As part of a graduation project, student Hussein Ali from the Department of Information and Communications Engineering supervised by Dr. Haidar Abdulhameed designed an intelligent system aimed at detecting plastic waste in the Tigris River using artificial intelligence and sensing technology. The project combines environmental sustainability with smart, low-cost solutions.

The system is built around an ESP32-CAM module that captures real-time images of the river. These images are analyzed by a machine learning model trained on the Edge Impulse platform to detect plastic waste such as bottles and bags. Once the number of detected items reaches a predefined threshold (e.g., 10 items), an automated alert is sent to relevant authorities through a Telegram bot. The alert includes an Arabic message with the detection time and item count.

Automation is powered by the n8n platform, which ensures alerts are sent immediately and without manual intervention.

Key Results:

• Achieved 85% accuracy in detecting plastic items.
• Enabled automatic alerts for timely action.
• Ensured fast detection and response for real-time monitoring.
• Used cost-effective components, making it feasible for wide deployment.
• Designed with scalability in mind, allowing application in various environments.

This project highlights how innovative technology can contribute to sustainable environmental solutions and provides a scalable model for protecting waterways from plastic pollution.