Plant Water Stress Detection Using Leaf Temperature and Microclimatic Information

Abstract

A proximal sensor suite consisting of an infrared thermometer, an air temperature sensor, a humidity sensor, a PAR sensor, and an anemometer was developed to measured leaf temperature and other relevant microclimatic information to determine plant water status. A series of experiments were conducted in almond and walnut orchards to study relationship between data obtained using the sensor suite and stem water potential measured using a standard pressure chamber. Multiple linear regression models of leaf temperature as functions of stem water potential, air temperature, relative humidity, photosynthetically active radiation, and wind speed were developed and validated for almond and walnut crops under sunlit and shaded conditions. Models yielded high correlation with R2 values ranging from 0.82 to 0.90. Discriminant analyses of the data obtained from the sensor suite resulted in error rates of 9 to 11% in walnuts and 16 to 17% in almonds. However, critically wrong decision error, which is the overall misclassification of stressed trees, was limited to 5 to 10% in almonds, and 2 to 7% in walnuts. Since shaded leaf datasets were better correlated to plant water status in regression analysis and resulted in good discrimination power in classification analyses, shaded leaf data that is easier to gather using the sensor suite may be used in future studies.