This project focuses on monitoring the surface condition of solar panels using basic image processing techniques. Solar panels are often affected by dust accumulation and surface defects such as cracks or stains, which can reduce their efficiency over time. Manual inspection of large solar installations can be time-consuming and difficult, especially in large solar farms. The purpose of this project is to demonstrate a simple automated approach for analyzing solar panel images and identifying potential surface issues.
The system processes images of solar panels using Python and OpenCV. Each image is converted to grayscale and preprocessed to reduce noise before applying edge detection and thresholding techniques. These methods help identify irregular regions on the panel surface that may represent defects. In addition to defect detection, the program also analyzes darker pixel regions to estimate the level of dust or dirt accumulation on the panel surface.
The output of the program includes the calculated percentage of detected defects and the estimated dirt level for each image. Based on these values, the system classifies the severity of defects and the overall cleanliness of the panel. This type of automated analysis can assist in monitoring solar panel conditions and can support maintenance planning in solar energy systems.