Performing Department
(N/A)
Non Technical Summary
Honey bees are not only of crucial importance for ecosystem stability but also for the human food supply. Their services are required for the production of more than 80 crops of agricultural interest or about a third of what we eat. Their yearly global value has been estimated to be up to $550 billion globally and $29 billion for the United States. However, drastic declines of honeybee populations have been documented over thepast two decades.This project proposes to develop and test Electronic Bee-Veterinarian (EbeeVet), a functional sensorframework that is specifically designed for bee hives and is fully integrated into the hive setups used by the majority of beekeepers, known as Langstroth hives. The framework will consist of machine learning (ML) methods to analyze a wealth of data and to propose timely and appropriate solutions, as well as scalable data management systems for multiple hives, and apiaries and cost-effective and self-sustainable sensing devices.
Animal Health Component
30%
Research Effort Categories
Basic
40%
Applied
30%
Developmental
30%
Goals / Objectives
How can machine learning technology be used to maximize the health of honey bee colonies?Honey bees are not only of crucial importance for ecosystem stability but also for the human food supply.Their services are required for the production of more than 80 crops of agricultural interest or about a thirdof what we eat. Their yearly global value has been estimated to be up to $550 billion globally and $29 billionfor the United States. However, drastic declines of honeybee populations have been documented over thepast two decades.This project proposes to develop and test Electronic Bee-Veterinarian (EbeeVet), a functional sensorframework that is specifically designed for bee hives and is fully integrated into the hive setups used by themajority of beekeepers, known as Langstroth hives. The framework will consist of machine learning (ML)methods to analyze a wealth of data and to propose timely and appropriate solutions, as well as scalabledata management systems for multiple hives, and apiaries and cost-effective and self-sustainable sensingdevices. The PI team consists of a unique combination of experts, including biologists, computer engineers,and machine learning experts. The team has collected over 10 years' worth of sensor data (temperature,humidity, etc) from multiple bee colonies, providing a solid foundation for in-depth analysis and significanttechnological advancements.
Project Methods
** EFFORTSWe plan to develop state-of-the-art machine-learning methods for measuring and monitoring the health of bee-hives. The goal is to issue early-warnings to the bee-keepers, when we notice anomalous behavior, like unexpected temperature or humidity inside the hive. As mentioned in the proposal, we will continue our practice of disseminating our results through graduate courses, bee-health conferences,and publications in machine learning and database conferences and journals.** EVALUATIONOur project involves a thorough plan for individual thrust evaluation as well as integrated system validation. We will implement our approaches and provide tools, documents, and datasets in public.* Testbeds.We will leverage the experimental apiary located at UCR for our testbed. This is being managed by the CIBER center led by PI Baer and other PIs Kim and Tsotras also have access to the apiary and have been working with beekeepers and entomologists to help understand honeybee health using wireless sensors and real-time data analysis and control.* Evaluation of Individual Tasks (Thrusts 1 - 3).Each of the research tasks will first be evaluated individually relative to existing results in the relevant literature. We plan to make use of open-source tools, datasets, and the aforementioned testbed in the individual evaluation.* Integrated System Evaluation (Thrust 4).We will follow the procedures discussed in Sec. 4 for system integration and validation. The results from the UCR testbed will be shared with our beekeeper collaborators (see Facilities and letters of collaboration).