CAREER: Enhancing Honey Bee Health and Productivity through Vibroacoustic and Electric Field Diagnostics

About This Grant

This Faculty Early Career Development Program (CAREER) award supports research, education, and outreach activities dedicated to understanding and improving the health and swarming behavior of honeybee colonies. Honeybees are essential pollinators, supporting over 35 percent of global crop production and contributing $15 billion annually to the US agricultural economy. Despite their importance, bee populations face significant declines due to environmental stressors such as pesticides, pathogens, and climate change. Research activities under this award will focus on creating novel diagnostic methodologies that integrate vibroacoustic and electric field measurements to monitor the health of bee colonies. These methodologies aim to reduce labor costs and increase the effectiveness of beekeeping practices. Educational activities will engage students in bioinspired engineering through hands-on experiences and create graduate-level course modules covering vibroacoustics, mathematical modeling, and signal processing. Outreach activities will disseminate findings to academic and beekeeping communities, enhancing the societal relevance of bioinspired engineering systems while broadly promoting STEM education. This research aims to develop a new methodology to accurately assess the dynamic responses of honeybee colonies to external stressors and environmental changes, including prediction of the swarming behavior triggered by the arrival of new queens. The approach combines mathematical modeling, vibroacoustic and electric field signal processing, and machine learning algorithms to create a robust data-driven platform for real-time bee colony monitoring and predictive swarm management. By harnessing the vibroacoustic responses, communication patterns, and electric field signals of honeybees, this research will pioneer a methodological framework for the bioinspired engineering systems. This framework will facilitate the development of a data processing platform equipped with machine learning algorithms capable of analyzing and interpreting diverse data on honeybee dynamics. These advancements will pave a new way for precision beekeeping, real-time monitoring and proactive management of colonies that will improve crop pollination and honey production, reduce labor costs for beekeepers, and support honeybee conservation efforts. This project is jointly funded by the Dynamics, Control and Systems Diagnostics (DCSD) program, and the Established Program to Stimulate Competitive Research (EPSCoR). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.