Zonglin Li ¹ and Austin Amakye Ansah ² , ¹ Singapore, ² The University of Texas at Arlington, USA

Manual inspection of photovoltaic systems is expensive, hazardous, and prone to inconsistency. This paper presents SunScout, a mobile application for offline solar-panel image management and on-device fault classification. The current mobile release organizes drone, gallery, and camera captures into reusable datasets and analyzes each stored asset with a fine-tuned EfficientNetB0 classifier deployed through ONNX Runtime. On the cleaned 1,575-image dataset, a frozen MobileNetV2 baseline reached 90.79% validation accuracy, while the proposed EfficientNetB0 model achieved 94.29%, a 3.50 percentage point improvement. Together, these results show that accurate solar fault analysis can be delivered on a consumer smartphone without requiring a persistent network connection.

Solar panel inspection, Fault classification, EfficientNet, Computer vision, Mobile deployment, On-device inference