Project Grant 2520320
- This Project Grant award from the National Science Foundation's Office of International Science and Engineering (CFDA 47.079) provides $225,000 to the University of Missouri System to develop an AI-driven "HARVEST" system that assists farmers in addressing agricultural challenges such as pest outbreaks, crop diseases, nutrient deficiencies, and water stress. The project leverages sensors, drones, and mobile robots to enable early detection and provide real-time guidance to farmers on...
- 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...
- This Project Grant award from the National Science Foundation's Office of International Science and Engineering (OISE) program (CFDA 47.079) provides $400,000 to Iowa State University to develop AI-based decision support tools for farmers to quickly identify and manage agricultural pests, diseases, and weeds. The project will create smartphone apps and a chatbot that allow farmers to take photos of problems and receive instant identification and management advice, making advanced pest control...
- This EAGER (Early-concept Grants for Exploratory Research) federal Project Grant award of $300,000 is provided by the National Science Foundation through its Office of International Science and Engineering (OISE) program (CFDA 47.079). The goal of this 2-year project is to develop and deploy artificial intelligence (AI)-driven tools to enhance agricultural productivity and sustainability worldwide. The key products and services to be delivered include: (1) Developing hybrid machine learning...
- This federal Project Grant award from the National Science Foundation's Office of International Science and Engineering (CFDA 47.079) provides $175,000.00 to the University of Tennessee to develop an AI-powered agricultural response system called HARVEST. The project investigates smart farming technologies that use artificial intelligence (AI) to help farmers protect crops, use resources more efficiently, and reduce reliance on chemicals. Key innovations include an early-warning module to detect...
- This federal Project Grant award, funded by the U.S. Department of Agriculture's National Institute of Food and Agriculture (USDA NIFA) under the Capacity Building Grants for Non-Land Grant Colleges of Agriculture (CFDA 10.326) program, aims to develop advanced Artificial Intelligence (AI) technologies to help soybean farmers manage and mitigate the impacts of drought. The $300,000 award, with a period of performance from July 1, 2024 to June 30, 2027, will enable the utilization of unmanned...
- This Project Grant award from the National Science Foundation's Office of International Science and Engineering (CFDA 47.079) provides $300,000 in funding to Washington State University (WSU) from May 1, 2024 to April 30, 2026. The project aims to develop collaborative research using AI/ML methods to improve agricultural production and climate resilience in India. Key objectives include: Building and testing new AI-enabled infrastructure for in-field crop monitoring and phenotyping, with a focus...
- This Project Grant award of $195,013 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program will establish the AI4AG living lab at Cornell University. AI4AG will serve as an accessible testbed to accelerate the development and deployment of artificial intelligence (AI) technologies for agriculture. The project will create a shared space equipped with tools, data, and expertise to support AI research and testing in real-world agricultural...
- This Cooperative Agreement award from the National Science Foundation (NSF) Technology, Innovation, and Partnerships (CFDA 47.084) program provides $1,249,394 to Advanced Growing Resources Inc. for the continued development of a portable hyperspectral imaging system for early detection of crop afflictions. The project aims to create a commercial-ready solution that integrates a ruggedized hyperspectral camera and AI-powered diagnostics into a vehicle-mounted system. This will enable farmers to...
- The National Science Foundation awarded a $304,786 project grant to the Donald Danforth Plant Science Center under the Computer and Information Science and Engineering program (CFDA 47.070) to develop a cyber-physical system for securely sharing agricultural data and lessons learned via edge computing. The three-year award beginning October 1, 2022 will support intersecting expertise in plant science, secure networked systems, software engineering, and geospatial science to mitigate challenges...
This Project Grant award from the National Science Foundation's Office of International Science and Engineering (CFDA 47.079) provides $399,992 to Kansas State University to develop an AI-enabled computer vision system called "Smart Scout" to help soybean farmers monitor crop health and estimate yields. The project aims to address the challenge of soybean lodging, where plants fall over before harvest, reducing yields and making harvesting more difficult. Smart Scout will integrate visual data on pests, plant traits, and yield indicators to provide real-time, georeferenced insights to farmers. The system is designed to be flexible, allowing deployment as a handheld tool, robotic platform, or machinery-integrated sensor. This award, which runs from October 2025 to September 2028, seeks to enhance agricultural productivity, reduce losses, and support economically prosperous farming globally through the use of this advanced AI-powered monitoring and decision support tool.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $400.0k | 8/1/25 |