WISRD Research & Engineering Journal

4.2 General Methods of Computer Vision As mentioned in the Introduction, determining the growth rate of coral in the lab is not trivial. Methods were recently reviewed by Koch, et al., 2021 in which they identified that most research researchers use photogrammetry methods, however these tend to be time consuming and/or expensive which meant it was not a viable option for this study. These methods are summarized in Appendix A. Plant phenotyping is a research area that focuses on quantitatively measuring the outward appearance of the plant (the phenotype), which is influenced by the organism’s interactions with the environment. Agriculture is a major driving force for development of plant phenotyping tools, and emerging, non-invasive methods come from computer vision, as recently reviewed by Pieruschka and Schurr (2019). Several research labs are actively working on computer vision such as the Danforth Center and the University of Nottingham. No matter what software is used for phenotyping, the basic steps are: ● Collecting images and creating metadata ● Methods for transferring data ● Segmenting the image (separating the region of interest (roi) from the background) 4.2.1 PlantCV In this work, we area investigating the application of PlantCV, developed at the Danforth Center, to quantify the growth of animals, small polyp stony corals, over time. PlantCV is a collection of open-source, modular packages. A useful overview of PlantCV can be found at the Friday’s Hands-On Workshop Series - "An introduction to image analysis workflows with PlantCV,” posted by Phenome Force in October, 2020. Researchers can ask questions through a newly formed Forum created by the PlantCV group at the Danforth Center. The sections below describe the installation and running of PlantCV. The following versions were ● Extracting the trait ● Analyzing the data

used during this internship: Anaconda.Navigator v2.04 Jupyter Notebook v6.4.0 Python v3.7 Plantcv v3.13.0 Anaconda.Navigator Channel: plantcv

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