Employment of artificial neural networks for non-invasive estimation of leaf water status using color features: a case study in Spathiphyllum wallisii

Abstract

The potential of combining artificial neural networks (ANNs) and image processing for assessing leaf relative water content (RWC) and water content (WC) was addressed. Spathiphyllum wallisii was employed as model species, because it has broad leaves and very responsive stomata. In the course of desiccation, leaves were periodically weighted (to calculate RWC and WC conventionally) and imaged. Image acquisition was performed by a scanner, and was, thus, independent of ambient light environment. Color feature extraction was performed in three color spaces (RGB, HSI, and CIELAB), while six texture statistical features were calculated for each of the (nine) computed color channels. Prior to model development via ANNs, the obtained feature vector underwent feature reduction using principal component analysis. The presented methodology yielded very precise estimations of leaf RWC and WC (correlation coefficient > 0.95). Therefore, the technique under study was proven to be very promising for non-invasive in situ measurements of leaf water status.