CyIndiBee

Collaborative investigation on AI development for the study of Puerto Rican honeybees, sponsored by the National Science Foundation.

Monitoring bees with Artificial Intelligence (AI) for long-term climate change research

The CyIndiBee project studies large amounts of Puerto Rican honeybees, at the individual level, through the creation of innovative tools powered by modern AI technologies.

Pollinators play a critical role in sustaining the lives of plants, animals and humans alike. Climate change and other anthropogenic activities are endangering pollinators and their habitats, causing behavioral changes.

This research gains insight into pollinator insects’ behaviors and ecosystems, while developing advanced digital infrastructure for detailed, long-term collaborative investigations.

Leading AI research in the Caribbean

This project is a first step towards positioning the University of Puerto Rico, Río Piedras (UPR-RP) campus as the leading research center, in the Caribbean, for AI applied to transdisciplinary investigations on Climate Change.

The UPR-RP, a hispanic Minority Serving Institution (MSI), creates a deep network of professors and students conducting state-of-the-art investigations, by integrating undergraduate and graduate training into its research-intensive activities.

This project is jointly funded by the CISE MSI Research Expansion Program and the Established Program to Stimulate Competitive Research (EPSCoR), awarded by the National Science Foundation (NSF).

News and updates

CyIndiBee's research

AI models that identify and classify bees

The deep learning models analyze noisy, unannotated data to extract information about the bee’s behavior, phenotype and identity.

Web application for behavior analysis with graphical interfaces

The platform integrates interactive dashboards that allow the researcher to visualize, study and annotate data, collected from pollinator video monitoring stations.

Data for the analysis of long-term individual bee behavior

The research involves two new curated datasets: a large-scale honeybee identification dataset, and a foraging behavior dataset, which span multiple colonies and foraging trips.

Related projects

BigDBee

Barcode tagging, video and sensor monitoring, and long-term tracking of individual bees during their foraging lifespan

DeepPollinator

The platform integrates interactive dashboards that allow the researcher to visualize, study and annotate data, collected from pollinator video monitoring stations.

For information on our investigators, professors and participants: