Water Stress Early Detection of Eggplant Plants by Hyperspectral Fluorescence Spectroscopy

Abstract

Water stress early detection is essential for precision farming to improve crop productivity and product quality. The methods usually used are destructive, long and expensive. In this work, we used hyperspectral chlorophyll fluorescence technology as a rapid, non-destructive approach to detect the water deficiency of eggplant plants using their spectral footprint. So, an experiment was made on 54 eggplant plants subjected to three water treatments: normal irrigation (T100), intermediate irrigation (T50) and no irrigation (T0). The fluorescence spectra were acquired in vivo and in situ using a USB4000 spectrometer from Ocean optics. For the classification of the plants subjected to three water treatments, we used three pretreatments of the raw hyperspectral data in order to suppress the non-informative variability present in these spectra and to obtain robust models. These are the Savitzky-Golay smoothing (SG), the standard normal variable (SNV) and the first derivative of Savitzky-Golay (SG-D1). The preprocessed data were then subjected to two partial least squares discriminant analyses (PLS-DA): Hard PLS-DA and Soft PLS-DA. These statistical approaches are suitable for large samples as it reduces the dimensionality of the data but improves the accuracy of the prediction. The SG-D1 combined with the Soft PLS-DA gave the best discrimination of plants with scores of sensitivity, specificity and total efficiency respectively of 97.33%, 94% and 95% for calibration, 6 days after hydric stress induction. For the plants used for the prediction, the scores are 86%, 91% and 90% respectively. This study shows that hyperspectral chlorophyll fluorescence spectroscopy is a fast and non-destructive technology allowing early detection of water stress in eggplant plants.