<p>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.</p>