Last week, The Ohio State University College of Food, Agricultural, and Environmental Sciences (CFAES) Research & Graduate Education Internal Grants Program announced which projects would receive funding for the upcoming year.
Department of Food, Agricultural and Biological Engineering (FABE) Ph. D. candidate Sushma Katari's project, titled “Combining satellite and unmanned aerial system data for identifying the crop growth stages using image processing and machine learning techniques” was one of the selected projects.
The CFAES Internal Grants Program (IGP) Graduate Competition is a unique funding opportunity for CFAES graduate students and is designed to provide graduate students with an opportunity to gain experience with research methods in food, agriculture, environmental sciences, human ecology and related social sciences, introduce graduate students to the grant-writing and peer-review processes, and stimulate faculty-graduate student collaborations and mentoring of graduate students by CFAES faculty.
Sushma's research is primarily concentrated on harnessing remote sensing methodologies alongside artificial intelligence (AI) techniques to devise automated solutions for precision agriculture. More recently, she worked on developing an automated labeling process for training deep-learning models to accurately quantify corn plants. Additionally, she focuses on discerning crop phenology and extracting plant spatial information from high-resolution images captured through a small Unmanned Aerial System, or sUAS. Currently in the second year of her Ph.D., she received her bachelor's degree in Computer Science and Engineering, coupled with a master's degree in Geoinformatics.
"It’s very exciting to receive the CFAES IGP Grant for my proposal," said Sushma. "I extend my sincerest gratitude to my advisor, Dr. Sami Khanal, for guiding me through this invaluable research opportunity. Under her guidance, I have been privileged to work in a highly collaborative laboratory environment."
Going forward, she is in the process of planning data collection in a soybean field for this project, utilizing the sUAS during the upcoming summer. This project aims to assess the significance of both high-resolution and medium-resolution images in capturing crucial crop phenology parameters, particularly soybean plants.