Sara Lafkih

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About

Sara Lafkih’s research focuses on the intersection of computer vision and renewable energy, specifically addressing the critical challenge of large-scale solar farm monitoring. Her work pioneers automated methods to detect dust, damage, or broken panels—key issues that directly impact solar plant efficiency and maintenance costs. In her most-cited paper (8 citations), she introduced a novel approach using video frames mosaicing from drone or robot footage to create comprehensive panel surveys, simplifying the detection of anomalies across vast installations. Her second notable contribution (4 citations) explores digital video watermarking for solar panel indexation, embedding identifiers directly into panel images to enable precise tracking and state evaluation over time. Though early in her career, Lafkih’s work tackles a practical bottleneck in renewable energy: reducing the cost and labor of remote monitoring. By combining image processing techniques with real-world energy infrastructure needs, she offers scalable solutions that could help solar farms operate more efficiently. Her research is especially relevant for students and engineers interested in applied computer vision, sustainable technology, and the growing field of automated infrastructure inspection.