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)âbased imaging and satelliteâbased 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 Ramanâbased diagnostics of water deficit and salinity stresses on two greenhouseâgrown peanut accessions: tolerant and susceptible to water deficit. Plants were grown for 76âdays prior to application of the water deficit and salinity stresses. Water deficit treatments received no irrigation for 5âdays, and salinity treatments received 1.0âL of 240âmM salt water per day for the duration of 5âday sampling. Every day after the stress was imposed, plant leaves were collected and immediately analyzed by a handâheld Raman spectrometer. RS and chemometrics could identify control and stressed (either water deficit or salinity) susceptible plants with 95% and 80% accuracy just 1âday 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 Ramanâbased surveillance in commercialâscale agriculture and digital farming.