Samenvatting

This study focuses on forecasting tomato sap flow in relation to various climate and
irrigation variables. The proposed study utilizes different machine learning (ML) techniques, including linear
regression (LR), least absolute shrinkage and selection operator (LASSO), elastic net regression (ENR),
support vector regression (SVR), random forest (RF), gradient boosting (GB) and decision tree (DT). The
forecasting performance of different ML techniques is evaluated. The results show that RF offers the best
performance in predicting sap flow.