AI-Based Human–Animal Conflict Management System Using Deep Learning and Early Warning

Abstract

Human–animal conflict has increased significantly due to rapid urbanization, habitat loss, deforestation, and agricultural expansion. Encounters between humans and wild animals often result in loss of human life, crop destruction, property damage, and threats to wildlife conservation. This paper presents the implementation of an AI-Based Human–Animal Conflict Management System that utilizes Deep Learning, Computer Vision, and IoT technologies to detect and classify wild animals in real time and generate immediate alerts for nearby communities and authorities. The proposed system employs a Convolutional Neural Network (CNN)-based object detection model trained on wildlife datasets to identify animals such as lions, tigers, elephants, leopards, and bears. A camera module continuously captures images, which are processed by the AI model. Upon detecting a dangerous animal, the system generates alerts through a graphical user interface (GUI), SMS notifications, and warning signals. Experimental results demonstrate high detection accuracy and low response time, making the system suitable for deployment in wildlife-prone regions. The implementation contributes to reducing human–animal conflicts while supporting wildlife conservation and public safety.