## 2 Material and Methods  
### Classifiers  
This study introduces SPHERE (Solar Panel Hidden-Defect Evaluation for Renewable Energy), implementing and evaluating three classifiers--YOLOv8, YOLOv9, and a custom CNN--for categorizing solar panel images into three classes, i.e., broken, clean, and dirty. The detailed information underpinning each classifier within the SPHERE framework is in the following sections. YOLOv8 is a state-of-the-art object detection model widely applied across various domains due to its high accuracy and speed. It has shown superior performance in violence detection, underwater debris monitoring, and agricultural applications. In violence detection, YOLOv8 outperformed other architectures like VGG16, VGG19, and MobileNetV2, demonstrating lower validation loss and better performance during the training phase [32]. In underwater environments, YOLOv8 was evaluated alongside other models like Faster R-CNN and SSD, which showed competitive results, although YOLOv9 outperformed it in some metrics [33]. For agricultural applications, YOLOv8 has been used for tasks such as tomato detection and disease identification in plants, where it achieved high mean average precision (mAP) and demonstrated robust performance even in complex environments [22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39]. YOLOv9 is an advanced iteration of the YOLO series designed to enhance precision and recall in object detection tasks. It has been particularly effective in challenging environments, such as underwater debris detection, where it consistently outperformed other models, including YOLOv8, regarding precision, recall, and mAP values. YOLOv9's stability and adaptability during training make it a robust solution for environmental mon itoring, highlighting its potential for applications that require high accuracy and reliability in diverse conditions [33]. Convolutional Neural Networks (CNNs) are a class of deep learning models particularly effective for image classification and object detection tasks. They have been applied in various fields, including agriculture and surveillance. In violence detection, CNN architectures like VGG16, VGG19, and MobileNetV2 were evaluated for their efficacy in identifying aggressive behaviors, although YOLOv8 showed superior performance in this specific application [32]. CNNs are also used with YOLO models for tasks such as license plate recognition, where they classify segmented characters with high accuracy [35]. Additionally, CNNs have been employed for early pest detection in agriculture, demonstrating their versatility and effectiveness in diverse applications [38]. K-fold cross-validation, used in this study, is a widely utilized validation method employed to evaluate model performance when data are limited and prevent overlearning (overfitting). This method divides the dataset into k equal parts; in each iteration, k-1 parts are used for training and one part for testing. The performance metrics are averaged when all parts are used as a test set. This approach provides a more reliable measure of the model's generalization ability and enables efficient data utilization. In the context of CNN k-fold literature applications, [PERSON] et al. underscored the pivotal function of k-fold cross-validation in model evaluation and hyperparameter optimization, a method that fosters the model's equitable treatment of all classes, particularly in scenarios where datasets are imbalanced (e.g., the \"broken\" panel class in this study) [40]. [PERSON] further elaborated on using k-fold cross-validation in deep learning models, underscoring its pivotal role in enhancing model stability, particularly in scenarios involving limited datasets. Additionally, he proposed the integration of k-fold to minimize variability in layered training processes [41]. [PERSON] et al. [42] expounded on the practical applications of k-fold, utilizing the scikit-learn library [43] as a case study.