Home▸AI & Machine Learning▸TinyML Hive Anomaly Detector

🐝 TinyML Hive Anomaly Detector

Interactive 3D hive with an embedded microcontroller where streaming simulated audio and vibration data shows how an on-device TinyML model flags varroa mite infestations.

AI & Machine Learning3DModerate60 FPS

A coin-sized sensor board inside a cutaway beehive streams simulated audio and vibration data through an on-device neural network, flagging the vibration signature of a varroa mite infestation in real time.

🔬 What It Demonstrates

Healthy hive hum is mixed with a bursty, higher-frequency varroa signature whose strength scales with infestation level. A smoothed anomaly score is compared against a sensitivity threshold to produce a live model verdict — just like an embedded TinyML classifier would.

🎮 How to Use

Raise colony activity to hear a busier hive, increase varroa infestation to inject the mite signature, and tune detection sensitivity to see how the trade-off between early warning and false alarms plays out. Switch between the microphone and accelerometer feed to compare sensors.

💡 Did You Know?

Field TinyML hive monitors can run compressed models under a few hundred kilobytes on microcontrollers that sip milliwatts of power, reporting mite risk for months on a small battery or solar cell.

⚙ Under the hood

Interactive 3D hive with an embedded microcontroller where streaming simulated audio and vibration data shows how an on-device TinyML model flags varroa mite infestations.

tinymledge-aivarroa-mitesanomaly-detectionembedded-machine-learningprecision-apicultureai-ml

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install