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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM45379
Volume:
9 |
4 |
Apr 2025
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Wildlife monitoring and conservation require accurate and efficient detection of animals in real time. Traditional methods like manual tracking or using motion-triggered cameras often fall short due to low accuracy and lack of immediate response. In this project, we present an intelligent animal detection system that utilizes computer vision and deep learning with the YOLO (You Only Look Once) algorithm to identify various animal species in real time through a camera feed. The system is integrated with an Arduino via serial communication to enable physical alerts or actions based on the detected animal class. It recognizes multiple animal classes including bear, deer, elephant, lion, tiger, and more, and sends a corresponding character signal to the Arduino to trigger specific functions such as alarms or lights. This project demonstrates the potential of AI-powered object detection combined with embedded systems for real-time applications in wildlife security, forest surveillance, and human-wildlife conflict mitigation. The model also incorporates a delay timer and class comparison logic to prevent repetitive alerts for the same animal. It is designed to work reliably in outdoor conditions, including low-light and occluded environments. The alert system can be expanded using a GSM module to send SMS notifications directly to forest officials or farmers. This innovative approach enhances proactive response and contributes significantly to preserving biodiversity and protecting farmlands.
KEYWORDS
Publication Date
4/21/2025
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
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