Integrating machine learning and IoT for real-Time wildlife tracking and crowd sourcing

Abstract

This research introduces a smart system to enhance wildlife safari experiences by integrating real-Time location tracking, animal behavior prediction, and emergency communication technologies. Traditional safari tours rely heavily on human guides, often leading to inefficiencies in wildlife spotting and navigation. Proposed system employs machine learning, GPS tracking, and voice assistance to provide an interactive and informative safari experience. The machine learning algorithm uses past and current movement patterns of animals and generates predictive information to guide tourists to optimal wildlife observation points. The system uses LoRa-based offline communication to ensure seamless connectivity in network-poor regions, facilitating smooth coordination between safari vehicles and park authorities. The voice guidance feature also enhances accessibility by providing real-Time educational content on observed wildlife. This study adds to wildlife tourism with a technology-based framework that improves visitor experience, minimizes environmental footprint by route optimization, and aids conservation through data-driven monitoring of wildlife behavior.