Non‑invasive leaf hydration status determination through convolutional neural networks based on multispectral images in chrysanthemum

Abstract

The potential of employing multispectral data (400–1050 nm) for estimating leaf relative water content (RWC) and water content (WC) was investigated in chrysanthemum (Chrysanthemum morifolium L.). Detached leaves were exposed to desiccation (0–24 h). The abaxial leaf side showed a higher reflectance (0.1–0.2%) than the adaxial one in the visible spectrum (400–700 nm), whereas differences between leaf sides were minor in the near-infrared region (750–1050 nm). The overall reflectance of either leaf side increased in the course of desiccation. Leaf RWC and WC could not be accurately retrieved based on the whole reflectance range or eleven commonly-employed indices (R² = 0.000–0.469). A convolutional neural network (CNN) predictive model was further developed. The input data were the multispectral images of either one (adaxial or abaxial) or both (adaxial and abaxial) leaf sides. These first underwent size enlargement and cropping and then a reduction in both size and wavelength band number. Pairs of convolutional-pooling layers, followed by a fully connected layer, were chosen as network architecture. The developed CNN model generated very accurate predictions of leaf RWC and WC (R² = 0.852–0.964). The obtained protocol provides real-time, non-invasive and accurate determinations of leaf water status.