DuckWild Wildlife Tracking System
Sept. 2025 – Present
A low-power wildlife monitoring system that uses on-device computer vision and a LoRa mesh network to detect and identify animals without relying on cellular or Wi-Fi connectivity.
Project Overview
DuckWild is an off-grid wildlife monitoring system that combines edge AI and long-range mesh networking. The system uses a Raspberry Pi, motion sensing, and an infrared camera to detect animals, identify species locally, and transmit lightweight data packets over a LoRa mesh network using the ClusterDuck Protocol. The goal is to enable wildlife tracking in remote environments without relying on cellular, Wi-Fi, or manual SD card retrieval.
The Problem
- Traditional wildlife cameras store images locally, requiring physical retrieval
- Cellular models are expensive and unreliable in remote areas
- Most capture far more useless data than actionable insight
- DuckWild filters at the edge—sending structured data instead of raw images
System Architecture
Edge node: Raspberry Pi + IR camera + PIR motion sensor
AI pipeline: MegaDetector → SpeciesNet for species classification
Networking: LoRa radio (SX1262) using the ClusterDuck Protocol
Mesh routing: Packets hop node-to-node until reaching a gateway (PapaDuck)
What We’ve Built So Far
- Python-based ClusterDuck implementation for Raspberry Pi
- Successful serialization and transmission of ClusterDuck-compatible packets over LoRa
- Working computer vision pipeline running fully on-device
- ~94% species classification accuracy on a real trail-camera dataset
- Power-aware design decisions keeping average consumption under ~5W
Key Technical Decisions
- Reimplemented ClusterDuck in Python instead of modifying upstream C++ for maintainability and reuse
- Dropped hardware AI accelerators due to power cost with little benefit for solar operation
- Motion-triggered, event-driven inference so AI only runs when needed
My Contributions
- Helped design the system state machine and overall software flow
- Responsible for motion sensing -> camera capture pipeline
- Implemented a systemd service to manage dependencies, start the networking stack on boot, and automatically restart on failure, enabling reliable system bring-up
- Contributed to ClusterDuck packet handling and Raspberry Pi compatibility
- Made power and architecture decisions based on real measurements
Current Focus / What’s Next
- Full integration of AI pipeline + mesh networking
- Solar and battery system bring-up
- Weatherproof enclosure design
- Long-range field testing with multiple nodes
- Public demo video + open-source release
Why This Project Matters
DuckWild shows how embedded systems, networking, and AI intersect in the real world. It’s not about maximizing benchmarks—it’s about making technology usable where infrastructure doesn’t exist.