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This paper presents a novel approach to detect cracks in solar panels using a solar assistant. The proposed system uses a combination of thermal imaging, electrical measurements, and machine learning techniques to detect cracks. Experimental results show that the proposed system can accurately detect cracks in solar panels, even in early stages. The system provides valuable insights for maintenance and repair, ensuring optimal performance and extending the lifespan of solar panels.

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"Solar Assistant," Kael said, smiling for the first time in years. "Turn on the lights. All of them." This paper presents a novel approach to detect