Innovative Solutions for Solar Panel Maintenance: A VGG16-Based Approach for Early Damage Detection

This study examines the creation and assessment of a deep learning-based method for the early identification of surface impurities and damage, such as dust, snow, bird droppings, physical damage, and electrical damage, on solar panels. Eighty-seven photos total from the collection are categorised into six groups: dusty, clean, bird drop, electrical damage, physical damage, and snow-covered. The study examines how well the transfer learning model performs in correctly identifying these photos by utilising and getting access to the VGG16 model. Maintaining maximum solar panel efficiency, cutting social maintenance costs, and minimising resource usage all depend on the early discovery of such problems. Using cutting-edge Deep learning algorithms, the suggested methodology analyses the photographs and assigns them to the appropriate categories. The results reveal that the proposed technique is successful in recognising and classifying surface concerns on solar panels, which can lead to increased energy output and sustainability. The collected results show a high accuracy and precision rate of 92.7%.