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About

Dr. Vikas Khullar is a pioneering researcher at the intersection of artificial intelligence, robotics, and the Internet of Things (IoT), with a primary focus on solving real-world challenges in agriculture, infrastructure, and healthcare. His most impactful work, "IoT-Fog-enabled robotics-based robust classification of hazy and normal season agricultural images for weed detection" (26 citations), introduces a novel mechanized farming system that leverages fog computing and robotic vision to accurately identify weeds under varying environmental conditions—a critical step toward sustainable, autonomous agriculture. In the domain of civil infrastructure, his paper "ROAD: Robotics-Assisted Onsite Data Collection and Deep Learning Enabled Robotic Vision System for Identification of Cracks on Diverse Surfaces" (15 citations) presents a deep learning-powered robotic platform that automates crack detection, significantly enhancing road safety and maintenance efficiency. Dr. Khullar also applies his expertise to social communication disorders, developing the "Vocal-friend" IoT-social framework and a deep learning robotic system for identifying emotional pragmatics deficits. His work consistently integrates cutting-edge deep learning with physical robotic systems, demonstrating a unique ability to translate complex algorithms into tangible, field-deployable solutions. With a growing citation record and a clear trajectory toward impactful, interdisciplinary innovation, Dr. Khullar is establishing himself as a key figure in applied intelligent robotics.