Artificial Intelligence (AI) Program

Artificial Intelligence (AI) Program is sponsored by USDA NIFA. Encourages AI applications in agriculture, including machine learning and data visualization for landscape management and agricultural trade.

Artificial Intelligence | NIFA The lifecycle of grants and cooperative agreements consists of four phases: Pre-Award, Award, Post-Award, and Close Out. The National Institute of Food and Agriculture is committed to serving its stakeholders, Congress, and the public by using new technologies to advance greater openness.

The Data Gateway enables users to find funding data, metrics, and information about research, education, and Extension projects that have received grant awards from NIFA. This website houses a large volume of supporting materials. In this section, you can search the wide range of documents, videos, and other resources.

Technical Assistance Webinar: The Equipment Grants Program (EGP) The Equipment Grants Program seeks to provide eligible institutions with the opportunity to acquire a shared-use piece of equipment/instrument that supports their research, research training, and extension goals. The National Institute of Food and Agriculture provides leadership and funding for programs that advance agriculture-related sciences.

Building on a long history of artificial intelligence (AI) activities that span a realm of disciplines and program areas, NIFA seeks to catalyze efforts that harness the power of AI in applications throughout agriculture and the food supply chain.

The AI activities supported through a variety of NIFA programs advance the ability of computer systems to perform tasks that have traditionally required human intelligence, including machine learning, data visualization, natural language processing and interpretation, intelligent decision support systems, autonomous systems, and novel applications of these techniques to agriculture and food production.

Areas that NIFA currently funds AI research, education, and extension activities Agricultural systems and engineering: Systems for crop and soil monitoring are leveraging machine learning, remote sensing, satellite imagery, drones, and precision technologies for informed production and management.

Autonomous robots are being developed to perform previously labor-intensive tasks like harvesting crops in greater volumes and faster than traditional human laborers. Nano-based sensing mechanisms and smart sensors are being developed and evaluated for accurate, reliable and cost-effective early and rapid detection of pathogens, allergens, chemicals and contaminants in foods, plant and animal production systems, water and soil.

Natural resources and environment: Decision support tools and models are increasingly used in the assessment and development of new management practices and processes leading to substantial improvements in soil health (e.g., microbiome, water, nutrients, carbon, chemicals of environmental concern) and improved ecosystem services from agricultural production.

Economics and rural communities: Applications of AI are expanding in development of new models to assist farm, forest, and ranch managers in decision-making with appropriate scale management strategies and technologies to enhance economic efficiency and sustainability.

The contributions and impact of AI are being examined to understand agricultural market structure and performance; international trade; agricultural production and resource use; consumer behavior; food safety; food waste and loss; and farm labor and immigration and policy; agricultural policy design and impacts; technology development and adoption; and science and innovation policy.

Efforts are supported by AI to create and examine innovative approaches for advancing economic opportunities for rural entrepreneurs and communities. Agriculture and Food Research Initiative (AFRI) Foundational and Applied Science Request for Applications .

The subsections of the AFRI Foundational and Applied Science program that provide funding in AI are Agriculture Systems and Technology; Bioenergy, Natural Resources and Environment; Agricultural Economics and Rural Community program areas.

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