Dr. Manoj Semwal is a highly accomplished scientist specializing in Precision Agriculture, Remote Sensing, Unmanned Aerial Vehicles (UAV), Machine Learning, and ICT applications in crop science. He holds degrees including B.Sc. (Physics, Chemistry, Mathematics), PGDCSA, MCA, M.Tech in Remote Sensing and GIS from the Indian Institute of Remote Sensing (ISRO), and a Ph.D. in Precision Agriculture from the Academy of Council of Scientific and Industrial Research. With over two decades of professional experience, he has served in progressive scientific roles at CSIR-CIMAP, culminating in his current position as Chief Scientist at CSIR Headquarters. His research interests span drone-based agri-technologies, smart farming systems, hyperspectral imaging, and data-driven decision tools for crop management. Demonstrating advanced research skills in remote sensing analytics, GIS modeling, and machine learning applications, he has led several national and international projects, including the Aroma Mission and a major Haleon UK-funded initiative on sustainable mint-based agro-technologies. His achievements have been recognized through numerous awards such as the CSIR Technology Award for Life Science, CAIRD Award for Rural Development, and the National Geomatics Award (ISRO, 2024). With over 40 peer-reviewed publications, book chapters, and innovative technology developments, Dr. Semwal’s work embodies scientific excellence and transformative innovation in modern agricultural research.
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Featured Publications
Bisht, D., Chanotiya, C. S., Rana, M., & Semwal, M. (2009). Variability in essential oil and bioactive chiral monoterpenoid compositions of Indian oregano (Origanum vulgare L.) populations from northwestern Himalaya and their chemotaxonomy. Industrial Crops and Products, 30(3), 422–426.
Kumar, Y., Prakash, O., Tripathi, H., Tandon, S., Gupta, M. M., Rahman, L. U., … (2018). AromaDb: A database of medicinal and aromatic plants’ aroma molecules with phytochemistry and therapeutic potentials. Frontiers in Plant Science, 9, 1081.
Khan, M. S., Semwal, M., Sharma, A., & Verma, R. K. (2020). An artificial neural network model for estimating Mentha crop biomass yield using Landsat 8 OLI. Precision Agriculture, 21, 18–33.
Prasad, N., Semwal, M., & Roy, P. S. (2015). Remote sensing and GIS for biodiversity conservation. In Recent Advances in Lichenology: Modern Methods and Approaches in Lichen Systematics and Culture Techniques (pp. 123–140). Springer.
Lothe, N. B., Mazeed, A., Pandey, J., Patairiya, V., Verma, K., Semwal, M., … (2021). Maximizing yields and economics by supplementing additional nutrients for commercially grown Menthol mint (Mentha arvensis L.) cultivars. Industrial Crops and Products, 160, 113110.