Ms. Pooja Garg
Designation: Ad-Hoc Assistant Professor
Miss Pooja Garg is an Ad-Hoc Assistant Professor and a researcher in the fields of Machine Learning, Deep Learning, Internet of Things (IoT), and Precision Agriculture. She is pursuing her Ph.D. at Dhirubhai Ambani University, Gandhinagar, where her research focuses on multimodal data fusion, transformer-based architecture, sensor systems, and artificial intelligence for plant disease detection.
She completed her M.Sc. in Mathematics from The LNM Institute of Information Technology (LNMIIT), Jaipur, and her B.Sc. from the University of Kota. Her research integrates IoT-enabled sensor systems, computer vision, and machine learning techniques to develop smart solutions for agricultural applications.
Miss Garg has authored several research articles in reputed IEEE journals, including IEEE Sensors Journal, IEEE Transactions on AgriFood Electronics, and IEEE Sensors Letters. Her work has also been presented at international conferences such as the IEEE Sensors Applications Symposium (SAS), United Kingdom. She led a sponsored research project under the CHANAKYA Fellowship funded by the IITI DRISHTI CPS Foundation, IIT Indore.
Her research contributions have been recognized through the DAU Student Research Excellence Award for two consecutive academic years (2024–25 and 2025–26). Her current research interests include machine learning, deep learning, multimodal AI, sensor systems, computer vision, and intelligent agricultural technologies.
- Ph.D. – Dhirubhai Amabni University
- M.Sc. (Mathematics) – The LNM Institute of Information Technology
- B.Sc. (Science)– University of Kota
- Teaching Assistant- Conducted lab and tutorials
- Machine learning, Deep learning, Plant disease,
- Garg, P., Shah, D., Vora, K., Kumar, A., Joshi, M. V., & Palaparthy, V. S. ”Transformer-Based Framework Using In-house Sensors and Lightweight Images for Accurate Plant Disease Classification,” in IEEE Sensors Journal, doi: 10.1109/JSEN.2026.3656207.
- Garg, P., Mishra, A., Raja, R., Kumar, A., Joshi, M. V., & Palaparthy, V. S. (2025). Multimodal Data Fusion by Integrating IoT-Enabled Sensors and Images for Jamun Crop Disease Detection With Machine Learning. IEEE Transactions on AgriFood Electronics.
- Yogi, P., Pawar, A. D., Khaparde, P., Garg, P., Kalita, H., & Palaparthy, V. S. (2025). Detection of Small Water Droplets on Flexible Leaf Wetness Sensor Considering Effect of Spatiotemporal Variation. IEEE Sensors Journal.
- Saini, R., Garg, P., Chaudhary, N. K., Joshi, M. V., Palaparthy, V. S., & Kumar, A. (2024). Identifying the Source of Water on the Plant Using the Leaf Wetness Sensor and Deep Learning-Based Ensemble Method. IEEE Sensors Journal.
- Garg, P., Khaparde, P., Patle, K. S., Bhaliya, C., Kumar, A., Joshi, M. V., & Palaparthy, V. S. (2023). Environmental and Soil Parameters for Germination of Leaf Spot Disease in Groundnut. IEEE Sensors Letters.
- Garg, P., Shah, S., Joshi, S., Gupta, A., Yogi, P., Joshi, M. V., & Palaparthy, V. S. (2025). Multi-Sensor System for Optimum Irrigation and Plant Disease Detection Using MLP Model on Mango Plant. IEEE Sensors Applications Symposium (SAS), Newcastle, United Kingdom.
- Garg, P., Joshi, M. V., Kumar, A., & Palaparthy, V. S. (2025). IoT Sensor System Feature Ablation Study for Robust Anthracnose Disease Classification. IEEE Sensors Applications Symposium (SAS), Newcastle, United Kingdom.
- A Novel Orthogonal Measurements for Accurate Plant Disease Prediction
- Funding Agency: IITI DRISHTI CPS Foundation (CHANAKYA Fellowship, IIT Indore)
- Funding Amount: 12,80,160 (Development Grant: 2,00,000)
- DAU Student Research Excellence Award (2025–26, 2024–25)
