
Animal Detection System using AI/ML
Forest departments monitoring animal movement along forest boundaries had no automated way to detect intrusions near human settlements, highways, or railway lines. Manual patrols were infrequent and slow when elephants, leopards, or other large animals crossed into monitored zones, alerts reached patrol teams too late. This delay directly contributed to human-animal conflict incidents and road accidents.
We designed and deployed a real-time animal detection system that runs entirely on edge hardware, no cloud dependency required. IP cameras with night vision feed live footage into an ARM Cortex-A55 edge device running a custom-trained object detection model. The moment an animal is detected, the system instantly sends a WhatsApp alert with a snapshot image, species label, zone ID, and GPS stamp to forest patrol teams and highway authorities via 4G. The system runs 24x7, supports multiple simultaneous camera zones, and was field-deployed at pilot forest boundary locations with calibrated confidence thresholds to minimize false positives.
Edge-based ML inference delivers alerts within seconds no cloud latency, no connectivity bottleneck. Patrol teams receive instant WhatsApp snapshots with no additional hardware or app setup required. A single edge device manages multiple camera zones simultaneously, keeping costs low as coverage scales. The system was fully handed over with operator training, a configuration dashboard, and remote firmware update support, the forest department runs it independently.


24/7
Continuous surveillance across all camera zones, including night hours, covering the window manual patrols could never match.
<5 Sec
From animal detection to WhatsApp alert with snapshot patrol teams are notified before a conflict can escalate.
100% Edge
All ML inference runs locally on the ARM Cortex-A55, no cloud dependency, no latency, works in the most remote forest zones.
Let's build the future, together.
Fullstack engineering partner: from hardware to cloud to Vision AI.
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