Smartphone-assisted real-time estimation of chlorophyll and carotenoid concentrations and ratio using the inverse of red and green digital color features

Abstract

Chlorophyll (Chl) concentration 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)concentration has been largely ignored in the process. The present study describes a smartphone-based leaf image analysis method for real-time estimation of Chl concentration and Chl/CAR ratio. Color 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) concentrations was performed, followed by principal component analysis (PCA). Linear mathematical modeling was performed for describing regression equations for predicting PP concentration. 1/R and 1/G indices showed strong positive linear correlation (0.93 < r2 < 0.96) with Chl and CAR concentrations, 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 concentration and Chl/CAR ratio. 1/R and 1/G indices indicate the concentrations of Chl and CAR, respectively, via linear models. The smartphone application developed using the linear models enables real-time estimation of Chl concentration and Chl/CAR ratio without complicated image preprocessing steps or mathematical derivations.