This $305,007 federal Project Grant awarded by the National Science Foundation (NSF) Biological Sciences program (CFDA 47.074) aims to develop an automated computer vision system for identifying North American bee species. The key objectives are to: Create a large, expertly-labeled image dataset of at least 1,000 North American bee species to train the computer vision algorithms. Develop an AI-based classification model using convolutional neural networks to identify bee species from images....
This Project Grant award, valued at $300,000.00, was provided by the U.S. Department of Agriculture's (USDA) National Institute of Food and Agriculture through the Agriculture and Food Research Initiative (AFRI) program (CFDA 10.310). The grant supports the development and testing of an "Electronic Bee-Veterinarian (eBeeVet)" sensor framework specifically designed for beehives used by the majority of beekeepers. The framework will utilize machine learning methods to analyze data and...
This federal Project Grant award from the United States Department of Agriculture's (USDA) Agriculture and Food Research Initiative (AFRI) (CFDA #10.310) provides $700,000 to develop and test an "Electronic Bee-Veterinarian (eBeeVet)" sensor framework for honeybee hives. The project, led by the Regents of the University of California at Riverside, aims to create a system that utilizes machine learning to analyze hive data and provide timely solutions for addressing the significant...
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...
This Project Grant award from the National Science Foundation (NSF) Biological Sciences program (CFDA 47.074) supports the development of a research hub for automated bee identification, data sharing, and citizen science using computer vision technology. The University of Kansas Center for Research Inc. will receive $342,866 over 3 years to achieve the following key objectives: Create a large, expertly-labeled image dataset of at least 1,000 North American bee species to train computer vision...
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...
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 $372,442 National Science Foundation (NSF) Biological Sciences (CFDA 47.074) Project Grant award to Kansas State University will develop a research hub for automated bee identification, data sharing, and citizen science using computer vision technology. The key objectives are to: Create a large, expertly labeled image dataset of over 1,000 North American bee species to train machine learning models. Develop an AI-based classification model using convolutional neural networks to identify bee...
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 federal Project Grant award, totaling $587,749.00, was made by the U.S. Department of Agriculture's (USDA) National Institute of Food and Agriculture (NIFA) through the Agriculture and Food Research Initiative (AFRI) program. The award will support research by Virginia Polytechnic Institute & State University (Virginia Tech) with the long-term goal of developing automated pest detection and surveillance capabilities for crops. The project aims to optimize low-cost samplers for...