AI for Elephant
Conservation
Using advanced acoustic sensors, computer vision, and predictive analytics to protect South Africa's giants from poaching and human conflict.
System Architecture
Real-Time Monitoring Ecosystem
Our sensor-to-server pipeline ensures rapid response times in remote areas by processing data at the edge.
Acoustic Sensors
High-fidelity microphones deployed in the canopy detect low-frequency rumbles (infrasound) unique to elephant communication.
Edge AI Processing
On-device neural networks filter wind noise and identify species instantly, sending only confirmed alerts to conserve bandwidth.
Cloud Analytics
Aggregated data creates migration heatmaps. Rangers receive instant SMS or WhatsApp coordinates for confirmed sightings.
Live Intelligence Dashboard
Tracking movement corridors in the Greater Kruger Area.
Recent Detections
Low frequency rumble detected. High probability of herd presence.
Camera Trap 04 triggered. Single large male moving North.
Faint rumble detected. Filtered for wind noise.
Seeing what human eyes miss.
Our proprietary CV models are trained on over 50,000 images of African wildlife. They can distinguish between elephants, rhinos, and vehicles in near-total darkness, reducing false alarms by 94% compared to standard motion sensors.
- check_circle Sub-second inference time on Edge devices
- check_circle Individual identification via tusk & ear patterns
- check_circle Behavior analysis (panic vs. grazing)
Project Goals & Impact
Technology is just the tool. The goal is coexistence.
Human-Wildlife Conflict
By predicting herd movement towards villages, we can alert community leaders hours in advance, allowing for non-lethal diversion tactics instead of conflict.
Anti-Poaching Support
Sudden panicked movements or silence in acoustic data can indicate poacher presence. Our system cross-references these anomalies with vehicle sounds to dispatch rangers.