Date Thesis Awarded
5-2022
Access Type
Honors Thesis -- Open Access
Degree Name
Bachelors of Science (BS)
Department
Biology
Advisor
Josh Puzey & Ron Smith
Committee Members
Dana Willner
William Soto
Abstract
The UN estimates that the global population could reach 9.7 billion by 2050 (United Nations). As a result, the amount of food required to feed humanity is thought to double by 2050 (Ray et al., 2012). Humanity must find a way to increase crop production without increasing fertilizer usage and eutrophication, which can be done using the soil microbiome. Using potted plants with soils inoculated with Pseudomonas alcaligenes, Pseudomonas denitrificans, Bacillus polymyxa, and Mycobacterium phlei, both the shoot and root growth of pea and cotton plants was significantly increased (Egamberdieva & Höflich, 2004). In this study, utilizing a random forest model, the presence or absence of inflorescences of an Asclepias (milkweed) plant was predicted using the soil microbiome as an input with 64% accuracy on test data. Euryarchaeota, Acidobacteria, and Chlorobi were identified as the most important phyla in predicting the presence of inflorescences.
Recommended Citation
Denoncourt, Luke, "Using a Machine Learning Model to Predict Plant Inflorescences based upon its Soil Microbiome" (2022). Undergraduate Honors Theses. William & Mary. Paper 1896.
https://scholarworks.wm.edu/honorstheses/1896
Included in
Data Science Commons, Environmental Microbiology and Microbial Ecology Commons, Plant Sciences Commons
