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A mobile sensor suite was developed and evaluated to predict plant water status by measuring the leaf temperature of nut trees and grapevines. It consists of an infrared thermometer to measure leaf temperature along with relevant ambient condition sensors to measure microclimatic variables in the vicinity of the leaf. Sensor suite was successfully evaluated in three crops (almonds, walnuts and grapevines) for both sunlit and shaded leaves. Stepwise linear regression models developed for shaded leaf temperature yielded coefficient of multiple determination values of 0.90, 0.86, and 0.86 for almonds, walnuts, and grapevines, respectively. Stem water potential (SWP) and air temperature (Ta) were found to be significant variables in all models. Regression models were used to classify trees into stressed and unstressed categories with critical misclassification error (i.e., predicting a stressed tree as unstressed) for sunlit and shaded leaf models of 8.8 and 5.2% for almonds, 5.4 and 6.9% for walnuts, and 12.9 and 8.1% for grapevines, respectively. Canonical discrimination analyses were also conducted using sensor suite data to classify stressed and unstressed trees with critical misclassification error for sunlit and shaded leaves of 9.3 and 7.8% for almonds, 2.0 and 4.1% for walnuts, and 9.6and 1.6% for grapevines, respectively. These results show the feasibility that the sensor suite can be used to determine plant water status for irrigation and quality management of nut and vineyard crops.