<strong>A YOLO-Driven Automated PV Health Monitoring Cleaning System with Combined IoT Sensor Fusion</strong>

This paper proposes an automated PV health monitoring and cleaning system that integrates IoT sensor data and YOLOv8-based computer vision. Sensor inputs including light intensity, temperature, humidity, voltage, and current are fused with live image analysis using Raspberry Pi. The system classifies the panel as “clean” or “dusty” and activates a water sprinkler if dust is detected. Results are logged on cloud (ThingSpeak), and the system is field-tested in different time slots. This real-time fusion approach enhances solar panel efficiency and reduces manual maintenance.<br>