Open AccessNondestructive automated workflow for analyzing diverse leaf morphologies using computed tomography
Author(s) -
Kuo Nathanael,
Hahne Nadeau,
Iwaskiw Alex,
Stone William,
Wu Susan,
Yang Kimberly,
Timm Collin M.
Publication year - 2020
Publication title -
the plant phenome journal
Language(s) - English
Resource type - Journals
ISSN - 2578-2703
DOI - 10.1002/ppj2.20009
Subject(s) - workflow , computed tomography , tomography , botany , computer science , biology , medicine , radiology , database
Automated plant analysis methods can inform basic and applied plant research. Computed tomography (CT) has been used to image plants in canopies and individual plants; however this method is underutilized for dynamic analysis of plant growth. In this work we present a workflow and associated algorithm to nondestructively extract leaf area from 120 individual CT scans for plants with flat [soybean, Glycine max (L.) Merr.], textured (tomato, Solanum lycopersicum L.), and grassy (wheat, Triticum aestivum L.) leaves. Under low water conditions, we see significant changes in leaf area depending on leaf type. This work enables future automated phenotyping using CT scanning of whole plants.