Theses and Dissertations
ORCID
https://orcid.org/0009-0003-5652-0806
Issuing Body
Mississippi State University
Advisor
Chen, Jingdao
Committee Member
Chen, Zhiqian
Committee Member
Jenkins, Johnie N.
Committee Member
Perkins, Andy D.
Committee Member
Huang, Yanbo
Date of Degree
12-8-2023
Document Type
Graduate Thesis - Open Access
Major
Computer Science
Degree Name
Master of Science (M.S.)
College
James Worth Bagley College of Engineering
Department
Department of Computer Science and Engineering
Abstract
Breeding compact cotton plants is desirable for many reasons, but current research for this is restricted by manual data collection. Using unmanned aircraft system imagery shows potential for high-throughput automation of this process. Using multispectral orthomosaics and ground truth measurements, I developed supervised models with a wide range of hyperparameters to predict three compactness traits. Extreme gradient boosting using a feature matrix as input was able to predict the height-related metric with R2=0.829 and RMSE=0.331. The breadth metrics require higher-detailed data and more complex models to predict accurately.
Sponsorship
United States Department of Agriculture - Agricultural Research Service
Recommended Citation
Waldbieser, Joshua Carl, "Phenotyping cotton compactness using machine learning and UAS multispectral imagery" (2023). Theses and Dissertations. 6035.
https://scholarsjunction.msstate.edu/td/6035