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The USDA NIFA Small Business Innovation Research / Small Business Technology Transfer (SBIR/STTR) program provides non-dilutive funding to small businesses developing innovative, commercially viable technologies for agriculture and food systems.
AI and machine learning applications are increasingly funded across topic areas including precision agriculture, crop and livestock production, food safety and quality, natural resource and water management, agricultural manufacturing, and rural economic development. The program moves discoveries from concept (Phase I feasibility) to market (Phase II development), distributing roughly $40-50 million annually.
AgTech startups applying AI to sensing, robotics, decision support, and supply chain modeling are well-aligned with current priorities.
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Or search similar grants →According to the current listing, eligibility includes: U.S. small businesses and proprietorships operating for profit that qualify as small business concerns for research and development purposes. STTR requires partnership with a nonprofit research institution. Confirm the full requirements in the official notice before applying.
The current listing shows phase I awards provide up to approximately $125,000 to $181,500 over roughly eight months to establish technical feasibility; Phase II awards scale to up to $600,000 over approximately 24 months for further R&D and commercialization. No cost-sharing is required. Verify award ceilings, matching requirements, and allowable costs in the official notice.
USDA NIFA SBIR/STTR Program for AgTech Including AI and Precision Agriculture is funded by USDA National Institute of Food and Agriculture (NIFA). Verify program details on the funder's official page before applying.
Start from the official opportunity page linked in this listing — it carries the sponsor's submission instructions.
The NEH Humanities Research Centers on Artificial Intelligence program funds the creation of university-based humanities research centers focused on the ethical, legal, and societal implications of artificial intelligence. Funded centers undertake interdisciplinary humanities-led research that brings ethics, law, history, philosophy, anthropology, religious studies, literature, linguistics, and cultural studies to bear on questions raised by AI systems. Topics include responsible AI governance frameworks, AI and civil rights, AI and labor, cultural impact of generative AI, AI and creative authorship, philosophical foundations of machine reasoning, history of AI thought, and humanistic evaluation of AI safety and alignment. Centers are expected to convene researchers, train new humanities scholars in AI, host public-facing programming, and produce publications and translational tools that inform policy and public understanding. Strong fit for universities seeking to launch sustained interdisciplinary AI humanities research programs in partnership with computer science and other STEM departments.
NSF SaTC 2.0 (Security Privacy and Trust in Cyberspace) is the largest open solicitation for university-led cybersecurity research in the federal portfolio now expanded with AI security as an explicit priority area. The 2.0 reboot added generative AI security open-source software security quantum computing security and supply chain security as topics of interest addressing the bidirectional role of AI as both a cybersecurity threat and a defensive tool. Research awards support adversarial machine learning and attacks on AI systems AI weaponization against people information and systems privacy-preserving machine learning and responsible AI use for detecting and responding to cyber threats. The program funds three award types: Research awards up to $1.2M for four years Education awards up to $500K for three years and Seedling awards up to $300K for two years through Dear Colleague Letters. Proposals are accepted on a recurring annual basis with two windows per year. This is distinct from NSF CyberAICorps which focuses on scholarship and workforce development and from NSF AIMing which focuses on AI formal methods and mathematical reasoning.
The Air Force Research Laboratory Information Directorate's Geospatial Intelligence Processing and Exploitation (GeoPEX) Broad Agency Announcement, FA8750-21-S-7006, is an open two-step BAA soliciting white papers for research, development, integration, test, and evaluation of technologies and techniques to provide geospatial intelligence (GEOINT) in all its forms and from whatever source, including imagery, imagery intelligence, and geospatial data. Explicit focus areas include AI/ML techniques for full-spectrum GEOINT, multi-INT data fusion, cloud-based high-performance computing for geospatial analytics, photogrammetry, computer vision for overhead imagery, automated target recognition, change detection, multi-modal foundation models for geospatial data, and edge AI for tactical reconnaissance. The BAA is open and effective until 30 September 2026 with rolling white paper submission; only white papers are accepted as initial submissions and formal proposals are accepted by invitation only. Strong fit for AI and computer vision performers building geospatial analytics, foundation models, or autonomous reconnaissance tools for Air Force, intelligence community, and combatant command users.
NIFA posted USDA-NIFA-SBIR-012221 on October 1 with $24.75 million, awards of $600,000 to $650,000, and a November 17 close. The 50% rate is real, and the same-topic-area rule is why.
Read articleUSDA-NIFA-SLBCD-012281, the Tribal Colleges Extension Program Special Emphasis, puts $1.5 million behind roughly 10 awards of $50,000 to $200,000 with no cost share. The 71% rate is not reviewer generosity — it is a closed applicant pool that does not fill its own competition.
Read articleUSDA-NIFA-HEP-011737 puts $14 million behind roughly 28 awards of up to $705,000 for graduate fellowships in food and agricultural sciences. No match is required. But two eligibility rules — the new-graduate-student definition and the citizenship requirement — disqualify most of the students a department would naturally nominate.
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