AI Grants for Agriculture: Precision Farming, Supply Chain, and Food Security

Half a billion dollars in federal investment across 25 National AI Research Institutes. A $1.4 billion Gates Foundation commitment to smallholder farming innovation. A Quad-nation initiative deploying autonomous robots in apple orchards. The money flowing into AI-driven agriculture is no longer a trickle from a few niche programs — it is a coordinated, multi-funder push that spans continents and disciplines.

Looking for AI agriculture funding? Browse our AI Agriculture Grants page for current opportunities.

What makes this moment different from previous waves of ag-tech enthusiasm is the breadth of funders involved. USDA still anchors the federal portfolio (we covered that in depth here), but NSF, DOE, the Gates and Rockefeller Foundations, CGIAR, and the World Bank are all running active programs — often with different eligibility requirements and far less competition than the usual NIH or DARPA channels.

NSF's Agriculture AI Pipeline: Institutes, Testbeds, and International Partnerships

NSF has built the most layered infrastructure for agricultural AI research of any U.S. agency. The National AI Research Institutes program — a joint effort with USDA-NIFA totaling $220 million — funds five agriculture-focused centers at roughly $20 million each over five years. The $20 million AI-LEAF Institute at the University of Minnesota (formerly AI-CLIMATE) works on climate-smart agriculture and forestry. UC Davis runs AIFS, applying AI and bioinformatics across the entire food system — molecular breeding, crop production, processing, distribution, and nutrition.

Beyond the institutes, NSF launched AI-ENGAGE in early 2026, a Quad-nation collaboration with Australia, India, and Japan. The first six awards, totaling over $6 million across partner countries, fund projects including autonomous disease-detection robots for orchards (Purdue), an AI pest-identification chatbot called BRIDGE (Iowa State), and a computer vision yield-estimation system for soybeans (Kansas State). Every project requires researchers from at least three Quad nations, which creates a natural pathway for international partnerships that most domestic-only programs lack.

On the infrastructure side, NSF invested in AI4Ag, a national testbed at Cornell's Farm of the Future where AI innovations can be stress-tested under authentic field conditions. The planning grant is modest — part of a $2 million initiative — but the testbed itself is designed to become a shared resource for the research community.

Foundation Funding: Gates, Rockefeller, and the Global South

The private foundation landscape for agricultural AI is dominated by two organizations with very different approaches.

The Gates Foundation announced a $1.4 billion, four-year commitment at COP30 in November 2025 to expand agricultural innovation for smallholder farmers across sub-Saharan Africa and South Asia. A significant share targets digital advisory services: mobile apps, SMS platforms, and AI-driven tools that deliver planting decisions, weather forecasts, and pest alerts directly to farmers. The AIM for Scale initiative, backed by the foundation, delivered AI-powered weather forecasts to nearly 40 million farmers across 13 Indian states during the 2025 monsoon season. A $5 million grant to Heritable Agriculture — a startup spun out of Google X — funds the JASON initiative, which uses AI combined with genomics to develop climate-resilient crops for low-income countries.

The Rockefeller Foundation operates at a smaller scale but with sharp focus. Its partnership with Cassava Technologies gives grantees access to computing infrastructure, including a planned NVIDIA-backed AI factory in Africa. Initial participants include Digital Green, which uses real-time data to improve smallholder productivity in Ethiopia and Kenya. The foundation also funds AI-powered extension tools that deliver regenerative agriculture advice through field agents — a model that pairs the technology with human trust networks rather than replacing them.

For U.S.-based researchers, the foundation programs are relevant mainly through collaboration: joint proposals with institutions in target countries, or technology development partnerships where the U.S. team builds the AI system and a local partner handles deployment and farmer engagement.

CGIAR and the World Bank: Infrastructure for AI at Scale

International organizations are investing less in individual AI research projects and more in the data infrastructure that makes agricultural AI possible at all.

CGIAR — the global agricultural research network — launched its Digital Transformation Accelerator with several flagship platforms. AgriLLM is an open-source agricultural large language model offering multilingual digital advisory. AgPile is a federated data-sharing architecture designed to connect fragmented agricultural datasets across countries. A $200 million UAE-Gates Foundation partnership announced at COP28 funds four related initiatives, including the CGIAR AI Hub and the Institute for Agriculture and Artificial Intelligence.

The World Bank launched AgriConnect in October 2025 — a platform combining satellite data, mobile money records, and AI credit scoring to de-risk lending for smallholders who lack collateral. AgriConnect is designed as an operating model rather than a standalone project, coordinating the World Bank, IFC, IFAD, AfDB, FAO, and private sector partners.

DOE's Bioenergy Connection

The Department of Energy's Bioenergy Technologies Office funds AI applications at the intersection of agriculture and energy — feedstock optimization, biomass supply chain modeling, and machine learning for bioenergy crop yields. Starting in FY2026, DOE's Biological and Environmental Research program shifted from targeted solicitations to an open call model, meaning researchers can propose AI-agriculture-energy crossover projects on a rolling basis rather than waiting for a specific funding announcement. For teams working on biofuel feedstocks, carbon sequestration in agricultural systems, or energy-crop genomics, this is an underused entry point.

Where the Gaps Are

The funding landscape is rich but uneven. Computer vision for crop monitoring and pest detection is well-served across multiple funders. Supply chain and food safety AI has strong USDA support. Climate-adaptive breeding attracts both federal and foundation money.

The gaps — and therefore the opportunities with less competition — sit in areas like AI for soil health monitoring at scale, post-harvest loss reduction in developing-country supply chains, agricultural robotics for labor-scarce specialty crops, and food safety prediction systems that work across international trade networks. Proposals that bridge two or more of these underserved areas, especially with international collaborators, will find receptive reviewers at NSF, Gates, and CGIAR simultaneously.

Whether you are targeting a $650,000 NIFA grant or a multimillion-dollar foundation partnership, Granted can help you identify which programs match your work and track deadlines before they close.

Sources:

• USDA-NIFA and NSF Invest $220M in Artificial Intelligence Research Institutes | NIFA
• NSF Announces First AI-ENGAGE Awards to Modernize Global Agriculture | NSF
• AI Institute for Next Generation Food Systems (AIFS) | UC Davis
• University of Minnesota AI-LEAF Institute
• Cornell Awarded NSF Grant for AI-Ready Living Lab | Cornell Chronicle
• Gates Foundation Smallholder Farmers Investment | COP30
• Heritable Agriculture Wins $5M Gates Grant | FoodBev
• Cassava Technologies, Rockefeller Foundation Advance AI in Africa
• CGIAR Digital Transformation
• World Bank AgriConnect Initiative
• DOE BER Funding Opportunities