## 5 Conclusions  
The main objective of this study was to conduct a comprehensive performance comparison of state-of-the-art deep learning models--YOLOv8, YOLOv9, and CNNs--for classifying solar panel conditions into three categories: broken, clean, and dirty. One of the unique features of this work is the utilization of a novel, proprietary dataset comprising 6079 high-resolution images captured under diverse environmental conditions, which has not been previously employed in any other research. The results demonstrated that the YOLOv8-m model outperformed its counterparts, achieving the highest accuracy (97.26%) and specificity (95.94%) while maintaining 100% sensitivity. These findings directly contribute to advancing automated inspection systems by providing a robust framework for defect detection, which is critical for maintaining grid stability and optimizing solar energy output.  
Another key contribution of this study lies in its methodological rigor. By employing advanced data augmentation techniques to address class imbalance and enhance model generalizability, this work establishes a blueprint for handling imbalanced datasets in photovoltaic defect detection. The comparative analysis of YOLO variants and CNNs highlights the superiority of object detection models over traditional classification architectures for spatially complex tasks. This work will increase the efficiency of other studies in the field by offering insights into model selection, dataset design, and augmentation strategies, thereby accelerating the development of reliable AI-driven maintenance systems.  
Future research should focus on expanding the dataset to include additional defect categories and environmental factors to improve real-world applicability. Integrating the proposed framework with IoT-enabled autonomous systems for real-time monitoring, as well as exploring hybrid architectures, could further enhance detection accuracy and operational efficiency. By addressing these challenges, this study paves the way for scalable, sustainable solutions that ensure the longevity and reliability of solar energy infrastructure, ultimately supporting global renewable energy goals.  
K.A.: methodology, conceptualization, software, data querying, validation, formal analysis, investigation, visualization, writing--original draft preparation, and writing--review and editing; B.S.: software, data querying, validation, conceptualization, and writing--review and editing; M.F.A.: validation, writing--review and editing, formal analysis, supervision, and project administration. U.K.: supervision, writing--review and editing, formal analysis, and project administration. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request. Publicly available datasets were analyzed in this study. This data can be found here: [[https://www.kaggle.com/datasets/kublayyturan/sphere-solar-panel-hidden-defect-evaluation-for-re/data](https://www.kaggle.com/datasets/kublayyturan/sphere-solar-panel-hidden-defect-evaluation-for-re/data)]([https://www.kaggle.com/datasets/kublayyturan/sphere-solar-panel-hidden-defect-evaluation-for-re/data](https://www.kaggle.com/datasets/kublayyturan/sphere-solar-panel-hidden-defect-evaluation-for-re/data)) (accessed on 22 April 2025).  
The authors would like to thank the editors and anonymous reviewers for providing insightful suggestions and comments to improve the quality of this research paper.  
The authors declare no conflicts of interest.