AI-Based Human–Animal Conflict Management System: A Review

Abstract

Human–animal conflict has become a significant challenge in many regions due to urban expansion, deforestation, habitat fragmentation, and increasing interaction between wildlife and human settlements. Traditional monitoring and mitigation methods are often reactive, labor-intensive, and inefficient in preventing damage to crops, property, and human lives. Recent advancements in Artificial Intelligence (AI), Machine Learning (ML), Computer Vision (CV), Internet of Things (IoT), and sensor networks have enabled the development of intelligent systems capable of real-time animal detection, tracking, classification, and early warning generation. This review paper presents a comprehensive analysis of AI-based human–animal conflict management systems. Various technologies, including deep learning models, image processing techniques, wireless sensor networks, thermal imaging, edge computing, and predictive analytics, are examined. The paper discusses current research trends, implementation challenges, datasets, performance metrics, and future directions for developing reliable and scalable wildlife monitoring solutions. The study concludes that AI-driven systems can significantly reduce human–animal conflicts while promoting wildlife conservation and sustainable coexistence.