Automated leaf temperature monitoring of glasshouse tomato plants by using a leaf energy balance model

In order to detect biotic and abiotic stress at leaf level thermal indices based on leaf temperature measurements have been commonly used. The application of these indices within glasshouse crops is, however, restricted due to the specific humid conditions and the large spatial variability of irradiance and air temperature inside a glasshouse. In this study, a novel diagnostic algorithm is proposed as an alternative method to automatically monitor the leaf temperature of a glasshouse tomato crop based on the ecophysiological interactions between a leaf and its surrounding microclimate. Given that this algorithm is intended to be implemented as a software tool in glasshouse climate control systems, a critical overview of all relevant equations found in literature was first given. Next, the most appropriate equations were selected by using two objective criteria, i.e. the commonly used R2 and the less conventional Young Information Criterion, which also takes into account the complexity of an algorithm, so that the most feasible algorithm for automated monitoring purposes was built. Our results also showed that an in situ calibration of the selected algorithm was needed, for which a novel procedure was proposed. Once calibrated, this algorithm successfully simulated the leaf temperature of a well-watered tomato plant during several days given that the environmental conditions in its microclimate were accurately measured. Finally, the 95% confidence limits on the leaf temperature simulations provided the requested dynamic thresholds necessary for an effective automated monitoring tool. It was demonstrated that by using this novel diagnostic algorithm unexpected and likely harmful stomatal closure can be detected before visual signs of turgidity loss are observed.

► We have developed an automated early-warning system for glasshouse tomato plants.
► The system is based on a leaf energy balance model.
► The system foresees in in situ model calibrations.
► It automatically provides dynamic thresholds on the simulated leaf temperature.
► The novel system successfully detected stress before visual signs were observed.