Dr. Lokeshwari M

Dr. Lokeshwari M. is an Assistant Professor specializing in Agricultural Statistics and Data Analytics at Karunya Institute of Technology and Sciences. She holds a Ph.D. in Agricultural Statistics from ICAR-Indian Agricultural Research Institute, New Delhi, completed in 2024 with an OGPA of 8.7, an M.Sc. in Agricultural Statistics from Bidhan Chandra Krishi Viswa Vidyalaya, West Bengal, completed in 2020 with an OGPA of 8.6, and a B.Sc. in Agriculture from Agricultural College and Research Institute, Madurai, completed in 2018 with an OGPA of 8.4. Her academic expertise includes statistical analysis, experimental design, statistical modelling, data analytics and the application of modern analytical tools in agricultural research.

Her research interests encompass agricultural statistics, data analysis, machine learning, deep learning, crop yield prediction, forecasting and statistical applications in agricultural and environmental sciences. She has experience using R, SPSS, SAS and Python, along with TensorFlow, for statistical modelling and data analysis. Her research projects have included statistical assessment and forecasting of horticultural crops, comparative time-series analysis of crop production and costs, and deep learning approaches for cereal crop yield prediction using spectral indices and weather variables. She has worked with machine learning models such as random forest regression and support vector machines, as well as deep learning approaches including deep neural networks, convolutional neural networks and long short-term memory models.

Dr. Lokeshwari has served as an Assistant Professor of Agricultural Statistics at Adhiyamaan College of Agriculture and Research, where she taught Agricultural Statistics, Experimental Designs and Data Analysis, guided student projects, and contributed to interdisciplinary research and academic activities. She subsequently joined Karunya Institute of Technology and Sciences in August 2025 as an Assistant Professor in Agricultural Statistics/Data Analytics, where she teaches UG, PG and Ph.D. courses related to statistics, data analytics and research methodology, guides postgraduate dissertations and assists Ph.D. scholars in statistical analysis and research design. Her academic achievements include securing seventh rank in the ICAR-PG JRF examination in 2018, qualifying ASRB-NET in Agricultural Statistics in 2019, securing first rank in the ICAR-PG SRF examination in 2020, receiving the Senior Research Fellowship, and winning first prize in the National Statistics Day “STATISTICIANS” Quiz in 2021.

She has published research articles on artificial intelligence for crop yield prediction, wheat yield prediction using artificial intelligence and deep learning, and spectral-index-based crop yield modelling. Her research contributions have been presented at national and international conferences on agricultural statistics, statistics and data science, machine learning, crop yield prediction, agricultural price forecasting and sustainable agricultural development. She has also attended advanced training programmes and workshops in deep learning, statistics and informatics, research methodology, statistical data analysis using SPSS, systematic reviews and evidence mapping. Her additional academic training includes online courses in TensorFlow, neural networks, deep learning and Python-based data science, supporting her interdisciplinary application of statistical and computational methods in agricultural research.