Date of Award
8-2016
Degree Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Agronomy
First Advisor
Katy M. Rainey
Second Advisor
William M. Muir
Committee Chair
Katy M. Rainey
Committee Co-Chair
William M. Muir
Committee Member 1
Shaun Casteel
Committee Member 2
Bruce Craig
Committee Member 3
Tobert Rocheford
Abstract
Increasingly, new sources of data are being incorporated into plant breeding pipelines. Enormous amounts of data from field phenomics and genotyping technologies places data mining and analysis into a completely different level that is challenging from practical and theoretical standpoints. Intelligent decision-making relies on our capability of extracting from data useful information that may help us to achieve our goals more efficiently. Many plant breeders, agronomists and geneticists perform analyses without knowing relevant underlying assumptions, strengths or pitfalls of the employed methods. The study endeavors to assess statistical learning properties and plant breeding applications of supervised and unsupervised machine learning techniques. A soybean nested association panel (aka. SoyNAM) was the base-population for experiments designed in situ and in silico. We used mixed models and Markov random fields to evaluate phenotypic-genotypic-environmental associations among traits and learning properties of genome-wide prediction methods. Alternative methods for analyses were proposed.
Recommended Citation
Xavier, Alencar, "Learning from data: Plant breeding applications of machine learning" (2016). Open Access Dissertations. 883.
https://docs.lib.purdue.edu/open_access_dissertations/883
Included in
Agriculture Commons, Biostatistics Commons, Plant Sciences Commons