International Research Journal of Education and Technology

Automated wildlife monitoring has become increasingly important in ecological research, biodiversity conservation, and environmental sustainability studies. Traditional bird observation methods rely heavily on manual surveillance, which is time-consuming, labor-intensive, and prone to human error. Recent advances in artificial intelligence, deep learning, and computer vision provide scalable alternatives for automated species recognition and behavioral analysis. This research presents a comprehensive Real-Time Bird Detection and Activity Prediction System that integrates computer vision techniques, deep learning-based image classification, structured data logging, automated alert mechanisms, and temporal activity analytics into a unified intelligent framework.

The system utilizes OpenCV for real-time video capture and frame processing, combined with a pre-trained bird species classification model from the Transformers library for accurate species recognition. Softmax probability distributions are employed to evaluate classification confidence and filter unreliable detections. Upon valid detection, the system captures and stores images, logs metadata—including species name, confidence score, timestamp, and geographic location—in a structured JSON database, and automatically sends email alerts with image attachments. A history module enables retrospective analysis of past detections, while an activity prediction module leverages Pandas and Matplotlib to generate graphical insights into daily and temporal bird activity patterns.

The proposed framework demonstrates the practical deployment of deep learning in ecological monitoring, offering scalability, automation, and real-time responsiveness. Experimental evaluations indicate high classification accuracy, low latency processing, and reliable activity trend analysis. This system contributes toward intelligent biodiversity monitoring solutions and supports data-driven ecological research.

Keywords

Bird Detection, Wildlife Monitoring, Deep Learning, Computer Vision, Transformers, OpenCV, Activity Prediction, Ecological AI, Streamlit Interface, Environmental Monitoring.