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Honeybees play a vital role in pollinating a third of the world’s food and beverages, from coffee to almonds. However, bee colonies are facing significant decline due to extreme weather, pesticide exposure, and parasitic infestations.
To address this growing crisis, researchers from Carnegie Mellon University’s School of Computer Science (SCS) and the University of California, Riverside (UC Riverside) have developed an advanced system to help beekeepers monitor hive health and take timely action to prevent colony collapse. This condition, where most worker bees abandon the hive and queen, threatens global agriculture and food security. The Science Behind Hive Health Monitoring
Honeybees regulate their hive’s internal temperature between 33 and 36 degrees Celsius (91 to 97 degrees Fahrenheit) through thermoregulation. They cluster together to generate heat when it’s cold and fan their wings to cool down in hot conditions. However, when a hive faces environmental stressors like pesticides or extreme weather, it loses its ability to regulate temperature, signaling distress.
Traditionally, beekeepers rely on experience and intuition to assess hive conditions, which can lead to undetected issues. The new system, called the Electronic Bee-Veterinarian (EBV), offers a data-driven alternative. By using low-cost heat sensors and predictive forecasting, EBV provides real-time insights into hive health.
Researchers placed two sensors—one inside the hive and one outside—to continuously measure temperature fluctuations. This data is then fed into a predictive model that calculates a hive health factor, providing an easy-to-interpret measure of hive stability. A Simple Yet Powerful Approach
Christos Faloutsos, Fredkin University Professor of Computer Science at CMU, explained that the EBV system is based on principles of thermal diffusion, heat transfer, and control theory.
"We combined these equations with historical data to compute a single number: the hive health factor," Faloutsos said. "If the health factor is close to one, the bees are thriving and maintaining thermoregulation. If it drops significantly below one, the hive is in distress and may require intervention. With daily computations, beekeepers can predict and prevent potential colony losses."
Simplicity was key in designing the system, ensuring that even beekeepers with no technical background could easily interpret and act on the results. A Multidisciplinary Effort with Real-World Impact
The research team’s diverse expertise played a crucial role in developing EBV. In addition to CMU researchers, the project involved UC Riverside experts, including Shamima Hossain, a Ph.D. student in computer science; Boris Baer, a professor of entomology; Hyoseung Kim, an associate professor of electrical and computer engineering; and Vassilis Tsotras, a professor of computer science and engineering. The project was funded by the U.S. Department of Agriculture’s National Institute of Food and Agriculture and was presented at the 2024 SIAM International Conference on Data Mining.
Jeremy Lee, a doctoral student at CMU who contributed to the project, emphasized the importance of cross-disciplinary collaboration.
"This is something I’m passionate about—using computer science to solve real-world problems in other fields," Lee said. He previously worked with criminology experts to develop AI-based methods for detecting human trafficking. The Future: Automated Hive Climate Control
The next phase of the project aims to take EBV a step further by automating hive climate regulation. With additional USDA funding, Faloutsos and the UC Riverside team are researching how the system’s data can be integrated into automated heating and cooling mechanisms for beehives.
This advancement could increase honey production, enhance hive resilience against disease, and reduce manual intervention for beekeepers. By leveraging technology, researchers hope to create a sustainable solution that supports honeybee populations and strengthens global food systems.
To address this growing crisis, researchers from Carnegie Mellon University’s School of Computer Science (SCS) and the University of California, Riverside (UC Riverside) have developed an advanced system to help beekeepers monitor hive health and take timely action to prevent colony collapse. This condition, where most worker bees abandon the hive and queen, threatens global agriculture and food security. The Science Behind Hive Health Monitoring
Honeybees regulate their hive’s internal temperature between 33 and 36 degrees Celsius (91 to 97 degrees Fahrenheit) through thermoregulation. They cluster together to generate heat when it’s cold and fan their wings to cool down in hot conditions. However, when a hive faces environmental stressors like pesticides or extreme weather, it loses its ability to regulate temperature, signaling distress.
Traditionally, beekeepers rely on experience and intuition to assess hive conditions, which can lead to undetected issues. The new system, called the Electronic Bee-Veterinarian (EBV), offers a data-driven alternative. By using low-cost heat sensors and predictive forecasting, EBV provides real-time insights into hive health.
Researchers placed two sensors—one inside the hive and one outside—to continuously measure temperature fluctuations. This data is then fed into a predictive model that calculates a hive health factor, providing an easy-to-interpret measure of hive stability. A Simple Yet Powerful Approach
Christos Faloutsos, Fredkin University Professor of Computer Science at CMU, explained that the EBV system is based on principles of thermal diffusion, heat transfer, and control theory.
"We combined these equations with historical data to compute a single number: the hive health factor," Faloutsos said. "If the health factor is close to one, the bees are thriving and maintaining thermoregulation. If it drops significantly below one, the hive is in distress and may require intervention. With daily computations, beekeepers can predict and prevent potential colony losses."
Simplicity was key in designing the system, ensuring that even beekeepers with no technical background could easily interpret and act on the results. A Multidisciplinary Effort with Real-World Impact
The research team’s diverse expertise played a crucial role in developing EBV. In addition to CMU researchers, the project involved UC Riverside experts, including Shamima Hossain, a Ph.D. student in computer science; Boris Baer, a professor of entomology; Hyoseung Kim, an associate professor of electrical and computer engineering; and Vassilis Tsotras, a professor of computer science and engineering. The project was funded by the U.S. Department of Agriculture’s National Institute of Food and Agriculture and was presented at the 2024 SIAM International Conference on Data Mining.
Jeremy Lee, a doctoral student at CMU who contributed to the project, emphasized the importance of cross-disciplinary collaboration.
"This is something I’m passionate about—using computer science to solve real-world problems in other fields," Lee said. He previously worked with criminology experts to develop AI-based methods for detecting human trafficking. The Future: Automated Hive Climate Control
The next phase of the project aims to take EBV a step further by automating hive climate regulation. With additional USDA funding, Faloutsos and the UC Riverside team are researching how the system’s data can be integrated into automated heating and cooling mechanisms for beehives.
This advancement could increase honey production, enhance hive resilience against disease, and reduce manual intervention for beekeepers. By leveraging technology, researchers hope to create a sustainable solution that supports honeybee populations and strengthens global food systems.