Raman spectroscopy‐based diagnostics of water deficit and salinity stresses in two accessions of peanut

Abstract

Water deficit and salinity are two major abiotic stresses that have tremendous effect on crop yield worldwide. Timely identification of these stresses can help limit associated yield loss. Confirmatory detection and identification of water deficit stress can also enable proper irrigation management. Traditionally, unmanned aerial vehicle (UAV)‐based imaging and satellite‐based imaging, together with visual field observation, are used for diagnostics of such stresses. However, these approaches can only detect salinity and water deficit stress at the symptomatic stage. Raman spectroscopy (RS) is a noninvasive and nondestructive technique that can identify and detect plant biotic and abiotic stress. In this study, we investigated accuracy of Raman‐based diagnostics of water deficit and salinity stresses on two greenhouse‐grown peanut accessions: tolerant and susceptible to water deficit. Plants were grown for 76 days prior to application of the water deficit and salinity stresses. Water deficit treatments received no irrigation for 5 days, and salinity treatments received 1.0 L of 240‐mM salt water per day for the duration of 5‐day sampling. Every day after the stress was imposed, plant leaves were collected and immediately analyzed by a hand‐held Raman spectrometer. RS and chemometrics could identify control and stressed (either water deficit or salinity) susceptible plants with 95% and 80% accuracy just 1 day after treatment. Water deficit and salinity stressed plants could be differentiated from each other with 87% and 86% accuracy, respectively. In the tolerant accessions at the same timepoint, the identification accuracies were 66%, 65%, 67%, and 69% for control, combined stresses, water deficit, and salinity stresses, respectively. The high selectivity and specificity for presymptomatic identification of abiotic stresses in the susceptible line provide evidence for the potential of Raman‐based surveillance in commercial‐scale agriculture and digital farming.

Confirmatory detection and identification of biotic and abiotic stresses in wheat using Raman spectroscopy

“…For instance, in the Raman spectra collected from wheat exposed to drought, we observed a small decrease in the intensity of carotenoid (1000,1048,1120,1155,1184,1218, and 1525 cm -1 ) bands, Figure 1 and Table S1. These observations suggest that drought causes a decrease in the concentration of carotenoids in plants, thereby showing harmony with earlier findings by Morey and co-workers (Morey et al, 2021). We observed even greater decrease in the intensity of carotenoid vibrations in the spectra collected from ND plants.…”

Spectral-Based Classification of Genetically Differentiated Groups in Spring Wheat Grown under Contrasting Environments

“…For instance, Partial least squares-discriminant analysis (PLS-DA) is one of the most widely used classification algorithms to deal with the multicollinearity problem of high-dimensional spectral data [ 16 ]. This method has been used in the context of genotype classification, disease detection, and the physiological states of crops [ 16 , 17 , 18 ]. On the other hand, Artificial Neural Networks (ANNs) have emerged as a powerful tool for the predictive modeling of big data.…”