International Research Journal of Education and Technology

With the rapid growth of solar energy adoption, maintaining the efficiency and reliability of solar panels has become increasingly important. Manufacturing defects such as cracks, hotspots, broken grid lines, and inactive regions significantly reduce panel performance and lifespan. Traditional manual inspection methods are time-consuming, inconsistent, and prone to human error. This paper presents an AI-based solar cell defect detection system that automatically identifies faulty regions using deep learning and image processing techniques. The proposed system employs a Convolutional Neural Network (CNN) to analyze solar panel images and classify them as defective or non-defective. Image preprocessing and dataset training enable the model to learn defect patterns accurately. Once trained, the system performs real-time predictions on new images, providing fast and reliable inspection results. Experimental evaluation shows that the model achieves high detection accuracy while reducing inspection time and human involvement. The proposed approach offers an efficient, scalable, and cost-effective solution for automated quality control in solar panel manufacturing and maintenance, improving overall energy output and operational reliability.

Index Terms - Solar Cell Defect Detection, Deep Learning, Convolutional Neural Network, Image Processing, Renewable Energy, Fault Classification, Automated Inspection, Artificial Intelligence