Project Grant 20246701442301
- This Project Grant award for $249,947.00 from the National Science Foundation's Biological Sciences (CFDA 47.074) program supports the development, testing, and implementation of non-lethal methods to monitor pollinator populations in managed forests using acoustic and camera-based artificial intelligence (AI) technology. The primary goal is to create easy-to-use software that can track pollinator activity patterns, providing valuable insights to inform evidence-based forest management practices...
- This Project Grant award from the National Science Foundation (NSF) Biological Sciences program (CFDA 47.074) provides $372,442 to Kansas State University to develop an automated bee identification and data sharing platform. The key objectives are: Creating a large, expertly-labeled image dataset of at least 1,000 North American bee species to train computer vision algorithms. Developing an AI-based classification model using convolutional neural networks to identify bee species from images....
- This federal Project Grant award of $649,999 from the United States Department of Agriculture's (USDA) Agriculture and Food Research Initiative (AFRI) program will support research to develop methods for determining the value of pollination services by wild and managed pollinators, as well as efficient levels and locations of wild pollinator habitat preservation and managed pollinator stocking rates. The project, which will be carried out by the University of Maryland, College Park, the...
- This $797,762 Project Grant awarded by the National Science Foundation (CFDA 47.074 Biological Sciences) to Mississippi State University seeks to develop non-lethal methods for monitoring bee pollinators in managed forests using acoustics and camera-based artificial intelligence. The primary goal is to create easy-to-use software that can help track pollinator activity patterns, providing valuable insights to support evidence-based forest management practices and pollinator conservation efforts....
- This Project Grant awarded by the National Science Foundation (NSF) Biological Sciences program (CFDA 47.074) aims to develop a large dataset of expertly labeled bee images, create an AI-based classification model to identify bee species, and establish a research hub website for automated bee identification, data sharing, and citizen science. The total funding amount is $342,866.00, to be provided over the award period from August 15, 2024 to July 31, 2027. The University of Kansas Center for...
- This $305,007 federal Project Grant award from the National Science Foundation's Biological Sciences (CFDA 47.074) program will support the development of a research hub for automated bee identification, data sharing, and citizen science using computer vision technology. The key products and services to be delivered under this 3-year award include: Creation of a large, expertly-labeled image dataset of at least 1,000 North American bee species to train an AI-based bee classification model...
- The U.S. Department of Agriculture's Agriculture and Food Research Initiative (CFDA 10.310) awarded a $435,178 project grant to Utah State University to explore and exploit a reservoir of multi-sensor data collected from field experiments with managed honey bee colonies. The project will curate this dataset, which includes thousands of frame photographs and millions of sensor measurements, and make it publicly available to the U.S. precision apiculture community. The university will use...
- This Project Grant award of $750,000.00 from the USDA National Institute of Food and Agriculture under the 1890 Institution Capacity Building Grants Program (CFDA 10.216) supports research at Central State University, an 1890 land-grant institution, to study the impacts of transportation on the health and behavior of honeybees, a critical managed pollinator for U.S. agriculture. The key objectives of the 3-year project are to: 1) Detect viral prevalence and distribution in transported honeybee...
- This $300,000 Project Grant award from the U.S. Department of Agriculture's Agriculture and Food Research Initiative (CFDA 10.310) will support the development and testing of the "eBeevet" functional sensor framework designed for bee hives. The framework will utilize machine learning methods to analyze data and propose solutions to address the drastic declines in honeybee populations that have occurred over the past two decades. The project aims to create scalable data management...
- This $742,173 Project Grant award from the USDA National Institute of Food and Agriculture (CFDA 10.310, Agriculture and Food Research Initiative) to Colorado State University will support a 4-year research initiative to evaluate interactions between wild bee populations and managed honeybees in peri-urban landscapes of the American West. The research aims to (1) characterize the extent and strength of resource overlap and competition between wild bees and honeybees, (2) determine if honeybees...
This Project Grant award, with a total funding amount of $749,086, was provided by the U.S. Department of Agriculture's (USDA) Agriculture and Food Research Initiative (AFRI) to Villanova University. The project aims to address two of the USDA's Pollinator Health priorities - understanding factors that influence pollinator abundance and diversity, and developing innovative tools and management practices to support healthy pollinators. Specifically, the project will use unmanned aerial vehicles (UAVs) and artificial intelligence (AI) to provide a critical understanding of the relationship between pollinator abundance and floral presence across agricultural fields, prairies, and other land areas. The researchers will develop AI image detection and classification models to accurately identify and quantify both pollinator (Bombus) and flower species from aerial imagery. A free, public web application will also be created to enable experts and non-experts to leverage these AI models. By matching pollinators with their host flowers and evaluating best practices for UAV-based real-time monitoring, the project aims to provide researchers, land managers, and environmental organizations with the tools needed to observe and make more informed decisions about pollinator health and conservation. This grant includes a sub-award to Kansas State University, where researchers will assist in developing the AI image classifiers and coordinate the use of high-performance computing resources. Additional entomology experts from Kansas State will provide guidance on the image datasets, neural network models, and field data collection.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $749.1k | 4/30/24 |
GrantNumber | Description | Subgrantee | Prime Award | Dollars Obligated | Updated At |
|---|---|---|---|---|---|
530193KANSASMARCH2025S | Kansas State University | Project Grant 20246701442301 | $163.0k | 3/20/25 | |
530193KANSASS | Kansas State University | Project Grant 20246701442301 | $167.9k | 7/22/24 |