Fig. 2

Schematic representation of the image processing and transpiration rate prediction model development pipeline. The top light gray square summarizes the image processing for the Red–Green–Blue (RGB: green), Thermal Infrared (TIR: orange) and hyperspectral visible and near-infrared (VNIR) and shortwave-infrared (SWIR) (blue) imaging systems. The RGB image processing consisted of a plant segmentation using a convolutional neural network (CNN). The TIR image processing included a radiation to temperature (T) conversion, TIR plant segmentation by aligning RGB and TIR images and the calculation of median plant temperature (T_p) and TIR indices. The hyperspectral processing consisted of a radiometric calibration and plant segmentation using the Red-edge Normalized Difference Vegetation Index (Re-NDVI) and a random forest (RF) model for VNIR and SWIR, respectively. After the hyperspectral segmentation, a correction of the distance between the white reference plate and the plant was performed followed by a brightness classification of the VNIR data and a VNIR-SWIR alignment to extract illumination classes from the data. Indices and reflectance were calculated for the intermediate light class [42]. The bottom gray square summarizes the different modeling approaches applied to develop transpiration rate (E) prediction models. Mechanistic energy balance and empirical models were developed by combining different data types including environmental data (env: pink), TIR data and hyperspectral data (hyp). The empirical modeling algorithms were Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), linear model with stepwise selection, Partial Least Square Regression (PLSR) and linear models combining one TIR index with environmental data and hyperspectral indices