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📷 Computer Vision for Bee Counting and Hive Activity Monitoring

A 3D hive-entrance landing board with a camera tracking bees in and out, drawing live bounding boxes and building an activity index from detections.

AI & Machine Learning3DAdvanced60 FPS

A camera watches a hive's landing board, detecting each bee that crosses in or out, drawing a live bounding box, and rolling those detections into a running activity index — just like the small edge-computing devices real apiaries use.

🔬 What It Demonstrates

Bees crossing the green detection zone trigger a simulated model pass: a confidence roll against your threshold decides whether the bee is flagged (green box) or missed (red dashed box), and every detection feeds a rolling activity chart on the monitor panel.

🎮 How to Use

Raise the traffic rate to see busier crossings, lower the confidence threshold to see more false negatives, and flip on low-light / IR mode to see how poor lighting degrades detection — the same trade-off real hive-cam hardware has to manage.

💡 Did You Know?

Production bee-counters typically log only aggregate in/out counts from an edge device rather than streaming raw video, because a rural apiary rarely has the bandwidth to upload continuous footage.

⚙ Under the hood

A 3D hive-entrance landing board with a camera tracking bees in and out, drawing live bounding boxes and building an activity index from detections.

machine learningcomputer visionbeeshivetrackingactivity recognitionThree.js

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