Abstract
Color patterning contributes to important plant traits that influence ecological interactions, horticultural breeding, and agricultural performance. High-throughput phenotyping of color is valuable for understanding plant biology and selecting for traits related to color during plant breeding. Here we present ColourQuant, an automated high-throughput pipeline that allows users to extract color phenotypes from images. This pipeline includes methods for color phenotyping using mean pixel values, a Gaussian density estimator of CIELAB color, and the analysis of shape-independent color patterning by circular deformation.
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Acknowledgments
Z.M. was supported by National Science Foundation (NSF) Plant Genome Research Program 1546869. M.H.F. is supported through startup funds from Cornell University’s College of Agriculture and Life Science and by the NSF (CAREER IOS-1942437). The coleus samples used in Figs. 1 and 3 were imaged from Dr. David Clark’s breeding program at the University of Florida in Gainesville, FL.
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Li, M., Frank, M.H., Migicovsky, Z. (2022). ColourQuant: A High-Throughput Technique to Extract and Quantify Color Phenotypes from Plant Images. In: Lorence, A., Medina Jimenez, K. (eds) High-Throughput Plant Phenotyping. Methods in Molecular Biology, vol 2539. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2537-8_9
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DOI: https://doi.org/10.1007/978-1-0716-2537-8_9
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