Project Grant 2524622
- This $596,594 Project Grant awarded by the National Science Foundation's Biological Sciences (CFDA 47.074) Federal Grant Program will develop artificial intelligence-based tools to automate the imaging and identification of ground-dwelling arthropods, such as insects and other arthropods, in research samples. The project aims to greatly minimize the time and expertise required to process large sample collections and unlock vast amounts of undiscovered biodiversity data that is currently...
- The National Science Foundation (NSF) awarded a $249,947 Project Grant under its Biological Sciences (CFDA 47.074) federal grant program to the University of Arkansas in Fayetteville, AR. The grant funds a collaborative research project to develop and implement non-lethal methods for monitoring wild bee pollinators in managed forests using acoustic and camera-based artificial intelligence (AI) technologies. The goal is to create user-friendly software that can track pollinator activity...
- The National Science Foundation (NSF) awarded a $230,552 Project Grant under the Integrative Activities program (CFDA 47.083) to the University of Maine System. The goal of this 2-year grant, with a performance period from January 1, 2025 to December 31, 2026, is to leverage artificial intelligence (AI) to extract information on beetles from imagery generated by the NSF-funded National Ecological Observatory Network (NEON). This effort aims to fuel scientific discovery related to biodiversity...
- This $198,141 project grant awarded by the National Science Foundation's Biological Sciences program (CFDA 47.074) seeks to enhance predictive understanding of global species distributions through the development of advanced generative artificial intelligence (AI) models. The project aims to create a versatile foundation model that can be used by scientists, conservationists, and educators to better understand and protect the natural world. By training this model on a vast array of environmental...
- The National Science Foundation (NSF) awarded a $1,040,107 project grant under its Biological Sciences (CFDA 47.074) Federal Grant Program to the University of Idaho. The grant will fund the development of an artificial intelligence-powered instrument that can identify small mammal species, discriminate between individuals, and measure their body weight and morphological traits in real-time without the need for animal capture and handling. This innovative technology will significantly reduce the...
- The National Science Foundation (NSF) awarded a $981,168 Cooperative Agreement under its Technology, Innovation, and Partnerships (CFDA 47.084) program to Solarid Ar LLC, a minority-owned, veteran-owned, Asian-Pacific American-owned limited liability company. This 2-year project aims to develop an artificial intelligence (AI)-driven insect trapping and identification system that can detect, identify, and quantify insects entering insect control devices in real-time. The primary objective is to...
- The National Science Foundation (NSF) Biological Sciences (CFDA 47.074) Federal Grant Program awarded the Division of Agriculture of the University of Arkansas a $399,831 project grant titled "STAR: MODELING THE PHYLOGENETIC ARCHITECTURE OF BIODIVERSITY". The grant will support an integrated analytics initiative to synergize evolutionary biology and statistical learning in order to address key challenges in biodiversity research. The project will develop new statistical solutions for...
- This $207,291 four-year Project Grant award from the National Science Foundation's Biological Sciences program (CFDA 47.074) supports research to develop Artificial Intelligence (AI) tools to analyze camera trap imagery of animals with seasonal camouflage adaptations. The research aims to understand how these species cope with environmental variability, including changes in snow cover, and remain camouflaged against their backgrounds. The project will create publicly available AI tools to assess...
- This Project Grant award from the National Science Foundation (CFDA 47.074 - Biological Sciences) provides $797,762 to Mississippi State University to develop and implement non-lethal methods for monitoring pollinator species in forests using acoustics and camera-based artificial intelligence (AI). The key goals of this 4-year project are to: 1) Create AI-powered software that can automatically identify pollinator species in real-time, allowing scientists and conservationists to track changes in...
- 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 National Science Foundation (NSF) Biological Sciences Program (CFDA 47.074) $785,872 Project Grant will develop artificial intelligence-based tools to automate the imaging and identification of arthropods (insects and other invertebrates) collected from terrestrial habitats. The project aims to minimize the time and expertise required to process large sample collections, which currently contain hundreds of millions of unidentified specimens representing vast undiscovered biodiversity. By automating the imaging and identification of these samples, the research will extract more data than previously possible and unlock significant biodiversity information from the backlog of collected specimens. The project is being conducted by the Division of Agriculture at the University of Arkansas, a state-controlled land grant institution, over a 3-year period from October 2025 to September 2028. Successful development of these AI-powered identification tools could have broad impacts on ecology, biodiversity research, and the advancement of AI technologies across various domains.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $785.9k | 8/14/25 |