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Machine learning classification of plant genotypes grown under different light conditions through the integration of multi-scale time-series data

  • Author / Creator
    Sakeef, Nazmus
  • In order to mitigate the effects of a changing climate, agriculture requires
    more effective evaluation, selection, and production of crop cultivars in order
    to accelerate genotype-to-phenotype connections and the selection of beneficial traits. Critically, plant growth and development are highly dependent on
    sunlight, with light energy providing plants with the energy required to photosynthesize as well to directly intersect with the environment in order
    to develop. In plant analyses, machine learning and deep learning techniques
    have a proven ability to learn plant growth patterns, including detection of
    disease, plant stress, and growth using a variety of image data. To date, however, studies have not assessed machine learning and deep learning algorithms
    for their ability to differentiate a large cohort of genotypes grown under several
    growth conditions using time-series data automatically acquired across multiple scales (daily and developmentally). Here, we extensively evaluate a wide
    range of machine learning and deep learning algorithms for their ability to differentiate 17 well-characterized photoreceptor deficient genotypes differing in
    their light detection capabilities grown under several different light conditions.
    Using algorithm performance measurements of precision, recall, F1-Score, and
    accuracy, we find that Suport Vector Machine (SVM) maintains the greatest
    classification accuracy, while a combined ConvLSTM2D deep learning model
    produces the best genotype classification results across the different growth
    conditions. Critically, our successful integration of time-series growth data
    across multiple scales, genotypes and growth conditions sets a new foundational baseline from which more complex plant science traits can be assessed
    for genotype-to-phenotype connections.

  • Subjects / Keywords
  • Graduation date
    Fall 2023
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/r3-k00r-9795
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.