Experimental study demonstrates 91% gunshot detection accuracy in forest environments using low-power edge AI, suggesting a scalable tool to combat illegal poaching.
Illegal poaching in remote forest regions poses a critical threat to global biodiversity, with traditional monitoring methods such as manual patrols and camera traps proving inadequate due to delayed response times and limited coverage. This paper presents a novel low-power, AI-Powered Edge-Based Gunshot Detection (AI-PEGD) designed for deployment in harsh forest environments. The system employs edge AI computing, utilizing a Raspberry Pi Pico microcontroller interfaced with an INMP441 digital MEMS microphone for real-time audio acquisition and processing. A lightweight TensorFlow Lite model, optimized for microcontroller deployment, performs on-device gunshot classification with minimal power consumption. Detection alerts are transmitted wirelessly via nRF24L01 modules to a centralized STM32-based receiver for event logging and monitoring. The hardware is protected by a biodegradable weather-resistant enclosure fabricated from coconut fiber-epoxy composites. Field testing demonstrates the system’s effectiveness in detecting gunshots with 94.2% accuracy in Line of Sight with less than 200 ms response time and system also tested in forest region and observed the accuracy of 91% while maintaining sustainable operation in remote forest environments. This interdisciplinary approach combines machine learning, embedded systems, and sustainable materials to provide a scalable solution for wildlife protection in the Indian context.
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