Project Grant 20245140242007
- This Project Grant award for $300,000 from the USDA National Institute of Food and Agriculture (NIFA) under the Agriculture and Food Research Initiative (CFDA 10.310) aims to develop a robotic precision spraying system to improve the sustainability of small fruit crop production in the United States. The key deliverables include designing and evaluating an unmanned ground robot-based sprayer that can autonomously apply pesticides based on canopy density, with the goal of significantly reducing...
- This $325,000 Project Grant awarded by the USDA National Institute of Food and Agriculture (NIFA) under the Crop Protection and Pest Management Competitive Grants Program (CFDA 10.329) will develop new tools and approaches to better predict and manage fungal foliar diseases in major crops like corn, soybeans, and potatoes. The project will use a combination of low-cost air sampling devices, weather monitoring stations, and DNA sequencing to track disease-causing organisms. This data will be used...
- This Project Grant award from the National Science Foundation's Office of International Science and Engineering (CFDA 47.079) provides $400,000 to Purdue University to develop a novel robotic platform that combines unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) with advanced sensing and artificial intelligence (AI) technologies. The goal is to efficiently detect and control diseases like apple scab, fire blight, and powdery mildew in apple orchards. The UAVs equipped with...
- This Project Grant award from the U.S. Department of Agriculture's (USDA) National Institute of Food and Agriculture (NIFA) under the Agriculture and Food Research Initiative (AFRI) program provides $998,051 to North Carolina State University to develop an early detection system for downy mildew, a devastating plant disease affecting cucurbit crops like cucumbers and pumpkins. The project aims to create a portable test for identifying disease spores and a robotic system to automatically...
- The Department of Agriculture National Institute of Food and Agriculture awarded a $324,833 Project Grant to Virginia Polytechnic Institute & State University (Virginia Tech) under the Crop Protection and Pest Management Competitive Grants Program (CFDA 10.329). The purpose of the award is to standardize the use of unmanned aerial systems (UAS), or drone, spray application technologies for pest management in cucurbit crops, particularly watermelon. This project aims to advance UAS spray...
- This $295,750 Project Grant award from the USDA National Institute of Food and Agriculture (NIFA) Crop Protection and Pest Management (CPPM) Competitive Grants Program, with a period of performance from September 1, 2024 to August 31, 2027, aims to develop effective integrated pest management (IPM) strategies for cucurbit production in the Delmarva region. The key objectives are to: 1) determine host plant preferences of cucumber beetles, squash bugs, and vine borers; 2) identify volatile...
- 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 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...
- The Department of Agriculture National Institute of Food and Agriculture awarded a $650,000 Project Grant to Agrofocal Technologies, Inc. under the Small Business Innovation Research (SBIR) Program / Small Business Technology Transfer (STTR) Program (CFDA 10.212). The funding supports the development of Agrofocal's real-time crop monitoring system that can be mounted on any vehicle to provide high-resolution, under-the-canopy images of crops. This system enables farmers to monitor crop health,...
- This $174,922 Project Grant award from the U.S. Department of Agriculture's (USDA) National Institute of Food and Agriculture (NIFA) Small Business Innovation Research (SBIR) / Small Business Technology Transfer (STTR) program supports the development and testing of a low-cost autonomous robotic weeding system for small and mid-sized farms. The key objectives are to create an affordable solution that can reliably distinguish between crops and weeds, then remove weeds using simple mechanical...
CURRENT CUCURBIT PRODUCTION PRACTICES INVOLVE MULTIPLE BROADCAST APPLICATIONS OF FUNGICIDES FROM PLANTING TO HARVEST. OUR GOAL IS TO DEVISE AN AI-BASED SYSTEM THAT ADMINISTERS FUNGICIDES ONLY WHERE THE CROP CANOPY OR DISEASE IS PRESENT. THE SPECIFIC OBJECTIVES ARE: (1) CREATE ADVANCED AI MODELS FOR DETECTION AND IDENTIFICATION OF CUCURBIT CROP CANOPIES, FLOWERS, FRUITS, AND DISEASE SYMPTOMS; (2) DEVELOP A FULL-SCALE PROTOTYPE SMART SPRAY SYSTEM, USING THE AI MODELS AND MACHINE VISION FOR PRECISE PESTICIDE APPLICATIONS ON CUCURBITS. WE PRIORITIZE CUCURBITS AS OUR INITIAL FOCUS DUE TO AN EXISTING IMAGE DATABASE, DISEASE SUSCEPTIBILITY, AND THEIR VINING GROWTH PATTERN, WHICH OFTEN RESULTS IN OFF-TARGET APPLICATIONS. THIS OFFERS A SUBSTANTIAL CHANCE TO MINIMIZE PESTICIDE USE, ESPECIALLY EARLY IN THE SEASON. OUR APPROACH FACILITATES RAPID TRAINING FOR THE DETECTION OF VARIOUS CUCURBIT TYPES IN DIVERSE PRODUCTION ENVIRONMENTS. WE CAN PRODUCE AMPLE SYNTHETIC IMAGE DATASETS FOR MODEL TRAINING, EVEN WITH LIMITED ORIGINAL IMAGES, WITH THE USE OF GENERATIVE AI. THIS IS CRUCIAL FOR EARLY DETECTION OF EXOTIC DISEASES WHICH LACK LARGE IMAGE DATASETS, THUS PREVENTING POTENTIAL INDUSTRY-WIDE DAMAGE IF SUCH DISEASES ARE INTRODUCED. ULTIMATELY, WE WILL COMMERCIALIZE A TRACTOR-MOUNTED OR AUTONOMOUS SYSTEM THAT APPLIES FUNGICIDES SELECTIVELY AND MONITORS DISEASE.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $0 | 6/11/25 | ||
| Not listed | $174.7k | 6/14/24 | ||
| Not listed | ($175k) | 6/4/24 | ||
| Not listed | $174.7k | 5/1/24 |