Using Machine Learning Algorithms to Forecast the Sap Flow of Cherry Tomatoes in a Greenhouse

Abstract

The sap flow of plants directly indicates their water requirements and provides farmers with a good understanding of a plant's water consumption. Water management can be improved based on this information. 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. SVR performs poorly in this study. Given water/m2, room temperature, given water EC, humidity and plant temperature are the best predictors of sap flow. The data are obtained from the Ideal Lab greenhouse, in the Netherlands, in the framework of the European Funds for Regionale Ontwikkeling (EFRO) EVERGREEN Greenport Noord Holland Noord project (2018-2020).