Regular inspection and maintenance are crucial for ensuring the optimal performance of solar panels. However, conventional manual methods can be laborious, time consuming, and expensive, especially for large and inaccessible installations. Aerial inspection has the potential to overcome these limitations and improve operational flexibility. To fully leverage the potential of aerial inspection, we present a summary overview of drone‐based photovoltaic module inspection and a case study demonstrating the integration of autonomous navigation and machine learning techniques for defect detection. In the case study, a convolutional neural network ( CNN ) based framework that can autonomously detect defective solar cells using aerial robots is integrated with the autonomous navigation of the aerial robot. There are two main phases for this framework: detection of the solar panel location and identification of the solar cell defect with a feasible set of trajectories. The solar panel is identified with a shape detection algorithm and the defects are classified using electroluminescence ( EL ) images with a CNN, based on the VGG16 architecture; various approaches to avoid overfitting are presented to achieve better performance. Seven solar cell states can be detected including breaks, finger interruptions, material defects, and microcracks. This pipeline is demonstrated virtually on the NEST building at the Swiss Federal Laboratories for Materials Science and Technology. The research hub is reconstructed in AirSim with real data and it is shown that the aerial robot can detect the location, conduct an approaching trajectory, extract the image, and transfer it to the CNN for defect identification.