Wild Animal Detection and Alerting System

Human–wildlife conflict is a major challenge in areas where agricultural fields and human settlements border forests, often resulting in crop damage, property loss, and safety risks. Traditional preventive measures such as fencing, firecrackers, and manual patrolling are labor-intensive and unreliable. This study proposes a Wild Animal Detection and Alerting System that utilizes the YOLOv8 deep learning model integrated with Python and OpenCV for accurate real-time detection of wild animals. A Streamlit-based interface provides users with interactive dashboards for uploading images or videos, while an SQLite database stores detection records including species, timestamp, and confidence level. To ensure timely response, the system triggers both audio alarms and automated email notifications upon detection. Experimental evaluation demonstrated that the system achieves high accuracy in detecting multiple species, performs efficiently on GPU-enabled systems, and remains functional on CPU environments with slightly reduced speed. The results confirm that the system is effective in reducing human–animal conflicts by providing early alerts, supporting farmers and forest officials, and contributing to wildlife conservation. This makes the solution scalable, cost-effective, and adaptable for large-scale deployment in real-world environments.