Damage to the surface of solar panels, such as cracks, scratches, and stains, can reduce the energy efficiency produced. The surface of solar panels often experiences various types of damage such as cracks, scratches, stains, or being in good condition, which can affect energy absorption efficiency. The data used in this study consists of 4000 images covering various categories of surface conditions. The method used in this research is the Texture Feature Extraction Method with statistical indicators, namely Mean, Variance, Standard Deviation, Skewness, Kurtosis, and Entropy, to identify existing damage patterns. These features are then analyzed and classified to determine the type of damage on the panel surface. The feature extraction process generates data representations that depict the texture patterns of each surface condition category. This research aims to identify damage on the surface of solar panels using texture-based feature extraction techniques to support the efficient maintenance of solar panels.
Wiliani et al. (Thu,) studied this question.