AI Based Smart Animal Tracking and Detection with Multi-Faceted Alert System

Abstract

Recent developments in Internet of Things (IoT) combined with artificial intelligence (AI) have produced creative solutions in a number of fields, including animal conservation. This paper proposes a novel smart animal detection system that leverages the capabilities of the YOLOv8 AI model for real-time object detection and IoT integration for immediate response mechanisms. By training the YOLOv8 deep learning algorithm on a comprehensive dataset of wildlife images, the system can achieve accurate identification of animals in diverse environments. The integration of IoT devices further enhances functionality by enabling rapid response actions upon detection. These IoT devices include buzzers, automated phone calls and SMS alerts, which are triggered based on the severity of the detected event. For example, detecting a potential threat such as poaching or habitat destruction can trigger a loud buzzer to deter intruders and simultaneously notify authorities via phone calls and SMS, providing real-time information for quick intervention. This system presents significant advantages over traditional wildlife monitoring methods, including real-time threat detection, prompt alert delivery to relevant stakeholders, and adaptability for deployment in various environments. Thus, it represents a versatile and effective tool for wildlife conservation efforts.

Keywords: AI, Automated alert system, IoT, Real-time object detection, Wildlife econservation, YOLOv8.

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