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Activity Number:
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79
- Functional Data Analysis: Methods and Applications
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Type:
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Contributed
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Date/Time:
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Sunday, July 28, 2019 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #305225
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Title:
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Estimating Plant Growth Curves and Derivatives by Modeling Crowdsourced Imaged-Based Data
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Author(s):
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Haozhe Zhang* and Dan Nettleton and Stefan Hey and Talukder Jubery and Patrick Schnable
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Companies:
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Iowa State University and Iowa State University and Iowa State University and Iowa State University and Iowa State University
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Keywords:
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crowdsourcing image analysis;
functional data analysis;
genotype-by-environment interaction;
spline smoothing;
plant growth rate;
principal component analysis
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Abstract:
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Recent advances in plant phenotyping have increased interest in statistical approaches for analysis of longitudinal phenotypic data derived from sequential images. In a maize growth study, plants of various genotypes were imaged daily during the growing season by hundreds of cameras. Amazon MTurk workers were hired to manually mark plant bodies on these images, from which plant heights were obtained. An important scientific problem is to estimate the effect of genotype and its interaction with environment on plant growth while adjusting for measurement errors from crowdsourcing image analysis. We model plant height measurements as discrete observations of latent growth curves contaminated with online worker random effects and worker-specific measurement errors. We allow the mean function of the growth curve and its first derivative to depend on replicates and environmental conditions, and model the phenotypic variation between genotypes and genotype-by-environment interactions by functional random effects. The proposed model leads to a new method for assessing the quality of MTurk worker data and a novel index for measuring the sensitivity to drought for various genotypes.
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Authors who are presenting talks have a * after their name.
