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Machine learning for species classification of the invasive Centaurea Jacea hybrid complex

Linde, Sophie
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Abstract
Effectively managing biodiversity requires accurate taxonomic classification, which is challenging when species look similarly. Centaurea jacea and C. nigra are invasive weeds that hybridize extensively, leading to phenotypic variability that blurs easy classification. Using measurements and imagery from individual flowering heads and associated genetic ancestry estimates, I assessed the utility of machine learning models (decision trees, support vector machines, random forests, and neural networks) for species classification. Models were most accurate at separating C. nigra from C. jacea and hybrids. These models are not currently reliable for classifying Centaurea, but if refined, could become a helpful tool for their management.
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Date
2023-01-01
Student Status
Undergraduate
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Type of presentation
Poster Presentation
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UVM Student Research Conference
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URI
https://hdl.handle.net/20.500.14849/8594
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Advisor(s)
Stephen Keller
Department
Program/Major
Biological Science
College/School
College of Arts and Sciences
Patrick Leahy Honors College
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Life Sciences
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