AI-based visual analysis of photovoltaic panels for fault detection and maintenance support.

As the global transition toward renewable energy accelerates, ensuring the operational reliability of photovoltaic (PV) systems has become increasingly critical. Manual inspection procedures remain labor-intensive and economically inefficient, particularly in large-scale solar installations. This study proposes an automated diagnostic framework for classifying anomalies associated with PV panels using deep learning approaches built on the EfficientNet as a backbone, techniques such as Fine-tuned Transfer Learning (FTL), Deep Feature Extraction + Classifier (DFE-C), and Alternative Fine-tuning Setup (AFS) are applied and their efficacy in detecting various defect categories is evaluated. Furthermore, the analysis focuses on a comprehensive evaluation of these strategies in terms of robustness and accuracy regarding classification capabilities. The approaches are evaluated under a consistent data partitioning strategy derived from the same PV image dataset, enabling a systematic comparison of their classification accuracy, robustness, and consistency. The results indicate that FTL enhances domain adaptability, while DFE-C exhibits the greatest overall stability and performance under limited and variable data conditions. The AFS approach provides a balanced trade-off between flexibility and convergence. The experimental framework incorporates structured training pipelines, hyperparameter control, and performance benchmarking using accuracy, macro F1-score, and fold-based stability analysis. Specifically, the DFE-C approach achieved a superior overall accuracy of 94.05%, demonstrating near-perfect diagnostic capability in critical categories such as physical damage (100%) and snow coverage (96%). In short, the proposed framework provides a comparative evaluation methodology for automated PV inspection and offers useful insights for the development of more reliable AI-based diagnostic systems for PV energy applications.