Spectral monitoring of salinity stress in tomato plants

Abstract

Water salinity is a widespread agricultural hazard that affects approximately 20% of irrigated land, causing a significant yield reduction in crops. Stress coping mechanisms by plants were thoroughly examined but understanding of plant adaptation and acclimation is still lacking and is often species- and variety-specific. Presently, the biochemical and physiological methods that are used to assess plant stress are costly, destructive, and time-consuming. Alternatively, spectroscopy is a potential method to monitor biochemical components and physiological states of plants. The objective of the current work was to build a spectral-based model for detecting plants under salt stress, in order to optimise plant-status monitoring in a non-destructive manner. In this study, five different tomato graft combinations were examined under four different salinity treatments in a greenhouse. Hyperspectral measurements were conducted in the range of 400–2500 nm, and chemometrics was used for data analysis and modelling. Salt treatments were found to affect the physiological performance of plants, although environmental conditions had a greater influence on plant temporal physiological trends. Spectral data acquisition with chemometrics showed high ability to predict salt accumulation in plants (root mean square error of prediction (RMSEP) of 0.47 mg g−1 and 2.8 mg g−1 for Na+ and Cl−, respectively). Moreover, a hyperspectral, robust decision-supporting classification model was established for detecting plants under salt stress (prediction specificity: 0.94). The presented capabilities of predicting Cl−, Na+, and the K:Na ratio in a non-destructive manner, by utilising spectroscopy, could serve as the basis for developing a low-cost, fast, and efficient stress detection method, independent of environmental conditions.