Graduation Semester and Year
Summer 2024
Document Type
Thesis
Degree Name
Master of Science in Computer Science
Department
Computer Science and Engineering
First Advisor
William Beksi
Second Advisor
Chris Conly
Third Advisor
Alex Dillhoff
Abstract
Sugarcane roots are not understood and previous methods of collecting and processing data have proved to be laborious and time consuming. Using Minirhizotrons, Researchers observe and photograph roots without disturbing the soil and are useful for studying root growth over time. Software such as Rhyzovision exists to allow quick processing of root images. These software tools require clean or well annotated images of only the roots to provide accurate information. Current annotations of the images are done manually and requires a Scientist with domain knowledge of roots to accurately annotate the root images. We are employing the use of CNN and Transformer deep learning models to learn from correct root annotations and create root segmentations that can provide context of root architectures in cases where roots are occluded.
Disciplines
Artificial Intelligence and Robotics | Other Computer Sciences
License
This work is licensed under a Creative Commons Attribution-No Derivative Works 4.0 International License.
Recommended Citation
Salas-Leon, Joseph, "Automated In Situ Segmentation of Sugarcane Roots" (2024). Computer Science and Engineering Theses - Archive. 367.
https://mavmatrix.uta.edu/cse_theses/367