Srikanth Panthulugiri

Position: Ph.D. Plant breeding

Education History

• 2021 – M.S. Genetics and Plant Breeding, Dr. Panjabrao Deshmukh Krishi Vidyapeeth, India
• 2019 – B.S. Agricultural Sciences, Professor Jayashankar Telangana State Agricultural University, India

Biography

Srikanth Panthulugiri is a Ph.D. student in Plant Breeding at Iowa State University, working under the guidance of Dr. Asheesh K. Singh (Danny Singh). His doctoral research focuses on developing high-yielding, flood-tolerant soybean varieties and utilizing advanced sensing methodologies to study plant responses to flooding stress and post-stress recovery, and genomic studies for identifying the candidate genes underlying excess water stress tolerance. In addition to his work on soybeans, Srikanth is engaged in mutation breeding of finger and proso millets, aiming to generate novel genetic variation and identify superior mutants adapted to Iowa and the Midwest. He is also investigating effective emasculation techniques to standardize the hybridization protocol in finger millet. Before beginning his Ph.D., Srikanth worked as a Research Fellow at the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), India, from 2021 to 2022, contributing to breeding and mutant population development in finger millet. He has served as a speaker committee member for the Corteva-sponsored R.F. Baker Plant Breeding Symposium and is an active member of Gamma Sigma Delta, the honor society of agriculture. 

Current Project:  

Field screening of Soybean Diversity Panel for excess waterlogging stress using high-throughput phenotyping

Soybean is highly sensitive to excess water stress, making it particularly vulnerable to the increasing frequency of heavy rainfall and flooding events driven by climate change, especially in Iowa and the broader Midwest region of the United States. This project aims to screen a diverse panel of 292 soybean accessions from maturity groups I to III to identify flood-tolerant lines. Physiological responses are assessed using Li-COR 600 (LI-600), while high-throughput phenotyping tools like hyperspectral and multispectral imaging, and LiDAR are being utilized to collect large-scale phenomic data. These advanced sensing approaches enable the precise identification and selection of genotypes with improved tolerance to flooding stress.