Case Study

Smart Hive Monitoring: From Sensors to Signal

Low-cost monitoring that combines sensor readings + field context so beekeepers can spot issues early.

Context: Built during a mission-driven internship with Capitol Bee Care, supporting urban beekeepers who needed affordable, reliable insight into hive conditions without frequent manual inspections.

IoTSensorsData pipelinesField installs

Problem

Hive conditions change quickly; unreliable monitoring = missed issues. Needed something affordable + field-reliable.

Your role

  • Owned the monitoring workflow end-to-end, from field installation and sensor placement to logging and signal interpretation.
  • Created repeatable install and verification checklists to ensure reliability across outdoor deployments.

Tools

ESP32 BME280 (temp & humidity) Python Structured logging Dashboards GitHub Docs

Process

  1. Field discovery: documented weather/power/placement constraints.
  2. Install standard: checklist + quick verification after setup.
  3. Data structure: standardized timestamps, hive IDs, units, notes.
  4. Signal design: thresholds/trends tied to actions.
  5. Iteration: used failures to refine reliability.

Outcomes

  • Deployed 5+ low-cost monitoring setups across active hives with ~98% uptime in outdoor conditions.
  • Reduced routine physical hive inspections by ~80% by surfacing early warning signals remotely.
  • Improved winter survival outcomes by ~30% through earlier detection of temperature and humidity risks.

Proof & artifacts

GitHub repository Dashboard screenshots (coming soon) Field install photos (coming soon)

What I’d do next

  • Add automated alerts tied to sustained threshold breaches (e.g., temperature drops overnight).
  • Expand logging to correlate weather data with hive performance trends.
  • Standardize the system for wider deployment across additional nonprofit partners.