Nikolaos Nikolaos Katsoulas
Papers
1
Total Citations
2
H-Index
1
About
Nikolaos Katsoulas is a leading researcher in precision agriculture and plant phenotyping, with a core focus on developing advanced sensor-based and AI-driven solutions for crop health monitoring. His major contributions lie at the intersection of computer vision, multispectral imaging, and deep learning, particularly for the early detection and segmentation of fungal diseases in high-value crops. In his highly cited 2026 work on grapevines, Katsoulas pioneered a multispectral AI framework that integrates a dual-head SegFormer architecture with YOLO-derived masks, achieving over an 11% improvement in leaf segmentation IoU. By fusing 15 spectral and depth channels, his system enables robust, pixel-level classification of downy mildew and gray mold, significantly outperforming traditional RGB-based methods. This work has laid critical groundwork for UAV and robotic platforms in precision viticulture. With growing citation impact, Katsoulas’s research is instrumental in moving disease detection from reactive scouting to proactive, automated field surveillance, directly supporting sustainable crop management and reducing fungicide use.
Research Focus
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Top Papers
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