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Academic Experts

Lovely Raghav

Biography

I am an Assistant Professor at Jaypee Institute of Information Technology (JIIT). I hold a Master of Computer Applications (MCA) and a Bachelor of Computer Applications (BCA), with a strong academic background in computer science and software development. I have nearly three years of professional experience as an Android Application Developer, working with technologies such as Java, Kotlin, MVVM architecture, REST APIs, Firebase, Room Database, and SQLite to develop and maintain Android applications. 
My research interests include Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision, the Internet of Things (IoT), and Explainable Artificial Intelligence (XAI). My research primarily focuses on applying AI techniques to agricultural applications, particularly plant disease detection using image-based analysis and the integration of environmental and sensor data to improve decision-making. I am interested in developing practical and efficient AI solutions that can support real-world applications.

I have presented and published research in international conference proceedings, including a Springer publication indexed in Scopus. Alongside my research activities, I continuously enhance my technical knowledge through academic learning, professional development programs, and hands-on implementation of emerging technologies. My areas of interest also include Generative AI, edge computing, and intelligent systems.

I am committed to continuous learning and to applying research and technology to solve practical problems. My goal is to contribute to the development of reliable, efficient, and accessible AI-based solutions that create meaningful impact in agriculture and other application domains while strengthening the connection between research and real-world implementation. 

Research Highlights

My research interests focus on Artificial Intelligence (AI), Machine Learning (ML), Deep Learning, Computer Vision, the Internet of Things (IoT), and their applications in agriculture. My work aims to explore practical AI techniques that can assist in the early identification of plant diseases and support technology-driven solutions for sustainable farming.

One of my research contributions is the paper titled "Plant Disease Detection Using Lightweight CNNs and GAN Augmentation," which was presented at the 9th International Conference on Innovative Computing and Communication (ICICC 2026). The paper was accepted for publication in Springer's Lecture Notes in Networks and Systems (LNNS) series and is indexed in Scopus. The research investigates the use of lightweight convolutional neural networks along with Generative Adversarial Networks (GANs) for improving plant disease detection while maintaining computational efficiency.

To strengthen my understanding of AI applications in agriculture, I successfully completed the ICAR-NAARM Massive Open Online Course (MOOC) on "Artificial Intelligence in Agriculture." The course provided practical knowledge of AI techniques, agricultural data analysis, and digital technologies used in modern farming systems.

My current research interests include image-based plant disease detection, multimodal data integration, explainable AI, and resource-efficient machine learning models for agricultural applications. I am committed to continuous learning and to conducting research that bridges the gap between artificial intelligence and real-world agricultural challenges through practical, reliable, and sustainable technological solutions. 

Areas of Interest
  • Machine Learning
  • Responsible Artificial Intelligence (Responsible AI)
  • Retrieval-Augmented Generation (RAG) and Generative AI
  • Computer Vision
  • Cyber Security
Publications

L. Raghav, J. Kaur, and G. Sakya, "Plant Disease Detection Using Lightweight CNNs and GAN Augmentation," accepted for publication in Proceedings of the 9th International Conference on Innovative Computing and Communication (ICICC 2026), Lecture Notes in Networks and Systems, Springer Nature, 2026 (in press)