GreenLeafVI: A FIJI Plugin for High-Throughput Analysis of Leaf Chlorophyll Content.

Abstract

Chlorophyll breakdown is a central process during plant senescence or stress responses, and leaf chlorophyll content is therefore a strong predictor of plant health. Chlorophyll quantification can be done in several ways, most of which are time-consuming or require specialized equipment. A simple alternative to these methods is the use of image-based chlorophyll estimation, which uses the color values in RGB images to calculate colorimetric visual indexes as a measure of the leaf chlorophyll content. Image-based chlorophyll measurement is non-destructive and requires no specialized equipment, apart from a digital camera. Here, we developed the ImageJ plugin Green Leaf Visual Index that facilitates high-throughput image analysis for quantifying leaf chlorophyll content. Our plugin offers the option to white-balance images to decrease variation between images and has an optional background removal step. We show that this method can reliably quantify leaf chlorophyll content in a variety of plant species. In addition, we show that image-based chlorophyll quantification can replicate Genome-Wide Association Study results based on traditional chlorophyll extraction methods, showing that this method is highly accurate.

Code and data availability

The paper's authors publicly released the GreenLeafVI FIJI plugin source code and documentation on GitHub, which is the computational tool used for the paper's image-based chlorophyll phenotyping. The underlying phenotype/trait datasets (RGB image measurements and chlorophyll extraction values) are not publicly posted;