A Hyperspectral Imaging System for Plant Water Stress Detection: Calibration and Preliminary Results

Much progress has been made on optimizing plant water supply based on several methods of irrigation scheduling, in both open-field and greenhouse cultivations, such as real-time measurements of solar radiation and soil or substrate moisture. However, only a limited number of such methods use plant-based physiological indicators to detect plant water stress and adapt irrigation scheduling accordingly. In addition, even fewer indicators can be estimated by non-contact, remote sensors (RS) that do not affect plant development. Hyperspectral imaging technology could be an accurate remote way to detect moisture content of plants, taking into account crop characteristics. In this work, a methodology of hyperspectral imaging calibration and acquisition is presented. The method uses the reflectance characteristics in hyperspectral bands from 400 to 1000 nm and incorporates the appropriate radiometric and geometric corrections. The basic statistical parameters of mean and standard deviation values are used to estimate spatial and spectral correlation of each band on the extracted areas/pixels of interest. Several statistical techniques are used for the selection of optimal features that will lead to the development of appropriate plant water stress indices that could be used for incipient water stress detection in optimal irrigation scheduling systems.