NIFA Invests Over $7 Million in Big Data, Artificial Intelligence, and Other Cyberinformatics Research

National Institute of Food and Agriculture has awarded 12 grants totaling over $7 million to initiate research on big data analytics, machine learning, artificial intelligence, and predictive technologies.

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USDA’s National Institute of Food and Agriculture has awarded 12 grants totaling over $7 million to initiate research on big data analytics, machine learning, artificial intelligence, and predictive technologies needed to keep U.S. agriculture on the leading edge of food and agricultural production. These grants are awarded through the USDA-NIFA Agriculture and Food Research Initiative (AFRI) Food and Agriculture Cyberinformatics and Tools (FACT). 

“Big data and artificial intelligence will increasingly play a vital role in the future of agricultural technologies,” said Parag Chitnis, acting director of USDA-NIFA. “As we work to realize precision nutrition for consumers and enhance farmer profitability and agricultural sustainability, these predictive technologies will keep research and development moving quickly to provide the tools needed for success.”

Chitnis added, “Undoubtedly, the work of these scientists will deliver data-driven solutions to help farmers and ranchers across the nation approach today’s challenges, but will also contribute to the growing body of science that will lead us to future technological advances not yet imagined to meet both current and future challenges.”

FY20 AFRI FACT grants were awarded to:

  • Auburn University in Auburn, Alabama, for “FACT: Interactive Deep Learning Platform and Multi-Source Data Integration for Improved Soil Moisture Forecasting” (Grant amount: $499,251)
  • Clemson University in Clemson, South Carolina, for “FACT-CIN: Expanded Food and Nutrition Education Program - Web Based Nutrition Education and Evaluation Reporting System (WebNEERS)” (Grant amount: $901,085)
  • Colorado State University in Fort Collins, Colorado, for “FACT: A Scalable Infrastructure for High-Precision Evapotranspiration Estimations and Effective Farm-Level Decision Making” (Grant amount: $495,585)
  • University of Georgia in Athens, Georgia, for “FACT-AI: Big Data-Enabled Real-Time Learning and Decision Making for Field-Based High Throughput Plant Phenotyping” (Grant amount: $500,000)
  • University of Illinois in Urbana, Illinois, for “FACT: Innovative Big Data Analytics Technology for Microbiological Risk Mitigation Assuring Fresh Produce Safety” (Grant amount: $499,815)
  • Iowa State University in Ames, Iowa, for “FACT: Web-Based Dynamic Data-Analytics Framework for On-Farm Research Networks” (Grant amount: $394,988)
  • Woods Hole Research Center in Falmouth, Massachusetts, for “FACT CIN: Soil Spectroscopy for the Global Good” (Grant amount: $999,702)
  • University of Minnesota in St. Paul, Minnesota, for “FACT: Development of a National Manure Composition Database” (Grant amount: $500,000)
  • University of Minnesota in Minneapolis, Minnesota, for “FACT: Cyber-Infrastructure for Landscape Impacts on Biocontrol” (Grant amount: $877,990)
  • North Carolina State University in Raleigh, North Carolina, for “FACT: Near-Real Time Spatiotemporal Resource Allocation to Improve Swine Health” (Grant amount: $497,560)
  • Texas A&M University in College Station, Texas, for "FACT: Big Data Analytics to Harmonize Disease and Pest Management with Agricultural Logistics" (Grant amount: $499,764)
  • Washington State University in Pullman, Washington, for “FACT: Predicting Wheat Hagberg Falling Number from Near Infrared Spectrometers” (Grant amount: $499,660)
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