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 for the leaf chlorophyll content. Image-based chlorophyll measurement is non-destructive and, apart from a digital camera, requires no specialized equipment. Here, we developed the ImageJ plugin GreenLeafVI that facilitates high-throughput image analysis for measuring 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 GWAS results based on traditional chlorophyll extraction methods, showing that this method is highly accurate.

Plant phenotyping relevance

葉のクロロフィル量を画像から推定するFIJIプラグインを開発し、複数植物種で信頼性とGWAS再現性を検証しており、植物フェノタイピング手法が中心である。

Code and data availability

The paper's GreenLeafVI FIJI plugin (the authors' phenotyping analysis code) is publicly available on GitHub with explicit availability language. The underlying phenotype/trait datasets are only available upon request, so they do not qualify as public assets.