Smartphone-assisted real-time estimation of chlorophyll and carotenoid contents in spinach following the inversion of red and green color features

Abstract

PurposeChlorophyll (Chl) content is a reliable indicator of leaf nitrogen content and plant health status. Currently available methods for image-based Chl estimation require complex mathematical derivations and high-throughput imaging set-up along with multiplex image-preprocessing steps. Further, the influence of carotenoid (CAR) content has been largely ignored in the process. The present study describes a smartphone-based leaf image analysis method for real-time estimation of Chl content and Chl/CAR ratio. MethodsColor features were obtained from RGB (red, green, blue) images of spinach leaves using a smartphone, and inverse R and G values were calculated. Correlation analysis of color indices and photosynthetic pigment (PP) contents was performed, followed by principal component analysis (PCA). Linear mathematical modeling was performed for describing regression equations for predicting PP contents. Results1/R and 1/G showed strong positive linear correlation (0.93 < r2 < 0.96) with Chl and CAR contents, respectively. Furthermore, 1/R+1/G and [1/R]/[1/G] presented strong positive linear correlation with Chl + CAR (r2 = 0.95) and Chl/CAR (r2 = 0.88), respectively. PCA confirmed the association of color indices with the respective PP features, which were subsequently estimated using the correlation models. A smartphone-based companion application was developed using the linear models for non-invasive, real-time estimation of Chl content and Chl/CAR ratio. ConclusionThe ratios 1/R and 1/G indicate the contents of Chl and CAR via linear models. The smartphone application developed using the linear models enables real-time estimation of Chl content and Chl/CAR ratio without complicated image preprocessing steps or mathematical derivations.

Plant phenotyping relevance

スマートフォン画像から葉のクロロフィル・カロテノイド含量を推定する画像解析法とアプリを開発しており、植物形質取得が研究の中心である。

Code and data availability

The paper's spinach leaf images, spectrophotometric Chl/CAR measurements, and R-based analysis are not publicly deposited; the authors state data are available only upon request. The smartphone app is said to be on Google Play but no authors' public URL is provided; the only code-like URL (Glide) is a third-party image

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