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Abstract:

This study developed an audio-video monitoring system utilizing machine-based learning to analyze colony activity and detect intrusions in Tetragonula biroi (stingless bee) hives. The system integrates sensors and embedded devices, including an ESP32-S3 microcontroller, Raspberry Pi 4 Model B, temperature and humidity sensor, microphone, load cell, and Raspberry Pi Camera Module 3, to collect environmental, acoustic, and visual data from the hive. The collected data are processed to monitor hive conditions and identify abnormal activities or potential predator intrusions in real time.

Results showed reliable environmental monitoring, stable sensor performance, and consistent data transmission. The machine learning-based intrusion detection system achieved high detection reliability, with accuracy reaching up to 85% and real-time alert notifications delivered within approximately 3–4 seconds. A web-based dashboard was also implemented to allow users to monitor hive conditions and receive alerts across multiple devices.

The system demonstrated fast response times, reliable alert delivery, and stable operation during field testing. Overall, the proposed system provides a non-invasive and automated monitoring solution that can help beekeepers detect hive disturbances early and improve colony management.

Keywords: Stingless bee (Tetragonula biroi), machine learning, audio-video analysis, hive monitoring, intrusion detection, precision apiculture