Reptile and Rodent Detection System using Artificial Intelligence
Abstract
In this project presents a Reptile and Rodent Detection System for wildlife reservation areas. The main aim of this system is to detect reptiles (like snakes) and rodents (like rats) in protected forest areas using modern technology. In wildlife reserves, reptiles and rodents can sometimes create problems for other animals, forest staff, and visitors. Early detection helps in preventing danger and maintaining ecological balance. The system uses sensors and a camera to identify the movement of reptiles and rodents. When an animal is detected, the system sends notification. This helps them take quick action without harming the animals. The proposed system is safe, automatic, cost-effective, and easy to install in forest areas. It reduces human effort and improves wildlife monitoring. This project helps in improving wildlife safety, protecting biodiversity, and supporting smart forest management.
References
R. Sharma and P. Kulkarni (2022) “Deep Learning-Based Wildlife Detection Using Convolutional Neural Networks for Forest Surveillance”, International Journal of Computer Vision and Artificial Intelligence.
M. Patel, S. Shah, and R. Mehta (2023) “Real-Time Pest and Small Animal Monitoring System Using YOLO-Based Object Detection”, Proceedings of the International Conference on Smart Agriculture and IoT Systems.
A. Singh and R. Verma (2021) “Deep Learning Framework for Snake Detection in Natural Environments”, Journal of Image Processing and Intelligent.
K. Reddy, V. Rao, and P. Nair (2024) “IoT-Based Smart Surveillance System with Embedded Edge Processing for Real-Time Intrusion Detection”, International Conference on Emerging Trends in Embedded Systems and IoT.
S. Kumar and A. Deshmukh (2023) “Multi-Object Detection in Agricultural Monitoring Using YOLOv7”, International Journal of Advanced Computing and Applications.
Refbacks
- There are currently no refbacks.