Papers
3
Total Citations
44
H-Index
3
About
Jyoti Verma is at the forefront of integrating robotics, IoT, and deep learning to solve critical real-world problems in agriculture and infrastructure. Her research focuses on developing intelligent robotic vision systems that can operate effectively under challenging conditions, such as hazy environments for weed detection or on diverse surfaces for crack identification. Her most impactful work, "IoT-Fog-enabled robotics-based robust classification of hazy and normal season agricultural images for weed detection," has garnered 26 citations, showcasing its significance in advancing precision agriculture through automated, fog-assisted robotics. She further demonstrated her expertise in infrastructure monitoring with a 15-cited study on a Robotics-Assisted Onsite Data collection (ROAD) system for crack detection, blending robotic navigation with deep learning. Additionally, her exploration of Ant Colony Optimization algorithms for robotic path planning (3 citations) highlights her commitment to efficient autonomous navigation. Through these contributions, Dr. Verma is pioneering the next generation of field-deployable robotic systems that enhance safety, productivity, and sustainability across multiple domains.
Research Focus
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