An artificial neural network approach to the estimation of stem water potential from frequency domain reflectometry soil moisture measurements and meteorological data

Stem water potential seems to be a sensitive measure of plant water status. Nonetheless, it is a labour-intensive measurement and is not suited for automatic irrigation scheduling or control. This study describes the application of artificial neural networks to estimate stem water potential from soil moisture at different depths and standard meteorological variables, considering a limited data set. The experiment was carried out with ‘Navelina’ citrus trees grafted on ‘Cleopatra’ mandarin. Principal components analysis and multiple linear regression were used preliminarily to assess the relationships among observations and to propose other models to allow a comparative analysis, respectively. Two principal components account for the systematic data variation. The optimum regression equation of stem water potential considered temperature, relative humidity, solar radiation and soil moisture at 50 cm as input variables, with a determination coefficient of 0.852. When compared with their corresponding regression models, ANNs presented considerably higher performance accuracy (with an optimum determination coefficient of 0.926) due to a higher input-output mapping ability.

► First step approach to estimate stem water potential from soil moisture and standard meteorological variables using ANNs.
► Two principal components are enough to describe systematic variability of data.
► Optimum input combination: temperature, relative humidity, solar radiation and soil moisture at 50 cm.
► Artificial neural networks present higher accuracy than corresponding multi-linear regression models.