• A Nidhi
• B Aditya
• B Sai Krishna
• Sk Afeef Ur Rehman
• Dr Ravi Mathey

This study endeavors to autonomously identify crucial characteristics in wild animal detection without human intervention. Utilizing a wealth of wildlife images, the system autonomously learns distinctive features for various animal species, harnessing the computational efficiency of YOLOv8. In this article, we propose a novel implementation of a wild animal detection and alert system employing YOLOv8. Deep learning models like YOLOv8 have demonstrated remarkable efficacy in identifying animals in diverse habitats. By integrating with Telegram, the system disseminates real-time alerts to a designated group, accommodating an unlimited number of participants. This integration holds promise for enhancing wildlife conservation efforts, providing timely notifications to stakeholders and facilitating swift responses to wildlife sightings, thereby bolstering conservation initiatives and minimizing human-wildlife conflicts.

Detection of wildlife, identification of objects, employing YOLOv8, and continuous real-time surveillance

"Wild Animal Detection and Alert System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.a175-a180, April-2024, Available :http://www.jetir.org/papers/JETIR2404025.pdf

"Wild Animal Detection and Alert System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppa175-a180, April-2024, Available at : http://www.jetir.org/papers/JETIR2404025.pdf