IoT Based Real-Time Animal Detection System For Smart Agricultural Crop Protection

Abstract

Human–wildlife conflict has intensified in recent years due to deforestation, agricultural expansion, and habitat fragmentation, forcing wild animals to enter human settlements and farmlands. This study proposes a cost-effective and automated animal intrusion detection system that leverages an ESP32-CAM module integrated with Internet of Things (IoT) technology and deep learning algorithms.The system continuously captures real-time images and processes them using a trained object detection model to identify the presence of wild animals. Upon detection, instant alerts are transmitted to users such as farmers or forest authorities via mobile applications, enabling timely preventive action. Additionally, a local buzzer mechanism is activated to deter animals and provide immediate on-site warning.The ESP32-CAM functions as an edge device for image acquisition, while cloud-based platforms facilitate communication and notification delivery. The proposed solution minimizes human intervention, reduces crop damage, and enhances safety in rural and forest-adjacent areas. Its affordability, low power consumption, and ease of deployment make it particularly suitable for remote regions.