// EDGE AI + IoT
ESP32-based edge AI system for real-time beehive health monitoring using environmental, weight, and acoustic sensing.
Built an ESP32-based IoT monitoring system for beehive health analysis by combining environmental, weight, and acoustic sensors. Developed an MFCC-based audio classification pipeline to analyze bee sounds and support lightweight edge inference.
Beehive health can be affected by changes in environmental conditions, colony activity, and acoustic behavior. Continuous monitoring across multiple sensor modalities can provide useful signals for detecting changes in hive conditions without relying entirely on manual inspection.
Name
Beehive Sensor & Audio Data
Metadata Matrix Size
Project-specific
Telemetry Source
ESP32 sensor collection
Pipeline Preprocessing Steps
Core Pattern
MFCC-based Audio Classification
Technology Stack
Python + ESP32
Key Components
Sensor Acquisition
Collect temperature, humidity, hive weight, and acoustic data using ESP32-connected sensors.
Environmental Monitoring
Record DHT22 environmental measurements and HX711-based hive weight readings.
Audio Recording
Capture bee sounds using a microphone connected to the embedded monitoring system.
Audio Feature Extraction
Process bee sounds and extract MFCC features for acoustic classification.
Edge Inference
Perform lightweight audio classification suitable for resource-constrained edge deployment.
Data Storage
Store collected hive monitoring data for visualization and further analysis.
3+
Sensors
ESP32
Platform