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

Amora Amir,Marya Butt,Olaf Van Kooten

doi:10.1109/access.2021.3127453

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).

Highlights

In alignment with artificial intelligence (AI) and big data technology, machine learning (ML) introduces new opportunities to unravel, measure, mine and understand the hidden patterns of data processes in dynamic and static environments [1]
The tomato, an herbaceous plant, was chosen as the research object to contribute to the sap flow database
An ML-based prediction system was used to predict sap flow, and the results show that random forest (RF) performed best

Summary

Introduction

In alignment with artificial intelligence (AI) and big data technology, machine learning (ML) introduces new opportunities to unravel, measure, mine and understand the hidden patterns of data processes in dynamic and static environments [1]. ML is defined as the scientific field of statistical techniques that confers machines with the ability to learn from a series of input and output examples. ML is applied in many scientific fields, for example, bioinformatics, medicine, finance and economic sciences, robotics and vision engineering, sentiment analysis of social media, agriculture, climatology and food security. ML has potential to address existing and future challenges in agriculture by means of massive volumes of data containing a wide variety of indicators that can be captured, analyzed, processed and used for decision making. It is essential to gather data from various sources when making predictive decisions e.g., preventing crop loss and increasing yield while minimizing the use of resources. Kaul et al [3] applied artificial neural networks for highly accurate corn and soybean yield prediction.

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