Computational corn hybrid selection integrating phenotypic, environmental, and genomic information

Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Bioinformatics

Grantor

University of Illinois Urbana-Champaign

Year dc:date

2025

Author dc:creator

• Chow, Tsz Yau Iris

Contributors dc:contributor

• Martin, Nicolas
• Dokoohaki, Hamze
• Monteverde Dominguez ,. Eliana

• Maize Hybrid Prediction
• Genotype-by-environment Interaction (g×e)
• Corn Yield Modeling
• Genomic Selection
• Machine Learning In Agriculture
• Random Forest Classification
• Principal Component Analysis (pca)
• Regression And Classification Models
• Feature Importance Analysis
• Genomes-to-fields (g2f) Initiative
• High-throughput Genotyping
• Public Yield Prediction Competitions

Statement dc:rights

• Copyright 2025 Tsz Yau Iris Chow

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/129925

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/129925