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

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