AI-Driven Detection and Conservation of Rare Bird Species and Wildlife Using Computer Vision and Edge Computing

This research paper presents the development and implementation of an artificial intelligence (AI) system designed to detect and conserve rare bird species and wildlife. Leveraging computer vision and edge computing, the system captures real-time images of wildlife and integrates advanced algorithms to classify species and monitor their behaviors. Utilizing tools such as TensorFlow, OpenCV, and MediaPipe, the system aims to assist conservationists by providing timely and accurate data on endangered species. Edge computing is employed to ensure functionality in remote and resource-constrained environments, enhancing accessibility and effectiveness in wildlife monitoring. The primary objective is to develop an AI-based system that detects rare bird species and wildlife in real time using computer vision techniques. Secondary objectives include enabling behavior analysis and environmental context awareness, automating data collection for researchers and conservationists, and implementing edge computing for deployment in remote areas. This project aims to revolutionize wildlife monitoring by making it more accessible and efficient. By capturing and analyzing data in real time, the system can significantly enhance conservation efforts, offering precise information to aid in the protection of endangered species. It will also foster community engagement and awareness in wildlife conservation efforts.