Aerial assessment of solar panel surfaces using drone

The concept of image processing has come a long way in the recent past. There are multiple softwares based on image processing with better accuracy and efficiency even when compared to the human eye. With open-source libraries like Python growing rapidly in development, there are many image processing libraries like OpenCV, Mahotas, Pillow / PIL, NumPy are being used widely and in most image processing softwares. This project is a demonstration of how image processing and machine learning is used to identify and differentiate different contaminants on the surface of a solar panel. A solar panel is surrounded by lots of empty areas like on rooftops of buildings and solar farms. Due to this, it is very easy for contaminants like dirt and bird droppings to make the solar panel dirty. When this happens and with more accumulation of contaminants, the solar panel efficiency exponentially decreases. Hence it is important to keep such surfaces clean as much as possible. Although, cleaning the surfaces is a tedious and moreover, a dangerous job. It is better to be prepared with the nature of the contaminants on the surface before actually going to clean it so that the cleaners will not have to take anything more than the necessary equipment.