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A Low-Cost, Low-Power, Multisensory Device and Multivariable Time Series Prediction for Beehive Health Monitoring.

Iraklis Rigakis1,2, Ilyas Potamitis3, Nicolas-Alexander Tatlas2

  • 1INSECTRONICS, 55 An. Mantaka Str, Chania, GR-73100 Crete, Greece.

Sensors (Basel, Switzerland)
|February 11, 2023
PubMed
Summary

This study introduces a low-cost, custom platform for long-term beehive monitoring, integrating novel sensors for real-time health alerts and future predictions. The system offers a power-efficient solution for comprehensive apiary management.

Keywords:
apis melliferabeehive monitoringremote sensingtime series prediction

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Area of Science:

  • Agricultural Science
  • Sensor Technology
  • Data Analytics

Background:

  • Existing beehive monitoring solutions often rely on expensive, ready-made platforms.
  • There is a need for cost-effective, long-term monitoring solutions with integrated, diverse sensor data.
  • Comprehensive data collection is crucial for understanding and maintaining hive health.

Purpose of the Study:

  • To develop a custom, low-cost, and power-efficient platform for long-term beehive monitoring.
  • To integrate a diverse range of sensors, including gas, vibration, and bee counters, beyond typical monitoring tools.
  • To establish a multivariable time series for real-time health alerts and predictive analysis of hive conditions.

Main Methods:

  • Designed and implemented a custom data acquisition platform integrating multiple sensors.
  • Utilized synchronous sampling every 5 minutes to create multivariable time series data.
  • Developed predictive models incorporating additional regressors for enhanced variable forecasting.

Main Results:

  • The platform successfully integrates and transmits data wirelessly to a cloud server.
  • Synchronous sampling enables immediate alerting based on predefined healthy hive boundaries.
  • Historical data analysis and predictive modeling show correlation with hive health, demonstrating the benefit of additional regressors.

Conclusions:

  • The custom platform provides a viable, low-cost alternative for comprehensive beehive monitoring.
  • Integrated sensor data and predictive analytics offer significant potential for proactive apiary management.
  • Open-sourcing the database and code facilitates further research and development in beekeeping technology.