Project Grant 20236701740165
- This $268,500 cooperative agreement from the Department of Agriculture Animal and Plant Health Inspection Service will support the development of artificial intelligence algorithms for the automatic detection of pests in high water content plants and fruits. Awarded to Stanford University on August 12, 2022 under the Plant and Animal Disease, Pest Control, and Animal Care program (CFDA #10.025), the project aims to modify an existing X-ray phase-contrast imaging system to enable continuous...
- This Project Grant award of $1,000,000 from the National Science Foundation's (NSF) Technology, Innovation, and Partnerships (CFDA 47.084) program aims to develop an automated pest management system for rural farms. The key products and services to be delivered include: Integrating artificial intelligence (AI), internet of things (IoT), machine learning, and affordable communication networks to reduce reliance on manual labor and outdated practices in pest management. This will enhance...
- Summary of Federal Project Grant Award The Trustees of Stevens Institute of Technology received a $728,000 Project Grant from the USDA's National Institute of Food and Agriculture under the Agriculture and Food Research Initiative (AFRI) Foundational and Applied Science Program (CFDA 10.310), effective February 1, 2026, through January 31, 2029. This award supports the development of a rapid, non-destructive food quality assessment and prediction system combining data science with ultra-wideband...
- Federal Project Grant Award Summary The National Science Foundation's Office of Advanced Cyberinfrastructure awarded $599,721 to the University of California, Davis on September 1, 2025, under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070) to establish a secure, accessible ecosystem for evaluating and deploying multi-modal large language models (MLLMs) in food science research. The project delivers three primary products: (1) a comprehensive profiling framework...
- Washington State University received a $650,000 Project Grant from the U.S. Department of Agriculture's National Institute of Food and Agriculture under the Agriculture and Food Research Initiative (AFRI) program (CFDA 10.310), awarded September 15, 2025, with a completion date of September 14, 2028. The project will develop a real-time, on-site verification platform to assess surface sanitation effectiveness in food processing environments. The platform integrates a biomimetic surrogate...
- This $324,213 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports the development of a novel AI infrastructure to integrate multi-scale sensing data and 3D modeling for enhanced crop monitoring and assessment. The key products to be delivered include: An AI system that integrates satellite, drone, and in-situ sensor data to accurately model 3D crop structures, including both above- and below-ground...
- Federal Grant Award Summary Texas A&M Agrilife Research received a $591,491 Project Grant from the Institute of Food Production and Sustainability under the Agriculture and Food Research Initiative (AFRI) program (CFDA 10.310), awarded February 15, 2026, with completion scheduled for February 14, 2029. The project develops an artificial intelligence-assisted decision support system for controlled environment agriculture (CEA) greenhouse operations, specifically targeting leafy green...
- Federal Grant Award Summary Oregon State University received a $611,000 project grant from the U.S. Department of Agriculture's National Institute of Food and Agriculture (NIFA) under the Agriculture and Food Research Initiative (AFRI) program (CFDA 10.310), effective September 1, 2025 through August 31, 2028. The project will develop an artificial intelligence (AI)-based imaging approach for the early detection of spoilage yeasts in fruit and vegetable juices. Yeasts are the predominant...
- Federal Grant Award Summary Washington State University received a $225,000 Project Grant award from the Institute of Food Production and Sustainability (via the USDA National Institute of Food and Agriculture) under the Agriculture and Food Research Initiative (AFRI) program (CFDA 10.310), effective July 1, 2025, with completion targeted for September 30, 2027. The project addresses the challenge of excessive and inefficient pesticide use in tree fruit and vegetable production by developing...
- This $649,999 Project Grant award from the National Science Foundation (NSF) under the Technology, Innovation, and Partnerships (CFDA 47.084) program supports Farmsense, Inc., a self-certified small disadvantaged business, in developing novel sensor technologies for precise insect pest monitoring and surveillance in agricultural settings. The project aims to create a system inspired by how bats detect and discriminate between different insects, allowing for tracking of insect pests down to the...
** AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** BETTER DATA ANALYSIS MEANS BETTER FOOD SAFETY. IN RECENT YEARS, ARTIFICIAL INTELLIGENCE (AI)-POWERED SOLUTIONS ARE EMERGING IN AGRICULTURE AND FOOD SCIENCE DUE TO THEIR SUPERIOR PATTERN RECOGNITION AND PREDICTION CAPABILITY. INTEGRATION OF FOOD SCIENCE AND AI CAN HELP ADDRESS NEW CHALLENGES SUCH ASINCREASING VOLUME OF DATA ACQUIRED FROM MIXED SAMPLES AND COMPLEX FOOD MATRICES, WHICH POSES CHALLENGES TO DATA ANALYSIS USING TRADITIONAL METHODS.THE OVERALL GOAL IS TOINTEGRATE NOVEL MACHINE LEARNING (ML) METHODS, SUCH ASATTENTION-BASED DEEP NETWORKS,WITHSURFACE-ENHANCED RAMAN SPECTROSCOPY (SERS) PLATFORM FORMULTIPLEXDETECTION AND QUANTIFICATIONOF FOOD CONTAMINANTS WITH HIGH ACCURACY.SPECIFIC OBJECTIVES ARE TOSYNTHESIZE NANOSUBSTRATES AND ACQUIRE SERS SPECTRAL DATA OF DIFFERENT TYPES AND QUANTITIES OF PESTICIDES BY SERS MEASUREMENT;DEVELOPATTENTION-BASEDDEEP LEARNING PREDICTION METHODS FOR QUALITATIVE AND QUANTITATIVE ANALYSIS OF SINGLE FOOD CONTAMINANTS AND MULTIPLE/MIXED FOOD CONTAMINANTS; VALIDATE AND ASSESS SERS-ML TECHNIQUES FOR MULTIPLEX DETECTION OF CHEMICAL CONTAMINANTS IN FRESH PRODUCE; AND ESTABLISH PROTOCOLS/DATABASES FOR MEASURING FOOD CONTAMINANTS.THIS STUDY WILL BE THE FIRST SYSTEMATICINVESTIGATION OF PESTICIDES IN FRESH PRODUCE BY SERS COUPLED WITH MACHINE LEARNING ALGORITHMS, WHICH WILL BE MORE ACCURATE, SENSITIVE, AND RELIABLE THAN CURRENT METHODS. THE PROJECT WILL BROADEN THE APPLICATIONS OF DATA SCIENCE IN MAINTAINING THE SUSTAINABILITY OF U.S. AGRICULTURE AND FOOD SYSTEMS.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $0 | 3/24/26 | ||
| Not listed | $649.5k | 5/25/23 |