressing competition between land for biomass production and ecosystem services, scientific and technological challenges of actionable (e.g., less expensive and more accurate) sequestered carbon verification, computational integration of the incentive structures of land managers that implement agriculture and forest management practices, and innovations to encourage large-scale equitable adoption of regenerative agricultural practices.
Research Mission and Objectives
AI-LEAF Institute’s mission is to revolutionize AI and enhance the sustainability and resiliency of agriculture and forestry, aiming to tackle previously insurmountable challenges and expedite adaptation and mitigation efforts, while informing policy and bolstering markets.
We aim to advance agriculture and forestry decision support by delivering AI-powered models, such as COMET emulators, digital twins, and Earth Economy models, that directly inform farm management, forest stewardship, and policy decisions, with success measured by tool adoption, user training, and policy impact. The initiative also seeks to build more resilient agricultural and forestry systems through predictive modeling of sustainability challenges, including geographic shifts, pest and disease risks, and carbon storage potential, as reflected in the production of resilience maps, agency use of risk assessments, and documented adaptation case studies. In parallel, AI-LEAF is committed to developing a highly qualified and diverse AI workforce by expanding interdisciplinary curricula, mentoring, and extension training that prepare students and professionals to lead at the intersection of AI, agriculture, and forestry, with outcomes tracked through participation, new educational offerings, and career trajectories. The program will further create and disseminate benchmark datasets and standards by releasing harmonized soil, crop, forest, and greenhouse gas datasets with transparent protocols that establish a gold standard for agricultural and forestry AI, measured through dataset releases, citations, and integration into decision systems. Finally, AI-LEAF will build and sustain a strong community of practice by deepening partnerships with USDA, industry, and non-governmental organizations and embedding stakeholder input throughout the research lifecycle, with success indicated by partnerships formed, resources leveraged, and stakeholder-informed tools and datasets.
Research Themes
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Knowledge-Guided Machine Learning (KGML)
Usability, trust, and co-design are essential for KGML’s success and require active integration of stakeholder needs and feedback. AI-LEAF’s distinctive strength lies in its ability to connect advanced AI research with the practical needs of agriculture and forestry through intentional integration across science, policy, and education. The KGML theme embodies this value by combining technical innovation with stakeholder engagement to ensure that tools are not only accurate but also usable, trusted, and relevant. This integrated approach enables AI-LEAF to co-design solutions with partners, bridge the gap between research and policy, and prepare the next generation of practitioners who will apply these methods in the field.
Goals:
- Advance hybrid AI models that integrate process-based science with machine learning for improved estimation of soil carbon and trace gas emissions.
- Enable faster, more accurate predictions that are easier to interpret and integrate into on-farm decision support systems for estimating biogeochemical trace gases (COMET Planner, COMET Farm) at the regional and national scale.
- Position AI-LEAF as a national leader in KGML research, benchmark datasets, and stakeholder-ready applications.
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Agriculture Resilience and Forest Sustainability
The Climate Risks and Adaptation team examines how diverse climate-related stressors impact crop and forestry systems, as well as the effects of various adaptation efforts to minimize risks through shifts in system vulnerability and exposure. In crop systems, the team is building advanced machine learning models to better understand the impacts of hydroclimatic stressors on crop yields and examining adaptation-mitigation tradeoffs with irrigation. In forest systems, the team is building benchmark datasets, improving estimates of forest biomass, and examining risks from fires and other disturbances.
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Combined Learning and AI Reasoning (CLeAR)
CLeAR integrates reasoning and learning for various applications, including estimating uncertainties in greenhouse gas flux, dynamic forest productivity modeling, and stochastic optimization in Earth-Economy scenarios.
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(AI-MOD) Multi-Criteria Optimization Decision-Making for Climate Adaptation
The research focuses on AI-guided multi-objective optimization for CSAF decision-making. This involves optimizing mitigation practices and bio-productivity across scales with multiple criteria, including equity and GHG reduction. The challenges involve complex problems necessitating fast response times, handling large areas and addressing spatial fragmentation. The research outlines three tasks: large-scale stochastic optimization for sequential decision-making, developing methods to compute Pareto frontiers for trade-offs and designing spatial optimization techniques to ensure contiguous solutions. Researchers are drawn on prior work in complex network optimization and spatial dependency modeling, particularly in the Amazon basin, showcasing their expertise in addressing CSAF challenges.
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Digital Twins
The research focuses on AI-aided Digital Twins (AIDT) to enhance resilience planning for climate scenarios in CSAF. Digital twins are necessary for evaluating CSAF and AI concepts under diverse climate scenarios, considering impacts on agriculture and forestry. The digital twin is an in-silico representation of phenomena. One focus is on the creation of a digital for soils encompassing the contiguous United States. Soils play a critical role in agriculture settings including: (1) providing essential nutrients to plants for their growth and development, (2) Supporting water retention and drainage, and thus helping to regulate soil moisture levels, and (3) serving as a habitat for beneficial microorganisms that contribute to soil health.
Our work on digital twins involves modeling encompassing diverse meteorological and environmental phenomena to estimate critical soil properties such as moisture levels, salinity, and nutrient concentrations. By integrating diverse data sources alongside these models, we create a highly detailed and dynamic representation of real-world conditions. Users can interact with this digital twin through a visualization engine that enables the layering of multiple datasets, real-time animations, and the rendering of complex environmental processes, providing deeper insights and facilitating informed decision-making.
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Computer Vision and Analysis
AI-LEAF’s Computer Vision and Analysis (CV&A) theme applies multi-sensor imagery and advanced machine learning to transform the monitoring of crops, soils, and forests. By combining UAV, satellite, and hyperspectral data, CV&A detects drought, pests, and disease early and refines estimates of biomass, yield, and forest carbon. Co-designed with producers, foresters, and policymakers, these tools provide critical visual data to Digital Soil Twins, Agricultural Resilience, and AI-MOD, while releasing benchmark datasets, dashboards, and training modules to accelerate the adoption of improved agricultural and forestry practices.