| Summary: | Nowadays, educational institutions particularly colleges, engaged with students and staff, frequently confront various security challenges in their day-to-day activities. One prominent concern involves the threat of animal bites on the campus. In response to this issue, campus management has traditionally resorted to human patrols and physical barriers to deter animals. To address this multifaceted security challenge, the proposed method “An IoT-based Animal Detection System Using Interdisciplinary Approaches” introduces an innovative solution that leverages the power of IoT technology to enhance campus safety and security significantly. The system deploys a surveillance robot equipped with ultrasonic sensors and ESP32 cameras, employingthe machine learning technique R-CNN for Animal Detection. This proposed method uses an interdisciplinary approach to develop an animal detection system capable of identifying and classifying various species. This proposed method aims to revolutionize campus security by seamlessly integrating advanced technology, mitigating risks proactively, streamlining processes through automation, and presenting a cost-effective alternative to traditional security approaches. Beyond the traditional methods, the proposed system achieves an impressive accuracy rate of animal detection approximately 97.6% enabling real-time alerts through push notifications to security personnel upon detection.
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