Recent Trends and Challenges in Animal Intrusion Detection and Repellent Systems

The increasing frequency of animal intrusions into human-inhabited or agri-cultural areas has emphasized the need for efficient detection and repellent systems to mitigate human-wildlife conflict.This paper examines advancements in technologies such as Passive Infrared sensors, Convolutional Neural Networks, and IoTbased frameworks that form the foundation of modern animal intru-sion management systems.PIR sensors are explored for their effectiveness in motion detection, while CNNs enable accurate species identification, crucial for activating targeted responses.Deterrent mechanisms, including ultrasonic frequency modulation and light-based systems, are analyzed for their ability to repel animals humanely and species-specifically.Additionally, the work highlights IoT-integrated systems that facilitate real-time alerts and communication with administrators, enhancing response efficiency.Case studies on CNN-based animal detection, IoT-enabled agricultural protection systems, and ultrasonic repellent solutions underscore the potential of combin-ing hardware and AI technologies.The evaluation focuses on detection accuracy, scalability, adaptability to diverse environments, and responsiveness.By consolidating insights from these methodologies, the work identifies trends in sustainable and humane animal intrusion management systems for applications in agriculture, wildlife conservation, and public safety.This analysis underscores the transformative role of AI and IoT in addressing the challenges of coexistence with wildlife, paving the way for scalable and ethical solutions to mitigate conflicts.