Using Machine Learning Algorithms to Evaluate the TVPD Evapotranspiration Prediction Model for Use in Irrigation Management

In the future, agriculture will need better irrigation management options to produce more food and decrease its air and water pollution contributions. Hydroponic systems conserve water over field production, but up to 50% of applied irrigation could be discharged from open-drain systems. TVPD is an evapotranspiration model developed for greenhouse production, particularly for hydroponics. In this study, we calibrate and evaluate TVPD on environmental and evapotranspiration data from hydroponic tomato production and compare predictions to those of random forest (RF) and K-nearest neighbors (KNN). Using five time-ordered data splits, we sought to gauge prediction accuracy for data-limited settings, where the model needs to be implemented with the least calibration time possible, and we evaluated TVPD, RF, and KNN with a 10-fold cross-validation to assess overall model robustness. Across the five data splits, TVPD produced more accurate predictions (r2: 0.86 to 0.90; RMSE: 0.1739 to 0.5796 L tray−1) than RF (r2: 0.06 to 0.73; RMSE: 0.7354 to 2.0505 L tray−1) and KNN (r2: 0.06 to 0.59; RMSE: 0.7694 to 1.7090 L tray−1). With calibration on only the first five days of data, TVPD was able to produce acceptable predictions (r2 = 0.87, RMSE = 0.5796 L tray−1). The mean r2 for a 10-fold cross-validation was 0.81 for TVPD, 0.88 for RF and 0.81 for KNN, and mean RMSE values were slightly better for the cross-validation for RF (0.4970 L tray−1) and KNN (0.4968 L tray−1) than for TVPD (0.5922 L tray−1). Overall, TVPD could be a useful model to predict evapotranspiration for irrigation management and could decrease the volume of discharged hydroponic waste solution.