Sofia Faliagka
Papers
1
Total Citations
2
H-Index
1
About
Sofia Faliagka is a leading researcher at the intersection of precision agriculture and artificial intelligence, specializing in advanced computer vision and multispectral imaging for plant disease detection. Her work focuses on developing robust, deployable frameworks for automated crop health monitoring, with a particular emphasis on grapevine pathologies such as downy mildew and gray mold. In her most cited work, Faliagka introduced a groundbreaking multispectral AI-driven imaging system that leverages a dual-head SegFormer architecture to achieve highly accurate disease segmentation. Her key contributions include the integration of YOLO-derived masks to boost leaf segmentation Intersection over Union (IoU) by over 11%, and a novel 15-channel data fusion technique that significantly enhances pixel-level classification performance. By combining multispectral and depth data, her framework supports deployment on UAVs and robotic platforms, making real-time, field-deployable disease detection a practical reality. Though early in its citation trajectory, this work has already garnered attention for its methodological rigor and practical impact, positioning Faliagka as an innovator in sustainable, AI-driven viticulture and precision agriculture.
Research Focus
Key Achievements
Top Papers
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