Detection of Plant Water Stress Using Leaf Temperature and Microclimatic Measurements in Almond, Walnut, and Grape Crops

Abstract

Abstract. A mobile sensor suite was developed and evaluated to predict plant water status by measuring the leaf temperature and microclimatic variables in nut crop trees and grapevines. The sensor suite consists of an infrared thermometer to measure leaf temperature along with other relevant sensors to measure microclimatic variables. The sensor suite was successfully evaluated in commercial orchards in the Sacramento Valley of California on three orchard crops, i.e., almond (Prunus dulcis), walnut (Juglans regia), and grape (Vitis vinifera), 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 almond, walnut, and grape crops, respectively. Stem water potential (SWP) was found to be a significant variable in all models. The regression models were used to classify trees into water stressed and unstressed categories. Critical misclassification errors (classifying a water stressed tree as unstressed) for sunlit and shaded leaf models were 8.8% and 5.2% for almond, 5.4% and 6.9% for walnut, and 12.9% and 8.1% for grapevine, respectively. Canonical discriminant analyses were also conducted using the sensor suite data to classify trees into water stressed and unstressed trees with critical misclassification errors for sunlit and shaded leaves of 9.3% and 7.8% for almond, 2.0% and 4.1% for walnut, and 9.6% and 1.6% for grapevine, respectively. These results show the feasibility of the sensor suite to determine plant water status for irrigation management of nut and vineyard crops.