Toward accurate estimating of crop leaf stomatal conductance combining thermal IR imaging, weather variables, and machine learning

Leaf stomata regulate the process of gas exchange between the plant and the atmosphere, therefore play an important role in plant growth and water use. Thermal infrared sensing of leaf surface temperature is proved to be an indirect but effective approach to estimate leaf stomatal conductance, and shows the potential to rapidly differentiate genotypes for water-use related traits. The objective of this study was to estimate leaf stomatal conductance from thermal IR images of crops and relevant environmental parameters. The experiment was conducted in the NU-Spidercam field phenotyping facility near Mead, NE. Leaf stomatal conductance was measured from soybean, sorghum, maize, and sunflower using a leaf porometer. Thermal IR images of the crop canopies were captured by a thermal IR camera and then processed to extract crop canopy temperature (Tc). In addition, weather variables including solar radiation, air temperature, relative humidity, and wind speed were extracted from a nearby weather station. Correlation analysis was implemented to explore the relationships between these variables. Multiple linear regression (MLR), random forest (RF), gradient boosting machine (GBM) were applied to model stomatal conductance from Tc and weather variables. The Pearson correlation coefficients between predicted and measured stomatal conductance were 0.495 for MLR, 0.591 for RF, and 0.878 for GBM when Tc was not used as an input variable. After adding Tc as input, Pearson correlation coefficients were improved to 0.584 for MLR, 0.593 for RF, and 0.896 for GBM. The mean absolute errors for the three models were 225, 237, and 129 mmol/(m2·s) when Tc was included as a model input. This research would lead to rapid assessment of leaf stomatal conductance and crop water status using thermal IR imaging.