## 1 Introduction
The cracked cells' classification accuracy was relatively low, especially compared to the good and corroded groups, possibly due to the unbalanced dataset (as most cells did not have any fault). [PERSON] et al., who manually categorized the database into four labels: good, cracked, cell edge darkening, and heavily busbar corroded, later extended their study in Reference [19]. They used an unsupervised clustering technique to correlate intrinsic patterns in the images with the supervised labels. The method is based on binary classification ('degraded' and 'non-degraded' cells), achieving a mean accuracy of 98.9% and 98.2% for SVM and convolutional neural network (CNN) ML algorithms. Additionally, several automated fault detection methods have been proposed [20; 21; 22]. [PERSON] et al., achieved an overall prediction accuracy of 98.4% using 2000 training steps (25 epochs) [20], while [PERSON] et al., employed a binary clustering of features to detect only finger interruptions. However, it seems challenging to detect defects with more elaborate structures due to shape assumptions [21]. SVM and RF classifiers were evaluated in Reference [22] using two cell region extraction algorithms. The study focused on the cracked and faulty region's geometry to distinguish it from a healthy area. Another approach that integrates mini-batch k-means with state-of-the-art clustering CNN has been proposed [23]. The method uses a feature drift compensation to reduce errors caused by a feature mismatch. The technique demonstrates a high accuracy and can efficiently compute millions of images, outperforming existing state-of-art clustering methods [14]. A different approach that uses an independent component analysis (ICA) has been demonstrated to achieve a 93.4% accuracy with a relatively small training dataset of only 300 solar cell images [24]. However, material defects such as finger interruptions are treated equally to cell cracks. Moreover, an algorithm using anisotropic diffusion filtering to locate micro-cracks in polycrystalline solar cells is described in Reference [25]. The method precisely detected the micro-cracks with an accuracy and sensitivity of 88% and 97%, respectively. Recently, deep learning-based approaches have been suggested for classification [26; 27; 28; 29; 30]. A pre-trained visual geometry group (Vgg)-16 CNN network architecture combined with an SVM decision layer was used to classify different faults, achieving 90.2% accuracy [26]. The work was later extended in Reference [27] by developing an enhanced CNN model proposing an algorithmic solution, which extensively evaluated the model performance using different inputs (dataset sizes, learned features, conventional solution, and more). The study demonstrated efficient defect detection of faults, achieving a mean accuracy of 97.9%. A transfer learning-based solution was proposed by [PERSON] et al. [28], which can identify visible defects in large-scale PV plants and distributed rooftop systems. The study uses an enhanced CNN-based model for classification, reaching a 98.9% mean accuracy. A visual defect detection method based on multi-spectral deep CNN has also been proposed [29], achieving an overall defect-recognition accuracy of 94.3%. The effectiveness of a data augmented method, auxiliary classifier-progressive growing generative adversarial networks, was evaluated using three selected CNN models [30]. It has been shown to improve the classification accuracy maximum by 14% in the material defect category compared to a more traditional data augmentation approach. In this study, we use extracted statistical parameters from the image histogram as a feature descriptor. The vector is then fed into different ML classifiers to distinguish between various defects. We demonstrate that processing the images into a feature vector of statistical parameters has a significant advantage over the standard methods that use many features.