SENSORBEES equips standard beehives with sensors that monitor queen activity, worker behavior, and brood dynamics to turn honeybee colonies into a distributed ecological monitoring network. By analyzing hive‑entrance data and weather inputs, the system estimates regional pollination services and plant diversity across up to 10 km (≈314 km²) and can detect colony health issues or pest infestations.
Funding
Funding not disclosed
Founders
Product
Problem
Ecological monitoring of pollination services and plant diversity is limited by sparse, labor-intensive measurements, while beekeepers lack real-time insight into colony health and pest pressures.
Solution
SENSORBEES transforms conventional beehives into autonomous sensor platforms that continuously record queen activity, worker behavior, brood development, and hive‑entrance payloads. By analyzing foraging trips up to 10 km from the hive, the system infers regional pollination levels and the diversity of flowering plants across an area of roughly 314 km². Integrated anomaly detection compares behavioral and environmental data streams to flag colony health issues such as pest infestations or resource shortages. Data from multiple hives are fused to map ecosystem‑wide feeding hotspots and potential problem sites, providing actionable information for researchers, land managers, and beekeepers.
Target Audience
Primary customers are ecological researchers, agricultural monitoring agencies, and commercial beekeepers who require detailed, continuous data on pollination services and colony health.
Features
- Multi‑modal sensor suite monitoring queen presence, worker traffic, brood temperature, and inbound/outbound pollen and nectar loads
- GPS‑linked foraging range analysis that quantifies pollination intensity and plant species diversity within a 10 km radius
- Real‑time health diagnostics using machine‑learning models to detect anomalies in brood growth, activity patterns, and resource fluctuations
- Weather data integration for contextualizing hive behavior and improving model accuracy
- Scalable data fusion across multiple colonies to generate spatial maps of pollination services and identify ecosystem stress points
- Secure cloud storage and API access for researchers and agricultural stakeholders to retrieve processed ecological metrics