Assessing nutritional pigment content and plant health status of green and red leafy vegetables by image analysis: Catching the 'red herring' of plant digital color processing

Abstract

Estimating pigment content in leafy vegetables via digital image analysis is a reliable method for real-time assessment of plant health status and nutritive value. However, the present leaf color analysis models developed using green-leafed plants do not perform reliably while analyzing images of anthocyanin-rich leaves, often giving misleading or "red herring" trends. Hence, the present study investigates variations in different digital color features for six types of leafy vegetables with varying levels of laminar anthocyanin to identify holistic digital color analysis models that could be implemented for real-time assessment of health status as well as nutritional pigment contents of leafy vegetables irrespective of laminar anthocyanin status. For this, datasets from three digital color spaces, viz., RGB (red, green, blue), HSI (hue, saturation, intensity), and L*a*b* (lightness, redness-greenness, yellowness-blueness), were compared with pigment contents of n = 320 leaf samples, and were analyzed via linear and non-linear regression using single variables, multiple linear regression, Support Vector regression, and Random Forest regression to predict chlorophyll and anthocyanin contents. While most digital color features presented abrupt shifts between anthocyanin-rich and low-anthocyanin samples, the R digital color feature did not show any deviation due to leaf anthocyanin content and was found to have the best correlation with SPAD chlorophyll meter readings (R2 = 0.83). Concomitantly, H (R2 = 0.82) and a* (R2 = 0.79) features correlated most strongly with leaf anthocyanin content. In general, prediction of pigment contents was more accurate when data from all channels within a color space was analyzed simultaneously. Further, most reliable estimates of pigment content were provided by Support Vector and Random Forest regression models (0.7 < R2 < 0.85). Thus, the present findings demonstrate how digital color analysis of green as well as anthocyanin-rich leafy vegetables could be implemented for assessing plant health status and nutritional pigment content non-invasively.