AI Based Animal Detection and Repellent System
Ms. KV Sneha, Amaljith pp, Jeffery Lino Joseph, Lisa Mary Mathew, Swathi Priya p
Professor, EEE Department, Vimal Jyothi Engineering College, Kannur, India EEE Department,
Vimal Jyothi Engineering College, Kannur, India
Abstract - Wild animals entering agricultural fields has become a growing concern, especially for farmers living near forest areas and wildlife corridors. In many regions, farmers experience significant crop damage when animals wander into their fields in search of food, most often during the night. Due to rapid urbanization, deforestation, and the gradual loss of natural habitats, wild animals are forced to move closer to human settlements as their traditional food sources become scarce.
Farmers have traditionally relied on methods such as electric fencing, staying awake to guard fields at night, or using firecrackers to scare animals away. However, these methods are often temporary, costly to maintain, physically exhausting, and sometimes unsafe for both humans and animals. In many cases, they also fail to provide a long-term or sustainable solution.
To overcome these challenges, this project proposes an AI-Powered Animal Intrusion Detection and Repelling System that offers an intelligent, automated, and humane approach to field protection. The system uses the advanced YOLOv11 object detection model to identify different animal species in real time through live video captured by an IP camera installed in the field. The model runs on a GPU-enabled laptop, ensuring fast and accurate detection with minimal delay.
Once an animal is detected, the system automatically activates non-lethal deterrent mechanisms such as water spraying, flashing lights, and species-specific sound alerts. These methods are designed to safely scare the animal away without causing injury. At the same time, important details such as the time of detection, the type of animal identified, and the confidence level of the prediction are stored in a Firebase Realtime Database. Instant notifications are also sent to farmers through a user-friendly Android application developed using Kodular, allowing them to monitor their fields remotely.
Overall, this system provides continuous real-time monitoring, remote access through a mobile application, cost-effective operation, and humane animal control. By combining Artificial Intelligence, IoT technology, and smart automation, the proposed solution aims to significantly reduce crop losses while promoting peaceful coexistence between humans and wildlife.
Key Words: Artificial Intelligence, Human–Wildlife Conflict, YOLOv11, Object Detection, Smart Agriculture, IP Camera Monitoring, IoT System, Firebase Database, Android App, Real-Time Alerts, Non-Lethal Animal Repellent, Automated Field Protection.
