Researchers have developed a low-cost sensor system to help beekeepers monitor hive health and prevent colony collapse—a major step in protecting honeybee populations.
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Honeybees play a crucial role in pollinating about a third of the food and drinks we consume—from coffee to almonds. But their colonies are increasingly at risk due to extreme weather, pesticides, and parasites.
To help beekeepers monitor hive health and prevent colony collapse, researchers from Carnegie Mellon University's School of Computer Science (SCS) and the University of California, Riverside (UC Riverside) have developed a new system that provides real-time insights into hive conditions. Colony collapse occurs when most worker bees abandon the hive and its queen, often leaving the colony unable to survive.
Beehives naturally regulate their temperature, keeping it between 33 and 36 degrees Celsius (91 to 97 degrees Fahrenheit). Bees cluster together for warmth in cold weather and fan their wings to cool the hive when it's hot. However, external stressors—like pesticides or sudden weather changes—can disrupt this thermoregulation, signaling trouble. Traditionally, beekeepers rely on experience and observation to assess hive health, but this method can miss early warning signs.
The new system, called the Electronic Bee-Veterinarian (EBV), uses low-cost heat sensors and predictive forecasting to track hive temperature and overall colony health. Researchers placed one sensor inside the hive and another outside to measure real-time temperature changes. This data feeds into a model that calculates a "hive health factor," providing beekeepers with a simple, daily metric to assess their colonies.
Christos Faloutsos, the Fredkin University Professor of Computer Science at CMU, explained that the team based their health forecasting model on fundamental principles of heat transfer and control theory.
We derived equations based on the first principles of thermal diffusion, heat transfer, and control theory. We put these equations together and then compressed all the historical data into one number, the hive health factor. If the health factor is close to one, the bees are healthy and thermoregulating. If it is much lower than one, it means the beehive isn't healthy and might need an intervention. Once we have this health factor computed every day, we can do standard forecasting and the beekeeper can take further action.
Christos Faloutsos, Study Lead and Fredkin Professor, Department of Computer Science, Carnegie Mellon University
Simplicity was a priority for the team, Faloutsos noted. The goal was to make the system accessible to beekeepers of all experience levels by summarizing complex data into one easy-to-understand number.
This is something I'm very interested in — using our expertise from computer science and working with other domain experts to make an impact in another area.
Jeremy Lee, Doctoral Research Assistant, Department of Computer Science, Carnegie Mellon University
This isn’t Lee’s first experience using computational methods for real-world challenges—he and his colleagues at CMU and McGill University previously worked with criminology experts to detect human trafficking.
The next phase of the project aims to automate hive climate control. Faloutsos and the UC Riverside team have secured additional USDA funding to explore how EBV data can be used to automatically regulate hive temperature, reducing the need for manual intervention. By improving climate control, the system could help beekeepers boost honey production and better protect their colonies from future threats, including diseases.